Authors: P. Ramamoorthy
Abstract: The study area Panruti Block has chosen to assess the groundwater quality. There are 15 groundwater samples were collected and analyzed for various physical and chemical parameters such as pH, TDS, EC, Ca, Mg, Na, K, Cl, NO3,SO4,Fl, etc. The groundwater samples are compared with the WHO standards. The quality of ground water in the study area is fresh to Brackish, slightly basic in nature, Salinity nature.
Authors: Smita Tanvade, Jyoti Sathe, Niha Kudchikar, Samruddhi Patil, Mrs. P.D Nasalapure
Abstract: Smart farming integrates advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and data analytics to improve the efficiency, productivity, and sustainability of agricultural processes. This paper presents an AI-driven smart farming system that uses real-time soil data to help farmers make accurate and effective decisions. The system collects essential soil parameters such as moisture, pH, temperature, electrical conductivity, and nutrient levels through IoT sensors and analyzes them using machine learning algorithms. Based on this analysis, the system predicts the most suitable crop for the existing soil condition and also recommends the appropriate fertilizers needed to improve soil health and support optimal crop growth. By reducing manual guesswork, minimizing resource wastage, and offering clear data-based guidance, the proposed system helps farmers increase productivity, reduce costs, and adopt sustainable farming practices. Cloud integration and an easy-to-use interface ensure that the solution remains accessible, scalable, and suitable for different types of farms. Overall, this AI-driven system provides a practical and efficient approach to modern agriculture, enabling higher yield, better soil management, and smarter decision-making.
Authors: Dr. Prabodhini B Wakhare, Bhagyashree S. Borhade, Professor Dr. Shivaji Borhade
Abstract: This study investigates the emerging trend of naked resignation among the digital workforce with a focus on IT professionals in Pune (India). Naked resignation refers to employees leaving abruptly without prior communication, gradual disengagement or attempts at negotiation. Utilising a mixed-methods approach the research gathered responses from IT employees and HR professionals to examine how factors such as techno-stress, burnout, emotional fatigue, workplace relationships and organisational trust influence resignation intentions. The findings reveal that emotional fatigue is the most significant factor followed by burnout and techno-stress. Participants noted a diminished trust in leadership and a sense of organisational disconnect as contributing factors to sudden resignations. Qualitative responses indicate that employees view naked resignation not as an impulsive decision but as a necessary reaction to psychological strain and declining workplace well-being. The study concludes that resignation is increasingly seen as a means of setting boundaries rather than a response to isolated incidents. The results underscore the need for improved communication channels, mental health resources and employee-centered leadership practices. This research sheds light on the evolving behaviours in the post-pandemic workforce and adds to the literature on employee retention, digital work culture and mental well-being in technology-driven organisations.
Research field is of patience, hard work with innovation. So every step of innovation need some recognition hence international journals plays an important role for doing the same work. Many of scholars do deep search to get the List of Free Journals for Paper Publication but mostly they get same of journals that are common. Hence mentors of scholars or researchers keep a list of journals that have following criteria like ISSN number, proper online presence, open access for published articles.
# Publisher should be old enough have atleast 10 years of publishing expeirnce.
# Publication coount in the journal should be regular and have variety of authors belings from different college, universities, nation.
Out of various set of publishers an international journal focus on the papers that were current trends of research so journal audience get what they are looking for these days. Many of journals give a proper feedback tot the author for improving the content before rejecting or modification. Authors are requested to always find the current trending topics of publication do by the journal. To get this information check the current issue of publication if current publication are same of author research area then approach to the journal for publication otherwise find other.
Other important factor of journal selection is publication fees and review time. In some of case journals fees is zero means publication is FREE but review time of such publisher is high with chance of rejection is also very high. Journals that make fast review take some charges from the author for the publication hence such fees should be nominal. Author should always check the fees of publication before submitting the paper to the editor, as after acceptance if publication fees feels very high turns into time waste.
Finally List of FREE Journals for Paper Publication should be regularly updated by the people, mentor, author’s as many of journal may change their policy of publication. It is always expected that journal provide all necessary information to the author publically as few of journals hide the publication terms and condition, so do not submit paper for publication as they may charge later once paper got accepted. Some of college universities prepare that list every year or semester they can even approach to the editor to confirm about fees, review time, etc.
Authors: Radhika Marathe, Jagruti Patil, Vraj Patel, Nakka Narendar
Abstract: The travel and tourism industry is rapidly transi- tioning toward digital ecosystems that provide seamless user ex- periences. However, most travel applications remain fragmented, requiring customers to access different platforms for hotels, packages, transfers, and visa services. World Trip Deal resolves this by offering an integrated web platform that centralizes all essential travel-related modules. The system is built using Angular, Node.js, Express, Firebase, and MongoDB, ensuring scalability, responsiveness, and security. This paper presents the system architecture, methodology, workflow, implementation details, and performance evaluation of the platform.
Authors: Harsh Manvani, Niraj Shrimali, Sahil Ahir, Ved Patel
Abstract: The tourism sector is changing quickly as digital tools be- come more common, but many travelers still struggle to plan trips that are both convenient and affordable. A major problem is the absence of a single platform that can offer clear, reliable, and budget-focused itineraries. Although there is plenty of information available on travel websites and search engines, it is scattered and often difficult to com- pare, which makes the planning process tiring and confusing.Another issue appears once tourists reach their destination. Local transport frequently becomes a challenge, especially for first-time visitors who may end up paying high prices or relying only on taxis and ride-hailing services. This also reduces their chances of exploring places more freely and experiencing the local culture. In many tourist locations, two-wheeler rentals are actually a more convenient and enjoyable way to move around, yet they are not well integrated into existing travel systems.To overcome these problems, this study proposes an AI-based platform that combines personalized trip planning with a peer-to-peer bike rental service. The system uses machine learning to create tailored itineraries by collecting and organizing travel data from multiple sources. At the same time, it offers a safe and easy-to-use marketplace where travelers can rent two-wheelers from local owners. By bringing together artificial intelligence and community-driven mobility options, the platform aims to make travel more accessible, reduce instances of overcharging, and improve the overall experience for tourists.
DOI: http://doi.org/10.5281/zenodo.17746212
Authors: Pratik Pandey, Nagendra Patel
Abstract: Brain tumor detection through magnetic resonance imaging (MRI) plays a crucial role in early diagnosis and treatment planning. This study presents a comparative analysis of five deep learning models—CNN, ResNet50, U-Net, YOLOv7, and InceptionResNetV2 for accurate classification of brain tumors. The dataset was preprocessed with cleaning, augmentation, and normalization before training and evaluation. Performance was measured using accuracy, precision, recall, and F1-score. Results demonstrate that all models achieved strong outcomes, but InceptionResNetV2 significantly outperformed others, reaching nearly 100% across all metrics. This superior performance highlights its effectiveness in minimizing false positives and false negatives, thereby offering a robust tool for clinical applications. The findings emphasize the importance of advanced deep learning architectures in medical imaging for reliable tumor detection.
Authors: Harshal Patel, Jitendra Shrivastav, Kamlesh Patidar
Abstract: The integration of machine learning into agriculture has shown great promise in improving decision-making, particularly in crop recommendation systems. This review focuses on XGBoost and supervised learning approaches for crop recommendation based on soil composition analysis. Soil properties such as pH, nutrient levels, moisture, and texture play a crucial role in determining crop suitability and yield. However, many existing models fail to adequately capture the complex interactions among these variables or account for regional soil variability. By examining current supervised learning methods and the growing application of XGBoost, this paper highlights their strengths, limitations, and potential for enhancing prediction accuracy. Furthermore, it identifies key research gaps, including the scarcity of diverse soil–crop datasets and the need for models that can adapt across geographical regions and climates. The review concludes that integrating advanced supervised learning with robust soil data can significantly optimize crop recommendations, promoting sustainable and precise agricultural practices.
