SDG 6: Program, Community, Planning Framework Amit Vajpayee, Mannat Thakur Water Suitability Analysis Advanced Research Approaches for Sustainable and Resilient Resource Management, 2026 Sustainable Development Goal 6 (SDG 6) aims to ensure the availability and sustainable management of water, while providing hygiene for all by 2030. This chapter details a cross-stakeholder Vision Program that guides and plans community engagement and strategic governance efforts through a bottom-up, integrated approach to developing a roadmap for achieving SDG 6, including all Governance Program agendas, Assessment and Reporting governance breaches, all identified financial constraints and challenges to achieving programs, unexplained gaps, and risks, particularly implementation and other critical bottlenecks. Community-driven models, such as Water User Associations and Community-Led Total Hygiene models, and strategic community governance approaches on implementation in the last mile, are outlined. The chapter analyzes relevant aspects of strategic planning, with an emphasis on Integrated Water Resources Management (IWRM) as the primary cross-sectoral planning and policy IWRM and on other strategic agenda frameworks to avoid integrated decision-making systems in record-keeping silos and to manage environmental, social, and governance (ESG) risks. The chapter highlights emerging applications of strategic IWRM, field-level Internet of Things (IoT), remote sensing, and other ultra-modern technologies to automate governance and ESG frameworks for real-time decision-making and adaptive, data-driven, responsive governance. The chapter aligns with the rest of the other chapters of the book to present a holistic view, including next steps such as innovative data-governance silos; data that form a collaborative, transparent, and open system; capacity and governance silos; bottom-up models; and frameworks to breakthrough in global access, equity, and advancement in water and hygiene as a primary strategic goal frames.
Wheat Disease Detection: Bridging the Gap with Deep Learning Approaches Vijay Bharatbhai Vasaiya, Amit Vajpayee, Ankita Gandhi Proceedings of the 11th International Conference on Bio Signals Images and Instrumentation Icbsii 2025, 2025 Wheat is a vital crop for global food security, yet its production faces significant threats from various diseases, leading to substantial yield losses. Traditional detection methods, such as manual scouting and laboratory analysis, are often labour-intensive and inefficient. The advent of deep learning techniques has revolutionized agricultural disease detection, offering more efficient and accurate solutions. We provide a comprehensive overview of common wheat diseases, methodologies, and datasets utilized in recent studies. A critical analysis of the literature reveals performance metrics of various deep learning models compared to traditional methods, Furthermore, the review suggests future research directions, emphasizing the integration of IoT technologies for real-time monitoring and the use of advanced sensors for improved detection accuracy. This synthesis aims to enhance strategies for effective disease management in wheat cultivation.
Leveraging the PneuNet Deep Learning Model to Effectively Identify Pneumonia from Chest X-rays Santosh Varshney, Amit Vajpayee, Bhupinder Kaur, Poonam Kukana 2025 IEEE International Conference on Advances in Computing Research on Science Engineering and Technology Acroset 2025, 2025 Pneumonia is a potentially deadly disease that affects people's lungs. Pneumonia is caused by the Streptococcus pneumonia bacteria. An automated method for detecting pneumonia would be beneficial and easy for a physician to utilize for early detection of pneumonia. Chest X-ray images from Kaggle dataset are used as the input dataset for this diagnosis. Chest X-ray estimation for pneumonia is a costly procedure that need for specialized radiotherapists. Pneumonia patients are immediately diagnosed and treated when it is still early. One can download the chest X-ray images from the Kaggle dataset. The attributes of the photos are learned using CNN (Convolutions Neural Network) models that have already been trained. To analyze picture features, CNN is employed. Physicians can diagnose pneumonia in patients with the use of this method. Early detection plays a pivotal role in enabling early diagnosis.
Deep Learning-Based Framework for Automated Detection and Classification of Rice Leaf Diseases Santosh Varshney, Amit Vajpayee, Poonam Kukana, Bhupinder Kaur 2025 IEEE International Conference on Advances in Computing Research on Science Engineering and Technology Acroset 2025, 2025 It is impossible to overstate the importance of rice production in providing food for India's enormous population. Rice production is hampered by issues such as bacterial leaf blight, brown spot, and smut. The time-consuming nature of manually checking for diseases necessitates the development of automated methods. In this study, a Deep Learning model has been developed to handle the illness detection and classification of several common rice diseases using machine learning techniques. The three disease classes—Bacterial Leaf Blight, Brown Spot, and Leaf Smut—are represented in the dataset's 119 photos. We conducted exploratory data analysis in an effort to comprehend the distribution and character of the photos. Next, we employed the Convolutional Neural Network (CNN) architecture to create the classification model. We now introduce the suggested model architecture: Fundamental Functional Elements Pooling layers and Convolutional layers. The Layers that are dense Grouping Image’s spatial features are down-sampled by pooling layers after being processed by convolutional layers. In the current study, we employed a variety of techniques, such as picture augmentation, to improve the model's capacity to effectively categorize unknown data. The partitioned training, validation, and test datasets were used to construct and evaluate this model. According to the experiments, the suggested method performs well in classifying rice diseases with an emphasis on minimal loss and high accuracy. Therefore, the work contributes to the development of automated early diagnosis and maturity of rice illnesses, which would help safeguard potential yield and national food security.
Chronic Kidney Disease Classification: Comparing the Effectiveness of Multiple Ensemble Classifiers Praveen Kumar Patidar, Navdeep Kaur, Amit Vajpayee, Pradeepta Kumar Sarangi, Alok Kumar Agrawal Proceedings of 2025 AI Driven Smart Healthcare for Society 5 0 Adsoc5 0 2025, 2025 Recent advancements in machine learning (ML) and artificial intelligence (AI) have greatly enhanced the detection and diagnosis of healthcare issues, including chronic kidney disease (CKD). CKD, also known as chronic kidney failure, involves the gradual deterioration of kidney function, which is essential for filtering waste and excess fluids from the blood. As the disease progresses, harmful fluids, electrolytes, and toxins can accumulate, posing serious health risks. The term "chronic" indicates the slow progression of this condition, which is seeing rising incidence globally. Patients with severely impaired kidney function typically survive without intervention for only about 18 days, resulting in urgent demand for kidney transplants and dialysis treatments. In addressing CKD, this study employs seven different ML algorithms to analyze kidney disease data and identify the most effective model. Many popular models such as Random Forest and Decision Tree models have been proved as useful tools. This study evaluates six distinct machine learning algorithms to analyze kidney disease data and identify the most effective model. The findings indicate that the LG Boost model achieved an impressive accuracy rate of 98.3%. Additionally, Stochastic Gradient Boosting and XGBoost models also have performed very well with an impressive accuracy of 97.5%.
An Overview of Computer Vision Techniques for Image Retrieval Amit Vajpayee, Palak Preet Kaur, Ankit Sharma, Sakshi 8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions Csitss 2024, 2024
An Extensive Analysis of Neuromorphic Computing Amit Vajpayee, Palak Preet Kaur, Ankit Sharma, Santosh Varshney 2024 International Conference on Advances in Computing Research on Science Engineering and Technology Acroset 2024, 2024
Disease Prediction Based on Symptoms and Drug Recommendation Apoorva Jindal, Riya Kamboj, Sakshi Pathak, Kunal Dubey, Amit Vajpayee 2024 11th International Conference on Reliability Infocom Technologies and Optimization Trends and Future Directions Icrito 2024, 2024
Early Diabetes Prediction Using Supervised Machine Learning Techniques Ramamani Tripathy, Dibyahash Bordoloi, Amit Vajpayee, Merry Saxena, Srikanta Kumar Mohapatra, Pradeepta Kumar Sarangi 2024 International Conference on Advances in Computing Research on Science Engineering and Technology Acroset 2024, 2024
