Most of the research conducted in action recognition is mainly focused on general human action recognition, and most of the available datasets support studies in general human action recognition. In more specific cont...
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Water resource management and disaster response have become some of the most challenging tasks, especially when disasters pose a threat, as delays could lead to more impacts. The centralized system used for water dyna...
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Water resource management and disaster response have become some of the most challenging tasks, especially when disasters pose a threat, as delays could lead to more impacts. The centralized system used for water dynamics and disaster control usually presents itself as a scalability problem since more clients present a problem, the system's latency is high, and the system is always prone to a single-point failure. The previous approach lacks flexibility and does not synchronously guarantee the integration of several subjects in real time, especially during unpredictable disaster conditions. The proposed FL-MAPPO model surpasses current methods by facilitating decentralized, privacy-protecting decision-making minimizing latency and single-point failures. In contrast to LSTM, Bi-LSTM, and DRNN, which are based on centralized data processing, FL-MAPPO provides real-time adaptability and effective resource management. Experimental results validate that it has lower MSE, higher R² scores, and quicker response times, making it better suited for flood prediction and disaster response. To this end, this study advances a solution through a Decentralized Learning-Driven Multi-Agent Autonomous System (DL-MAAS). The new feature is a Decentralized Cooperation environment in which intelligent and self-managing agents learn utilizing Reinforcement Learning (RL) and Federated Learning (FL) algorithms for enhancing smart water management and real-time disaster relief. IoT devices are adopted for sensing and data acquisition, adaptive learning for decision-making, and optimization of energy use among the agents in the system through metaheuristic algorithms. The research methodology for implementing the proposed solution involves the design of a multi-layered architecture, including data acquisition, decentralized learning, and real-time execution. With a Mean Squared Error (MSE) of 0.112, R-squared (R²) of 0.953, and Mean Absolute Error (MAE) of 0.207, the proposed method is better
作者：
Khadse, ShrikantGourshettiwar, PalashPawar, Adesh
Faculty of Engineering and Technology Wardha442001 India
Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Wardha442001 India
Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India
Meta-learning aims to create Artificial Intelligence (AI) systems that can adapt to new tasks and improve their performance over time without extensive retraining. The advent of meta-learning paradigms has fundamental...
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This study introduces the System for Calculating Open Data Re-identification Risk (SCORR), a framework for quantifying privacy risks in tabular datasets. SCORR extends conventional metrics such as k-anonymity, l-diver...
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This study introduces the System for Calculating Open Data Re-identification Risk (SCORR), a framework for quantifying privacy risks in tabular datasets. SCORR extends conventional metrics such as k-anonymity, l-diversity, and t-closeness with novel extended metrics, including uniqueness-only risk, uniformity-only risk, correlation-only risk, and Markov Model risk, to identify a broader range of re-identification threats. It efficiently analyses event-level and person-level datasets with categorical and numerical attributes. Experimental evaluations were conducted on three publicly available datasets: OULAD, HID, and Adult, across multiple anonymisation levels. The results indicate that higher anonymisation levels do not always proportionally enhance privacy. While stronger generalisation improves k-anonymity, l-diversity and t-closeness vary significantly across datasets. Uniqueness-only and uniformity-only risk decreased with anonymisation, whereas correlation-only risk remained high. Meanwhile, Markov Model risk consistently remained high, indicating little to no improvement regardless of the anonymisation level. Scalability analysis revealed that conventional metrics and Uniqueness-only risk incurred minimal computational overhead, remaining independent of dataset size. However, correlation-only and uniformity-only risk required significantly more processing time, while Markov Model risk incurred the highest computational cost. Despite this, all metrics remained unaffected by the number of quasi-identifiers, except t-closeness, which scaled linearly beyond a certain threshold. A usability evaluation comparing SCORR with the freely available ARX Tool showed that SCORR reduced the number of user interactions required for risk analysis by 59.38%, offering a more streamlined and efficient process. These results confirm SCORR’s effectiveness in helping data custodians balance privacy protection and data utility, advancing privacy risk assessment beyond existing tools
作者：
Warbhe, Mohan K.Bore, Joy JordanChaudari, Shiv Nath
Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India
Faculty of Engineering and Technology Department of Computer Science and Medical Engineering MaharashtraSawangi Wardha442001 India
The proposed web application for tomato leaf disease detection exemplifies the transformative power of Artificial Intelligence and computer Vision in modern agriculture. Addressing the critical issue of early and accu...
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作者：
Petkar, Taniya G.Kumar, PraveenSarate, Kirtiksha U.
Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Sawangi Wardha442001 India
Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India
By enabling precise, individualized, and effective treatments, the integration of artificial intelligence (AI) and machine learning (ML) into wound and skin healing is revolutionizing healthcare. Artificial intelligen...
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Artificial Intelligence (AI) and Big Data Analytics are greatly transforming the healthcare scenario by facilitating the early detection of diseases, personalized medicine, and improved public health interventions. AI...
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Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and att...
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The growing demand for real-time disease prediction in healthcare necessitates advanced AI frameworks capable of ensuring both computational efficiency and patient privacy. This study introduces an Edge-Assisted Feder...
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In the era of big data, with the increase in volume and complexity of data, the main challenge is how to use big data while preserving the privacy of users. This study was conducted with the aim of finding a solution ...
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