The study promotes a thorough research effort to create a predictive model specifically for medical malpractice litigation, highlighting the intricate relationships between insurance and medical malpractice claims. Th...
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In power distribution systems, common issues such as voltage fluctuations, voltage instability, current harmonics, and power imbalances often arise, negatively impacting the stability and power quality (PQ) of the pow...
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Identifying important factors that frequently affect a team’s success is very important, that leads to the usage of mathematical models in the sports especially in cricket, in order to predict which side might win a ...
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Epilepsy is the second most prevalent neurological disorder. It poses significant challenges to both diagnosis and treatment. The information obtained from electroencephalography serves as crucial for understanding th...
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Traditional methods of violence detection in public spaces often struggle with low accuracy, limited real-time capabilities, and an inability to handle complex spatiotemporal patterns. They lack the sophistication nee...
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Traditional methods of violence detection in public spaces often struggle with low accuracy, limited real-time capabilities, and an inability to handle complex spatiotemporal patterns. They lack the sophistication needed to accurately distinguish between violent and non-violent activities, and their reliance on rule-based systems hinders adaptability to diverse scenarios. Moreover, their communication channels for alerts might be slow and inefficient. Mitigating the pervasive issue of violence within public spaces demands a technologically advanced approach. Addressing this imperative, we present a novel solution encompassing a profound neural network architecture. Our method harmoniously integrates a pre-trained Darknet19 model with both Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models, collectively orchestrated to achieve unprecedented efficacy in violence detection and prevention. Our approach commences with the extraction of spatial intricacies, meticulously executed by leveraging the potent capabilities of the Darknet19 model. Subsequently, these extracted spatial features serve as the foundational dataset for training the CNN, which in turn captures and distills essential temporal attributes inherent to the video sequences. These temporal features are then seamlessly channeled into the LSTM component of our architecture, which adeptly discerns and categorizes video-based activities into two distinct classes: manifestations of violence and non-violent behaviors. Validation and verification of our proposed model transpire upon the Fight dataset, resulting in a suite of commendable experimental outcomes. The integration of multi-modal alert dissemination mechanisms further enhances our system's efficacy. Notably, pertinent alerts are expeditiously communicated to relevant law enforcement entities through the synergistic utilization of WhatsApp, Telegram, and e-mail applications. This technologically fortified paradigm promises a transfo
Despite India's global leadership in dairy production, the industry faces persistent challenges in animal productivity due to healthcare gaps and remote veterinary access. This hinders farmer profits and industry ...
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Early detection is vital in crop health, yet improvement in productivity faces time-consuming and inefficient challenges due to traditional manual techniques of plant disease detection. Thus, we present a deep learnin...
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作者：
Maiti, PradiptaBanerjee, SudiptaDhanbad
Department of Electronics Engineering Dhanbad India
Symbiosis Institute of Technology Pune Department of Computer Science and Engineering Pune India
This study examines three-dimensional (3D) and two-dimensional (2D) interpolation approaches for indoor 3D radio environment map (REM), on received signal strength (RSS) data of ultrahigh frequency (UHF) active televi...
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The Real-Time Worker Monitoring System employing deep learning and web integration represents a cutting-edge solution for enhancing workplace safety and *** advanced deep learning algorithms, this system continuously ...
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This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neut...
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