Authors: Yash Solunke, Ketan Bharambe, Nidhi Gandhi, Himani Suryawanshi, Khushi Raktate

Abstract: Sustainable irrigation is a critical component of modern agriculture due to increasing water scarcity, climate variability, and the need for precision resource management. Traditional irrigation systems, often based on fixed schedules or coarse environmental data, frequently lead to over-irrigation, under-irrigation, and inefficient water use. To address these limitations, this work introduces an AI-powered irrigation advisory framework that combines microclimate simulation, machine learning models, and real-time field-level sensing to generate accurate and adaptive water-use recommendations. The proposed system models localized microclimate parameters, including soil moisture, evapotranspiration, humidity flux, and temperature gradients, to provide more accurate short-term water demand estimates than traditional farm-level predictions. Machine learning algorithms continuously optimize the system, forecast crop-specific water needs, and dynamically identify patterns. To ensure robustness across diverse farming scenarios, the framework incorporates adaptive calibration mechanisms that adjust recommendations based on changing crop phenology and environmental conditions. We describe the implementation of this software-driven decision-support tool and its validation using both simulated and real-world agricultural datasets. Results demonstrate improved prediction reliability, a reduction in irrigation waste, and enhanced water-use efficiency compared to conventional scheduling methods. The proposed AI-powered sustainable irrigation advisor illustrates how microclimate-aware systems can advance next-generation smart agriculture, supporting productivity, environmental sustainability, and water conservation.

DOI: https://doi.org/10.5281/zenodo.18596198

Published by: vikaspatanker

Authors: Yash Solunke, Om Nikam, Shubham Chavan, Rutuja Raut, Pallavi Gulia

Abstract: BreathSafe is an innovative AIdriven system designed to monitor and diagnose respiratory conditions through breath analysis and real-time data processing. By leveraging machine learning algorithms on sensor data from wearable devices, BreathSafe enables early detection of diseases like COPD, asthma, and lung infections with over 90% accuracy in clinical trials. This paper presents the system's architecture, implementation, and evaluation for sustainable healthcare innovation.

DOI: http://doi.org/10.5281/zenodo.18619328

Published by: vikaspatanker

Authors: Piyush Kotkar, Pratik Halnor, Sakshi Kapse, Harshal Adhav, Atharva Dhawale

Abstract: Real-time foot traffic monitoring is now a key part of retail analytics, campus management, and smart surveillance. However, limitations in computing power make it hard to use heavy deep-learning models in low-power settings. This paper introduces a lightweight footfall counting system that uses YOLOv8n and YOLOv8s along with a centroid-based tracking method for effective ID persistence and directional counting. Experimental results indicate that YOLOv8n reaches 4.1 FPS on CPU-only systems with 98–99% ID stability, surpassing YOLOv8s in real-time performance. The system works well for embedded platforms, public monitoring, and budget-sensitive deployments.

DOI: https://doi.org/10.5281/zenodo.18596112

Published by: vikaspatanker

Authors: Mr. Viraj Kishor Chitte, Mr. Om Anant Aher, Mr. Darshan Santosh Bhandari, Mr. Sai Yogesh More, Mrs. Smita Manohar Dighe

Abstract: Neural networks, fuzzy logic, and expert systems are fundamental to the development of intelligent systems capable of addressing complex decision-making challenges across various domains. Neural networks, inspired by the structure of the human brain, demonstrate proficiency in pattern recognition, data classification, and high-accuracy prediction. Fuzzy logic facilitates reasoning under uncertainty, enabling systems to process imprecise inputs and generate responses that resemble human reasoning. Expert systems employ rule-based reasoning to emulate expert decision-making, delivering reliable solutions across healthcare, diagnostics, and industrial automation. This paper examines the underlying principles, strengths, limitations, and applications of these three artificial intelligence techniques. Through comparative analysis, it highlights their performance distinctions and unique contributions to intelligent problem-solving. Additionally, the study investigates the advantages of integrating these methods to create hybrid intelligent systems with improved adaptability, accuracy, and reliability. Such integrated approaches have the potential to advance AI-driven solutions in smart systems, real-time monitoring, and automated decision support.

DOI: https://doi.org/10.5281/zenodo.18596011

Published by: vikaspatanker

Authors: Kewal Manish Patel, Gaurav Tushar Kokate, Durvesh Amit Amale, Shubham Musmade, Atharva Gare

Abstract: Agriculture in India faces challenges such as unpredictable rainfall, improper irrigation planning, and inefficient use of water resources. To address these issues, this paper proposes a Smart Agriculture System that integrates Internet of Things (IoT) sensors with a lightweight Machine Learning model to optimize irrigation. The system collects real-time soil moisture, temperature, humidity, and light intensity data using low-cost sensors such as the soil moisture sensor and DHT11. The data is sent to a cloud platform through an ESP8266/NodeMCU microcontroller for monitoring. A simple ML model, such as Linear Regression or Decision Tree, predicts the required watering level based on sensor patterns. When moisture falls below the predicted threshold, the system automatically activates a water pump and sends an alert to the farmer’s mobile dashboard. The proposed solution reduces water wastage, increases crop health, and facilitates precision agriculture. This work demonstrates how IoT and ML together can support sustainable agricultural practices, contributing to UN Sustainable Development Goals (SDG-2 and SDG-12). The prototype is easy to implement, low-cost, and scalable for real-world applications.

DOI: https://doi.org/10.5281/zenodo.18595980

Published by: vikaspatanker

Authors: Jayashree Chava, Prasad Chavan, Dr. Pritish Vibhute

Abstract: Women’s safety continues to be a pressing concern globally, and timely access to help often determines the outcome of critical situations. With rapid advances in electronics and communication technology, there is growing potential to build practical tools that can offer support when it is needed most. This work presents a compact, AI-enabled wearable safety device developed specifically to assist women during emergencies. The device operates hands-free and relies on on-device voice recognition, implemented on an ESP32-S3 microcontroller trained using Edge Impulse. It uses Bluetooth Low Energy (BLE) to connect with a companion Android application. When the system recognizes the spoken keyword “Help! Help!” it functions entirely offline to activate the mobile app. The app then automatically fetches the user’s GPS location and sends an SOS alert to selected emergency contacts. It also uses the Google Places API to identify nearby police stations for quicker support. To strengthen post-incident reporting, the wearable includes an AI-based motion and image-capture module that records relevant visual evidence through its built-in camera. The prototype is designed to be power-efficient, affordable, and mindful of user privacy, making it suitable for both rural and urban environments. Overall, the proposed system shows how edge AI and IoT connectivity can be combined to create a practical and reliable personal-safety solution.

DOI: https://doi.org/10.5281/zenodo.18595926

Published by: vikaspatanker

Authors: Ashwini Shinde, Dr. Kiran Wakchaure

Abstract: India’s agriculture sector remains the backbone of rural livelihoods and national food security, contributing substantially to economic growth and employment. However, farmers continue to encounter a wide range of structural and socio-economic barriers, including small and fragmented landholdings, heavy reliance on monsoon rains, inadequate technological adoption, post-harvest inefficiencies, financial vulnerabilities, and unstable market prices. Additional constraints such as rising labour expenses, low levels of mechanization, limited irrigation coverage, and insufficient knowledge of sustainable practices further limit agricultural productivity. This review paper explores these complex challenges in detail while assessing the effectiveness of major government programmes designed to address them. Key schemes—such as the Pradhan Mantri Fasal Bima Yojana (PMFBY), PM-Kisan income support, Soil Health Card initiative, e-NAM digital marketplace, Pradhan Mantri Krishi Sinchai Yojana (PMKSY), Minimum Support Price (MSP) mechanisms, and emerging digital agriculture efforts—are evaluated for their role in improving productivity, farmer income, and risk management. The study identifies notable policy successes as well as areas requiring improvement, emphasizing the need for integrated, technology-oriented, and farmer-focused strategies.

DOI: https://doi.org/10.5281/zenodo.18595876

Published by: vikaspatanker

Authors: Aliza Sayyad, Dr. Pravin Khatkale

Abstract: The prevalence of mental health conditions in- cluding stress, anxiety, and depression is on the rise worldwide, but stigma, ignorance, and a lack of mental health experts con- tinue to hinder early detection and ongoing emotional support. The Smart Mental Health Assistant, an AI-powered support sys- tem intended to assess user symptoms, forecast potential mental health issues, and offer tailored self-care advice, is the idea behind this project. The system incorporates a chatbot interface for user interaction and advice, Random Forest Classifier for mental health prediction, and Natural Language Processing (NLP) for symptom extraction. In order to effectively diagnose mental health disorders, the system transforms retrieved symptoms into binary vectors using datasets that include symptoms, severity lev- els, and preventative measures. This paper examines the body of research on AI in mental health, pinpoints important variables affecting technology uptake, and emphasizes the significance of scalable and easily accessible mental health resources. The re- sults show that early diagnosis, emotional monitoring, and pre- ventive treatments could all be enhanced by AI-based screening systems. By facilitating ongoing assistance, lowering stigma, and enhancing psychological well-being, the suggested assistant bene- fits the mental health ecosystem.

DOI: https://doi.org/10.5281/zenodo.18595836

Published by: vikaspatanker

Authors: Chinmay Chouhan, Assistant Professor Srashti Thakur

Abstract: Brain tumor segmentation is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amount of MRI images generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain tumor image segmentation. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. Recently, automatic segmentation using deep learning methods proved popular since these methods achieve the state-of-the-art results and can address this problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based image data. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. Different than others, in this paper, we focus on the recent trend of deep learning methods in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of deep learning methods are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.

Published by: vikaspatanker

Authors: Neha Sahu, Prof. Ruchika Saini

Abstract: This study focuses on the design and structural analysis of helical gears with varying helix angles to investigate their influence on mechanical performance. By designing helical gears with different helix angles and analyzing them under identical loading and boundary conditions, the study aims to evaluate changes in bending stress, contact stress, deformation, and axial force. The results of this investigation will help identify optimal helix angle ranges that enhance gear strength and longevity while minimizing undesirable effects such as excessive axial loads and material failure. The findings of this study are expected to contribute to improved gear design practices by providing insights into the relationship between helix angle variation and structural performance. Such insights are valuable for engineers and designers seeking to develop efficient, durable, and high-performance gear systems for modern mechanical applications.

Published by: vikaspatanker