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1
Dachepalli, Veeresh. "AI-Driven Decision Support Systems in ERP." International Journal of Computer Science and Data Engineering 2, no. 2 (2025): 1–7. https://doi.org/10.55124/csdb.v2i2.248.
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The integration of artificial intelligence (AI) into enterprise resource planning (ERP) systems has fundamentally transformed organizational decision-making by providing more precise, data-driven insights. This paper examines how AI has been incorporated into ERP systems, focusing on improving strategic decision-making through the use of advanced data visualization techniques and algorithmic decision support algorithms. By leveraging the power of machine learning (ML) and business intelligence (BI) tools, a robust decision support algorithm is proposed that facilitates real-time data analysis, predictive forecasting, and actionable insights. The integration of ML models allows ERP systems to analyze a wide range of historical and real-time data, identify trends, and make predictions, thereby improving forecast accuracy. Meanwhile, BI tools provide intuitive dashboards and visualizations that help decision-makers interpret complex data and effectively monitor key performance indicators (KPIs). This combination significantly improves operational efficiency, streamlines decision-making processes, and reduces time spent on manual tasks. The proposed decision support system enhances the adaptability of ERP systems, helping organizations respond proactively to changing business environments. These findings demonstrate considerable advancements in predictive analytics, operational effectiveness, and the overall adaptability of ERP systems, enabling businesses to remain proactive in market trends and make well-informed strategic choices.
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Saiyam Arora. "Transforming AI Decision Support System with Knowledge Graphs & CAG." International Journal on Engineering Artificial Intelligence Management, Decision Support, and Policies 2, no. 2 (2025): 15–23. https://doi.org/10.63503/j.ijaimd.2025.110.
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Artificial Intelligence (AI) serves as a fundamental component of decision support systems (DSS), enabling organizations to process large-scale data and derive actionable insights. However, traditional AI models utilizing relational databases (RDBMS) exhibit limitations in retaining context and applying knowledge-driven reasoning. This study examines the integration of Knowledge Graphs (KGs) and Context-Aware Graphs (CAGs) to enhance AI-driven decision-making systems. A hybrid framework is proposed in which structured knowledge graphs improve the contextual understanding of large language models (LLMs), thereby optimizing information retrieval, similarity-based search, and multi-query handling. The system employs semantic embeddings to map entities and relationships, utilizing Neo4j and machine learning techniques to enhance inference capabilities. A comparative analysis with conventional RDBMS-based AI models demonstrates significant improvements in query accuracy, explainability, and relevance for decision-making tasks. The proposed approach is evaluated in various domains, including business intelligence, financial analysis, and strategic policymaking. Results indicate that KGs and CAGs enable organizations to obtain more reliable, transparent, and context-aware insights. Additionally, user feedback mechanisms are incorporated to dynamically refine the knowledge graph, ensuring continuous enhancement of AI responses. By bridging structured data with generative AI, this research contributes to the advancement of decision support systems, predictive analytics, and expert recommendation frameworks. The findings suggest that knowledge-enhanced AI models substantially outperform traditional methods in contextual reasoning and decision optimization, offering a scalable and explainable AI framework for enterprise applications. This approach ensures adaptability in AI-driven decision systems by facilitating continuous learning from emerging data trends, thereby enabling more intelligent and data-informed business strategies.
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.Parimala, M. "Soul Support : AI Driven Emotional Assistance ChatBot." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50039.
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Abstract— This paper introduces the development of an AI-based Mental Health Therapist Chatbot designed to provide immediate, accessible emotional support and mental health information. Utilizing deep learning and natural language processing (NLP), the chatbot identifies user intents and delivers appropriate responses across various mental health topics, including anxiety, stress, and sadness. The system is deployed via a web interface that supports both text and voice input, enhancing user accessibility. On the backend, the chatbot uses a Flask server, a trained Keras model for intent classification, and preprocessing pipelines built with NLTK and SpaCy. Experimental evaluations show high accuracy in intent recognition and efficient real-time interaction. Additionally, user feedback reflects a positive reception in terms of helpfulness and ease of use. While the chatbot does not replace professional care, it acts as a supportive digital tool for initial engagement and emotional guidance. Future enhancements will focus on multilingual support, contextual dialogue, and integration with mental health services.
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Latek, Dorota, Khushil Prajapati, Paulina Dragan, Matthew Merski, and Przemysław Osial. "GPCRVS - AI-driven Decision Support System for GPCR Virtual Screening." International Journal of Molecular Sciences 26, no. 5 (2025): 2160. https://doi.org/10.3390/ijms26052160.
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G protein-coupled receptors (GPCRs) constitute the largest and most frequently used family of molecular drug targets. The simplicity of GPCR drug design results from their common seven-transmembrane-helix topology and well-understood signaling pathways. GPCRs are extremely sensitive to slight changes in the chemical structure of compounds, which allows for the reliable design of highly selective and specific drugs. Only recently has the number of GPCR structures, both in their active and inactive conformations, together with their active ligands, become sufficient to comprehensively apply machine learning in decision support systems to predict compound activity in drug design. Here, we describe GPCRVS, an efficient machine learning system for the online assessment of the compound activity against several GPCR targets, including peptide- and protein-binding GPCRs, which are the most difficult for virtual screening tasks. As a decision support system, GPCRVS evaluates compounds in terms of their activity range, the pharmacological effect they exert on the receptor, and the binding mode they could demonstrate for different types and subtypes of GPCRs. GPCRVS allows for the evaluation of compounds ranging from small molecules to short peptides provided in common chemical file formats. The results of the activity class assignment and the binding affinity prediction are provided in comparison with predictions for known active ligands of each included GPCR. Multiclass classification in GPCRVS, handling incomplete and fuzzy biological data, was validated on ChEMBL and Google Patents-retrieved data sets for class B GPCRs and chemokine CC and CXC receptors.
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Ekmalle, Mr Abhiraj. "“Conversational AI for Customer Support”." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04356.
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ABSTRACT: This research paper presents a web-based Conversational AI system developed for enhancing customer support services using Natural Language Processing (NLP), Machine Learning (ML), and Flask for deployment. The system is designed to simulate human-like conversation, resolve customer queries, and operate 24/7 without human intervention. By leveraging intelligent dialogue management and intent recognition, the system can handle frequently asked questions, route complex queries, and significantly reduce human workload. This work demonstrates the integration of AI-driven models within a lightweight web interface and proposes a scalable solution for modern customer service challenges. KEYWORDS: Conversational AI, NLP, Customer Support, Flask, Intent Recognition, Dialogue Management, Chatbot
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Venkateswari, Mrs G. "AI-Driven Voice Transcription with Multilingual Support and Summarization." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5234–40. https://doi.org/10.22214/ijraset.2025.69553.
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Abstract: This paper presents an AI-powered platform for real-time voice transcription and multilingual summarization, aimed at streamlining communication and documentation in global collaborative settings. The system combines cutting-edge Artificial Intelligence and Natural Language Processing to accurately transcribe speech, extract critical information, and generate clear, context-aware summaries across multiple languages. Utilizing OpenAI's Whisper for speech recognition, the platform integrates sentiment analysis, topic modeling, and both extractive and abstractive summarization methods. A built-in translation engine enables seamless cross-language understanding, supporting diverse teams and user groups. Applicable in domains such as business, education, healthcare, and public administration, the system minimizes manual workload while improving information accuracy and accessibility.
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Nithya, S. "Eco Sort: AI-Driven Waste Segregation System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41870.
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An important environmental problem is waste mishandling, which calls for effective and automated segregation systems. An AI-driven garbage segregation system that uses deep learning and image processing techniques to separate waste into biodegradable and non-biodegradable categories is shown in this research. To achieve precise categorization based on visual cues, a Convolutional Neural Network (CNN) is trained on a variety of trash datasets. The system processes garbage photos using OpenCV to ensure accurate identification. Under many circumstances, image preprocessing methods like scaling and normalization improve model performance. Over time, the algorithm learns from new trash data, increasing the accuracy of its classifications. Effective trash disposal management is also made possible by a real-time monitoring tool that keeps track of the amount of waste in bins and updates users through an interactive interface. By integrating cloud storage, waste management authorities may access and analyze data remotely, which helps them make better decisions. Deep learning, automation, and real-time monitoring are all combined in this system to improve trash management effectiveness, encourage recycling, and support environmental sustainability. Keywords— Deep Learning, Image Processing, Convolutional Neural Network (CNN), OpenCV, Waste Classification and Artificial Intelligence.
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Zhang, Jiangang, and S. B. Goyal. "AI-Driven Decision Support System Innovations to Empower Higher Education Administration." Journal of Computers, Mechanical and Management 3, no. 2 (2024): 35–41. http://dx.doi.org/10.57159/gadl.jcmm.3.2.24070.
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This study explores the utilization, perceptions, and impacts of Decision Support Systems (DSS) in higher education administration. With a focus on DSS, a cross-sectional survey was conducted among higher education administrators from various institutions. The findings underscore the essential role of DSS in higher education administration, with administrators reporting significant utilization and praising their effectiveness and user-friendliness. The study reveals the positive influence of DSS on strategic planning, enrollment management, resource allocation, and student success initiatives. Moreover, it demonstrates the association between DSS usage and favorable outcomes, including increased efficiency and perceived positive consequences. However, persistent challenges such as data quality issues, privacy concerns, and resistance to change highlight the need for improved data management strategies, ethical considerations, and change management approaches. These findings contribute to the ongoing discourse on the transformative potential of DSS in higher education administration and provide valuable insights for businesses seeking to enhance decision-making, resource allocation, and data-driven initiatives. The innovative integration of AI in DSS for higher education administration represents a paradigm shift in decision-making processes, offering unprecedented opportunities for improvement and innovation.
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Soujanya, Dr K. L. S., Dr D. V. Latitha Parameswari, A.Sirisahasra, P.Nishitha, and M.Varshini. "AI Driven Decision Support System for Sustainable Agriculture and Zero Hunger." International Research Journal of Innovations in Engineering and Technology 09, Special Issue ICCIS (2025): 172–77. https://doi.org/10.47001/irjiet/2025.iccis-202528.
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Abstract - Agriculture remains a vital pillar of India’s economy, with a substantial portion of the population relying on farming as their primary livelihood. Despite its importance, many farmers continue to face barriers in maximizing crop productivity and maintaining soil health due to limited access to scientific guidance and data-driven tools. To address these challenges, this study presents an AI-based Decision Support System (DSS) designed to deliver personalized, real-time agricultural recommendations. The system encompasses three key modules: a Crop Recommendation Model, a Fertilizer Recommendation Model, and an interactive bilingual Chatbot supporting both Telugu and English. The crop recommendation module identifies optimal crops based on soil nutrient profiles and environmental parameters, while the fertilizer module suggests suitable nutrient combinations for sustainable and efficient soil management. The integrated chatbot functions as a virtual assistant, providing user-friendly support and addressing common queries in local languages, thereby enhancing accessibility for rural farmers.
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Sheikh, Nuruddin. "AI-Driven Observability: Enhancing System Reliability and Performance." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 7, no. 01 (2025): 229–39. https://doi.org/10.60087/jaigs.v7i01.322.
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AI-powered observability will revolutionize how modern systems are monitored, analyzed, and optimized for performance and resilience. With traditional observability, it requires manual analysis of logs, metrics, and traces, which can often make it too late to respond to system anomalies. With the integration of AI and machine learning in observability platforms, they can use the data collected to find out patterns, identify anomalies, bad actors, late trends, and offer insights based on alert patterns and defined ratios. It assesses how the tools of tomorrow will build on observability for inner systems. The discussion also highlights key challenges including data complexity, model interpretability, and scalability. It concludes with a focus on AI-driven observability as a key strategy to help support resilient and high performing systems in complex and dynamic IT environments.
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Manimala, S. "AI-Driven Bilingual Voice Chatbot." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 6075–89. https://doi.org/10.22214/ijraset.2025.71640.
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This project proposes an AI-driven, bilingual voice- enabled health chatbot that aims to enhance healthcare access in Karnataka’s rural and semi-urban zones. The system was initially developed with a fine-tuned GPT-2 model; however, it generated unpredictable and sometimes unrelated answers, particularly for advanced or bilingual inputs. To address these constraints, the model was substituted with Mistral LLM through integration on LangChain, allowing Retrieval-Augmented Generation (RAG) to generate more precise and context-specific responses from a carefully curated health QA dataset. Supporting both Kannada and English languages, voice interaction in real time, and a friendly Gradio interface, the chatbot offers inclusive, voice-to- voice health support customized for those with low literacy and restricted digital exposure
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B G, DARSHAN. "AI-Powered Healthcare Chatbot and Personalized Medical Support." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42567.
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For real-time medical help, disease prediction, and emergency warnings, the AI-Powered Healthcare Chatbot and Personalized Medical Support System uses artificial intelligence, natural language processing (NLP), and OpenAI. It has a heart-monitoring system for keeping track of health in real time, a chatbot for user contact, an image processing module for medical analysis, and a search tool for finding patient records. The system uses an AI-driven message processing layer to process customer inquiries, guaranteeing precise and customized answers. Additionally, an automatic alert system uses health data analysis to provide emergency messages. This method boosts accessibility, accuracy, and prompt medical intervention by combining AI with healthcare, making it a useful tool for contemporary digital healthcare. Key words: Natural language processing (NLP), medical image processing, AI-powered healthcare, healthcare chatbots, and AI-based disease prediction.
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Pradhan, Sneha S. "AI-Driven Travel Planner." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 3107–15. https://doi.org/10.22214/ijraset.2025.68012.
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The AI-Driven Travel Planner project leverages artificial intelligence and the Flutter framework to transform the travel planning process, making it more efficient, personalized, and user-friendly. The platform analyses user preferences such as destination type, budget, and travel dates to provide tailored destination recommendations. It generates detailed itineraries that optimize time, cost, and convenience, while also offering real-time assistance for on-the-go adjustments. With the ability to continuously learn from user behavior and feedback, the AI ensures that each recommendation becomes more accurate over time, enhancing the overall travel experience. Additionally, the platform helps manage budgets by suggesting affordable options and providing cost breakdowns, ensuring travelers can plan within their financial constraints. This project also incorporates AIpowered discovery and custom recommendations, making it adaptable to individual needs. Travelers can receive personalized suggestions, accommodations, and local experiences based on their preferences, while real-time assistance ensures that users have support throughout their planning and travels. The mobile-first design, built with Flutter, provides seamless access across Android devices, allowing users to change their plans anytime. The platform also benefits from a feedback-driven learning system, which enables the AI to improve and refine its suggestions with each interaction, creating a more intuitive and dynamic tool for future travel planning
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Dileesh, Chandra Bikkasani. "Leveraging Artificial Intelligence for Business Analytics: A Data-Science based Decision Support System Framework." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 2 (2025): 1507–15. https://doi.org/10.5281/zenodo.14964501.
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Artificial Intelligence (AI) transforms business intelligence (BI) by enhancing decision-making speed, accuracy, and depth in today’s data-driven landscape. Traditional Decision Support Systems (DSS), once foundational to BI, struggle to handle modern data's complexity, scale, and diversity, often resulting in limited decision-making agility. Integrating AI into DSS has become essential to bridge this gap, enabling these systems to process vast datasets in real-time and make predictive, data-informed recommendations. This study presents an AI-powered DSS framework designed to address the limitations of conventional DSS by incorporating machine learning, natural language processing, and adaptive feedback mechanisms. Through real-world simulations and industry-specific use cases, the framework demonstrates marked improvements in decision quality, response times, and user satisfaction compared to traditional systems. Findings suggest that AI-driven DSS can substantially enhance BI processes, equipping organizations with a proactive, scalable approach to decision support. By addressing key technical and ethical challenges, this research offers valuable insights for businesses aiming to leverage AI to stay competitive in increasingly complex environments, positioning AI-powered DSS as critical to the future of BI.
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kumar, K. Rajesh. "AI-Driven Mental Health Forecasting and Solutions Using SVM." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44657.
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ABSTRACT- The identification and evaluation of stress levels through advanced tracking technologies represents an essential research domain which supports wellness improvement and performance enhancement. Deep learning algorithms in this smart system conduct behavioral and physiological indicator analysis alongside Naive Bayes which recognizes emotions through speech and text inputs to detect emotions including anger, sadness, fear, and happiness. Through transfer learning the examination system obtains better capabilities in making measurements across different domains which leads to extra accuracy. Within the system a chatbot running on an artificial neural network (ANN) detects emotional states and provides customized stress support through actionable advice that includes mindfulness training and physical activity and social interaction suggestions and professional resources and recreational activities. This system integrates stress recognition with emotional detection while offering specific guidance which gives users the power to deal in advance with their mental health requirements to maintain balanced well-being. Keywords-Stress Detection, Mental Health, Machine Learning, Deep Learning, Transfer Learning, Chatbot, Artificial Neural Network (ANN), Stress Management
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Zulu, Jacob, and Mr Peter Munyenyembe. "Design and Development of AI Driven Mental Health Support System for Teenagers." International Journal of Advances in Scientific Research and Engineering 11, no. 03 (2025): 08–25. https://doi.org/10.31695/ijasre.2025.3.2.
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Mental health issues among teenagers are on the rise due to academic stress, social pressures, and the challenges of adolescence. Access to timely and effective mental health support is crucial, yet traditional methods of seeking help may feel intimidating or inaccessible to many young people. This articlefocuses on developing an AI-driven mental health support apptailored for teenagers, aimed at providing a safe, accessible, and personalized platform for emotional well-being. The app leverages artificial intelligence to offer empathetic, real-time support through an AI-powered Chabotthat can engage users in meaningful conversations, detect emotional cues, and provide relevant coping strategies. Key features include a mood-tracking system to help users log and identify emotional patterns, personalized self-care plans, and access to a library of mental health resources. Additionally, the app integrates crisis intervention features, enabling immediate connection to professional support or helplines when high-risk behaviors are detected. Data privacy and security are prioritized, with advanced encryption and authentication mechanisms ensuring sensitive user data remains confidential. The app's user-friendly interface and scalable design make it adaptable to diverse educational and social settings. Regular updates and user feedback loops drive continuous improvements, ensuring the app remains relevant and effective in addressing the mental health needs of teenagers. This innovative AI-driven solution aims to enhance the accessibility, efficiency, and effectiveness of mental health support for teenagers, empowering them to manage their well-being with confidence and fostering a proactive approach to mental health care.
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Shobhana Sanjay Singh. "Use of AI-driven support system to help courts in predicting child custody outcomes in India." World Journal of Advanced Research and Reviews 23, no. 1 (2024): 1088–92. http://dx.doi.org/10.30574/wjarr.2024.23.1.2108.
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Predicting child protection outcomes using AI-driven support systems under Indian law raises interesting possibilities and challenges. This study examines the feasibility of using AI in Indian courts to assist in child custody decision-making. The Indian legal system is complex and topical in child custody cases, where sociocultural and family factors heavily influence judicial decisions. The potential integration of AI provides an opportunity for objectivity and continuity in accuracy to enter the system. However, there are several challenges to implementing such a system in India. These areas include addressing ethical concerns related to algorithmic decision-making about sensitive family matters using the right, this research topic raises questions about the feasibility of introducing AI into the Indian childcare decision-making process. This suggests the need to carefully consider these challenges and their potential benefits when assessing the utility and appropriateness of using AI-driven support systems in Indian courts to set childcare outcomes to help judges in faster fact-finding and to make decisions on the same. In addition, we analyzed the use of AI in other countries.
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Shobhana, Sanjay Singh. "Use of AI-driven support system to help courts in predicting child custody outcomes in India." World Journal of Advanced Research and Reviews 23, no. 1 (2024): 1088–92. https://doi.org/10.5281/zenodo.14794480.
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Predicting child protection outcomes using AI-driven support systems under Indian law raises interesting possibilities and challenges. This study examines the feasibility of using AI in Indian courts to assist in child custody decision-making. The Indian legal system is complex and topical in child custody cases, where sociocultural and family factors heavily influence judicial decisions. The potential integration of AI provides an opportunity for objectivity and continuity in accuracy to enter the system. However, there are several challenges to implementing such a system in India. These areas include addressing ethical concerns related to algorithmic decision-making about sensitive family matters using the right, this research topic raises questions about the feasibility of introducing AI into the Indian childcare decision-making process. This suggests the need to carefully consider these challenges and their potential benefits when assessing the utility and appropriateness of using AI-driven support systems in Indian courts to set childcare outcomes to help judges in faster fact-finding and to make decisions on the same. In addition, we analyzed the use of AI in other countries.
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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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Das, Archana, S. Padmavathy, N. S. Shilpa, et al. "Cyber-Physical Systems Integration in Healthcare: AI-Enabled Decision Support Systems." Health Leadership and Quality of Life 4 (June 4, 2025): 659. https://doi.org/10.56294/hl2025659.
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Abstract structured in: The convergence of Cyber-Physical Systems (CPS) and healthcare is bringing about a transformation in the delivery of patient care by bridging the gap between the digital and physical realms. By utilizing modern technologies, these systems make it possible to make intelligent decisions and gain insights that are driven by data in real time.Introduction: The complexity of data integration, the mitigation of sophisticated cyber threats, and the guaranteeing of system scalability within a variety of healthcare infrastructures are among the most significant obstacles. Methods: This research presents the Artificial Intelligence-Enabled Intrusion Quantum Predictive Detection System (AI-IQPDS), an innovative approach that is intended to improve the operational reliability of healthcare CPS, as well as the security and predictive analytics capabilities of the system. AI-IQPDS combine quantum computing and machine learning to provide accurate intrusion detection and predictive decision assistance. Intelligent patient monitoring systems powered by AI can optimize hospital resource management, transmit data securely between connected devices, and detect emergencies early working. Results: Simulation results show that the system outperforms modern techniques in terms of precision of detection, speed of processing, and reduction of false-positives. The results of this research demonstrate the revolutionary possibilities of using CPS driven by AI in healthcare. Conclusion: Healthcare ecosystems that are both intelligent and scalable may be possible as a result of this integration, which might lead to better efficiency, security, and patient outcomes.
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Ahmed, Shahan, Anisur Rahman, and Md Ashrafuzzaman. "A SYSTEMATIC REVIEW OF AI AND MACHINE LEARNING-DRIVEN IT SUPPORT SYSTEMS: ENHANCING EFFICIENCY AND AUTOMATION IN TECHNICAL SERVICE MANAGEMENT." American Journal of Scholarly Research and Innovation 2, no. 02 (2023): 75–101. https://doi.org/10.63125/fd34sr03.
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The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) has brought significant advancements in IT support systems, transforming the efficiency, automation, and responsiveness of technical service management (TSM). Traditional IT support methods, which rely heavily on manual troubleshooting, rule-based ticketing systems, and reactive maintenance approaches, often suffer from delayed issue resolution, increased operational costs, and inefficiencies in service management. This systematic review, analyzing 563 peer-reviewed studies published before 2023, investigates the application of AI-driven solutions in automated troubleshooting, predictive maintenance, intelligent ticketing systems, and AI-powered virtual assistants. The findings indicate that AI-driven troubleshooting models reduce mean time to resolution (MTTR) by 50-60%, improving system uptime and minimizing service disruptions. Predictive maintenance models leveraging ML algorithms achieve up to 90% accuracy in failure detection, leading to a 40-50% reduction in unplanned downtime and optimizing IT infrastructure reliability. AI-based intelligent ticketing systems enhance classification accuracy by 50-60%, reducing misclassification errors by 30-40%, while sentiment-based prioritization improves critical incident response speed by 35%, ensuring faster resolution of high-priority issues. Additionally, AI-powered virtual assistants autonomously manage 50-60% of IT service requests, significantly decreasing first-level support workload by 40% and enabling IT personnel to focus on complex technical challenges. Despite these advancements, challenges persist, including algorithmic bias, model misclassification risks, and limitations in handling complex, non-standard IT issues, which impact the overall effectiveness of AI-driven IT support automation. A comparative analysis between AI and human-led IT support reveals that while AI-driven systems outperform human-led models in automation, scalability, and cost efficiency, human intervention remains critical for addressing high-complexity IT problems, strategic decision-making, and exception handling. This review highlights the transformative role of AI in IT service management, emphasizing its capabilities in optimizing IT workflows, improving service efficiency, and reducing operational burdens. However, the findings also reinforce the need for continuous improvements in AI fairness, adaptability, interpretability, and hybrid AI-human integration models to maximize the benefits of AI-driven IT support systems.
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Srikanth Perla. "AI-driven Test Automation for Salesforce and System Integration." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1120–29. https://doi.org/10.32628/cseit251112116.
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Integrating artificial intelligence into test automation frameworks has transformed quality assurance practices in Salesforce environments and system integrations. AI-driven solutions have revolutionized testing approaches through smart test selection, risk-based analysis, and dynamic element identification capabilities. These advancements enable organizations to detect defects earlier, reduce false positives, and significantly decrease test maintenance efforts. Self-healing locators and context-aware selection mechanisms have enhanced test stability across dynamic web applications, while pattern recognition and anomaly detection capabilities proactively identify potential issues. Real-world implementations demonstrate substantial improvements in testing efficiency, reliability, and cost-effectiveness. Despite the challenges of data requirements and implementation complexity, AI-powered testing solutions have proven particularly effective in handling complex Salesforce configurations and multi-system integrations. The continuous evolution of these technologies promises enhanced predictive capabilities, improved integration support, and more sophisticated automated testing approaches, marking a significant shift in how organizations approach quality assurance in modern software development.
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Mudili, Yaswanth. "Machine Aided Diagnosis System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43375.
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—Machine-Aided Diagnosis System is an intelligent healthcare solution designed to enhance diagnostic accuracy and accessibility through machine learning and AI-driven interactions. The system employs the Random Forest algorithm to analyze multiple symptom correlations, enabling precise disease prediction based on both general and chronic symptoms. An interactive disease dashboard provides a comprehensive symptom profile, suggested specialists, and available treatment methods, ensuring users can interpret their health conditions effectively. Additionally, an LLM( Large Language Model )-powered chatbot offers real-time conversations, assisting users with symptom explanations, treatment suggestions, and specialist recommendations. By integrating machine learning for prediction, interactive visualization for clarity, and AI-driven conversations for support, the system enhances user experience and medical accessibility. This innovative approach bridges the gap between advanced healthcare technology and everyday diagnostic needs, making disease prediction more intuitive and efficient. Index Terms— AI Chatbot, Disease Prediction, Healthcare Accessibility, Machine Learning, Random Forest.
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Dhanvijay, Nikita Ishwar. "AI-Based Smart Utility Management System." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 186–88. https://doi.org/10.22214/ijraset.2025.67207.
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This research explores the development and implementation of an AI-based Smart Utility Management System that allows users to book home services such as cleaning, electrical, plumbing, and more. The system integrates AI-powered features like personalized user experiences, chatbots, predictive analytics, image recognition, and smart scheduling to enhance efficiency and user satisfaction. Additionally, it incorporates non-AI features like user dashboards, loyalty programs, multilingual support, and service provider profiles to improve accessibility and engagement. This paper outlines the system’s methodology, architecture, AI models, and evaluation metrics. The results demonstrate that AI-driven home service management improves user experience, service efficiency, and business scalability. Challenges such as data privacy, AI bias, and service provider optimization are discussed, along with future directions for AI-powered home service platforms.
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Ms.M. Savitha, S J Gopika, E K Jayaprakash, and G J Lashman. "Implementation of AI-Driven Academic Achievement Tracker." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 04 (2025): 1479–85. https://doi.org/10.47392/irjaeh.2025.0211.
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Managing certificates and academic records manually is inefficient and time-consuming, leading to administrative overhead, errors, and delays in report generation. Physical storage of certificates for academic, extracurricular, and faculty development activities makes it difficult to maintain accurate records and provide personalized academic guidance. To address this, an AI-driven system has been developed to automate certificate digitization, skill extraction, and academic record management. The system integrates OCR to extract text from certificates, NLP to identify event names, and an ontology-based model to map relevant skills. Extracted skills are stored and analyzed to generate real-time reports and personalized recommendations for students and faculty. Additionally, an AI-powered recommendation engine suggests projects and courses based on skill gaps, improving academic and career growth. This solution enhances efficiency, accuracy, and accessibility in record management, reducing administrative workload while enabling data-driven academic support for institutions and students.
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Bharti, Karpatti. "AI-Driven Smart Irrigation System for Sustainable Agriculture." International Journal of Advance and Applied Research S6, no. 22 (2025): 821–26. https://doi.org/10.5281/zenodo.15533466.
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<em>The global agricultural sector faces unprecedented challenges in meeting the rising demand for food amidst escalating issues such as water scarcity, climate variability, and the depletion of natural resources. Conventional irrigation practices, often characterized by inefficiency and over-reliance on manual intervention, contribute to substantial water wastage, energy consumption, and suboptimal crop yields. In response, AI-driven smart irrigation systems have emerged as a groundbreaking innovation, integrating cutting-edge technologies such as the Internet of Things (IoT), machine learning, and data analytics to revolutionize water management in agriculture. These systems enable real-time monitoring of soil moisture, weather conditions, and crop health, facilitating precise and automated irrigation decisions that minimize water usage while maximizing crop productivity.</em> <em> Traditional irrigation methods are often inefficient, leading to significant water wastage and reduced crop yields. AI-driven smart irrigation systems have emerged as a transformative solution, leveraging advanced technologies such as IoT, machine learning, and data analytics to optimize water usage and enhance agricultural productivity. This paper provides a comprehensive analysis of AI-driven smart irrigation systems, exploring their components, functionality, applications, benefits, challenges, and future prospects. Through case studies and real-world examples, the paper highlights the potential of these systems to promote sustainable agriculture and ensure global food security.</em> <em> However, the adoption of AI-driven irrigation technologies is not without challenges, including high initial costs, data privacy concerns, and the need for technical expertise among farmers. Through an analysis of case studies and real-world implementations, this paper underscores the transformative impact of AI-driven smart irrigation systems in advancing sustainable agriculture. It also discusses future prospects, emphasizing the role of ongoing research, policy support, and technological advancements in scaling these solutions globally. By optimizing resource utilization and fostering resilience in the face of environmental uncertainties, AI-driven smart irrigation systems hold immense promise in ensuring food security and supporting the transition toward a more sustainable agricultural future.</em>
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Dachepalli, Veeresh. "Chatbots for ERP User Support Using AI." International Journal of Robotics and Machine Learning Technologies 1, no. 1 (2025): 1–9. https://doi.org/10.55124/ijrml.v1i1.236.
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The need for effective user assistance methods has increased due to the quick development of enterprise resource planning (ERP) systems. Traditional support models often strain resources and incur significant costs. This paper introduces AI-driven chatbots as a solution to streamline ERP user support, reduce overhead, and enhance user satisfaction. Leveraging advanced transformer-based Natural Language Processing (NLP) models, the proposed chatbot architecture facilitates real-time, accurate query resolution. Through a detailed exploration of methodology, implementation outcomes, and system capabilities, this study demonstrates the potential of AI chatbots to revolutionize ERP user assistance.
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Crosley, Nathaniel, and Ito Wasito. "Improving IT Support Efficiency Using AI-Driven Ticket Random Forest Classification Technique." sinkron 8, no. 4 (2023): 2283–93. http://dx.doi.org/10.33395/sinkron.v8i4.12925.
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This research project aims to improve IT support efficiency at Indonesian company XYZ by using AI-based IT support ticket classification integration. This method involved collecting over 1,000 support tickets from the company's IT ticketing system, GLPI, and pre-processing the data to ensure the quality and relevance of the data for analysis. Claims data is enriched with relevant features, including textual information and categorical attributes such as urgency, impact, and requirement expertise. To improve the ticket preference matrix, AI-based language models, especially OpenAI's GPT-3, are used. These templates help to reclassify and improve the work of IT support teams. In addition, the ticket data is used to train the Random Forest classifier, allowing automatic classification of tickets based on their specific characteristics. The performance of the ticket classification system is evaluated using a variety of metrics, and the results are compared with alternative methods to assess effectiveness. of the Random Forest algorithm. This evaluation demonstrates the system's ability to correctly classify and prioritize incoming tickets. The successful implementation of this project at Company XYZ is a model for other organizations looking to optimize their IT support through AI-driven approaches. By providing simplified ticket classification and admission ticket reclassification based on AI algorithms, this research helps leverage AI technologies to improve IT support processes. Ultimately, the proposed solution benefits both support providers and users by improving efficiency, response times, and overall customer satisfaction.
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Nagarajan, Fnu. "Automating Customer Experience in Ride-Hailing Platforms via AI-Powered Support Systems." International Journal of Multidisciplinary Research and Growth Evaluation. 3, no. 1 (2022): 834–36. https://doi.org/10.54660/.ijmrge.2022.3.1.834-836.
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With the rapid expansion of ride-hailing services, customer support has become a pivotal aspect of ensuring user satisfaction and operational efficiency. Traditional customer support systems, relying on third-party platforms, have often faced challenges in customization, performance optimization, and seamless integration with internal ride-hailing data. This paper presents a case study on how Lyft developed an AI-powered, machine-learning-driven customer support system to address these limitations. By leveraging internal infrastructure, automation, and intelligent decision-making, Lyft successfully improved issue resolution speed, enhanced customer satisfaction, and reduced operational costs. A detailed discussion on machine learning applications in dispute resolution, ethical considerations, safeguards, and future enhancements provides insights into the evolving landscape of AI-driven customer support. This research highlights how AI, when integrated into customer service frameworks, transforms user experiences and optimizes support operations, offering a scalable solution for ride-hailing companies and beyond.
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Khandare, Sakshi. "Sentiment-Driven Defense Donation System Using AI and Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 4968–75. https://doi.org/10.22214/ijraset.2025.69591.
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Abstract: The National Defense Fund Initiative website serves as a centralized platform designed to facilitate contributions and support for the nation's defense and security forces. This initiative aims to provide financial assistance for the welfare of members of the Armed Forces, paramilitary forces, and their dependents, particularly those affected by conflict or disasters. The website features a user-friendly interface that allows citizens, organizations, and institutions to make donations, ensuring transparency in the use of funds and guaranteeing that every contribution is allocated to defense-related welfare programs and emergency needs. Furthermore, the platform provides real-time updates, success stories, and pertinent information about ongoing projects and their impact, thereby enhancing public engagement in national security and defense support efforts. Through this initiative, the website not only fosters a spirit of patriotism but also encourages a culture of collective responsibility towards the nation's defenders.
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Meshach W, Thamba, and S Aswini. "AI-Integrated Learning and Support Platform for Individuals with Autism." International Research Journal of Innovations in Engineering and Technology 09, Special Issue (2025): 14–23. https://doi.org/10.47001/irjiet/2025.inspire03.
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This paper presents an AI-driven assistive system designed to support autistic individuals by leveraging multi-modal artificial intelligence (AI) technologies for communication enhancement, emotional well-being, and social interaction. The system comprises four core modules: Speak and Learn, See and Learn, Collaborations, and ASD Community, each integrating advanced AI methodologies. The Speak and Learn module utilizes speech-to-text (STT) and text-to-speech (TTS) technologies, reinforced by a Natural Language Processing (NLP) model, to facilitate real-time, adaptive communication. A custom chatbot trained on predefined conversational patterns enhances user engagement and interaction with 95% Accuracy. The See and Learn module employs a TensorFlow Lite (TFLite)-based deep learning model for real-time emotion detection, classifying emotions into angry, sad, and happy categories. Based on the detected emotional state, the system dynamically suggests curated videos and GIFs to promote emotional regulation and engagement with 95% accuracy. The Collaborations module features a secure, low-latency realtime messaging system, enabling direct communication between autistic individuals and psychologists for tailored professional support. Lastly, the ASD Community module serves as an interactive, AI-powered social engagement platform where users can share experiences, provide feedback, and connect with peers. The system is optimized for low-latency performance using resource-efficient AI models, making it compatible with mobile and embedded platforms. By integrating NLP-driven conversational agents, deep learning-based emotion recognition, and secure communication channels, this application creates a comprehensive, intelligent ecosystem tailored to the needs of autistic users. Experimental results demonstrate the system's efficiency, accuracy, and real-time performance, ensuring seamless user experiences across diverse deployment environments.
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Pinsky, Michael R., Artur Dubrawski, and Gilles Clermont. "Intelligent Clinical Decision Support." Sensors 22, no. 4 (2022): 1408. http://dx.doi.org/10.3390/s22041408.
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Early recognition of pathologic cardiorespiratory stress and forecasting cardiorespiratory decompensation in the critically ill is difficult even in highly monitored patients in the Intensive Care Unit (ICU). Instability can be intuitively defined as the overt manifestation of the failure of the host to adequately respond to cardiorespiratory stress. The enormous volume of patient data available in ICU environments, both of high-frequency numeric and waveform data accessible from bedside monitors, plus Electronic Health Record (EHR) data, presents a platform ripe for Artificial Intelligence (AI) approaches for the detection and forecasting of instability, and data-driven intelligent clinical decision support (CDS). Building unbiased, reliable, and usable AI-based systems across health care sites is rapidly becoming a high priority, specifically as these systems relate to diagnostics, forecasting, and bedside clinical decision support. The ICU environment is particularly well-positioned to demonstrate the value of AI in saving lives. The goal is to create AI models embedded in a real-time CDS for forecasting and mitigation of critical instability in ICU patients of sufficient readiness to be deployed at the bedside. Such a system must leverage multi-source patient data, machine learning, systems engineering, and human action expertise, the latter being key to successful CDS implementation in the clinical workflow and evaluation of bias. We present one approach to create an operationally relevant AI-based forecasting CDS system.
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Krishnamoorthy, A., K. S. Karthika, S. Arunkumar, and N. Prabakaran. "An AI-Driven Clinical Text-Based Decision Support System for Pancreatic Cancer Diagnosis." Migration Letters 20, S13 (2023): 460–67. http://dx.doi.org/10.59670/ml.v20is13.6476.
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The high mortality rate of pancreatic cancer and its detection at a late stage require advanced early diagnostic methods. The proposed AI-based clinical decision support system that leverages natural language processing (NLP) to extract key information from medical texts, such as patient records and reports. Using machine learning algorithms, including recurrent neural networks and transformer models, the system aims to identify early signs of pancreatic cancer with high accuracy. This research aims to bridge the gap between the increasing complexity of medical data and the need for user-friendly diagnostic tools. The model focus on a text analytics approach, integrating NLP techniques such as named entity recognition and sentiment analysis with machine learning for predictive modeling. The system interface will help healthcare professionals make informed decisions with a great accuracy for treatment recommendations. By facilitating early detection and providing actionable insights, the model hopes to significantly reduce the burden of pancreatic cancer.
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Yadav, Naveen. "AI-Driven Personalized Cancer Treatment App using React.js, Vite, and Gemini AI." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem03283.
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Abstract This paper presents the design and implementation of a web-based, AI-driven personalized cancer treatment platform leveraging React.js, Vite, and Gemini AI models. We address the pressing need for individualized oncology care by facilitating rapid, secure, and dynamic recommendations based on patient-specific data. The proposed architecture integrates state-of-the-art generative AI for clinical decision support, delivers a fast and robust frontend with modern web technologies, and ensures user data privacy and compliance. We discuss system design, core AI workflow, frontend engineering, integration challenges, and preliminary evaluation results, demonstrating the effectiveness and promise of such technology in aiding clinicians and patients.
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G.Lokeswari, Anthapu Neha, Gopidinna Sheeba Shaik, Danduboina Surya Teja, and Mannala Shilpa. "AI-DRIVEN SYSTEM FOR PAVEMENT DISTRESS AND OBSTACLE CLASSIFICATION." Journal of Engineering Sciences 16, no. 02 (2025): 18–24. https://doi.org/10.36893/jes.2025.v16i02.02.
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Monitoring the road condition has acquired a critical significance during recent years. A few major factors add up to the importance of current research : the information retrieved may support the decision-making process of drivers to the use or avoidance of certain highways, smooth road surface causes less damage to the vehicle chassis and suspension system, the dependability of the vehicles control system remains, valid information on the road surface quality is the basis for updating the knowledge base of the road management companies and organizations and thus challenges them for regular surface reviews and repairs. The tool considered in the paper is the real-time IoT-complex with Android application that automatically collects the data from the mobile triaxial accelerometer and gyroscope, shows the road trace on a geographic map using GPS and sends all recorded entries to the cloud-based computation algorithms. Different types of artificial neural networks are applied to training data to classify road segments and build the model. The experimental results show a consistent accuracy of 90 and higher percent. Using this approach the expected output is the visualization of the road quality map of a selected region. Hence, the constructive feedback may be provided to drivers and local authorities. The long-term benefit from this system is the performing of the road network state comparison throughout various time intervals and checking up on the road construction projects, whether or not they meet the assigned quality prerequisites.
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Omkar R. Shelke, Jai S. Hardas, Nilesh K. Mohite, Atul G. Rathod, and Prof. Smruti S. Barik. "AI-Driven Doctor Scheduling for Efficient Patient Appointments." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 06 (2025): 2791–97. https://doi.org/10.47392/irjaeh.2025.0411.
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This paper presents an AI-enhanced doctor appointment system designed to optimize the scheduling of medical appointments. The system is composed of four main modules: patient, hospital administration, doctor, and Admin. Upon logging in, users can input their location and symptoms, enabling the system to suggest nearby hospitals based on distance and ratings. Patients can then choose specialist doctors from comprehensive profiles and reviews. Doctors have the capability to update their availability, while hospital administrators can manage appointments for walk-in patients. The system also includes features for notifications and cancellation alerts to improve the user experience. The primary goal of this system is to enhance patient satisfaction, optimize hospital resource usage, and increase the efficiency of medical services. By integrating AI components, the system aims to refine scheduling processes, reduce congestion, and offer a smooth user experience. It utilizes a machine learning model based on support vector machines (SVM) to predict appointment attendance. In today’s fast-paced environment, reliable healthcare services are essential. This approach attempts to improve the connection between patients and healthcare providers by implementing a practical and user-friendly system. Furthermore, the technology provides medical personnel with a powerful tool for effectively managing their calendars, reducing administrative work and assuring a great patient experience. AI-powered doctor appointment system, medical schedule optimization, patient happiness, hospital administration, doctor availability management, location-based hospital search, special- ized doctor referral, notification system, cancelation alerts, and healthcare efficiency. Index Terms—The Doctor Appointment System leverages AI technology to streamline the process of scheduling medical appointments. This system is structured into four main modules: Patient, Hospital Administration, Doctor, and Admin. Key features include: patient satisfaction, hospital administration, doctor scheduling, location-based hospital search, specialist doctor recommendation, notification system, cancellation alerts, healthcare efficiency.
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Kumar, Manish. "AIRA : AI-Powered Code Review & Bug Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42592.
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The increasing complexity of software develop- ment presents significant challenges for developers, including bug detection, code inefficiencies, and security vulnerabilities. Traditional methods of code review and debugging often result in increased workload and reduced productivity. To address these issues, AI-powered tools are emerging as a solution to enhance code quality, streamline development, and minimize human error. Introducing AIRA (AI-powered Intelligent Review Assistant), an advanced AI-driven code review and bug detection system designed to assist developers in improving code quality and ensuring robust security. AIRA leverages advanced AI models, including Pylint, SonarQube, and Bandit, to perform real- time static and dynamic code analysis. It identifies bugs, security vulnerabilities, and performance bottlenecks, providing actionable insights to enhance code efficiency. AIRA is built on a Flask-based Python backend integrated with a React.js frontend, offering a high-performance and intuitive interface. The system supports real-time code analysis, automated code optimization, and AI-based refactoring, en- abling developers to identify and resolve issues efficiently. AIRA also features a secure authentication system using Firebase, providing multi-platform support and seamless user experience with light and dark mode options. AIRA empowers developers by automating repetitive tasks, reducing the time required for code review, and enhancing overall code quality. By combining AI-driven analysis with an intuitive user interface, AIRA aims to transform the software development process, making it faster, more secure, and highly efficient. Index Terms: AI-powered Code Review, Bug Detection, Python Development, Flask API, Security Analysis, Static Code Anal- ysis, AI-Based Code Optimization, Code Efficiency, Software Security, AIRA.
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Bamanikar, Ashvini. "Enhancing Medical Knowledge Sharing and Decision Support with AI- Driven Collaborative Platforms in Healthcare." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem02919.
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Healthcare professionals require efficient systems for instant collaboration, continuous learning, and informed decision-making. While digital tools exist, they often lack tailored AI integration for medical contexts. This paper proposes an advanced, AI-enhanced collaborative platform focused on medical knowledge exchange, contextual query resolution, and decision support. Through modular architecture and semantic AI models, the platform encourages evidence-based practice, personalized learning, and expert-led discussions. Real-time updates, role-based access, and secure communication protocols ensure adaptability in dynamic healthcare settings. Evaluation shows improved knowledge dissemination and user engagement, pointing to future possibilities of AI- driven support in clinical practice. Keywords: Medical Collaboration, Semantic AI, Decision Support System, NLP in Healthcare, Medical Forums.
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Sinha, Abhinav. "Style Craft: AI-Driven Fashion Platform." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47571.
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1. ABSTRACT As artificial intelligence continues to reshape industries, personalized and intelligent systems are becoming essential for enriching digital experiences. Style Craft: AI-Driven Fashion Platform introduces a next-generation fashion assistant designed to redefine how users discover, interact with, and personalize their style choices. The platform delivers curated fashion recommendations, enables virtual outfit trials, and helps users stay updated with current trends through an intuitive and immersive interface. Built with Python and enhanced by cutting-edge AI methodologies, the system leverages computer vision, natural language understanding, and recommendation engines to offer dynamic suggestions tailored to individual preferences, body profiles, and browsing behavior. Core components include an AI-powered virtual try-on system, style compatibility analysis, and trend forecasting modules, all accessible through a responsive web interface. This paper details the system's architecture and the technologies that power it, emphasizing how AI elevates personalization, visual recognition, and interaction design in the fashion domain. It also addresses implementation challenges, including optimizing garment recognition, adapting to user variability, and maintaining fluid performance. Looking forward, the platform envisions broader capabilities such as conversational AI for voice-guided fashion navigation, AR/VR support for immersive try-ons, and integration with real-time retail inventories for seamless shopping. Style Craft underscores the innovative potential of AI in crafting tailored, engaging, and futuristic fashion experiences for modern users. ACM Reference Format: Mitansh Sehgal, Nikhil Maurya, Abhinav Sinha. 2025. Style Craft: AI-Driven Fashion Platform. Keywords – Artificial Intelligence, Personalized Recommendations, Fashion Technology, , Trend Forecasting, Recommendation Systems, User Personalization, Human-Computer Interaction, Conversational AI, Style Analysis, Intelligent Fashion Assistant, E-commerce Innovation
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Chen, Meng-Han, Guanling Lee, and Lun-Ping Hung. "AI-Driven Data Analysis for Asthma Risk Prediction." Healthcare 13, no. 7 (2025): 774. https://doi.org/10.3390/healthcare13070774.
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Background: Asthma is a well-known otolaryngological and immunological disorder that affects patients worldwide. Currently, the primary diagnosis relies on a combination of clinical history, physical examination findings consistent with asthma, and objective evidence of reversible airflow obstruction. However, the diagnostic process can be invasive and time-consuming, which limits clinical efficiency and accessibility. Objectives: In this study, an AI-based prediction system was developed, leveraging voice changes caused by respiratory contraction due to asthma to create a machine learning (ML)-based clinical decision support system. Methods: A total of 1500 speech samples—comprising high-pitch, normal-pitch, and low-pitch recitations of the phonemes [i, a, u]—were used. Long-Term Average Spectrum (LTAS) and Single-Frequency Filtering Cepstral Coefficients (SFCCs) were extracted as features for classification. Seven machine learning algorithms were employed to assess the feasibility of asthma prediction. Results: The Decision Tree, CNN, and LSTM models achieved average accuracies above 0.8, with results of 0.88, 0.80, and 0.84, respectively. Observational results indicate that the Decision Tree model performed best for high-pitch phonemes, whereas the LSTM model outperformed others in normal-pitch and low-pitch phonemes. Additionally, to validate model efficiency and enhance interpretability, feature importance analysis and overall average spectral analysis were applied. Conclusions: This study aims to provide medical clinicians with accurate and reliable decision-making support, improving the efficiency of asthma diagnosis through AI-driven acoustic analysis.
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Noor Nafees, Muhammad Azam, Dr. Aneesa Sohail, and Qaiser Janjua. "Exploring the Integration of AI for Social-Emotional Learning: A Psychological, Technological, and Educational Approach." Critical Review of Social Sciences Studies 3, no. 2 (2025): 810–27. https://doi.org/10.59075/3y8ma270.
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This research focused on how artificial intelligence (AI) has been implemented in social-emotional learning (SEL) and how it influenced emotional well-being and academic performance among Punjab, Pakistan-based university teachers. A quantitative design was employed with a random sampling approach to gather data from 271 participants. The research verified that AI-driven SEL technologies significantly impact emotional well-being by delivering customized support to students and making real-time emotional interventions through AI systems. Correlation analysis indicated a strong positive correlation between AI integration and emotional well-being, and regression analysis identified AI's significant impact on emotional support outcomes. Besides, analysis using ANOVA also reaffirmed the presence of significant performance differences in AI-based SEL instruments across demographic characteristics like teaching background and university category. The paper highlights the need for culturally relevant, ethically driven AI mechanisms that enable humans to interact freely, thereby equipping the teaching environment with an even emotional support system. The findings suggest that AI-driven SEL tools can effectively encourage a more emotionally supportive classroom culture, provided that ethical concerns, such as privacy and emotional authenticity, are considered.
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Aftab Ahmed Soomro, Muhammad Hayat Khan, Muhammad Umar, Sajid Khan, and Dr. Osama Ali. "AI-Driven Academic Advising in Higher Education: Leveraging Intelligent Systems to Personalize Student Support, Improve Retention, and Optimize Career Pathways." Critical Review of Social Sciences Studies 3, no. 2 (2025): 229–48. https://doi.org/10.59075/vy3v7k17.
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The integration of Artificial Intelligence (AI) into academic advising is rapidly transforming how higher education institutions support student success. This study explores the effectiveness of AI-driven academic advising systems in enhancing student support, improving retention rates, and optimizing career pathways. Using a survey-based methodology, data was collected from 1,200 university students across three institutions that have implemented AI-powered advising platforms. The survey assessed students’ experiences with the AI system, focusing on usability, personalization, academic confidence, retention likelihood, and alignment of academic choices with career goals. Results indicate a significant positive impact of AI on students’ perception of guidance quality, timely decision-making, and clarity in career planning. Furthermore, students reported increased satisfaction and a stronger sense of being supported academically, with many noting that AI-generated insights helped them make informed choices about courses and careers. These findings suggest that when implemented ethically and with proper institutional support, AI-driven academic advising can serve as a powerful tool for personalized education and long-term academic success.
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Raj, J. Durai, and G. Sathiyan. "Enhancing Life Skill Progression and Psychological Well-being of Undergraduate Students through AI-driven Recommendation System." Multidisciplinary Science Journal 7, no. 2 (2024): 2025054. http://dx.doi.org/10.31893/multiscience.2025054.
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Professionals may keep up with changed work settings and difficulties by continuing their education throughout their lives. Given the variety of courses offered, it is critical for professionals to have mechanisms that can connect them to appropriate programs. Although there is little information available regarding how teenagers utilize these courses, smartphone delivered life competency courses are a new and potential approach to support psychological wellness and discourage addiction to substances among young people. This paper explores the integration of AI-driven recommendation systems (AI-RSs) to enhance the mental health and professional skills of undergraduate students. Leveraging machine learning (ML) techniques and ontological frameworks, the proposed system aims to provide personalized recommendations for lifelong learning courses. By understanding individual preferences and learning objectives, the system facilitates the discovery of relevant courses tailored to the unique needs of students. This approach not only supports the continuous development of professional skills but also contributes to promoting mental well-being by fostering a sense of purpose and accomplishment through lifeskills learning. Through this abstract, we delve into the potential of AI-driven technologies to revolutionize education and support the holistic growth of undergraduate students.
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Janaekiram, G. "AI IMPACT TRACKER: A DECISION SUPPORT SYSTEM FOR WORKFORCE TRANSITION." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem02906.
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Abstract: A decision-support tool called the AI Impact Tracker was created to help people make the shift to AI-driven employment. It offers a thorough, individualized method that evaluates users' abilities, passions, and objectives to create learning pathways that are specifically tailored to them. Users can evaluate their technical and soft abilities, choose pertinent skills, and get career development recommendations based on market expectations through an interactive dashboard. To make sure that recommendations match changing employment requirements, the system incorporates real-time market facts, such as skill shortages, salary trends, and industry growth estimates. It also provides a skills evaluation tool that gives users tailored feedback after evaluating their competency in data analysis, machine learning, programming, and other important technical areas. A tailored learning plan with carefully chosen materials, certifications, and practical tasks is also provided by the site. In addition, the system creates a career transition roadmap to assist users in visualizing their path from laying the groundwork to creating a portfolio and applying for jobs. An adaptive learning experience is ensured by the AI Impact Tracker's recommendations, which are continuously improved in response to user feedback. The tool's ultimate goal is to provide people with the knowledge and self-assurance they need to succeed in the rapidly changing field of artificial intelligence. The AI Impact Tracker is a flexible and dynamic tool that is always improving its suggestions based on input from users. This technology is positioned to be a vital tool in preparing the workforce for the future of work as companies move toward a greater reliance on AI. Keywords: AI, career transition, skill assessment, personalized learning, workforce development, machine learning, soft skills, career roadmap, market insights, cloud computing.
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Baba, Khalid, Nour-Eddine El Faddouli, and Nicolas Cheimanoff. "Mobile-Optimized AI-Driven Personalized Learning: A Case Study at Mohammed VI Polytechnic University." International Journal of Interactive Mobile Technologies (iJIM) 18, no. 04 (2024): 81–96. http://dx.doi.org/10.3991/ijim.v18i04.46547.
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With the rise of mobile learning platforms, it has become increasingly evident that individuals require personalized experiences that are tailored to the strengths and limitations of mobile devices. The present study explores the significant impact that personalized mobile learning environments, powered by artificial intelligence (AI), could have. This study specifically evaluates the impact of an AI-driven personalized educational platform, designed for mobile devices, on the academic achievement and educational progress of students at Mohammed VI Polytechnic University. The platform, designed for mobile devices, allows instructors to easily upload information. Learners can interact with an AI mentor through a chat interface that is seamlessly integrated into their mobile course materials. The system, constructed using cutting-edge technologies such as Langchain, Pinecone, and the LLM Model, excels at providing personalized, real-time feedback and support for learners who are frequently mobile. This study compared two groups of students. One group had access to a mobile personalized learning platform powered by AI, whereas the control group did not have access to it. We conducted a comparative analysis of mobile educational experiences, levels of engagement, and academic outcomes across these groups. In addition, qualitative feedback was gathered from educators and students to evaluate the mobile usability and effectiveness of the system. The results of our study demonstrate that the AI-driven mobile-tailored learning system significantly improves the experience of mobile learners. The increased levels of engagement, improved understanding, and superior academic achievements support our claim. This study not only supports the potential of AI-driven personalized mobile learning in higher education but also emphasizes the importance of continuous innovation to improve its usefulness and effectiveness.
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Shankar, Dr Venkatesh. "AI Startup Assistant: Empowering Entrepreneurs to Launch and Scale Startups with AI-driven Insights and Tools." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48877.
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Abstract – The rapid acceleration of entrepreneurial activity in the technology sector necessitates intelligent, scalable solutions for startup ideation, development, and growth management. This paper presents the design and implementation of an AI Startup Assistant, a comprehensive system powered by cutting-edge techniques in machine learning and natural language processing, with mechanisms for identifying content duplication data analytics techniques. The assistant is engineered to support founders through critical phases including business model generation, market research, competitive analysis, funding strategy, and operational execution. Leveraging a modular architecture, the system delivers personalized, real-time insights and automates key tasks such as pitch deck creation, investor matching, and product road mapping. Experimental evaluation demonstrates the assistant’s efficacy in reducing time-to-market, enhancing decision-making accuracy, and increasing early-stage success rates. Upcoming studies aim to integrate autonomous learning mechanisms to further adapt to diverse industry domains and evolving market conditions. Index Terms – Artificial Intelligence, Startup Assistant, Entrepreneurship, Business Automation, The Role of NLP in Enhancing Decision Support Systems.
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K H, Vandana. "AI-Driven Real-Time Surveillance: Anomaly Detection and Notification System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47628.
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ABSTRACT Surveillance technologies are rapidly transitioning from traditional passive monitoring systems to intelligent platforms capable of detecting anomalies in real-time. This study presents an AI-powered surveillance framework designed to identify violent behavior across diverse input formats, such as static images, recorded videos, and live webcam feeds. Leveraging advanced deep learning architectures, including YOLO and TensorFlow-based models, the system delivers high-precision violence detection with minimal latency. On identifying a threat, it initiates instant alerts through visual prompts and sound notifications, ensuring rapid situational awareness. The interface is intuitively built to support real-time configuration of detection parameters and the monitoring of performance indicators such as detection frequency, operational uptime, and frame processing speed. Engineered for scalability and resilience, the system demonstrates strong applicability in domains like public safety, institutional monitoring, and content regulation. Its core advantages include high detection accuracy, efficient real-time processing, and adaptability to various data sources. By combining cutting edge AI strategies with responsive caution instruments, the framework offers a reliable, computerized arrangement for improving danger location in observation situations. Keywords: Real-Time Surveillance, Anomaly Detection, Violence Detection, YOLO, TensorFlow, Deep Learning, Smart Monitoring, Alert System
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Chauhan, C. U., Sumit Bhamkar, Kalyani Mogre, Sangharsh Ghadse, Pratiksha Bhandekar, and Vaibhavi Kinnake. "AI-Driven Project Guidance for Idea Execution and Collaboration." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43416.
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VisionCraft is an AI-powered project management system designed to assist users from project initiation to completion. It provides technology stack recommendations, time and cost estimations, resource allocation, and real-time progress tracking to enhance efficiency. The system integrates a research paper recommendation tool to streamline academic exploration and a community forum for expert collaboration. By leveraging machine learning, predictive analytics, and workflow automation, VisionCraft ensures structured guidance, optimised decision-making, and increased project success rates. Keywords: Project Management, AI-Driven Planning, Technology Stack Recommendation, Time and Cost Estimation, Resource Allocation, Predictive Analytics, Real-Time Tracking, Risk Assessment, Research Paper Integration, Community Collaboration, Expert Support, Machine Learning, Workflow Automation, Data-Driven Decision Making
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Mandepudi, Pavani, Gade Sindhu, S. S. Rohan, Dr L. Chandrasekhar Reddy, and Parameswar M. "AI-Driven Medical Chatbot for Early Disease Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 2324–29. https://doi.org/10.22214/ijraset.2025.67802.
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Abstract: This paper presents the design and implementation of an AI-powered medical chatbot for predicting infectious diseases and providing medical assistance. The chatbot leverages Machine Learning (ML) techniques, including Support Vector Machine (SVM), Long Short-Term Memory (LSTM) networks, and Natural Language Processing (NLP), to accurately interpret and respond to user queries. It is trained on medical datasets consisting of symptom descriptions, disease history, and treatment plans. Therefore, the chatbot can suggest an accurate diagnosis, preventive measures, and possible treatment options. The chatbot serves as an intelligent healthcare assistant that provides immediate replies and individualized medical indications at all times, minimizing the necessity for immediate physician consultations. It is intended to be used on various platforms to make it available through the internet and mobile application. The system has a high accuracy rate. As a result, it is effective in predicting diseases and engaging users. The purpose of the study is the role of AI chatbots in the transformation of healthcare. AI chatbots connect patients with medical professionals, provide immediate support, and alert patients of the early signs of diseases (especially during pandemics and emergencies).
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Samuel Olaoluwa Folorunsho, Olubunmi Adeolu Adenekan, Chinedu Ezeigweneme, Ike Chidiebere Somadina, and Patrick Azuka Okeleke. "Utilizing AI for predictive maintenance and problem resolution to optimize technical support operations." International Journal of Frontiers in Engineering and Technology Research 7, no. 1 (2024): 012–32. http://dx.doi.org/10.53294/ijfetr.2024.7.1.0038.
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This paper explores the application of artificial intelligence (AI) in enhancing technical support operations through predictive maintenance and problem resolution. The objective is to examine how AI-driven solutions can optimize support efficiency, reduce downtime, and improve overall customer satisfaction. The research methodology involves a comprehensive review of existing literature, case studies, and the implementation of AI models in a controlled technical support environment. Key findings indicate that AI can significantly improve predictive maintenance by analyzing historical data, identifying patterns, and forecasting potential system failures before they occur. This proactive approach not only minimizes operational disruptions but also extends the lifespan of technical equipment. Additionally, AI-powered problem resolution tools, such as chatbots and virtual assistants, have demonstrated their ability to provide real-time support, reduce response times, and handle a large volume of inquiries with high accuracy. The study also highlights the integration of machine learning algorithms in technical support workflows, enabling continuous learning and adaptation to new issues. By automating routine tasks and providing data-driven insights, AI facilitates more efficient allocation of human resources to complex problems that require expert intervention. The utilization of AI in predictive maintenance and problem resolution presents a transformative opportunity for technical support operations. The findings underscore the potential for AI to not only enhance operational efficiency and reliability but also to deliver superior customer experiences. Future research should focus on scaling AI applications across diverse technical environments and addressing challenges related to data privacy and algorithmic bias.