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1
Brouchet, Edouard, François de Brondeau, Marie-José Boileau, and Masrour Makaremi. "Apport de l’intelligence artificielle dans la prévision de croissance mandibulaire : revue systématique de la littérature." Revue d'Orthopédie Dento-Faciale 58, no. 2 (June 2024): 185–209. http://dx.doi.org/10.1051/odf/2024021.
Повний текст джерелаАнотація:
L’orthodontiste intervient principalement auprès d’enfants en cours de croissance. L’examen clinique initial ne fournit qu’une image statique qui doit être interprétée en tenant compte de son évolution potentielle. Une prédiction précise de la croissance mandibulaire, permettrait au praticien d’améliorer le diagnostic, la planification du traitement et ainsi la prise en charge du patient. De nombreux travaux de recherche ont été menés, basés sur des signes structuraux, des analyses céphalométriques et des valeurs d’agrandissement moyen, mais restent imprécis. Les limites rapportées comprennent principalement une variabilité interindividuelle extrême, des schémas de croissance variables selon l’âge, un manque de signes structuraux caractéristiques avant la puberté, l’utilisation de normes statistiques et de résultats dépendant directement de l’expérience du clinicien. À ce jour, il n’existe aucun consensus sur la meilleure méthode pour prédire la croissance mandibulaire, et l’orthodontiste ne peut se fier uniquement à son intuition d’expert. Ces dernières années, la combinaison de l’intelligence artificielle (IA) et des sciences cognitives dans le domaine médical a révolutionné l’interprétation des radiographies. Les techniques d’apprentissage automatique (ML) et profond (DL) représentent une approche novatrice grâce à leur capacité à analyser d’énormes volumes de données tout en éliminant les biais humains. L’objectif de cette revue systématique était d’examiner les différents résultats des prévisions de croissance mandibulaire par intelligence artificielle chez des patients en cours de croissance. Ces résultats suggèrent que nous ne sommes encore qu’aux débuts de l’orthodontie tirant parti du diagnostic et de la prise de décision de l’IA, mais ces modèles de prévision de croissance devraient devenir, dans un avenir proche, des systèmes de support clinique fiables pour les orthodontistes.
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AFTAB, Ifra, Mohammad DOWAJY, Kristof KAPITANY, and Tamas LOVAS. "Artificial Intelligence (AI) – based strategies for point cloud data and digital twins." Nova Geodesia 3, no. 3 (August 19, 2023): 138. http://dx.doi.org/10.55779/ng33138.
Повний текст джерелаАнотація:
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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Choudhary, Laxmi, and Jitendra Singh Choudhary. "Deep Learning Meets Machine Learning: A Synergistic Approach towards Artificial Intelligence." Journal of Scientific Research and Reports 30, no. 11 (November 16, 2024): 865–75. http://dx.doi.org/10.9734/jsrr/2024/v30i112614.
Повний текст джерелаАнотація:
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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Zhang, Shengzhe. "Artificial Intelligence and Applications in Structural and Material Engineering." Highlights in Science, Engineering and Technology 75 (December 28, 2023): 240–45. http://dx.doi.org/10.54097/9qknfc57.
Повний текст джерелаАнотація:
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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Iadanza, Ernesto, Rachele Fabbri, Džana Bašić-ČiČak, Amedeo Amedei, and Jasminka Hasic Telalovic. "Gut microbiota and artificial intelligence approaches: A scoping review." Health and Technology 10, no. 6 (October 26, 2020): 1343–58. http://dx.doi.org/10.1007/s12553-020-00486-7.
Повний текст джерелаАнотація:
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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Gokcekuyu, Yasemin, Fatih Ekinci, Mehmet Serdar Guzel, Koray Acici, Sahin Aydin, and Tunc Asuroglu. "Artificial Intelligence in Biomaterials: A Comprehensive Review." Applied Sciences 14, no. 15 (July 28, 2024): 6590. http://dx.doi.org/10.3390/app14156590.
Повний текст джерелаАнотація:
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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Gayatri, T., G. Srinivasu, D. M. K. Chaitanya, and V. K. Sharma. "A Review on Optimization Techniques of Antennas Using AI and ML / DL Algorithms." International Journal of Advances in Microwave Technology 07, no. 02 (2022): 288–95. http://dx.doi.org/10.32452/ijamt.2022.288295.
Повний текст джерелаАнотація:
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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Drikakis, Dimitris, and Filippos Sofos. "Can Artificial Intelligence Accelerate Fluid Mechanics Research?" Fluids 8, no. 7 (July 19, 2023): 212. http://dx.doi.org/10.3390/fluids8070212.
Повний текст джерелаАнотація:
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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An, Ruopeng, Jing Shen, and Yunyu Xiao. "Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies." Journal of Medical Internet Research 24, no. 12 (December 7, 2022): e40589. http://dx.doi.org/10.2196/40589.
Повний текст джерелаАнотація:
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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Ali, Zulfiqar, Asif Muhammad, Nangkyeong Lee, Muhammad Waqar, and Seung Won Lee. "Artificial Intelligence for Sustainable Agriculture: A Comprehensive Review of AI-Driven Technologies in Crop Production." Sustainability 17, no. 5 (March 5, 2025): 2281. https://doi.org/10.3390/su17052281.
Повний текст джерелаАнотація:
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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Pelayes, David Eduardo, Jose A. Mendoza, and Anibal Martin Folgar. "Artificial intelligence use in diabetes." Latin American Journal of Ophthalmology 5 (December 10, 2022): 6. http://dx.doi.org/10.25259/lajo_4_2022.
Повний текст джерелаАнотація:
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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Kumar, Sanjeet, Urmila Pilania, and Neha Nandal. "A Systematic Study of Artificial Intelligence-Based Methods for Detecting Brain Tumors." Informatics and Automation 22, no. 3 (May 22, 2023): 541–75. http://dx.doi.org/10.15622/ia.22.3.3.
Повний текст джерелаАнотація:
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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Wang, Zichang. "Enhancing Cancer Prediction Accuracy Through Real-Time Monitoring and Artificial Intelligence Analysis for Patients." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 309–15. http://dx.doi.org/10.54097/rw17nn71.
Повний текст джерелаАнотація:
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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Razzaq, Kamran, and Mahmood Shah. "Machine Learning and Deep Learning Paradigms: From Techniques to Practical Applications and Research Frontiers." Computers 14, no. 3 (March 6, 2025): 93. https://doi.org/10.3390/computers14030093.
Повний текст джерелаАнотація:
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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Annapoorani, S. "AN IN-DEPTH ANALYSIS OF ARTIFICIAL INTELLIGENCE APPROACHES FOR RAINFALL PREDICTION." international journal of advanced research in computer science 15, no. 2 (April 20, 2024): 48–58. http://dx.doi.org/10.26483/ijarcs.v15i2.7061.
Повний текст джерелаАнотація:
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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Umesh Kumar. "Scientific Analysis of Various Computational Intelligence Methods used for Weather Forecasting." Journal of Information Systems Engineering and Management 10, no. 9s (February 9, 2025): 482–92. https://doi.org/10.52783/jisem.v10i9s.1246.
Повний текст джерелаАнотація:
Numerous studies in the field of weather forecasting have been conducted since technology started to develop in order to better understand how to manage weather by employing the appropriate kind of forecasting. This work is an evaluation report of facts and figures from the literature on weather forecasting incorporate with machine learning(ML), and deep learning(DL) models. Meteorologists, scientists, and researchers have created a wide range of designs, models, simulation systems, and prototypes to increase prediction accuracy. The first portion examines the literature on previous weather forecasting work, the application of numerous ML and DL models for weather forecasting along with the associated challenges. The second Section is all about the analysis description and drawbacks of current DL weather forecasting models. Several flaws were discovered following the study of prior models. The most common concerns are that running several equations simultaneously which are non-linear in nature requires a significant amount of computer resources and takes a long time to process. computer using Data-driven modeling techniques can be used to reduce the complexity of earlier models. ML and DL, in particular, can more accurately reflect a physical process's nonlinear or intricate underlying features.
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Mahjabeen, Farhana. "Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review." Formosa Journal of Science and Technology 3, no. 10 (October 26, 2024): 2397–406. http://dx.doi.org/10.55927/fjst.v3i10.11552.
Повний текст джерелаАнотація:
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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Jain, Rituraj, Sitesh Kumar Singh, Damodharan Palaniappan, Kumar Parmar, and Premavathi T. "Data-Driven Civil Engineering: Applications of Artificial Intelligence, Machine Learning, and Deep Learning." Turkish Journal of Engineering 9, no. 2 (January 20, 2025): 354–77. https://doi.org/10.31127/tuje.1581564.
Повний текст джерелаАнотація:
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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Khan, Irfan Ullah, Nida Aslam, Malak Aljabri, Sumayh S. Aljameel, Mariam Moataz Aly Kamaleldin, Fatima M. Alshamrani, and Sara Mhd Bachar Chrouf. "Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients." International Journal of Environmental Research and Public Health 18, no. 12 (June 14, 2021): 6429. http://dx.doi.org/10.3390/ijerph18126429.
Повний текст джерелаАнотація:
The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.
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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, no. 7 (July 1, 2022): 491–97. http://dx.doi.org/10.1530/eor-21-0107.
Повний текст джерелаАнотація:
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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R, Kusuma, and R. Rajkumar. "Plant leaf disease detection and classification using artificial intelligence techniques: a review." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (May 1, 2025): 1308. https://doi.org/10.11591/ijeecs.v38.i2.pp1308-1323.
Повний текст джерелаАнотація:
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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Mahjabeen, Farhana. "Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review." Formosa Journal of Applied Sciences 3, no. 10 (October 26, 2024): 4175–84. http://dx.doi.org/10.55927/fjas.v3i10.11536.
Повний текст джерелаАнотація:
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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R. Arthy, Et al. "Harvesting Intelligence: A Comprehensive Study on Transforming Aquaponic Agriculture with AI and IoT." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 8s (August 18, 2023): 782–95. http://dx.doi.org/10.17762/ijritcc.v11i8s.9473.
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Aquaponics, an agricultural technique that merges aquaculture and hydroponics, is on the brink of a transformative advancement with the amalgamation of Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT). The incorporation of these cutting edge technologies in the field of aquaponics is bringing about a profound transformation in the realm of sustainable agriculture. This extensive investigation delves into the profound influence of these cutting-edge technologies on aquaponics, with a focus on predictive analysis, system optimization, environmental monitoring, and disease prevention. By means of ML and DL algorithms, historical and real-time data are scrutinized in order to forecast environmental fluctuations, optimize resource allocation, and facilitate the growth of crops and fish. IoT devices consistently gather data pertaining to crucial parameters, thereby enabling real-time monitoring and control of the aquaponic system. Furthermore, IoT technology enhances resource utilization and grants the ability to remotely monitor and manage the system. The detection of abnormalities in fish behavior and plant health through the utilization of ML and DL algorithms allows for the implementation of proactive measures aimed at preventing outbreaks and minimizing losses. Furthermore, these advanced technologies also offer personalized recommendations for effective management of various crop and fish species. The incorporation of ML, DL, and IoT into the field of aquaponics signifies a substantial advancement towards a more sustainable, efficient, and productive form of agriculture. These innovative technologies possess the capability to effectively address the challenges associated with global food security by optimizing the utilization of resources, maintaining environmental equilibrium, and mitigating the occurrence of disease outbreaks. In the context of the examined research endeavors presented in this article, it is anticipated that the utilization of smart control units in conjunction with the aquaponics system will yield greater profitability, increased intelligence, enhanced precision, and heightened efficacy. In the context of the examined research endeavors presented in this article, it is anticipated that the utilization of ML, DL and IoT in conjunction with the aquaponics system will yield greater profitability, increased intelligence, enhanced precision, and heightened efficacy.
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Choo, Min Soo, Ho Young Ryu, and Sangchul Lee. "Development of an Automatic Interpretation Algorithm for Uroflowmetry Results: Application of Artificial Intelligence." International Neurourology Journal 26, no. 1 (March 31, 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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Chandu, D. Vaidya, Botre Mayuri, Rokde Yash, Kumbhalkar Sagar, Linge Soham, Pitale Soham, and Bawne Shreyash. "Unveiling sentiment analysis: A comparative study of LSTM and Logistic regression models with XAI insights." i-manager's Journal on Computer Science 11, no. 3 (2023): 36. http://dx.doi.org/10.26634/jcom.11.3.20471.
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In this study, we delve into sentiment analysis and the role of Explainable Artificial Intelligence (XAI), with a focus on techniques such as Lime that bring transparency to machine learning (Logistic Regression) and deep learning (LSTM) models. We explore how ML predictions can be biased using XAI and how XAI helps us understand DL models used in sentiment analysis through research that has been made. Examining various research, we notice a gap – the lack of training and interpretation for both ML and DL models on the same dataset using XAI. Our research fills this gap, shedding light on ML and DL model predictions through XAI's lens. By completing our research work, we come to know that even with an accuracy level of 83% for the DL model, they outperform the ML model with an accuracy level of 92% in some cases. This distinction is only identified with XAI techniques, particularly Lime.
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El-den, B. M. El, and Marwa M. Eid. "Watermarking Models and Artificial Intelligence." Journal of Artificial Intelligence and Metaheuristics 1, no. 2 (2022): 24–30. http://dx.doi.org/10.54216/jaim.010203.
Повний текст джерелаАнотація:
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, and Ricardo Moreira Borges. "Nuclear Magnetic Resonance and Artificial Intelligence." Encyclopedia 4, no. 4 (October 18, 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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Shirinova, Simuzar. "Leveraging Artificial Intelligence in Linguistics: Innovations in Language Acquisition and Analysis." EuroGlobal Journal of Linguistics and Language Education 2, no. 1 (February 11, 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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Alafif, Tarik, Abdul Muneeim Tehame, Saleh Bajaba, Ahmed Barnawi, and Saad Zia. "Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions." International Journal of Environmental Research and Public Health 18, no. 3 (January 27, 2021): 1117. http://dx.doi.org/10.3390/ijerph18031117.
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With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.
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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, and Hannah Theresa Sony. "Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development." International Journal of Molecular Sciences 24, no. 3 (January 19, 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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Islam, Mahmudul, Masud Rana Rashel, Md Tofael Ahmed, A. K. M. Kamrul Islam, and Mouhaydine Tlemçani. "Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review." Energies 16, no. 21 (November 3, 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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Amarasingam, Narmilan, Fernando Vanegas, Melissa Hele, Angus Warfield, and Felipe Gonzalez. "Integrating Artificial Intelligence and UAV-Acquired Multispectral Imagery for the Mapping of Invasive Plant Species in Complex Natural Environments." Remote Sensing 16, no. 9 (April 29, 2024): 1582. http://dx.doi.org/10.3390/rs16091582.
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The proliferation of invasive plant species poses a significant ecological threat, necessitating effective mapping strategies for control and conservation efforts. Existing studies employing unmanned aerial vehicles (UAVs) and multispectral (MS) sensors in complex natural environments have predominantly relied on classical machine learning (ML) models for mapping plant species in natural environments. However, a critical gap exists in the literature regarding the use of deep learning (DL) techniques that integrate MS data and vegetation indices (VIs) with different feature extraction techniques to map invasive species in complex natural environments. This research addresses this gap by focusing on mapping the distribution of the Broad-leaved pepper (BLP) along the coastal strip in the Sunshine Coast region of Southern Queensland in Australia. The methodology employs a dual approach, utilising classical ML models including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) in conjunction with the U-Net DL model. This comparative analysis allows for an in-depth evaluation of the performance and effectiveness of both classical ML and advanced DL techniques in mapping the distribution of BLP along the coastal strip. Results indicate that the DL U-Net model outperforms classical ML models, achieving a precision of 83%, recall of 81%, and F1–score of 82% for BLP classification during training and validation. The DL U-Net model attains a precision of 86%, recall of 76%, and F1–score of 81% for BLP classification, along with an Intersection over Union (IoU) of 68% on the separate test dataset not used for training. These findings contribute valuable insights to environmental conservation efforts, emphasising the significance of integrating MS data with DL techniques for the accurate mapping of invasive plant species.
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Alkhurayyif, Yazeed, and Abdul Rahaman Wahab Sait. "A Review of Artificial Intelligence-Based Dyslexia Detection Techniques." Diagnostics 14, no. 21 (October 23, 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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Zhang, Zhao, Guangfei Li, Yong Xu, and Xiaoying Tang. "Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review." Diagnostics 11, no. 8 (August 3, 2021): 1402. http://dx.doi.org/10.3390/diagnostics11081402.
Повний текст джерелаАнотація:
Artificial intelligence (AI) for medical imaging is a technology with great potential. An in-depth understanding of the principles and applications of magnetic resonance imaging (MRI), machine learning (ML), and deep learning (DL) is fundamental for developing AI-based algorithms that can meet the requirements of clinical diagnosis and have excellent quality and efficiency. Moreover, a more comprehensive understanding of applications and opportunities would help to implement AI-based methods in an ethical and sustainable manner. This review first summarizes recent research advances in ML and DL techniques for classifying human brain magnetic resonance images. Then, the application of ML and DL methods to six typical neurological and psychiatric diseases is summarized, including Alzheimer’s disease (AD), Parkinson’s disease (PD), major depressive disorder (MDD), schizophrenia (SCZ), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Finally, the limitations of the existing research are discussed, and possible future research directions are proposed.
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Hagos, Desta Haileselassie, Theofilos Kakantousis, Sina Sheikholeslami, Tianze Wang, Vladimir Vlassov, Amir Hossein Payberah, Moritz Meister, Robin Andersson, and Jim Dowling. "Scalable Artificial Intelligence for Earth Observation Data Using Hopsworks." Remote Sensing 14, no. 8 (April 14, 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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Faisal Ghazi Bishaw. "Review Artificial Intelligence Applications in Renewable Energy Systems Integration." Journal of Electrical Systems 20, no. 3 (April 30, 2024): 566–82. http://dx.doi.org/10.52783/jes.2983.
Повний текст джерелаАнотація:
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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Bhattiprolu, Sreenivas. "From Machine Learning to Deep Learning: Revolutionizing Microscopy Image Analysis." Microscopy Today 32, no. 6 (November 2024): 13–19. https://doi.org/10.1093/mictod/qaae082.
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Abstract Artificial intelligence (AI) has transformed microscopy workflows, enhancing efficiency from image acquisition to analysis. This article explores the evolution from conventional machine learning (ML) to deep learning (DL) in microscopy applications, discussing how AI assists at various stages of the microscopy process. It explains the fundamental differences between ML and DL, using real-world examples to demonstrate DL's superiority in complex scenarios such as organelle segmentation in life sciences and grain boundary analysis in materials sciences. The article also covers advanced topics like semantic and instance segmentation, providing insights into customizing DL models. By demystifying AI for microscopists, this work bridges the gap between cutting-edge technology and practical applications in microscopy.
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Baashar, Yahia, Gamal Alkawsi, Hitham Alhussian, Luiz Fernando Capretz, Ayed Alwadain, Ammar Ahmed Alkahtani, and Malek Almomani. "Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis." Computational Intelligence and Neuroscience 2022 (February 24, 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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Dabboor, Mohammed, Ghada Atteia, Souham Meshoul, and Walaa Alayed. "Deep Learning-Based Framework for Soil Moisture Content Retrieval of Bare Soil from Satellite Data." Remote Sensing 15, no. 7 (April 3, 2023): 1916. http://dx.doi.org/10.3390/rs15071916.
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Machine learning (ML) is a branch of artificial intelligence (AI) that has been successfully applied in a variety of remote sensing applications, including geophysical information retrieval such as soil moisture content (SMC). Deep learning (DL) is a subfield of ML that uses models with complex structures to solve prediction problems with higher performance than traditional ML. In this study, a framework based on DL was developed for SMC retrieval. For this purpose, a sample dataset was built, which included synthetic aperture radar (SAR) backscattering, radar incidence angle, and ground truth data. Herein, the performance of five optimized ML prediction models was evaluated in terms of soil moisture prediction. However, to boost the prediction performance of these models, a DL-based data augmentation technique was implemented to create a reconstructed version of the available dataset. This includes building a sparse autoencoder DL network for data reconstruction. The Bayesian optimization strategy was employed for fine-tuning the hyperparameters of the ML models in order to improve their prediction performance. The results of our study highlighted the improved performance of the five ML prediction models with augmented data. The Gaussian process regression (GPR) showed the best prediction performance with 4.05% RMSE and 0.81 R2 on a 10% independent test subset.
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Said, Noha Mostafa Mohamed, Sabna Machinchery Ali, Naseema Shaik, Khan Mohamed Jarina Begum, Dr Anwaar Ahmed Abd elLatif Shaban, and 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, no. 4 (November 30, 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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Elizabeth Kuukua Woode Amoako, Victor Boateng, Ola Ajay, Tobias Kwame Adukpo, and Nicholas Mensah. "Exploring the role of Machine Learning and Deep Learning in Anti-Money Laundering (AML) strategies within U.S. Financial Industry: A systematic review of implementation, effectiveness, and challenges." Finance & Accounting Research Journal 7, no. 1 (February 13, 2025): 22–36. https://doi.org/10.51594/farj.v7i1.1808.
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As the U.S. financial sector confronts evolving threats from financial crimes, the integration of Machine Learning (ML) and Deep Learning (DL) into Anti-Money Laundering (AML) strategies has become imperative. This paper explores the role of ML and DL technologies in enhancing AML frameworks to identify, mitigate, and prevent money laundering activities. The paper begins by analyzing prevalent money laundering schemes and the methods used by criminals to bypass traditional AML controls. The study underscores the importance of educating and training financial institution personnel to ensure the effective implementation of AML strategies powered by ML and DL. The findings revealed that a culture of awareness and accountability is vital for managing risks associated with financial crimes. Furthermore, the paper highlights the value of collaboration and information-sharing between financial institutions, regulatory bodies, and technology providers. Industry partnerships, public-private initiatives, and shared threat intelligence are identified as key components in strengthening AML defenses. This research also examines the transformative potential of ML and DL in AML. It shows how these technologies enhance pattern recognition, anomaly detection, and decision-making processes, allowing financial institutions to stay ahead of evolving money laundering tactics. Moreover, the dynamic and self-learning capabilities of ML and DL models enable continuous adaptation to new risks. Through adaptation of a vigilant, collaborative, and technology-driven approach, U.S. financial institutions can leverage ML and DL to enhance AML frameworks, safeguard consumer trust, and protect the integrity of the financial system. Keywords: Money Laundering, Financial Industry, Deep Learning, USA
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Alboaneen, Dabiah, Razan Alqarni, Sheikah Alqahtani, Maha Alrashidi, Rawan Alhuda, Eyman Alyahyan, and Turki Alshammari. "Predicting Colorectal Cancer Using Machine and Deep Learning Algorithms: Challenges and Opportunities." Big Data and Cognitive Computing 7, no. 2 (April 13, 2023): 74. http://dx.doi.org/10.3390/bdcc7020074.
Повний текст джерелаАнотація:
One of the three most serious and deadly cancers in the world is colorectal cancer. The most crucial stage, like with any cancer, is early diagnosis. In the medical industry, artificial intelligence (AI) has recently made tremendous strides and showing promise for clinical applications. Machine learning (ML) and deep learning (DL) applications have recently gained popularity in the analysis of medical texts and images due to the benefits and achievements they have made in the early diagnosis of cancerous tissues and organs. In this paper, we intend to systematically review the state-of-the-art research on AI-based ML and DL techniques applied to the modeling of colorectal cancer. All research papers in the field of colorectal cancer are collected based on ML and DL techniques, and they are then classified into three categories: the aim of the prediction, the method of the prediction, and data samples. Following that, a thorough summary and a list of the studies gathered under each topic are provided. We conclude our study with a critical discussion of the challenges and opportunities in colorectal cancer prediction using ML and DL techniques by concentrating on the technical and medical points of view. Finally, we believe that our study will be helpful to scientists who are considering employing ML and DL methods to diagnose colorectal cancer.
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Drakaki, Maria, Yannis L. Karnavas, Ioannis A. Tziafettas, Vasilis Linardos, and Panagiotis Tzionas. "Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey." Journal of Industrial Engineering and Management 15, no. 1 (February 1, 2022): 31. http://dx.doi.org/10.3926/jiem.3597.
Повний текст джерелаАнотація:
Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research.Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithmsFindings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated.Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015.
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Rhafes, Mohamed Yassine, Omar Moussaoui, and Maria Simona Raboaca. "Literature review on forecasting green hydrogen production using machine learning and deep learning." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (April 1, 2025): 884. https://doi.org/10.11591/ijai.v14.i2.pp884-893.
Повний текст джерелаАнотація:
Green hydrogen is a sustainable and clean energy source, for this purpose, it conducts the global energy transition. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) with the process of green hydrogen production is essential in enhancing its production. This literature review studies in detail the intersection between AI and green hydrogen. Firstly, it concentrates on ML and DL algorithms used in forecasting green hydrogen production. Secondly, it presents an analysis of the studies released from 2021 to March 2024. Finally, the focus is on the results realized by the ML and DL algorithms proposed by the studies reviewed. This study provides a summary that explains the trends and methods used, as well as highlights the gaps and the opportunities in the field of AI and green hydrogen production. This liternature review presents a solid foundation for future research initiatives in this field.
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Gyamfi, Nana Kwame, and Adam Amril Jaharadak. "Ml/Dl Analytical Approaches to Assist Software Project Managers: Dashboard." International Journal of Membrane Science and Technology 10, no. 1 (October 17, 2023): 1075–84. http://dx.doi.org/10.15379/ijmst.v10i1.2748.
Повний текст джерелаАнотація:
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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Khyade, Ms Mahadevi Pundlik. "Artificial Intelligence (AI): Brain Tumor Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (December 31, 2024): 761–63. https://doi.org/10.22214/ijraset.2024.65886.
Повний текст джерелаАнотація:
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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Naderisorki, Mohammad, Maryam Rezapour, and Mehdi Naderi Soorki. "Investigating the Application of Artificial Intelligence in the Pediatric Oncology." Journal of Pediatrics Review 12, no. 1 (January 1, 2024): 1–4. http://dx.doi.org/10.32598/jpr.12.1.786.3.
Повний текст джерелаАнотація:
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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Muhaimil, Ali, Saikiran Pendem, Niranjana Sampathilla, Priya P S, Kaushik Nayak, Krishnaraj Chadaga, Anushree Goswami, Obhuli Chandran M, and Abhijit Shirlal. "Role of Artificial intelligence model in prediction of low back pain using T2 weighted MRI of Lumbar spine." F1000Research 13 (October 10, 2024): 1035. http://dx.doi.org/10.12688/f1000research.154680.2.
Повний текст джерелаАнотація:
Background Low back pain (LBP), the primary cause of disability, is the most common musculoskeletal disorder globally and the primary cause of disability. Magnetic resonance imaging (MRI) studies are inconclusive and less sensitive for identifying and classifying patients with LBP. Hence, this study aimed to investigate the role of artificial intelligence (AI) models in the prediction of LBP using T2 weighted MRI image of the lumbar spine. Methods This was a prospective case-control study. A total of 200 MRI patients (100 cases and controls each) referred for lumbar spine and whole spine screening were included. The scans were performed using 3.0 Tesla MRI (United Imaging Healthcare). T2 weighted images of the lumbar spine were segmented to extract radiomic features. Machine learning (ML) models, such as random forest, decision tree, logistic regression, K-nearest neighbors, adaboost, and deep learning methods (DL), such as ResNet and GoogleNet, were used, and performance measures were calculated. Results Our study showed that Random forest and AdaBoost are the most reliable ML models for predicting LBP. Random forest showed high performance with area under curve (AUC) values from 0.83 to 0.88 across all lumbar vertebrae and L2-L3, L3-L4, and L4-L5 intervertebral discs (IVDs), with AUCs of 0.88 the highest at L5-S1 IVD (0.92). Adaboost demonstrated high performance at the L2-L5 vertebrae with AUC values of 0.82 to 0.90, with the highest AUC (0.97) at the L5-S1 IVD. Among the DL models, GoogleNet outperformed the other models at 30 epochs with an accuracy of 0.85, followed by ResNet 18 (30 epochs) with an accuracy of 0.84. Conclusion The study demonstrated that ML and DL models can effectively predict LBP from MRI T2 weighted image of the lumbar spine. ML and DL models could also enhance the diagnostic accuracy of LBP, potentially leading to better patient management and outcomes.
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Muhaimil, Ali, Saikiran Pendem, Niranjana Sampathilla, Priya P S, Kaushik Nayak, Krishnaraj Chadaga, Anushree Goswami, Obhuli Chandran M, and Abhijit Shirlal. "Role of Artificial intelligence model in prediction of low back pain using T2 weighted MRI of Lumbar spine." F1000Research 13 (September 10, 2024): 1035. http://dx.doi.org/10.12688/f1000research.154680.1.
Повний текст джерелаАнотація:
Background Low back pain (LBP), the primary cause of disability, is the most common musculoskeletal disorder globally and the primary cause of disability. Magnetic resonance imaging (MRI) studies are inconclusive and less sensitive for identifying and classifying patients with LBP. Hence, this study aimed to investigate the role of artificial intelligence (AI) models in the prediction of LBP using T2 weighted MRI image of the lumbar spine. Methods This was a prospective case-control study. A total of 200 MRI patients (100 cases and controls each) referred for lumbar spine and whole spine screening were included. The scans were performed using 3.0 Tesla MRI (United Imaging Healthcare). T2 weighted images of the lumbar spine were segmented to extract radiomic features. Machine learning (ML) models, such as random forest, decision tree, logistic regression, K-nearest neighbors, adaboost, and deep learning methods (DL), such as ResNet and GoogleNet, were used, and performance measures were calculated. Results Our study showed that Random forest and AdaBoost are the most reliable ML models for predicting LBP. Random forest showed high performance with area under curve (AUC) values from 0.83 to 0.88 across all lumbar vertebrae and L2-L3, L3-L4, and L4-L5 intervertebral discs (IVDs), with AUCs of 0.88 the highest at L5-S1 IVD (0.92). Adaboost demonstrated high performance at the L2-L5 vertebrae with AUC values of 0.82 to 0.90, with the highest AUC (0.97) at the L5-S1 IVD. Among the DL models, GoogleNet outperformed the other models at 30 epochs with an accuracy of 0.85, followed by ResNet 18 (30 epochs) with an accuracy of 0.84. Conclusion The study demonstrated that ML and DL models can effectively predict LBP from MRI T2 weighted image of the lumbar spine. ML and DL models could also enhance the diagnostic accuracy of LBP, potentially leading to better patient management and outcomes.
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Khanna, Narendra N., Mahesh Maindarkar, Ajit Saxena, Puneet Ahluwalia, Sudip Paul, Saurabh K. Srivastava, Elisa Cuadrado-Godia, et al. "Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction—A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review." Diagnostics 12, no. 5 (May 17, 2022): 1249. http://dx.doi.org/10.3390/diagnostics12051249.
Повний текст джерелаАнотація:
Purpose: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. Methods: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. Summary: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.