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Localised actor roles in post-disaster housing recovery: A case study from Kerala
The effectiveness of post-disaster housing reconstruction (PDHR) is increasingly being challenged by the frequency and complexity of climate-induced disasters. In the Indian state of Kerala-particularly the highland regions of Kottayam and Idukki-landslides and floods have caused significant housing losses in recent years. While the government initiated housing recovery interventions after the 2021 landslide event, multiple civil society actors, including faith-based organisations, political parties, and professional groups, also participated in reconstruction efforts. This study examines the actor-specific approaches to community consultation in PDHR and their impact on beneficiary satisfaction. Using a qualitative case study design, the analysis identifies variations in participation across planning, design, and construction stages, and maps these to outcomes such as reconstruction speed, satisfaction levels, and community cohesion. While some actors offered comprehensive engagement strategies, others limited their consultation, resulting in mismatches between needs and outcomes. Findings suggest that community consultation remains uneven and often symbolic, with beneficiaries perceiving external aid as benevolence rather than entitlement. The study underscores the importance of meaningful participation in PDHR, especially in the context of localized climate events. These insights offer practical implications for designing inclusive recovery frameworks and enhancing community resilience in hazard-prone regions. The Authors, published by EDP Sciences, 2025. -
Urban Heat Island in Bengaluru: Built-up Growth and Temperature Trends
Bangalore, once renowned as India's "Garden City"has transformed into a "Silicon Valley"metropolis over the past five decades, experiencing profound environmental changes. This study investigated the direct correlation between the city's extensive built-up area expansion and rising temperatures, drawing upon comprehensive literature and climate data. The analysis revealed a dramatic 1055% increase in built-up areas, from 7.97% in 1973 to 93.3% in 2023.Concurrently, vegetation cover has plummeted by 88% (from 68.27% to approximately 6%), and water bodies have decreased by 79%. These significant land-use alterations have led to notable thermal shifts in the region's climate. Land Surface Temperatures (LST) increased by 7.9 C, from 33.08 C in 1992 to 41 C in 2017, while average air temperatures rose by 0.23 C per decade since 1975.The urban heat island (UHI) effect was pronounced, with an average annual nighttime surface UHI of 0.99 C. A strong inverse relationship between vegetation cover and LST (R =-0.74 in dry seasons,-0.34 in wet seasons) confirms the critical role of green spaces in urban areas. This evidence unequivocally attributes the escalating UHI effect to the rapid, unplanned urban growth of Bangalore, underscoring the urgent need for sustainable urban planning. The Authors, published by EDP Sciences, 2025. -
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. -
Improvised hand layup fabrication of alkali treated jute epoxy composites: A comparative study of positive and vacuum-assisted compaction
There lie several benefits of using fiber composites which have increased the desire for using these materials in various higher-level applications. They have been widely used in automobile sector, aerospace, sports industry, medical field, and so on. This has created a demand for better manufacturing techniques with cost-effectiveness. This work has been focused on improvising the hand layup procedure. To enhance the properties of the samples prepared by this conventional method, surface treatment was incorporated. Woven jute fiber was chemically treated with KOH under various sizing conditions. Hand layup was carried out for the samples followed by applying pressure considering two different methods; vacuum- assisted compaction and positive compaction. The jute composites prepared by the positive compaction hand layup technique were found to be better than the vacuum-assisted or negative compaction composites for the same set of sizing samples. There is a maximum increase of 32.4% in the tensile strength of treated composites prepared by positive compaction in comparison to untreated samples. On the other hand, the values of all the treated samples showed a reduction in tensile strength with a maximum decrease of 50% than the untreated sample for the negative compaction technique. The Authors, published by EDP Sciences, 2026. -
Assessment of AI Companies Operational Performance in India
The article has delved into understanding the importance and performance of Artificial Intelligence companies in India with the data of the past five years. It scrutinizes the performance and significance of Artificial Intelligence (AI) companies in India over the past five years. It delves into the financial data, specifically examining Profit After Tax as an independent variable and its relation to net cash flows in these companies. The analysis involves nine prominent AI companies in India and employs statistical tools such as correlation, regression, standard deviation, and one-way ANOVA. The findings indicate varying relationships between Profit After Tax and net cash flows across different companies, underscoring the complexities within their financial dynamics. While some companies exhibit a positive correlation, others show no direct relationship. Additionally, the article explores government guidelines and existing laws that impact AI companies in India, emphasizing ethical and responsible AI usage. Despite the evolving market, the article suggests a promising future for AI companies in India, contingent upon their ability to tailor solutions to the unique challenges and opportunities in the Indian landscape. 2025 Author(s). -
Automated Door with Password-Based Lock
The application of this work is to lock the door and ensure the safety of our space. This was done with heavy locks earlier. Locks do not ensure safety completely and there is a lot of tension around them. The main problem with traditional locks is that they are heavy, and their system is completely mechanical. The three basic ideas of this project are safety, privacy, and automation. This device is a password-based door lock system in which the door is opened and closed without any physical work, i.e. automatically. The key here is the password that the user has to enter to open the door. When the correct password is entered into the keypad, the microcontroller gives a command to the servo motor to rotate at a specific angle. If the incorrect password is entered, the motor will not do any operation and the user will not be allowed to enter. 2025 Author(s). -
Lightweight Sybil attack detection framework for wireless sensor network with cluster topology
The development of communication and networking technology has made it possible for wireless sensor networks to play a significant role in many fields. Wireless sensor networks are vulnerable to a variety of security threats because of their remote hostile features. The Sybil attack, which generates several identities to gain access to wireless sensor networks, is one such devastating but simple to spread exploit. This Paper proposes a novel identity and trust-based system to ensure protection against Sybil attacks. Analysis of the RSSI and location parameter increases the accuracy. It recognises the attackers and broadcasts information about them to all adjacent sensor nodes. Additionally, it offers other crucial security features. 2025 Author(s). -
On the influencing facets of infant mortality in Karnataka: A study based on birth orders
The infant mortality rate (IMR) is used to assess the overall physical health of any community. Reducing this and spreading awareness among people can improve the well-being of society. In India, IMR is high due to the complex and challenging health policies and increased population, but various socio-economic and demographic factors play a significant role in determining the infant mortality rate. This study majorly focuses on identifying the factors influencing infant mortality, and a model has been proposed to estimate the likelihood of an infant's survival in Karnataka. For the empirical analysis, data has been taken from the National Family Health Survey-4 (2015-16), India. It is found that mothers' education and female literacy are the most significant factors affecting the IMR irrespective of the birth order. It is also found that the various socio-economic and demographic factors do not have a significant influence on the survival status of an infant as the birth order increases. Other factors like preceding birth interval, wealth index, caste, and religion also influence infant mortality. Hence, it is suggested that parents should have access to quality education and health facilities near their place of residence to reduce infant mortality at each order of birth. 2025 Author(s). -
A study of failure prediction of Indian banks using various machine learning algorithms - An examination of predictive accuracy
Banks play a key role in strengthening the economy; hence their survival is very important. It is necessary to evaluate the failure probability of banks correctly based on the factors associated with it. Over half of the assets in the financial sector in India are held by the banking sector, which holds a strong position. Phased implementation of financial sector reforms has resulted in an exciting moment of rapid transformation for Indian banks. The study here focuses on the establishment of machine learning approach to compute and compare the extent of bankruptcy based on the accuracy measure-Support Vector Machine classification, Random Forest, Logistic Regression, Nae Bayes classification using the data of 250 Indian banks having qualitative variables from 2015 to 2020. The feature selection in this paper is based on correlation and relief algorithms. The explanatory features of the dataset are drawn by implementing a two-step feature selection technique and the selected features are fed and further used for prediction using the Random Forest technique, Logistic Regression, Support Vector Machine, and Nae Bayes classification techniques. The results reveal, that the support vector machine shows a score of 99.8% forecasting the highest accuracy. This research serves as a foundation for the decisions made by a variety of stakeholders, including analysts, policymakers, shareholders, and bank management, and it facilitates the comparison of the qualitative ratios of bankruptcy. The goal is to develop a prediction system that will allow the firms and businesses to be categorized according to the level of risk. 2025 Author(s). -
Customer churn behaviour prediction in telecommunication using classification algorithms and modelling
The cost of obtaining a high-quality client is usually five times more than the cost of keeping an existing customer. This is why it is very important that businesses keep their customers at home. To retain and improve their customers' satisfaction, researchers in various fields such as marketing, information technology, and business intelligence studied various ways to deliver the best possible services. Despite the good performance of the work done before, there is still a considerable gap in their prediction of the churners. In most cases, the training dataset is too large, and the high dimensionality of it causes the classification algorithms to fail. In the present paper, an attempt was made to estimate customer churn with greater accuracy in the membership of cellular wireless services using a call details records dataset consisting of 3333 clients having 21 attributes each. With the advancement of Machine Learning (ML) and artificial intelligence, most popular approaches such as logistic regression, CART, and C5 algorithms have been used with and without using the data balancing technique SMOTE. The performance evaluation of these predictive models is done using the model accuracy, confusion matrix, AUC value, ROC curve, and Cohen's Kappa statistics. The study results indicate that the C5 algorithm could estimate customer churn with an accuracy of more than 92% for both balanced and imbalanced datasets. 2025 Author(s). -
Enhancing Video Surveillance for Crime Detection Using Anomaly Detection Techniques
Security cameras are widely used to detect and prevent crimes, but the number of surveillance videos has increased due to this prevalence. The process of detecting similarities or data points that significantly depart from the norm or expected behavior of a given system is known as anomaly detection. Predictive maintenance, network intrusion detection, and fraud detection are just a few of the areas where anomaly detection is applied. By processing these videos with the help of a suitable machine learning algorithm, unfavorable events can be brought to the attention of experts to manually monitor. Since these unfavorable events are of various types and few in number, this problem can be addressed in the anomaly detection structure. An anomaly detection algorithm has been developed using the UCF-Crime dataset consisting of 1900 surveillance videos of various lengths. In this context, video surveillance refers to observing the scenes of improper human behaviors which are termed as real world anomalies. Depending on the availability of data sets, anomaly detection algorithms can be supervised, unsupervised, or semi- supervised. The quality of the data and the selection of the best algorithms determine how well anomaly detection techniques work. This paper proposes the use of anomaly detection techniques to enhance video surveillance systems for crime detection. By identifying unusual activities in surveillance footage, the system can alert authorities to potential criminal activity and improve overall security measures. The effectiveness of this approach is demonstrated through experiments and analysis of real-world surveillance data. 2025 Author(s). -
Prediction of CO and NOx Emission from Gas Turbine Using Machine Learning
In gas-turbine-based power plants, predictive emission monitoring systems (PEMS) are used to validate and back up the expensive continuous emission monitoring systems. Increasing energy consumption increased deforestation and carbon and flue gas emissions, harming the environment. The availability of relevant and ecologically sound data is crucial to their successful deployment. In this article, we adopted the Gas Turbine CO and NOx Emission Data Set Data Set from UCI machine learning repository to predict the CO And NOx emission from gas turbine using machine learning (ML). We developed the model using random forest and support vector algorithms. The random forest algorithm performs better for the data. 2025 Author(s). -
Novel mammography images approach for breast cancer diagnosis using ensemble feature extraction
By using ensemble feature extraction methods to mammography pictures, this study introduces a novel strategy for the early detection of breast cancer. Beginning with preprocessing stages that use data augmentation to improve the dataset, the technique incorporates a methodical flowchart. Following the creation and individual training of an ensemble model that incorporates CNN architectures like as DenseNet, AlexNet, and i-Alex, the final model attains an impressive level of accuracy. Optimized feature vectors are the end result of a process that begins with feature fusion and continues with dimensionality reduction methods like principal component analysis (PCA). Utilizing LASSO and ReliefF for feature selection helps to refine the collection of features, which in turn improves accuracy metrics. Utilizing cross-validated hyperparameter optimization, classifier training showcases the effectiveness of SVM, Random Forest, and XGBoost. The ensemble method is clearly better according to the performance assessment, which takes into account sensitivity, specificity, F1-score, and AUC. Integrating the chosen classifier into a mammography screening system ensures clinical interpretability by providing clear visualizations. Updating the model with fresh data on a regular basis and doing continuous monitoring ensure that it remains accurate. By working together in the clinic and taking radiologists' comments into account, we can improve the system's performance and reveal its capabilities as a cutting-edge instrument for accurate breast cancer detection. 2025 Author(s). -
Survey on deep learning techniques used for object identification of underwater forward looking sonar images
Underwater object identification using forward-looking sonar (FLS) images is crucial for autonomous underwater vehicles (AUVs) for navigation and obstacle avoidance. Deep learning techniques have emerged as powerful tools for object recognition in various domains. This paper surveys deep learning approaches employed for object identification in FLS images. We examine the effectiveness of popular deep learning frameworks such as YOLOv5, EfficientDet, and MobileNet, and transfer learning, data enhancements to improve object recognition performance, and the role of adversaries training. We also examine the potential of focusing and lightweight CNN algorithms developed for FLS images despite these advances, challenges still exist due to the limited number of registered cases. The paper analyzes how deep learning methods address these challenges and highlights their effectiveness in object identification. We aim to provide a comprehensive overview of the current state-of-the-art in deep learning for FLS object identification, paving the way for further research and development in this field. Results of this study show that the proposed algorithms improve obstacle detection accuracy and processing speed of sonar images. At the same time, the proposed algorithms ensure AUV navigation safety in a complex obstacle environment. 2025 Author(s). -
Comparative analysis of machine learning algorithms for predicting student success and enhancing their educational outcomes
The primary objective of this study is to predict the performance of students and evaluate the efficacy of various machine learning algorithms in predicting student success based on their marks and grades (academic factors). Through a comprehensive review of literature and experimentation, this research compares the performance of different machine learning models, including but not limited to decision trees, random forests, support vector machines, logistic regression, and neural networks. The evaluation metrics considered in this comparative analysis include accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Fourteen experiments have been performed and preliminary results suggest that performances of students on the basis of academic factors might be predictable and by understanding the strengths and weaknesses of student's educational outcomes and foster student achievement can be improved. Through extensive experimentation and comparative analysis, XGBoost(ExtremeGradient Boosting) and AdaBoost demonstrated as the most effective predictive models to analyze the students' performance. 2025 Author(s). -
A bibliometric analysis of fruit disease prediction using machine learning
In recent years, there has been a growing interest in leveraging machine learning techniques for the early detection and prediction of diseases affecting fruit crops. This study presents a comprehensive bibliometric analysis of research literature focused on fruit disease prediction using machine learning algorithms. Through systematic review and analysis of a large corpus of scholarly articles, conference papers, and patents, this paper aims to provide insights into the current trends, key research themes, influential authors, and popular machine learning methods in this domain. This paper conducts a literature review and bibliometric analysis to explore a significant increase in research activity in fruit disease prediction using machine learning, indicating the increasing importance of this area in agriculture and plant pathology. Various machine learning and deep learning algorithms, including convolutional neural network (CNN), decision trees, random forests and LSTM have been widely employed for disease prediction tasks. Moreover, the study identifies common datasets, evaluation metrics, and challenges encountered in this field. Overall, this bibliometric analysis provides valuable insights for researchers, practitioners, and policymakers interested in fruit disease prediction, highlighting opportunities for collaboration, innovation, and advancement in agricultural technology and plant health management. 2025 Author(s). -
Skin cancer prediction using AI: A bibliometric analysis
Skin cancer is a major public health concern globally, with early detection being crucial for successful treatment and management. Artificial intelligence (AI) has emerged as a promising tool for aiding in the early detection of skin cancer [15, 19, 23, 41]. This paper conducts a literature review and bibliometric analysis to explore the current landscape of AI-based skin cancer prediction. This bibliometric analysis systematically examines the landscape of research on skin cancer prediction using AI. The aim of the study is to identify the research trends, keyword contributors, influential authors, and research hotspots [13, 31]. Through this bibliometric analysis, this study offers insights into the evolution of AI-based approaches for skin cancer prediction. By producing and analyzing bibliometric data from relevant scholarly publications, this study provides a comprehensive overview of the current state of research in this domain, informing future directions for advancing skin cancer prediction using AI technologies. 2025 Author(s). -
Thermal performance evaluation of vertical slotted circular fins over turbulence flow regime in a heat sink
The study investigates the thermal performance of vertical slotted circular fins in heat sinks under turbulent flow conditions. As electronic devices become more compact and power-dense, efficient thermal management is critical. This research uses numerical simulations to evaluate six different slot sizes (0.5 mm to 3 mm) in terms of their impact on heat transfer and flow dynamics. The fins, modeled in 3D and subjected to Reynolds numbers ranging from 8490 to 23300, were analyzed for heat transfer efficiency and friction factors. Results indicate that slotted fins outperform solid fins, with the S-D slot configuration achieving a 17.73% increase in the Nusselt number and a 28.77% reduction in friction factor at a Reynolds number of 13,365. The Thermal Evaluation Criteria highlight the S-D slot as the most effective, providing a 15.17% improvement in overall performance. These findings underscore the potential of slotted fins in optimizing thermal management systems by balancing enhanced heat dissipation with reduced energy consumption. 2025 Author(s). -
Chain funds: Transforming healthcare crowdfunding through blockchain technologies
Crowdfunding in healthcare refers to the practice of raising funds from a large number of individuals through online platform. This will eventually support the various medical expenses involving hospitalization and treatment. This method enables individuals with health problems or an urgent need for medical treatment to go out and seek financial assistance from a large number of people. Crowdfunding on health is now widely recognized as the means of catering for medical bills, surgeries, experimental treatments and other health-related expenses. The research presents a decentralized blockchain based crowdfunding platform built using ReactJS, Solidity and Thirdweb SDK. This new platform aims to change traditional crowdfunding techniques through utilization of the benefits of blockchain such as transparency, security and automation provided by smart contracts. The user interface has been designed using ReactJS; creating smart contracts on the Ethereum Blockchain was done under Solidity; Thirdweb SDK was used to create a link between the system and the blockchain. Some significant features of this platform involve start-up campaigns organization, contribution management, milestone tracking and automatic distribution of funds in order to enhance customer satisfaction and minimize operational costs. Moreover, users are awarded with certificates and crypto tokens. In addition, this policy ensures high level security that comes with immutability in blockchain technology by eliminating middlemen hence live monitoring of fund is possible through it at all times and disbursements. Rigorous testing has been conducted to assess performance, security, and scalability, highlighting the advantages of decentralization in contrast to centralized crowdfunding models. 2025 Author(s). -
Analysis of Biometric Systems for Secure Human Recognition
In the realm of contemporary computing, the recognition of humans has emerged as a crucial element, finding utility in both mundane daily tasks and sophisticated operations across diverse IT applications. The process of identifying individuals often involves harnessing their distinctive biological, chemical, and behavioral attributes. To achieve this, a biometric system, functioning as a computer-based automated mechanism, is employed to authenticate and confirm alter the perspective or framing of users by exploiting their biological characteristics. In present-day applications, an existing entity exists notable emphasis on the biological aspects of individuals as the primary means of identification. While utilizing the chemical traits of humans for identification yields greater accuracy and reliability, practical implementation proves to be challenging. This article presents the execution or outcome of automatic human recognition systems derived from diverse sources or perspectives parameters such as user psychology, ease of use, security, reliability, and market share. The results suggest that these systems offer authentication and recognition capabilities, but it is noteworthy that the security of these systems at the template level poses a significant challenge for designers. 2025 Author(s)