Predictive analytics for cardiac arrhythmia using machine intelligence
Myocardial Infarction (MI) is the primary cause of death worldwide. MI occurs when a plaque buildup in the inner surface of the coronary artery suddenly ruptures and prevents the blood flow. A heart attack is medically termed as MI. It is the irreversible damage caused by the prolonged ischemia. Ischemia is nothing but the heart organ doesn’t get enough blood and oxygen which is also termed as coronary artery disease or coronary heart disease. The heart gets damaged if it has not received enough blood or oxygen. In connection to the damage of the heart, arrhythmia would occur. Arrhythmia is the problem based on the heart rhythm or rate of the heartbeat. Tachycardia, when there is a fast beat in the heart. Bradycardia, when the heart beats too slow. The common type of arrhythmia is atrial fibrillation. The great concern is that the patient who has arrhythmia has to be treated immediately. They lose consciousness in a few minutes when the heart is not pumping enough blood mainly to the brain. Death occurs when the patient is not given emergency treatment.
Myocardial Infarction (MI) is the primary cause of death worldwide. MI occurs when a plaque buildup in the inner surface of the coronary artery suddenly ruptures and prevents the blood flow. A heart attack is medically termed as MI. It is the irreversible damage caused by the prolonged ischemia. Ischemia is nothing but the heart organ doesn t get enough blood and oxygen which is also termed as coronary artery disease or coronary heart disease. The heart gets damaged if it has not received enough blood or oxygen. In connection to the damage of the heart, arrhythmia would occur. Arrhythmia is the problem based on the heart rhythm or rate of the heartbeat. Tachycardia, when there is a fast beat in the heart. Bradycardia, when the heart beats too slow. The common type of arrhythmia is atrial fibrillation. The great concern is that the patient who has arrhythmia has to be treated immediately. They lose consciousness in a few minutes when the heart is not pumping enough blood mainly to the brain. Death occurs when the patient is not given emergency treatment. newline Treatment which is included in the emergency is defibrillation and Cardiopulmonary Resuscitation (CPR). CPR is an emergency procedure which is combined with the chest compressions. It is through artificial ventilation which gives manual effort, preserves the brain functions until further treatment for the restoration of spontaneous blood circulation. The common symptoms of sudden cardiac death are chest pain, shortness of breath, severe wheezing, irregular heartbeats, fainting etc. newlineHeart Scar tissue which is not like heart muscle. It doesn t contrast like the normal heart muscle. Heart muscles get damaged for the heart attack patient based on the time of the treatment. The damage of the heart is based on the blockage of the artery. Arrhythmia can be predicted based on the volume of the scar region in the heart. Arrhythmia patients are treated by fixing Implantable Cardioverter Defibrillator (ICD).
Predictive Analytics for Network Traffic Management
LSTM networks; machine learning; network traffic management; predictive analytics; real-time processing
It examines how this can be applied to monitoring network traffic and carrying out predictive analysis to improve the functionality and effectiveness of network management. The study uses historical data of the network traffics and uses machine learning techniques such as the Long Short Term Memory based models and the Ensemble Methods to predict the traffic patterns in the future. It includes data gathering, data pre-processing, feature selection, model choice, model training, model validation, and the architectural setup of the machine learning solution in a real-time stream processing pipeline using Apache Kafka and Apache Flink. It is evident from the results that the proposed models yield a high level of accuracy in terms of prediction and that the Ensemble method alone gives a slightly higher accuracy than LSTM in the specific metrics. Real-time values closely followed actual traffic level, thus allowing real-time adjustments in network usage. In light of this, there is a clear understanding of the significance of having reliable data preprocessing, feature engineering, and model optimization process. The study also notes the need in prediction concerning data quality and scalability issues taking into account that current and future networks are characterized as dynamic and highly complex to offer more effective solutions for intelligent and proactive networking. 2024 IEEE.
Jain N.; Husain S.O.; Goyal S.; Hariharasudhan S.; Victor M.; Manjula
2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/IICCCS61609.2024.10763554 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214438436&doi=10.1109%2fIICCCS61609.2024.10763554&partnerID=40&md5=d44f26ed7daefa23fc7dd5486e317a68
Jain N., Ies College of Technology, Department of Electrical & Electronics Engineering, M.P., Bhopal, India; Husain S.O., The Islamic University, College Of Technical Engineering, Department Of Computers Techniques Engineering, Najaf, Iraq, The Islamic University Of Al Diwaniyah, College Of Technical Engineering, Department Of Computers Techniques Engineering, Al Diwaniyah, Iraq; Goyal S., Chandigarh Group of Colleges, Chandigarh Engineering College, Department of Computer Science Engineering, Punjab, Mohali, 140307, India; Hariharasudhan S., Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Victor M., Christ (Deemed to be University), School of Business and Management, India; Manjula, Saveetha Institute of Medical and Technical Sciences, Department of Mathametics, Tamilnadu, Chennai, India
Predictive Analytics for Stock Market Trends using Machine Learning
Data Engineering; Data Preprocessing; Decision Trees; Deep Learning; Feature Selection; Financial Data; Linear Regression; Machine Learning; Machine Learning Algorithms; Random Forests; Recurrent Neural Networks (RNNs); Stock Market Trends
Navigating the intricacies of stock market trends demands a novel approach capable of deciphering the web of financial data and market sentiment. This research embarks on a transformative journey into the realm of machine learning, where we harness the power of data to forecast stock market trends with increased precision and accuracy. Commencing with an exploration of stock market dynamics and the inherent limitations of traditional forecasting techniques, this paper takes a bold step into the future by embracing the potential of machine learning. The study begins with an in-depth analysis of data preprocessing, unraveling the complexity of feature selection and engineering, setting the stage for a data-driven odyssey. As our exploration progresses, we dive into the deployment of diverse machine learning algorithms, including linear regression, decision trees, random forests, and the formidable deep learning models such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). These algorithms act as our guiding lights, revealing intricate patterns concealed within historical stock price data. Our journey reaches new heights as we recognize the significance of augmenting predictive models with external data sources. Incorporating elements like news sentiment analysis and macroeconomic indicators enriches our understanding of the market landscape, enhancing the predictive capabilities of our models. We also delve into the crucial aspects of model evaluation, guarding against overfitting, and selecting appropriate performance metrics to ensure robust and reliable predictions. The research reaches its zenith with a meticulous analysis of real-world case studies, providing a comparative perspective between machine learning models and traditional forecasting methods. The results underscore the remarkable potential of machine learning in predicting stock market trends more accurately. 2023 IEEE.
Manasa N.; Praveenraj D.W.; Lakshmi S.R.
2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023
https://doi.org/10.1109/ICCAKM58659.2023.10449528 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187556499&doi=10.1109%2fICCAKM58659.2023.10449528&partnerID=40&md5=171d658a35aeb60e8ca851d7aaae7a84
Manasa N., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Praveenraj D.W., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Lakshmi S.R., School of Business and Management, Christ (Deemed to Be University), Bangalore, India
Predictive Analytics for Stock Price Forecasting: Machine Learning Techniques in Financial Markets
financial market volatility; generative adversarial networks; LSTM-GAN hybrid model; stock price forecasting; time-series prediction
Stock market forecasting is significantly challenging because financial data generally exhibits non-linearity, and volatility is highly presented. Traditional methods such as the ARIMA model and NN fail to take a good grasp of intricate and complex temporal patterns in changes related to market trends. By overcoming these limitations, it makes use of LSTM and combines GAN networks. The LSTM exploits the historical stock price data for temporal dependencies, whereas GAN produces realistic synthetic data to augment model training. The Stock Market Dataset was used, and the proposed model was implemented using Python with TensorFlow and PyTorch frameworks. The hybrid LSTM-GAN model resulted in better performance with an RMSE of 0.0125, MAE of 0.0093, and R2 of 0.926, thus outperforming LSTM and traditional forecasting models. This work greatly enhances the accuracy of forecasting, avoids overfitting, and promotes performance in volatile market environments. The results are extremely useful for investors, financial analysts, and trading platforms because they can make better predictions. 2025 IEEE.
Sao, Ameet; Verma, Rajesh; Lokannadha, Irala; Nagarajan, S.; Mary, S. Suma Christal; Raj, Infant
International Conference on Intelligent Systems and Computational Networks, ICISCN 2025;
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICISCN64258.2025.10934289 https://www.scopus.com/pages/publications/105002687206?origin=resultslist
Sao A., Amity University, Rics School of Built Environment, Noida, India; Verma R., Christ (Deemed to be University), School of Business and Management, Ghaziabad, India; Lokannadha I., University of Hyderabad, School of Management Studies, Hyderabad, India; Nagarajan S., Bannari Amman Institute of Technology, Department of School of Management Studies, Erode, India; Mary S.S.C., Panimalar Engineering College, Department of Information Technology, Chennai, India; Raj I., K.Ramakrishnan College of Engineering, Department of Cse, Trichy, India
Predictive Analytics for Train Timeliness Using Long Short Term Memory and Machine Learning Techniques
Decision Trees; Machine Learning; RNN; Train Delay; XGBoost
Train delays are one of the most persistent issue faced by Indian Railways and it has still been the issue with all the modern infrastructure and increasing travel demands. Traditional methods mainly depend on historical averages and simple modelling which fail to capture complex patterns in delays caused. This research aims to build machine learning and neural network models to analyse historical data from past train journeys and make predictions for future train journeys. Machine learning models include Decision Trees, XGBoost, Random Forest, Extra Trees and a neural network model LSTM to predict the delay for a particular train on a given day based on the previous running status. The highest accuracy of 94.02% was found using LSTM model and a lowest of 72.65% for Decision Tree Regressor algorithm. 2025 IEEE.
Johny, Akshay; Senthil Vadivu, M.; Jeevanand, E.S.
4th International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2025;pp.1049-1054
https://doi.org/10.1109/ICAAIC64647.2025.11330264 https://www.scopus.com/pages/publications/105034854796?origin=resultslist
Johny A., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, India; Senthil Vadivu M., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, India; Jeevanand E.S., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, India
Predictive analytics in cryptocurrency using neural networks: A comparative study
Convolutional Neural Network (CNN); Cryptocurrency; Gated Recurrent Unit (GRU); Long Short Term Memory (LSTM)
This paper is concerned with assessing different neural network based predictive models. Each of these predictive models has one goal and that is to predict the price of a cryptocurrency, Bitcoin is the cryptocurrency taken into consideration. The models will be focusing on predicting the USD equivalent value of bitcoin using historical data and live data. The neural network models being assessed are a Convolutional Neural Network, and two variations of the Recurrent Neural Network that are Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The goal is to observe the validation loss of each model and also the time it takes to train or epoch for each training set which basically just determine its efficiency and performance. The results that are achieved are almost what was expected as LSTM outperforms CNN but the when we take a look at GRU, it is at par with LSTM. However, CNN is quicker at training or creating epochs and the validation loss is acceptable and not too high but it looks so when it is compared with the Recurrent Neural Networks such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). BEIESP.
International Journal of Recent Technology and Engineering, Vol-7, No. 6, pp. 425-429.
Blue Eyes Intelligence Engineering and Sciences Publication
Khan A.S., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India; Augustine P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India
Predictive Analytics in Wealth Management and the Role of Machine Learning for Investment Professionals
Data-Driven Decision Making; Financial Forecasting; Machine Learning; Predictive Analytics; Risk Assessment; Wealth Management
The paper focuses on the use of predictive analytics and machine learning (ML) to potentially transform the landscape of contemporary wealth management and provide financial decision-makers with cutting-edge tools that improve decision-making and portfolio management practices with clients. Conventional investment models are usually based on the past performance and unchanging risk evaluation, which is not flexible in a fluctuating market. As compared, the suggested ML-based framework would use the real-time data source dynamic, that is, market trends, behavioral financial indicators, and macroeconomic signals to capture the real-time forecast and provide individual investment advice. Multi-model ensemble comprising Random Forest, XGBoost, and LSTM networks were created to predict the asset performance and to evaluate investor risk profiles with high accuracy. Empirical analysis proved that the proposed system proved to be more accurate (95.3 %), have a higher precision (92 %), recall (94.1 %), and F1-score (92.1 %) as compared to current approaches. The strategy improves risk-adjusted returns, minimises human bias and decision lag. These results reinforced the useful application of ML in enhancing the strategic potential of investment professionals and establishing a more robust and flexible ecosystem of managing wealth. 2025 IEEE.
Manoharan, Geetha; Kulshrestha, Nitin; Sugatha Kumari, B.; Sidhu, Kawerinder Singh; Sanghavi, Himanshu; Anix Joel Singh, J.
Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.1316-1321
https://doi.org/10.1109/SISIMPACT67725.2025.11439363 https://www.scopus.com/pages/publications/105037465455?origin=resultslist
Manoharan G., School of Business, SR University, Telangana, Warangal, India; Kulshrestha N., Christ Deemed to Be University, India; Sugatha Kumari B., School of Commerce, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu, Chennai, India; Sidhu K.S., Uttaranchal Institute of Management, Uttaranchal University, Uttarakhand, India; Sanghavi H., C Z Patel College of Business and Management, The Charutar Vidya Mandal (CVM) University, Gujarat, Vallabh Vidyanagar, India; Anix Joel Singh J., St.Joseph University, Mechanical Engineering Department, Tanzania, India
Predictive Machine Learning Approaches for Estimating Residential Rental Rates in India
Decision Tree Algorithm; Gradient Boosting Technique; Linear Regression Model; Random Forest Algorithm; Rental Rate
As urban areas like Chennai and Bangalore witness a continuous surge in land and housing prices, accurately estimating the market value of houses has become increasingly crucial. This presents a formidable challenge, prompting a growing demand for an accessible and efficient method to predict house rental prices, ensuring dependable forecasts for future generations. In response to this need, this study delves into the core factors influencing rental prices, with a keen focus on location and area. Leveraging a dataset comprising ten essential features tailored for detecting Rental Price in Metropolitan cities, the research meticulously preprocesses the data using a Python library to ensure data cleanliness, laying a robust foundation for constructing the predictive model. Employing a diverse range of Machine Learning algorithms, including Random Forest, Linear Regression, Decision Tree Regression, and Gradient Boosting, the study evaluates their efficacy in forecasting rental prices. Notably, feature extraction underscores the significance of area and property type in shaping rental prices. In comparison with existing methodologies, this research adopts gradient boosting as its preferred approach, achieving the most satisfactory predictive outcomes. Evaluation metrics are meticulously analyzed to validate the model's performance. Through this comprehensive analysis, the study not only offers valuable insights into rental price prediction but also ensures a rigorous comparison with existing approaches, maintaining originality and relevance in addressing the pressing challenges of housing market dynamics. 2024 IEEE.
Jayadharshini P.; Ragunath R.; Prabhakaran D.; Krishnasamy L.; Abinaya N.; Priyanka S.
TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
https://doi.org/10.1109/TQCEBT59414.2024.10545253 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204457681&doi=10.1109%2fTQCEBT59414.2024.10545253&partnerID=40&md5=e1120ea3fb4ecffa3d27417986fdc147
Jayadharshini P., Kongu Engineering College, Department of Artificial Intelligence, Tamilnadu, Erode, India; Ragunath R., Nandha Engineering College, Department of Information Technology, Tamilnadu, Erode, India; Prabhakaran D., Nandha Engineering College, Department of Information Technology, Tamilnadu, Erode, India; Krishnasamy L., Christ University, Department of Cse, Bangalore, India; Abinaya N., Kongu Engineering College, Department of Artificial Intelligence, Erode, India; Priyanka S., Kongu Engineering College, Department of Artificial Intelligence, Tamilnadu, Erode, India
Predictive Maintenance and Asset Management Using Motion Analytics
In this chapter, the researcher studies how motion analytics enabled by smart sensing and data modeling provides an effective way of predictive maintenance and managing assets within industrial systems. It talks about how time based diagnostics and anomaly detection algorithms in the past time along with the history trends could be used to predict machine failures even before it has happened. The combination of machine learning and Internet of Things (IoT) platforms is stressed as one of the enablers of real- time decision making and low rates of downtimes. Such implementation issues as data quality, sensor calibration, system scalability are also tackled in this chapter. It lays stress on the importance of motion analytics with respect to reliable and efficient planning of lifecycle management and key assets. 2026, IGI Global Scientific Publishing. All rights reserved.
Pradeepa, S.V.; Gokilavani, R.; Deepan, A.; Sridevi, J.; Selvi, K.
Methodologies and Applications of Intelligent Motion Control Systems;pp.75-103
https://doi.org/10.4018/979-8-3693-3637-3.ch004 https://www.scopus.com/pages/publications/105025077369?origin=resultslist
Pradeepa S.V., JSPM University, Pune, India; Gokilavani R., Chirst University, Banglore, India; Deepan A., Sambhram University, Uzbekistan; Sridevi J., Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology, India; Selvi K., Saveetha Engineering College, Chennai, India
Predictive Modeling for Uber Ride Cancellation and Price Estimation: An Integrated Approach
decision tree algorithms; fare estimation; gradient boosting; logistic regression; machine learning; predictive modeling; ride cancellation prediction; rideshare; transportation efficiency; Uber
In the realm of ridesharing services, exemplified by Uber, two formidable challenges have surfaced: ride cancellations and precise fare estimation. This research introduces an innovative, integrated approach that leverages predictive modeling to address both issues. By analyzing historical ride data, we identify the intricate factors influencing cancellations, and through machine learning techniques, we develop predictive models to forecast cancellation likelihood. Additionally, we pioneer a dynamic approach to fare estimation by considering historical data alongside real-time variables. By unifying these strategies, we aim to enhance user satisfaction, optimize driver allocation, and promote trust and transparency within the ridesharing ecosystem. 2024 IEEE.
Gupta A.; Pipaliya A.; Jacob L.; Balagangadhar K.
Gupta A., Christ (Deemed to Be University), School of Science, Lavasa, India; Pipaliya A., Christ (Deemed to Be University), School of Science, Lavasa, India; Jacob L., Christ (Deemed to Be University), Department of Data Science, Lavasa, India; Balagangadhar K., Christ (Deemed to Be University), Department of Data Science, Lavasa, India
Predictive modeling of mechanical behavior in waste ceramic concrete using machine learning techniques
Artificial Neural Network; Construction Industry; Environmental Sustainability; LightGBM; Machine Learning; Waste Ceramic Concrete
This study identifies the critical demand for a certain approach that aims to predict and ascertain the mechanical behavior of concrete admixed with waste ceramic, a method to overcome and mitigate the related environmental challenges as it pertains to the construction field. Concrete modification with ceramic wastes has received significant attention due to its potential improvement in sustainability. The developed predictive models on waste ceramic concrete (WCC) involved the use of advanced machine learning techniques such as Artificial Neural Network (ANN) and Light Gradient Boosting Machine (LightGBM). Experimental datasets were formulated based on 5% and 20% variability of ceramic waste percentages as input variables for training and testing data for validation of the proposed model. In each case, iterative training improved model performance, with the ANN showing moderate predictability (R = 0.70 and 0.67) and LightGBM demonstrating stronger accuracy. Predictive values ranged between 1.02 MPa and 0.12 MPa for compressive and splitting tensile strengths and had R values of 0.70 and 0.67 for the ANN model, respectively. The established findings will lead to a dependable framework for assessing and improving the performance of ceramic waste-modified concrete. In this regard, these findings have reinforced the potential of machine learning in developing sustainable construction practices. This paper is of value to engineers and decision-makers within the construction industry, providing an informed choice towards environmental sustainability and better risk management. Kamal Upreti et al.
Upreti, Kamal; Pandey, Adesh Kumar; Kushwah, Virendra Singh; Kshirsagar, Pravin R.; Sharma, Kamal Kant; Singh, Jagendra; Parashar, Jyoti; Jain, Rituraj
International Journal of Basic and Applied Sciences;Volume;14;Issue;1;pp.124-135
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.14419/ywwvvd04 https://www.scopus.com/pages/publications/105005015895?origin=resultslist
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi-NCR, Uttar Pradesh, Ghaziabad, India; Pandey A.K., Department of Information Technology, KIET Group of Institutions, Ghaziabad, India; Kushwah V.S., School of Computing Science & Engineering, VIT Bhopal University, Highway, Kothrikalan, Madhya Pradesh, Sehore, India; Kshirsagar P.R., Department of Electronics & Telecommunication Engineering, J D College of Engineering &; Management, Maharashtra, Nagpur, India; Sharma K.K., Department of Information Technology, KIET Group of Institutions, Ghaziabad, India; Singh J., School of Computer Science Engineering & Technology, Bennett University, Greater Noida, India; Parashar J., Bharati Vidyapeeths Institute of Computer Applications and Management, New Delhi, India; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India
Predictive Modeling of Solar Energy Production: A Comparative Analysis of Machine Learning and Time Series Approaches
Machine Learning approaches; Solar Energy; Time Series and ARIMA
In this study, we dive into the world of renewable energy, specifically focusing on predicting solar energy output, which is a crucial part of managing renewable energy resources. We recognize that solar energy production is heavily influenced by a range of environmental factors. To effectively manage energy usage and the power grid, it's vital to have accurate forecasting methods. Our main goal here is to delve into various predictive modeling techniques, encompassing both machine learning and time series analysis, and evaluate their effectiveness in forecasting solar energy production. Our study seeks to address this by developing robust models capable of capturing these complex dynamics and providing dependable forecasts. We took a comparative route in this research, putting three different models to the test: Random Forest Regressor, a streamlined version of XGBoost, and ARIMA. Our findings revealed that both the Random Forest and XGBoost models showed similar levels of performance, with XGBoost having a slight edge in terms of RMSE.. By providing a comprehensive comparison of these different modeling techniques, our research makes a significant contribution to the field of renewable energy forecasting. We believe this study will be immensely helpful for professionals and researchers in picking the most suitable models for solar energy prediction, given their unique strengths and limitations. 2024 IEEE.
Sucharitha M.M.; Sowjanya S.; Sumalatha K.N.; Ayesha S.; Basha M.S.A.
Proceedings of ICWITE 2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, pp. 235-241.
Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Sowjanya S., Rajeev Gandhi Memorial College of Engineering and Technology, Department of Management Studies, Nandyal, India; Sumalatha K.N., Maris Stella College, Vijayawada, India; Ayesha S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Basha M.S.A., Gandhi Institute of Technology and Management (Deemed to Be University), Gitam School of Business, Bengaluru, India
Predictive Modeling of Student Learning Outcomes Through Cognitive and Emotional Skill Integration
Classification; Cognitive Learning; Education; Machine Learning; Non-Cognitive Learning; Student Performance
The interplay of factors, including both cognitive and non-cognitive, plays a significant role in the learning patterns of students. However, the majority of the research conducted on such issues mainly puts forward the role of cognitive skills but forgets that a very important role is played by the non-cognitive factor, specifically motivation and emotional intelligence. Therefore, this study focuses on bridging that gap by investigating the combined influence of cognitive and non-cognitive factors on the learning capacities of engineering students during their transition to higher education. A two-year longitudinal study on engineering students of AITAM, Tekele, India was considered in relation to their academic performance, learning preference, and socio-emotional aspects. The approach adopted makes use of predictive analytics. It is deployed here as machine learning algorithms in the form of Logistic Regression (LR), Naive Bayes, k-Nearest Neighbors (k-NN), Decision Trees (DT), and Support Vector Machines (SVM) to classify the learners into very fast, fast, average, and slow learners. The algorithm of k-NN also achieved the highest accuracy classification and showed good robustness for learning the students' learning rates. This study underscores the combination of new teaching approaches as well as personalized self-learning methods to enhance learning performance, especially for slow learners. Indeed, the outcome gives avenues for much more extensive studies done on large datasets using advanced algorithms which can be applied across a range of educational fields to support tailored learning interventions. 2025, Iquz Galaxy Publisher. All rights reserved.
Kumar, Ajay; Tak, Tan Kuan; Ali, S Md Shakir; Haque, Mustafizul; Paralkar, Tejasvini Alok; Pravin Kshirsagar, R.; Upreti, Kamal
International Research Journal of Multidisciplinary Scope;Volume;6;Issue;1;pp.892-910
https://doi.org/10.47857/irjms.2025.v06i01.02895 https://www.scopus.com/pages/publications/85218767526?origin=resultslist
Kumar A., Indraprastha Engineering College, Ghaziabad, India; Tak T.K., Singapore Institute of Technology, Singapore; Ali S.M.S., Auroras PG College, Hyderabad, India; Haque M., Dr. D.Y. Patil Vidyapeeth's Centre For Online Learning, Dr. D.Y. Patil Vidyapeeth (Deemed To Be University), Pune, India; Paralkar T.A., Symbiosis Centre for Management Studies, Symbiosis International (Deemed University), Nagpur Campus, India; Pravin Kshirsagar R., Department of Electronics & Telecommunication Engineering, JD College of Engineering & Management, Maharashtra, Nagpur, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India
Predictive Modeling of Substance Abuse Risks using Big Data Analytics and Social Media Mining
Addiction Prevention; Big Data Analytics; Predictive Modelling; Public Health Informatics; Risk Detection; Social Media Influence; Substance Abuse; Treatment Barriers
The worldwide increase in substance abuse among teenagers and young adults has become serious concern in recent times. One way this pattern has developed is through the evolution of social media. Social media has transformed people's attitudes towards certain behaviors and has encouraged risky behavior to the point of actually causing addiction by exposing them to drug-related material. Despite the existence of preventative measures, such as education programs in schools, many children and youth have not had adequate access to educational interventions or evidence-based measures due to barriers created by geography, economic circumstances, and social factors, particularly in less developed countries. The research proposed is focusing on addressing this gap using a big data approach. This research employs a unique analytical framework that integrates multiple large data sets from a variety of sources to better identify and assess the effectiveness of interventions. This model employs an analytical approach that uses statistical learning techniques and predictive analytics to identify historical patterns and anticipate future trends, and assess the effectiveness of various interventions conducted in different countries. The results of the analysis suggest that this big data approach will provide decision-makers with clearly documented evidence related to various risk-taking behaviors as they relate to available prevention interventions, and will assist decision-makers in developing targeted prevention intervention strategies. This study demonstrates the revolutionary aspect behind the application of computational intelligence in preventing substance abuse and informing evidence-based community health interventions. 2025 IEEE.
Anees Moidheen, M.M.; Parande, Athreya S; Jomy, Rayal; Presannakumar, Krishna; Souza, Mithun D
Proceedings of the 2025 International Conference on Computational Innovations and Sustainable Technologies, ICCIST 2025;
https://doi.org/10.1109/ICCIST67338.2025.11438824 https://www.scopus.com/pages/publications/105037460726?origin=resultslist
Anees Moidheen M.M., Department Of Computer Science, Christ (Deemed to be University) Bangalore Yeshwanthpur Campus, Bangalore, India; Parande A.S., Department Of Computer Science, Christ (Deemed to be University) Bangalore Yeshwanthpur Campus, Bangalore, India; Jomy R., Department Of Computer Science, Christ (Deemed to be University) Bangalore Yeshwanthpur Campus, Bangalore, India; Presannakumar K., Department Of Computer Science, Christ (Deemed to be University) Bangalore Yeshwanthpur Campus, Bangalore, India; Souza M.D., Department Of Computer Science, Christ (Deemed to be University) Bangalore Yeshwanthpur Campus, Bangalore, India
Predictive Modelling of Heart Disease: Exploring Machine Learning Classification Algorithms
Cross-validation; F1 Score; Grid Search; Heart attack prediction; Machine Learning; overfitting; Recall
In addressing the critical challenge of early and accurate heart failure diagnosis, this study explores the application of five machine learning models, including XGBoost, Decision Tree, Random Forest, Logistic Regression, and Gaussian Naive Bayes. Employing cross-validation and grid search techniques to enhance generalization, the comparative analysis reveals XGBoost as the standout performer, achieving a remarkable accuracy of 85%. The findings emphasize the significant potential of XGBoost in advancing heart failure diagnosis, paving the way for earlier intervention, and potentially improving patient prognosis. The study suggests that integrating XGBoost into diagnostic processes could represent a valuable and impactful advancement in the realm of heart failure prediction, offering promising avenues for improved healthcare outcomes. 2024 IEEE.
Yadav J.; Nair A.M.; George J.; Alapatt B.P.
International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024
Yadav J., Christ (Deemed to Be University), Delhi NCR, India; Nair A.M., Christ (Deemed to Be University), Delhi NCR, India; George J., Christ (Deemed to Be University), Delhi NCR, India; Alapatt B.P., Christ (Deemed to Be University), Delhi NCR, India
Predictive Modelling of Microwave Link Failures Using Machine Learning and Deep Learning Techniques
Deep Learning; Long Short-Term Memory (LSTM); Machine Learning; Microwave Link Failure; Microwave radio; Quality of Service
Microwave radio links play a vital role in keeping mobile networks running, especially when it comes to backhaul-the part of the network that connects base stations to the core. Gradual failure in these links could disrupt services and cost providers a lot in both revenue and customer trust. In this study, we explore how machine learning can help predict such failures before they happen. Network performance data from a mobile network operator in Nigeria was collected, cleaned and used to achieve the purpose of the study. Four algorithms belonging to machine learning (ML) and deep learning (DL) were adopted and used for training the dataset and predicting link failures. Results show that the Long ShortTerm Memory (LSTM - a deep learning model effective for handling time-series data) performed best with prediction accuracy of 92%, distantly followed by others. These findings indicate that the LSTM is better in modelling temporal patterns in network behaviours. This study provides a practical framework for automating microwave link monitoring and maintenance, thereby reducing manual diagnostics, preventing outages, and improving service reliability. The proposed solution supports the integration of predictive intelligence into network operations, enhancing the quality of service and operational efficiency for telecom providers. 2025 IEEE.
Okewumi, Peter Olufeyi; Ojo, Olayinka Anthony; Iwendi, Celestine; Sharma, Vandana; Uwah, Salome Enoshi; Aboutorabi, Negin
2025 12th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2025;
https://doi.org/10.1109/ICRITO66076.2025.11241758 https://www.scopus.com/pages/publications/105029853032?origin=resultslist
Okewumi P.O., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Ojo O.A., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Iwendi C., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India; Uwah S.E., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Aboutorabi N., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom
Predictive value of IL-6, IL-1?, TNF-?, and vaginal pH in diagnosing vaginal microbial infections: A host-inflammatory axis perspective
Bacterial vaginosis; Inflammatory biomarkers; Vaginal microbial infections
Microbial-associated vaginal infections are common among women of reproductive age and are linked to alterations in the local immune environment. Inflammatory biomarkers such as IL-6, IL-?, and TNF-?, along with vaginal pH have emerged as potential indicators of microbial dysbiosis. This study aimed to statistically evaluate the ability of these specific inflammatory cytokines and vaginal pH to identify infection status. Cytokine concentrations and vaginal pH were measured in clinically characterized samples. The group differences were analyzed using Mann-Whitney U tests and Cliff's Delta for effect size. ROC-AUC analysis was also performed to assess the discriminative power, and correlation heatmaps explored marker synergy. The infected individuals showed increased levels of all cytokines (p < 0.001), with large effect size (? > 0.9 for IL-6, IL-1?, TNF-?). Vaginal pH also differed significantly (? = 0.60). In addition, the combination of IL-6 and vaginal pH achieved excellent discriminative performance (AUC = 0.98). These findings suggest that IL-6, IL-1?, and TNF-?, when combined with vaginal pH, can function as reliable non-invasive biomarkers for the early detection and improved diagnostic triaging of vaginal microbial infections. 2024
Kharsodiya, Kiran; Das, Kaushik Pratim; Dhingra, Harish Kumar
Journal of Microbiological Methods;Volume;237;Issue;;Article No.;107212;
https://doi.org/10.1016/j.mimet.2025.107212 https://www.scopus.com/pages/publications/105012545069?origin=resultslist
Kharsodiya K., Department of Biosciences, School of Liberal Arts and Science, Mody University of Science and Technology, Rajasthan, Lakshmangarh, 332311, India; Das K.P., Department of Computer Science, School of Science, Christ University, Karnataka, Bengaluru, 560029, India; Dhingra H.K., Department of Biosciences, School of Liberal Arts and Science, Mody University of Science and Technology, Rajasthan, Lakshmangarh, 332311, India
Predictors of behavioral and emotional issues in children involved in custody disputes: A cross sectional study in urban Bengaluru
Child Custody Disputes; Child Mental health; Divorce; India; SDQ
Background: The increasing rates of divorce in urban India has led to the subsequent parental battle for the child's custody. This paper discusses the behavioral and emotional issues of these children in relation to their psychosocial environmental factors and other relevant socio-demographic variables. Methods: We used samples from parent interviews concerning 52 children aged 717-years-old, involved in child custody cases in the Family court of urban Bengaluru. The Strengths and Difficulties Questionnaire was used to measure response variables of behavioral and emotional issues in these children. Predictor models of quantile and multiple linear regression were used to assess the influence of psychosocial environmental factors and socio-demographic variables on the response variables. Results: The predictor models revealed that risk of child suffering emotional and behavioral issues increased with factors such as excessive parental control, change of academic environment, general unrest at school, frequency of child's court visit, child's visitation of non-custodian parent on occasions and vacations, and negatively altered family relationship. The model however intriguingly showed that residing in nuclear household rather than with their grandparents in a non-nuclear household, decreased the risk of mental health issues in these children. Conclusions: This study is a novel attempt to understand the influence of the psychosocial issues on the child's mental health in the context of custody cases in India. Despite the minimum sample size, the findings imply that family-based intervention is the need of the hour in these cases. The implications for clinical practice and research are discussed. 2021 Elsevier B.V.
Nambiar P.P.; Jangam K.V.; Jose A.; Seshadri S.P.
https://doi.org/10.1016/j.ajp.2021.102930 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120302855&doi=10.1016%2fj.ajp.2021.102930&partnerID=40&md5=b427065c75a430aff4f6040947cb83d4
Nambiar P.P., Department of Psychiatric Social Work, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India; Jangam K.V., Department of Psychiatric Social Work, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India; Jose A., Department of Statistics, Christ University, Bengaluru, India; Seshadri S.P., Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
Predictors of compassion competence among nurses working in the non-profit healthcare sector in India
compassion competence; engagement; nurse practice environment; the non-profit sector
Objectives: For many years, the non-profit healthcare sector in India has been able to instil a sense of goodwill in the society through the provision of healthcare services, which are not only affordable and accessible, but also deliver compassionate care. This study was an attempt to evaluate the compassionate care and competence of the nurses working in India's non-profit healthcare sector, and to identify the predictive factors associated with their work environment and engagement. Methods: A cross-sectional survey of nurses working in the medical college hospitals managed by private trusts in the non-profit sector in India was conducted using an online questionnaire. The study was conducted in April 2021 after the second wave of the Covid-19 pandemic. Socio-demographic factors, compassion competence, nurse practice environment, and nurse engagement were assessed. Linear regression analysis was conducted to identify the variance and the predictors of compassion competence among Indian nurses. Results: We found that nurses practice environment (?=0.982, p=< .001) and engagement (?=0.842, p=< .001) predicted compassion competence during the Covid-19 pandemic. Moreover, nurse practice environment and engagement positively influenced compassion competence. Conclusion: There was a considerably high level of compassion competence among nurses working in the non-profit healthcare sector during the Covid-19 pandemic. The compassion phenomenon was statistically significantly impacted by the nurses practice environment and their level of engagement. Consequently, not only does competent compassion behaviour require positive work environments and engaged nurses, but also nurses compassion competence and its relationship with practice environment factors and engagement are critical in the non-profit healthcare sector in India. These findings support previous reviews that a high degree of compassion competence increases healthcare quality. 2024 Jismon, M. G., Rofin T. M., Thekkekkara, J. V., Asha K. C., & Vijesh P. V.
George J.M.; Rofin T.M.; Thekkekkara J.V.; Asha K.C.; Vijesh P.V.
International Journal of Practice-Based Learning in Health and Social Care, Vol-12, No. 2, pp. 34-48.
https://doi.org/10.18552/ijpblhsc.v12i2.1054 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212955375&doi=10.18552%2fijpblhsc.v12i2.1054&partnerID=40&md5=b69c51b00891f061eb4c92a750f47a0e
George J.M., Christ University, NCR, Delhi, India; Rofin T.M., Indian Institute of Management, Mumbai, India; Thekkekkara J.V., St. Johns Medical College, Bangalore, India; Asha K.C., St. Thomas Hospital, Kerala, Chethipuzha, India; Vijesh P.V., Rajagiri College of Social Sciences, Kerala, Kalamassery, India