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Dr., Christine Wacta, and Edugaming Geo. "Project Matrix: a Virtual Experience Platform for RE-Conciling Human Ecology & Urban Ecology." INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS 06, no. 03 (2023): 850–58. https://doi.org/10.5281/zenodo.7687887.
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A flashback to previous centuries testifies extraordinary human-lead technological prowess; from the invention of the telescope in the 17th-century, the telephone in 19th-century, the Worldwide-Web and technological revolution of 20th-21st century; it is undeniable that Humans constantly strive to overcome challenges through technological innovation by empowering/advancing humanity. Yet, we’ve witnessed a global disruption by a pandemic that exposed unforeseen weaknesses and exacerbated existing issues (human negligence or not). Suddenly all well-structured and reliable system-organization failed, exposing inadequacies, causing chaos and harmful damages of interconnected components including the flows of people, health, matters, information, economy, politic, materials, communication networks, deadlines, etc. Covid-19 spread across the globe defeated technological advancements. How could that happen in such technological era? Is Human in control of digital evolution or is the technology we invent holding us on leash? Was technology in place to leverage and tackle risks? Where we not prepared to it, or were we focused on the wrong tracks? In any case, whether by failure or negligence, Covid-19 has made history and will continue generating ink flow for decades to come. It is to help address these multi layered-multidimensional and highly complex socio-technical and temporal systems that this project intends to develop a series of applications in alignment with the SDGs. The focus is to blend the capabilities of environment scanning Apps with that of the human habitus-emotions capture, tracing and feedback collector in time and place. It requires advance analytical studies to fully understand interdependencies while respecting the relationships of all subsystems.
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Li, Xinyi, Annabelle Yin, Ha Young Choi, Virginia Chan, Margaret Allman-Farinelli, and Juliana Chen. "Evaluating the Quality and Comparative Validity of Manual Food Logging and Artificial Intelligence-Enabled Food Image Recognition in Apps for Nutrition Care." Nutrients 16, no. 15 (2024): 2573. http://dx.doi.org/10.3390/nu16152573.
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For artificial intelligence (AI) to support nutrition care, high quality and accuracy of its features within smartphone applications (apps) are essential. This study evaluated popular apps’ features, quality, behaviour change potential, and comparative validity of dietary assessment via manual logging and AI. The top 200 free and paid nutrition-related apps from Australia’s Apple App and Google Play stores were screened (n = 800). Apps were assessed using MARS (quality) and ABACUS (behaviour change potential). Nutritional outputs from manual food logging and AI-enabled food-image recognition apps were compared with food records for Western, Asian, and Recommended diets. Among 18 apps, Noom scored highest on MARS (mean = 4.44) and ABACUS (21/21). From 16 manual food-logging apps, energy was overestimated for Western (mean: 1040 kJ) but underestimated for Asian (mean: −1520 kJ) diets. MyFitnessPal and Fastic had the highest accuracy (97% and 92%, respectively) out of seven AI-enabled food image recognition apps. Apps with more AI integration demonstrated better functionality, but automatic energy estimations from AI-enabled food image recognition were inaccurate. To enhance the integration of apps into nutrition care, collaborating with dietitians is essential for improving their credibility and comparative validity by expanding food databases. Moreover, training AI models are needed to improve AI-enabled food recognition, especially for mixed dishes and culturally diverse foods.
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Alzahrani, Adel Saeed, Valerie Gay, Ryan Alturki, and Mohammad J. AlGhamdi. "Towards Understanding the Usability Attributes of AI-Enabled eHealth Mobile Applications." Journal of Healthcare Engineering 2021 (December 21, 2021): 1–8. http://dx.doi.org/10.1155/2021/5313027.
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Mobile application (app) use is increasingly becoming an essential part of our daily lives. Due to their significant usefulness, people rely on them to perform multiple tasks seamlessly in almost all aspects of everyday life. Similarly, there has been immense progress in artificial intelligence (AI) technology, especially deep learning, computer vision, natural language processing, and robotics. These technologies are now actively being implemented in smartphone apps and healthcare, providing multiple healthcare services. However, several factors affect the usefulness of mobile healthcare apps, and usability is an important one. There are various healthcare apps developed for each specific task, and the success of these apps depends on their performance. This study presents a systematic review of the existing apps and discusses their usability attributes. It highlights the usability models, outlines, and guidelines proposed in previous research for designing apps with improved usability characteristics. Thirty-nine research articles were reviewed and examined to identify the usability attributes, framework, and app design conducted. The results showed that satisfaction, efficiency, and learnability are the most important usability attributes to consider when designing eHealth mobile apps. Surprisingly, other significant attributes for healthcare apps, such as privacy and security, were not among the most indicated attributes in the studies.
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Tseng, Hsiao-Ting, Chia-Lun Lo, and Chun-Chih Chen. "The Moderation Role of AI-Enabled Service Quality on the Attitude Toward Fitness Apps." Journal of Global Information Management 31, no. 1 (2023): 1–20. http://dx.doi.org/10.4018/jgim.318694.
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Artificial intelligent technology is rapidly transforming the fitness apps landscape by applying data mining technologies within given parameters. These wide-ranging AI-enabled services improve user interface and enhance customers' experience when exercising with the fitness apps. The current study integrated the four antecedents—technological functions, intrinsic information quality, perceived enjoyment, and social connection—to investigate the moderating influence of AI-enabled service quality on users' attitude toward physical activity. PLS-SEM was used to analyze and validate a sample of 170 participants. The findings posited that individuals' attitude toward physical activity is encouraged by the (1) technological functions (2) intrinsic information quality, and (3) perceived enjoyment. Further, the moderating role of AI-enabled service positively influencing the attitude toward physical activity with technological functions was also established.
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Yue, Peng, Kaixuan Wang, Hanwen Xu, Jianya Gong, and Longgang Xiang. "From Geospatial Data Cube to AI Cube: the Open Geospatial Engine (OGE) Approach." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4-2024 (October 18, 2024): 441–46. http://dx.doi.org/10.5194/isprs-annals-x-4-2024-441-2024.
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Abstract. The Earth Observation (EO) analytics are moving from local systems to online cloud computing platforms such as Google Earth Engine (GEE) and Open Geospatial Engine (OGE). A typical approach in existing efforts is to leverage geospatial data cubes with cloud computing to support large-scale big EO data analytics in Digital Earth systems. While online analytical processing (OLAP) can be enabled using the cube approach, it is still not clear how geospatial artificial intelligence (GeoAI) can be incorporated in data cubes to benefit the cube infrastructure. Such an investigation can consolidate the vision of an AI-ready SDI (Spatial Data Infrastructure). The paper presents a systematic approach to incorporate GeoAI models into geospatial data cubes to help create an AI Cube. It covers on-demand model retrieval, cube data and model integration, and distributed model inference. The approach is demonstrated in OGE, which is an EO cloud computing platform layered on the GeoCube implementation. The results show that such an AI Cube enriches a cube infrastructure with GeoAI capabilities, facilitates the on-demand coupling of cube data and GeoAI models, and improves the performance of GeoAI inference.
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Taneja, Avina. "AI enabled right swipe: Enhancing user experience in dating apps." Journal of Global Communication 17, no. 2 (2024): 117–25. https://doi.org/10.5958/0976-2442.2024.00024.0.
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Kirti, Vasdev. "Enhancing Network Security with GeoAI and Real-Time Intrusion Detection." International Journal on Science and Technology 16, no. 1 (2025): 1–8. https://doi.org/10.5281/zenodo.14802799.
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GeoAI (Geospatial Artificial Intelligence) combines geospatial data analysis with AI capabilities to enhance decision-making. Its application in network security offers a revolutionary approach to detecting and mitigating cyber threats. This paper explores the integration of GeoAI with real-time intrusion detection systems (IDS), discussing theoretical foundations, practical applications, and challenges. Case studies illustrate GeoAI’s role in identifying geographically contextualized cyber threats. We also examine the integration of machine learning, geospatial analytics, and real-time processing to improve network resilience. Challenges like data privacy and system complexity are discussed, alongside future trends in GeoAI-enabled network security.
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Sofianopoulos, Sofianos, Antigoni Faka, and Christos Chalkias. "SDI-Enabled Smart Governance: A Review (2015–2025) of IoT, AI and Geospatial Technologies—Applications and Challenges." Land 14, no. 7 (2025): 1399. https://doi.org/10.3390/land14071399.
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This paper presents a systematic, narrative review of 62 academic publications (2015–2025) that explore the integration of spatial data infrastructures (SDIs) with emerging smart city technologies to improve local governance. SDIs provide a structured framework for managing geospatial data and, in combination with IoT sensors, geospatial and 3D platforms, cloud computing and AI-powered analytics, enable real-time data-driven decision-making. The review identifies four key technology areas: IoT and sensor technologies, geospatial and 3D mapping platforms, cloud-based data infrastructures, and AI analytics that uniquely contribute to smart governance through improved monitoring, prediction, visualization, and automation. Opportunities include improved urban resilience, public service delivery, environmental monitoring and citizen engagement. However, challenges remain in terms of interoperability, data protection, institutional barriers and unequal access to technologies. To fully realize the potential of integrated SDIs in smart government, the report highlights the need for open standards, ethical frameworks, cross-sector collaboration and citizen-centric design. Ultimately, this synthesis provides a comprehensive basis for promoting inclusive, adaptive and accountable local governance systems through spatially enabled smart technologies.
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Arjun Varma, Jampana Venkata. "YOLOv8-Enabled Real-Time Crop Health Monitoring with Conversational Diagnosis and Geospatial Support." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47655.
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Abstract—Agriculture is a cornerstone of global economies, supplying food, employment, and raw materials for numerous industries. Yet, one of the sector’s enduring challenges is crop disease, which can drastically reduce yields and threaten food security. Traditional approaches to identifying plant diseases rely on manual inspections and expert evaluations, which are often slow, costly, and vulnerable to human error. Without early diagnosis, diseases can spread uncontrollably, leading to major economic setbacks for farmers and decreased crop output. To overcome these issues, this project introduces an AI- powered system for detecting and diagnosing crop diseases. It combines advanced deep learning, natural language processing (NLP), and geospatial mapping technologies. At its core is YOLOv8 (You Only Look Once, version 8), a powerful Convolu- tional Neural Network (CNN) designed for real-time image-based detection. Trained on a robust, annotated dataset from Roboflow, the model accurately identifies a variety of diseases affecting key crops such as rice, wheat, and maize. Beyond detection, the system includes an intelligent chatbot powered by Large Language Models (LLMs). This virtual as- sistant offers instant, tailored advice on diagnosis, treatment options, and preventive strategies. It provides farmers with user- friendly guidance in natural language, making it accessible even to those with limited technical knowledge. The chatbot serves as a virtual agricultural consultant, recommending effective pesticides, organic treatments, and disease management practices. A standout feature of this project is its geospatial mapping capability. By integrating OpenStreetMap’s Overpass API, the system helps farmers locate nearby agricultural supply stores after a disease is identified. This allows quick access to the necessary products like pesticides or fertilizers, helping farmers respond promptly to disease outbreaks. Overall, the system presents a comprehensive AI-driven approach to crop disease management by combining image- based detection, interactive chatbot support, and location-based resource mapping. By reducing the dependency on manual inspection, enhancing decision-making, and streamlining access to agricultural inputs, it promotes more efficient, tech-enabled farming. This real-time, intelligent solution not only boosts productivity but also minimizes economic losses, paving the way for a more sustainable and resilient agricultural future. Keywords: Plant Disease Detection, YOLOv8 CNN, Agricul- tural AI, Intelligent Chatbot, LLMs in Farming, Plant Pathology AI, GIS Mapping, OpenStreetMap API, Smart Farming, AI- Enhanced Agriculture.
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Ghosh, Samadrita, Stephanie Ness, and Shruti Salunkhe. "The Role of AI Enabled Chatbots in Omnichannel Customer Service." Journal of Engineering Research and Reports 26, no. 6 (2024): 327–45. http://dx.doi.org/10.9734/jerr/2024/v26i61184.
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Currently, organizations are progressively embracing artificial intelligence (AI) and chatbots to transform omnichannel customer service in the modern digital age. This study examines the revolutionary impact of artificial intelligence (AI) and chatbots in providing seamless, personalized, and efficient consumer experiences across various communication channels. AI technology, such as machine learning and natural language processing, enable organizations to analyze large volumes of data, predict client requirements, and offer immediate support. Chatbots, functioning as artificial intelligence-powered virtual assistants, have a key function in interacting with customers through natural language conversations and providing immediate assistance through several channels, including websites, mobile apps, and messaging platforms. Through the utilization of artificial intelligence (AI) and chatbots, organizations may optimize efficiency, responsiveness, and personalization in consumer interactions, resulting in heightened satisfaction and loyalty. Nevertheless, the implementation of AI and chatbots in omnichannel customer service also gives rise to ethical concerns, including data protection, transparency, and fairness, which need to be resolved in order to guarantee appropriate utilization of these technologies. Notwithstanding these difficulties, the capacity of AI and chatbots to revolutionize the customer service industry is unquestionable, providing organizations with novel prospects to distinguish themselves and provide extraordinary experiences in the digital era. This article offers valuable insights into the developing patterns, difficulties, and possibilities linked to AI and chatbots in Omnichannel customer service. It emphasizes the revolutionary influence of these technologies on the future of managing customer experience.
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Alkahtani, Bader N. "The Impact of Artificial Intelligence on Quality of Life for Deaf and Hard of Hearing Students." American Annals of the Deaf 169, no. 4 (2024): 329–47. https://doi.org/10.1353/aad.2024.a946587.
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Abstract: This research study synthesizes research on applications of artificial intelligence (AI) to transform and improve quality of life for deaf and hard of hearing students. Twenty studies were analyzed, spanning domains including AI-enabled captioning, interpreters, personalized tutors, social robots, and assistive apps. Key findings demonstrate emerging AI innovations show promise for enhancing communication, learning, inclusion, and independence for deaf and hard of hearing youth. Technologies such as automatic speech recognition, natural language processing, and intelligent tutoring systems increased classroom participation and academic skills by removing barriers. However, small samples, lack of longitudinal data, and technology accessibility limitations affect the strength of conclusions. Additional rigorous comparative research is warranted, but cautious adoption of AI appears beneficial if implemented ethically. With thoughtful design and integration, AI technologies exhibit strong potential for creating more empowering, equitable learning for deaf and hard of hearing students. However, achieving an inclusive AI future requires ongoing collaboration among developers, researchers, and educators to ensure that innovations are leveraged to amplify capacities, not replace them.
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Can, R., S. Kocaman, and C. Gokceoglu. "DEVELOPMENT OF A CITSCI AND ARTIFICIAL INTELLIGENCE SUPPORTED GIS PLATFORM FOR LANDSLIDE DATA COLLECTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B5-2020 (August 24, 2020): 43–50. http://dx.doi.org/10.5194/isprs-archives-xliii-b5-2020-43-2020.
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Abstract. Geospatial data are fundamental to understand the relationship between the geographical events and the Earth dynamics. Although the geospatial technologies aid geodata collection, the increasing possibilities yield new application areas and cause even a greater demand. Considering the increment in data quantity and diversity, to be able to work with the data, they must be collected, stored, analysed and presented with the help of specifically designed platforms. Geographical Information Systems (GIS) with mobile and web support are the most suitable platforms for these purposes. On the other hand, the location-enabled mobile, web and geospatial technologies empowered the rise of the citizen science (CitSci) projects. With the CitSci, mobile GIS platforms enable the data to be collected from almost any location. As the size of the collected data increases, considering automatic control of the data quality has become a necessity. Integrating artificial intelligence (AI) with the CitSci based GIS designs allows automatic quality control of the data and helps eliminating data validation problem in CitSci. For this reason, the purpose of the present study is to develop a CitSci and AI supported GIS platform for landslide data collection because landslide hazard mitigation efforts require landslide susceptibility, hazard and risk assessments. Especially, landslide hazard assessments are necessary the time of occurrence of a landslide. Although this information is crucial, it is almost impossible to collect time of occurrence in regional hazard assessment efforts. Consequently, use of CitSci for this purpose may provide valuable information for landslide hazard assessments.
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Dakić, Vedran, Zlatan Morić, Ana Kapulica, and Damir Regvart. "Leveraging Microsoft sentinel and logic apps for automated cyber threat response." Edelweiss Applied Science and Technology 8, no. 6 (2024): 4319–48. http://dx.doi.org/10.55214/25768484.v8i6.2933.
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An integrated approach to automated cyber threat response is explored in this paper, with Microsoft Sentinel's Security Information and Event Management (SIEM) capabilities being leveraged alongside Logic Apps' workflow automation within the Azure ecosystem. Efficient identification and automated mitigation of security incidents are enabled by a combination of AI-driven analytics and advanced threat-hunting capabilities, resulting in a substantial reduction of manual intervention and response times. The approach is demonstrated to contribute scientifically across three core areas: (1) a novel integration of Sentinel's SIEM with Logic Apps is proposed to streamline response workflows using automated playbooks; (2) the effectiveness of the system is assessed through multiple cyber threat scenarios, including automated account blocking and virtual machine isolation in response to identified threats; and (3) Sentinel's performance is evaluated relative to other SIEM solutions, such as Splunk and IBM QRadar, particularly in Azure-centric and hybrid environments. The potential of Microsoft Sentinel and Logic Apps to advance proactive cybersecurity defenses is underscored, while key limitations in scalability and cross-platform adaptability are also identified.
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Shorbaji, Mohamad Fouad, Ali Abdallah Alalwan, and Raed Algharabat. "AI-Enabled Mobile Food-Ordering Apps and Customer Experience: A Systematic Review and Future Research Agenda." Journal of Theoretical and Applied Electronic Commerce Research 20, no. 3 (2025): 156. https://doi.org/10.3390/jtaer20030156.
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Artificial intelligence (AI) is reshaping mobile food-ordering apps, yet its impact on customer experience (CX) has not been fully mapped. Following systematic review guidelines (PRISMA 2020), a search of SCOPUS, Web of Science, ScienceDirect, and Google Scholar in March 2025 identified 55 studies published between 2022 and 2025. Since 2022, research has expanded from intention-based studies to include real-time app interactions and live app experiments. This shift has helped to identify five key CX dimensions: (1) instrumental usability: how quickly and smoothly users can order; (2) personalization value: AI-generated menus and meal suggestions; (3) affective engagement: emotional appeal of the app interface; (4) data trust and procedural fairness: users’ confidence in fair pricing and responsible data handling; (5) social co-experience: sharing orders and interacting through live reviews. Studies have shown that personalized recommendations and chatbots enhance relevance and enjoyment, while unclear surge pricing, repetitive menus, and algorithmic anxiety reduce trust and satisfaction. Given the limitations of this study, including its reliance on English-only sources, a cross-sectional design, and limited cultural representation, future research should investigate long-term usage patterns across diverse markets. This approach would help uncover nutritional biases, cultural variations, and sustained effects on customer experience.
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R., Senthil Prabhu, Priyanka R., and Rajasoundarya S. "Artificial Intelligence – Emerging Trends in Management of Pandemics." Pharmaceutical and Chemical Journal 8, no. 2 (2021): 6–17. https://doi.org/10.5281/zenodo.13960762.
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Digital Health strategically leverages digital technologies, empowering patients to manage their illness, reduce health risk, and promote health and wellness. This era of digital innovation within our health care system provides new alternatives for managing chronic conditions, diagnosing diseases earlier, and promoting prevention. The extensive scope of digital health encompasses technologies such as mobile health (m Health) apps; artificial intelligence (AI) enabled medical devices, and smart connected devices. Artificial intelligence (AI) is a branch of computer science that deals with the problem solving by the aid of symbolic programming. One of the pivotal applications of AI in development of the expert system AI is not new concept, yet it has been accepted in many areas such as education, business, medical and manufacturing. Health care delivery requires the support of new technologies like AI, Internet of things (IoT), big data and machine learning to fight and look ahead against the new pandemic diseases. The role of AI as a decisive technology to analyze, prepare us for prevention and fight with COVID-19 pandemic. This descriptive article gives a broad overview of AI in medicine, pharmacy dealing with terms and concepts as well as the current and future applications.
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Cresswell, Kathrin, Ahsen Tahir, Zakariya Sheikh, et al. "Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence–Enabled Social Media Analysis." Journal of Medical Internet Research 23, no. 5 (2021): e26618. http://dx.doi.org/10.2196/26618.
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Background The emergence of SARS-CoV-2 in late 2019 and its subsequent spread worldwide continues to be a global health crisis. Many governments consider contact tracing of citizens through apps installed on mobile phones as a key mechanism to contain the spread of SARS-CoV-2. Objective In this study, we sought to explore the suitability of artificial intelligence (AI)–enabled social media analyses using Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the United Kingdom. Methods We extracted and analyzed over 10,000 relevant social media posts across an 8-month period, from March 1 to October 31, 2020. We used an initial filter with COVID-19–related keywords, which were predefined as part of an open Twitter-based COVID-19 dataset. We then applied a second filter using contract tracing app–related keywords and a geographical filter. We developed and utilized a hybrid, rule-based ensemble model, combining state-of-the-art lexicon rule-based and deep learning–based approaches. Results Overall, we observed 76% positive and 12% negative sentiments, with the majority of negative sentiments reported in the North of England. These sentiments varied over time, likely influenced by ongoing public debates around implementing app-based contact tracing by using a centralized model where data would be shared with the health service, compared with decentralized contact-tracing technology. Conclusions Variations in sentiments corroborate with ongoing debates surrounding the information governance of health-related information. AI-enabled social media analysis of public attitudes in health care can help facilitate the implementation of effective public health campaigns.
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Lawal, Oluwatoyin O., Nawari O. Nawari, and Omobolaji Lawal. "AI-Enabled Cognitive Predictive Maintenance of Urban Assets Using City Information Modeling—Systematic Review." Buildings 15, no. 5 (2025): 690. https://doi.org/10.3390/buildings15050690.
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Predictive maintenance of built assets often relies on scheduled routine practices that are disconnected from real-time stress assessment, degradation and defects. However, while Digital Twin (DT) technology within building and urban studies is maturing rapidly, its use in predictive maintenance is limited. Traditional preventive and reactive maintenance strategies that are more prevalent in facility management are not intuitive, not resource efficient, cannot prevent failure and either underserve the asset or are surplus to requirements. City Information Modeling (CIM) refers to a federation of BIM models in accordance with real-world geospatial references, and it can be deployed as an Urban Digital Twin (UDT) at city level, like BIM’s deployment at building level. This study presents a systematic review of 105 Scopus-indexed papers to establish current trends, gaps and opportunities for a cognitive predictive maintenance framework in the architecture, engineering, construction and operations (AECO) industry. A UDT framework consisting of the CIM of a section of the University of Florida campus is proposed to bridge the knowledge gap highlighted in the systematic review. The framework illustrates the potential for CNN-IoT integration to improve predictive maintenance through advance notifications. It also eliminates the use of centralized information archiving.
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Bajwa, Ammar. "AI-BASED EMERGENCY RESPONSE SYSTEMS: A SYSTEMATIC LITERATURE REVIEW ON SMART INFRASTRUCTURE SAFETY." American Journal of Advanced Technology and Engineering Solutions 1, no. 01 (2025): 174–200. https://doi.org/10.63125/xcxwpv34.
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Artificial intelligence (AI)-based emergency response systems have emerged as critical enablers of smart infrastructure safety, offering enhanced real-time decision-making, risk assessment, and disaster mitigation strategies across various domains. This systematic literature review, encompassing 424 eligible studies, investigates the integration of machine learning (ML), deep learning (DL), computer vision, IoT-enabled predictive analytics, and AI-powered robotics in optimizing emergency response mechanisms. The study comprehensively examines AI applications in disaster management, real-time incident detection, healthcare emergency response, industrial hazard prevention, cybersecurity frameworks, and intelligent traffic control, providing a detailed assessment of technological advancements and challenges in AI adoption. The findings reveal that AI has significantly improved predictive accuracy, automated hazard detection, and emergency resource optimization, leading to faster response times, minimized human error, and enhanced situational awareness in crisis management. AI-driven predictive analytics models have enabled early warning systems for earthquakes, floods, and wildfires, facilitating proactive disaster preparedness and risk mitigation. In real-time emergency response, AI-powered computer vision and sensor-based surveillance technologies have improved incident detection, reducing intervention delays and ensuring more efficient allocation of emergency resources. In the healthcare sector, AI-enhanced diagnostic tools, triage automation, and geospatial analytics for ambulance dispatch have streamlined medical crisis management, improving survival rates and reducing treatment delays. Additionally, AI-integrated industrial safety frameworks, robotic automation, and cybersecurity intelligence systems have strengthened workplace hazard prevention, cyber threat detection, and emergency communication resilience, ensuring safer and more secure operational environments. Despite these advancements, several challenges related to interoperability, regulatory constraints, cybersecurity vulnerabilities, algorithmic biases, and ethical concerns persist, hindering large-scale AI adoption in emergency response systems. This review provides a comprehensive synthesis of AI’s transformative role in modern emergency management, offering insights into technological developments, limitations, and policy considerations necessary to enhance AI-driven crisis response strategies and ensure more effective, scalable, and resilient emergency safety infrastructures worldwide.
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Shah, Krishna Bikram, S. Visalakshi, and Ranjit Panigrahi. "AI-enabled geospatial solutions for waste collection and forecasting for smart cities application: insights from Kathmandu municipality." Future Technology 4, no. 1 (2025): 37–49. https://doi.org/10.55670/fpll.futech.4.1.5.
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Effective waste management is a critical global concern, especially in urban areas where efficient systems are essential for reducing litter, minimizing environmental contamination, and enhancing urban aesthetics. This study presents a comprehensive framework for optimizing waste management in Kathmandu Municipality, focusing on the spatial allocation of collection points, bin requirements, and predictive waste level modeling. This approach is based on the primary parameters of the rate at which waste is generated, the capacity of a bin, the density of waste, and the frequency of collection. The model also accommodates waste segregation-this means effective bin deployment across categories of waste to avoid wasting resources. It includes a time-series forecasting model, simulating waste accumulation for 7 days with seasonality influences, holiday influence, fluctuation of the population, and socio-economic influence. Trend of generation waste is reported at an interval of 6 hours in order to enhance precision within the schedule of collecting wastes. Lower risk of overflow of the bins due to the services before bins overflow. This holistic framework shall consequently provide data-driven scalable solutions to Kathmandu Municipality in optimizing its routes for collecting wastes, enhancing resource efficiency, and adapting the patterns of producing wastes on a real-time basis.
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Elkhodr, Mahmoud, Ergun Gide, Omar Darwish, and Shorouq Al-Eidi. "BioChainReward: A Secure and Incentivised Blockchain Framework for Biomedical Data Sharing." International Journal of Environmental Research and Public Health 20, no. 19 (2023): 6825. http://dx.doi.org/10.3390/ijerph20196825.
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In the era of digital healthcare, biomedical data sharing is of paramount importance for the advancement of research and personalised healthcare. However, sharing such data while preserving user privacy and ensuring data security poses significant challenges. This paper introduces BioChainReward (BCR), a blockchain-based framework designed to address these concerns. BCR offers enhanced security, privacy, and incentivisation for data sharing in biomedical applications. Its architecture consists of four distinct layers: data, blockchain, smart contract, and application. The data layer handles the encryption and decryption of data, while the blockchain layer manages data hashing and retrieval. The smart contract layer includes an AI-enabled privacy-preservation sublayer that dynamically selects an appropriate privacy technique, tailored to the nature and purpose of each data request. This layer also features a feedback and incentive mechanism that incentivises patients to share their data by offering rewards. Lastly, the application layer serves as an interface for diverse applications, such as AI-enabled apps and data analysis tools, to access and utilise the shared data. Hence, BCR presents a robust, comprehensive approach to secure, privacy-aware, and incentivised data sharing in the biomedical domain.
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Shukla, Arvind Kumar, and V. Suresh Kumar. "Cloud Computing with Artificial Intelligence Techniques for Effective Disease Detection." International Journal of Data Informatics and Intelligent Computing 2, no. 1 (2023): 32–41. http://dx.doi.org/10.59461/ijdiic.v2i1.45.
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With the current rapid advancement of cloud computing (CC) technology, which enabled the connectivity of many intelligent objects and detectors and created smooth data interchange between systems, there is now a strict need for platforms for data processing, the Internet of Things (IoT), and data management. The field of medicine in CC is receiving a lot of attention from the scientific world, as well as the private and governmental sectors. Thousands of individuals now have a digital system due to these apps where they may regularly obtain helpful medical advice for leading a healthy life. The use of artificial intelligence (AI) in the medical field has several advantages, including the ability to automate processes and analyze large patient databases to offer superior medicine more quickly and effectively. IoT-enabled smart health tools provide both internet solutions and a variety of features. CC infrastructure improves these healthcare solutions by enabling safe storage and accessibility. We suggest a novel Cloud computing and artificial intelligence (CC-AI) premised smart medical solution for surveillance and detecting major illnesses to provide superior solutions to the users. For disease detection, we suggested AI-based whale optimization (WO) and fuzzy neural network (FNN) (WO-FNN). Patients' IoT wearable sensor data is gathered for detection. The accuracy, sensitivity, specificity, and computation time are evaluated and compared with existing techniques.
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TOBOULA,, Coffi Martinien ZOUNHIN. "Exploring the Impact ofAI-powered Collaborative and Interactive NLP Apps on EFL Teaching in the Post-COVID-19 Era." International Journal on Cybernetics & Informatics 12, no. 2 (2023): 171–93. http://dx.doi.org/10.5121/ijci.2023.120213.
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The COVID-19 pandemic has disrupted traditional language learning, leading to a shift towards online teaching and requiring new approaches to language education. This study examines the effectiveness of AI-powered collaborative and interactive Natural Language Processing (NLP) applications on English as a Foreign Language (EFL) instruction in a post-COVID-19 online education environment. The study used a mixed-methods approach, incorporating statistical and in-depth qualitative data gathering and processing strategies. EFL teachers and students from the University of Abomey-Calavi (UAC) in Benin were surveyed, interviewed, and observed during online language learning sessions. The data were analysed using both descriptive and inferential statistics. The study employed questionnaire surveys to analyse quantitative data and used the thematic (content) analysis method to isolate the most important trends and themes hidden within the qualitative data collected through semi-structured interviews and online class observations. Results showed the challenges and opportunities of using AI-powered collaborative and interactive language learning in EFL teaching, the learning methodologies and assessment approaches used in AI-enabled collaborative e-learning, the role of technology in supporting pervasive learning, and the impact of professional development for teachers in ICT on integrating AI-assisted collaborative e-learning in EFL instruction. The findings offer new perspectives on the effects of AI-supported collaborative and interactive language learning on EFL teaching and its implications for EFL teachers and students in the post-pandemic era.
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Biliavska, Valeriia, Rui Alexandre Castanho, and Ana Vulevic. "Analysis of the Impact of Artificial Intelligence in Enhancing the Human Resource Practices." Journal of Intelligent Management Decision 1, no. 2 (2022): 128–36. http://dx.doi.org/10.56578/jimd010206.
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Artificial intelligence, in a larger sense, refers to computers that have human intelligence-specific capabilities such as obtaining information, perceiving, seeing, thinking, and making decisions. At first glance, artificial intelligence, often known as "Artificial Intelligence" (AI) in the literature, causes everyone to associate something distinct. According to researches, the concept of artificial intelligence evokes an electro-mechanical robot replacing human beings, but everyone involved in this field is aware that there is a definite difference between human beings and machines. The aim of this article is to show the importance of using AI in today’s HR practices. In this context, one of the qualitative research designs, phenomenological research, was deemed 1appropriate for the thesis study. Because phenomenology establishes a framework for exploring subjects that aren't utterly unfamiliar but whose meaning isn't quite clear.AI-based HR apps have the ability to boost employee productivity while also assisting HR personnel in becoming educated advisers who can boost employee performance. AI-enabled HR solutions are capable of evaluating, predicting, diagnosing, and locating more powerful and capable employees.
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Alzahrani, Adel, Valerie Gay, and Ryan Alturki. "Enabled Artificial Intelligence (AI) to Develop Sehhaty Wa Daghty App of Self-Management for Saudi Patients with Hypertension: A Qualitative Study." Information 14, no. 6 (2023): 334. http://dx.doi.org/10.3390/info14060334.
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(1) Background: The prevalence of uncontrolled hypertension is rising all across the world, making it a concern for public health. The usage of mobile health applications has resulted in a number of positive outcomes for the management and control of hypertension. (2) Objective: The study’s primary goal is to explain the steps to create a hypertension application (app) that considers cultural and social standards in Saudi Arabia, motivational features, and the needs of male and female Saudi citizens. (3) Methods: This study reports the emerged features and content needed to be adapted or developed in health apps for hypertension patients during an interactive qualitative analysis focus group activity with (n = 5) experts from the Saudi Ministry of Health. A gap analysis was conducted to develop an app based on a deep understanding of user needs with a patient-centred approach. (4) Results: Based on the participant’s reviews in this study, the app was easy to use and can help Saudi patients to control their hypertension, the design was interactive, motivational features are user-friendly, and there is a need to consider other platforms such as Android and Blackberry in a future version. (5) Conclusions: Mobile health apps can help Saudis change their unhealthy lifestyles. Target users, usability, motivational features, and social and cultural standards must be considered to meet the app’s aim.
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Lee, Jung-Chieh, and Rongrong Lin. "The continuous usage of artificial intelligence (AI)-powered mobile fitness applications: the goal-setting theory perspective." Industrial Management & Data Systems 123, no. 6 (2023): 1840–60. http://dx.doi.org/10.1108/imds-10-2022-0602.
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PurposeDue to the popularity of mobile devices and the development of artificial intelligence (AI), AI-powered mobile fitness applications (MFAs) have entered people's daily lives. However, the extant literature lacks empirical investigations that explore users' continuance usage intentions regarding AI-powered MFAs. To fill this research gap, this paper employs goal-setting theory to establish a research model for exploring how AI-enabled features (i.e. intelligence and anthropomorphism) affect users' perceptions of goal difficulties and goal specificities, which in turn affect their MFA continuance usage intentions.Design/methodology/approachThis paper uses a survey method to analyze the research model, and a total of 223 responses are collected. The partial least squares (PLS) technique is utilized for data analysis.FindingsThe results show that intelligence and anthropomorphism affect the continuance usage intention of MFA users through their goal difficulty and specificity. Both intelligence and anthropomorphism positively influence goal specificity, whereas they negatively affect goal difficulty. In addition, goal specificity increases users' MFA continuance usage intention, whereas goal difficulty decreases users' continuance usage intention. The findings of this study provide theoretical contributions for AI technology adoption research and offer practical strategies for firms to retain MFA users.Originality/valueBased on goal-setting theory, this study reveals that as two primary AI features of contemporary mobile fitness apps, intelligence and anthropomorphism, can increase comprehension of users' perceptions regarding goal difficulty and specificity in the context of users' continuance usage intentions toward AI-powered MFAs.
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N G, Suma. "Diagnosis of Acute Diseases Using AI (RogiDoot)." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40685.
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Rural healthcare in India faces critical challenges due to limited infrastructure, a shortage of medical professionals, and difficulties in accessing timely diagnostics. Advancements in Artificial Intelligence (AI) and the Internet of Things (IoT) offer significant potential to bridge gaps in healthcare delivery. This paper examines the integration of AI and IoT technologies in improving healthcare accessibility, focusing on the RogiDoot app—an innovative platform aimed at enhancing healthcare services in rural areas. Drawing on recent advancements in AI-powered diagnostics, IoT-based health monitoring, and telemedicine, the study showcases how RogiDoot addresses critical challenges, including early disease detection, real-time health data tracking, and providing inclusive care with multi-language support. AI techniques such as predictive analytics and decision-support systems complement IoT's role in wearable health monitoring, enabling efficient healthcare services.By leveraging insights from case studies and prior research on AI and IoT in healthcare systems, this study demonstrates RogiDoot's potential to improve patient outcomes, reduce healthcare inequities, and create a scalable model for rural regions. This paper contributes to the growing discourse on how innovative technologies can revolutionize rural healthcare and promote equitable access to medical resources. Key Words: Rural Healthcare, Artificial Intelligence (AI), Internet of Things (IoT), AI Diagnostics, Health Monitoring Systems, Telemedicine, Digital Healthcare Solutions, Remote Patient Monitoring, Predictive Analytics in Healthcare, IoT-Enabled Devices, Healthcare Accessibility, Multi-Language Support in Healthcare Apps, Real-Time Health Data, Early Disease Detection, Scalable Healthcare Technology
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Siolos, Pavlos, Saif Pasha, Maria Triantafyllou, et al. "Harnessing AI for Improved Diagnosis and Management of Pediatric Sepsis: Current Advances, Challenges, and Future Directions." Pediatric Emergency Care 41, no. 7 (2025): 576–85. https://doi.org/10.1097/pec.0000000000003397.
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Artificial intelligence (AI) has been applied to early recognition and management of rapidly progressive, community-acquired pediatric sepsis, a leading cause of childhood mortality. The broad adoption of electronic health records combined with rapid advances in digital technologies have enabled the federated training of both knowledge-driven AI, known as expert systems, trained by teams of collaborating clinicians, and data-driven AI, known as machine learning (ML), to derive predictive, clustering algorithms trained on “big data.” An important subset of ML is “deep learning,” which includes tools that understand, interpret, and manipulate human imagery and language, such as natural language processing and its subset large language models. We are in an era of rapid deployment of AI/ML-powered tools ranging from real-time electronic health records–embedded decision support tools to continuous wearable vital sign monitors and mobile/conversational virtual assistants/triage apps. These applications have the potential of transforming the timeliness of life-saving sepsis care delivery. This review explores the current and potential AI/ML applications in sepsis care, including tools for screening/early detection, risk stratification/outcome prediction, personalized treatment, and continuous patient monitoring. We highlight successful implementations and ongoing clinical trials, emphasizing the impact on patient outcomes. Finally, we address practical considerations for the future, such as bias mitigation and integration into clinical workflows.
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Swaroopa, D. "Smart PH Monitoring System: IOT, Cloud and AI Driven Solutions." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46791.
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Abstract The Smart pH Monitoring System addresses the challenge of effective air quality awareness in a time of growing environmental concern. Many individuals remain unaware of harmful pH levels and pollution indicators in their immediate surroundings, leading to exposure without timely preventive action. Current monitoring apps often rely on generalized or third-party data, lack precise location-based feedback, and do not support user-specific interactions or real-time environmental visibility. A key issue is the absence of IoT integration and live geospatial mapping, which limits the ability to deliver personalized and actionable insights. Most existing systems fail to incorporate intelligent assistants and lack support for offline environments, reducing accessibility. The proposed Smart pH Monitoring System tackles these issues by utilizing IoT sensors to measure real-time pH levels, which are visualized in a mobile application built with Flutter. Users log in securely through Firebase Authentication, after which they are presented with a Google Maps interface displaying their current location. A color-coded circular overlay spanning a 1 km radius indicates the pH severity level—green for safe, yellow for moderate, and red for hazardous. The current pH value is displayed beneath the map, offering concise and critical insight. Additionally, the app includes an AI-powered chatbot to address user queries and offer tips related to pollution and safety. The system ensures cloud-based data logging through ThingSpeak and Firebase, with offline support for continued usability. This dual-layered approach—real-time monitoring combined with interactive guidance—empowers users to make informed decisions and contributes toward greater environmental consciousness. Index Terms – IoT, Flutter, Firebase Authentication, ThingSpeak, Google Maps API, pH Monitoring, Air Quality Index, AI Chatbot, Cloud Integration, Environmental Awareness
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Choudhury, Avishek, and Onur Asan. "Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review." JMIR Medical Informatics 8, no. 7 (2020): e18599. http://dx.doi.org/10.2196/18599.
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Background Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. Objective The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. Methods We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. Results We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. Conclusions This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.
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Passmore, Jonathan, and Woody Woodward. "Coaching education: Wake up to the new digital and AI coaching revolution!" International Coaching Psychology Review 18, no. 1 (2023): 58–72. http://dx.doi.org/10.53841/bpsicpr.2023.18.1.58.
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In this article we argue that coach education has been through three distinct phases of development over the past three decades: 1990-2020. These phrases reflect changes in the coaching industry, which itself has seen significant change over the same period. These phases include ‘pre-profession’, reflected in ad hoc and non-qualification based training, ‘practice based professionalisation’, which saw a growth in small scale coach providers using professional body competencies, and ‘evidenced-based professionalisation’, which stimulated the growth in university based coach education programmes focused on evidenced based and research informed training. We argue that as we enter the Mid 2020’s we are witnessing a new shift in the coaching industry from ‘professionalisation’ to ‘productization’, with the emergence of large scale, digitally enabled, coaching providers. These new providers employ thousands of home working coaches and are focused on delivering coaching at scale to tens of thousands of workers in enterprise size organisations using digital channels. This industrial change calls for a need to rethink and modernise coach education. We must acknowledge the shift towards the management of industrial scale delivery and the focus on data, alongside a movement towards mastery of the technologies which have enabled coaches to work globally. We conclude by suggesting coach education should offer two new career pathways: one for those commissioning and managing coaching services and a second for those working in digital coaching firms in coaching service management, in roles such as Customer Success and Coach Relations, alongside a revitalised coach training which equips coaches to operate in digital environments through a mastery of the communication platforms, tools and apps which they employ and a deeper understanding of new technologies such as AI, VR and MR.
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Wang, Hanqing. "Analysis Of Deutsche Bank's Sales Policy——Impact of The Digital Transformation on Deutsche Bank's Sales Policy." Highlights in Business, Economics and Management 52 (March 25, 2025): 115–23. https://doi.org/10.54097/ysaea050.
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The digital transformation has significantly reshaped various industries, particularly the financial sector. This paper examines the Deutsche Bank's transformation of its sales strategy through the adoption of digital technologies. The shift from traditional face-to-face banking to digital channels, including online banking and mobile apps, has enabled Deutsche Bank to offer customers greater accessibility and improved services. The integration of artificial intelligence (AI), big data, the Internet of Things (IoT), cloud computing, and blockchain technologies has enhanced operational efficiency, reduced costs, and allowed for more personalized financial solutions. However, despite these advancements, Deutsche Bank faces several challenges, including internal resistance to change, integration of new technologies into legacy systems, and competition from digital-first banks and fintech companies. The paper discusses these challenges and proposes strategies for optimizing Deutsche Bank's digital transformation, including strengthening digital infrastructure and customer-oriented services.
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A.Shaji, George, and S.Sagayarajan. "Acoustic Eavesdropping: How AIs Can Steal Your Secrets by Listening to Your Typing." Partners Universal International Innovation Journal ( PUIIJ) 01, no. 04 (2023): 1–14. https://doi.org/10.5281/zenodo.8260814.
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<strong>Abstract -</strong> Recent advances in artificial intelligence have enabled a disturbing new form of cyberattack - acoustic side channel attacks, where AIs can identify keystrokes and steal passwords simply by listening to the sounds of typing. A study published in 2021 demonstrated this is possible with 93% accuracy using common video conferencing apps like Zoom. By analyzing audio recordings, AIs can detect the precise timings between keystrokes, and map patterns to reveal what is being typed. This poses serious risks, as acoustic eavesdropping can steal passwords, private messages, credit card numbers and other sensitive information. This paper examines the technical details of how acoustic side channel attacks work, using machine learning algorithms to match audio signals to keystroke inputs. It outlines real-world examples of AIs stealing credentials, text messages, and financial data simply by listening to typing sounds. The paper then discusses the broader dangers of acoustic snooping attacks, which exploit ubiquitous apps like video calls to turn devices into surveillance bugs. With acoustic attacks operating silently in the background, users may have their most confidential information extracted without their knowledge. To defend against this novel threat, the paper provides recommendations like using strong randomized passwords, enabling two-factor authentication, and avoiding typing sensitive information during video calls in public spaces. It also explores emerging countermeasures like audio masking, localized jamming signals, and AI detection of acoustic anomalies. However, these defenses are still in early stages. The paper concludes with an outlook on the future of acoustic security, emphasizing the need for continued research and increased user awareness to combat the disturbing privacy risks posed by AI listening hacks.
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Kumar, Mr Lachigoudugari Pavan. "Automated Blood Bank Management System: Integrating RFID and AI for Real-Time Inventory Tracking." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5581–87. https://doi.org/10.22214/ijraset.2025.69387.
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Traditional blood bank systems face critical inefficiencies due to manual processes, leading to delays, errors, and significant blood wastage. This paper proposes an Automated Blood Bank Management System leveraging RFID technology and AI-driven analytics to optimize real-time inventory tracking, reduce wastage, and enhance emergency response. The system integrates RFID tags for unit-level monitoring, enabling precise tracking of blood stock levels, expiry dates, and storage conditions. AI algorithms predict demand patterns and prioritize emergency requests through dynamic priority queuing, reducing response times by 40% compared to manual systems. A hybrid cloud-edge architecture ensures scalability, while compliance with WHO and FDA standards is maintained through AES-256 encryption and role-based access control. Through automated SMS/IVR reminders, a mid-sized hospital's pilot study showed a 25% increase in donor retention and a 30% decrease in blood waste. The IoT-enabled dashboard of the system offers real-time information into compliance audits, donor involvement, and inventory trends. The findings demonstrate how the framework's inclusive design—which includes multilingual voice interfaces for non-tech users—can bridge healthcare gaps between urban and rural areas. By fusing AI's predictive capabilities with RFID's granular tracking, our work enhances blood bank automation and provides a scalable solution for international healthcare systems. AI-powered mobile apps for tailored donor interaction and blockchain integration for impenetrable audits are examples of future additions.
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Oyetunji, Tosin Samuel, Fasasi Lanre Erinjogunola, Rasheed O. Ajirotutu, Abiodun Benedict Adeyemi, Tochi Chimaobi Ohakawa, and Saliu Alani Adio. "Smart Data-Driven Analysis of Affordable Housing Crisis Impact on Underserved Communities." International Journal of Multidisciplinary Research and Growth Evaluation 5, no. 1 (2024): 1617–25. https://doi.org/10.54660/.ijmrge.2024.5.1.1617-1625.
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The affordable housing crisis has become a critical socio-economic challenge, disproportionately affecting underserved communities and exacerbating inequalities. Outdated data, reactive strategies, and inefficiencies in resource allocation have often hindered traditional policy interventions. This paper explores the role of smart data-driven analysis in addressing housing affordability challenges through predictive analytics, big data integration, and artificial intelligence. By leveraging large-scale datasets, including census data, rental market trends, and socioeconomic indicators, data-driven methodologies can provide real-time insights into housing affordability, enabling policymakers to design targeted and proactive interventions. The study reviews existing research on housing affordability, highlighting the limitations of conventional approaches and the emerging role of machine learning, geospatial analysis, and blockchain in improving transparency and efficiency in housing markets. A methodological framework is proposed that integrates predictive modeling, geographic information systems, and real-time data processing to assess housing crises at both macro and micro levels. Furthermore, the paper discusses policy implications and technology-enabled solutions such as AI-driven rent control models, smart subsidies, and blockchain-based housing registries. Key findings emphasize the necessity of public-private collaboration, ethical AI implementation, and the development of dynamic affordability tracking systems. Future research directions include refining predictive modeling techniques, enhancing real-time monitoring capabilities, and exploring AI-driven decision-support systems for urban planning. This paper provides a foundation for more effective, equitable, and sustainable housing interventions by integrating data science and housing policy.
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Dr., A. Saravanakumar. "Innovations of Digitalisation of Indian Financial Sector." SEKA : JOURNAL OF MULTIDISCIPLINARY STUDIES 1, no. 1 (2024): 27. https://doi.org/10.5281/zenodo.13977094.
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Abstract The financial sector has witnessed one of the most significant evolutions and continues to grow and expand. One of the biggest changing factors for the banking sector was the pandemic .With the lockdown enforcement, people started moving more towards digital banking for their financial needs. While digitization has been integrated into this sector for some time, it was only during the pandemic that people had to actually take the time to understand its benefits and make the most of these services Digital technology has enabled banks to offer a wide range of financial services to customers through online platforms, mobile apps, and other digital channels, allowing customers to understand the many benefits being offered and choose between different plans and packages.
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Shayegh, Soheil, Javier Andreu-Perez, Caroline Akoth, et al. "Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework." PLOS ONE 18, no. 8 (2023): e0275037. http://dx.doi.org/10.1371/journal.pone.0275037.
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Objectives To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs). Methods A combination of geospatial data analysis and artificial intelligence methods for evaluating vulnerability factors at the local level and allocate vaccines according to a dynamic mechanism for updating vulnerability and vaccine uptake. Results A novel approach is introduced including (I) Vulnerability data collection (including country-specific data on demographic, socioeconomic, epidemiological, healthcare, and environmental factors), (II) Vaccination prioritization through estimation of a unique Vulnerability Index composed of a range of factors selected and weighed through an Artificial Intelligence (AI-enabled) expert elicitation survey and scientific literature screening, and (III) Values consideration by identification of the most effective GIS-assisted allocation of vaccines at the local level, considering context-specific constraints and objectives. Conclusions We showcase the performance of the 3Vs strategy by comparing it to the actual vaccination rollout in Kenya. We show that under the current strategy, socially vulnerable individuals comprise only 45% of all vaccinated people in Kenya while if the 3Vs strategy was implemented, this group would be the first to receive vaccines.
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Biswas, Milon, Marzia Hoque Tania, M. Shamim Kaiser, Russell Kabir, Mufti Mahmud, and Atika Ahmad Kemal. "ACCU3RATE: A mobile health application rating scale based on user reviews." PLOS ONE 16, no. 12 (2021): e0258050. http://dx.doi.org/10.1371/journal.pone.0258050.
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Background Over the last decade, mobile health applications (mHealth App) have evolved exponentially to assess and support our health and well-being. Objective This paper presents an Artificial Intelligence (AI)-enabled mHealth app rating tool, called ACCU3RATE, which takes multidimensional measures such as user star rating, user review and features declared by the developer to generate the rating of an app. However, currently, there is very little conceptual understanding on how user reviews affect app rating from a multi-dimensional perspective. This study applies AI-based text mining technique to develop more comprehensive understanding of user feedback based on several important factors, determining the mHealth app ratings. Method Based on the literature, six variables were identified that influence the mHealth app rating scale. These factors are user star rating, user text review, user interface (UI) design, functionality, security and privacy, and clinical approval. Natural Language Toolkit package is used for interpreting text and to identify the App users’ sentiment. Additional considerations were accessibility, protection and privacy, UI design for people living with physical disability. Moreover, the details of clinical approval, if exists, were taken from the developer’s statement. Finally, we fused all the inputs using fuzzy logic to calculate the new app rating score. Results and conclusions ACCU3RATE concentrates on heart related Apps found in the play store and App gallery. The findings indicate the efficacy of the proposed method as opposed to the current device scale. This study has implications for both App developers and consumers who are using mHealth Apps to monitor and track their health. The performance evaluation shows that the proposed mHealth scale has shown excellent reliability as well as internal consistency of the scale, and high inter-rater reliability index. It has also been noticed that the fuzzy based rating scale, as in ACCU3RATE, matches more closely to the rating performed by experts.
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Abdi, Sarah, Luc de Witte, and Mark Hawley. "Exploring the Potential of Emerging Technologies to Meet the Care and Support Needs of Older People: A Delphi Survey." Geriatrics 6, no. 1 (2021): 19. http://dx.doi.org/10.3390/geriatrics6010019.
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Some emerging technologies have potential to address older people’s care and support needs. However, there is still a gap in the knowledge on the potential uses of these technologies in some care domains. Therefore, a two-round Delphi survey was conducted to establish a consensus of opinion from a group of health and social technology experts (n = 21) on the potential of 10 emerging technologies to meet older people’s needs in five care and support domains. Experts were also asked to provide reasons for their choices in free-text spaces. The consensus level was set at 70%. Free-text responses were analyzed using thematic analysis. Voice activated devices was the technology that reached experts consensus in all assessed care domains. Some technologies (e.g., Artificial intelligence (AI) enabled apps and wearables and Internet of things (IoT) enabled homes) also show potential to support basic self-care and access to healthcare needs of older people. However, most of the remaining technologies (e.g., robotics, exoskeletons, virtual and augmented reality (VR/AR)) face a range of technical and acceptability issues that may hinder their adoption by older people in the near future. Findings should encourage the R & D community to address some of the identified challenges to improve the adoption of emerging technologies by older people.
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Malik, Tanya, Adrian Jacques Ambrose, and Chaitali Sinha. "Evaluating User Feedback for an Artificial Intelligence–Enabled, Cognitive Behavioral Therapy–Based Mental Health App (Wysa): Qualitative Thematic Analysis." JMIR Human Factors 9, no. 2 (2022): e35668. http://dx.doi.org/10.2196/35668.
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Background Digital mental health apps are rapidly becoming a common source of accessible support across the world, but their effectiveness is often influenced by limited helpfulness and engagement. Objective This study’s primary objective was to analyze feedback content to understand users’ experiences with engaging with a digital mental health app. As a secondary objective, an exploratory analysis captured the types of mental health app users. Methods This study utilized a user-led approach to understanding factors for engagement and helpfulness in digital mental health by analyzing feedback (n=7929) reported on Google Play Store about Wysa, a mental health app (1-year period). The analysis of keywords in the user feedback categorized and evaluated the reported user experience into the core domains of acceptability, usability, usefulness, and integration. The study also captured key deficits and strengths of the app and explored salient characteristics of the types of users who benefit from accessible digital mental health support. Results The analysis of user feedback found the app to be overwhelmingly positively reviewed (6700/7929, 84.50% 5-star rating). The themes of engaging exercises, interactive interface, and artificial intelligence (AI) conversational ability indicated the acceptability of the app, while the nonjudgmentality and ease of conversation highlighted its usability. The app’s usefulness was portrayed by themes such as improvement in mental health, convenient access, and cognitive restructuring exercises. Themes of privacy and confidentiality underscored users’ preference for the integrated aspects of the app. Further analysis revealed 4 predominant types of individuals who shared app feedback on the store. Conclusions Users reported therapeutic elements of a comfortable, safe, and supportive environment through using the digital mental health app. Digital mental health apps may expand mental health access to those unable to access traditional forms of mental health support and treatments.
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Ruan, Yichen, Xiaoyi Zhang, Shaohua Wang, Xiuxiu Chen, and Qiuxiao Chen. "Street View-Enabled Explainable Machine Learning for Spatial Optimization of Non-Motorized Transportation-Oriented Urban Design." Land 14, no. 7 (2025): 1347. https://doi.org/10.3390/land14071347.
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To advance evidence-based urban design prioritizing non-motorized mobility, this study proposes a street view-enabled explainable machine learning framework that systematically links built environment semantics to non-motorized transportation vitality optimization. By integrating Baidu Street View images with deep learning-based object detection (Faster R-CNN), we quantify fine-grained human-powered and mechanically assisted mobility vitality. These features are fused with multi-source geospatial data encompassing 23 built environment variables into an interpretable machine learning pipeline using SHAP-optimized random forest models. The key findings reveal distinct nonlinear response patterns between HP and MA modes to built environment factors; for instance, a notable promotion in mechanically assisted NMT vitality is observed as enterprise density increases beyond 0.2 facilities per ha. Emergent synergistic and threshold effects are evident from variable interactions requiring multidimensional planning consideration, as demonstrated in phenomena such as the peaking of human-powered NMT vitality occurring at public facility densities of 0.2–0.8 facilities per ha, enterprise densities of 0.6–1 facilities per ha, and spatial heterogeneity patterns identified through Bivariate Local Moran’s I clustering. This research contributes an innovative technical framework combining street view image recognition with explainable AI, while practically informing urban planning through evidence-based mobility zone classification and targeted strategy formulation, enabling more precise optimization of pedestrian-/cyclist-oriented urban spaces.
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Serrano, Will. "Smart or Intelligent Assets or Infrastructure: Technology with a Purpose." Buildings 13, no. 1 (2023): 131. http://dx.doi.org/10.3390/buildings13010131.
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Smart or intelligent built assets including infrastructure, buildings, real estate, and cities provide enhanced functionality to their different users such as occupiers, passengers, consumers, patients, managers or operators. This enhanced functionality enabled by the Internet of Things (IoT), Artificial Intelligence (AI), Big Data, Mobile Apps, Virtual Reality (VR) and 5G does not only translate into a superior user experience; technology also supports sustainability and energy consumption to meet regulation (ESG, NZC) while optimising asset management and operations for enhanced business economic performance. The main peculiarity is that technology is standardised, ubiquitous and independent from the physical built assets whereas asset users including humans, machines and devices are also common to different assets. This article analyses the atomic differences between built assets and proposes an asset omni-management model based on micro-management of services that will support the macro-functionality of the asset. The proposed key concept is based on the standardisation of different assets based on common and specific functionality and services delivered by the technology stack that is supporting already the transition to Industry 5.0 based on Web 3.0 and Tokenisation.
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Min, Kyung-suk. "A Study on the Utilization of Smart Technology for Effective CPTED in Construction Sites." Korea CPTED Association 15, no. 3 (2024): 9–40. https://doi.org/10.26470/jcssed.2024.15.3.9.
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This study aims to propose methods for the effective application of CPTED in construction sites by utilizing smart technologies. To achieve this, security-vulnerable zones were identified, and appropriate smart technology adoption strategies were suggested for each area. For external entrances and access control rooms, biometrics was employed to prevent unauthorized access, while monitoring systems utilized AI-based CCTV to analyze access patterns and abnormal behaviors. Additionally, IoT and drones supported real-time surveillance and data transmission. For equipment and material storage zones, drones and IoT enabled real-time detection and intrusion prevention, and modular systems were designed to allow rapid construction and expansion. Mobile apps and robotic automation were used to track the locations of materials and equipment, improving management efficiency. Parking lots and vehicle access pathways were managed automatically using AI and biometrics for vehicle access control. 3D printing was employed to manufacture various equipment, ensuring clear and precise management. Sensors detected vehicle and human movements in real time, enabling immediate responses to abnormal situations. Temporary fences and perimeter areas around the construction site were enhanced with drones and sensors for real-time detection and intrusion prevention. AR and VR were utilized to simulate risk factors, and BIM (Building Information Modeling) optimized 3D design and data-driven management systems. This study demonstrates that smart technologies serve as key tools for security innovation in construction sites, contributing to maximizing the effectiveness of CPTED.
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Saydazimova, Dr Umida, Dr Durdona Lutfullaeva, Dr Jasur Ziyamukhamedov, et al. "A Conceptual Framework and Examination of Online Learning Applications Using Software Infrastructure for Pedagogy." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 15, no. 4 (2024): 213–25. https://doi.org/10.58346/jowua.2024.i4.014.
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Integrating modern technology into teaching is not recent; hence, studies in this field have been conducted over the years. The implementation of Augmented Reality (AR) as a software infrastructure has captured the interest of educators and generated new possibilities in teaching. The advancement of Artificial Intelligence (AI) has enabled educators to adopt innovative approaches and tactics for evaluating and analyzing teaching and learning processes. Learning Analytics (LA) has developed the capacity to revolutionize traditional teaching methodologies through systematic and multifaceted approaches aimed at improving the effectiveness of online education. The study seeks to establish a conceptual framework for expanding online learning, particularly incorporating mobile apps into educational activities. This is accomplished by analyzing the current techniques implemented to improve academic standards via the active application of technology advancements and the existing frameworks for online learning. The results indicated that incorporating technology gadgets in classrooms is crucial for pupils to succeed, highlighting the imperative for exemplary educators to adjust to digital innovations and prepare learners for opportunities in the changing digital environment. The study contended that successful curriculum implementation requires a synthesis of technical advancement and business platform modification as essential mechanisms for achieving worldwide educational sustainability.
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Giansanti, Daniele. "Advancing Dermatological Care: A Comprehensive Narrative Review of Tele-Dermatology and mHealth for Bridging Gaps and Expanding Opportunities beyond the COVID-19 Pandemic." Healthcare 11, no. 13 (2023): 1911. http://dx.doi.org/10.3390/healthcare11131911.
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Mobile health (mHealth) has recently had significant advances in tele-dermatology (TD) thanks to the developments following the COVID-19 pandemic. This topic is very important, as telemedicine and mHealth, when applied to dermatology, could improve both the quality of healthcare for citizens and the workflow in the health domain. The proposed study was centered on the last three years. We conducted an overview on the opportunities, the perspectives, and the problems involved in TD integration with mHealth. The methodology of the narrative review was based on: (I) a search of PubMed and Scopus and (II) an eligibility assessment, using properly proposed parameters. The outcome of the study showed that during the COVID-19 pandemic, TD integration with mHealth advanced rapidly. This integration enabled the monitoring of dermatological problems and facilitated remote specialist visits, reducing face-to-face interactions. AI and mobile apps have empowered citizens to take an active role in their healthcare. This differs from other imaging sectors where information exchange is limited to professionals. The opportunities for TD in mHealth include improving service quality, streamlining healthcare processes, reducing costs, and providing more accessible care. It can be applied to various conditions, such as (but not limited to) acne, vitiligo, psoriasis, and skin cancers. Integration with AI and augmented reality (AR), as well as the use of wearable sensors, are anticipated as future developments. However, integrating TD with mHealth also brings about problems and challenges related to regulations, ethics, cybersecurity, data privacy, and device management. Scholars and policymakers need to address these issues while involving citizens in the process.
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Zhao, Zhonglin. "Understanding the Role of Personalized Learning Pathways in Enhancing Academic Confidence among ESL Students in Overseas Study Contexts." Journal of Education and Educational Research 13, no. 2 (2025): 19–27. https://doi.org/10.54097/beez2p44.
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This paper reviews the role of personalized learning pathways in building academic confidence among ESL students studying in higher education abroad. With over 6.9 million tertiary-level students studying internationally and many in English-speaking contexts, language-related challenges remain common, particularly in academic communication. Confidence, closely tied to self-efficacy, is a key factor in whether students engage, persist, and succeed. Drawing on more than 20 peer-reviewed empirical studies, this review explores how personalized approaches ranging from differentiated classroom instruction to adaptive digital technologies affect confidence in second-language academic contexts. Findings suggest that when instruction addresses individual needs and offers scaffolded support, learners report higher motivation, reduced anxiety, and increased willingness to participate. Classroom strategies such as individualized projects, flexible assessments, and targeted skills workshops were found to foster a stronger sense of academic readiness. Similarly, technology-enabled personalization, including adaptive apps and AI-driven feedback systems, supported self-directed practice and improved language self-efficacy. However, limitations remain around the generalizability and long-term impact of such interventions. The paper concludes that embedding structured, needs-based personalization into mainstream academic provision may help ESL learners gain the confidence necessary to thrive, outlining areas for future study, including longitudinal outcomes, cultural responsiveness, and the role of human support in digitally mediated learning.
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Metwaly, Ahmed Fadiel, Adam Thomas Koster, Mohammad Abbasi, et al. "Abstract 1973: A geo inelegant approach to cancer disparity." Cancer Research 83, no. 7_Supplement (2023): 1973. http://dx.doi.org/10.1158/1538-7445.am2023-1973.
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Abstract UCRShiny is an intelligent user interface application that utilizes R and R-Shiny tools and statistical intelligence to allow users to visualize geospatial cancer data and perform advanced analyses and predictive modeling. UCRShiny's focus is the catchment area (in Illinois and Indiana) near UCCC and utilizes various metadata. This includes fitting univariate and multivariate models to make statistically sound inferences from the data. It also allows the user to plot specific data on a map of the catchment area as layers for descriptive analysis and visual geospatial assessment. As well as assessing the significance of the relation between these factors. These statistical tools should have a level of intelligence that can determine the correctness of the model's assumptions. An advantage of our tool is that it provides an overall diagnosis of the model to the user without the need for the intervention of an expert statistician. The data selected for analysis are assessed to ensure they meet the assumptions of the statistical model being used, and proper data transformations are applied. The presence of confounding factors is assessed using linear models in sequence. Initially, a single variable linear model is fit using the primary variable of interest (i.e., Cancer mortality) as the response variable and the second variable in the univariate analysis as the covariate. The second model adds the internal metadata columns as potential confounders to the model. In the third step, the percent change in the coefficient of the second variable in the first and second linear models is calculated. Multivariate analysis using ML is used to simultaneously assess the effect of multiple variables on the outcome variable. Here, we use various ML algorithms based on the problem and data at hand, report the significant features, and quantify their impact on the response of interest. The tool allows users to utilize the in-app data, load their dataset, or combine both datasets. It helps to visualize geospatial health data on a map, perform statistical analysis and build ML/AI models without being concerned about the validity of the statistical models. After uploading data and selecting the appropriate statistical test/analysis, results will be visualized as a plot on a map with the specified layers collared by the layer variable values. The layers can be turned on or off using specific buttons on the map. GIS maps may be easily shared and included in apps and are available to almost everyone, anywhere. The user can study the effect of multiple variables and rank their importance in predicting the response variable using multivariate ML/AI models. The UI allows the user to select an appropriate ML algorithm for the model or the application's intelligent backend algorithms to choose the proper ML model (This will be implemented in phase II). The outcome feature and the covariates to use in the model can be selected using searchable textboxes in the UI, and the model is run using the Run model button. Citation Format: Ahmed Fadiel Metwaly, Adam Thomas Koster, Mohammad Abbasi, Aya Hassouneh, Jasmin Tiro, Nita Lee, Adekunle Odunsi. A geo inelegant approach to cancer disparity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1973.
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Leslie-Miller, Calissa J., Shellen R. Goltz, Pamela L. Barrios, et al. "Evaluating the Acceptability and Utility of a Personalized Wellness App (Aspire2B) Using AI-Enabled Digital Biomarkers: Engagement Enhancement Pilot Study." JMIR Formative Research 9 (May 14, 2025): e63471-e63471. https://doi.org/10.2196/63471.
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Abstract Background There is significant global interest in promoting wellness, with digital solutions like mobile health apps being broadly downloaded; yet, there is a challenge in maintaining engagement for long-term behavior change. Developing a widely accepted mobile wellness app is imperative for advancing personalized wellness interventions. Objective The primary objective of this study was to evaluate the Aspire2B wellness app (powered by Salus Optima), designed to exceed industry standards for participant engagement by incorporating evidence-based behavior change strategies and to assess its acceptability (eg, liking the face scan) and utility (eg, willing to use the face scan technology for other health insights) as a digital health solution. Methods Participants aged 18-65 years, who were smartphone and fitness tracker users, were recruited in the United States during March-May 2022. Participants received US $5 compensation for downloading the app, with no further incentive for usage. Following completion of onboarding (ie, survey questions about lifestyle behaviors), participants were placed in either a nutrition, sleep, or fitness 4-week challenge. During the challenge, participants used various app features at their own will, such as a facial scan for wellness insights (eg, heart rate and biological age), recipes, and workout videos. These interactions with the app were cumulatively evaluated as engagement metrics. Participants were also asked to answer offboarding questions to evaluate any changes to lifestyle behaviors and experience using the app features (eg, acceptability of face scan experience). Results Out of the 398 people who created an account, 85.9% (342/398) completed onboarding and a face scan. Following this, 74.9% (298/398) of users completed additional survey questions about current wellness behaviors. Notably, interaction with the app was relatively stable from week 2 to 4 (173/398, 43.5%), outperforming industry standards by roughly 3×. In addition, on average, participants completed 2.1‐2.7 face scans per week, with approximately 7% (24/342) of participants maintaining regular use of face scan technology for 4 weeks. In users who completed offboarding questions, 88.8% (111/125) found Aspire2B credible, 64.8% (81/125) liked the face scan experience, 7.2% (9/125) disliked the face scan experience, and 83.2% (104/125) said they would use face scan technology for other insights into their health. Conclusions These findings highlight strong initial engagement with Aspire2B, followed by significant sustained user engagement over a 4-week period. Furthermore, users indicated high levels of credibility and willingness to use face scan technology for wellness insights. These findings collectively demonstrate the capability of a personalized wellness app using AI-enabled digital biomarkers and evidence-supported behavior change techniques to yield positive user perception and provide long-term engagement.
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Nagayo, Analene Montesines, Mahmood Zayid Al Ajmi, Naga Rama Krishna Guduri, and Fatma Al Buradai. "Monitoring stress levels and associated clinical health risks utilizing IoT and AI technologies to promote mental health awareness in educational institutions." Multidisciplinary Science Journal 6 (July 12, 2024): 2024ss0327. http://dx.doi.org/10.31893/multiscience.2024ss0327.
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This paper presents the development and implementation of a stress monitoring system and its associated health risks using IoT, GSM, and AI technologies. The designed system was implemented with IoT-enabled Particle Photon microcontrollers that read and processed data from biosensors and input switches to evaluate physiological factors including body thermal heat rate, systemic arterial pressure, heartbeat rate, electrical skin resistance, oxygen saturation level, pain or aches and breathing rate, as well as emotional and behavioral stress symptoms such as restlessness, being irritable or over-reactive, being upset or agitated, nervousness, and impatience. Real-time and distant access to the measured parameters were made feasible through IoT cloud platforms and customized smartphone apps. The system displayed the user's stress score and level (normal, mild, moderate, severe, and extremely severe), health risk level (zero, low, medium, or high), and stress and health management recommendations. When the resulting stress level was extremely high, the system generated a referral message and sent it by SMS to the guidance counselor for necessary mental health care support. As per the research findings, the IoT-based stress level detection prototype achieved an accuracy of 87.5% when compared to the data collected from the AI interactive stress evaluation mobile phone application. The developed application was based on the DASS-21 test and was administered to participants who volunteered from an institution in Oman. Furthermore, when compared to the NEWS2 early warning score system, the system's health risk level results had an accuracy of 93.75%. With the help of this research project, guidance counselors, psychologists, and medical professionals will be able to assess and address some of the mental and physical health concerns that university students and faculty members have, as well as raise mental health awareness among individuals.
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Atmadjaja, Agnes, Minarni Watinin, and Stefani Nurhadi. "LITERATURE REVIEW: Effectiveness of Chatbot to Reduce the Risk of Coronary Heart Disease using Android-based application." Journal of Widya Medika Junior 4, no. 5 (2022): 181–92. http://dx.doi.org/10.33508/jwmj.v4i3.4094.
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Background: An innovative approach in the form of a discussion platform designed to help users deal with health issues related to coronary artery disease. Chatbot platforms allow the collection of users' data, which is analyzed through natural language processing and behavioral analysis, to provide each user with a customized solution based on their current situation. The data collected and analyzed is accessible. The platform is developed using chatbot technology. Users can interact with chatbots to generate personal chat data stored on the platform. Conflicting information and sensitivity to Coronary Heart Disease (CHD) issues hinder effective communication. Recent technological solutions to maintain weight loss are limited. A chatbot would be suitable to support weight loss as it requires no human intervention, is available 24 hours a day, and supports natural communication while maintaining anonymity. The health system needs an effective and low-cost way to provide optimal health outcomes using conversation-enabled Artificial Intelligence (AI). Humans can interact well with this AI in the form of a fully automated and self-contained text-based mobile tutoring service. CHD is a serious health problem worldwide with multiple and interrelated causes. At the same time, chatbots are becoming more popular for interacting with users in mobile health apps. Objective: We evaluated an Android application. Its main goal is to prevent lifestyle-related diseases that are a risk for CHD, which has been considered at risk for multiple coronary artery disease (CAD), with the overarching goal of gaining compassion through mobile health improvements. The insights gained in this preview article will be used to plan future health care systems and to design an AI capable of advanced healthcare, chronic disease prevention, and self-treatment. Results: The Role of Artificial Intelligence in preventing Coronary Heart Disease (CHD) is done through health screenings. The app warns the user to exercise regularly and maintain food intake by reducing foods high in calories and adding foods high in fiber. Conclusion: Usage of AI in healthcare is associated with CHD prevention, which alters healthy lifestyles. It can also encourage a change in attitude, a high level of user concern for health, and obtain complete health information. Research on artificial intelligence and its use in telemedicine needs to be continued, with clinical trials examining the impact on blood pressure, body mass index, smoking, diabetes mellitus, and user engagement and feedback.
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Sivakumar, R. D. Assistant Professor Department of Computer Science, and S. Former Assistant Professor of Business Administration Brindha. "VIRTUAL ASSISTANTS AND CHATBOTS IN RETAIL." Indian Journal of Research and Development Systems in Technologization 1, no. 4 (2024): 1–11. https://doi.org/10.5281/zenodo.12643572.
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<em>Today, virtual assistants and chatbots are playing a very important role in retail to boost the customer experience, reduce costs, and change the way of selling the products. The aim of this paper is to study the virtual assistants and chatbots for retail business in terms of feasibility, advantages, and drawbacks. Consumer services in the form of Virtual Assistants enabled with Artificial Intelligence (AI) and Natural Language Processing (NLP) mechanisms enhance the support and selling propositions to consumers improving customer satisfaction and retention. They can be employed as virtual assistants in the retail websites as well as the messaging apps where they interact with customers, answer questions, offer updates on order status, and provide other services. Organizations should embrace and implement virtual assistants and chatbots as they drive down the operational costs of retail especially in the sectors of customer advocacy. However, there are concerns that need to be addressed including data security, accuracy of responses as well as compatibility with other systems. However, the future of virtual assistants and chatbots in retail seems rather bright due to the continuous improvements in the technologies used and the growing adoption of automated services by consumers. This paper concludes by discussing the future vision to explore, such as voice assistants and personal shopping which are likely to revolutionize the future of retail with a brand new level of efficiency and customer satisfaction.</em>