Category Archives: Uncategorized

Authors: Mr. Satish Yadav, Ashish Khandagale, Dr. Jasbir Kaur, Ms. Sandhya Thakar, MS. Ifra Kampoo

Abstract: The exponential growth of social media platforms has generated unprecedented volumes of user-generated content, creating vast repositories of public opinion and sentiment. This research investigates the application of sentiment analysis techniques to social media big data, examining methodologies for extracting, processing, and analyzing emotional insights from large-scale social media datasets. Through a comprehensive review of machine learning approaches, natural language processing techniques, and big data analytics frameworks, this study evaluates the effectiveness of various sentiment classification models when applied to Twitter, Facebook, and Instagram data. Our findings demonstrate that hybrid approaches combining lexicon-based methods with deep learning architectures achieve superior accuracy rates of 89.3% compared to traditional rule-based systems. The research also addresses critical challenges including data preprocessing, feature engineering, and scalability issues inherent in social media sentiment analysis. The implications of this work extend to business intelligence, political analysis, brand monitoring, and public health surveillance applications.

Published by: vikaspatanker

Authors: Kalpesh Desai, Komal Yadav, Jasbir Kaur, Suraj Kanal, Sandhya Thakkar

Abstract: The Internet of Things (IoT) based smart agriculture system is an emerging technology that uses sensors, gateways, cloud platforms, and mobile applications to provide real-time data on weather, soil moisture, crop growth, and livestock health to farmers. This research paper focuses on developing and implementing an IoT-based smart agriculture system. The system offers several benefits: increased efficiency, improved resource management, enhanced crop quality, better decision-making, and remote monitoring. However, potential negative impacts, such as cost, technical skills, dependence on technology, data privacy and security, and environmental impact, must be considered. Careful planning, implementation, and monitoring can help to mitigate these risks and ensure that smart agriculture systems are sustainable and effective. This research aims to give an overview of how predictive analysis and Internet of Things (IoT) devices, along with cloud management and security systems, can be used in agriculture to support multiple crops. It also takes into account the experiences of farmers and highlights the challenges and difficulties that may arise when introducing modern technology into traditional farming practices. By utilizing statistical and quantitative methods, this research seeks to bring about significant and positive changes in the current agriculture system. In simpler terms, this study explores how smart farming can enhance food production, resource management, and labor efficiency, while acknowledging the challenges and benefits of integrating modern technology into traditional farming practices.

 

 

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Authors: Rishav Bairagya, Barshan Kundu, Arghya Biswas, Nikhilesh Sil

Abstract: Artificial intelligence (AI)-based emergency response systems provide emerging as key enablers of smart infrastructure safety, improved real-time decision-making, risk assessment, and catastrophe mitigation tactics across multiple domains. The combination of machine learning (ML), deep learning (DL), computer vision, IoT-enabled predictive analytics, and AI-powered robotics in optimising emergency response mechanisms is examined in this systematic literature review, which includes more than 100 eligible papers. In addition to offering a thorough evaluation of technological developments and adoption bar-riers, the study thoroughly investigates AI applications in disaster management, real-time incident detection, healthcare emergency response, industrial hazard prevention, cybersecurity frameworks, and intelligent traffic control. According to the findings, artificial intelligence (AI) has greatly increased automated hazard detection, predictive accuracy, and emergency resource optimisation. These improvements have sped up reaction times, reduced human error, and improved situational awareness in crisis management. Early warning systems for earth-quakes, floods, and wildfires have been made possible by AI-driven predictive analytics models, promoting proactive risk mitigation and catastrophe prepar-edness. Artificial intelligence (AI)-driven computer vision and sensor-based surveillance technologies have enhanced incident detection in real-time emergency response, cutting down on intervention delays and guaranteeing more effective use of emergency resources. Triage automation, geospatial analytics for ambulance dispatch, and AI-enhanced diagnostic technologies have simplified medical crisis management in the healthcare industry, increasing survival rates and cutting down on treatment delays. Furthermore, cybersecurity intelligence systems, robotic automation, and AI-integrated industrial safety frameworks have improved workplace hazard prevention and cyber threat identification. In an emergency, an AI-powered drone-based system facilitates communication between firefighters via light, sound, and a graphical user interface. AI-driven technology solves problems based on situational awareness (SA). Emergency response systems powered by AI improve the security of smart infrastructure. Real-time emergency response optimisation, automated hazard detection, and enhanced risk assessment are all made possible by AI technologies. Real-time fire scenarios are identified using an AI-powered smart firefighting system. During disasters, artificial intelligence (AI) can improve the efficiency of emergency response.AI's role as a crucial enabler of intelligent, data-driven emergency response frameworks, fire fighting, and emergency response will be further reinforced as AI technologies continue to advance and are incorporated into emergency management strategies. This will improve crisis preparedness, real-time intervention capabilities, and global disaster resilience. // This review offers a thorough synthesis of AI's revolutionary role in contemporary emergency management, including insights into technological advancements, constraints, and policy considerations.

 

Published by: vikaspatanker

Authors: Ramlah Ampatuan Duge,, Taya Panigel Adam, Salahudin D. Solaiman

Abstract: – This research determined the level of cooperative learning strategies in group activities, group games, peer mentoring, and problem-solving activities; the students' level of learning outcomes on the 2nd and 3rd quarter of the S.Y. 2023-2024; and the relationship of demographic profile to cooperative learning strategies on students' learning outcomes. Additionally, this study determined the relationship between cooperative learning strategies and learning outcomes; and the significant influence of cooperative learning strategies on students' learning outcomes. Descriptive-correlation research design was utilized to analyze the gathered data from the respondents who were identified using stratified sampling with proportional allocation and complete enumeration. Mean and the spearman's rho with correlation coefficient were used to describe the results and to test the hypotheses of the study correspondingly.Results of the statistical analyses revealed that the majority of the respondents strongly agreed on their cooperative learning strategies in peer mentoring and problem-solving activities. Subsequently, most of the respondents strongly agreed that there are learning outcomes in their subjects such as English, Mathematics, and Science. Findings revealed that cooperative learning strategies have a significant relationship with the learning outcomes in English, Mathematics and Science subjects. Moreover, cooperative learning strategies have a significant influence on learning outcomes in the same subjects.

DOI: https://doi.org/10.5281/zenodo.15738047

 

Published by: vikaspatanker

Authors: Ms. Tanvi Parab, Ms.Saloni Pawar, Dr. Jasbir Kaur, Assistant Professor Ms. Sandhya Thakkar

Abstract: The field of drug discovery has undergone a remarkable transformation with the integration of artificial intelligence (AI) techniques. AI-driven approaches have the potential to significantly accelerate and enhance the drug discovery pipeline by streamlining key stages such as target identification, compound screening, lead optimization, and preclinical prediction. This paper provides a comprehensive overview of the various AI methodologies employed in drug discovery, including machine learning, deep learning, reinforcement learning, and natural language processing. We explore how these technologies are being utilized to analyze complex biological data, predict molecular interactions, and identify promising drug candidates with greater efficiency and accuracy. Furthermore, the paper examines the challenges and limitations associated with data quality, model interpretability, and regulatory acceptance. We also highlight recent advancements and successful case studies demonstrating real-world applications of AI in pharmaceutical research. Ethical implications, data privacy concerns, and the evolving role of human expertise in AI- assisted workflows are critically discussed. Finally, the paper outlines future prospects, emphasizing the potential of AI to revolutionize personalized medicine and accelerate the development of novel therapeutics in a cost-effective and time- efficient manner.

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Dr. Soumalya Kundu
Affilation:	Assistant Professor (Physics) Department of Basic Science NSHM Knowledge Campus, Durgapur, India
Email-Id:	physics.soumalya@gmail.com
Publication: R. Majumder, S. Kundu, R. Ghosh, M. Pradhan, D. Ghosh, S. Roy, S. Roy, M. P. Chowdhury, “Expeditious UV detection of tungstite (WO3·H2O) and tungsten oxide (WO3) decorated multiwall carbon nanotubes (MWCNT) based photodetector: ultrafast response and recovery time”, SN Applied Sciences 2020, 2, article no. 81. R. Ghosh, R. Majumder, S. Kundu, M. Pradhan, S. Roy, R. Gayen, M. P. Chowdhury, “Effect of grain–grain boundary on ZnOnanorod-based UV photosensor: a complex impedance spectroscopic study”, J. Mater. Sci. 2021, 56, 19128–19143. R. Majumder, S. Kundu, M. P. Chowdhury, “Investigation of ambient regulated enhanced photo-responsive properties of GO-ZnO and transition metal doped GO-ZnO nanocomposite: Improved photocurrent and swift response”, Optical Materials, 2023, 142, 113981. S. Kundu, R. Majumder, R. Ghosh, MP Chowdhury, “Enhanced relative humidity sensing property of porous Al:ZnO thin films”, Materials Today: Proceedings 2020, 26, 138-141. S. Kundu, R. Majumder, S. Roy and MP Chowdhury, “Electro-polymerization of Polyaniline on CVD grown transferrable vertically aligned CNT forest and its application in resistive detection of relative humidity”, Materials Today: Proceedings 2021, 43, 3591-94. R. Ghosh, S. Kundu, R. Majumder, M. P. Chowdhury, “Hydrothermal synthesis and characterisation of multifunctional ZnO nanomaterials”, Materials Today: Proceedings 2020, 26, 77-81.

 

Published by: vikaspatanker

Authors: Assistant Professor K.M.Jadhav, Ms.Shreya Deshmukh, Ms.Anushka Kshirsagar, Ms.Sanika Patil, Ms.Gauri Mharanur, Ms.Priya Kolar, Ms.Tanuja Patil, Ms.Ashish Katkar, Ms.Shlok Katu, Ms.Umer Ibuse

Abstract: Augmented Reality (AR) and Virtual Reality (VR) are rapidly transforming how users interact with digital environments by enhancing real-world experiences and simulating fully immersive virtual scenarios. This paper explores the current landscape, practical applications, and future directions of AR and VR technologies, with a focus on their role in education, healthcare, engineering, and retail. A qualitative research approach was adopted, incorporating academic literature, platform documentation, and real-world use cases. The study also highlights sector-specific challenges including usability, cost, and cultural limitations, while discussing technological trends such as gesture recognition, virtual simulations, and cross-platform development. Through this analysis, the paper underscores the importance of context-aware, user-centred design in maximizing the impact of immersive technologies.

Published by: vikaspatanker

Authors: Assistant Professor S.N.Sudhal, Mr.Vishal Patil, Mr.Aniruddha Jagadale, Mr.Harshvardhan Jadhav

Abstract: The growing global demand for sustainable energy solutions has accelerated the development and integration of biofuels in modern engine technologies. Biofuels—renewable fuels derived from biological sources such as crops, algae, and waste—offer a cleaner and more environmentally friendly alternative to fossil fuels. This paper explores the types of biofuels, including first, second, and third-generation fuels, and examines their physical and chemical properties relevant to combustion performance. Emphasis is placed on the compatibility of various biofuels with current internal combustion engine (ICE) systems, including spark-ignition and compression-ignition engines. Advances in engine modifications, fuel injection systems, and emission control technologies are discussed in the context of optimizing engine performance while minimizing environmental impact. The paper also addresses the technical and economic challenges in large-scale biofuel adoption and outlines future directions for research and development. Ultimately, the synergy between biofuels and evolving engine technology presents a promising pathway toward a more sustainable and energy-secure future.

Published by: vikaspatanker

Authors: Madhura.M.Raste, Aniruddha. R. Sawant,, Prathmesh.S.Patil,, Sourabh. S. Kurne, Sandip.S.Sawant,

Abstract: – In today’s digital age, farmers and livestock owners still face challenges when it comes to buying and selling animals or cattle feed. Traditional methods are often time-consuming, limited by geography, and involve middlemen who may increase costs. This project aims to develop a user-friendly website that serves as an online marketplace where farmers, feed suppliers, and livestock traders can connect directly. The platform allows users to list livestock for sale, browse available cattle feed, compare prices, and make purchases or inquiries all from their mobile or computer. It includes features like secure user accounts, search filters (by location, type of animal or feed, price range), and contact options for buyers and sellers. By bringing these transactions online, the platform helps reduce market inefficiencies, increase transparency, and give rural communities better access to trade opportunities. This website is designed to be simple, multilingual, and accessible even in low-connectivity areas. Overall, the goal is to modernize livestock and cattle feed trading, empowering farmers with the tools they need to grow their businesses more efficiently.

 

 

Published by: vikaspatanker

Authors: Assistant Professor H.S.Bhore, Mr.Shreyas Shivankar, Ms.Payal Kamble, Ms.Aishwarya Bansode

Abstract: This paper explores the applications of Artificial Intelligence (AI) in smart city management, focusing on traffic, waste, and resource management. We discuss the benefits and challenges of implementing AI-powered solutions in urban settings and propose a framework for integrating AI into smart city infrastructure.

Published by: vikaspatanker