Machine Learning in Financial Distress: A Scoping Review
subject: Artificial Neural Networks; Financial Distress Prediction; Machine Learning; Support Vector Machine
description: 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.
creator: Peralungal A.; Natchimuthu N.
source: International Research Journal of Multidisciplinary Scope, Vol-5, No. 3, pp. 457-474.
publisher: Iquz Galaxy Publisher
date: 2024-01-01
rights: All Open Access; Gold Open Access
relation: ISSN: 2582631X
format: Online
language: English
type: Article
coverage: 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 smart agriculture
description: 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.
creator: Raja A.T.; Arunachalam A.S.; Gobinath R.
source: Advanced Technologies for Smart Agriculture, pp. 129-151.
publisher: River Publishers
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 978-877022883-1; 978-877022848-0
format: Online
language: English
type: Book chapter
coverage: 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
description: 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.
creator: Bose M.; Aloysius G.C.; Rajendran R.K.
source: The Climate Change Crisis and Its Impact on Mental Health, pp. 72-84.
publisher: IGI Global
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-836933273-3; 979-836933272-6
format: Online
language: English
type: Book chapter
coverage: 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
subject: Academic Achievement; Machine Learning Models; Smart Phone; Student Health
description: 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.
creator: Biswas P.; Krishnan D.R.; Basha M.S.A.; Sucharitha M.M.
source: 2024 3rd International Conference for Advancement in Technology, ICONAT 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835035417-1
format: Online
language: English
type: Conference paper
coverage: 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
subject: Crude Oil; Data Analysis; Feature Engineering; Machine Learning Algorithms; Model Evaluation; Predictive Analysis; Random Forest; Support Vector Machine
description: 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.
creator: Toppo A.; Mahajan J.; Singh V.P.; Paswan A.S.; Saxena A.
source: TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835038427-7
format: Online
language: English
type: Conference paper
coverage: 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
description: 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.
creator: Winster Praveenraj D.D.; Madeswaran A.; Pastariya R.; Sharma D.; Abootharmahmoodshakir K.; Dhablia A.
source: E3S Web of Conferences, Vol-540
publisher: EDP Sciences
date: 2024-01-01
rights: All Open Access; Gold Open Access
relation: ISSN: 25550403
format: Online
language: English
type: Conference paper
coverage: 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
subject: Education; etc.; Machine Learning; Online class
description: 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 onl…
creator: Rajagopal M.; Ali B.; Sharon Priya S.; Aisha Banu W.; Madhavi M.G.; Punamkumar
source: Proceedings of 8th IEEE International Conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835034779-1
format: Online
language: English
type: Conference paper
coverage: 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
subject: ADFA-LD; Anomaly Detection; Host Intrusion Detection System; Linux; Machine Learning
description: 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.
creator: Khandelwal P.; Likhar P.; Yadav R.S.
source: 2022 2nd International Conference on Intelligent Technologies, CONIT 2022
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022-01-01
rights: Restricted Access
relation: ISBN: 978-166548407-7
format: Online
language: English
type: Conference paper
coverage: 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
subject: Cyber-aggressive; Cyber-bullying; Machine learning
description: 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.
creator: Pawar V.; Jose D.V.
source: Lecture Notes in Networks and Systems, Vol-490, pp. 507-513.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981194051-4
format: Online
language: English
type: Conference paper
coverage: 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
subject: Accuracy; Classification Algorithms; Coronary Heart Disease; Cross-Validation; Data Preprocessing; Feature Engineering; Machine Learning; Model Generalization; Prediction; Risk Assessment
description: 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.
creator: Kanth P.C.; Vijayalakshmi S.; Palathara T.S.
source: TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835038427-7
format: Online
language: English
type: Conference paper
coverage: 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
subject: covid-19; depression; machine learning; pandemic; Prediction
description: 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.
creator: Joshi S.; Ghosh A.; Shukla S.
source: IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022-01-01
rights: Restricted Access
relation: ISBN: 978-166548316-2
format: Online
language: English
type: Conference paper
coverage: 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
subject: Chronic lymphocytic Leukemia; Chronic Myeloid Leukemia; image processing; Machine Learning; Random Forest; Watershed Segmentation
description: 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.
creator: Patil A.P.; Hiremath M.
source: Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033577-4
format: Online
language: English
type: Conference paper
coverage: 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 Observation on the Prediction of Diabetes Mellitus Disease
subject: Diabetes; Machine Learning; Prediction; Training Model
description: 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.
creator: Kumar P.D.; Priya S.L.; Kumar K.P.; Rego A.M.
source: Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033577-4
format: Online
language: English
type: Conference paper
coverage: 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 Technique to Detect Radiations in the Brain
subject: Brain morphology; Electromagnetic field radiations; Image processing; Machine learning; Segmentation
description: 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,…
creator: Gothai E.; Baseera A.; Prabu P.; Venkatachalam K.; Saravanan K.; SathishKumar S.
source: Computer Systems Science and Engineering, Vol-42, No. 1, pp. 149-163.
publisher: Tech Science Press
date: 2022-01-01
rights: All Open Access; Hybrid Gold Open Access
relation: ISSN: 2676192; CODEN: CSSEE
format: Online
language: English
type: Article
coverage: 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

Machine Learning Techniques for Automated Nuclear Atypia Detection in Histopathology Images: A Review
subject: Breast cancer; Deep learning; Histopathological image; Machine learning; Nuclear atypia detection; Nuclear pleomorphism
description: Nuclear atypia identification is an important stage in pathology procedures for breast cancer diagnosis and prognosis. The introduction of image processing techniques to automate nuclear atypia identification has made the very tedious, error-prone, and time-consuming procedure of manually observing stained histopathological slides much easier. In the last decade, several solutions for resolving this problem have emerged in the literature, and they have shown positive incremental advancements in this fieldof study. The nuclear atypia count is an important measure to consider when assessing breast cancer. This work provides a comprehensive review of automated nuclear atypia scoring process which includes the current advancements and future prospects for this critical undertaking, which will aid humanity in the fight against cancer. In this study, we examine the various techniques applied in detecting nuclear atypiain breast cancer as well as the major hurdles that must be overcome and the use of benchmark datasets in this domain. This work provides a comprehensive review of automated nuclear atypia scoring process which includes the current advancements and prospects for this critical undertaking, which will aid humanity in the fight against cancer. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Varghese J.; Saleema J.S.
source: Lecture Notes in Networks and Systems, Vol-563, pp. 717-740.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981197401-4
format: Online
language: English
type: Conference paper
coverage: Varghese J., Department of Computer Science, Christ Deemed to Be University, Karnataka, Bengaluru, 560029, India, Christ Academy Institute for Advanced Studies, Karnataka, Bengaluru, 560083, India; Saleema J.S., Department of Computer Science, Christ Deemed to Be University, Karnataka, Bengaluru, 560029, India

Machine Learning Techniques for Resource-Constrained Devices in IoT Applications with CP-ABE Scheme
subject: CP-ABE; Encryption; IoT; Machine learning; Security
description: Ciphertext-policy attribute-based encryption (CP-ABE) is one of the promising schemes which provides security and fine-grain access control for outsourced data. The emergence of cloud computing allows many organizations to store their data, even sensitive data, in cloud storage. This raises the concern of security and access control of stored data in a third-party service provider. To solve this problem, CP-ABE can be used. CP-ABE cannot only be used in cloud computing but can also be used in other areas such as machine learning (ML) and the Internet of things (IoT). In this paper, the main focus is discussing the use of the CP-ABE scheme in different areas mainly ML and IoT. In ML, data sets are trained, and they can be used for decision-making in the CP-ABE scheme in several scenarios. IoT devices are mostly resource-constrained and has to process huge amounts of data so these kinds of resource-constrained devices cannot use the CP-ABE scheme. So, some solutions for these problems are discussed in this paper. Two security schemes used in resource-constrained devices are discussed. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Ancy P.R.; Krishna A.V.N.
source: Lecture Notes in Networks and Systems, Vol-613 LNNS, pp. 557-566.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981199378-7
format: Online
language: English
type: Conference paper
coverage: Ancy P.R., Computer Science and Engineering Department, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Krishna A.V.N., Computer Science and Engineering Department, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India

Machine Learning Techniques in Predicting Heart Disease a Survey
subject: and random forest; decision tree; heart disease; logistic regression; support vector machine
description: The heart serves an important role in living creatures. Diagnosis and forecast of cardiac illnesses demand greater precision, perfection, and accuracy because such tiny mistakes can lead to weariness and death. Numerous heart-related deaths have occurred, and the incidence rates have been rising over time. Predicting the development of heart disorders is important to work in the medical industry. Every month, many databases related to the patient are kept. The information gathered can be used to predict the occurrence of future diseases. This article gives an outline of cardiovascular diseases and modern treatments. Also, the focus of this research is to outline some current research on applying machine learning techniques to predict heart disease, analyze the many machine learning algorithms employed, and determine which technique(s) are useful and efficient. Artificial neural network (ANN), decision tree (DT), fuzzy logic, K-nearest neighbor (KNN), Naive bayes (NB), and support vector machine (SVM) are data mining and machine learning approaches used to predict cardiac disease. This paper includes an overview of the present method based on features, the algorithms are compared, and the most accurate algorithm is analyzed. 2022 IEEE.
creator: Roy R.E.; Kulkarni P.; Kumar S.
source: Proceedings - 2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022, pp. 373-377.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022-01-01
rights: Restricted Access
relation: ISBN: 978-150905001-7
format: Online
language: English
type: Conference paper
coverage: Roy R.E., Christ (Deemed to Be University), School of Engineering and Technology, Department of Computer Science and Engineering, Bangalore, 560074, India; Kulkarni P., Christ (Deemed to Be University), School of Engineering and Technology, Department of Computer Science and Engineering, Bangalore, 560074, India; Kumar S., Christ (Deemed to Be University), School of Engineering and Technology, Department of Computer Science and Engineering, Bangalore, 560074, India

Machine Learning Technology-Based Heart Disease Detection Models
description: At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Nae Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters…
creator: Nagavelli U.; Samanta D.; Chakraborty P.
source: Journal of Healthcare Engineering, Vol-2022
publisher: Hindawi Limited
date: 2022-01-01
rights: All Open Access; Gold Open Access; Green Open Access
relation: ISSN: 20402295; PubMed ID: 35265303
format: Online
language: English
type: Article
coverage: Nagavelli U., Dayananda Sagar Research Foundation, University of Mysore (UoM), Karnataka, Mysore, India; Samanta D., Dayananda Sagar Research Foundation, University of Mysore (UoM), Karnataka, Mysore, India, Department of Computer Science, CHRIST (Deemed to be) University, Karnataka, Bangalore, India; Chakraborty P., Department of Computer Science and Engineering, Comilla University, Cumilla, 3506, Bangladesh

Machine Learning with Data Science-Enabled Lung Cancer Diagnosis and Classification Using Computed Tomography Images
subject: data science; disease diagnosis; image processing; Lung cancer; machine learning; predictive models
description: In recent times, the healthcare industry has been generating a significant amount of data in distinct formats, such as electronic health records (EHR), clinical trials, genetic data, payments, scientific articles, wearables, and care management databases. Data science is useful for analysis (pattern recognition, hypothesis testing, risk valuation) and prediction. The major, primary usage of data science in the healthcare domain is in medical imaging. At the same time, lung cancer diagnosis has become a hot research topic, as automated disease detection poses numerous benefits. Although numerous approaches have existed in the literature for lung cancer diagnosis, the design of a novel model to automatically identify lung cancer is a challenging task. In this view, this paper designs an automated machine learning (ML) with data science-enabled lung cancer diagnosis and classification (MLDS-LCDC) using computed tomography (CT) images. The presented model initially employs Gaussian filtering (GF)-based pre-processing technique on the CT images collected from the lung cancer database. Besides, they are fed into the normalized cuts (Ncuts) technique where the nodule in the pre-processed image can be determined. Moreover, the oriented FAST and rotated BRIEF (ORB) technique is applied as a feature extractor. At last, sunflower optimization-based wavelet neural network (SFO-WNN) model is employed for the classification of lung cancer. In order to examine the diagnostic outcome of the MLDS-LCDC model, a set of experiments were carried out and the results are investigated in terms of different aspects. The resultant values demonstrated the effectiveness of the MLDS-LCDC model over the other state-of-The-Art methods with the maximum sensitivity of 97.01%, specificity of 98.64%, and accuracy of 98.11%. 2023 World Scientific Publishing Company.
creator: Vishwa Kiran S.; Kaur I.; Thangaraj K.; Saveetha V.; Kingsy Grace R.; Arulkumar N.
source: International Journal of Image and Graphics, Vol-23, No. 3
publisher: World Scientific
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 2194678
format: Online
language: English
type: Article
coverage: Vishwa Kiran S., Department of AI and ML, BMS Institute of Technology and Management, Karnataka, Bangalore, 560064, India; Kaur I., Department of CSE, Ajay Kumar Garg Engineering College, Uttar Pradesh, Ghaziabad, 201009, India; Thangaraj K., Department of IT, Sona College of Technology, Tamil Nadu, Salem, 636005, India; Saveetha V., Department of IT, Dr. N. G. P Institute of Technology, Tamil Nadu, Coimbatore, 641048, India; Kingsy Grace R., Department of CSE, Sri Ramakrishna Engineering College, Tamil Nadu, Coimbatore, 641022, India; Arulkumar N., Department of Computer Science, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560029, India

Machine Learning-Based Classification of Autism Spectrum Disorder across Age Groups
subject: Autism Spectrum Disorder; classifiers; cuckoo search; machine learning
description: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that has gained significant attention in recent years due to its increasing prevalence and profound impact on individuals, families, and society as a whole. In this study, we explore the use of different machine learning classifiers for the accurate detection of ASD in children, adolescents, and adults. Furthermore, we conduct feature reduction to identify key features contributing to ASD classification within each age group using Cuckoo Search Algorithm. Logistic Regression has the highest accuracy compared to the other two models. 2024 by the authors.
creator: Reghunathan R.K.; Venkidusamy P.N.P.; Kurup R.G.; George B.; Thomas N.
source: Engineering Proceedings, Vol-62, No. 1
publisher: Multidisciplinary Digital Publishing Institute (MDPI)
date: 2024-01-01
rights: All Open Access; Hybrid Gold Open Access
relation: ISSN: 26734591
format: Online
language: English
type: Article
coverage: Reghunathan R.K., Department of Computer Science, CHRIST University, Bengaluru, 560029, India; Venkidusamy P.N.P., Department of Computer Science, CHRIST University, Bengaluru, 560029, India; Kurup R.G., Department of Computer Science, CHRIST University, Bengaluru, 560029, India; George B., Department of Computer Science, Nirmala College Muvattupuzha, Muvattupuzha, 686661, India; Thomas N., Department of Computer Science, Santhigiri College, Vazhithala, Thodupuzha, 685583, India