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AN OPTIMIZATION AND PREDICTIVE MODELING TO ENHANCE THE WEAR AND MECHANICAL PERFORMANCE OF Al 5054 ALLOY FOR DEFENSE APPLICATIONS WITH TiO2 NANOPARTICLES
This study examines the effects of 2%, 4%, and 6% additions of TiO2 nanoparticles on the wear and mechanical characteristics of Al 5054 alloy reinforcement. The results demonstrate that the addition of TiO2 nanoparticles considerably increases the alloys tensile and impact strengths. Tensile strength reaches a peak of 221 MPa at 6% reinforcement and it rises gradually as the percentage of TiO2 reinforcement increases. Similarly, impact strength rises with time and, with TiO2 reinforcement, it reaches a maximum of 63 Joules at 6%. Wear analysis using Taguchi-based design determines the optimal combination of composition, disc rotation speed, load, and sliding distance to minimize a given wear rate and friction force. The SEM analysis validates that the composites exhibit enhanced wear resistance due to the uniform distribution of TiO2 nanoparticles. An Artificial Neural Network (ANN) model is also developed to predict the responses, and it achieves an overall accuracy of 83.549%. The mechanical properties and wear resistance of TiO2-reinforced Al 5054 composites can be enhanced, as it is demonstrated by these results. This information is crucial for material design and optimization across a range of engineering applications. 2024, Scibulcom Ltd.. All rights reserved. -
A thorough investigation of various goals and responses for mobile software-defined networks
Cloud computing has caused some companies to modify their IT infrastructure and maintenance procedures and may eliminate their current hardware altogether. Conventional methods of setting up a switch or router may be error-prone and unable to make full use of the capabilities of current network architectures. As many intelligent networking designs as possible must be developed for intellectualization, activation, and customization in future networks. Due to software-defined networking (SDN) technology, it's possible to control, secure, and optimize network resources, eliminating the rigid coupling between the control plane and the data plane in traditional network architectures. Here, the chapter explores the problems, difficulties, and potential solutions associated with software-defined networks (SDN), a novel concept in computer networking. Through SDN, the network gains the ability to be programmable, quick, and adaptable thanks to its separation of data and its ability to control traffic. 2023, IGI Global. All rights reserved. -
Human-Machine Interactions andAgility inSoftware Development
A modern organization cannot function without project management. Organizations, governments, and non-profits recognize how important modern project techniques are to the success of their IT projects. Many people understand that excellent project skills are crucial for remaining competitive in the workplace. Many project management concepts will help them with their everyday interactions with people and technology. Project management aims to plan, organize, motivate, and control resources to accomplish specific objectives and meet specific success criteria. The major challenge is to achieve all the project goals and objectives while respecting the preconceived constraints of the project. Project management for data science is easy with Agile. Understanding the different approaches to project management and how they can fit into information science is essential. Several project management tools are available to maintain and report on a projects progress. As proposed in this paper, a comprehensive study on project management and Agile methodologies helps enhance the teams interactions when working for data science project management. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Sustainable Business Model for Converting Construction and Demolition Waste to Wealth
India's rapid urbanisation necessitates a planning approach that ensures the sustainability of its cities through efficient waste management. This swift urban growth has significantly accelerated modern construction and demolition of older infrastructure or structures within Indian cities. C&D (Construction and Demolition) waste is accountable for approximately 30 percent of urban municipal waste within metropolitan areas. Managing C&D waste and transforming it into valuable resources presents considerable challenges for all urban local bodies (ULBs). Recycling C&D waste offers dual benefits: it reduces pressure on the extraction of virgin construction materials and helps mitigate environmental pollution. Recycled C&D waste can produce various valuable products, including aggregates of different sizes, manufactured sand, paver blocks, concrete bricks, double-tee precast panels for boundary walls, manhole covers, water tanks, and more. These products are durable and eco-friendly building materials that contribute to the conservation of natural resources. However, a sustainable business model is essential for understanding the volume of C&D waste produced and for addressing current challenges and opportunities at the city, regional, and state levels. The current research aims to gather information about the overall scenario of C&D waste management procedures in India, relying on secondary resources. It proposes a sustainable business model for C&D waste handling that transforms this specific waste into a valuable resource, identifying possible advantages and the resource efficiency of recycled items. permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. -
Explainable Hybrid Deep Learning Framework with Multimodal Inputs for Diabetic Retinopathy Detection
Diabetic Retinopathy (DR) is a leading cause of vision loss, making accurate and interpretable detection critical. This study proposes a hybrid interpretable machinedeep learning framework that integrates multimodal data for enhanced DR severity classification. The model combines unstructured fundus images from EyePACS, Messidor, and APTOS with structured clinical and lifestyle variables such as age, sex, HbA1c, BMI, blood pressure, and diabetes duration. Fundus images undergo preprocessing through resizing, normalization, augmentation, and noise reduction, while clinical data are imputed, normalized, and one-hot encoded. For feature extraction, EfficientNetV2, ResNet50, and Swin Transformer are applied to images, and XGBoost, LightGBM, and TabNet to clinical data. Features are fused via concatenation and attention, followed by classification using Logistic Regression, Random Forest, and MLP. Explainability is provided by Grad-CAM for imaging data and SHAP/LIME for clinical data, supporting clinical interpretability. The proposed model outperformed unimodal baselines, achieving 99.34% accuracy, 98.5% precision, 98.0% recall, 99.0% specificity, 98.2% F1-score, and 0.99 AUC-ROC, with a 10% gain over ResNet50 alone. Performance improvements included a 9% increase in recall and 8% in F1-score, alongside excellent calibration. Confusion matrix analysis confirmed balanced severity detection, and clinicians validated the interpretability outputs. This framework demonstrates robust accuracy, generalization, and clinical applicability for DR screening. 2026, An-Najah National University. All rights reserved. -
LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection
Early and accurate diagnosis, however, is still lacking for the most common form of lung cancer, and this remains one of the leading cancers leading to mortality. CT scans are widely used for lung cancer screening; however, their manual interpretation is time-consuming and prone to variability. This study introduces LungDxNet, a deep learning-based framework that integrates transfer learning to enhance diagnostic accuracy and efficiency. Using a large dataset of Low Dose CT (LDCT) scans, the system is built with fine-tuned pre-trained Convolutional Neural Networks (CNNs) such that feature extraction is reliable though minimal reducing radiation exposure. Consequently, LungDxNet involves the integration of component segmentation techniques that have been used to isolate the lung regions and discriminate the cancerous nodules from the malignant and benign cases. Very rigorous evaluations were performed on the model against both conventional machine learning and state of the art deep learning architectures. Results show that there is a substantial reduction of false positive and false negative resulting in a superior accuracy (98.88), sensitivity, and specificity. This design is to be scaled, robust and clinically applicable, making it a potential real world lung cancer diagnosis tool. Deep learning and transfer learning has excellent power to transform lung cancer detection, and this research brings awareness of how far we can optimise and integrate into clinical workflow. The model is enhanced for future work and adapted for real time diagnostic applications. 2025, Sakarya University. All rights reserved. -
Decomposition of graphs into induced paths and cycles
A decomposition of a graph G is a collection ? of subgraphs H1,H2,..., Hr of G such that every edge of G belongs to exactly one Hi. If each Hi is either an induced path or an induced cycle in G, then ? is called an induced path decomposition of G. The minimum cardinality of an induced path decomposition of G is called the induced path decomposition number of G and is denoted by ?i(G). In this paper we initiate a study of this parameter. -
Decomposition of graphs into induced paths and cycles
A decomposition of a graph G is a collection ? of subgraphs H1,H2,...,Hr of G such that every edge of G belongs to exactly one Hi. If each Hi is either an induced path or an induced cycle in G, then ? is called an induced path decomposition of G. The minimum cardinality of an induced path decomposition of G is called the induced path decomposition number of G and is denoted by ?i(G). In this paper we initiate a study of this parameter. -
The role of artificial intelligence in business model innovation
Businesses are fundamentally changing because of AI, allowing for a greater focus on creativity. The chapter introduces advancements in AI like machine learning, predictive analytics, and natural language processing that help boost customer service, operational efficiencies and asset monetization opportunities. Key considerations will include personalized customer engagements, process efficiencies, optimizing resources and planning to set up new business models-subscription- based ser- vices/platform economies, etc. Emerging technologies like artificial intelligence have broader implications in e- commerce, finance, manufacturing-showing how firms use AI strategies to gain competitive advantage and foster business growth. In addition, the chapter addresses ethical considerations like bias, privacy as well as workforce implications and their role in ensuring responsible AI practices. AI can improve flexibility, realizing the highest level of value and maintaining competitive advantage. 2025, IGI Global Scientific Publishing. All rights reserved. -
Classification of Skin Diseases Using Convolutional Neural Networks (VGG) with Histogram Equalization Preprocessing
Skin diseases are a major global health concern for which prompt and precise diagnosis is necessary for effective treatment. Convolutional neural networks (CNN), one of the deep learning techniques, have shown potential in automating the diagnostic procedure. The goal of this research is to enhance the effectiveness of skin disease categorization by fusing the capabilities of CNNs - particularly the VGG architecture - with the histogram equalization preprocessing method. In image processing, histogram equalization is a commonly used approach to enhance the contrast and general quality of medical photographs, which include photos of skin conditions. In order to improve the characteristics and details of dermatological pictures for this study, we employed histogram equalization as a preprocessing step. This allowed CNN to extract pertinent features more quickly. 2024 IEEE. -
Intricate Plane of Adversarial Attacks in Sustainable Territory and the Perils faced Machine Intelligent Models
The issue of model security and reliability in Artificial Intelligence (AI) is a concern due to adversarial attacks. In order to tackle this issue, researchers have developed sustainable defense strategies, but certain challenges remain. These challenges involve transferability, higher computing costs, and adaptability. Striking a balance between accuracy and robustness is difficult, as defense mechanisms often come with trade-offs between the two. Real-world situations demonstrate the practical implications of sustainable adversarial AI. For example, it improves the security of self-driving vehicles, enhances the accuracy of medical imaging diagnoses, and incorporates AI-driven defenses into network intrusion detection and phishing detection systems. It is crucial to consider ethical aspects throughout this process. Future trends in adversarial AI research for cybersecurity will involve ensemble defense mechanisms, adversarial learning from limited data, and hybrid attacks. By embracing the evolving landscape, researchers and practitioners can develop sustainable AI systems that are more secure and resilient, effectively countering adversarial threats. 2023 IEEE. -
AI-Based Medical Assistance forProactive Healthcare Predictions andServices
Healthcare systems must adapt to the requirements of the digital era. The proposed healthcare Artificial Intelligence (AI) assistance provides a safe and user-friendly platform for physicians, patients, and administrators to meet their specific needs. The systems architecture prioritizes user authentication and role-based access control to ensure that only authorized users have access to certain features. The technology allows patients to input their symptoms, which is the platforms cornerstone offering. The technology uses a Machine Learning (ML) model and a large medical database to properly forecast probable illnesses based on the symptoms presented. This predictive feature helps individuals make educated decisions about their health and seek medical assistance proactively. The systems creative approach extends to online consultations. Patients may seek consultations, schedule appointments, and conduct secure video chats from the comfort of their homes. This online consultation service offers a convenient and flexible option for medical treatment, especially for people with restricted mobility or wanting immediate assistance. This paper evaluates disease prediction using parameters like accuracy and confusion matrix performance. The neural network model performs better for the above parameters in comparison to the random forest and K-nearest neighbor ML models. The proposed system uses ML technology to deliver fast, accurate, and secure medical services, breaking down traditional healthcare barriers. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Understanding environmentally sustainable Indian travel behaviour: an analysis of 2011 census data
Using census data of non-agricultural workers for 2011, this study aims to examine trends and determinants of travel behaviour in India. Descriptive statistics accompanied by a beta regression model of proportional outcomes are implemented on the obtained data. The study finds that men are the dominant users of motorized transport in the country. Most workers travel a short distance of less than 5km, irrespective of area or gender. Population density, the share of married population and the share of rural population in a district significantly influence the share of environmentally sustainable travel behaviour displayed by that region. To the best of our comprehension, this is one of the primary studies elucidating the comparison of travel behaviour in ruralurban areas of Indian states. Not many studies in India have addressed the issue of influence of socio-demographic factors on environmentally sustainable travel choices. With this analysis, policymakers in the transportation sector can get a clearer idea of the behaviour and demands of different divisions of society. The findings of this study demand the evolution of infrastructure of public transportation and non-motorized transportation in the country in such a way that is both efficient and secure to neither impede the goals of empowerment or sustainability. The Author(s), under exclusive licence to Institute for Social and Economic Change 2023. -
Gender gap in travel behaviour and public opinion on proposed policy measures: Evidence from India
Employing primary survey data collected from Jaipur city in India, this work attempts to evaluate inconsistencies in travel behaviour based on gender. It also intends to discuss the public opinion on a few proposed policy changes which can aid in bridging the established gender gap. Stratified random sampling approach is used to gather data on travel pattern measures and socioeconomic attributes. Descriptive statistics complemented with bivariate probit model and seemingly unrelated bivariate probit model is applied on the data acquired. The obtained results confirm the existence of a gender gap in all observed measures of travel behaviour. Compared to men, women travel shorter distances, use more of non-motorised modes of transport, have lower frequency of travelling, and travel majorly for purposes other than work. Results of the study also highlight how a majority of the respondents are in favour of policy changes aimed at narrowing the observed gender disparities. The analysis demands infrastructural development of non-motorised transportation and public transportation in the city in such a way which is both efficient and secured, so as to neither obstruct the objective of empowerment nor of sustainability. 2023 John Wiley & Sons Ltd. -
Examining the Prevalence and Impact of Physical Violence from a Psychological Perspective
Physical violence within marriages are pervasive issues that affect individuals globally, including in India. This essay examines the prevalence and impact of physical and sensual violence in India from a psychological perspective. The underreporting and stigmatization of these forms of violence pose significant challenges in understanding their true extent. Factors such as power imbalances, gender inequality, and cultural norms contribute to their perpetuation. The psychological consequences experienced by survivors include trauma, post-traumatic stress disorder (PTSD), depression, anxiety, emotional distress, sexual dysfunction, substance abuse, and self-harm. Additionally, the cycle of violence and revictimization further compounds the psychological impact. Addressing this issue requires legal reforms, raising awareness, promoting education, challenging cultural norms, providing support services, and ensuring accessible mental health support. By addressing the cultural, legal, and psychological dimensions, it is possible to create a society that is free from physical violence and supports survivors in their journey towards healing and empowerment. The objective of this study is to enhance comprehension of the issue of physical violence in India by examining its prevalence and impact. The findings of this research endeavour to facilitate the development of efficacious strategies and interventions to combat this widespread problem in the nation. 2023, Journal for ReAttach Therapy and Developmental Diversities. All Rights Reserved. -
Lesion detection in women breasts dynamic contrast-enhanced magnetic resonance imaging using deep learning
Breast cancer is one of the most common cancers in women and the second foremost cause of cancer death in women after lung cancer. Recent technological advances in breast cancer treatment offer hope to millions of women in the world. Segmentation of the breasts Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is one of the necessary tasks in the diagnosis and detection of breast cancer. Currently, a popular deep learning model, U-Net is extensively used in biomedical image segmentation. This article aims to advance the state of the art and conduct a more in-depth analysis with a focus on the use of various U-Net models in lesion detection in womens breast DCE-MRI. In this article, we perform an empirical study of the effectiveness and efficiency of U-Net and its derived deep learning models including ResUNet, Dense UNet, DUNet, Attention U-Net, UNet++, MultiResUNet, RAUNet, Inception U-Net and U-Net GAN for lesion detection in breast DCE-MRI. All the models are applied to the benchmarked 100 Sagittal T2-Weighted fat-suppressed DCE-MRI slices of 20 patients and their performance is compared. Also, a comparative study has been conducted with V-Net, W-Net, and DeepLabV3+. Non-parametric statistical test Wilcoxon Signed Rank Test is used to analyze the significance of the quantitative results. Furthermore, Multi-Criteria Decision Analysis (MCDA) is used to evaluate overall performance focused on accuracy, precision, sensitivity, F 1 -score, specificity, Geometric-Mean, DSC, and false-positive rate. The RAUNet segmentation model achieved a high accuracy of 99.76%, sensitivity of 85.04%, precision of 90.21%, and Dice Similarity Coefficient (DSC) of 85.04% whereas ResNet achieved 99.62% accuracy, 62.26% sensitivity, 99.56% precision, and 72.86% DSC. ResUNet is found to be the most effective model based on MCDA. On the other hand, U-Net GAN takes the least computational time to perform the segmentation task. Both quantitative and qualitative results demonstrate that the ResNet model performs better than other models in segmenting the images and lesion detection, though computational time in achieving the objectives varies. 2023, The Author(s). -
Machine Learning in Intrusion Detection: A Comprehensive Analysis
Intrusion detection systems (IDS) are employed to investigate anomalous behavior in a network system, which monitors a network system for suspicious behavior, which is essential for maintaining network security. Improving accuracy in intrusion detection is necessary to lower false alarms and boost detection rates. Support Vector Machine (SVMLinear and Quadratic), Long Short-Term Memory (LSTM), and k-nearest neighbors (kNN), machine learning techniques for intrusion detection in network environments, are compared in this research. The effectiveness of SVM, which is well-known for its resilience in high-dimensional environments, in differentiating between normal and malicious behavior is examined. Straightforward yet powerful algorithms, namely KNN and LSTM, are analyzed to see how well they can adjust to different types of intrusions. Regarding detection accuracy, false positive rates, and response times, the experimental results on a benchmark intrusion detection dataset highlight the advantages and disadvantages of the models considered for the study. This study suggests incorporating machine-learning approaches into real-time intrusion detection systems to improve network security and lessen cyber risks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Study on the Ethics of using Artificial Intelligence in Mental Health Treatment and its Legality
The development of artificial intelligence (AI) has transformed mental health care through offering new and feasible solutions to old assumptions. The moral concerns related to the use of AI in the mental health, however, could not be overlooked. A thorough grasp of how AI can be used throughout the patient journey is essential to advancing AI technology in the realm of mental health and overcoming its present restrictions. To reduce it to three columns, or one dataset, five Facebook datasets were gathered from Kaggle. The preprocessing procedure enhances the dataset's quality by using user tweets. Four datasets about depression were taken from the Kaggle website. After the preprocessing is finished, we will refine four pre-trained BERT models using the Hugging Face package. We will be able to create a predictive model for identifying depression with this method. The effectiveness of our refined BERT models for depression identification was assessed using a number of metrics. Our healthcare system could be greatly enhanced by AI, but we can only realise this potential if we begin addressing the moral and legal issues that currently confront us. 2025 IEEE. -
Enablers of Successful Fiscal Decentralisation: A Case Study of Three Gram Panchayats in Kerala
Kerala is among the few states that have a successful record in fi scal decentralisation. This study qualitatively analyses primary data from three gram panchayats in Kerala to identify the factors that enable successful decentralised fi scal governance through panchayati raj. Based on the fi ndings of the study, we have constructed a framework to assess the readiness of gram panchayats to carry out successful decentralised fiscal governance. 2022 Economic and Political Weekly. All rights reserved. -
Dynamic Connectedness and Volatility Spillover Effects of Indian Stock Market with International Stock Markets: An Empirical Investigation Using DCC GARCH
This study employs the DCC-GARCH model to investigate the dynamic connectedness between the Indian and significant global stock markets. Specifically, we examine daily log returns data of the National Stock Exchange (NSE) index and several international indices, including the United States, Australia, China, Germany, England, Japan, and Taiwan. Our analysis indicates a significant level of volatility spillover between the Indian stock market and the international stock market. Notably, we observe a significant positive spillover effect from the S&P 500 and FTSE 100 to the Indian stock market, suggesting contagion effects. Additionally, we find bidirectional spillover between the Indian stock market and the Nikkei 225 and Hang Seng, indicating a high level of interdependence between these markets. Our research contributes to the growing literature on the dynamic connectedness of stock markets and has important implications for policymakers and investors in emerging economies such as India. Overall, this study provides valuable insights into the nature and extent of spillover effects between the Indian and international stock markets. 2023 University of Pardubice. All rights reserved.