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Journal articles on the topic 'Advance RISC machine'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Huang, Rui. "A Man-machine Interaction System Based on the Advanced RISC Machines." Journal of Applied Sciences 13, no. 12 (2013): 2246–51. http://dx.doi.org/10.3923/jas.2013.2246.2251.

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2

Prakash Manjappasetty Masagali, Bhanu. "Machine Learning Algorithms for Advanced Risk Stratification and Personalized Intervention Planning in Long-Term Care: A Focus on Gradient Boosting Machine (GBM) Algorithm." International Journal of Science and Research (IJSR) 14, no. 1 (2025): 219–23. https://doi.org/10.21275/sr25103124013.

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3

Vidhya, S., S. Dharani, and Kumar S. Ajith. "Surveillance Robot Capturing Intruder Using PIR Sensor." Journal of Remote Sensing GIS & Technology 5, no. 3 (2019): 36–40. https://doi.org/10.5281/zenodo.3576712.

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This paper focuses on a surveillance mission performed by an autonomous mobile robot in an environment. In this mission, by using flexibility and monitoring function, the Robert is expected to detect as many intruders as possible. However, the robot does not know where and how many environmental intruders are present without the data, estimating an intrusion pattern and detecting unknown intruders is impossible for the robot. By proposing a novel surveillance method this challenges in order to estimate intrusion trend for the robot. For this purpose, Bayes’ rule is basically used. This p

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Odette Boussi, Grace, Himanshu Gupta, and Syed Akhter Hossain. "Enhancing financial cybersecurity via advanced machine learning: analysis, comparison." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1281. https://doi.org/10.11591/ijai.v14.i2.pp1281-1289.

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The financial sector is a prime target for cyber-attacks due to the sensitive nature of the data it handles. As the frequency and sophistication of cyber threats continue to rise, implementing effective security measures becomes paramount. In this paper we provide a comprehensive comparison of six prominent machine learning techniques utilized in the financial industry for cyber-attack prevention. The study aims to identify the best-performing model and subsequently compares its performance with a proposed model tailored to the specific challenges faced by financial institutions. This paper lo

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Grace, Odette Boussi, Gupta Himanshu, and Akhter Hossain Syed. "Enhancing financial cybersecurity via advanced machine learning: analysis, comparison." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1281–89. https://doi.org/10.11591/ijai.v14.i2.pp1281-1289.

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The financial sector is a prime target for cyber-attacks due to the sensitive nature of the data it handles. As the frequency and sophistication of cyber threats continue to rise, implementing effective security measures becomes paramount. In this paper we provide a comprehensive comparison of six prominent machine learning techniques utilized in the financial industry for cyber-attack prevention. The study aims to identify the best-performing model and subsequently compares its performance with a proposed model tailored to the specific challenges faced by financial institutions. This paper lo

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Haw, Su-Cheng. "Editorial: Perspectives on Machine Learning." Journal of Telecommunications and the Digital Economy 12, no. 3 (2024): 1–6. http://dx.doi.org/10.18080/jtde.v12n3.1042.

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Progress in machine learning technology has truly impacted our lives by tailoring many of our daily experiences to be seamless and intuitive. This innovation has brought about changes in day-to-day routines; from suggesting music based on our emotions to offering recommendations for places to visit or meals to try out. This special issue explores various Machine Learning technologies. Among some are Machine Learning advances that improve human interaction, predict user behaviours, analyse user reviews, and optimize high-risk investments like Bitcoin trading. These technologies enhance user exp

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Onyshchenko, Borys, Volodymyr Onyshchenko, Volodymyr Nazarenko, and Vasyl Achkevych. "Experimental study of the time of pressure rise and fall in the sprayer pipe." Naukovij žurnal «Tehnìka ta energetika» 15, no. 1 (2024): 95–103. http://dx.doi.org/10.31548/machinery/1.2024.95.

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A wide variety of meteorological factors, soil and climatic conditions, the saturation of fields with many types of weeds, a significant set of cultivated plants and many other factors necessitate the implementation of innovative technological schemes for the use of pesticides, which will reduce the pesticide load as much as possible and determine the safe environmental effect of preparations. Experimental studies were carried out to determine the time of pressure rise and fall in the sprayer pipe and to establish the corresponding functional dependencies. The automatic adjustment system of th

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Bindu Sree. "A Comprehensive Machine Learning Approach for Advanced Vehicle Detection and Counting." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 4 (2024): 219–24. http://dx.doi.org/10.32628/cseit2410415.

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The exponential rise of urban areas and the associated surge in transportation congestion. Consequently, this study offers a thorough method for vehicle recognition and counting via the use of machine learning, as well as an effective system for real-time traffic monitoring, with the aim of reducing traffic. The first step is to develop a model that can identify and follow moving cars in still photos or video. This research delves into the topic of teaching a computer to count automobiles using machine learning, a kind of artificial intelligence. The purpose of this study is to provide a compu

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Prof. M. S. Patil, Benkar Anuradha, Gaikwad Madhuri, and Sawant Supriya. "CARDIO PREDICT: HARNESSING MACHINE LEARNING FOR ADVANCED HEART DISEASE RISK ASSESSMENT." International Journal of Innovations in Engineering Research and Technology 11, no. 4 (2024): 28–32. http://dx.doi.org/10.26662/ijiert.v11i4.pp28-32.

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Heart disease prediction using machine learning algorithms has gained significant attention due to its potential to improve diagnosis and treatment. This study explores various machine learning techniques and an algorithm applied to heart disease prediction. We analyze the performance of popular algorithms such as logistic regression, decision trees, random forests, support vector machines, and artificial neural networks on heart disease datasets. Additionally, we investigate the impact of feature selection, data preprocessing techniques, and model evaluation metrics on the predictive performa

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AFANASIEVA, Maryna. "RISK ANALYSIS OF INEFFICIENCY AT UKRAINE’S MACHINE BUILDING ENTERPRISES." Economy of Ukraine 2019, no. 3 (2019): 22–34. http://dx.doi.org/10.15407/economyukr.2019.03.022.

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The paper considers the risk identification of inefficiency concerning 51 Ukrainian joint-stock companies of machine building in 2012–2017. The value added at factor cost (VA) is determined as the resulting indicator of production efficiency, which is a source of income of various social groups, so it contributes to combined efforts. To support advanced production and management technologies, rather than an extensive market capture, the multiplicative model of VA has been suggested with the VA share in output to assess the quality processes within the enterprise. Economic analysis of the annua

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G, Ramani, and Menakambal S. "Advanced RISC Machine Based Data Acquisition Development and Control." International Journal of Electrical and Electronics Engineering 1, no. 8 (2021): 1–4. http://dx.doi.org/10.14445/23488379/ijeee-v1i8p101.

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Alqaraleh, Muhyeeddin, Mowafaq Salem Alzboon, and Mohammad Subhi Al-Batah. "Real-Time UAV Recognition Through Advanced Machine Learning for Enhanced Military Surveillance." Gamification and Augmented Reality 3 (January 1, 2025): 63. https://doi.org/10.56294/gr202563.

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In an era where the military utilization of Unmanned Aerial Vehicles (UAVs) has become essential for surveillance and operational operations, our study tackles the growing demand for real-time, accurate UAV recognition. The rise of UAVs presents numerous safety hazards, requiring systems that distinguish UAVs from non-threatening phenomena, such as birds. This research study conducts a comparative examination of advanced machine learning models, aiming to address the challenge of real-time aerial classification in diverse environmental conditions without model retraining. This research employs

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Md Al Amin, Irin Akter Liza, Shah Foysal Hossain, Ekramul Hasan, Md Musa Haque, and Joy Chakra Bortty. "Predicting and Monitoring Anxiety and Depression: Advanced Machine Learning Techniques for Mental Health Analysis." British Journal of Nursing Studies 4, no. 2 (2024): 66–75. http://dx.doi.org/10.32996/bjns.2024.4.2.8.

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Anxiety and depression are considered among the most prevailing mental illnesses; they affect millions in the USA and worldwide. Besides being highly prevalent, these conditions have major implications for individuals and American society as a whole. The prime objective of this research project was to design and evaluate advanced machine learning methodologies for the monitoring and prediction of anxiety and depression. The rise in recent advances in Machine Learning and AI technologies has unleashed tremendous potential in the diagnosis and monitoring of mental health conditions such as anxie

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Sharma, Harshita, and K. L. Bansal. "Machine Learning in Healthcare: A Comparative Review of Techniques and Applications." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1490–97. https://doi.org/10.22214/ijraset.2025.68587.

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The rise of machine learning has profoundly impacted healthcare, enhancing the interpretation and utilization of medical data. It emphasizes how machine learning may improve diagnosis accuracy, maximize treatment choices, and advance precision medicine. According to previous research, machine learning algorithms are highly accurate in disease diagnosis. But comprehensive information on algorithms accuracy is rarely available in a single study, making access time-consuming. So, the objective of this work is to provide necessary information about these algorithms used in healthcare and to review

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Rakibul Hasan Chowdhury, Abdullah Al Masum, Md Zahidur Rahman Farazi, and Israt Jahan. "The impact of predictive analytics on financial risk management in businesses." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 1378–86. http://dx.doi.org/10.30574/wjarr.2024.23.3.2807.

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This paper examines the impact of predictive analytics on financial risk management in businesses. Predictive analytics involves the use of statistical algorithms, machine learning, and data mining techniques to analyze historical data and predict future outcomes. In the context of financial risk management, predictive analytics plays a critical role in identifying, assessing, and mitigating potential financial risks. This paper explores various machine learning algorithms, including neural networks, decision trees, and support vector machines, and their applications in risk management. It als

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Rakibul, Hasan Chowdhury, Al Masum Abdullah, Zahidur Rahman Farazi Md, and Jahan Israt. "The impact of predictive analytics on financial risk management in businesses." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 1378–86. https://doi.org/10.5281/zenodo.14945320.

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This paper examines the impact of predictive analytics on financial risk management in businesses. Predictive analytics involves the use of statistical algorithms, machine learning, and data mining techniques to analyze historical data and predict future outcomes. In the context of financial risk management, predictive analytics plays a critical role in identifying, assessing, and mitigating potential financial risks. This paper explores various machine learning algorithms, including neural networks, decision trees, and support vector machines, and their applications in risk management. It als

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Ling Jiang and Yiting Zhang. "Ethical Risk and Pathway of AIGC Cross-Modal Content Generation Technology." International Journal of Social Sciences & Humanities (IJSSH) 9, no. 1 (2024): 85–99. http://dx.doi.org/10.58885/ijllis.v9i1.85.lj.

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This study analyses the core technologies underlying AI-generated cross-modal content (AIGC), identifying data, algorithms, and computing power as the fundamental pillars supporting AIGC operation. And data are recognized as the underlying logic driving AI's continuous development and the source of ethical issues within AIGC. By integrating Gilbert Hottois' concept of technological accompaniment, this research incorporates multiple stakeholders to dissolve the binary opposition between humans and machines. This study explores pathways to scientifically and positively advance AIGC technologies

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GC, Chetan, Anuj M R, Anusha GN, M. Venkat Anil Kumar, and Asha K. "Applications of Advance Risk Machine Microcontroller in Embedded Systems Design." Journal of Information Technology and Sciences 7, no. 1 (2021): 30–35. http://dx.doi.org/10.46610/joits.2021.v07i01.006.

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19

Amadasun, Osamuyimen Odion, Bukola Onasoga, Ahmed Aliyu, Kingsley Eghonghon Ukhurebor, Adeyinka Oluwabusayo Abiodun, and Moses Ashawa. "A virtual machine-based e-malpractice mitigation strategy in e-assessment and e-learning using system resources and machine learning techniques." Journal of Autonomous Intelligence 7, no. 5 (2024): 1484. http://dx.doi.org/10.32629/jai.v7i5.1484.

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<p>Since the introduction of online learning and the widespread use of AI-proctored examination systems, protecting the integrity of assessments has faced new difficulties. The development of reliable methods for detecting electronic cheating, notably the use of virtual machines (VMs) during examinations, has become essential with the rise of advanced cheating methods. Hence, in this research, a thorough methodology for identifying virtual machine usage in an AI-proctored test system is presented. In order to uncover suspicious activities connected with the use of VMs, the study offers a

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20

Shahin Mammadov, Ramil, Sadiq Faig Isgandarli, Nijat Arif Malikov, Kanan Meheddin Mustafazade, and Sabina Natig Samed-zade. "ARTIFICIAL INTELLIGENCE MODELS USED IN CREDIT RISK PREDICTION." Elmi Əsərlər 34, no. 1 (2025): 28–32. https://doi.org/10.61413/fqgv3089.

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Artificial intelligence (AI) models are making a significant contribution to improving the accuracy, efficiency, and decision-making of credit risk prediction in modern financial institutions. Traditional credit risk assessment is largely based on statistical methods such as logistic regression, which, while effective in certain cases, have limitations in identifying complex and nonlinear relationships between financial data. Artificial intelligence methods such as machine learning (ML) and deep learning (DL) provide more advanced forecasting capabilities due to their ability to process large

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21

Tandon, Kartik, and Dr Priya Menon. "LEVERAGING MACHINE LEARNING TO IDENTIFY MATERNAL RISK FACTORS FOR CONGENITAL HEART DISEASE IN OFFSPRING." International Journal of Intelligent Data and Machine Learning 2, no. 05 (2025): 1–7. https://doi.org/10.55640/ijidml-v02i05-01.

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Congenital Heart Defects (CHDs) represent a significant global health challenge, being the most common birth anomalies. Early identification of mothers at risk of having a child with a CHD is crucial for timely intervention, improved prenatal counseling, and better neonatal outcomes. This article explores the application of machine learning (ML) methodologies to predict the risk of CHDs in offspring based on maternal characteristics and health data. We review various ML algorithms, including traditional classifiers and advanced neural networks, that have been or could be employed for this pred

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Ahn, Seungil, Jinsub Won, Jangchoon Lee, and Changhyun Choi. "Comprehensive Building Fire Risk Prediction Using Machine Learning and Stacking Ensemble Methods." Fire 7, no. 10 (2024): 336. http://dx.doi.org/10.3390/fire7100336.

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Building fires pose a critical threat to life and property. Therefore, accurate fire risk prediction is essential for effective building fire prevention and mitigation strategies. This study presents a novel approach to predicting fire risk in buildings by leveraging advanced machine learning techniques and integrating diverse datasets. Our proposed model incorporates a comprehensive range of 34 variables, including building attributes, land characteristics, and demographic information, to construct a robust risk assessment framework. We applied 16 distinct machine learning algorithms, integra

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Valencia-Arias, Alejandro, Jesus Alberto Jimenez Garcia, Erica Agudelo-Ceballos, et al. "Machine learning applications in risk management: Trends and research agenda." F1000Research 14 (April 7, 2025): 233. https://doi.org/10.12688/f1000research.161993.2.

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Abstract Risk management has become a foundational aspect in numerous industries, propelling the implementation of machine learning technologies for impact assessment, prevention, and decision-making processes. Nevertheless, lacunae in the extant literature persist, particularly with regard to the identification of emergent trends and transversal applications. This study addresses this limitation through a bibliometric analysis of scientific production in Scopus and Web of Science, adhering to the PRISMA-2020 declaration. The findings reveal a substantial growth in publications on machine lear

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Lakhani, Alisha, Abhishek Chaudhary, Aarti Khatri, et al. "Different machine learning language models for cardiovascular disease risk prediction: a systematic review." International Journal of Research in Medical Sciences 13, no. 1 (2024): 331–39. https://doi.org/10.18203/2320-6012.ijrms20244132.

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Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, prompting the urgent need for accurate and efficient predictive tools. This systematic review evaluates the efficacy of various machine learning algorithms in predicting cardiovascular disease risk by analyzing multiple studies that employed diverse techniques, including support vector machines, decision trees, and neural networks. The results consistently demonstrate that machine learning algorithms outperform traditional risk assessment models in predicting critical outcomes such as myocardial infarction, heart f

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Crossley, Scott A. "Technological disruption in foreign language teaching: The rise of simultaneous machine translation." Language Teaching 51, no. 4 (2018): 541–52. http://dx.doi.org/10.1017/s0261444818000253.

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The fear of technology replacing jobs can be traced back to Aristotle, who, before great technological advances existed, ventured that machines may one day end the need for human labor (Campa 2014). In the current era, there is overwhelming evidence of technological unemployment. This evidence comes in the form of jobs that were once common, but have largely been replaced by technology such as switchboard operators, travel agents, booth cashiers, bank tellers, and typists. These jobs still exist, but their numbers have declined sharply because they were easily replaced by technology. Statistic

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Pearson, Katelin D., Gil Nelson, Myla F. J. Aronson, et al. "Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research." BioScience 70, no. 7 (2020): 610–20. http://dx.doi.org/10.1093/biosci/biaa044.

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Abstract Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and societ

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Sundar, S. Shyam. "Rise of Machine Agency: A Framework for Studying the Psychology of Human–AI Interaction (HAII)." Journal of Computer-Mediated Communication 25, no. 1 (2020): 74–88. http://dx.doi.org/10.1093/jcmc/zmz026.

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Abstract Advances in personalization algorithms and other applications of machine learning have vastly enhanced the ease and convenience of our media and communication experiences, but they have also raised significant concerns about privacy, transparency of technologies and human control over their operations. Going forth, reconciling such tensions between machine agency and human agency will be important in the era of artificial intelligence (AI), as machines get more agentic and media experiences become increasingly determined by algorithms. Theory and research should be geared toward a dee

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Raviraja, K. N., R. Pavan, P. Sanjay, D. Srikanth, and Kumar T. Kiran. "Anytime Medicine Vending Machine." Journal of Communication Engineering and Its Innovations 5, no. 2 (2019): 1–4. https://doi.org/10.5281/zenodo.2667053.

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<em>Medicines were essential part in looking after wellbeing, averting ailment, overseeing, interminable conditions and curing sickness. Unsurpassed Medicine (ATM) is a machine which conveys the medication in crisis cases and guarantee accessibility of medications 24x7 and thus the name "Record breaking Medicine". ATM will be extremely valuable in sparing life if there should arise an occurrence of a mischance on parkways, remote ranges, provincial territories and spots where therapeutic stores are not inside the compass in event of crisis. In any event, first help can be made effect

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Kong, Xian Jun, Hong Zhi Zhang, Xue Feng Wu, and Yang Wang. "Laser-Assisted Machining of Advanced Materials." Materials Science Forum 800-801 (July 2014): 825–31. http://dx.doi.org/10.4028/www.scientific.net/msf.800-801.825.

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Laser-assisted machining (LAM) is a hybrid cutting process in which a laser beam is used to heat and soften the workpiece locally in front of the cutting tool edge. The rapid temperature rise at the shear zone reduces the yield strength and work hardening of the workpiece, which makes the plastic deformation of difficult-to-machine materials easier during machining. LAM provides a reduction in the cutting forces/specific cutting energy, longer tool life, better surface integrity, and high productivity over traditional cutting. This paper presents the technical characteristics, material removal

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Nan, Tianyu. "The advantage of artificial intelligence application in financial risk assessment and management." Applied and Computational Engineering 48, no. 1 (2024): 148–53. http://dx.doi.org/10.54254/2755-2721/48/20241289.

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The increasing perfection of artificial intelligence technology has brought subversive changes to the field of financial risk management. The application of artificial models such as neural networks, support vector machines, and mixed intelligence in financial risk management can improve the speed of data processing, provide deep insight into data analysis, reduce human labour costs, and hence improve the efficiency of financial risk control. Meanwhile, the increasing amount of data and the application of AI also bring new challenges to financial risk management, such as the risk of program er

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Jennifer Muhindo, Kevin Mukasa, Doreen Kitakufe, and Jimmy Kato. "Advancing credit risk assessment and financial decision-making: Integrating modern techniques and insights." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 2019–27. http://dx.doi.org/10.30574/wjarr.2024.23.2.2565.

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Credit risk assessment and fraud detection are critical functions in the financial industry, necessary for ensuring the stability and integrity of financial institutions. Traditional approaches often struggle to accurately assess risk and detect fraudulent activities in a timely manner. However, the rise of machine learning has introduced powerful tools that leverage large datasets and advanced algorithms to improve these processes. This research paper investigates the application of machine learning techniques in credit risk assessment and fraud detection within financial transactions. The pa

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Jennifer, Muhindo, Mukasa Kevin, Kitakufe Doreen, and Kato Jimmy. "Advancing credit risk assessment and financial decision-making: Integrating modern techniques and insights." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 2019–27. https://doi.org/10.5281/zenodo.14869154.

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Credit risk assessment and fraud detection are critical functions in the financial industry, necessary for ensuring the stability and integrity of financial institutions. Traditional approaches often struggle to accurately assess risk and detect fraudulent activities in a timely manner. However, the rise of machine learning has introduced powerful tools that leverage large datasets and advanced algorithms to improve these processes. This research paper investigates the application of machine learning techniques in credit risk assessment and fraud detection within financial transactions. The pa

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Deepa Shukla. "A Survey of Machine Learning Algorithms in Credit Risk Assessment." Journal of Electrical Systems 20, no. 3 (2024): 6290–97. https://doi.org/10.52783/jes.6788.

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Credit risk assessment is a critical process for financial institutions, designed to predict the likelihood of borrower default and reduce potential financial losses. Traditionally, credit scoring relied on statistical models; however, the advent of machine learning (ML) has significantly transformed these methods. Machine learning provides more accurate, scalable, and flexible solutions for analyzing vast amounts of financial data. This survey examines key ML algorithms—including decision trees, random forests, support vector machines, and deep neural networks—that are used in credit risk ass

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Mubarakova,, S. R., S. T. Amanzholova,, and R. K. Uskenbayeva,. "USING MACHINE LEARNING METHODS IN CYBERSECURITY." Eurasian Journal of Mathematical and Computer Applications 10, no. 1 (2022): 69–78. http://dx.doi.org/10.32523/2306-6172-2022-10-1-69-78.

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Abstract Cybersecurity is an ever-changing field, with advances in technology that open up new opportunities for cyberattacks. In addition, even though serious secu- rity breaches are often reported, small organizations still have to worry about security breaches as they can often be the target of viruses and phishing. This is why it is so important to ensure the privacy of your user profile in cyberspace. The past few years have seen a rise in machine learning algorithms that address major cybersecu- rity issues such as intrusion detection systems (IDS), detection of new modifications of know

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Song, Zheng, and Shu Luo. "Application of machine learning and data mining in manufacturing industry." Frontiers in Computing and Intelligent Systems 2, no. 1 (2022): 47–53. http://dx.doi.org/10.54097/fcis.v2i1.2966.

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With the rise of machine learning in various industries, the traditional manufacturing industry is facing a new disruption, which requires the use of different technologies and tools to achieve its production targets; In this regard, machine learning (ML) and data mining (DM) play a key role. This paper provides a statistical understanding of the main methods and algorithms used to improve manufacturing processes over the past 20 years by dividing them into four main themes: Scheduling, Monitoring, Quality and Failure, presents previous ML research and the latest advances in manufacturing, fol

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Song, Zhen, and Shu Luo. "Application of Machine Learning and Data Mining in Manufacturing Industry." International Journal of Computer Science and Information Technology 2, no. 1 (2024): 425–36. http://dx.doi.org/10.62051/ijcsit.v2n1.45.

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With the rise of machine learning in various industries, the traditional manufacturing industry is facing a new disruption, which requires the use of different technologies and tools to achieve its production targets; In this regard, machine learning (ML) and data mining (DM) play a key role. This paper provides a statistical understanding of the main methods and algorithms used to improve manufacturing processes over the past 20 years by dividing them into four main themes: Scheduling, Monitoring, Quality and Failure, presents previous ML research and the latest advances in manufacturing, fol

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Shakya, Sanjeev, Attaphongse Taparugssanagorn, and Chaklam Silpasuwanchai. "Convolutional Neural Network-Based Low-Powered Wearable Smart Device for Gait Abnormality Detection." IoT 4, no. 2 (2023): 57–77. http://dx.doi.org/10.3390/iot4020004.

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Gait analysis is a powerful technique that detects and identifies foot disorders and walking irregularities, including pronation, supination, and unstable foot movements. Early detection can help prevent injuries, correct walking posture, and avoid the need for surgery or cortisone injections. Traditional gait analysis methods are expensive and only available in laboratory settings, but new wearable technologies such as AI and IoT-based devices, smart shoes, and insoles have the potential to make gait analysis more accessible, especially for people who cannot easily access specialized faciliti

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Akan, Oguzhan, Abhishek Verma, and Sonika Sharma. "Prediction of customer churn risk with advanced machine learning methods." International Journal of Data Science 10, no. 1 (2025): 70–95. https://doi.org/10.1504/ijds.2025.144832.

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Shaik Nazrin Tarannum, Chaitanya Udatha, and Sushmitha Polishetty. "A Comprehensive Risk Assessment of Genetic Disorders in Children." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 04 (2025): 1293–97. https://doi.org/10.47392/irjaeh.2025.0183.

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Genetic disorders pose major challenges to paediatric care, impacting the health and development of children. Detection at an early stage and proper risk assessment are critical to minimize adverse effects and enhance outcomes. Advances in genetic testing and risk assessment technologies in recent times have enhanced our capability to detect genetic predispositions, leading to mobile and web-based applications that provide individualized insights for parents and clinicians. The platforms integrate genetic information, family history, and lifestyle information to deliver comprehensive risk asse

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Choudhary, Rahul, Rajkumar Choudhary, and Karuna Soni. "MACHINE LEARNING ALGORITHMS FOR CYBER ATTACKS AND FRAUD DETECTION." International Journal of Technical Research & Science 9, no. 05 (2024): 17–20. http://dx.doi.org/10.30780/ijtrs.v09.i05.004.

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Cyber-attacks and fraud pose significant risks to individuals, organizations, and nations. The consequences of such malicious activities range from financial losses and reputational damage to national security threats. As cyber attackers continuously evolve their techniques, traditional defense mechanisms often fall short in providing adequate protection. Consequently, there is a pressing need for advanced, adaptive, and efficient solutions to detect and mitigate these threats. The rise of cyber-attacks and fraud has become a significant concern for both individuals and organizations. As a res

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Han, Bowen. "Evaluating Machine Learning Techniques for Credit Risk Management: An Algorithmic Comparison." Applied and Computational Engineering 112, no. 1 (2024): 29–34. http://dx.doi.org/10.54254/2755-2721/112/20251785.

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The evaluation of credit risk has become an indispensable element within the financial sector. This research aims to conduct a comparative examination of several machine learning model's performance in predicting credit risk. This research uses comprehensive metrics to give a comparative examination of six machine learning models, including Random Forests (RF) and Support Vector Machines (SVM). The features used in the training of these models were screened by a combination of Random Forest feature importance and Recursive Feature Elimination (RFE) to ensure model accuracy. After comparing the

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Lin, Hanhui, Ken Cai, Huazhou Chen, and ZhaoFeng Zeng. "The Construction of a Precise Agricultural Information System Based on Internet of Things." International Journal of Online Engineering (iJOE) 11, no. 6 (2015): 10. http://dx.doi.org/10.3991/ijoe.v11i6.4847.

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The promotion of the Internet of Things (IOT) in agriculture is an important symbol in the modern agricultural industry. It can efficiently lower the labor consumption and exert a positive impact on farmlands through wireless sensor networks. It can precisely acquire data on crops and the environment to achieve the scientific cultivation and management of the production equipment by means of automation, intelligence, and remote control and to advance the transformation of agricultural development in modern times. An intelligent system of high precision, which is based on the IOT, is formulated

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Zafar, Aziz, Ziad Attia, Mehret Tesfaye, et al. "Machine learning-based risk factor analysis and prevalence prediction of intestinal parasitic infections using epidemiological survey data." PLOS Neglected Tropical Diseases 16, no. 6 (2022): e0010517. http://dx.doi.org/10.1371/journal.pntd.0010517.

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Background Previous epidemiological studies have examined the prevalence and risk factors for a variety of parasitic illnesses, including protozoan and soil-transmitted helminth (STH, e.g., hookworms and roundworms) infections. Despite advancements in machine learning for data analysis, the majority of these studies use traditional logistic regression to identify significant risk factors. Methods In this study, we used data from a survey of 54 risk factors for intestinal parasitosis in 954 Ethiopian school children. We investigated whether machine learning approaches can supplement traditional

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Dr., Amit Sharma. "Advanced Cyber Defense: Machine Learning Techniques with TensorFlow." Career Point International Journal of Research (CPIJR) 1, no. 3 (2024): 123–30. https://doi.org/10.5281/zenodo.13821739.

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The rise of cyber threats has necessitated the development of advanced defense mechanisms to protect digital infrastructures. Traditional cybersecurity techniques, while effective in many scenarios, are often inadequate to address the growing complexity of modern attacks. Machine learning (ML) has emerged as a promising solution for enhancing cybersecurity, allowing for more accurate threat detection, prediction, and response. TensorFlow, a widely-used open-source ML framework, offers a robust platform for deploying sophisticated ML models aimed at mitigating cyber threats. This paper investig

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Misemer, Sarah M. "What a Bawd from the Renaissance Can Teach Us about AI: Celestina, Robots, and Free Will." South Central Review 42, no. 1-2 (2025): 140–43. https://doi.org/10.1353/scr.2025.a961463.

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Abstract: Fernando de Rojas’ masterwork La Celestina (published under various titles between 1499 and 1518) in early Renaissance Spain, appears at a transitional moment that pits the rise of humanist inquiry and experimentation against a new monarchy’s fierce control of its expanding territory through Catholicism. Rojas uses the recognizable structure of courtly love in La Celestina but overturns it so the tale can be read either as a warning or celebration of an individual’s ability to think, choose, and rebel against systems of control. Our current transition from analog to virtual and augme

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Zong, Yue. "House Prices Prediction Advanced Regression Techniques." Advances in Economics, Management and Political Sciences 50, no. 1 (2023): 181–89. http://dx.doi.org/10.54254/2754-1169/50/20230580.

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In recent years, with the development of the real estate industry, housing prices have continued to rise. The nation, society, and individuals are all concerned about these prices. For commodity housing prices, there are many factors that influence the housing prices. Apart from national regulations, factors such as lighting, layout, and environment of the houses themselves also have a certain impact on the prices, leading to significant fluctuations in the real estate market. Therefore, researching an accurate model for predicting housing prices has practical significance. It can guide reside

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Anwulika Ogechukwu Scott, Prisca Amajuoyi, and Kudirat Bukola Adeusi. "Advanced risk management solutions for mitigating credit risk in financial operations." Magna Scientia Advanced Research and Reviews 12, no. 1 (2024): 212–23. http://dx.doi.org/10.30574/msarr.2024.11.1.0085.

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Advanced risk management solutions are essential for mitigating credit risk in financial operations, particularly in today's volatile economic environment. This Review explores the innovative approaches and technologies being utilized to enhance credit risk management and safeguard financial institutions against potential losses. Credit risk, the possibility that a borrower will default on their obligations, poses a significant threat to financial stability. Traditional methods of assessing and managing credit risk, such as credit scoring and historical data analysis, are no longer sufficient

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Angelopoulos, John, and Dimitris Mourtzis. "An Intelligent Product Service System for Adaptive Maintenance of Engineered-to-Order Manufacturing Equipment Assisted by Augmented Reality." Applied Sciences 12, no. 11 (2022): 5349. http://dx.doi.org/10.3390/app12115349.

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Under the framework of Industry 4.0, machines and machine tools have evolved to smart and connected things, comprising the Internet of Things (IoT) and leading to the Mass Personalization (MP) paradigm, which enables the production of uniquely made products at scale. MP, fueled by individualization trends and enabled by increasing digitalization, has the potential to go beyond current mass customization. Although this evolution has enabled engineers to gain useful insight for the production, the machine status, the quality of products, etc., machines have become more complex. Thus, Maintenance

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Mohammad, Fokhrul Islam Buian, Anan Arde Ramisha, Masum Billah Md, Debnath Amit, and Md Siddique Iqtiar. "Advanced analytics for predicting traffic collision severity assessment." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 2007–18. https://doi.org/10.5281/zenodo.14043830.

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Accurate prediction of accident risks plays a crucial role in proactively implementing safety measures and allocating resources effectively. This paper introduces an innovative approach aimed at improving accident risk prediction by harnessing unique data sources and extracting insights from diverse yet sparse datasets. Traditional models often face limitations due to a lack of diversity and scope in the available data, which hinders their predictive capabilities. In response to this challenge, our study integrates a broad spectrum of heterogeneous data, encompassing traffic flow, weather cond

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Stanley Chidozie Umeorah, Adesola Oluwatosin Adelaja, Bibitayo Ebunlomo Abikoye, Oluwatoyin Funmilayo Ayodele, and Yewande Mariam Ogunsuji. "Data-driven credit risk monitoring: Leveraging machine learning in risk management." Finance & Accounting Research Journal 6, no. 8 (2024): 1416–35. http://dx.doi.org/10.51594/farj.v6i8.1399.

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This review explores the evolution of credit risk monitoring, tracing its journey from traditional qualitative assessments to the current integration of machine learning (ML). It highlights how the integration of ML and big data has introduced unprecedented capabilities for analyzing extensive datasets and detecting subtle patterns beyond human capacity. These advanced technologies enable more accurate, efficient and dynamic credit risk predictions through techniques such as random forests, gradient boosting and decision trees. The transformative potential of these methodologies in credit risk

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