Machine Learning in Cyber Threats Intelligent System
Cybercriminals disrupt services, exfiltrate sensitive data, and exploit victim machines and networks to perform malicious activities against organizations. A malicious adversary seeks to steal, destroy, or compromise business assets that have a specific financial, reputational, or intellectual value. As a result, organizations are complementing their perimeter defenses with threat intelligence platforms to address these security challenges and eliminate security blind spots for their systems. Any type of information useful for identifying, assessing, monitoring, and responding to cyber threats is considered cyber threat intelligence. Organizations can benefit from increased visibility into cyber threats and policy violations. An organizations threat intelligence allows them to prevent or mitigate various types of cyberattacks. The use of machine learning and artificial intelligence is a key component of cybersecurity conflict, which together allows attackers and defenders to function at new speeds and scales. In spear-phishing attacks, relatively frivolous machine learning algorithms have been used to overwhelming effect as adversarial artificial intelligence. This chapter discusses the various cyber threats, cyber security attack types, publicly available datasets for research work, and machine learning techniques in cyber-physical systems. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors.
Artificial Intelligence for Cyber Defense and Smart Policing, pp. 1-20.
https://doi.org/10.1201/9781003251781-1 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190232809&doi=10.1201%2f9781003251781-1&partnerID=40&md5=ec3bada096b848bc8f7a6e2ff5b650c8
Jaisingh W., Presidency University, Bengaluru, India; Nanjundan P., Department of Data Science, Christ University, Pune, Lavasa, India; George J.P., Department of Data Science, Christ University Delhi NCR Campus, India
Machine Learning in Financial Distress: A Scoping Review
Artificial Neural Networks; Financial Distress Prediction; Machine Learning; Support Vector Machine
Predicting financial distress is crucial for stakeholders, policymakers, governments, and management in decision-making processes. Researchers have developed various prediction models encompassing both traditional and machine-learning approaches. Notably, recent attention has shifted towards employing machine learning models to address the limitations of traditional methods. This study seeks to offer insights into current trends, identify gaps, and suggest future research directions using machine learning models for financial distress prediction, employing the PRISMA Extension for Scoping Reviews methodology. To achieve this, a comprehensive search was conducted across three databasesScience Direct, EBSCO, and ProQuestspanning from 2020 to 2023, identifying 34 relevant articles for analysis. The findings underscore the prevalent use of Support Vector Machine in financial distress prediction, followed by the Random Forest Classifier and Artificial Neural Network, with little attention paid to other models. Furthermore, the study underscores the necessity for more research in developing countries, noting the predominance of studies from developed nations. While machine learning models hold promise for enhancing the accuracy and efficiency of financial distress prediction, additional research is imperative to evaluate their effectiveness and applicability across diverse contexts. This scoping review aims to furnish researchers, policymakers, and institutions with valuable insights and policy recommendations, shedding light on underexplored machine-learning techniques. 2024, Iquz Galaxy Publisher. All rights reserved.
International Research Journal of Multidisciplinary Scope, Vol-5, No. 3, pp. 457-474.
https://doi.org/10.47857/irjms.2024.v05i03.0779 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201817039&doi=10.47857%2firjms.2024.v05i03.0779&partnerID=40&md5=ed1da499cedf8d19b4d67202007fa1c5
Peralungal A., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Karnataka, Bengaluru, India; Natchimuthu N., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Karnataka, Bengaluru, India
Machine Learning in Intrusion Detection: A Comprehensive Analysis
Intrusion detection system; k-nearest neighbors; Long short-term memory; Multiclass classification; Network security; Support vector machine
Intrusion detection systems (IDS) are employed to investigate anomalous behavior in a network system, which monitors a network system for suspicious behavior, which is essential for maintaining network security. Improving accuracy in intrusion detection is necessary to lower false alarms and boost detection rates. Support Vector Machine (SVMLinear and Quadratic), Long Short-Term Memory (LSTM), and k-nearest neighbors (kNN), machine learning techniques for intrusion detection in network environments, are compared in this research. The effectiveness of SVM, which is well-known for its resilience in high-dimensional environments, in differentiating between normal and malicious behavior is examined. Straightforward yet powerful algorithms, namely KNN and LSTM, are analyzed to see how well they can adjust to different types of intrusions. Regarding detection accuracy, false positive rates, and response times, the experimental results on a benchmark intrusion detection dataset highlight the advantages and disadvantages of the models considered for the study. This study suggests incorporating machine-learning approaches into real-time intrusion detection systems to improve network security and lessen cyber risks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Saila, B.; Emmanuel, Alen; Teja, Ekila; Kokatnoor, Sujatha Arun; Mandala, Jyothi; Kumar, Sandeep
Lecture Notes in Networks and Systems;Volume;1265 LNNS;pp.591-603
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-96-2299-3_40 https://www.scopus.com/pages/publications/105009909585?origin=resultslist
Saila B., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, 560074, India; Emmanuel A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, 560074, India; Teja E., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, 560074, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, 560074, India; Mandala J., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, 560074, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, 560074, India
Machine Learning in Investment Analysis-Enhancing or Replacing Human Judgment
Financial Forecasting; Human Judgment; Hybrid Decision-Making; Investment Analysis; Machine Learning; Portfolio Optimization
Machine Learning (ML) involvement in investment analysis is quickly revolutionizing the investment-based decisions through becoming highly accurate, quick, and embracing increased data processing capabilities. This paper is to research on whether ML is complementary or a possible replacement to human financial judgment. We run experiments over 1.2 million financial transactions between 150 firms comparing old style analyst recommendations and ML-models, including XGBoost, LSTM and Random Forest. The findings indicate that ML models outperformed prediction capability by 19.6 percent and lowered the volatility of the portfolios by 14.3 percent in 5-year investment. Also, the ML-Aided decision-making was better than human (only) approaches in 78 percent of the cases in markets with high volatility or that involved trading in complicated assets. The qualitative variables like regulatory policy changes and investor sentiment however were too difficult to decipher under the leadership of ML only. Our results indicate that ML supports rather than supers the human judgement and thus demonstrates a hybrid paradigm of decision making that resolves computational exactitude with context sensitive understanding in the modern investment scenarios. 2025 IEEE.
Kafila; Ghai, Bhupaesh; Champaneria, Tushar; Sidhu, Kawerinder Singh; Lourens, Melanie; Acharjee, Purnendu Bikash
Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.295-300
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/SISIMPACT67725.2025.11439080 https://www.scopus.com/pages/publications/105037469838?origin=resultslist
Kafila, School of Business, SR University, Telangana, Warangal, India; Ghai B., University Institute of Computing, Chandigarh University, India; Champaneria T., L. D. College of Engineering, Computer Engineering, Ahmedabad, India; Sidhu K.S., Uttaranchal Institute of Management, Uttaranchal University, Uttarakhand, Dehradun, India; Lourens M., Durban University of Technology, Faculty of Management Sciences, South Africa; Acharjee P.B., CHRIST University, Computer Science, Bengaluru, India
Agriculture is the cultivation of the soil, the growth of crops and the raising of livestock. Agriculture is critical to the economic development of a country. Farming generates nearly 58% of a country's primary income. Previously, cultivators had accepted conventional farming practices. Because these methods were imprecise, they produced less and took longer time. Precise farming boosts productivity by precisely determining which steps must be completed at what time. Precision farming entails forecasting the weather, analyzing soil, recommending crops for cultivation and calculating the amount of fertilizer and pesticides that must be used. Precise farming uses advanced technologies such as IoT, data mining, data analytics, and machine learning (ML) to collect data, train systems and predict outcomes. Precision farming employs technology to reduce manual labor and boost productivity. Farmers have recently faced several difficulties, such as crop failure due to insufficient rainfall, soil infertility and so on. The proposed work in determining the soil, managing crops and harvesting efficiently can solve the problems caused by environmental changes. It guides a person's farming strategy to produce better results through a proper prediction process. The goal of this research is to assist an individual in efficiently cultivating crops, resulting in high productivity at a low cost. It also assists in estimating the total cost of cultivation and forecasting the likely economic barriers. This would help a person plan activities prior to cultivation, resulting in an integrated farming solution. 2023 River Publishers. All rights reserved.
Raja A.T.; Arunachalam A.S.; Gobinath R.
Advanced Technologies for Smart Agriculture, pp. 129-151.
Raja A.T., Directorate of Distance Education (DDE), SRM Institute of Science and Technology (SRMIST), India; Arunachalam A.S., Department of Computer Science, Vels Institute of Science Technology and Advanced Studies (VISTAS), India; Gobinath R., Department of Computer Science, Christ University, India
Machine learning insights into mental health risk factors associated with climate change: Impact on schoolchildren's cognitive abilities
In this chapter, we use machine learning techniques to investigate how the effects of climate change and certain risk factors for mental health affect students' cognitive skills in the classroom. The mental health of at-risk populations, especially students, must be considered in light of the fact that the world's environment is changing significantly. Using state-of-the-art machine learning algorithms, we analyze large datasets that include environmental variables, socio-economic characteristics, and markers of mental health among school-aged persons. We are primarily interested in identifying key relationships and trends that might help us understand the complex relationship between climate change and cognitive health in this population. In order to uncover complex insights, the chapter takes a holistic approach by combining feature selection, model training, and interpretability analysis. The cognitive capacities of school-aged children may be significantly impacted by some climate- related stresses, according to preliminary results. The findings add to our knowledge of the interconnected webs of environmental shifts, psychological susceptibilities, and cognitive consequences. Educators, legislators, and healthcare providers can benefit from this study's use of machine learning insights into the possible effects of climate change on students' mental health. It also paves the way for the creation of tailored treatments and adaptive techniques to deal with the highlighted dangers, fostering resilience and prosperity in the face of a changing environment. 2024, IGI Global. All rights reserved.
The Climate Change Crisis and Its Impact on Mental Health, pp. 72-84.
Bose M., Christ University, India; Aloysius G.C., Christ University, India; Rajendran R.K., Christ University, India
Machine Learning Insights into Mobile Phone Usage and Its Effects on Student Health and Academic Achievement
Academic Achievement; Machine Learning Models; Smart Phone; Student Health
The research intends to find how students' health and academic performance are affected by their smartphone use. Considering how widely smartphones are used among students, it is important to know how they could affect health and learning results. This study aims to create prediction models that can spot trends and links between smartphone usage, health ratings, and academic achievement, thereby offering insightful information for teachers and legislators to encourage better and more efficient use among their charges. Data on students' mobile phone use, health evaluations, and academic achievement were gathered for the study. Preprocessing of the dataset helped to translate categorical variables into numerical forms and manage missing values. Trained and assessed were many machine learning models: Random Forest, SVM, Decision Tree, Gradient Boosting, Logistic Regression, AdaBoost, and K-Nearest Neighbors (KNN). The models' performance was evaluated in line with their accuracy in influencing performance effects and health ratings. Predictive accuracy was improved by use of feature engineering and model optimization methods. With 63.33% of accuracy for estimating health ratings, the SVM model was most successful in capturing the link between smartphone usage and health results. With an accuracy of 50%, logistic regression performed very well in forecasting performance effect, therefore stressing important linear connections between consumption habits and academic success. Random Forest and Decision Tree models were less successful for performance impact even if they showed strong performance in health forecasts. These results highlight the need of customized treatments to reduce the detrimental consequences of too high mobile phone use on students' academic performance and health. 2024 IEEE.
Biswas P.; Krishnan D.R.; Basha M.S.A.; Sucharitha M.M.
2024 3rd International Conference for Advancement in Technology, ICONAT 2024
https://doi.org/10.1109/ICONAT61936.2024.10774742 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214978797&doi=10.1109%2fICONAT61936.2024.10774742&partnerID=40&md5=4fa16545454a01d65796c2ab7cbfacd1
Biswas P., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Krishnan D.R., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Basha M.S.A., GITAM School of Business, (Deemed to Be University), Bengaluru, India; Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India
Machine Learning Insights into Predicting Crude Oil Prices
Crude Oil; Data Analysis; Feature Engineering; Machine Learning Algorithms; Model Evaluation; Predictive Analysis; Random Forest; Support Vector Machine
This research paper delves into the complexities of crude oil, highlighting its extraction, composition, and transformation into valuable derivatives. Examining the pricing dynamics, it explores the intricate interplay of social and economic factors that shape crude oil's value, emphasizing its critical role in global energy and industrial sectors. A forecasting model is introduced, focusing on key factors - heating oil, SPX, GPNY, and EU DOL EX - utilizing five machine learning models. Historical data reveals the efficacy of conventional models, particularly Random Forest, in predicting crude oil prices, enhanced by feature engineering techniques. The paper concludes by suggesting avenues for further exploration, offering valuable insights for readers in this dynamic research domain. 2024 IEEE.
Toppo A.; Mahajan J.; Singh V.P.; Paswan A.S.; Saxena A.
TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
https://doi.org/10.1109/TQCEBT59414.2024.10545299 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204464958&doi=10.1109%2fTQCEBT59414.2024.10545299&partnerID=40&md5=d51df646f57bffb56e148bf80810ba6a
Toppo A., Christ University, Bengaluru, India; Mahajan J., Christ University, Bengaluru, India; Singh V.P., Ewec, United Arab Emirates; Paswan A.S., Christ University, Bengaluru, India; Saxena A., Christ University, Bengaluru, India
Machine Learning Integration for Enhanced Solar Power Generation Forecasting
This paper reviews the advancements in machine learning techniques for enhanced solar power generation forecasting. Solar energy, a potent alternative to traditional energy sources, is inherently intermittent due to its weather-dependent nature. Accurate forecasting of photovoltaic power generation (PVPG) is paramount for the stability and reliability of power systems. The review delves into a deep learning framework that leverages the long short-term memory (LSTM) network for precise PVPG forecasting. A novel approach, the physics-constrained LSTM (PCLSTM), is introduced, addressing the limitations of conventional machine learning algorithms that rely heavily on vast data. The PC-LSTM model showcases superior forecasting capabilities, especially with sparse data, outperforming standard LSTM and other traditional methods. Furthermore, the paper examines a comprehensive study from Morocco, comparing six machine learning algorithms for solar energy production forecasting. The study underscores the Artificial Neural Network (ANN) as the most effective predictive model, offering optimal parameters for real-world applications. Such advancements not only bolster the accuracy of solar energy forecasting but also pave the way for sustainable energy solutions, emphasizing the integration of these findings in practical applications like predictive maintenance of PV power plants. The Authors, published by EDP Sciences, 2024.
Winster Praveenraj D.D.; Madeswaran A.; Pastariya R.; Sharma D.; Abootharmahmoodshakir K.; Dhablia A.
https://doi.org/10.1051/e3sconf/202454004007 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198115352&doi=10.1051%2fe3sconf%2f202454004007&partnerID=40&md5=b6a50a821b2979d6a981e5a9f686fe9e
Winster Praveenraj D.D., School of Business and Management, CHRIST (Deemed to Be University), Bangalore Yeshwantpur Campus, India; Madeswaran A., Scool of Business and Management, CHRIST University, Yeshwanthpur Campus, Bengaluru, India; Pastariya R., Department of Computer Science and Engineering, IES College of Technology, IES University, Madhya Pradesh, Bhopal, 462044, India; Sharma D., Department of Management Uttaranchal Institute of Management, Uttaranchal University, Dehradun, 248007, India; Abootharmahmoodshakir K., The Islamic University, Najaf, Iraq; Dhablia A., Altimetrik India Pvt Ltd, Maharashtra, Pune, India
Machine Learning Methods for Online Education Case
Education; etc.; Machine Learning; Online class
Online education has become a popular choice for learners of all ages and backgrounds due to its accessibility and flexibility. However, providing personalized learning experiences for a diverse range of students in online education can be challenging. Machine learning methods can be used to provide personalized learning experiences and improve student engagement in online education. In this case study, We're going to do some research on machine learning. methods in an online education platform. The platform provides courses in various subjects and is designed to be accessible to students from all over the world. The platform collects data on student behavior, such as the courses they enroll in, the time they spend on each course, and their performance on assignments and quizzes. We will explore several machine learning methods that can be applied to this data, including clustering, classification, and recommendation systems. Clustering algorithms can be used to group students based on their learning behavior and preferences, allowing instructors to provide personalized feedback and course recommendations. Classification algorithms can be used to predict student success in a particular course, allowing instructors to intervene and provide additional support if needed. Recommendation systems can be used to suggest courses to students based on their interests and past behavior. We will also discuss the potential benefits and challenges of using machine learning methods in online education. Benefits include increased student engagement, improved learning outcomes, and more efficient use of resources. Challenges include ensuring data privacy and security, preventing algorithmic bias, and maintaining transparency and fairness in the decision-making process. Overall, machine learning methods have the potential to transform online education by providing personalized learning experiences and improving student outcomes. By leveraging the vast amounts of data generated by online education platforms, we can create more effective and efficient learning experiences that meet the needs of students from diverse backgrounds and learning styles. 2023 IEEE.
Rajagopal M.; Ali B.; Sharon Priya S.; Aisha Banu W.; Madhavi M.G.; Punamkumar
Proceedings of 8th IEEE International Conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2023
Rajagopal M., CHRIST(Deemed to Be University), Lean Operations and Systems, School of Business and Management, Bangalore, 560029, India; Ali B., Maulana Azad National Urdu University, College of Teacher Education, Hyderabad, 500032, India; Sharon Priya S., B.S.Abdur Rahman Crescent Institute of Science and Technology, Chennai, 600100, India; Aisha Banu W., B.S.Abdur Rahman Crescent Institute of Science and Technology, Chennai, 600100, India; Madhavi M.G., SreeVidyanikethan Engineering College, Department of Basic Sciences and Humanities, Tirupati, 517 502, India; Punamkumar, Christuniversity, Bangalore, 560029, India
Machine Learning Methods leveraging ADFA-LD Dataset for Anomaly Detection in Linux Host Systems
ADFA-LD; Anomaly Detection; Host Intrusion Detection System; Linux; Machine Learning
Advancement in network technology and revolution in the global internet transformed the overall Information Technology (IT) infrastructure and its usage. In the era of the Internet of Things (IoT) and the Internet of Everything (IoE), most everyday gadgets and electronic devices are IT-enabled and can be connected over the internet. With the advancements in IT technologies, operating systems also evolved to leverage these advancements. Today's operating systems are more user-friendly and feature-rich to support current IT requirements and provide sophisticated functionalities. On the one hand, these features enabled operating systems accomplish all current requirements, but on the other hand, these modern operating systems increased their attack surface considerably. Intrusion detection systems play a significant role in providing security against the broad spectrum of attacks on host systems. Intrusion detection systems based on anomaly detection have become a prominent research area among diverse areas of cyber security. The traditional approaches for anomaly detection are inadequate to discover the operating system level anomalies. The advancement and research in Machine Learning (ML) based anomaly detection open new opportunities to tackle this challenge. The dataset plays a significant role in ML-based system efficacy. The Australian Defence Force Academy Linux Dataset (ADFA-LD) comprises thousands of normal and attack processes system call traces for the Linux platform. It is the benchmark dataset used for dynamic approach-based anomaly detection. This paper provided a comprehensive and structured study of various research works based on the ADFA-LD for host-based anomaly detection and presented a comparative analysis. 2022 IEEE.
2022 2nd International Conference on Intelligent Technologies, CONIT 2022
Khandelwal P., Christ(Deemed to Be University), Department of Computer Science, Bengaluru, India; Likhar P., Centre for Artificial Intelligence and Robotics (CAIR), Defence Research and Development Organisation (DRDO), Bengaluru, India; Yadav R.S., Centre for Artificial Intelligence and Robotics (CAIR), Defence Research and Development Organisation (DRDO), Bengaluru, India
Machine Learning Methods to Identify Aggressive Behavior in Social Media
Cyber-aggressive; Cyber-bullying; Machine learning
With the more usage of Internet and online social media, platforms creep with lot of cybercrimes. Texts in the online platforms and chat rooms are aggressive. In few instances, people target and humiliate them with the text. It affects victim mental health. Therefore, there is a need of detecting the abuse words in the text. In this paper, a study of machine learning methods is done to identify the aggressive behavior. Accuracy can be improved by incorporating additional features. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Networks and Systems, Vol-490, pp. 507-513.
Pawar V., Assistant Professor, Department of Computer Applications, CMR Institute of Technology, Bengaluru, India, Research Scholar, Department of Computer Science, Christ Deemed to be University, Bengaluru, India; Jose D.V., Christ Deemed to be University, Bangalore, India
Machine Learning Model Enabled with Data Optimisation for Prediction of Coronary Heart Disease
Accuracy; Classification Algorithms; Coronary Heart Disease; Cross-Validation; Data Preprocessing; Feature Engineering; Machine Learning; Model Generalization; Prediction; Risk Assessment
Cardiovascular disorders remain leading cause for mortality worldwide, necessitating robust early risk assessment. Although machine learning models show promise, most rely on conventional preprocessing, which lacks model portability across datasets. We propose an integrated preprocessing pipeline enhancing model generalizability. Our methodology standardises features solely based on training statistics and then transforms test data identically to prevent leakage. We handle class imbalance through synchronised oversampling, enabling consistent performance despite distribution shifts. This framework was evaluated on an open-source dataset of clinical parameters from an African cohort using classifiers like support vector machines and gradient boosting. All models achieved upto 80% accuracy. Remarkably, evaluating the identical models on five external European and Asian datasets maintains 80% - 86% accuracy. Our reproducible data conditioning strategy enables precise and transportable heart disease risk prediction, overcoming population variability. The framework provides the flexibility to readily retrain models on new data or update risk algorithms for clinical implementation in diverse locales. Our work accelerates the safe translation of machine learning to guide cardiovascular screening worldwide. 2024 IEEE.
Kanth P.C.; Vijayalakshmi S.; Palathara T.S.
https://doi.org/10.1109/TQCEBT59414.2024.10545194 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204476908&doi=10.1109%2fTQCEBT59414.2024.10545194&partnerID=40&md5=6eea7c45b36395074ef643eba4c6a2c6
Kanth P.C., Christ University, Department of Data Science, India; Vijayalakshmi S., Christ University, Department of Data Science, India; Palathara T.S., Christ University, Department of Data Science, India
Machine Learning Model for Depression Prediction during COVID-19 Pandemic
covid-19; depression; machine learning; pandemic; Prediction
Depression is an unfamous mental health disorder that has affected half the population worldwide. In December 2019, the break of the COVID-19 pandemic was first spotted in Wuhan, China, and later spread to 212 countries and territories worldwide, impacting half the population. It took a significant toll on their physical health and their mental health. Many among the population lost their loved ones, businesses, and being in quarantine for years, completely shifted to the online mode made everyone's life miserable. Many may be dealing with escalated levels of alcohol and drug use, sleeplessness, and an anxious state of mind. So, the need to address this and help the severely affected ones is significant. Self-quarantine also causes additional stress and challenges the mental health of citizens. This paper intends to identify the people who were mentally affected by the pandemic using machine learning techniques. A survey was conducted among college-going students and professionals. The paper used classification techniques such as Naive Bayes, KNN, Random Forest, Logistic Regression, k-fold cross-validation to get results. Support Vector Machine gave the maximum accuracy of 99.35%. 2022 IEEE.
IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022
https://doi.org/10.1109/ICDCECE53908.2022.9792792 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133415857&doi=10.1109%2fICDCECE53908.2022.9792792&partnerID=40&md5=2ce25bc59ff6dae5ee3c96ce79d588c3
Joshi S., Christ University, Department of Data Science, Bangalore, India; Ghosh A., Christ University, Centre for Counselling and Health Services, Bangalore, India; Shukla S., Christ University, Department of Data Science, Bangalore, India
Machine Learning Model to Detect Chronic Leukemia in Microscopic Blood Smear Images
Chronic lymphocytic Leukemia; Chronic Myeloid Leukemia; image processing; Machine Learning; Random Forest; Watershed Segmentation
Chronic leukemia is a slow-progressing form of disease, If not diagnosed on time can progress and increase the risk of life-threatening complications. It is essential to develop a fully automated system to recognize and categorize type of leukemia for proper evaluation and treatment. This paper aims to provide a machine learning model to identify and classify chronic lymphocytic leukemia, chronic myeloid Leukemia and healthy cells. Digital microscopic blood smear images were automatically cropped into single nucleus and segmented using watershed algorithm. Grey level co-occurrence matrix (GLCM) and geometrical features were extracted from the segmented nucleus images and random forest algorithm is used to classify chronic leukemia and healthy cells. This prognosis aids pathologists and physicians in identifying leukemic patients early and selecting the most effective course of action. 2023 IEEE.
Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
Patil A.P., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Hiremath M., CMR Institute of Technology, Department of Computer Application, Bengaluru, India
Machine Learning Models for Apple Disease Detection With Texture Feature Fusion and Feature Selection
Apple diseases; Boruta; feature fusion; LDA; PCA; Random Forest; ReliefF; texture features; XGBoost
Computer vision has become an integral part of modern agriculture. One of the key applications of Computer vision is the automatic detection and classification of plant disease from digital images of plant leaves. In this study we evaluate the discriminatory capability of selected texture features and their fusion in identifying plant diseases from leaf images. Further, the performance of four feature selection algorithms is also evaluated. Texture features are extracted from resized raw images. Experiments are carried out with public data sets of Apple plants. Through extensive experimentation, two classifiers - Random forest and XGBoost are chosen for the evaluation. The feature fusion and feature selection resulted in 85% accuracy. The result is promising as the features are extracted from whole leaf images, without any segmentation. 2025 IEEE.
Prashant, C.; Acharya, Biswaranjan; Vijaya, P.; Bejoy, B.J.; Raju, G.; Singh, Ajay Vikram
2025 12th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2025;
https://doi.org/10.1109/ICRITO66076.2025.11241915 https://www.scopus.com/pages/publications/105029839218?origin=resultslist
Prashant C., Christ University, Bengaluru, India; Acharya B., Marwadi University, Department of AI, Ml and Ds, Rajkot, India; Vijaya P., Modern College of Business and Science, Department of Mathematics and Computer Science, Bowshar, Muscat, Oman; Bejoy B.J., Christ University, Bengaluru, India; Raju G., Sahrdaya College of Engineering and Technology, Kerala, Thrissur, India; Singh A.V., Aiit, Amity University, Uttar Pradesh, India
Machine Learning Models for SMS Spam Detection
Decision Trees; Ham; Logistic Regression; Machine learning; Nae Bayes; Random Forest; Short Messaging Service; Spam; Spam detection
With the increasing reliance on mobile communication, detecting spam messages sent via Short Messaging Service (SMS) has become more important. This advent has created a new era for spam in peoples lives, one that calls for quick attention and automatization in categorizing messages. This study analyzes three machine learning algorithmsLogistic Regression, Naive Bayes, and Decision Tree resulting in the binary classification of SMS messages into either spam or not spam (ham). To achieve effective spam detection, the study highlights the significance of feature engineering, model selection, and evaluation metrics such as accuracy, precision, recall, and F1-score. The research challenges, including unbalanced data, changing spam strategies, and the requirement for scalable solutions, are handled in this study. During experimentation, it was observed that Logistic Regression increased performance by 98.07% accuracy. The results also showed the advantages and disadvantages of each model, providing guidance on which strategy, is best for SMS spam filtering apps in the real world. This analysis aims to give readers a thorough grasp of existing approaches and how they might be used to improve the effectiveness and security of mobile communication systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Basumatary, Baknai; Norbu, Tenzin Khetsuen; Kokatnoor, Sujatha Arun; Kumar, Sandeep
Lecture Notes in Networks and Systems;Volume;1266 LNNS;pp.423-433
https://doi.org/10.1007/978-981-96-2647-2_29 https://www.scopus.com/pages/publications/105008647438?origin=resultslist
Basumatary B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India; Norbu T.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India
Machine Learning Observation on the Prediction of Diabetes Mellitus Disease
Diabetes; Machine Learning; Prediction; Training Model
Diabetes disease has become as one of the common syndromes in many of the age groups. Diabetes can result in high blood sugar levels, a heart attack, or heart disease. This is one of the fastest developing illnesses, and it requires regular care. After seeing the doctor and being diagnosed, the patient is typically compelled to obtain their reports. Because this procedure is time-consuming and costly, we have the option of using ML approaches to solve this problem. Our research aims to foster a framework prepared to do all the more precisely foreseeing a patient's diabetes risk level. To develop models, classification methods such as Logistic Regression, K-Nearest Neighbor, Support Vector Machine, and Random Forest Classifier are employed. The results indicate that the techniques are quite accurate. The result showed that the prediction with the Logistic Regression model acquired the highest accuracy. 2023 IEEE.
Kumar P.D.; Priya S.L.; Kumar K.P.; Rego A.M.
Kumar P.D., CHRIST (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Priya S.L., CHRIST (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Kumar K.P., CHRIST (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Rego A.M., CHRIST (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India
Machine Learning Research Methods for Identifying Inaccurate Content
Artificial intelligence; Authenticity; Data analysis; Deception recognition; Deep learning; Detection; Facial expression; Fake news; Lie detection; Machine learning; Machine learning; Neural networks; Social networks; Technology
Social media, especially when disseminating news, is a valuable information resource. The paper presents methods for detecting fake news, comparing their effectiveness, identifying existing problems, and describes the vectors of further development of this research area. The paper begins with a description of the relevance of the Fake News problem, which clearly describes the negative impact of false news on all spheres of human life. The following is a description of methods for detecting false news, starting from the usual rules of text analysis and ending with complex ML algorithms. In this paper, a comparative analysis of detection methods is carried out, which is based on criteria of efficiency and accuracy. The author identifies the main problems of existing methods related to data quality, changing Fake News formats and the difficulties of automatically determining the reliability of information. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Lapina, Maria; Anita, Mary; Bagautdinova, Alina; Lapin, Vitalii; Rudenko, Marina
Lecture Notes in Networks and Systems;Volume;1295;pp.193-201
https://doi.org/10.1007/978-981-96-3311-1_16 https://www.scopus.com/pages/publications/105005482773?origin=resultslist
Lapina M., North-Caucasus Federal University, Stavropol, Russian Federation; Anita M., Christ University, Bangalore, India; Bagautdinova A., North-Caucasus Federal University, Stavropol, Russian Federation; Lapin V., North-Caucasus Federal University, Stavropol, Russian Federation; Rudenko M., V. I. Vernadsky Crimean Federal University, Simferopol, Russian Federation
Machine Learning Technique to Detect Radiations in the Brain
Brain morphology; Electromagnetic field radiations; Image processing; Machine learning; Segmentation
The brain of humans and other organisms is affected in various ways through the electromagnetic field (EMF) radiations generated by mobile phones and cell phone towers. Morphological variations in the brain are caused by the neurological changes due to the revelation of EMF. Cellular level analysis is used to measure and detect the effect of mobile radiations, but its utilization seems very expensive, and it is a tedious process, where its analysis requires the preparation of cell suspension. In this regard, this research article proposes optimal broadcasting learning to detect changes in brain morphology due to the revelation of EMF. Here, Drosophila melanogaster acts as a specimen under the revelation of EMF. Automatic segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of EMF. The geometrical characteristics of the brain image of that is microscopic segmented are analyzed. Analysis results reveal the occurrence of several prejudicial characteristics that can be processed by machine learning techniques. The important prejudicial characteristics are given to four varieties of classifiers such as nae Bayes, artificial neural network, support vector machine, and unsystematic forest for the classification of open or nonopen microscopic image of D. melanogaster brain. The results are attained through various experimental evaluations, and the said classifiers perform well by achieving 96.44% using the prejudicial characteristics chosen by the feature selection method. The proposed system is an optimal approach that automatically identifies the effect of revelation of EMF with minimal time complexity, where the machine learning techniques produce an effective framework for image processing. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Gothai E.; Baseera A.; Prabu P.; Venkatachalam K.; Saravanan K.; SathishKumar S.
Computer Systems Science and Engineering, Vol-42, No. 1, pp. 149-163.
All Open Access; Hybrid Gold Open Access
https://doi.org/10.32604/CSSE.2022.020619 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121609894&doi=10.32604%2fCSSE.2022.020619&partnerID=40&md5=bf5b14750fa5d3b433c59a2534f30d31
Gothai E., Department of Computer Science and Engineering, Kongu Engineering College, Erode, 638060, India; Baseera A., School of Computing Science and Engineering, VIT Bhopal University, Bhopal, 466114, India; Prabu P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Venkatachalam K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Saravanan K., Department of Computer Science and Engineering, Erode Sengunthar Engineering College, Thudupathi, 638057, India; SathishKumar S., Department of EEE, M.Kumarasamy College of Engineering, Tamilnadu, Karur, 639113, India