Daily Archives: May 8, 2026

Authors: Mani G

Abstract: The fast growth and acceptance of cloud computing technology have completely changed the IT infrastructure of organizations, but along with that transformation, there have been several emerging security concerns. These security concerns have become hard to detect using conventional security approaches, due to the complexity and the evolution of new cyber attacks. In this paper, a complete deep learning cybersecurity framework will be proposed, to detect any threats in real-time within cloud computing environments. The cybersecurity framework consists of several deep learning models. They include the TCN with an autoencoder to detect anomalies at 99% accuracy with a false positive rate of 2.2% based on CSE-CIC-IDS2018 dataset, a transformer with CNN to detect network intrusions with 99.12% accuracy, and a federated learning method for detecting attacks in distributed environment without violating any user’s privacy at 98.3% accuracy in 300 communication rounds.

DOI: https://doi.org/10.5281/zenodo.20085675

Published by: vikaspatanker

Authors: M.Indumathi, Uddandam Vinodkumar

Abstract: Tumor segmentation in Magnetic Resonance Imaging (MRI) plays an important role in diagnosis, treatment planning, and disease surveillance. But still there are many hurdles in the process because of low contrast tissues, unclear boundaries and high morphology variations. In this paper, we propose Contour-Aware U-Net (CAU-Net), which uses explicit contour refinement techniques along with multi-level feature fusion. Our framework includes three main components that are as follows: (1) Contour-Aware Decoder with Attention Fusion blocks for contour enhancement, (2) adversarial learning constraint for anatomically plausible results, and (3) combined hybrid loss function using cross entropy loss, dice loss, and sub-differentiable Hausdorff loss. Extensive experiments on tumor datasets have proven that our proposed approach outperforms existing approaches in terms of accuracy by producing Dice Similarity Coefficient score of 0.92 and reducing Hausdorff Distance by 38%. Our model performs exceptionally well in terms of boundary delineation that was the crucial requirement in clinical practice.

DOI: https://doi.org/10.5281/zenodo.20085607

Published by: vikaspatanker

Authors: Ashish Shrimali, Dr. Priyanka Pandey

Abstract: Eco-friendly cement-based bricks incorporating stone mine waste and quarry dust have attracted considerable interest in the quest for environmentally sustainable building resources. The global research trends in sustainable cement-based bricks made with granite waste, sandstone waste, quarry dust and other mining waste products are reviewed from a performance and a bibliometric perspective in this study. The research identified 400 research articles from Lens.org database using a search query on the keywords: cement/concrete, stone waste products and mechanical and durability properties. Bibliometric analysis of articles was performed by VOSviewer for the mapping of the growth of publications, notable authors, research hot spots and evolutionary trends. Publication trends indicate steady increase after 2018 and an explosion of research between 2020 and 2023, which are the factors related to the increase of research and industrial interest handling eco-efficient masonry products. country wise analysis for published literature shows India leads the study as it is the most studied, followed by China and Malaysia, indicating good regional interest in valourisation of stone waste and promotion of material innovations. Key journals, such as Materials and Sustainability, are important in this field, highlighting the interdisciplinary nature. Keyword co-occurrence analysis revealed that "compressive strength", "durability", "quarry dust" and "recycled aggregates" are commonly used; new directions are circular economy principles and eco-efficient designing of materials. The insight gained from the case studies towards performance suggest that partial replacem However, there are problems on standardization, large-scale adoption and long-term testing which need to be resolved. This research provides an overview of existing research directions and possibilities for further research, e.g. hybrid forms of waste recycling and life cycle assessment. Our results encourage the role of stone waste as a resource sustainable brick-making as well as to guide further research and development of the industry in producing affordable and sustainable building materials.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Aniket Mishra, Ajinkya Bagal, Jayesh Jadhav, Rushikesh Nath

Abstract: This study examines the incorporation of generative artificial intelligence (Gen-AI) into e-commerce recommendation systems. Traditional approaches, such as collaborative filtering and content-based filtering, face challenges like sparse data, cold-start issues, and changing user preferences. Gen-AI models, especially transformer-based frameworks like GPT and diffusion models, provide innovative solutions for understanding and creating personalized content. This paper reviews the progression of recommendation systems, introduces generative models, and proposes a framework that integrates Gen-AI with current recommendation strategies to enhance accuracy, diversity, and contextual relevance.

DOI: Name : aniket mishra Contact No : +919518352808

Published by: vikaspatanker

Authors: Anshika Singh, Sneha Chhabra, Rajat Takkar, Harshwardhan Singh Thakur

Abstract: This study investigates the rising global disease burden, emphasizing the need for early detection to minimize mortality and healthcare costs. This article proposes a machine learning model for predicting disease risk from a dataset of 4240 patient records. Each record is characterized by 15 clinical and demographic attributes. This research paper employed five classifiers—Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Naive Bayes—to identify disease presence. Using hold-out validation, the models were evaluated, and Logistic Regression achieved the highest accuracy of approximately 84%, followed by Random Forest (~83.7%), SVM (~83.3%), and KNN (~82–83%). These results show the potential for early disease detection, enabling timely interventions. By integrating such models into practice, clinicians can maximize patient outcomes and reduce the disease burden globally. Future development includes expanding the dataset and adding an accessible interface for real-time analysis of disease risk.

Published by: vikaspatanker

Authors: Daniel Karikari Frempong, Mutala Nakpan Jentina, Hannah Owusu Ansah, Gabriel Oduro Asirifi

Abstract: The pivotal role of intelligent sensors in building and running smart homes is discussed in this literature review. First, we present a brief overview of smart homes and intelligent sensors, emphasizing the critical importance of this sophisticated technology used to transform ordinary homes into intelligent AI-controlled houses. The review then delves into the principles of several types of intelligent sensors, including energy, health and wellbeing, environmental, security, and appliance sensors. Besides playing a critical role in gathering data for personalized home automation services, this section touches upon their remarkable contribution to sustainable living, energy-saving, and human wellbeing. The review next examines key technologies and standards that enable seamless communication between devices, such as Matter, Wi-Fi, and Zigbee. This section also sheds light on how artificial intelligence and machine learning could change the paradigm of processing information collected by these intelligent sensors, leading to advanced predictive analysis and decision-making. Finally, we propose ways to address some challenges that impede the widespread application of intelligent sensors, such as interoperability, security, privacy concerns, and affordability. We also present promising avenues for future research on intelligent sensors for smart living, such as increased autonomy, advanced sensor miniaturization, and human-centric design.

DOI: https://doi.org/10.5281/zenodo.20080909

Published by: vikaspatanker