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Journal articles on the topic 'Potentiel machine learning'

Author: Grafiati
Published: 21 December 2024

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1

Ben Zid, Afef, Asma Najjar, and Imen Hamrouni. "Classification automatique d’emprises au sol de maisons dites « andalouses » à l’aide de modèle de Machine Learning." SHS Web of Conferences 203 (2024): 02001. http://dx.doi.org/10.1051/shsconf/202420302001.

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L’apprentissage automatique (ML) est une branche de l’IA qui utilise des données et des algorithmes pour imiter l’apprentissage humain. Intégrant l’informatique, la robotique et les sciences cognitives, il offre des applications transformatrices dans divers domaines. En architecture du patrimoine, le ML analyse les motifs, les styles et les matériaux pour aider à la préservation. Cet Article présente un modèle de classification basé sur le ML pour l’architecture andalouse en Tunisie et en Espagne, comparant des maisons construites par les Morisques expulsés d’Espagne en 1609 à celles de l’Espagne musulmane médiévale. L’objectif est d’identifier les caractéristiques architecturales distinctives. Les données ont été générées à l’aide d’un algorithme DCGAN, et des modèles ML ont atteint des taux de succès de 87,55% avec k-NN et 84,21% avec SVM. Le modèle montre un potentiel pour des applications plus larges en architecture.
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BOUKHELEF, Faiza. "Investigating Students’ Attitudes Towards Integrating Machine Translation in the EFL Classroom: The case of Google Translate." Langues & Cultures 5, no. 01 (June 30, 2024): 264–77. http://dx.doi.org/10.62339/jlc.v5i01.243.

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This paper delves into the potential of machine translation tools, with a specific focus on Google Translate, to expand their role beyond traditional translation tasks to enhance language learning in EFL classrooms. While machine translation tools have become integral to translator training programs, their utilization in language education remains limited and understudied. The present study attempts to highlight the promising avenues for innovative pedagogy in language education by incorporating machine translation tools and EFL students’ attitudes towards them. It explores the application of machine translation in the context of English language acquisition for non-native speakers. The results demonstrate that students consider machine translation as a useful strategy to learn English, and Google Translate (GT) offers advantages in vocabulary expansion and quick translations. However, its limitations, such as reduced accuracy for longer texts and the inability to process idiomatic expressions, necessitate careful consideration when integrating it into language learning curricula. GT can serve as a supplementary tool to support learners, but it should not replace conventional language learning methods. Ultimately, this research emphasizes the need for cautious guidance and monitoring when utilizing automated translation to ensure effective language learning outcomes, bridging the gap between translation and language education while acknowledging the tool's limitations. Résumé Cet article explore le potentiel des outils de traduction automatique, avec un accent particulier sur Google Translate, en élargissant leur rôle au-delà des tâches de traduction traditionnelles pour améliorer l'apprentissage des langues dans les classes EFL. Bien que les outils de traduction automatique soient devenus une partie intégrante des programmes de formation des traducteurs, leur utilisation dans l'enseignement des langues reste limitée et peu étudiée. La présente étude tente de mettre en évidence les pistes prometteuses pour une pédagogie innovante dans l'enseignement des langues en intégrant les outils de traduction automatique et les attitudes des étudiants EFL à leur égard. Cet article explore l'application de la traduction automatique dans le contexte de l'acquisition de la langue anglaise pour les locuteurs non natifs. Les résultats montrent que les étudiants considèrent la traduction automatique comme une stratégie utile pour apprendre l'anglais, et Google Translate (GT) offre des avantages dans l'expansion du vocabulaire et les traductions rapides. Cependant, ses limites, telles que la précision réduite des textes plus longs et l'incapacité à traiter les expressions idiomatiques, nécessitent une attention particulière lors de leur intégration dans les programmes d'apprentissage des langues. GT peut servir d'outil supplémentaire pour soutenir les apprenants, mais ne devrait pas remplacer les méthodes conventionnelles d'apprentissage des langues. Enfin, cette recherche met l'accent sur la nécessité d'une orientation et d'un suivi prudent dans l'utilisation de la traduction automatisée pour assurer des résultats d'apprentissage linguistique efficaces, combler l'écart entre la traduction et l'éducation linguistique tout en reconnaissant les limites de l'outil.
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Ng, Wenfa. "Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization." Biotechnology and Bioprocessing 2, no. 9 (November 2, 2021): 01–07. http://dx.doi.org/10.31579/2766-2314/060.

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Successful engineering of a microbial host for efficient production of a target product from a given substrate can be viewed as an extensive optimization task. Such a task involves the selection of high activity enzymes as well as their gene expression regulatory control elements (i.e., promoters and ribosome binding sites). Finally, there is also the need to tune expression of multiple genes along a heterologous pathway to relieve constraints from rate-limiting step and help reduce metabolic burden on cells from unnecessary over-expression of high activity enzymes. While the aforementioned tasks could be performed through combinatorial experiments, such an approach incurs significant cost, time and effort, which is a handicap that can be relieved by application of modern machine learning tools. Such tools could attempt to predict high activity enzymes from sequence, but they are currently most usefully applied in classifying strong promoters from weaker ones as well as combinatorial tuning of expression of multiple genes. This perspective reviews the application of machine learning tools to aid metabolic pathway optimization through identifying challenges in metabolic engineering that could be overcome with the help of machine learning tools.
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Datta, Debaleena, Pradeep Kumar Mallick, Akash Kumar Bhoi, Muhammad Fazal Ijaz, Jana Shafi, and Jaeyoung Choi. "Hyperspectral Image Classification: Potentials, Challenges, and Future Directions." Computational Intelligence and Neuroscience 2022 (April 28, 2022): 1–36. http://dx.doi.org/10.1155/2022/3854635.

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Recent imaging science and technology discoveries have considered hyperspectral imagery and remote sensing. The current intelligent technologies, such as support vector machines, sparse representations, active learning, extreme learning machines, transfer learning, and deep learning, are typically based on the learning of the machines. These techniques enrich the processing of such three-dimensional, multiple bands, and high-resolution images with their precision and fidelity. This article presents an extensive survey depicting machine-dependent technologies’ contributions and deep learning on landcover classification based on hyperspectral images. The objective of this study is three-fold. First, after reading a large pool of Web of Science (WoS), Scopus, SCI, and SCIE-indexed and SCIE-related articles, we provide a novel approach for review work that is entirely systematic and aids in the inspiration of finding research gaps and developing embedded questions. Second, we emphasize contemporary advances in machine learning (ML) methods for identifying hyperspectral images, with a brief, organized overview and a thorough assessment of the literature involved. Finally, we draw the conclusions to assist researchers in expanding their understanding of the relationship between machine learning and hyperspectral images for future research.
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Srinivasaiah, Bharath. "The Power of Personalized Healthcare: Harnessing the Potential of Machine Learning in Precision Medicine." International Journal of Science and Research (IJSR) 13, no. 5 (May 5, 2024): 426–29. http://dx.doi.org/10.21275/sr24506012313.

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Kamoun-Abid, Ferdaous, Hounaida Frikha, Amel Meddeb-Makhoulf, and Faouzi Zarai. "Automating cloud virtual machines allocation via machine learning." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 1 (July 1, 2024): 191. http://dx.doi.org/10.11591/ijeecs.v35.i1.pp191-202.

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In the realm of healthcare applications leveraging cloud technology, ongoing progress is evident, yet current approaches are rigid and fail to adapt to the dynamic environment, particularly when network and virtual machine (VM) resources undergo modifications mid-execution. Health data is stored and processed in the cloud as virtual resources supported by numerous VMs, necessitating critical optimization of virtual node and data placement to enhance data application processing time. Network security poses a significant challenge in the cloud due to the dynamic nature of the topology, hindering traditional firewalls’ ability to inspect packet contents and leaving the network vulnerable to potential threats. To address this, we propose dividing the cloud topology into zones, each monitored by a controller to oversee individual VMs under firewall protection, a framework termed divided-cloud, aiming to minimize network congestion while strategically placing new VMs. Employing machine learning (ML) techniques, such as decision tree (DT) and linear discriminant analysis (LDA), we achieved improved accuracy rates for adding new controllers, reaching a maximum of 89%, and used the K-neighbours classifier method to determine optimal locations for new VMs, achieving an accuracy of 83%.
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Shoureshi, R., D. Swedes, and R. Evans. "Learning Control for Autonomous Machines." Robotica 9, no. 2 (April 1991): 165–70. http://dx.doi.org/10.1017/s0263574700010201.

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SUMMARYToday's industrial machines and manipulators have no capability to learn by experience. Performance and productivity could be greatly enhanced if a machine could modify its operation based on previous actions. This paper presents a learning control scheme that provides the ability for machines to utilize their past experiences. The objective is to have machines mimic the human learning process as closely as possible. A data base is formulated to provide the machine with experience. An optical infrared distance sensor is developed to inform the machine about objects in its working space. A learning control scheme is presented that utilizes the sensory information to enhance machine performance in the next trial. An adaptive scheme is proposed for the modification of learning gain matrices, and is implemented on an industrial robot. Experimental results verify the potentials of the proposed adaptive learning scheme, and illustrate how it can be used for improvement of different manufacturing processes.
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Aschepkov, Valeriy. "METHODS OF MACHINE LEARNING IN MODERN METROLOGY." Measuring Equipment and Metrology 85 (2024): 57–60. http://dx.doi.org/10.23939/istcmtm2024.01.057.

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In the modern world of scientific and technological progress, the requirements for the accuracy and reliability of measurements are becoming increasingly stringent. The rapid development of machine learning (ML) methods opens up perspectives for improving metrological processes and enhancing the quality of measurements. This article explores the potential application of ML methods in metrology, outlining the main types of ML models in automatic instrument calibration, analysis, and prediction of data. Attention is paid to the development of hybrid approaches that combine ML methods with traditional metrological methods for the optimal solution of complex measurement tasks.
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Levantesi, Susanna, Andrea Nigri, and Gabriella Piscopo. "Longevity risk management through Machine Learning: state of the art." Insurance Markets and Companies 11, no. 1 (November 25, 2020): 11–20. http://dx.doi.org/10.21511/ins.11(1).2020.02.

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Longevity risk management is an area of the life insurance business where the use of Artificial Intelligence is still underdeveloped. The paper retraces the main results of the recent actuarial literature on the topic to draw attention to the potential of Machine Learning in predicting mortality and consequently improving the longevity risk quantification and management, with practical implication on the pricing of life products with long-term duration and lifelong guaranteed options embedded in pension contracts or health insurance products. The application of AI methodologies to mortality forecasts improves both fitting and forecasting of the models traditionally used. In particular, the paper presents the Classification and the Regression Tree framework and the Neural Network algorithm applied to mortality data. The literature results are discussed, focusing on the forecasting performance of the Machine Learning techniques concerning the classical model. Finally, a reflection on both the great potentials of using Machine Learning in longevity management and its drawbacks is offered.
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Shak, Md Shujan, Aftab Uddin, Md Habibur Rahman, Nafis Anjum, Md Nad Vi Al Bony, Murshida Alam, Mohammad Helal, Afrina Khan, Pritom Das, and Tamanna Pervin. "INNOVATIVE MACHINE LEARNING APPROACHES TO FOSTER FINANCIAL INCLUSION IN MICROFINANCE." International Interdisciplinary Business Economics Advancement Journal 05, no. 11 (November 6, 2024): 6–20. http://dx.doi.org/10.55640/business/volume05issue11-02.

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This study examines the application of machine learning algorithms to enhance financial inclusion in microfinance, focusing on credit scoring, risk and fraud detection, and customer segmentation. We performed feature engineering and employed models such as Logistic Regression, Decision Trees, Random Forests, Gradient Boosting Machines (XGBoost and LightGBM), Support Vector Machines (SVM), Autoencoders, Isolation Forests, and K-means Clustering. LightGBM achieved the highest accuracy (89.6%) and AUC (0.92) in credit scoring, while Random Forests demonstrated strong performance in both loan approval (86.7% accuracy) and fraud detection (87.6% accuracy, AUC of 0.88). SVM also performed competitively, and unsupervised methods like Autoencoders and Isolation Forests showed potential for anomaly detection but required further refinement.K-means Clustering excelled in customer segmentation with a silhouette score of 0.72, enabling tailored services based on client demographics. Our findings highlight the significant impact of machine learning on improving credit scoring accuracy, reducing fraud risks, and enhancing customer service delivery in microfinance, thereby promoting financial inclusion for underserved populations. Ethical considerations and model interpretability are crucial, particularly for smaller institutions. This study advocates for the broader adoption of machine learning in the microfinance sector.
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Hossain, Nur, Nafis Anjum, Murshida Alam, Md Habibur Rahman, Md Siam Taluckder, Md Nad Vi Al Bony, S. M. Shadul Islam Rishad, and Afrin Hoque Jui. "PERFORMANCE OF MACHINE LEARNING ALGORITHMS FOR LUNG CANCER PREDICTION: A COMPARATIVE STUDY." International Journal of Medical Science and Public Health Research 05, no. 11 (November 14, 2024): 41–55. http://dx.doi.org/10.37547/ijmsphr/volume05issue11-05.

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This study compares the performance of five machine learning algorithms—logistic regression, support vector machines, random forests, gradient boosting, and neural networks—for lung cancer prediction using demographic, lifestyle, and medical data from the UCI Machine Learning Repository. Gradient boosting and random forests achieved the highest accuracy (89% and 87%, respectively) and AUC-ROC scores (0.93 and 0.92), while neural networks reached 90% accuracy but presented interpretability limitations. Key predictors included smoking history, chronic disease, and respiratory symptoms, aligning with established risk factors. Ensemble methods, particularly gradient boosting and random forests, provided an optimal balance of accuracy and interpretability, highlighting their potential for clinical applications in early lung cancer detection.
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Lyu, Nian. "The prospect and metaphysical analysis of conscious artificial intelligence." Applied and Computational Engineering 77, no. 1 (July 16, 2024): 32–36. http://dx.doi.org/10.54254/2755-2721/77/20240632.

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Artificial intelligence, also known as AI, has led the trend of evolution in the past and future decades, and the potential of consciousness artificial intelligence emerges as a renovative field to address. The computer machine aims to process repetitive and tedious tasks for humans since its concept was first developed. Whether AI is conscious does not raise unprecedented discussion before the appearance of the concept of machine learning. After it appears, the machine, instead of merely passing in input and generating output, is able to learn while processing the information, which resembles a human's learning process. Therefore, this paper delves into the complex terrain of AI to explore the theoretical possibility of endowing machines with consciousness and addresses the future concerns and potentials of AI. Illustrating through the aspects of ethical concerns, metaphysical perspectives on consciousness, and the latest advancements in AI, the study critically examines whether machines can possess a consciousness similar to human understanding.
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Keneskyzy, K., and S. B. Yeskermes. "Метод машинного обучения для обратных задач теплопроводности." INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES 2, no. 1(5) (March 26, 2021): 59–64. http://dx.doi.org/10.54309/ijict.2021.05.1.008.

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Investigated in this work is the potential of carrying out inverse problems with linear and non-linear behavior using machine learning methods and the neural network method. With the advent of ma-chine learning algorithms it is now possible to model inverse problems faster and more accurately. In order to demonstrate the use of machine learning and neural networks in solving inverse problems, we propose a fusion between computational mechanics and machine learning. The forward problems are solved first to create a database. This database is then used to train the machine learning and neural network algorithms. The trained algorithm is then used to determine the boundary conditions of a problem from assumed meas-urements. The proposed method is tested for the linear/non-linear heat conduction problems in which the boundary conditions are determined by providing three, four, and five temperature measurements. This re-search demonstrates that the proposed fusion of computational mechanics and machine learning is an effec-tive way of tackling complex inverse problems.
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Yang, Yinuo, Shuhao Zhang, Kavindri D. Ranasinghe, Olexandr Isayev, and Adrian E. Roitberg. "Machine Learning of Reactive Potentials." Annual Review of Physical Chemistry 75, no. 1 (June 28, 2024): 371–95. http://dx.doi.org/10.1146/annurev-physchem-062123-024417.

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In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction and training of MLPs enable fast and accurate simulations and analysis of thermodynamic and kinetic properties. This review focuses on the application of MLPs to reaction systems with consideration of bond breaking and formation. We review the development of MLP models, primarily with neural network and kernel-based algorithms, and recent applications of reactive MLPs (RMLPs) to systems at different scales. We show how RMLPs are constructed, how they speed up the calculation of reactive dynamics, and how they facilitate the study of reaction trajectories, reaction rates, free energy calculations, and many other calculations. Different data sampling strategies applied in building RMLPs are also discussed with a focus on how to collect structures for rare events and how to further improve their performance with active learning.
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Shi, Yang. "Research on the Stock Price Prediction Using Machine Learning." Advances in Economics, Management and Political Sciences 22, no. 1 (September 13, 2023): 174–79. http://dx.doi.org/10.54254/2754-1169/22/20230307.

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Stock price prediction is a complex and challenging problem that has attracted the attention of investors and researchers for decades. In recent years, machine learning algorithms have become powerful tools for predicting stock prices. This paper first introduces four popular machine learning algorithms used for stock price prediction which are linear regression, support vector machines, artificial neural networks and long short-term memory. In addition, applications and potential challenges of stock price prediction using machine learning are examined. Overall, this paper provides a comprehensive overview of ML-based models for stock price prediction and highlights the potential benefits and limitations of these models for financial researchers and artificial intelligence developers.
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Mueller, Tim, Alberto Hernandez, and Chuhong Wang. "Machine learning for interatomic potential models." Journal of Chemical Physics 152, no. 5 (February 7, 2020): 050902. http://dx.doi.org/10.1063/1.5126336.

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Shih, David, Matthew R. Buckley, Lina Necib, and John Tamanas. "via machinae: Searching for stellar streams using unsupervised machine learning." Monthly Notices of the Royal Astronomical Society 509, no. 4 (November 24, 2021): 5992–6007. http://dx.doi.org/10.1093/mnras/stab3372.

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ABSTRACT We develop a new machine learning algorithm, via machinae, to identify cold stellar streams in data from the Gaia telescope. via machinae is based on ANODE, a general method that uses conditional density estimation and sideband interpolation to detect local overdensities in the data in a model agnostic way. By applying ANODE to the positions, proper motions, and photometry of stars observed by Gaia, via machinae obtains a collection of those stars deemed most likely to belong to a stellar stream. We further apply an automated line-finding method based on the Hough transform to search for line-like features in patches of the sky. In this paper, we describe the via machinae algorithm in detail and demonstrate our approach on the prominent stream GD-1. Though some parts of the algorithm are tuned to increase sensitivity to cold streams, the via machinae technique itself does not rely on astrophysical assumptions, such as the potential of the Milky Way or stellar isochrones. This flexibility suggests that it may have further applications in identifying other anomalous structures within the Gaia data set, for example debris flow and globular clusters.
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Samahitha Kaliyuru Ravi, Sameera Kaliyuru Ravi, and A. Hema Prabha. "Advent of machine learning in autonomous vehicles." International Journal of Science and Research Archive 13, no. 1 (September 30, 2024): 1219–26. http://dx.doi.org/10.30574/ijsra.2024.13.1.1760.

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The advent of Machine Learning has significantly transformed the landscape of automation, heralding a new era of efficiency, precision, and innovation. This literature review explores the pivotal role of machine learning in advancing automation across various industries. By examining the evolution of machine learning algorithms and their integration into automated systems, the paper highlights key developments and breakthroughs that have enabled machines to perform complex tasks with minimal human intervention. The review delves into case studies from manufacturing, healthcare, finance, and driver-less vehicles, illustrating how machine learning-driven automation has improved productivity, enhanced decision-making, and reduced operational costs. Furthermore, the paper discusses the challenges and ethical considerations associated with the widespread adoption of machine learning in automation, such as data privacy, job displacement, and algorithmic bias. By synthesizing findings from recent research, this review provides a thorough study of the current state and future potential of machine learning in automation. The insights gained underscore the huge impact of machine learning technologies, along with the need for continuous innovation and regulation to harness their full potential while mitigating associated risks. This paper serves as a valuable resource for academics, industry professionals, and policymakers aiming to navigate and contribute to the rapidly evolving field of machine learning in automation.
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Abro, Safdar Ali, Lyu Guang Hua, Javed Ahmed Laghari, Muhammad Akram Bhayo, and Abdul Aziz Memon. "Machine learning-based electricity theft detection using support vector machines." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (April 1, 2024): 1240. http://dx.doi.org/10.11591/ijece.v14i2.pp1240-1250.

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Electricity theft is a serious issue that many nations face, especially in developing areas where non-technical losses can make up a significant percentage of the overall losses sustained by utilities. Electricity theft detection (ETD) is a very challenging task because it frequently introduces irregularities in customer electricity consumption patterns. In recent times, machine learning (ML) techniques have been investigated as a potential solution for ETD. In this research, author propose electricity theft detection based on four kernel functions of support vector machines (SVM). The proposed method analyzes the electricity consumption patterns and then predicts the category of the user. The kernel functions utilized includes polynomial, sigmoid, radial basis function (RBF) and linear kernel function. For experimentation and model training, a dataset of Pakistani utility company is used, which contains the electricity consumption information. The results highlight SVM method works well for accurate ETD. The detection accuracy of the various kernel functions of SVM is 83%, 79%, 80%, and 76% for RBF, polynomial, sigmoid, and linear kernel functions, respectively, demonstrating the effectiveness of the proposed SVM-based method for theft detection. By leveraging these ML-based methods, utility companies can strengthen their ability to detect and prevent electricity theft, leading to improved revenue management and dependability of services.
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Ramesh, Banoth, G. Srinivas, P. Ram Praneeth Reddy, M. D. Huraib Rasool, Divya Rawat, and Madhulita Sundaray. "Feasible Prediction of Multiple Diseases using Machine Learning." E3S Web of Conferences 430 (2023): 01051. http://dx.doi.org/10.1051/e3sconf/202343001051.

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Automated Multiple Disease Prediction System using Machine Learning is an advanced healthcare application that utilizes machine learning algorithms to accurately predict the likelihood of a patient having multiple diseases based on their medical history and symptoms. The system employs a comprehensive dataset of medical records and symptoms of various diseases, which are then analysed using machine learning techniques such as decision trees, support vector machines, and random forests. The system’s predictions are highly accurate, and it can assist medical professionals in making more informed decisions and providing better treatment plans for patients. Ultimately, the viable Multiple Disease Prediction System using Machine Learning has the potential to improve healthcare outcomes and reduce healthcare costs by predicting and preventing disease early.
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Nagaraju, Dr R. "XSS Attack Detection using Machine Learning Algorithms." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (December 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27487.

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This project focuses on the development of an XSS attack detection system using machine learning algorithms. The research involves the careful curation of diverse datasets encompassing XSS attacks and benign data. Key features are extracted, emphasizing HTML structure and JavaScript patterns. The study evaluates the efficacy of k- Nearest Neighbors, Logistic Regression, Random Forest, and Support Vector Machines (SVM) in detecting XSS threats. The training phase optimizes model accuracy, and performance metrics such as Precision, Recall, and F1 Score assess the model's effectiveness. Results provide a comparative analysis of machine learning algorithms, offering insights for future implementations. The study contributes to strengthening web security, showcasing the potential of machine learning in XSS attack detection.
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Kayathri, K., and Dr K. Kavitha. "CGSX Ensemble: An Integrative Machine Learning and Deep Learning Approach for Improved Diabetic Retinopathy Classification." International Journal of Electrical and Electronics Research 12, no. 2 (June 28, 2024): 669–81. http://dx.doi.org/10.37391/ijeer.120245.

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This research proposes an integrated approach for automated diabetic retinopathy (DR) diagnosis, leveraging a combination of machine learning and deep learning techniques to extract features and perform classification tasks effectively. Through preprocessing of retinal images to enhance features and mitigate noise, two distinct methodologies are employed: machine learning feature extraction, targeting texture features like Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run Length Matrix (GLRLM), and deep learning feature extraction, utilizing pre-trained convolutional neural networks (CNNs) such as VGG, ResNet, or Inception. Following feature extraction, various classifiers, including Support Vector Machines (SVM), Random Forests, and Gradient Boosting Machines, are trained on the extracted features for DR classification. Alternatively, deep learning classifiers like CNNs or recurrent neural networks (RNNs) may be trained directly on the extracted features or on raw images. This comprehensive framework shows promising potential to improve the accuracy and efficiency of diabetic retinopathy (DR) diagnosis, enabling timely intervention and management of this vision-threatening condition.
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Li, Keqin, Peng Zhao, Shuying Dai, Armando Zhu, Bo Hong, Jiabei Liu, Changsong Wei, Wenqian Huang, and Yang Zhang. "Exploring the Impact of Quantum Computing on Machine Learning Performance." Middle East Journal of Applied Science & Technology 07, no. 02 (2024): 145–61. http://dx.doi.org/10.46431/mejast.2024.7215.

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This paper delves into the integration of machine learning and quantum computing, highlighting the potential of quantum computing to enhance the performance and computational efficiency of machine learning. Through theoretical analysis and experimental studies, this paper demonstrates how quantum computing can accelerate traditional machine learning algorithms via its unique properties of superposition and entanglement, particularly in handling large datasets and solving high-dimensional problems. Detailed introductions to quantum-enhanced machine learning models such as quantum neural networks and quantum support vector machines are provided, and their efficacy is validated through experimental applications in tasks like handwriting digit recognition. Results indicate that the parallel processing capabilities of quantum computing significantly enhance the speed and precision of model training, while also addressing the challenges and potential solutions for practical applications of quantum computing. Finally, the paper discusses future research directions and the importance of interdisciplinary collaboration in the integration of machine learning and quantum computing.
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Chinnala Balakrishna and Rambabu Bommisetti. "Detecting psychological uncertainty using machine learning." International Journal of Science and Research Archive 12, no. 2 (July 30, 2024): 1365–70. http://dx.doi.org/10.30574/ijsra.2024.12.2.1399.

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Psychological uncertainty encompassing findings such as anxiety, depression, and bipolar disorder poses significant challenges for timely diagnosing and successful treatment. Traditional diagnostic methods often depend on subjective assessments, leading to inconsistencies and potential biases. This research finds the applications of machine learning techniques to identify Psychological uncertainty with greater accuracy and objectivity. It uses a comprehensive dataset of healthcare records, standardized mental health tests, and social media activity to train multiple machine learning models, including Support Vector Machines (SVM), Random Forests, and Convolutional Neural Networks (CNN). The models were assessed based on their accuracy, precision, recall, and F1-score. The results shows that the Random Forest model had the highest accuracy (87%), with major predictive characteristics including social media sentiment ratings, frequency of healthcare visits, and physiological data such as heart rate variability. These findings indicate that machine learning can considerably improve the detection of psychological ambiguity, providing a reliable alternative to traditional diagnostic methods. The work highlights the potential for incorporating machine learning into mental health diagnostics to enable earlier interventions and individualized treatment programs, ultimately improving patient outcomes. Future research should focus on increasing datasets and using real-time monitoring technology to improve these predictive models.
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Nivas, K., M. Rajesh Kumar, G. Suresh, T. Ramaswamy, and Yerraboina Sreenivasulu. "Facial Emotion Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (January 31, 2023): 427–33. http://dx.doi.org/10.22214/ijraset.2023.48585.

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Abstract: The use of machines to perform various tasks is ever increasing in society. By imbuing machines with perception, they will be able to perform a wide variety of tasks. There are also very complex ones, such as aged care. Machine perception requires the machine to understand the surrounding environment and the intentions of the interlocutor. Recognizing facial emotions can help in this regard. During the development of this work, deep learning techniques were used on images showing facial emotions such as happiness, sadness, anger, surprise, disgust, and fear. In this study, a pure convolutional neural network approach outperformed the results of other statistical methods obtained by other authors, including feature engineering. The use of convolutional networks includes a learning function. This looks very promising for this task where the functionality is not easy to define. Additionally, the network he was evaluated using two different corpora. One was used during network training and also helped tune parameters and define the network architecture. This corpus consisted of mimetic emotions. The network that yielded the highest classification accuracy results was tested on the second dataset. Although the network was trained on only one corpus, the network reported promising results when tested on another dataset showing non-real facial emotions. The results achieved did not correspond to the state of the art. Collected evidence indicates that deep learning may be suitable for facial expression classification. Deep learning therefore has the potential to improve human-machine interaction. Because the ability to learn functions allows machines to evolve cognition. And through perception, the machine could offer a smoother response, greatly improving the user's experience.
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William, Carter, Choki Wangmo, and Anjali Ranjan. "Unravelling the application of machine learning in cancer biomarker discovery." Cancer Insight 2, no. 1 (June 14, 2023): 1–8. http://dx.doi.org/10.58567/ci02010001.

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Machine learning is playing an increasingly important role in the healthcare industry by transforming the way cancer is diagnosed and treated. By analyzing patient data, genomic data, and imaging data, machine learning algorithms can identify molecular signatures that distinguish cancer patients from healthy patients. Biomarkers that can accurately detect and diagnose cancer can be identified through analysis of these data sources. Additionally, personalized cancer therapies can be developed by identifying the most effective treatments based on individual patient characteristics and cancer type. Some of the machine learning techniques used for cancer biomarker discovery include deep learning and support vector machines, which can respectively identify complex patterns in data and classify data to identify relevant biomarkers. The benefits of using machine learning for cancer biomarker discovery are significant, including more precise and personalized treatments, improved patient outcomes, and the potential to transform cancer diagnosis and treatment. However, there are also challenges associated with using machine learning for cancer biomarker discovery, such as data collection and privacy issues, as well as the need for more powerful computational resources. This article explores the potential of machine learning in cancer biomarker discovery and argues that ongoing research in this field has the potential to revolutionize cancer diagnosis and treatment. Future research directions should focus on further developing machine learning algorithms and effective data collection and privacy protocols.
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Patil, Rohit, Priyadarshani Alandikar, Vaibhav Chaudhari, Pradnya Patil, and Prof Swarupa Deshpande. "Water Demand Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 122–28. http://dx.doi.org/10.22214/ijraset.2022.47797.

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bstract: Water is of paramount importance for the existence of life on Earth. The causes of water depletion are both natural and anthropogenic. On Earth, the amount of freshwater has remained persistent over span but the population has mushroomed. Therefore, striving for freshwater intensifies day by day. Proper management and forecasting are required for better and effective water usage plans. Water demand and population forecasting are the major parameters for an Urban Water Management. Machine learning is among the best-known techniques for such forecasting. Machine learning is a data analytics technique that provides machines the potential to learn without being comprehensively programmed. Unlike the traditional methods of demand forecasting that were not suitable for historical unstructured and semi structured data, machine learning takes into account or has the capabilities for analyzing such data.
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Akrom, Muhamad. "Quantum Support Vector Machine for Classification Task: A Review." Journal of Multiscale Materials Informatics 1, no. 2 (July 5, 2024): 1–8. http://dx.doi.org/10.62411/jimat.v1i2.10965.

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Quantum computing has emerged as a promising technology capable of solving complex computational problems more efficiently than classical computers. Among the various quantum algorithms developed, the Quantum Support Vector Machine (QSVM) has gained significant attention for its potential to enhance machine learning tasks, particularly classification. This review paper explores the theoretical foundations, methodologies, and potential advantages of QSVM for classification tasks. We discuss the quantum computing principles underpinning QSVM, compare them with classical support vector machines, and review recent advancements and applications. Finally, we highlight the challenges and prospects of QSVM in the context of quantum machine learning.
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Patil, Bhagyashree A., Sri Adithya S, and Dr Jayanthi M G. "Detection of Malware using Machine Learning Approach." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 2023): 736–41. http://dx.doi.org/10.22214/ijraset.2023.55233.

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Abstract: This proposal abstract outlines a new approach to classification of malware using machine learning algorithms. Malware detection is an essential task in cybersecurity to identify and mitigate potential threats posed by malicious software. This research proposes a new framework of Support Vector Classifiers and Decision Tree these are very know machine learning algorithms which will be used on malware detection. The proposed work is designed to effectively classify whether the network data is malware or normal behavioural characteristics. The designed approach is compared with traditional algorithms of Machine Learning, such as Support Vector Machines (SVM) and Decision Tree to evaluate its performance. The outcomes of this research are expected to yield on the development of more effective malware detection and prevention systems.
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Sahoo, Abhilipsa, and Kaushika Patel. "Machine Learning-based Inverse Design Model of a Transistor." Indian Journal Of Science And Technology 17, no. 7 (February 15, 2024): 617–24. http://dx.doi.org/10.17485/ijst/v17i7.3076.

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Objectives: To develop an inverse design model for transistors, utilizing machine learning algorithms to predict key design parameters specifically, the length and width based on specified gain and bandwidth requirements. And to conduct a comprehensive comparative analysis with existing literature, evaluating the efficacy and novelty of the proposed model in the context of semiconductor engineering challenges and methodologies. Methods: The comprehensive dataset, comprising 30,000 values generated through LTspice simulations, forms the basis for training the machine learning model. Utilizing a Random Forest regressor as the base model and a multi-output regressor as the main model, the project involves extensive data analysis, model development, and iterative fine-tuning. Findings: The outcomes demonstrate the efficacy of the developed model in accurately predicting transistor dimensions. Performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared, highlight the precision of the model in fulfilling the specified objectives. Novelty: This study introduces a novel approach to semiconductor device design optimization, showcasing the potential of machine learning to streamline the inverse design process. The use of a multi-output regressor, feature engineering, and fine-tuning through log transformation contribute to the innovative nature of the developed model. Keywords: Machine Learning (ML) model, Random Forest regressor, multi­output regressor, Feature engineering, Fine­tuning
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Tiffin, Paul A., and Lewis W. Paton. "Rise of the machines? Machine learning approaches and mental health: opportunities and challenges." British Journal of Psychiatry 213, no. 3 (August 16, 2018): 509–10. http://dx.doi.org/10.1192/bjp.2018.105.

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SummaryMachine learning methods are being increasingly applied to physical healthcare. In this article we describe some of the potential benefits, challenges and limitations of this approach in a mental health context. We provide a number of examples where machine learning could add value beyond conventional statistical modelling.Declaration of interestNone.
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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.

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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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Pei, Jun, Lin Frank Song, and Kenneth M. Merz. "Pair Potentials as Machine Learning Features." Journal of Chemical Theory and Computation 16, no. 8 (June 19, 2020): 5385–400. http://dx.doi.org/10.1021/acs.jctc.9b01246.

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Kobayashi, Keita, Hiroki Nakamura, Akiko Yamaguchi, Mitsuhiro Itakura, Masahiko Machida, and Masahiko Okumura. "Machine learning potentials for tobermorite minerals." Computational Materials Science 188 (February 2021): 110173. http://dx.doi.org/10.1016/j.commatsci.2020.110173.

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Barbour, Dennis L., and Jan-Willem A. Wasmann. "Performance and Potential of Machine Learning Audiometry." Hearing Journal 74, no. 3 (February 26, 2021): 40,43,44. http://dx.doi.org/10.1097/01.hj.0000737592.24476.88.

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Therrien, Audrey C., Berthié Gouin-Ferland, and Mohammad Mehdi Rahimifar. "Potential of edge machine learning for instrumentation." Applied Optics 61, no. 8 (March 2, 2022): 1930. http://dx.doi.org/10.1364/ao.445798.

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Awan, Kamran H., S. Satish Kumar, and Indu Bharkavi SK. "Potential Role of Machine Learning in Oncology." Journal of Contemporary Dental Practice 20, no. 5 (2019): 529–30. http://dx.doi.org/10.5005/jp-journals-10024-2551.

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Dral, Pavlo O., Alec Owens, Alexey Dral, and Gábor Csányi. "Hierarchical machine learning of potential energy surfaces." Journal of Chemical Physics 152, no. 20 (May 29, 2020): 204110. http://dx.doi.org/10.1063/5.0006498.

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Wu, Yuexiang. "Potential pulsars prediction based on machine learning." Theoretical and Natural Science 12, no. 1 (November 17, 2023): 193–201. http://dx.doi.org/10.54254/2753-8818/12/20230466.

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The search for potential pulsars is a difficult job because of the complex nature of the signals and the vast amounts of data involved. In the last few years, a lot of researchers have tried to use machine learning to deal with complex data. This essay examines how machine learning could help to identify potential pulsars, exploring the various types of algorithms and the challenges and limitations associated with this approach. The essay mainly explored three themes: the training of 5 algorithms for the identification of pulsars, the improvement of 2 algorithms by adjusting parameters, and the simplification of the data to improve the processing speed and performance of the algorithms on prediction. All 5 algorithms reached great accuracy after adjustment and the simplification of the input data can help to boost the prediction time and accuracy for future research about pulsars. The essay highlights the need for further research in this area, as machine learning has demonstrated strong potential for pulsar prediction. By analyzing the results of several previous studies, this essay underscores the importance of machine learning as an approach for predicting potential pulsars and made improvements to the performance of current algorithms by adjusting parameters and simplifying the data.
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Zhou, Ziyun, Jingwei Shang, and Yimang Li. "Enhancing Efficiency in Hierarchical Reinforcement Learning through Topological-Sorted Potential Calculation." Electronics 12, no. 17 (September 1, 2023): 3700. http://dx.doi.org/10.3390/electronics12173700.

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Hierarchical reinforcement learning (HRL) offers a hierarchical structure for organizing tasks, enabling agents to learn and make decisions autonomously in complex environments. However, traditional HRL approaches face limitations in effectively handling complex tasks. Reward machines, which specify high-level goals and associated rewards for sub-goals, have been introduced to address these limitations by facilitating the agent’s understanding and reasoning with respect to the task hierarchy. In this paper, we propose a novel approach to enhance HRL performance through topologically sorted potential calculation for reward machines. By leveraging the topological structure of the task hierarchy, our method efficiently determines potentials for different sub-goals. This topological sorting enables the agent to prioritize actions leading to the accomplishment of higher-level goals, enhancing the learning process. To assess the efficacy of our approach, we conducted experiments in the grid-world environment with OpenAI-Gym. The results showcase the superiority of our proposed method over traditional HRL techniques and reward machine-based reinforcement learning approaches in terms of learning efficiency and overall task performance.
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Li, Jiarui. "Evaluative Comparison of Machine Learning Algorithms for Precision Diagnosis in Breast Cancer." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 354–62. http://dx.doi.org/10.54097/40fmfw48.

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Breast cancer remains a prominent issue in worldwide public health, exhibiting a gender disparity that primarily impacts women. This study systematically evaluates the diagnostic capabilities of various machine learning algorithms in predicting breast cancer recurrences. Utilising a dataset of 569 data points, the algorithms scrutinised include Artificial Neural Networks (ANN), Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), XGBoost (XGB), Logistic Regression (LR), and K-Nearest Neighbours (KNN). Principal Component Analysis (PCA) was applied and employed with the algorithmic evaluation for selecting features and reducing dimensionality. The study utilised multiple evaluative metrics, focusing on Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) values. The findings suggest that Logistic Regression and Support Vector Machines performed better than the other algorithms. Specifically, Logistic Regression achieved an AUC value of 99.77%, and Support Vector Machines achieved an AUC value of 99.74%. Additionally, these algorithms demonstrated an accuracy rate of 97.37%, precision of 97.62%, recall of 95.35%, F1 score of 96.47%, and Cohen's Kappa coefficient of 94.37%, consistent. The study suggests potential avenues for further investigation into the utility of machine learning algorithms and dimensionality reduction techniques in diagnosing breast cancer recurrence. These preliminary findings have the potential to make a valuable contribution to the current discourse around the use of machine learning technologies within healthcare environments
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Østerlund, Carsten, Kevin Crowston, Corey B. Jackson, Yunan Wu, Alexander O. Smith, and Aggelos K. Katsaggelos. "Supporting Human and Machine Co-Learning in Citizen Science: Lessons From Gravity Spy." Citizen Science: Theory and Practice 9, no. 1 (December 9, 2024): 42. https://doi.org/10.5334/cstp.738.

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We explore the bi-directional relationship between human and machine learning in citizen science. Theoretically, the study draws on the zone of proximal development (ZPD) concept, which allows us to describe AI augmentation of human learning, human augmentation of machine learning, and how tasks can be designed to facilitate co-learning. The study takes a design-science approach to explore the design, deployment, and evaluations of the Gravity Spy citizen science project. The findings highlight the challenges and opportunities of co-learning, where both humans and machines contribute to each other’s learning and capabilities. The study takes its point of departure in the literature on co-learning and develops a framework for designing projects where humans and machines mutually enhance each other’s learning. The research contributes to the existing literature by developing a dynamic approach to human-AI augmentation, by emphasizing that the ZPD supports ongoing learning for volunteers and keeps machine learning aligned with evolving data. The approach offers potential benefits for project scalability, participant engagement, and automation considerations while acknowledging the importance of tutorials, community access, and expert involvement in supporting learning.
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M, Senthil Raja, Arun Raj L, and Arun A. "Detection of Depression among Social Media Users with Machine Learning." Webology 19, no. 1 (January 20, 2022): 250–57. http://dx.doi.org/10.14704/web/v19i1/web19019.

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Mental illnesses are a significant and growing public health concern. They have the potential to tremendously affect a person’s life. Depression, in particular, is one of the major reasons for suicide. In recent times, the popularity of social media websites has burgeoned as they are platforms that facilitate discussion and free-flowing conversation about a plethora of topics. Information and dialogue about subjects like mental health, which are still considered as a taboo in various cultures, are becoming more and more accessible. The objective of this paper is to review and comprehensively compare various previously employed Natural Language Processing techniques for the purpose of classification of social media text posts as those written by depressed individuals. Furthermore, pros, cons, and evaluation metrics of these techniques, along with the challenges faced and future directions in this area of research are also summarized.
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D. Nageswara Rao. "Predictive Modeling of Breast Cancer Outcomes Using Supervised Machine Learning Algorithms." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 4 (August 15, 2024): 258–66. http://dx.doi.org/10.32628/cseit2410416.

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Breast cancer remains one of the leading causes of mortality among women, emphasizing the need for accurate predictive models to aid in early diagnosis and treatment. This study explores the application of supervised machine learning algorithms to predict breast cancer outcomes, leveraging patient data such as demographics, clinical features, and histopathological information. We evaluate several algorithms, including Logistic Regression, Support Vector Machines (SVM), Random Forests, and Gradient Boosting Machines (GBM), to identify their efficacy in predicting survival rates and disease progression. Our results indicate that ensemble methods, particularly Random Forests and GBMs, offer superior predictive performance compared to traditional approaches. This work demonstrates the potential of machine learning techniques to enhance decision-making in breast cancer management, providing a framework for future research and clinical application.
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Silva, Nuno A., Vicente Rocha, and Tiago D. Ferreira. "Optical Extreme Learning Machines with Atomic Vapors." Atoms 12, no. 2 (February 6, 2024): 10. http://dx.doi.org/10.3390/atoms12020010.

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Extreme learning machines explore nonlinear random projections to perform computing tasks on high-dimensional output spaces. Since training only occurs at the output layer, the approach has the potential to speed up the training process and the capacity to turn any physical system into a computing platform. Yet, requiring strong nonlinear dynamics, optical solutions operating at fast processing rates and low power can be hard to achieve with conventional nonlinear optical materials. In this context, this manuscript explores the possibility of using atomic gases in near-resonant conditions to implement an optical extreme learning machine leveraging their enhanced nonlinear optical properties. Our results suggest that these systems have the potential not only to work as an optical extreme learning machine but also to perform these computations at the few-photon level, paving opportunities for energy-efficient computing solutions.
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Sumathi, P., Arun Kumar S, and Balaji A. "Healthcare - Autism Predicting Tool Using Data Science / AI / ML." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 440–43. http://dx.doi.org/10.22214/ijraset.2024.60421.

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Abstract: This study presents a comprehensive analysis of the application of machine learning techniques for the prediction of autism spectrum disorder (ASD). The dataset used in this research comprises a range of demographic, behavioral, and diagnostic features. The study focuses on the use of various machine learning algorithms, including limited decision trees, support vector machines, and neural networks, to predict the likelihood of ASD in individuals. In addition, engineering and feature selection strategies are investigated to determine the most pertinent characteristics for precise prediction. Metrics like accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve are used to assess how well various algorithms perform. Results show promising potential for the utilization of machine learning models in predicting ASD, with certain algorithms exhibiting superior predictive capabilities. The findings of this study provide valuable insights into the potential use of machine learning in the early detection and intervention of autism, ultimately contributing to improved outcomes for individuals on the autism spectrum
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Hossain, Md Shakhaowat, S. M. Shadul Islam Rishad, Md Mohibur Rahman, Sanjida Akter Tisha, Farhan Shakil, Ashim Chandra Das, Radha Das, and Sadia Sultana. "MACHINE LEARNING FOR STOCK MARKET SECURITY MEASUREMENT: A COMPARATIVE ANALYSIS OF SUPERVISED, UNSUPERVISED, AND DEEP LEARNING MODELS." International journal of networks and security 04, no. 01 (November 22, 2024): 22–32. http://dx.doi.org/10.55640/ijns-04-01-06.

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This study presents a comprehensive analysis of machine learning techniques for measuring and predicting security in stock markets, comparing the performance of supervised, unsupervised, and deep learning models. Using a diverse dataset from Kaggle that includes historical stock prices, financial news sentiment, company fundamentals, and macroeconomic indicators, we applied feature engineering and rigorous preprocessing methods to optimize model accuracy. The study evaluated Random Forest, Support Vector Machines (SVM), K-Means clustering, and Long Short-Term Memory (LSTM) networks across key performance metrics. Results indicate that Random Forest outperformed other models in classification tasks with an accuracy of 92%, making it highly effective for real-time security assessment. SVM also demonstrated strong classification capabilities, particularly in high-dimensional spaces, with an accuracy of 88%. K-Means and DBSCAN clustering algorithms excelled in anomaly detection, identifying unusual patterns that could signal market irregularities. LSTM models, designed for time-series forecasting, achieved a root mean square error (RMSE) of 1.78, proving their utility in predicting future stock trends but requiring more computational resources.Our findings suggest that a hybrid approach, combining the strengths of supervised and deep learning models, can provide a robust solution for stock market security measurement. By leveraging explainable AI techniques such as SHAP and LIME, we also improved model interpretability, making these predictions more actionable for stakeholders. This research highlights the potential of machine learning in financial security monitoring and supports the growing integration of AI in the finance industry.
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Gittler, Thomas, Stephan Scholze, Alisa Rupenyan, and Konrad Wegener. "Machine Tool Component Health Identification with Unsupervised Learning." Journal of Manufacturing and Materials Processing 4, no. 3 (September 2, 2020): 86. http://dx.doi.org/10.3390/jmmp4030086.

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Unforeseen machine tool component failures cause considerable losses. This study presents a new approach to unsupervised machine component condition identification. It uses test cycle data of machine components in healthy and various faulty conditions for modelling. The novelty in the approach consists of the time series representation as features, the filtering of the features for statistical significance, and the use of this feature representation to train a clustering model. The benefit in the proposed approach is its small engineering effort, the potential for automation, the small amount of data necessary for training and updating the model, and the potential to distinguish between multiple known and unknown conditions. Online measurements on machines in unknown conditions are performed to predict the component condition with the aid of the trained model. The approach was exemplarily tested and verified on different healthy and faulty states of a grinding machine axis. For the accurate classification of the component condition, different clustering algorithms were evaluated and compared. The proposed solution demonstrated encouraging results as it accurately classified the component condition. It requires little data, is straightforward to implement and update, and is able to precisely differentiate minor differences of faults in test cycle time series.
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Adewusi, Michael Adelani, Adeshina Wasiu Adebanjo, Tokunbo Odekeye, and Sophia Kazibwe. "Rise of the Machines: Exploring the Emergence of Machine Consciousness." European Journal of Theoretical and Applied Sciences 2, no. 4 (July 1, 2024): 563–73. http://dx.doi.org/10.59324/ejtas.2024.2(4).48.

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Inquiry into the field of artificial intelligence (machines) and its potential to develop consciousness is presented in this study. This investigation explores the complex issues surrounding machine consciousness at the nexus of AI, neuroscience, and philosophy as we delve into the fascinating world of artificial intelligence (AI) and investigate the intriguing question: are machines on the verge of becoming conscious beings? The study considers the likelihood of machines displaying self-awareness and the implications thereof through an analysis of the current state of AI and its limitations. However, with advancements in machine learning and cognitive computing, AI systems have made significant strides in emulating human-like behavior and decision-making. Furthermore, the emergence of machine consciousness raises questions about the blending of human and artificial intelligence, and ethical considerations are also considered. The study provides a glimpse into a multidisciplinary investigation that questions accepted theories of consciousness, tests the limits of what is possible with technology, and do these advancements signify a potential breakthrough in machine consciousness.
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Hidayat, Taufik, Kalamullah Ramli, Nadia Thereza, Amarudin Daulay, Rushendra Rushendra, and Rahutomo Mahardiko. "Machine Learning to Estimate Workload and Balance Resources with Live Migration and VM Placement." Informatics 11, no. 3 (July 19, 2024): 50. http://dx.doi.org/10.3390/informatics11030050.

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Currently, utilizing virtualization technology in data centers often imposes an increasing burden on the host machine (HM), leading to a decline in VM performance. To address this issue, live virtual migration (LVM) is employed to alleviate the load on the VM. This study introduces a hybrid machine learning model designed to estimate the direct migration of pre-copied migration virtual machines within the data center. The proposed model integrates Markov Decision Process (MDP), genetic algorithm (GA), and random forest (RF) algorithms to forecast the prioritized movement of virtual machines and identify the optimal host machine target. The hybrid models achieve a 99% accuracy rate with quicker training times compared to the previous studies that utilized K-nearest neighbor, decision tree classification, support vector machines, logistic regression, and neural networks. The authors recommend further exploration of a deep learning approach (DL) to address other data center performance issues. This paper outlines promising strategies for enhancing virtual machine migration in data centers. The hybrid models demonstrate high accuracy and faster training times than previous research, indicating the potential for optimizing virtual machine placement and minimizing downtime. The authors emphasize the significance of considering data center performance and propose further investigation. Moreover, it would be beneficial to delve into the practical implementation and dissemination of the proposed model in real-world data centers.
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