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Pelayes, David Eduardo, Jose A. Mendoza i Anibal Martin Folgar. "Artificial intelligence use in diabetes". Latin American Journal of Ophthalmology 5 (10.12.2022): 6. http://dx.doi.org/10.25259/lajo_4_2022.
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Diabetic retinopathy (DR) affects the small vessels of the eye and is the leading cause of blindness in people on reproductive age; however, less than half of patients are aware of their condition; therefore, early detection and treatment is essential to combat it. There are currently multiple technologies for DR detection, some of which are already commercially available. To understand how these technologies work, we must know first some basic concepts about artificial intelligence (AI) such as machine learning (ML) and deep learning (DL). ML is the basic process by which AI incorporates new data using different algorithms and thus creates new knowledge on its base, learns from it, and makes determinations and predictions on some subject based on all that information. AI can be presented at various levels. DL is a specific type of ML, which trains a computer to perform tasks as humans do, such as speech recognition, image identification, or making predictions. DL has shown promising diagnostic performance in image recognition, being widely adopted in many domains, including medicine. For general image analysis, it has achieved strong results in various medical specialties such as radiology dermatology and in particular for ophthalmology. We will review how this technology is constantly evolving which are the available systems and their task in real world as well as the several challenges, such as medicolegal implications, ethics, and clinical deployment model needed to accelerate the translation of these new algorithms technologies into the global health-care environment.
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Gokcekuyu, Yasemin, Fatih Ekinci, Mehmet Serdar Guzel, Koray Acici, Sahin Aydin i Tunc Asuroglu. "Artificial Intelligence in Biomaterials: A Comprehensive Review". Applied Sciences 14, nr 15 (28.07.2024): 6590. http://dx.doi.org/10.3390/app14156590.
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The importance of biomaterials lies in their fundamental roles in medical applications such as tissue engineering, drug delivery, implantable devices, and radiological phantoms, with their interactions with biological systems being critically important. In recent years, advancements in deep learning (DL), artificial intelligence (AI), machine learning (ML), supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) have significantly transformed the field of biomaterials. These technologies have introduced new possibilities for the design, optimization, and predictive modeling of biomaterials. This review explores the applications of DL and AI in biomaterial development, emphasizing their roles in optimizing material properties, advancing innovative design processes, and accurately predicting material behaviors. We examine the integration of DL in enhancing the performance and functional attributes of biomaterials, explore AI-driven methodologies for the creation of novel biomaterials, and assess the capabilities of ML in predicting biomaterial responses to various environmental stimuli. Our aim is to elucidate the pivotal contributions of DL, AI, and ML to biomaterials science and their potential to drive the innovation and development of superior biomaterials. It is suggested that future research should further deepen these technologies’ contributions to biomaterials science and explore new application areas.
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Drikakis, Dimitris, i Filippos Sofos. "Can Artificial Intelligence Accelerate Fluid Mechanics Research?" Fluids 8, nr 7 (19.07.2023): 212. http://dx.doi.org/10.3390/fluids8070212.
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The significant growth of artificial intelligence (AI) methods in machine learning (ML) and deep learning (DL) has opened opportunities for fluid dynamics and its applications in science, engineering and medicine. Developing AI methods for fluid dynamics encompass different challenges than applications with massive data, such as the Internet of Things. For many scientific, engineering and biomedical problems, the data are not massive, which poses limitations and algorithmic challenges. This paper reviews ML and DL research for fluid dynamics, presents algorithmic challenges and discusses potential future directions.
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Zhang, Shengzhe. "Artificial Intelligence and Applications in Structural and Material Engineering". Highlights in Science, Engineering and Technology 75 (28.12.2023): 240–45. http://dx.doi.org/10.54097/9qknfc57.
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The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has become a vital tool attributed to Structural and Material Engineering and developed the way engineers approach design analysis and optimization. This paper explores the principal models of ML and DL, such as the generative adversarial network (GAN) and the artificial neural networks (ANN) and, and discusses their impacts on the applications of material design, structure damage detection (SDD), and archtecture design. It indicates that the high-quality of database is the essential key to training the model. Thus, the data preprocessing is required for expanding the data source and improving the quality of data. In material design process, ML and DL models reduce the time to predict the properties of construction materials, which makes SDD realistic as well. For architecture design, GAN is used to generate image data, such as drawing of the floor plan and this could be helpful to reduce the labor resources. However, some challenges of ML and DL are found while applying the algorithms to real-life applications. For example, sufficient data is needed to train the DL models and the ethic aspect is also a concern when thinking of AI.
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AFTAB, Ifra, Mohammad DOWAJY, Kristof KAPITANY i Tamas LOVAS. "Artificial Intelligence (AI) – based strategies for point cloud data and digital twins". Nova Geodesia 3, nr 3 (19.08.2023): 138. http://dx.doi.org/10.55779/ng33138.
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Artificial Intelligence (AI), specifically machine learning (ML) and deep learning (DL), is causing a paradigm shift in coding practices and software solutions across diverse fields. This study focuses on harnessing the potential of ML/DL strategies in the geospatial domain, where geodata possesses characteristics that align with the concept of a “lingual manuscript” in aesthetic theory. By employing ML/DL techniques, such as feature evaluation and extraction from 3D point clouds, we can derive concepts that are specific to software, geographical areas, and tasks. ML/DL-based interpretation of 3D point clouds extends geospatial modelling beyond implicit representations, enabling the resolution of complex heuristic-based reconstructions and abstract concepts. These advancements in artificial intelligence have the potential to optimize and expedite geodata computation and geographic information systems. However, ML/DL encounters notable challenges in this domain, including the need for abundant training data, advanced statistical methods, and the development of effective feature representations. Overcoming these challenges is essential to enhance the performance and efficacy of ML/DL systems. Additionally, ML/DL-based solutions can simplify software engineering processes by replacing certain aspects of current adoption and implementation practices, resulting in reduced complexities in development and management. Through the adoption of ML/DL, many of the existing explicitly coded GIS implementations may gradually be replaced in the long term. Overall, this research illustrates the transformative capabilities of ML/DL in geospatial applications and underscores the significance of addressing associated challenges to drive further advancements in the field.
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Iadanza, Ernesto, Rachele Fabbri, Džana Bašić-ČiČak, Amedeo Amedei i Jasminka Hasic Telalovic. "Gut microbiota and artificial intelligence approaches: A scoping review". Health and Technology 10, nr 6 (26.10.2020): 1343–58. http://dx.doi.org/10.1007/s12553-020-00486-7.
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Abstract This article aims to provide a thorough overview of the use of Artificial Intelligence (AI) techniques in studying the gut microbiota and its role in the diagnosis and treatment of some important diseases. The association between microbiota and diseases, together with its clinical relevance, is still difficult to interpret. The advances in AI techniques, such as Machine Learning (ML) and Deep Learning (DL), can help clinicians in processing and interpreting these massive data sets. Two research groups have been involved in this Scoping Review, working in two different areas of Europe: Florence and Sarajevo. The papers included in the review describe the use of ML or DL methods applied to the study of human gut microbiota. In total, 1109 papers were considered in this study. After elimination, a final set of 16 articles was considered in the scoping review. Different AI techniques were applied in the reviewed papers. Some papers applied ML, while others applied DL techniques. 11 papers evaluated just different ML algorithms (ranging from one to eight algorithms applied to one dataset). The remaining five papers examined both ML and DL algorithms. The most applied ML algorithm was Random Forest and it also exhibited the best performances.
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Choudhary, Laxmi, i Jitendra Singh Choudhary. "Deep Learning Meets Machine Learning: A Synergistic Approach towards Artificial Intelligence". Journal of Scientific Research and Reports 30, nr 11 (16.11.2024): 865–75. http://dx.doi.org/10.9734/jsrr/2024/v30i112614.
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The evolution of artificial intelligence (AI) has progressed from rule-based systems to learning-based models, integrating machine learning (ML) and deep learning (DL) to tackle complex data-driven tasks. This review examines the synergy between ML, which utilizes algorithms like decision trees and support vector machines for structured data, and DL, which employs neural networks for processing unstructured data such as images and natural language. The combination of these paradigms through hybrid ML-DL models has enhanced prediction accuracy, scalability, and automation across domains like healthcare, finance, natural language processing, and robotics. However, challenges such as computational demands, data dependency, and model interpretability remain. This paper discusses the benefits, limitations, and future potential of ML and DL and also provides a review study of a hybrid model makes use of both techniques (machine learning & deep learning) advantages to solve complicated problems more successfully than one could on its own. To boost performance, increase efficiency, or address scenarios where either ML or DL alone would not be able to manage, this approach combines deep learning structures with conventional machine learning techniques.
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Gayatri, T., G. Srinivasu, D. M. K. Chaitanya i V. K. Sharma. "A Review on Optimization Techniques of Antennas Using AI and ML / DL Algorithms". International Journal of Advances in Microwave Technology 07, nr 02 (2022): 288–95. http://dx.doi.org/10.32452/ijamt.2022.288295.
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In recent years, artificial intelligence (AI) aided communications grabbed huge attention to providing solutions for mathematical problems in wireless communications, by using machine learning (ML) and deep learning (DL) algorithms. This paper initially presents a short background on AI, CEM, and the role of AI / ML / DL in antennas. A study on ML / DL algorithms and the optimization techniques of antenna parameters using various ML / DL algorithms are presented. Finally, the application areas of AI in antennas are illustrated.
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El-den, B. M. El, i Marwa M. Eid. "Watermarking Models and Artificial Intelligence". Journal of Artificial Intelligence and Metaheuristics 1, nr 2 (2022): 24–30. http://dx.doi.org/10.54216/jaim.010203.
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Machine learning and deep learning are good bets for solving various intelligence-related problems. While it has practical applications in watermarking, it performs less well on more standard tasks like prediction, classification, and regression. This article offers the results of a thorough investigation into watermarking using modern tools like AI, ML, and DL. Watermarking's origins, some historical context, and the most fascinating and practical applications are also covered briefly.
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Kuhn, Stefan, Rômulo Pereira de Jesus i Ricardo Moreira Borges. "Nuclear Magnetic Resonance and Artificial Intelligence". Encyclopedia 4, nr 4 (18.10.2024): 1568–80. http://dx.doi.org/10.3390/encyclopedia4040102.
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This review explores the current applications of artificial intelligence (AI) in nuclear magnetic resonance (NMR) spectroscopy, with a particular emphasis on small molecule chemistry. Applications of AI techniques, especially machine learning (ML) and deep learning (DL) in the areas of shift prediction, spectral simulations, spectral processing, structure elucidation, mixture analysis, and metabolomics, are demonstrated. The review also shows where progress is limited.
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An, Ruopeng, Jing Shen i Yunyu Xiao. "Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies". Journal of Medical Internet Research 24, nr 12 (7.12.2022): e40589. http://dx.doi.org/10.2196/40589.
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Background Obesity is a leading cause of preventable death worldwide. Artificial intelligence (AI), characterized by machine learning (ML) and deep learning (DL), has become an indispensable tool in obesity research. Objective This scoping review aimed to provide researchers and practitioners with an overview of the AI applications to obesity research, familiarize them with popular ML and DL models, and facilitate the adoption of AI applications. Methods We conducted a scoping review in PubMed and Web of Science on the applications of AI to measure, predict, and treat obesity. We summarized and categorized the AI methodologies used in the hope of identifying synergies, patterns, and trends to inform future investigations. We also provided a high-level, beginner-friendly introduction to the core methodologies to facilitate the dissemination and adoption of various AI techniques. Results We identified 46 studies that used diverse ML and DL models to assess obesity-related outcomes. The studies found AI models helpful in detecting clinically meaningful patterns of obesity or relationships between specific covariates and weight outcomes. The majority (18/22, 82%) of the studies comparing AI models with conventional statistical approaches found that the AI models achieved higher prediction accuracy on test data. Some (5/46, 11%) of the studies comparing the performances of different AI models revealed mixed results, indicating the high contingency of model performance on the data set and task it was applied to. An accelerating trend of adopting state-of-the-art DL models over standard ML models was observed to address challenging computer vision and natural language processing tasks. We concisely introduced the popular ML and DL models and summarized their specific applications in the studies included in the review. Conclusions This study reviewed AI-related methodologies adopted in the obesity literature, particularly ML and DL models applied to tabular, image, and text data. The review also discussed emerging trends such as multimodal or multitask AI models, synthetic data generation, and human-in-the-loop that may witness increasing applications in obesity research.
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Annapoorani, S. "AN IN-DEPTH ANALYSIS OF ARTIFICIAL INTELLIGENCE APPROACHES FOR RAINFALL PREDICTION". international journal of advanced research in computer science 15, nr 2 (20.04.2024): 48–58. http://dx.doi.org/10.26483/ijarcs.v15i2.7061.
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Natural disasters and floods brought on by heavy rainfall pose serious threats to human health and lives every year on a global scale. The intricacy of meteorological data makes it difficult to provide accurate rainfall predictions, despite their critical importance in nations like India where agriculture is the primary occupation. Rainfall forecasting has recently benefited from Artificial Intelligence (AI) developments such as Deep Learning (DL) and Machine Learning (ML) techniques. This article provides a comprehensive survey of recent studies that use AI techniques for rainfall prediction, analyzing them based on the ML algorithms and DL methods used, organized by publication year. The findings show that DL approaches are more effective than traditional ML methods and shallow neural network models. This research is important as it has significant impacts on agriculture, disaster preparedness, and water resource management. Finally, it outlines future research directions for further advancements in rainfall prediction through AI methodologies.
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Kumar, Sanjeet, Urmila Pilania i Neha Nandal. "A Systematic Study of Artificial Intelligence-Based Methods for Detecting Brain Tumors". Informatics and Automation 22, nr 3 (22.05.2023): 541–75. http://dx.doi.org/10.15622/ia.22.3.3.
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The brain is regarded as one of the most effective body-controlling organs. The development of technology has enabled the early and accurate detection of brain tumors, which makes a significant difference in their treatment. The adoption of AI has grown substantially in the arena of neurology. This systematic review compares recent Deep Learning (DL), Machine Learning (ML), and hybrid methods for detecting brain cancers. This article evaluates 36 recent articles on these techniques, considering datasets, methodology, tools used, merits, and limitations. The articles contain comprehensible graphs and tables. The detection of brain tumors relies heavily on ML techniques such as Support Vector Machines (SVM) and Fuzzy C-Means (FCM). Recurrent Convolutional Neural Networks (RCNN), DenseNet, Convolutional Neural Networks (CNN), ResNet, and Deep Neural Networks (DNN) are DL techniques used to detect brain tumors more efficiently. DL and ML techniques are merged to develop hybrid techniques. In addition, a summary of the various image processing steps is provided. The systematic review identifies outstanding issues and future goals for DL and ML-based techniques for detecting brain tumors. Through a systematic review, the most effective method for detecting brain tumors can be identified and utilized for improvement.
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Ali, Zulfiqar, Asif Muhammad, Nangkyeong Lee, Muhammad Waqar i Seung Won Lee. "Artificial Intelligence for Sustainable Agriculture: A Comprehensive Review of AI-Driven Technologies in Crop Production". Sustainability 17, nr 5 (5.03.2025): 2281. https://doi.org/10.3390/su17052281.
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Smart farming leverages Artificial Intelligence (AI) to address modern agricultural sustainability challenges. This study investigates the application of machine learning (ML), deep learning (DL), and time series analysis in agriculture through a systematic literature review following the PRISMA methodology. The review highlights the critical roles of ML and DL techniques in optimizing agricultural processes, such as crop selection, yield prediction, soil compatibility classification, and water management. ML algorithms facilitate tasks like crop selection and soil fertility classification, while DL techniques contribute to forecasting crop production and commodity prices. Additionally, time series analysis is employed for demand forecasting of crops, commodity price prediction, and forecasting crop yield production. The focus of this article is to provide a comprehensive overview of ML and DL techniques within the farming industry. Utilizing crop datasets, ML algorithms are instrumental in classifying soil fertility, crop selection, and various other aspects. DL algorithms, when applied to farming data, enable effective time series analysis and crop selection. By synthesizing the integration of these technologies, this review underscores their potential to enhance decision-making in agriculture and mitigate food scarcity challenges in the future.
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Wang, Zichang. "Enhancing Cancer Prediction Accuracy Through Real-Time Monitoring and Artificial Intelligence Analysis for Patients". Highlights in Science, Engineering and Technology 85 (13.03.2024): 309–15. http://dx.doi.org/10.54097/rw17nn71.
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The integration of Artificial Intelligence (AI) into clinical medicine has recently garnered significant attention, particularly in the context of digital pathology and precision medicine. Real-time monitoring and analysis of patients play a pivotal role in the overall treatment process. This includes monitoring basic body detection indices, analyzing the patient's overall condition, and predicting future possibilities for their medical status. In addition to fundamental machine learning (ML), both deep learning (DL) and digital twin technology have demonstrated considerable potential in enhancing the accuracy of analysis and prediction in clinical cancer patients' condition monitoring. This study employs a comparative analysis approach to assess and compare how DL and digital twin technology contribute to improving accuracy in monitoring and analyzing clinical cancer patients' conditions. ML is well-suited for training models with large datasets, while DL excels in tackling more intricate problems. To enhance the precision and accuracy of analysis and future prediction, digital twin technology is leveraged to explore various patient scenarios, with basic twins providing real-time monitoring of the patient's current conditions. Markov Decision Progress (MDP) is employed when predicting potential outcomes. However, it's essential to consider the impact of the number of times the model is trained when making comparisons. The evaluation criterion centers on whether the use of DL and digital twin technology improves accuracy and precision compared to using ML alone. Examining the results reveals that using only ML or DL yields accuracies of 97.01% and 94.73%, respectively. However, when ML is combined with DL and digital twin technology, accuracy significantly improves to 99.73%.
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Khyade, Ms Mahadevi Pundlik. "Artificial Intelligence (AI): Brain Tumor Detection". International Journal for Research in Applied Science and Engineering Technology 12, nr 12 (31.12.2024): 761–63. https://doi.org/10.22214/ijraset.2024.65886.
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The detection and diagnosis of brain tumors, a critical medical challenge, have greatly benefited from the application of Artificial Intelligence (AI). This review paper explores the advancements, methods, and technologies of AI in the detection and classification of brain tumors from medical imaging modalities. It also highlights the importance of machine learning (ML) and deep learning (DL) algorithms, particularly Convolutional Neural Networks (CNNs), in improving diagnostic accuracy, early detection, and prognosis prediction. Moreover, the paper addresses challenges and future directions in integrating AI with clinical practices for brain tumor management.
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Jain, Rituraj, Sitesh Kumar Singh, Damodharan Palaniappan, Kumar Parmar i Premavathi T. "Data-Driven Civil Engineering: Applications of Artificial Intelligence, Machine Learning, and Deep Learning". Turkish Journal of Engineering 9, nr 2 (20.01.2025): 354–77. https://doi.org/10.31127/tuje.1581564.
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Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are a great advantage that is coming to civil engineering in ways that detail accuracy can be enhanced, many tasks automated, and predictive modeling improved. Across some of the significant subdomains, these technologies allow for eminent progress in structural health monitoring, geotechnical engineering, hydraulic systems, construction management. Currently, AI-powered models such as Artificial Neural Networks (ANNs), fuzzy logic, and evolution-based algorithms allow engineers to predict failure, optimize design, and better resource management of infrastructures. Yet, despite the potential, the adoption of AI, ML, and DL into civil engineering faces a host of challenges including data availability, computational complexity, model interpretability, integration with traditional systems, etc. High-quality, real-time data collection remains expensive and the resource-intensive nature of DL models limits their application to a large scale. In addition, the "black-box" nature of these models raises ethical and regulatory issues especially in decisions related to safety. Against this backdrop, this paper reviews current and potential applications of AI, ML, and DL in civil engineering within the framework of benefits and limitations of AI, ML, and DL, focusing on comparisons. Besides that, the paper outlines future directions regarding cloud computing, explainable AI, and regulatory frameworks. With all these changes within the scope of the discipline, AI-driven technologies will be major in safe, efficient, and sustainable infrastructure systems, provided that success is specifically dependent on addressing these key challenges.
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Naderisorki, Mohammad, Maryam Rezapour i Mehdi Naderi Soorki. "Investigating the Application of Artificial Intelligence in the Pediatric Oncology". Journal of Pediatrics Review 12, nr 1 (1.01.2024): 1–4. http://dx.doi.org/10.32598/jpr.12.1.786.3.
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Since Alan Turing proposed the concept of using computers for intelligent simulation of behavior and critical thinking, artificial intelligence has progressed in various fields. In medicine, artificial intelligence is used in three subfields: Machine learning (ML), deep learning (DL), and computer vision. Considering the increasing use of artificial intelligence in pediatric oncology and cancer treatment, there is a need for studies and research projects specifically focused on pediatric oncology. In oncology, especially childhood malignancies, artificial intelligence can help doctors as a new tool.
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Alkhurayyif, Yazeed, i Abdul Rahaman Wahab Sait. "A Review of Artificial Intelligence-Based Dyslexia Detection Techniques". Diagnostics 14, nr 21 (23.10.2024): 2362. http://dx.doi.org/10.3390/diagnostics14212362.
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Problem: Dyslexia is a learning disorder affecting an individual’s ability to recognize words and understand concepts. It remains underdiagnosed due to its complexity and heterogeneity. The use of traditional assessment techniques, including subjective evaluation and standardized tests, increases the likelihood of delayed or incorrect diagnosis. Motivation: Timely identification is essential to provide personalized treatment and improve the individual’s quality of life. The development of artificial intelligence techniques offers a platform to identify dyslexia using behavior and neuroimaging data. However, the limited datasets and black-box nature of ML models reduce the generalizability and interpretability of dyslexia detection (DD) models. The dimensionality reduction technique (DRT) plays a significant role in providing dyslexia features to enhance the performance of machine learning (ML)- and deep learning (DL)-based DD techniques. Aim: In this review, the authors intend to investigate the role of DRTs in enhancing the performance of ML- and DL-based DD models. Methodology: The authors conducted a comprehensive search across multiple digital libraries, including Scopus, Web of Science, PubMed, and IEEEXplore, to identify articles associated with DRTs in identifying dyslexia. They extracted 479 articles using these digital libraries. After an extensive screening procedure, a total of 39 articles were included in this review. Results: The review findings revealed various DRTs for identifying critical dyslexia patterns from multiple modalities. A significant number of studies employed principal component analysis (PCA) for feature extraction and selection. The authors presented the essential features associated with DD. In addition, they outlined the challenges and limitations of existing DRTs. Impact: The authors emphasized the need for the development of novel DRTs and their seamless integration with advanced DL techniques for robust and interpretable DD models.
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R, Kusuma, i R. Rajkumar. "Plant leaf disease detection and classification using artificial intelligence techniques: a review". Indonesian Journal of Electrical Engineering and Computer Science 38, nr 2 (1.05.2025): 1308. https://doi.org/10.11591/ijeecs.v38.i2.pp1308-1323.
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Agriculture is a cornerstone of human civilization, providing both food and economic stability. While not necessarily fatal, leaf diseases are a crucial threat to plant health. Accurate detection and classification of diseases in early stages are essential to minimize damage. Manual identification can be challenging, and delays in detection can lead to crop devastation. Fortunately, computer-aided image processing offers a solution. Researchers have explored several techniques for disease detection and classification by usage of affected leaf images, making significant progress over time. However, there's always room for improvement. Machine learning (ML), Deep learning (DL) techniques have shown hopeful results. ML, DL approaches act as black-box; eXplainable AI (XAI) provides clear explanations on decisions made by these black-boxes. This study aims to present a comprehensive review on plant leaf disease detection and classification by means of ML, DL and XAI methods with an overview of the outcomes of existing techniques, summarizes their performance, evaluation metrics, and analyses the challenges in existing systems, and offers the study's inferences.
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Bonkra, Anupam, Pramod Kumar Bhatt, Joanna Rosak-Szyrocka, Kamalakanta Muduli, Ladislav Pilař, Amandeep Kaur, Nidhi Chahal i Arun Kumar Rana. "Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis". International Journal of Environmental Research and Public Health 20, nr 4 (12.02.2023): 3222. http://dx.doi.org/10.3390/ijerph20043222.
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Infection in apple leaves is typically brought on by unanticipated weather conditions such as rain, hailstorms, draughts, and fog. As a direct consequence of this, the farmers suffer a significant loss of productivity. It is essential to be able to identify apple leaf diseases in advance in order to prevent the occurrence of this disease and minimise losses to productivity caused by it. The research offers a bibliometric analysis of the effectiveness of artificial intelligence in diagnosing diseases affecting apple leaves. The study provides a bibliometric evaluation of apple leaf disease detection using artificial intelligence. Through an analysis of broad current developments, publication and citation structures, ownership and cooperation patterns, bibliographic coupling, productivity patterns, and other characteristics, this scientometric study seeks to discover apple diseases. Nevertheless, numerous exploratory, conceptual, and empirical studies have concentrated on the identification of apple illnesses. However, given that disease detection is not confined to a single field of study, there have been very few attempts to create an extensive science map of transdisciplinary studies. In bibliometric assessments, it is important to take into account the growing amount of research on this subject. The study synthesises knowledge structures to determine the trend in the research topic. A scientometric analysis was performed on a sample of 214 documents in the subject of identifying apple leaf disease using a scientific search technique on the Scopus database for the years 2011–2022. In order to conduct the study, the Bibliometrix suite’s VOSviewer and the web-based Biblioshiny software were also utilised. Important journals, authors, nations, articles, and subjects were chosen using the automated workflow of the software. Furthermore, citation and co-citation checks were performed along with social network analysis. In addition to the intellectual and social organisation of the meadow, this investigation reveals the conceptual structure of the area. It contributes to the body of literature by giving academics and practitioners a strong conceptual framework on which to base their search for solutions and by making perceptive recommendations for potential future research areas.
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Liakos, Konstantinos G., Georgios K. Georgakilas, Fotis C. Plessas i Paris Kitsos. "GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS". Electronics 11, nr 2 (13.01.2022): 245. http://dx.doi.org/10.3390/electronics11020245.
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A significant problem in the field of hardware security consists of hardware trojan (HT) viruses. The insertion of HTs into a circuit can be applied for each phase of the circuit chain of production. HTs degrade the infected circuit, destroy it or leak encrypted data. Nowadays, efforts are being made to address HTs through machine learning (ML) techniques, mainly for the gate-level netlist (GLN) phase, but there are some restrictions. Specifically, the number and variety of normal and infected circuits that exist through the free public libraries, such as Trust-HUB, are based on the few samples of benchmarks that have been created from circuits large in size. Thus, it is difficult, based on these data, to develop robust ML-based models against HTs. In this paper, we propose a new deep learning (DL) tool named Generative Artificial Intelligence Netlists SynthesIS (GAINESIS). GAINESIS is based on the Wasserstein Conditional Generative Adversarial Network (WCGAN) algorithm and area–power analysis features from the GLN phase and synthesizes new normal and infected circuit samples for this phase. Based on our GAINESIS tool, we synthesized new data sets, different in size, and developed and compared seven ML classifiers. The results demonstrate that our new generated data sets significantly enhance the performance of ML classifiers compared with the initial data set of Trust-HUB.
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Hagos, Desta Haileselassie, Theofilos Kakantousis, Sina Sheikholeslami, Tianze Wang, Vladimir Vlassov, Amir Hossein Payberah, Moritz Meister, Robin Andersson i Jim Dowling. "Scalable Artificial Intelligence for Earth Observation Data Using Hopsworks". Remote Sensing 14, nr 8 (14.04.2022): 1889. http://dx.doi.org/10.3390/rs14081889.
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This paper introduces the Hopsworks platform to the entire Earth Observation (EO) data community and the Copernicus programme. Hopsworks is a scalable data-intensive open-source Artificial Intelligence (AI) platform that was jointly developed by Logical Clocks and the KTH Royal Institute of Technology for building end-to-end Machine Learning (ML)/Deep Learning (DL) pipelines for EO data. It provides the full stack of services needed to manage the entire life cycle of data in ML. In particular, Hopsworks supports the development of horizontally scalable DL applications in notebooks and the operation of workflows to support those applications, including parallel data processing, model training, and model deployment at scale. To the best of our knowledge, this is the first work that demonstrates the services and features of the Hopsworks platform, which provide users with the means to build scalable end-to-end ML/DL pipelines for EO data, as well as support for the discovery and search for EO metadata. This paper serves as a demonstration and walkthrough of the stages of building a production-level model that includes data ingestion, data preparation, feature extraction, model training, model serving, and monitoring. To this end, we provide a practical example that demonstrates the aforementioned stages with real-world EO data and includes source code that implements the functionality of the platform. We also perform an experimental evaluation of two frameworks built on top of Hopsworks, namely Maggy and AutoAblation. We show that using Maggy for hyperparameter tuning results in roughly half the wall-clock time required to execute the same number of hyperparameter tuning trials using Spark while providing linear scalability as more workers are added. Furthermore, we demonstrate how AutoAblation facilitates the definition of ablation studies and enables the asynchronous parallel execution of ablation trials.
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Rodríguez-Merchán, E. Carlos. "The current role of the virtual elements of artificial intelligence in total knee arthroplasty". EFORT Open Reviews 7, nr 7 (1.07.2022): 491–97. http://dx.doi.org/10.1530/eor-21-0107.
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The current applications of the virtual elements of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in total knee arthroplasty (TKA) are diverse. ML can predict the length of stay (LOS) and costs before primary TKA, the risk of transfusion after primary TKA, postoperative dissatisfaction after TKA, the size of TKA components, and poorest outcomes. The prediction of distinct results with ML models applying specific data is already possible; nevertheless, the prediction of more complex results is still imprecise. Remote patient monitoring systems offer the ability to more completely assess the individuals experiencing TKA in terms of mobility and rehabilitation compliance. DL can accurately identify the presence of TKA, distinguish between specific arthroplasty designs, and identify and classify knee osteoarthritis as accurately as an orthopedic surgeon. DL allows for the detection of prosthetic loosening from radiographs. Regarding the architectures associated with DL, artificial neural networks (ANNs) and convolutional neural networks (CNNs), ANNs can predict LOS, inpatient charges, and discharge disposition prior to primary TKA and CNNs allow for differentiation between different implant types with near-perfect accuracy.
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Faisal Ghazi Bishaw. "Review Artificial Intelligence Applications in Renewable Energy Systems Integration". Journal of Electrical Systems 20, nr 3 (30.04.2024): 566–82. http://dx.doi.org/10.52783/jes.2983.
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The transition to renewable energy (RE) sources is critical for addressing global energy demands and environmental concerns. This review paper focuses on the pivotal role of Machine Learning (ML) and Deep Learning (DL) in optimizing and predicting the performance of RE systems, particularly solar and wind power. We explore various applications of these advanced technologies in forecasting energy demand and consumption, predicting the output power of renewable systems, and optimizing the operation and maintenance of these systems. The paper also delves into the significance of Explainable AI (XAI) in enhancing the transparency and understandability of AI models in energy applications. Our comprehensive analysis reveals that while ML and DL offer transformative potential in the RE sector, challenges such as data complexity, system integration, and model interpretability remain. Concluding, this work aims to provide a foundation for future research and development in this rapidly evolving field, asserting that the continued advancement and integration of AI technologies in RE systems is essential for achieving a sustainable and efficient energy future.
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Shirinova, Simuzar. "Leveraging Artificial Intelligence in Linguistics: Innovations in Language Acquisition and Analysis". EuroGlobal Journal of Linguistics and Language Education 2, nr 1 (11.02.2025): 50–57. https://doi.org/10.69760/egjlle.250006.
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Artificial Intelligence (AI) has become a transformative tool in the field of linguistics, providing innovative approaches to studying language acquisition and analysis. This article offers a detailed exploration of AI’s applications in linguistics, with a focus on its contributions to understanding language learning and processing. Using methods such as Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL), researchers are uncovering new perspectives on linguistic phenomena and advancing the study of language. NLP, ML, and DL have enabled the automation of linguistic data analysis with remarkable accuracy and efficiency. NLP techniques allow researchers to process and analyze natural language text through tasks like part-of-speech tagging, syntactic parsing, named entity recognition, and sentiment analysis. Meanwhile, ML algorithms facilitate the development of predictive models for language acquisition and usage by leveraging large linguistic datasets. Additionally, DL models, particularly neural networks, have shown exceptional capabilities in identifying complex linguistic patterns and capturing semantic relationships. In the context of language acquisition research, AI is instrumental in modeling the cognitive processes involved in learning a language. By employing computational simulations and models, researchers can examine how learners acquire phonology, morphology, syntax, and semantics. AI methods also provide valuable tools for studying language development trajectories, analyzing learner productions, and identifying error patterns, offering deeper insights into the mechanisms of language acquisition.
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Choo, Min Soo, Ho Young Ryu i Sangchul Lee. "Development of an Automatic Interpretation Algorithm for Uroflowmetry Results: Application of Artificial Intelligence". International Neurourology Journal 26, nr 1 (31.03.2022): 69–77. http://dx.doi.org/10.5213/inj.2244052.026.
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Purpose: To develop an automatic interpretation system for uroflowmetry (UFM) results using machine learning (ML), a form of artificial intelligence (AI).Methods: A prospectively collected 1,574 UFM results (1,031 males, 543 females) with voided volume>150 mL was labelled as normal, borderline, or abnormal by 3 urologists. If the 3 experts disagreed, the majority decision was accepted. Abnormality was defined as a condition in which a urologist judges from the UFM results that further evaluation is required and that the patient should visit a urology clinic. To develop the optimal automatic interpretation system, we applied 4 ML algorithms and 2 deep learning (DL) algorithms. ML models were trained with all UFM parameters. DL models were trained to digitally analyze 2-dimensional images of UFM curves.Results: The automatic interpretation algorithm achieved a maximum accuracy of 88.9% in males and 90.8% in females when using 6 parameters: voided volume, maximum flow rate, time to maximal flow rate, average flow rate, flow time, and voiding time. In females, the DL models showed a dramatic improvement in accuracy over the other models, reaching 95.4% accuracy in the convolutional neural network model. The performance of the DL models in clinical discrimination was outstanding in both genders, with an area under the curve of up to 0.957 in males and 0.974 in females.Conclusions: We developed an automatic interpretation algorithm for UFM results by training AI models using 6 key parameters and the shape of the curve; this algorithm agreed closely with the decisions of urology specialists.
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Islam, Mahmudul, Masud Rana Rashel, Md Tofael Ahmed, A. K. M. Kamrul Islam i Mouhaydine Tlemçani. "Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review". Energies 16, nr 21 (3.11.2023): 7417. http://dx.doi.org/10.3390/en16217417.
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Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems, ranging from classical ML methods like k-nearest neighbor (KNN) and random forest to more advanced deep learning models such as Convolutional Neural Networks (CNNs). Quantum circuits and infrared imagery were also explored as potential solutions. The analysis found that DL models, in general, outperformed traditional ML models in accuracy and efficiency. This study shows that AI methodologies have evolved and been increasingly applied in PV fault detection. The integration of AI in PV fault detection offers high accuracy and effectiveness. After reviewing these studies, we proposed an Artificial Neural Network (ANN)-based method for PV fault detection and classification.
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Filipp, Fabian V. "Opportunities for Artificial Intelligence in Advancing Precision Medicine". Current Genetic Medicine Reports 7, nr 4 (grudzień 2019): 208–13. http://dx.doi.org/10.1007/s40142-019-00177-4.
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Abstract Purpose of Review We critically evaluate the future potential of machine learning (ML), deep learning (DL), and artificial intelligence (AI) in precision medicine. The goal of this work is to show progress in ML in digital health, to exemplify future needs and trends, and to identify any essential prerequisites of AI and ML for precision health. Recent Findings High-throughput technologies are delivering growing volumes of biomedical data, such as large-scale genome-wide sequencing assays; libraries of medical images; or drug perturbation screens of healthy, developing, and diseased tissue. Multi-omics data in biomedicine is deep and complex, offering an opportunity for data-driven insights and automated disease classification. Learning from these data will open our understanding and definition of healthy baselines and disease signatures. State-of-the-art applications of deep neural networks include digital image recognition, single-cell clustering, and virtual drug screens, demonstrating breadths and power of ML in biomedicine. Summary Significantly, AI and systems biology have embraced big data challenges and may enable novel biotechnology-derived therapies to facilitate the implementation of precision medicine approaches.
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Mahjabeen, Farhana. "Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review". Formosa Journal of Science and Technology 3, nr 10 (26.10.2024): 2397–406. http://dx.doi.org/10.55927/fjst.v3i10.11552.
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Undetected photovoltaic system faults can lead to significant energy losses, often exceeding 10%, necessitating efficient fault detection and diagnosis. Artificial intelligence, particularly machine learning and deep learning, offers promising solutions for real-time, high-volume fault detection and complex pattern recognition in PV systems. This research analyzes various PV fault detection studies, examining their objectives, methods, results, and the prevalence of ML/DL approaches. The analysis highlights the application of both classical ML algorithms, such as K-Nearest Neighbors and Random Forest, and advanced DL models, including Convolutional Neural Networks, for PV fault diagnosis.
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Sarkar, Chayna, Biswadeep Das, Vikram Singh Rawat, Julie Birdie Wahlang, Arvind Nongpiur, Iadarilang Tiewsoh, Nari M. Lyngdoh, Debasmita Das, Manjunath Bidarolli i Hannah Theresa Sony. "Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development". International Journal of Molecular Sciences 24, nr 3 (19.01.2023): 2026. http://dx.doi.org/10.3390/ijms24032026.
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The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as “message-passing paradigms”, “spatial-symmetry-preserving networks”, “hybrid de novo design”, and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.
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Blackledge, Jonathan, i Napo Mosola. "Applications of Artificial Intelligence to Cryptography". Transactions on Machine Learning and Artificial Intelligence 8, nr 3 (30.06.2020): 21–60. http://dx.doi.org/10.14738/tmlai.83.8219.
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This paper considers some recent advances in the field of Cryptography using Artificial Intelligence (AI) It specifically considers the applications of Machine Learning (ML) and Evolutionary Computing (EC) concepts used to generate ciphers. A short overview is given on Artificial Neural Networks (ANNs) and the principles of Deep Learning (DL) using Deep ANNs. In this context, the paper considers: (i) the implementation of EC and ANNs to generate unique and unclonable ciphers; (ii) ML strategies for detecting the genuine randomness (or otherwise) of binary streams for applications in Cryptanalysis. The paper aims to provide an overview on how AI can be applied for encrypting data and undertaking cryptanalysis of such data and other encrypted data classes in order to assess the cryptographic strength of an encryption algorithm. For example, to detect patterns of intercepted data streams that are signatures of encrypted data. An application is presented which includes authentication of high-value documents such as bank notes, using smartphones. Using an antenna of a smartphone to read (in the near field) an embedded flexible integrate circuit with a non-programmable coprocessor, ultra-strong encrypted information can be used on-line for validation.
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Baashar, Yahia, Gamal Alkawsi, Hitham Alhussian, Luiz Fernando Capretz, Ayed Alwadain, Ammar Ahmed Alkahtani i Malek Almomani. "Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis". Computational Intelligence and Neuroscience 2022 (24.02.2022): 1–12. http://dx.doi.org/10.1155/2022/5849995.
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Heart failure is the most common cause of death in both males and females around the world. Cardiovascular diseases (CVDs), in particular, are the main cause of death worldwide, accounting for 30% of all fatalities in the United States and 45% in Europe. Artificial intelligence (AI) approaches such as machine learning (ML) and deep learning (DL) models are playing an important role in the advancement of heart failure therapy. The main objective of this study was to perform a network meta-analysis of patients with heart failure, stroke, hypertension, and diabetes by comparing the ML and DL models. A comprehensive search of five electronic databases was performed using ScienceDirect, EMBASE, PubMed, Web of Science, and IEEE Xplore. The search strategy was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. The methodological quality of studies was assessed by following the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) guidelines. The random-effects network meta-analysis forest plot with categorical data was used, as were subgroups testing for all four types of treatments and calculating odds ratio (OR) with a 95% confidence interval (CI). Pooled network forest, funnel plots, and the league table, which show the best algorithms for each outcome, were analyzed. Seventeen studies, with a total of 285,213 patients with CVDs, were included in the network meta-analysis. The statistical evidence indicated that the DL algorithms performed well in the prediction of heart failure with AUC of 0.843 and CI [0.840–0.845], while in the ML algorithm, the gradient boosting machine (GBM) achieved an average accuracy of 91.10% in predicting heart failure. An artificial neural network (ANN) performed well in the prediction of diabetes with an OR and CI of 0.0905 [0.0489; 0.1673]. Support vector machine (SVM) performed better for the prediction of stroke with OR and CI of 25.0801 [11.4824; 54.7803]. Random forest (RF) results performed well in the prediction of hypertension with OR and CI of 10.8527 [4.7434; 24.8305]. The findings of this work suggest that the DL models can effectively advance the prediction of and knowledge about heart failure, but there is a lack of literature regarding DL methods in the field of CVDs. As a result, more DL models should be applied in this field. To confirm our findings, more meta-analysis (e.g., Bayesian network) and thorough research with a larger number of patients are encouraged.
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Brogi, Simone, i Vincenzo Calderone. "Artificial Intelligence in Translational Medicine". International Journal of Translational Medicine 1, nr 3 (12.11.2021): 223–85. http://dx.doi.org/10.3390/ijtm1030016.
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The huge advancement in Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques, enable the scientific community to gain access to biological datasets, clinical data and several databases containing billions of pieces of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields, including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as machine learning (ML)/deep learning (DL) and overall artificial intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, translational research is benefitting from computer-based approaches, transforming the design and execution of translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.
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Wang, Alan, Tam T. Doan, Charitha Reddy i Pei-Ni Jone. "Artificial Intelligence in Fetal and Pediatric Echocardiography". Children 12, nr 1 (25.12.2024): 14. https://doi.org/10.3390/children12010014.
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Echocardiography is the main modality in diagnosing acquired and congenital heart disease (CHD) in fetal and pediatric patients. However, operator variability, complex image interpretation, and lack of experienced sonographers and cardiologists in certain regions are the main limitations existing in fetal and pediatric echocardiography. Advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offer significant potential to overcome these challenges by automating image acquisition, image segmentation, CHD detection, and measurements. Despite these promising advancements, challenges such as small number of datasets, algorithm transparency, physician comfort with AI, and accessibility must be addressed to fully integrate AI into practice. This review highlights AI’s current applications, challenges, and future directions in fetal and pediatric echocardiography.
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Gyamfi, Nana Kwame, i Adam Amril Jaharadak. "Ml/Dl Analytical Approaches to Assist Software Project Managers: Dashboard". International Journal of Membrane Science and Technology 10, nr 1 (17.10.2023): 1075–84. http://dx.doi.org/10.15379/ijmst.v10i1.2748.
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Companies frequently turn to project management systems for advice with the ongoing data growth caused by stakeholders throughout a product life cycle. The team will be able to communicate more effectively, plan their next moves, have an overview of the current project state, and act before the projections are delivered with project-oriented business intelligence approaches. These technologies are becoming even more beneficial as agile working mindsets proliferate. It establishes a fundamental concept of how the project should function so that the implementation is simple to use and follow. Teams and the potential for economic generation are held back by the high project failure rates brought on by inadequate project planning. The advancement of Machine Learning (ML) and Deep Learning (DL) methodologies has greatly benefited business and project management. To assist project managers in planning their projects and evaluating risks, we have examined techniques that help them anticipate potential hazards when planning their project milestones based on their prior experiences. The system's three components are the database, the web-based platform, and the machine learning core. To do this, we applied a variety of artificial intelligence techniques. Our system must be able to do risk analysis as quickly as is practical and provide project managers with recommendations using the least amount of data necessary. This article thoroughly analyses much research that has addressed the use of machine learning in software project management. This study thoroughly analyses the literature on three critical subjects: software project management, machine learning, and methods from Web Science, Science Directs, and IEEE Explore. There are 111 papers divided into four categories in these three archives. Our contribution also offers context and a broader viewpoint, essential for potential project risk management initiatives.
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Azizi, Abdellah, Mostafa Azizi i M'barek Nasri. "Artificial Intelligence Techniques in Medical Imaging: A Systematic Review". International Journal of Online and Biomedical Engineering (iJOE) 19, nr 17 (15.12.2023): 66–97. http://dx.doi.org/10.3991/ijoe.v19i17.42431.
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This scientific review presents a comprehensive overview of medical imaging modalities and their diverse applications in artificial intelligence (AI)-based disease classification and segmentation. The paper begins by explaining the fundamental concepts of AI, machine learning (ML), and deep learning (DL). It provides a summary of their different types to establish a solid foundation for the subsequent analysis. The prmary focus of this study is to conduct a systematic review of research articles that examine disease classification and segmentation in different anatomical regions using AI methodologies. The analysis includes a thorough examination of the results reported in each article, extracting important insights and identifying emerging trends. Moreover, the paper critically discusses the challenges encountered during these studies, including issues related to data availability and quality, model generalization, and interpretability. The aim is to provide guidance for optimizing technique selection. The analysis highlights the prominence of hybrid approaches, which seamlessly integrate ML and DL techniques, in achieving effective and relevant results across various disease types. The promising potential of these hybrid models opens up new opportunities for future research in the field of medical diagnosis. Additionally, addressing the challenges posed by the limited availability of annotated medical images through the incorporation of medical image synthesis and transfer learning techniques is identified as a crucial focus for future research efforts.
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Thakker, Dhavalkumar, Bhupesh Kumar Mishra, Amr Abdullatif, Suvodeep Mazumdar i Sydney Simpson. "Explainable Artificial Intelligence for Developing Smart Cities Solutions". Smart Cities 3, nr 4 (13.11.2020): 1353–82. http://dx.doi.org/10.3390/smartcities3040065.
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Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results.
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Said, Noha Mostafa Mohamed, Sabna Machinchery Ali, Naseema Shaik, Khan Mohamed Jarina Begum, Dr Anwaar Ahmed Abd elLatif Shaban i Dr Betty Elezebeth Samuel. "Analysis of Internet of Things to Enhance Security Using Artificial Intelligence based Algorithm". Journal of Internet Services and Information Security 14, nr 4 (30.11.2024): 590–604. https://doi.org/10.58346/jisis.2024.i4.037.
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Exploring creative methods to secure IoT networks is vital due to the enormous security concerns created by the rapid proliferation of the Internet of Things (IoT). To increase the security of the IoT, this study examines the use of artificial intelligence (AI), specifically deep learning (DL) as well as machine learning (ML) techniques. Three state-of-the-art DL algorithms—Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), Convolutional Neural Networks (CNN)—along with three ML methods—CatBoost, LightGBM, and XGBoost—are examined. These algorithms are renowned for their capability to handle big, as well as unbalanced datasets. This work test how well each algorithm can identify anomalies, categorize attacks, and forecast vulnerabilities using an IoT security dataset, such as CICIDS 2017 as well as IoT-23. The research evaluates algorithms by comparing their accuracy and training time. Classification tasks are where CatBoost and LightGBM really good, but when it comes to sequential data and complicated attack patterns, DL algorithms like CNN and LSTM are good. To provide the groundwork for creating AI-driven security solutions optimised for IoT systems, this research sheds light on the benefits and drawbacks of each method.
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Mahjabeen, Farhana. "Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review". Formosa Journal of Applied Sciences 3, nr 10 (26.10.2024): 4175–84. http://dx.doi.org/10.55927/fjas.v3i10.11536.
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The increasing global demand for renewable energy has propelled the adoption of photovoltaic systems as a key component of sustainable energy infrastructure. Undetected photovoltaic system faults can lead to significant energy losses, often exceeding 10%, necessitating efficient fault detection and diagnosis. Artificial intelligence, particularly machine learning and deep learning, offers promising solutions for real-time, high-volume fault detection and complex pattern recognition in PV systems. This research analyzes various PV fault detection studies, examining their objectives, methods, results, and the prevalence of ML/DL approaches. The analysis highlights the application of both classical ML algorithms, such as K-Nearest Neighbors and Random Forest, and advanced DL models, including Convolutional Neural Networks, for PV fault diagnosis.
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Razzaq, Kamran, i Mahmood Shah. "Machine Learning and Deep Learning Paradigms: From Techniques to Practical Applications and Research Frontiers". Computers 14, nr 3 (6.03.2025): 93. https://doi.org/10.3390/computers14030093.
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Machine learning (ML) and deep learning (DL), subsets of artificial intelligence (AI), are the core technologies that lead significant transformation and innovation in various industries by integrating AI-driven solutions. Understanding ML and DL is essential to logically analyse the applicability of ML and DL and identify their effectiveness in different areas like healthcare, finance, agriculture, manufacturing, and transportation. ML consists of supervised, unsupervised, semi-supervised, and reinforcement learning techniques. On the other hand, DL, a subfield of ML, comprising neural networks (NNs), can deal with complicated datasets in health, autonomous systems, and finance industries. This study presents a holistic view of ML and DL technologies, analysing algorithms and their application’s capacity to address real-world problems. The study investigates the real-world application areas in which ML and DL techniques are implemented. Moreover, the study highlights the latest trends and possible future avenues for research and development (R&D), which consist of developing hybrid models, generative AI, and incorporating ML and DL with the latest technologies. The study aims to provide a comprehensive view on ML and DL technologies, which can serve as a reference guide for researchers, industry professionals, practitioners, and policy makers.
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Elste, James, Akash Saini, Rafael Mejia-Alvarez, Armando Mejía, Cesar Millán-Pacheco, Michelle Swanson-Mungerson i Vaibhav Tiwari. "Significance of Artificial Intelligence in the Study of Virus–Host Cell Interactions". Biomolecules 14, nr 8 (26.07.2024): 911. http://dx.doi.org/10.3390/biom14080911.
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A highly critical event in a virus’s life cycle is successfully entering a given host. This process begins when a viral glycoprotein interacts with a target cell receptor, which provides the molecular basis for target virus–host cell interactions for novel drug discovery. Over the years, extensive research has been carried out in the field of virus–host cell interaction, generating a massive number of genetic and molecular data sources. These datasets are an asset for predicting virus–host interactions at the molecular level using machine learning (ML), a subset of artificial intelligence (AI). In this direction, ML tools are now being applied to recognize patterns in these massive datasets to predict critical interactions between virus and host cells at the protein–protein and protein–sugar levels, as well as to perform transcriptional and translational analysis. On the other end, deep learning (DL) algorithms—a subfield of ML—can extract high-level features from very large datasets to recognize the hidden patterns within genomic sequences and images to develop models for rapid drug discovery predictions that address pathogenic viruses displaying heightened affinity for receptor docking and enhanced cell entry. ML and DL are pivotal forces, driving innovation with their ability to perform analysis of enormous datasets in a highly efficient, cost-effective, accurate, and high-throughput manner. This review focuses on the complexity of virus–host cell interactions at the molecular level in light of the current advances of ML and AI in viral pathogenesis to improve new treatments and prevention strategies.
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Vinayaka, Ambujakshi Manjunatha. "Artificial intelligence in periodontics: Transforming the future of periodontal care". IP International Journal of Maxillofacial Imaging 10, nr 3 (15.10.2024): 129–31. http://dx.doi.org/10.18231/j.ijmi.2024.028.
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Artificial intelligence (AI) has emerged as a transformative force across various medical fields, including dentistry. In periodontics, AI offers the potential to enhance diagnostic accuracy, optimize treatment planning, and provide predictive analytics for disease progression. By leveraging machine learning (ML), deep learning (DL), and computer vision techniques, AI is reshaping how clinicians approach periodontal care. This review explores the current and future applications of AI in periodontics, from diagnostics to personalized treatment strategies.
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Sivamani, M., P. Sathya i R. Narmadha. "Artificial Intelligence Methods for Data Science and Data Analytics". REST Journal on Data Analytics and Artificial Intelligence 3, nr 3 (6.09.2024): 82–84. http://dx.doi.org/10.46632/jdaai/3/3/9.
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Artificial intelligence (AI) represents a multidisciplinary field aimed at automating tasks that traditionally require human intelligence. This paper explores the evolution, methodologies, applications, and challenges of AI in the domains of data science and data analytics. Key AI techniques such as machine learning (ML), deep learning (DL), natural language processing (NLP), and computer vision are discussed, alongside their applications in various sectors including healthcare, finance, customer service, marketing, autonomous vehicles, manufacturing, and cyber security. The review also highlights current research challenges and future trends in AI and data analytics.
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Xiao, Haohan, Bo Xing, Yujie Wang, Peng Yu, Lipeng Liu i Ruilang Cao. "Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies". Applied Sciences 11, nr 21 (1.11.2021): 10264. http://dx.doi.org/10.3390/app112110264.
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The shield machine attitude (SMA) is the most important parameter in the process of tunnel construction. To prevent the shield machine from deviating from the design axis (DTA) of the tunnel, it is of great significance to accurately predict the dynamic characteristics of SMA. We establish eight SMA prediction models based on the data of five earth pressure balance (EPB) shield machines. The algorithms adopted in the models are four machine learning (ML) algorithms (KNN, SVR, RF, AdaBoost) and four deep learning (DL) algorithms (BPNN, CNN, LSTM, GRU). This paper obtains the hyperparameters of the models by utilizing grid search and K-fold cross-validation techniques and uses EVS and RMSE to verify and evaluate the prediction performances of the models. The prediction results reveal that the two best algorithms are the LSTM and GRU with EVS > 0.98 and RMSE < 1.5. Then, integrating ML algorithms and DL algorithms, we design a warning predictor for SMA. Through the historical 5-cycle data, the predictor can give a warning in advance if the SMA deviates significantly from DTA. This study indicates that AI technologies have considerable promise in the field of SMA dynamic prediction.
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Saikia, Gautam. "The Evolution of Exoplanet Detection Techniques using Artificial Intelligence". International Journal of Current Research and Techniques 15, nr 1 (13.01.2025): 50376–79. https://doi.org/10.61359/2024050046.
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The discovery and study of exoplanets have made tremendous strides, particularly with the aid of Artificial Intelligence (AI). The surge in data from space missions like Kepler, TESS, and the upcoming James Webb Space Telescope has necessitated the development of automated tools for efficient data processing. Machine learning (ML) and deep learning (DL) algorithms have significantly improved exoplanet detection, identifying planetary signals and refining the analysis of light curves, radial velocities, and other astronomical data. This review traces the evolution of exoplanet detection techniques, from traditional methods to AI-driven approaches, and explores the future of exoplanet exploration using AI.
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Cao, Zehong. "A review of artificial intelligence for EEG‐based brain−computer interfaces and applications". Brain Science Advances 6, nr 3 (wrzesień 2020): 162–70. http://dx.doi.org/10.26599/bsa.2020.9050017.
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The advancement in neuroscience and computer science promotes the ability of the human brain to communicate and interact with the environment, making brain–computer interface (BCI) top interdisciplinary research. Furthermore, with the modern technology advancement in artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods, there is vast growing interest in the electroencephalogram (EEG)‐based BCIs for AI‐related visual, literal, and motion applications. In this review study, the literature on mainstreams of AI for the EEG‐based BCI applications is investigated to fill gaps in the interdisciplinary BCI field. Specifically, the EEG signals and their main applications in BCI are first briefly introduced. Next, the latest AI technologies, including the ML and DL models, are presented to monitor and feedback human cognitive states. Finally, some BCI‐inspired AI applications, including computer vision, natural language processing, and robotic control applications, are presented. The future research directions of the EEG‐based BCI are highlighted in line with the AI technologies and applications.
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Imamoglu, Esra. "Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses". Bioengineering 11, nr 11 (13.11.2024): 1143. http://dx.doi.org/10.3390/bioengineering11111143.
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This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used in various aspects of microalgae processes, such as real-time monitoring, species identification, the optimization of growth conditions, harvesting, and the purification of bioproducts. Commonly employed ML algorithms, including the support vector machine (SVM), genetic algorithm (GA), decision tree (DT), random forest (RF), artificial neural network (ANN), and deep learning (DL), each have unique strengths but also present challenges, such as computational demands, overfitting, and transparency. Despite these hurdles, AI/ML technologies have shown significant improvements in system performance, scalability, and resource efficiency, as well as in cutting costs, minimizing downtime, and reducing environmental impact. However, broader implementations face obstacles, including data availability, model complexity, scalability issues, cybersecurity threats, and regulatory challenges. To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. This review contributes to the literature by offering a thorough analysis of the practical applications, obstacles, and benefits of AI/ML in microalgae processes, offering critical insights into this fast-evolving field.
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Priya, Aakriti Sharma, Ajay Yadav, Amit. "AI, Ml, and Deep Learning Models for Better Disease Detection in Lemon Plants". Tuijin Jishu/Journal of Propulsion Technology 44, nr 1 (24.11.2023): 84–87. http://dx.doi.org/10.52783/tjjpt.v44.i1.2211.
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Lemon plant illnesses must be promptly identified if the health and production of the crop are to be preserved. Traditional techniques of illness identification rely on professional visual inspection, which can be labor-intensive, arbitrary, and prone to mistakes. The use of automated and precise disease diagnosis in lemon trees is made possible by advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL). In this study, we explore the use of several AI, ML, and DL models for enhanced disease detection in lemon trees. The study evaluates the accuracy, precision, and recall of several models, such as random forests (RFs), convolutional neural networks (CNNs), and support vector machines (SVMs).The findings show that in terms of diagnosing and categorizing diseases of lemon plants, DL models, in particular CNNs, perform better than conventional ML models. Utilizing these cutting-edge methods can considerably improve the ability to identify diseases in crops, improving crop management procedures and raising agricultural yields.
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Ashrafuzzaman, Md. "Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics". Membranes 11, nr 9 (31.08.2021): 672. http://dx.doi.org/10.3390/membranes11090672.
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Ion channels are linked to important cellular processes. For more than half a century, we have been learning various structural and functional aspects of ion channels using biological, physiological, biochemical, and biophysical principles and techniques. In recent days, bioinformaticians and biophysicists having the necessary expertise and interests in computer science techniques including versatile algorithms have started covering a multitude of physiological aspects including especially evolution, mutations, and genomics of functional channels and channel subunits. In these focused research areas, the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms and associated models have been found very popular. With the help of available articles and information, this review provide an introduction to this novel research trend. Ion channel understanding is usually made considering the structural and functional perspectives, gating mechanisms, transport properties, channel protein mutations, etc. Focused research on ion channels and related findings over many decades accumulated huge data which may be utilized in a specialized scientific manner to fast conclude pinpointed aspects of channels. AI, ML, and DL techniques and models may appear as helping tools. This review aims at explaining the ways we may use the bioinformatics techniques and thus draw a few lines across the avenue to let the ion channel features appear clearer.