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Predicting Liver Injury Risk from Chemical Properties and Drug Label Information Using Machine Learning Models
This research aims to create a drug-induced liver injury (DILI) severity prediction system based on machine learning to aid healthcare professionals in safety assessment. FDA's Liver Toxicity Knowledge Base supplied a drug dataset of 1042 drugs, and later, after pre-processing and API data extraction, each drug was defined by 16 chemical features such as molecular descriptors and pharmacokinetic properties. To improve uniformity and get quality input for training, data preparation involved correcting missing values, encoding categorical values, and normalising numerical data. Various machine learning models were trained and evaluated to forecast the levels of DILI severity, i.e., Random Forest, Gradient Boosting, and XGBoost. The importance of features was approximated for identifying the predictors that impacted the most. The best overall performance was recorded for XGBoost, and it had 81% accuracy when it was evaluated. Its acceptable discrimination was established for mild, moderate, and severe cases. The aptness of being applied to the medical sector is demonstrated by drastically lowering the principal misclassifications, especially from mild to severe. The application of machine learning in improving medicine safety assessment and reducing risks associated with pharmaceutical development is illustrated here. 2025 IEEE. -
Election Forecasting with Machine Learning and Sentiment Analysis: Karnataka 2023
Data science is rapidly transforming the political sphere, enabling more informed and data- driven electoral processes. The ensemble machine model which is made up of Random Forest Classifier, Gradient Boosting Classifier, and Voting Classifier, introduced in this paper makes use of machine learning methods and sentiment analysis to correctly forecast the results of the Karnataka state elections in 2023. Election features such as winning party, runner- up party, district name, winning margin, and voting turnout are used to evaluate the effectiveness of different machine learning paradigms. Similarly, it also makes use of sentiment analysis through party tweet and public reactions for further breaking down reliance upon past elections data alone. This study demonstrates that using both past historical records and current public opinion yields precise predictions about how electable leaders are. This reduces reliance on a historical dataset. The experimented results shows that, how machine learning and sentiment analysis can predict election results and provide useful data for election decision making. We compared various machine learning models in this study, including logistic regression, Grid SearchCV, XGBoost, Gradient Boosting Classifier, and ensemble model. With an accuracy of 85%, we demonstrated that our ensemble model outperformed machine models such as XGBoost and Gradient Boosting Classifier. It also offers a novel method for predictive analysis. 2023 IEEE. -
Non-orthogonal multiple access wireless systems using deep learning
In 5G networks, non-orthogonal multiple access (NOMA) increases spectral efficiency and user capacity greatly by letting multiple users share the same time, frequency, and code resources. Wireless communication systems stand to benefit significantly from deep learning owing to its ability to model intricate patterns. This chapter centers around deep learning-NOMA integration with special attention given to areas like channel estimation, interference management, and dynamic resource allocation. Using advanced deep learning frameworks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and deep reinforcement learning (DRL), this chapter demonstrates how NOMA system performance can be optimized to meet the stringent requirements of 5G and beyond networks. Moreover, this chapter also discusses the challenges associated with implementing deep learning in NOMA including computational complexity and data requirements, alongside future trends like federated learning and edge computing among others. The integration of these technologies promises improved network efficiency, reduced latency, and enhanced user experience, thereby making NOMA a fundamental technology in wireless communication evolution. 2025 selection and editorial matter, Mariyam Ouaissa, Mariya Ouaissa, Hanane Lamaazi, Khadija Slimani, Ihtiram Raza Khan, and B. Sundaravadivazhagan. -
Transforming healthcare engagement in the medtech industry through digital marketing
[No abstract available] -
TAMIL- NLP: Roles and Impact of Machine Learning and Deep Learning with Natural Language Processing for Tamil
Reading information in your mother tongue gives the feeling of enjoying juice of fruit. Researchers are working on regional languages to provide convenient and perfect automated tools to convert the content of knowledge from other languages. There exist many challenges based on the grammar of language. One of the classic regional languages, Tamil which is rich in Morphology, contains more processing challenges. The Natural Language Processing (NLP) technique along with Machine Learning (ML) and Deep Learning (DL) algorithms have been used to overcome those challenges. The accuracy of work is depending on the corpus provided to train the model. Among the reviewed papers using Support Vector Machine (SVM) of ML produced higher accuracy then other ML techniques. As DL techniques for NLP are booming one the researchers are working with different DL algorithms. Most of the NLP with Review Discussion in this paper will direct the researchers doing NLP in Tamil language to move further and to choose the right Machine Learning and Deep Learning algorithm to come out with accurate outcomes. 2023 IEEE. -
Toward precision agriculture in Cyber-Physical Agricultural System
Agriculture 4.0 or Agri 4.0 is a newly developed system that consists of various digital technologies adapted from Industry 4.0 based on smart automation. Agriculture 4.0 is a subset of Industry 4.0 aimed at sustainable precision agriculture (PA) and increasing agricultural efficiency using digital technologies and the Internet of Things. The cyber-physical system (CPS) is the seamless integration of digital and physical domains and when CPS is applied in agriculture, it is termed cyber-physical agricultural system (CPAS). The application of CPS in carrying out PA with sustainable management of resources is termed Agri 4.0. Research papers are reviewed to understand the bigger picture behind various details of digital technologies and CPS with a focus on agriculture 4.0 and to determine its applications, challenges, and developments in the field. It is apparent that most of the small and marginal farms in remote areas are not able to use this technology due to a lack of knowledge and resources. It is the need of the hour to support these farmers by making favorable policies and appropriating budgets such that it will lead to more profitable and sustained PA and in the process contribute to the social and economic upliftment of farmers of India. 2024 Elsevier Inc. All rights reserved. -
Research aligned analysis on web access behavioral pattern mining for user identification
Human activity understanding includes activity recognition and activity pattern discovery. Monitoring human activity and finding abnormality in their activities used by many field like medical applications, security systems etc. Basically it helps and support in decision making systems. Mining user activity from web logs can helps in finding hidden information about the user access pattern which reveals the web access behaviour of the users. Clustering and Classification techniques are used for web user identification. Clustering is the task of grouping similar patterns for web user identification. Classification is the process of classifying web patterns for user identification. In this paper we have implemented the existing works and discussed the results here to find the limitations. In existing methods, many data mining techniques were introduced for web user behaviour identification. But, the user identification accuracy was not improved and time consumption was not reduced. Our objective is to study the existing work and explore the possibility to improve the identification accuracy and reduce the time consumption using machine learning and deep learning techniques. BEIESP. -
Performance Analysis of User Behavior Pattern Mining Using Web Log Database for User Identification
User behavior analytics is a progressive research domain. Understanding the users behavior patterns and identifying their behavior patterns will provide solutions to many issues like identity theft and user authentication. So many research works are done in analyzing the frequent access patterns of the users by pre-processing access logs and applying various algorithms to understand the frequent access behavior of the user. From the literature, it founds that the frequent user access pattern identification needs improvement on prediction accuracy and the minimal false positives. To accomplish these, three different approaches were proposed to overcome the existing issues and intended to reduce false positives and improve the frequent pattern mining accuracy based on web access logs. Proposed methods were found to be good while compared with the existing works. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Text-Based Sentimental Analysis to Understand User Experience Using Machine Learning Approaches
Data Analysis is turning into a driving force in every industry. It is a process in which data is analyzed in multiple ways to come to certain conclusions for the given situation. Sentiment analysis can be said to be a sub-section of data analysis where analysis is carried out on the emotions and opinions of the text. Social media has a plethora of sentiment data in various forms such as tweets, updates on the status, and so forth. Sentiment analysis on the huge volume of data can help in identifying the opinions of the general mass.The primary goal is to find the opinion of customers on the services of the Bangalore airport and to enhance the nature of these services according to the feedback provided. In this paper, we aim to measure customer opinion on services provided by Bangalore Airport through sentiment. Data is collected by a python-based scraper. The tweets are processed to determine whether they are of positive or negative opinion. These opinions are then analyzed to determine the factors which cause the negative opinions and the airport staff are alerted about the same. Various algorithms were used as part of the experimental analysis. LSTM produces more accuracy compared with existing approaches. 2023 IEEE. -
ResFruitGrader: Leveraging Residual Networks for Advanced Fruit Quality Grading Systems
The rising agricultural industrys requirement for effective sorting and grading procedures has increased the demand for automated and precise fruit quality assessment in recent years. This study aims to attain high classification accuracy by investigating the use of Convolutional Neural Networks for fruit quality identification. As customers place a higher value on fresh and wholesome options, the agriculture and food industries must meet rising demands for premium produce. Fruit quality must be guaranteed since it directly affects consumer happiness and the profitability of the sector. Preprocessing methods, CNN model creation, training, and evaluation utilizing cutting-edge deep learning techniques comprise the methodology applied in our study. The research demonstrates the CNN-based methods stability and dependability in identifying a range of quality attributes, such as fruit imperfections, size, color, and maturity. The suggested CNN architecture performs remarkably well, recognizing fruit quality parameters with a 99.5% accuracy rate by utilizing a collection of various fruit photos. A promising path for improving efficiency and accuracy in fruit quality assessment within the agricultural industry is provided by the researchs insights into the transferability and scalability of the developed model for practical applications in automated fruit sorting systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Green accounting and its application: A study on reporting practices of environmental accounting in India
Green Accounting is an important device for understanding the role of business ventures in the economy towards environmental security and welfare. It is a well-known term for environment and natural resources accounting. Many companies all over the world have initiated the practices of making environmental disclosures in their annual reports. However, these practices are still largely voluntary in nature. The objective of this research paper is to study the environment-related disclosures of companies taken from Nifty 50 based on the summary of Global Reporting Standards. Content Analysis, both sector-wise and keyword-wise is used on the annual reports of 29 sample companies using MAXQDA software. A high count of the formulated keywords is observed in some relevant sectors of Energy, Cement and Metals. 2022 Inderscience Enterprises Ltd. -
Student Performance Prediction in Learners Centric Approach with Machine Learning
Predicting student performance helps educators locate students who are at risk and tailor appropriate and timely interventions for those students. This research proposes a learner-centered machine learning framework to integrate demographic, academic and behavioral features in order to predict student grade performance. The dataset consists of 2392 students and 15 attributes including age, gender, parental education, study time, absences, and extracurricular activities. Four supervised learning models - Logistic Regression, Decision Tree, Random Forest and Support Vector Machines (SVM) were trained and measured using 70:30 stratified split. The performance of the model was evaluated using accuracy, precision, recall, and F1-score metrics. Among these, Decision Tree classifier achieved the highest accuracy (92.48%) which was followed by Random Forest (88.31%), SVM (83.51%) and Logistic Regression (75.16%). The results show that such factors as study time, absences, and parental involvement were the most predictive. The proposed learner-centered approach shows that the combination of contextual, behavioral, and academic data can greatly increase the predictive accuracy and the interpretability of the data, facilitating early risk detection and intervention in education. The Authors, published by EDP Sciences. -
Exploring Consumer Choices and Shopping Patterns: Examining Influences on Consumer Choices
This chapter explores the dynamic world of consumer behaviour and buying patterns, focusing on the psychological, social, cultural, and economic factors that shape decisions. It examines how consumers manage their preferences and choices in various market situations, highlighting trends like sustainable consumption, loyalty-driven purchases, and impulsive buying. The chapter also investigates the impact of the digital revolution, including social media and e-commerce, on consumer engagement and purchasing habits. By addressing elements such as peer influence, brand perception, and decision-making processes, it emphasises the importance of understanding consumer diversity in demographics, culture, and lifestyle. Combining theoretical frameworks, real-world examples, and data-driven insights, this chapter provides businesses and researchers with a foundation for predicting demands and creating effective marketing strategies. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Wound Healing, Cell Viability and Antimicrobial Potency of Mucus from Pangasianodon hypophthalmus
Acute and chronic wounds are the major cause of death according to World Health Organization (WHO), in which, antimicrobial resistance is considered to be a major plight. In this regard, our study is aimed at developing an antimicrobial agent using the mucus of Pangasianodon hypophthalmus against the clinically resistant microbial pathogens and to evaluate the cell cytotoxicity and cell viability followed by an in vitro wound healing analysis. The evaluation of antimicrobial activity was performed through well diffusion method and micro dilution method. The cell cytotoxicity and cell viability were assessed using MTT assay. The cell migration and in vitro wound healing was performed using scratch assay. The acidic extracts of mucus showed antimicrobial activity against the eight different selected bacterial strains while the organic extract showed against seven bacterial strains. L929 showed a cell viability of 102.96% at a concentration of 75 g/mL and did not show cell toxicity effect up to the concentration of 300 g/mL. In the in vitro wound healing analysis, the cell migration rate was 99.27% in the treated cells while, the untreated showed only 94.68%. The current research work clearly shows that the mucus of P. hypophthalmus possesses antimicrobial activity and wound healing potency. Furthermore, gene expression analysis and in vivo trials have to be performed for a thorough understanding of the actual cellular mechanism of wound healing. The Author(s) 2024. -
A Systematic Review of Fish-Based Biomaterial on Wound Healing and Anti-Inflammatory Processes
Objective: To conduct a systematic literature review to study the effects of fish-based biomaterials on wound healing in both in vivo and in vitro animal models. Approach: This review covers the study reported in different articles between 2016 and August 2022 concentrating mainly on the cytotoxicity evaluation of different fish-based biomaterials on inflammation, reepithelialization and wound healing. Significance: This review shows considerable amount of research work carried out with fish-based biomaterials and collagen for treating burn wounds. Surprisingly there are only a few commercial products developed so far in this particular regard for surgical purpose and therefore, there is a way out and need for developing medical support product from fish-based biomaterials to treat and cure wounds. Recent Advances: Three-dimensional skin bioprinting technique is a large-scale solution for severe burn wounds that requires collagen as a raw material for printing, wherein fish collagen can be used in place of bovine and porcine, as it is biocompatible, promotes cell proliferation, adhesion, and migration, and degrades enzymatically. In the recent times, there are a few fish-based surgical products that have been formulated by Kerecis in United States. Critical Issues: The different fish-based biomaterial products are all mere supplements taken in orally as food or supplements till date and there is no proper proven medications that has been formulated so far in the field of wound healing and inflammation based on fish biomaterials except the surgical products that can be finger counted. Future Directions: Fish-based biomaterials are known for the medicinal properties that are used throughout the world and further investigations should be carried out to understand the actual physiochemical properties of its derivatives for the discovery of novel products and drugs. Copyright 2024 by Mary Ann Liebert, Inc. -
Malicious node detection using heterogeneous cluster based secure routing protocol (HCBS) in wireless adhoc sensor networks
In wireless, every device can moves anywhere without any infrastructure also the information can be maintained constantly for routing the traffic. The open issues of wireless Adhoc network the attacks which are chosen the forwarding attack that is dropped by malicious node to corrupt the network performance then the information integrity exposure. Aim of the problem that existing methods in Adhoc network for malicious node detection which cannot assure the traceability of the node as well as the fairness of node detection. In this paper, the proposed heterogeneous cluster based secure routing scheme provides trust based secure network for detection of attacks such as wormhole and black hole caused by malicious nodes presence in wireless Adhoc network. The simulation result shows that the proposed model is detect the malicious nodes effectively in wireless Adhoc networks. The malicious node detection efficiency can be achieved 96% also energy consumption also 10% better than existing method. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
Design and validation of the digital well-being scale
As the reliance on digital products and services continues to increase, there arises the need to measure and understand how the use of digital devices affects our well-being. In order to do so, the researchers attempted to create and validate an instrument. The items for the instrument were identified through an extensive review of literature, followed by a brainstorming session. The statements were then validated by a panel of experts, post which the instrument was administered, and the data was collected and analyzed for reliability and validity. The final instrument returned a Cronbachs alpha score of 0.921, indicating high reliability. The validity of the instrument was also established through a confirmatory factor analysis. 2023, University of Bologna. All rights reserved. -
Virtual Community Mentoring Models for Middle School Underachievers Psychosocial Development and Well-Being During COVID-19
Recent studies highlight the outcomes of COVID-19 on the psychosocial skills of early adolescents. It shows the unavailability of virtual community mentoring models for teenagers' individual and interpersonal growth in the virtual scenario. Hence, there emerges a need to explore and apply the available virtual communication resources by facilitators, families, and other community professionals for teenagers self-development. This article reports the application of virtual resources like WhatsApp, graphic design platforms (CANVA and Adobe), graphic interchange formats (GIPHY App), all-in-one visual content editing forums (InShot App), and memes (Meme Generator App) in engaging and supporting community mentoring capacities leading to psychosocial development and well-being for teenagers during COVID-19. Through this article, contemporary virtual models are explored and executed with community guidance to integrate the personal developmental skills of middle school underachievers. There is also a need to work with community interventions by using virtual mentoring skillsets for positive youth development. 2022, Commonwealth of Learning. All rights reserved. -
Development and validation of middle school under performers checklist in India through virtual platforms post-COVID-19
Education is a holistic development that must be nurtured through hybrid or virtual educational practices. The pandemic brought a sense of psychosocial distress among teenagers that urged the need to understand these psychosocial competencies. Often, our Indian education system is unable to assess the challenging psychosocial competencies of learners in varied learning platforms. Hence, there is a need in todays context to harness adolescents holistic learning to be more flexible, and interactive with innovative instructional methodologies, creative assessment strategies, and virtual resource tools. These psychosocial open learning resources need to be advocated by educators and counsellors for the well-being of teenagers. Thus, this quantitative study aimed to develop a checklist to identify the psychosocial concerns of underperformers in an open learning system. Hence, educators and counsellors must be equipped to recognise their psychosocial concerns to handhold them into becoming autonomous thinkers and contributors in their society. This would further establish the seed of sustainability. Thus, this study aimed to develop and validate a checklist as a psychometric measure to identify middle school underperformers social and personal abilities. The study group comprised 359 school educators and counsellors in Bangalore and Mashed, India (299 educators and 60 counsellors). The checklist was developed using Develop (2016) and Oldenburgs principles of scale development (2021). The Cranach coefficient of the checklist was.924 for 12 items. The statistical results indicated the validation of the checklist as a tool for identifying psychosocial challenges of eighth-grade underperformers as reliable. Exploratory Factor Analysis reduced these items into two distinctive factors. The findings suggest that the checklist can be used as an innovative educational toolkit to identify middle school underperformers personal and social abilities. Further experimentation of this study can be taken up with a larger intergenerational population. 2025 selection and editorial matter, Asma Parveen and Rajesh Verma; individual chapters, the contributors. -
Design, analysis and fabrication of EV with level-1 autonomous vehicle capability
The fact to this day remains true and the same for over a hundred years the Automobile industry and vehicles, in general, have become the pivoting point in our day to day lives. We might as well call it a necessary evil. Although it is very true that they have made our lives more convenient when we speak in terms of transportation; the pollution that conventional IC engine vehicles produce hasn't done much to create a cleaner environment especially with Global warming on the rise as we speak. The simplest remedy would be is to replace IC engine vehicles with Electric one, EV. A Problem common to both conventional IC engine vehicles and EV's alike is the accidents occurring due to collision caused by human error on-road. While safety measures have greatly been taken in order to reduce the damage done to the driver and passengers in the event of a collision it would be far better to avoid the collision altogether. Thus having at least, a Level-1 Autonomous Vehicles capability where the system alerts the driver in the event of a crash or collision and deploy full braking capability. Thanks to increasing urbanization and the advent of modern technology the need of the hour of the 21st century has given rise to high demands for employment in the motorized transport sectors. The authors were successfully able to design, analyze and fabricate an EV with Level-1 Autonomous Vehicles capability. The successful implementation of this project will help in reducing not only pollution and accidents occurring on-road due to vehicle collision but also pave paths in alimenting Level-1 Autonomous Vehicles capability in EV's inexpensively. 2020 Author(s).