Category Archives: Uncategorized

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

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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

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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

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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

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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

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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

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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

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Authors: Ria Ranjan Kumar, Ms. Mitali Srivastava

Abstract: The growing adoption of Artificial Intelligence and its intervention in the legal sphere has been a welcome sight for everyone in the industry. From judicial institutions to corporations, AI has rapidly shown an overarching effect on judicial functioning. However, with this bargaining effect, what comes is the danger of Artificial intelligence. Due to the lack of regulatory mechanisms and compliance laws, a varied but unexpected stream of jeopardy is underway for all of us. This is somehow related to the growing correspondence being provided by human beings and the pervasive acceptance not just by the elite class of the world but across all classes. The only laws that are currently under the purview of this domain are the Digital Personal Data Protection Act, 2023, for privacy and personal data, the Information Technology Act, 2000 for cyber offences and intermediary liability, and the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021. With this dissertation, I shall try to lay down an intricate evaluation of the recent AI interception and reflect on the dire need to incorporate it with a proper legal regulatory system. Although the term "artificial intelligence" is not used in the DPDP act, one crucial word mentioned therein is "automated," which refers to a digital procedure that can process data automatically. That is the closest the Act gets to discussing artificial intelligence. The act does not specifically address algorithmic bias, deepfakes, facial recognition, automated decision-making, AI explainability, or generative AI training data. There are a lot of inconsistencies in the law itself, and the dystopian tone of the act adds insult to injury.

DOI: https://doi.org/10.5281/zenodo.20069843

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Authors: Khushi Wadhwa, Rajat Takkar, Vaani, Kashish Sharma, Kartikay Singh Manhas

Abstract: In the world of medical imaging, Edge artificial intelligence (AI) is driving a revolution by enabling real-time analysis and diagnosis decision making. The aforementioned article examines the constantly developing subject of edge AI-powered healthcare imaging, describing the most recent ad-vancements, creations, and concepts that could transform a variety of medical fields by instantly interpreting medical images, which can be crucial in life-saving circumstances. The Edge AI can be used in remote clinics and other medical imaging situations. In rural diabetes camps, diabetic retinopathy can be diagnosed with Fundus cameras and point-of-care ultrasound without radiologists. In emergency situations, portable X-ray devices can diagnose fractures. The three main types of diagnos-tic procedures—imaging-based, pathology-driven, and protective diagnostic approaches—as well as the alterations and adaptations brought about by the application of Edge AI are also covered in this article. Using medical records raises several ethical issues because they are very sensitive documents. These challenges have also been discussed in this article. The necessity for further developments in Edge AI-based diagnostic techniques is also covered in the article. Additionally, there is a great deal of potential for the future in the creation of tools and techniques that are easy to use and incorporate into routine operations. The increasing usage of clinical decision support systems makes edge AI a promising topic in healthcare and diagnostics. Despite a number of obstacles to its application and adoption, the research concludes that Edge AI in healthcare has a promising future. However, in order to guarantee that facilities are available for this, a high degree of precision must be attained and patients must have better medical outcomes. The potential of AI to transform healthcare and enhance patient outcomes is also highlighted in this paper, with a focus on responsible implementation and ongoing assessment.

DOI: http://doi.org/

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Authors: Mahantesh M. Ganganallimath, Dr. K. Vizayakumar, Dr. Umesh M. Bhushi

Abstract: By providing cast components to the automobile, aerospace, railroad, construction, defense, and heavy engineering industries, the Indian foundry sector is essential to the manufacturing sector. Casting flaws, process unpredictability, material waste, high rejection rates, energy inefficiency, and growing international competitiveness are some of the industry's major obstacles. In this regard, sustainable industrial growth now depends on quality assurance and quality-centric methods. The necessity of methodical quality assurance procedures, process control systems, and continuous improvement techniques in Indian foundries is examined in this study. The study highlights that quality-driven systems enhance customer satisfaction and product dependability while simultaneously lowering costs and promoting long-term competitiveness and environmental sustainability. The combination of Industry 4.0, automation, and statistical quality tools for stable growth is further supported by recent research on KPI-driven foundry quality systems and sustainable control models.

DOI: https://doi.org/10.5281/zenodo.20068127

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