http://irgu.unigoa.ac.in/drs/handle/unigoa/27 2026-04-07T09:15:22Z http://irgu.unigoa.ac.in/drs/handle/unigoa/7767 Detection of Avian Influenza-Infected Chickens using a Multi-Modal Audio-Visual Convolutional Neural Network Optimized for Indian Poultry Farms Gawas, M.; Patil, H.; Gawas, M.M. Avian influenza (AI) poses a recurring threat to poultry production and public health in India, where high-density farming and diverse environmental conditions create challenges for early detection. This study presents a novel Multi-Modal Chicken Health Monitoring System (MM-CHMS) that integrates audio-based spectral analysis of chicken vocalizations with visual-based posture and movement monitoring to detect AI infections. Our architecture combines Mel-spectrogram Convolutional Neural Networks (CNNs) for sound analysis with a ResNet-50 backbone for video feature extraction, fused through a fully connected feature integration layer. The system is designed for field deployment in Indian poultry farms, addressing constraints such as variable lighting, high background noise, and breed-specific vocal variations. A dataset was collected from farms in Goa, Karnataka, and Maharashtra, including both healthy and AI-infected flocks confirmed via RT-PCR tests. The proposed system supports both real-time edge-device inference and offline batch analysis. Field evaluations demonstrate an overall detection accuracy of 94.2 percent, with AI infections detected up to 48 hours prior to visible symptoms, enabling timely intervention. 2025-01-01T00:00:00Z http://irgu.unigoa.ac.in/drs/handle/unigoa/7766 Integrating Thermal and Fecal Image Analysis Using Deep Learning for Early Detection of Poultry Diseases Gawas, M.; Gawas, M.M. Timely and accurate detection of poultry diseases is vital for minimizing economic losses and safeguarding animal welfare in the poultry industry. Conventional diagnostic methods are often invasive, time-consuming, and reliant on human observation, leading to potential delays in intervention. This study explores the application of artificial intelligence (AI) and non-invasive technologies to enhance disease diagnosis in poultry. A thermal imaging-based framework was developed to detect Avian Influenza (AI) and Newcastle Disease (ND) within 24 h of infection. Thermal data collected from both infected and healthy broilers were processed to extract 23 statistical features, which were then analyzed using Support Vector Machines (SVM) and Artificial Neural Networks (ANN). SVM outperformed ANN, achieving classification accuracies of 97.2 percent for AI and 100 percent for ND. To address uncertain predictions, the Dempster-Shafer evidence theory was employed to enhance decision reliability. Additionally, the study proposes a deep learning-based model using fecal image classification through CNN architectures such as MobileNetV2 and Xception to identify Coccidiosis, Salmonella, and ND, achieving accuracies exceeding 98 percent. The integration of thermal imaging, fecal image analysis, and AI-based classification presents a robust, non-invasive, and rapid disease detection system. This multi-modal approach demonstrates significant potential for improving poultry health monitoring and enabling early, automated interventions. 2026-01-01T00:00:00Z http://irgu.unigoa.ac.in/drs/handle/unigoa/7765 Mathematical Modeling for Predictive Maintenance: Leveraging AI and Machine Learning for System Optimization and Failure Prediction Gawas, M.M.; Gawas, M. This paper presents a mathematical modeling framework for predictive maintenance using artificial intelligence (AI) and machine learning (ML) to improve system performance and accurately forecast component failures. The study integrates advanced mathematical tools-such as differential equations, optimization algorithms, and probabilistic models-with ML techniques to develop predictive insights for mechanical systems. A case study on engine life prediction demonstrates the application of Support Vector Machines (SVM) and Neural Networks (NN), showing how degradation over time can be effectively modeled and predicted using real-time data and simulations. The proposed model achieves failure prediction accuracy with error rates as low as 2.5 percent, along with notable improvements in operational efficiency. The research highlights the value of mathematical modeling in enhancing AI/ML-based maintenance systems and emphasizes its broad applicability in industrial settings. 2026-01-01T00:00:00Z http://irgu.unigoa.ac.in/drs/handle/unigoa/7764 Optimizing Energy Efficiency and Quality of Service in Fog Computing with a Decentralized Blockchain Framework Gawas, M.; Gawas, M.M. This study introduces an innovative framework that combines edge computing with blockchain-based IoT systems. Unlike conventional IoT-blockchain models, which offer limited capabilities such as running multiple applications simultaneously and supporting cross-platform operations, the proposed approach provides a more comprehensive solution. However, they still rely heavily on cloud infrastructures for data storage and processing. This reliance forces resource-constrained IoT devices to handle raw data, resulting in increased management overhead, power consumption, and degraded Quality of Service (QoS) due to their centralized architecture. Furthermore, most existing frameworks address only a limited set of security concerns, leaving the ecosystem susceptible to various vulnerabilities. To overcome these limitations, we propose an QoS-Aware and Energy-Efficient Decentralized Blockchain (EEQA-DBC) designed for fog computing environments. EEQA-DBC eliminates the need for trusted intermediaries while resolving challenges linked to centralized systems. In addition, we introduce a novel security authentication mechanism that safeguards data confidentiality, integrity, and availability. The framework we introduce significantly enhances the security of IoT-fog networks by defending against prevalent cyber threats such as eavesdropping, and denial-of-service (DoS) attempts, thereby improving the network's overall resilience. 2026-01-01T00:00:00Z