Category Archives: Uncategorized

Authors: Tejaswini Bagade, Preeti Wagh, Ms. Neeta Takawale

Abstract: This project focuses on the design and development of a Real-Time Traffic Flow Forecasting and Management System using machine learning and deep learning techniques. The system aims to predict traffic conditions accurately by analyzing real-time and historical traffic data collected from sensors, CCTV cameras, and GPS devices. Data preprocessing techniques are applied to remove noise and handle missing values for improved prediction accuracy. Advanced models such as LSTM, GRU, and CNN–LSTM are implemented to forecast traffic flow and support intelligent traffic management decisions. The proposed system helps reduce traffic congestion, improve road safety, optimize signal control, and enhance transportation efficiency through real-time monitoring and adaptive management strategies.

Published by: vikaspatanker

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: https://doi.org/10.5281/zenodo.20281288

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.

DOI: https://doi.org/10.5281/zenodo.20226307

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

Authors: Aziz A. Isra, Chaturvedi Rachna, Prakash Jyoti

Abstract: Chromium (Cr) metal and Cr compounds are primarily used in applications like making stainless steel, polishing, and leather tanning. Chromium naturally occurs in air, water, rocks, and soil, via natural or anthropogenic sources. It exists in different oxidation states ranging from +6 to -2. The most stable forms are the trivalent Cr(III) and the hexavalent Cr (VI), which are interconvertible with each other. Chromium is an important trace element for human beings as it stimulates the breakdown of fatty acids and cholesterol. However, if exposed to a higher dose of chromium particles for a longer period, it can lead to human health toxicity and fatality. It is introduced into the environment through chemical and physical processes or even by biological transport systems in living organisms. Over the past decades, chromium contamination has become a significant threat with a negative influence on the environment, especially soil and water, and its accumulation affects human health, plant metabolism, and animal tissues. By gathering information from various published literature, we have highlighted the adversities caused by Chromium toxicity, for example, acute and chronic toxicity among human beings like carcinogenic potential, apoptosis, oxidative stress, and DNA adducts. This review focuses on the complex chemistry of chromium, its exposure routes, and hazardous effect of chromium on human health, and the mechanism of chromium toxicity upon entering the cells. Therefore, it is now important to investigate and develop various useful sustainable remediation strategies to balance and reduce the increased levels of chromium in the environment.

DOI: https://doi.org/10.5281/zenodo.20075414

Published by: vikaspatanker

Authors: Research Scholar Udaya Kumar Nanubala, Professor Dr.Pankaj Khairnar

Abstract: Emotion recognition has surely become an important research field in artificial intelligence because it can improve how humans interact with computers. Moreover, this technology helps in building better intelligent systems. Basically, traditional methods using single type of data fail to understand human emotions properly because emotions are expressed through multiple ways – text, speech, and facial expressions – all at the same time. This paper actually reviews transformer deep learning models that definitely work with different types of data for recognizing emotions. This study looks at how emotion recognition methods have changed from old rule-based and machine learning ways to new deep learning and transformer systems. As per the research, regarding emotion detection techniques, there has been clear progress from basic approaches to advanced methods. Basically, deep learning models like CNNs and RNNs have made feature extraction and pattern recognition better, but the same models struggle with long-range connections and combining different types of data. Basically, Transformer models use attention mechanisms to understand context better and make different types of data work together in the same way. As per recent studies, multimodal transformer systems improve emotion detection by combining different types of data sources into one framework. Regarding performance, this approach gives more accurate and reliable results. As per the review, different multimodal fusion techniques like early, late, and hybrid fusion strategies are analyzed regarding their role in making system performance better. Despite good progress, challenges like different data types, matching different modes, high computing needs, and limited large multimodal datasets remain critical issues that need further attention, as the field itself faces these ongoing problems. Also, this study further identifies important research gaps and emphasizes that efficient fusion mechanisms, scalable architectures, and real-world deployment strategies itself need more development. The findings give important insights for developing better emotion recognition systems that can further improve human-machine interaction itself.

DOI: https://zenodo.org/records/20074005

Published by: vikaspatanker

Authors: Manas Kumar, Heera Sahu, Shivam Jaiswal, Amar Verma , Professor Pushpa Sahu

Abstract: With the mining industry moving from traditional manual methods to the advanced mechanised mining, the focus is also shifting to the energy efficiency of the equipment and system being employed. Most of the equipment used in mining like shovel, drill, elevator, continues miner, conveyor, pumps etc. runs on electricity. Electric energy being the only form of energy which can be easily converted to any other form plays a vital role for the growth of any industry. The Power Factor gives an idea about the efficiency of the system to do useful work out of the supplied electric power. A low value of power factor leads to increase is electric losses and also draws penalty by the utility. Significant savings in utility power costs can be realized by keeping up an average monthly power factor close to unity. To improve the power factor to desired level, reactive power compensators are used in the substations. The most common used device is capacitor bank which are switched on and off manually based on the requirement. If automatic switching can be employed for the correction devices, not only it will improve the response time but also removes any scope for error. The work carried out is concerned with developing power factor correction equipment based on embedded system which can automatically monitor the power factor in the mining electrical system and take care of the switching process to maintain a desired level of power factor which fulfils the standard norms. The Automatic Power Factor Correction (APFC) device developed is based on embedded system having 89S52 microcontroller at its core. The voltage and current signal from the system is sampled and taken as input to measure the power factor and if it falls short of the specified value by utility, then the device automatically switch on the capacitor banks to compensate for the reactive power. The number of capacitors switched on or off is decided by the microcontroller based on the system power factor and the targeted power factor. The measurement and monitoring of three different possible load types suggested that only the inductive loads required power factor correction. After employing the correction equipment the targeted power factor of 0.95 is achieved and the increase in power factor varied from 9% to 19% based on the combination of load. There is also a decrease of 1.7% in the total energy consumption due to reduction in load current. The economic analysis for power factor improvement considering the data from a local coal mine suggested the payback period to be around 9 months if the correction equipment is implemented.

DOI: https://zenodo.org/records/20070425

Published by: vikaspatanker