Статті в журналах: "Machine learning potential"

1

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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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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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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4

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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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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Zelinska, Snizhana. "Machine learning: technologies and potential application at mining companies." E3S Web of Conferences 166 (2020): 03007. http://dx.doi.org/10.1051/e3sconf/202016603007.

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Implementation of machine learning systems is currently one of the most sought-after spheres of human activities at the interface of information technologies, mathematical analysis and statistics. Machine learning technologies are penetrating into our life through applied software created with the help of artificial intelligence algorithms. It is obvious that machine learning technologies will be developing fast and becoming part of the human information space both in our everyday life and in professional activities. However, building of machine learning systems requires great labour contribution of specialists in the sphere of artificial intelligence and the subject area where this technology is to be applied. The article considers technologies and potential application of machine learning at mining companies. The article describes basic methods of machine learning: unsupervised learning, action learning, semi-supervised machine learning. The criteria are singled out to assess machine learning: operation speed; assessment time; implemented model accuracy; ease of integration; flexible deployment within the subject area; ease of practical application; result visualization. The article describes practical application of machine learning technologies and considers the dispatch system at a mining enterprise (as exemplified by the dispatch system of the mining and transportation complex “Quarry” used to increase efficiency of operating management of enterprise performance; to increase reliability and agility of mining and transportation complex performance records and monitoring. There is also a list of equipment performance data that can be stored in the database and used as a basis for processing by machine learning algorithms and obtaining new knowledge. Application of machine learning technologies in the mining industry is a promising and necessary condition for increasing mining efficiency and ensuring environmental security. Selection of the optimal process flow sheet of mining operations, selection of the optimal complex of stripping and mining equipment, optimal planning of mining operations and mining equipment performance control are some of the tasks where machine learning technologies can be used. However, despite prospectivity of machine learning technologies, this trend still remains understudied and requires further research.

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Sarkar, Soumyadip. "Quantum Machine Learning: A Review." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 352–54. http://dx.doi.org/10.22214/ijraset.2023.49421.

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Abstract: Quantum machine learning is an emerging field that aims to leverage the unique properties of quantum computing to accelerate machine learning tasks. In this paper, we review recent advances in quantum machine learning and discuss the potential applications and challenges associated with this technology. Specifically, we examine the current state of quantum machine learning algorithms, including variational quantum algorithms, quantum neural networks, and quantum generative models. We also discuss the challenges associated with practical quantum computing resources, algorithm design, and interdisciplinary collaboration. Furthermore, we highlight the potential applications of quantum machine learning in areas such as drug discovery, speech and image recognition, financial modeling, and many others. We also examine the ethical and societal implications of this technology, including the potential impact on privacy and security. Finally, we discuss future prospects for quantum machine learning, including the potential for quantum-inspired classical algorithms and the development of error correction techniques. We conclude by emphasizing the importance of interdisciplinary collaboration in the continued advancement of this field.

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11

M, Shah,. "Demystifying Machine Learning." Saudi Journal of Engineering and Technology 9, no. 07 (July 9, 2024): 299–303. http://dx.doi.org/10.36348/sjet.2024.v09i07.004.

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This paper delves into the rapidly evolving domain of Artificial Intelligence (AI), with a particular focus on Machine Learning (ML), a dynamic and influential subset of AI. It explores how ML empowers computers to learn from data, identify patterns, and make decisions with minimal human intervention. The manuscript examines the broad utility of ML across various real-world scenarios, emphasizing its critical role in enabling organizations to evolve and maintain a competitive edge in the fast-paced technological landscape. It discusses the necessity for organizations to adopt new ways of working and embrace the opportunities presented by AI to remain viable in the global, online marketplace. The paper reviews the evolution of ML, evaluates its advantages and disadvantages, and contemplates the future directions ML could lead organizations willing to integrate this powerful technology. The overarching theme is the transformative potential of ML in reshaping organizational strategies and operations for a more interconnected and intelligent future.

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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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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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14

Nikoulis, Giorgos, Jesper Byggmästar, Joseph Kioseoglou, Kai Nordlund, and Flyura Djurabekova. "Machine-learning interatomic potential for W–Mo alloys." Journal of Physics: Condensed Matter 33, no. 31 (June 18, 2021): 315403. http://dx.doi.org/10.1088/1361-648x/ac03d1.

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Wang, Peng-Ju, Jun-Yu Fan, Yan Su, and Ji-Jun Zhao. "Energetic potential of hexogen constructed by machine learning." Acta Physica Sinica 69, no. 23 (2020): 238702. http://dx.doi.org/10.7498/aps.69.20200690.

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Mukherjee, Debashis, and Rajesh Biswal. "Machine Learning in Automotive Data Potential, Analytics Power." Auto Tech Review 4, no. 5 (May 2015): 44–49. http://dx.doi.org/10.1365/s40112-015-0916-7.

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Sun, Lei, Badong Chen, Kar-Ann Toh, and Zhiping Lin. "Sequential extreme learning machine incorporating survival error potential." Neurocomputing 155 (May 2015): 194–204. http://dx.doi.org/10.1016/j.neucom.2014.12.029.

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Lorena, Ana C., Luis F. O. Jacintho, Marinez F. Siqueira, Renato De Giovanni, Lúcia G. Lohmann, André C. P. L. F. de Carvalho, and Missae Yamamoto. "Comparing machine learning classifiers in potential distribution modelling." Expert Systems with Applications 38, no. 5 (May 2011): 5268–75. http://dx.doi.org/10.1016/j.eswa.2010.10.031.

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Sharifipour, Behzad, Bahram Gholinejad, Ataollah Shirzadi, Himan Shahabi, Nadhir Al-Ansari, Asghar Farajollahi, Fatemeh Mansorypour, and John J. Clague. "Rangeland species potential mapping using machine learning algorithms." Ecological Engineering 189 (April 2023): 106900. http://dx.doi.org/10.1016/j.ecoleng.2023.106900.

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20

Yu, Jingyi. "Product potential user prediction based on machine learning." Highlights in Science, Engineering and Technology 92 (April 10, 2024): 146–51. http://dx.doi.org/10.54097/2h70m008.

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In today's ever-changing business environment, it is crucial for businesses to market their products with the needs of users in mind to increase profitability. With consumers increasingly receiving services through digital platforms, analyzing potential users of products through big data has attracted extensive attention from researchers. The obvious differences in characteristics between users have greatly increased the difficulty of predicting potential users of a product. Fortunately, machine learning-based data analysis methods offer solutions to this complex task. However, there are obvious differences in usage scenarios and performance between different algorithms, which brings inconvenience to practical applications. This study delves into machine learning-based methods for product prospecting, including k -nearest neighbours (KNN), support vector machine (SVM), decision tree, XGBoost and random forest. Meanwhile, the performance of these algorithms is compared with data to analyse their advantages and disadvantages. Finally, the full paper is summarized and future research directions are envisaged.

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Mei, Haojie, Luyao Cheng, Liang Chen, Feifei Wang, Jinfu Li, and Lingti Kong. "Development of machine learning interatomic potential for zinc." Computational Materials Science 233 (January 2024): 112723. http://dx.doi.org/10.1016/j.commatsci.2023.112723.

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Lee, Chien-Chang, James Yeongjun Park, and Wan-Ting Hsu. "Bridging expertise with machine learning and automated machine learning in clinical medicine." Annals of the Academy of Medicine, Singapore 53, no. 3 - Correct DOI (March 27, 2024): 129–31. http://dx.doi.org/10.47102/annals-acadmedsg.202481.

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In this issue of the Annals, Thirunavukarasu et al.’s systematic review on the clinical performance of automated machine learning (autoML) highlights its extensive applicability across 22 clinical specialties, showcasing its potential to redefine healthcare by making artificial intelligence (AI) technologies accessible to those without advanced computational skills.1 This enables the development of effective AI models that could rival or exceed the accuracy of traditional machine learning (ML) approaches and human diagnostic methods.

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Lee, Chien-Chang, James Yeongjun Park, and Wan-Ting Hsu. "Bridging expertise with machine learning and automated machine learning in clinical medicine." Annals of the Academy of Medicine, Singapore 53, no. 3 (March 27, 2024): 129–31. http://dx.doi.org/10.47102/https://doi.org/10.47102/annals-acadmedsg.202481.

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In this issue of the Annals, Thirunavukarasu et al.’s systematic review on the clinical performance of automated machine learning (autoML) highlights its extensive applicability across 22 clinical specialties, showcasing its potential to redefine healthcare by making artificial intelligence (AI) technologies accessible to those without advanced computational skills.1 This enables the development of effective AI models that could rival or exceed the accuracy of traditional machine learning (ML) approaches and human diagnostic methods.

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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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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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Elhadary, Mohamed, Mervat Mattar, Khalil Al Farsi, Salem Alshemmari, Basel ElSayed, Omar Metwalli, Amgad Elshoeibi, Ahmed Abdelrehim Badr, Awni Alshurafa, and Mohamed A. Yassin. "Machine Learning in CLL." Blood 142, Supplement 1 (November 28, 2023): 7185. http://dx.doi.org/10.1182/blood-2023-179388.

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Introduction: Chronic lymphocytic leukemia (CLL) and small lymphocytic lymphoma (SLL) are closely related diseases with similar characteristics, classified as mature B cell neoplasms. They involve the accumulation of monoclonal B lymphocytes and are considered as one disease with different manifestations. CLL is more prevalent and is usually detected through routine blood tests, while SLL is identified by primary lymph node involvement. Although many patients with CLL/SLL are asymptomatic, some may experience lymphoma-related symptoms. The diagnosis involves blood tests and immunophenotypic analysis. CLL prognosis varies, and staging systems help in determining the appropriate treatment regimen. Machine learning (ML) is being increasingly used in hematology to aid in diagnosis, treatment, and risk-stratification. This review explores the application of ML in the classification and diagnosis of CLL, discussing the performance and limitations of existing models, with the goal of encouraging further research and integration into clinical settings to improve patient care. Materials and Methods: A literature search was conducted using the PubMed/MEDLINE and EMBASE databases. EndNote and Rayyan software were used to eliminate duplicate entries and for screening the articles. In addition, the references of the identified articles were manually screened to identify additional relevant studies. Articles with primary data pertaining to the use of ML in CLL/SLL diagnosis and classification were included without language or time restrictions. Results: A total of 169 articles were identified and duplicates were removed using Endnote® and Rayyan® software, resulting in 149 articles eligible for screening. The included articles met specific criteria: they utilized ML methods, reported conclusions on the method's reliability or accuracy, and focused on CLL diagnosis and classification. Excluded articles were non-English, animal/in vitro studies, abstracts, and review articles. After screening, 14 studies met the inclusion criteria. Studies examining the potential of ML algorithms in enhancing the diagnosis of CLL used various data sources like blood smears, flow cytometry, genetic data, and histopathological images. The current evidence suggests that ML can effectively predict CLL diagnosis, aid in screening, identify potential biomarkers, and explore the underlying molecular mechanisms. Many of the ML models evaluated in this review demonstrate high accuracy, ranging from 83% to 100%. Implementing AI and ML in hematology can automate tasks involved in patient workup, risk assessment, and treatment, allowing hematologists to focus on critical aspects of patient care and research. Despite the promising potential, some important considerations should be acknowledged. First, many reviewed models had limited sample sizes from single centers, limiting their generalizability. It's crucial to develop models using larger, diverse datasets from multiple centers to improve generalizability. Second, there is a lack of prospective studies and research on the impact of ML models on patient outcomes. Future investigations should focus on prospectively assessing the effect of ML models on CLL diagnosis, prognosis, and patient outcomes. Third, the integration of ML applications into direct patient care raises ethical and medico-legal concerns, including liability, data privacy, and doctor-ML application interaction. An ethical framework specific to ML applications in healthcare is needed, and doctors should receive training on ML applications to understand their capabilities and limitations. Addressing these considerations can lead to successful implementation of ML applications in the care of CLL patients, improving diagnosis and patient outcomes. Conclusion: Our findings demonstrate the promising potential of ML algorithms in accurately predicting CLL diagnosis, conducting CLL screening, and identifying genetic biomarkers associated with CLL. Future research endeavors should prioritize the development of large, standardized datasets to effectively train ML models, conduct prospective evaluations to assess their performance, explore their impact on patient outcomes, and establish ethical frameworks to govern their utilization.

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Prakash, Ujjwal. "Advanced Dietitian Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 8, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem33347.

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The "Advanced Artificial Intelligence Dietitian" (AI-Dietitian) project explores the potential of AI to revolutionize personalized nutrition. Traditional dietary approaches often struggle with individual complexities and lack continuous guidance. We propose an AI-powered system that transcends these limitations, offering: · Comprehensive Data Integration: The AI-Dietitian analyzes diverse data sets, including medical records, genetic information, activity trackers, and food intake sensors, building a holistic understanding of individual needs and preferences. · Adaptive & Personalized Plans: Leveraging advanced machine learning algorithms, the AI-Dietitian generates dynamic, personalized meal plans that adapt to real-time data and user feedback, ensuring continued effectiveness and adherence. · Continuous Coaching & Support: The AI-Dietitian acts as a virtual coach, providing real-time feedback, nutritional education, and motivational support through natural language interaction and personalized content. · Predictive & Preventative Healthcare: The AI-Dietitian leverages its predictive capabilities to identify potential health risks and recommend preventative dietary interventions, promoting long-term health and well-being. This project addresses the limitations of traditional dietetics and explores the ethical considerations of AI in healthcare. We believe the AI-Dietitian has the potential to democratize personalized nutrition, promoting healthier communities and reducing healthcare costs. Keywords: Artificial Intelligence, Machine Learning, Personalized Nutrition, Health Coaching, Predictive Healthcare, Ethical AI

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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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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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Biswal M, Manas. "The Potential of Machine Learning for Future Mars Exploration." Acceleron Aerospace Journal 1, no. 6 (December 30, 2023): 119–20. http://dx.doi.org/10.61359/11.2106-2326.

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The pursuit of understanding Mars, our neighboring planet, is rife with challenges that range from treacherous conditions for potential human astronauts to the vast distances that complicate communication. However, a beacon of hope emerges in the form of machine learning, a technological frontier that promises to transform the landscape of Martian exploration. As we embark on this interplanetary journey, the recognition of machine learning's potential is growing. It offers innovative solutions to some of the most pressing challenges, ushering in a new era of autonomous exploration. Imagine rovers and orbiter spacecraft equipped with the ability to analyze Martian data on-site, reducing the need for slow communications with Earth. This revolutionary approach is already in action with rovers like Curiosity, where machine learning enables self-directed exploration and continuous data analysis on the Martian surface. The applications of machine learning extend beyond mere autonomy. They hold the promise of addressing communication limitations, providing greater operational autonomy, and unlocking the mysteries that shroud the Red Planet. From identifying sources of atmospheric gases, such as oxygen and methane, to interpreting geological features like cloud distributions and weather patterns, machine learning is proving itself to be a versatile and indispensable tool in unraveling the complexities of Mars. Venturing deeper into the Martian climate, machine learning becomes a powerful ally. By leveraging this technology to analyze climate data, we have the potential to generate predictive models crucial for planning future surface missions and assessing the habitability of Mars. Additionally, the application of machine learning on Earth offers a unique opportunity to decode uncertainties related to Martian atmospheric interactions, the dynamics of dust storms, and conditions beneath the surface. Anticipating the wealth of data that future Mars missions will yield, the integration of machine learning emerges as a game-changer. Its efficiency in discerning intricate patterns within extensive datasets has the potential to revolutionize our scientific understanding of Mars. As we delve deeper into the mysteries of the Red Planet, machine learning stands as a pivotal catalyst, promising not just incremental but transformative discoveries. It becomes the linchpin in our ongoing quest to answer the age-old question: Did life ever exist on Mars? In the realm of Martian exploration, machine learning is proving to be the technological cornerstone that propels us towards unprecedented scientific revelations.

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Ravindran, Anjana V., Anjana V. J, and Meenakshi P. "Prediction of Learning Disability Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 2023): 1248–54. http://dx.doi.org/10.22214/ijraset.2023.55332.

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Abstract: This A learning disability is a neurological disorder. The children predicted with learning disability may find it difficult to spell, read, write, organize things and so on. Learning disabilities are not related to intelligence or motivation. People with learning disabilities have average or above-average intelligence but may need special accommodations and support to learn and succeed. Early identification, assessment, and intervention are critical for managing learning disabilities. With appropriate support and accommodations, people with learning disabilities can achieve their full potential and lead fulfilling lives. Machine Learning algorithms can be useful in predicting learning disabilities because they can analyse large amounts of data quickly and accurately, and they can identify patterns that may not be apparent to human observers. Deep learning models can lead to better and faster predictions and they are capable to work with unstructured data as well. While Machine Learning and Deep Learning algorithms have shown promise in predicting learning disabilities, it’s important to use these tools in a responsible and ethical manner to ensure that individuals’ privacy and autonomy are protected. By leveraging the power of these algorithms, we can help to ensure that children with learning disabilities receive the support they need to reach their full potential. In this study, six models, ANN and CNN, were assessed for their effectiveness in predicting specific learning disabilities. The performance measure used to evaluate the models was accuracy. Among the models, KNN is found to be the most accurate with 90.33% followed by Random Forest with 79.22%, CNN with 77.69% LSTM with 77.57%, ANN with 57.57% and SVM with 57.57%.

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Sharma, Pratibha, and Manisha Joshi. "AWS Machine Learning Services." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 2 (September 10, 2019): 1171–74. http://dx.doi.org/10.61841/turcomat.v10i2.14390.

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As organizations seek to harness the power of machine learning (ML) to enhance decision-making and innovation, cloud platforms play a key role in democratizing access to ML capabilities types of This paper examines the state of machine learning services provided by Amazon Web Services (AWS). gunmaker etc. It provides an overview of the core AWS ML applications, exploring their use, use cases, and integration across applications Through a combination of textbooks, AWS documentation, and real-world case studies, this review aims to build highlights the transformational potential of AWS machine learning services , providing insights into the current state of technology, upcoming trends, and implications for various industries Industry. The abstract includes the abstract of AWS Machine Learning Services, which is a brief introduction to the detailed analysis of a full research paper.

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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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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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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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Veeramani, Sindhu, S. M. Ramesh, and B. Gomathy. "Exploring the Potential of Machine Learning in Healthcare Accuracy Improvement." WSEAS TRANSACTIONS ON COMPUTERS 22 (December 31, 2023): 374–79. http://dx.doi.org/10.37394/23205.2023.22.42.

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Machine learning techniques have shown great potential in the medical industry, particularly in the field of neuroimaging and the identification of neurological illnesses such as Autism Spectrum Disorder (ASD). By utilizing machine learning algorithms, researchers aim to predict the type of disability and analyze the predicted variations using different types of predictive models. These predictive models can be trained on neuroimaging data to identify patterns and markers that are indicative of ASD. By analyzing these patterns, machine learning algorithms can help in accurately predicting the presence and type of ASD in individuals. This can be immensely valuable in early diagnosis and intervention, leading to better outcomes for individuals with ASD. Furthermore, the applications of machine learning in the healthcare industry extend beyond just prediction. Machine learning algorithms can also be used to analyze large amounts of medical data, identify trends, and assist in decision-making processes. This can help healthcare professionals in providing more accurate diagnoses, personalized treatment plans, and improved patient care. It is important to note that the success and accuracy of machine learning models in the healthcare industry depend on various factors, including the quality and quantity of data available, the choice of algorithms, and the expertise of the researchers. Ongoing research and advancements in machine learning techniques hold great promise for improving the accuracy and effectiveness of medical diagnoses and treatments.

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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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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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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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Arora, Aaryan, and Nirmalya Basu. "Machine Learning in Modern Healthcare." International Journal of Advanced Medical Sciences and Technology 3, no. 4 (June 30, 2023): 12–18. http://dx.doi.org/10.54105/ijamst.d3037.063423.

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Traditional healthcare systems have long struggled to meet the diverse needs of millions of patients, leading to inefficiencies and suboptimal outcomes. However, the advent of machine learning (ML) has introduced a transformative paradigm shift towards value-based treatment, enabling healthcare providers to deliver personalized and highly effective care. Modern healthcare equipment and devices now incorporate internal applications that gather and store comprehensive patient data, presenting a valuable resource for ML-driven predictive models. In this research article, we delve into the profound impact of ML on modern healthcare, highlighting its potential to significantly enhance patient care and optimize resource allocation. Our study presents a robust predictive model capable of accurately forecasting patient diseases based on input information and various parameters, harnessing the power of extensive datasets encompassing diverse patient populations. We compared several ML algorithms, including Logistic Regression (accuracy: 0.796875), K-Nearest Neighbors (accuracy: 0.7864583333333334), XG Boost (accuracy: 0.78125), and Py Torch (accuracy: 0.7337662337662337), to determine the best-performing model. The achieved accuracies demonstrate the effectiveness of these ML techniques in disease prediction and showcase the potential for improving patient outcomes. Beyond the technical aspects, we explore the broader implications of value-based treatment and the integration of ML for various healthcare stakeholders. By emphasizing the numerous benefits of personalized and proactive medical care, our findings illustrate the substantial potential of ML-driven predictive healthcare models to revolutionize traditional healthcare systems. The adoption of ML in healthcare lays the foundation for a more efficient, effective, and patient-centered medical ecosystem, supporting the sustainability and adaptability of healthcare systems in the face of expanding patient populations and complex medical needs. This article significantly contributes to the field by providing comprehensive insights into the experimental stages, showcasing the achieved results, and highlighting the key conclusions derived from our study. By addressing the limitations of the previous abstract, we ensure a more informative and substantial overview of our research, offering valuable knowledge for for researchers, practitioners, and decision-makers striving to leverage the power of ML in healthcare innovation.

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Liu, Jinyan, Guanghao Zhang, Jianyong Wang, Hong Zhang, and Ye Han. "Research on Cu-Sn machine learning interatomic potential with active learning strategy." Computational Materials Science 246 (January 2025): 113450. http://dx.doi.org/10.1016/j.commatsci.2024.113450.

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Chen, Samuel Yen-Chi, and Shinjae Yoo. "Federated Quantum Machine Learning." Entropy 23, no. 4 (April 13, 2021): 460. http://dx.doi.org/10.3390/e23040460.

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Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

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Mukilan, K., K. Thaiyalnayaki, Yagya Dutta Dwivedi, J. Samson Isaac, Amarjeet Poonia, Arvind Sharma, Essam A. Al-Ammar, Saikh Mohammad Wabaidur, B. B. Subramanian, and Adane Kassa. "Prediction of Rooftop Photovoltaic Solar Potential Using Machine Learning." International Journal of Photoenergy 2022 (May 25, 2022): 1–8. http://dx.doi.org/10.1155/2022/1541938.

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Solar energy forecasting accuracy is essential for increasing the quantity of renewable energy that can be integrated into the existing electrical grid control systems. The availability of data at unprecedented levels of granularity allows for the development of data-driven algorithms to improve the estimation of solar energy generation and production. In this paper, we develop a prediction of solar potential across large photovoltaic panels from the roof tops using a machine learning method. The Restricted Boltzmann Machine (RBM) is the machine learning method used in the study to predict or forecast the solar potential in rooftops. The machine learning model is supplied with training dataset to get trained with the dataset for conversion into the model and then tested with the test dataset for validating the model. The results of simulation are conducted on R-package over various libraries to predict the rooftop solar potential. The results of simulation shows that the proposed method achieves higher rate of prediction accuracy than the other methods. The results of the simulation show that the proposed method achieves a higher rate of prediction accuracy of 99% than the other methods.

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Vaganov, A. V., V. F. Zaikov, O. S. Krotova, A. I. Musokhranov, Z. V. Pokalyakin, and L. A. Khvorova. "Modeling a Potential Plant Habitat Using Machine Learning Methods." Izvestiya of Altai State University, no. 4(126) (September 9, 2022): 85–92. http://dx.doi.org/10.14258/izvasu(2022)4-13.

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The article is devoted to modeling the potential distribution area of the species Pulsatilla turczaninovii Kryl. et Serg. Plant ecological niche modeling is the process of building models using modern computer algorithms and bioclimatic data to predict the distribution range of plant species. The result of the simulation is a model that can be used to map the area of growth or residence of species, predict the range or analyze the impact of the environment on species. Data are required on both the presence of species and their absence in a particular territory to build effective models for predicting ecological niches of plants. View absence points (or background points) are not registered in databases, but can be generated using different approaches. This article describes the implementation of three approaches to selecting pseudo-absence points of species in an operationally divided territory and presents the result of modeling the potential distribution area of the species Pulsatilla turczaninovii Kryl. et Serg. using the random forest algorithm — the most popular way to build ensembles of decision trees. The software implementation of the model is carried out in the high-level Python programming language.

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Zennaro, Federica, Elisa Furlan, Christian Simeoni, Silvia Torresan, Sinem Aslan, Andrea Critto, and Antonio Marcomini. "Exploring machine learning potential for climate change risk assessment." Earth-Science Reviews 220 (September 2021): 103752. http://dx.doi.org/10.1016/j.earscirev.2021.103752.

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Cesarini, Luigi, Rui Figueiredo, Beatrice Monteleone, and Mario L. V. Martina. "The potential of machine learning for weather index insurance." Natural Hazards and Earth System Sciences 21, no. 8 (August 11, 2021): 2379–405. http://dx.doi.org/10.5194/nhess-21-2379-2021.

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Abstract. Weather index insurance is an innovative tool in risk transfer for disasters induced by natural hazards. This paper proposes a methodology that uses machine learning algorithms for the identification of extreme flood and drought events aimed at reducing the basis risk connected to this kind of insurance mechanism. The model types selected for this study were the neural network and the support vector machine, vastly adopted for classification problems, which were built exploring thousands of possible configurations based on the combination of different model parameters. The models were developed and tested in the Dominican Republic context, based on data from multiple sources covering a time period between 2000 and 2019. Using rainfall and soil moisture data, the machine learning algorithms provided a strong improvement when compared to logistic regression models, used as a baseline for both hazards. Furthermore, increasing the amount of information provided during the training of the models proved to be beneficial to the performances, increasing their classification accuracy and confirming the ability of these algorithms to exploit big data and their potential for application within index insurance products.

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Erharter, Georg H., Jonas Weil, Franz Tschuchnigg, and Thomas Marcher. "Potential applications of machine learning for BIM in tunnelling." Geomechanics and Tunnelling 15, no. 2 (April 2022): 216–21. http://dx.doi.org/10.1002/geot.202100076.

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Ivanciuc, Ovidiu. "Weka Machine Learning for Predicting the Phospholipidosis Inducing Potential." Current Topics in Medicinal Chemistry 8, no. 18 (December 1, 2008): 1691–709. http://dx.doi.org/10.2174/156802608786786589.

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Rowe, Patrick, Volker L. Deringer, Piero Gasparotto, Gábor Csányi, and Angelos Michaelides. "An accurate and transferable machine learning potential for carbon." Journal of Chemical Physics 153, no. 3 (July 21, 2020): 034702. http://dx.doi.org/10.1063/5.0005084.

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Reich, Yoram, and Steven J. Fenves. "The potential of machine learning techniques for expert systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 3, no. 3 (August 1989): 175–93. http://dx.doi.org/10.1017/s0890060400001219.

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Expert systems employing current methodologies suffer from two major problems: they are brittle and their development is time-consuming and tedious.Learning, the key to intelligent human behavior and expertise, has the potential of alleviating these difficulties. The paper reviews a number of machine learning techniques and provides a framework for their classification. The description of each technique is followed by an example taken from the domain of structural design. The applicability of machine learning techniques to expert systems is discussed, including some prototype applications and their shortcomings. Three promising research directions are outlined as a partial solution for the shortcomings.

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