Multi-access Edge Computing presents a compelling solution for delivering seamless connectivity to computing services. In this study, we aim to optimize multicast throughput to ensure high-quality experiences for pass...
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Numerous deep learning (DL) models based on convolutional neural network (CNN) have been developed for image classification. Though the CNN models attained outstanding performance, its architecture contains certain li...
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Due to the considerable complexity of the multimedia data acquired, IoT (Internet of Things) and multimedia will encounter a number of energy and communication overload limits. One well-known solution to the problem o...
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A key development towards enhancing computer-human interaction is emotion recognition. This publication describes a technique called EmoCNN, which uses deep learning techniques to precisely identify and classify human...
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A key development towards enhancing computer-human interaction is emotion recognition. This publication describes a technique called EmoCNN, which uses deep learning techniques to precisely identify and classify human emotions, emphasizing improving model performance using different optimizers. Our research intends to contribute to the creation of more effective systems that improve computer-human interaction by solving the problems associated with emotion recognition. By bridging the gap between humans and robots, accurate emotion detection enables systems to perceive emotions for customized and responsive interactions. AI-powered assistants, chatbots, and social robots all benefit from emotion recognition by providing more responsive, empathic and interesting user experiences. Emotion-aware technologies can also enhance user feedback analysis, human-centered design, and monitoring of mental health. Using a human emotion detection dataset, we carried out comprehensive experiments focusing on the happy, sad, and neutral emotion classes. Constructing a customized EmoCNN model with convolutional layers, a hidden layer, ReLU activation, and max-pooling was the focus of our computational work. We investigated various optimizers and evaluated how they affected accuracy, convergence speed and loss minimization. The results demonstrated that the EmoCNN model, which had been trained using the Adam optimizer, gave the best accuracy in distinguishing between emotions. Our paper provides a comparative analysis, highlighting the superiority of EmoCNN over existing models, showcasing its ability to achieve higher validation accuracy (89%) and more efficient emotion recognition when compared to previous approaches with minimal loss. Our research advances the field of emotional computing by demonstrating how well EmoCNN can identify and categorizes various human emotions. This discovery has significant ramifications for the creation of emotion-aware computers, which can better und
Antimicrobial resistance (AMR) is one of the significant problems in the health sector and requires resources to create and develop accurate forecasting systems capable of recognizing resistant microbes and their trea...
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Modern information technology is driving the rapid development of the information and automation industry through the combination of computertechnology, communication technology, and control technology. Inspired by n...
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Relative overgeneralization (RO) occurs in cooperative multi-agent learning tasks when agents converge towards a suboptimal joint policy due to overfitting to suboptimal behaviors of other *** methods have been propos...
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Relative overgeneralization (RO) occurs in cooperative multi-agent learning tasks when agents converge towards a suboptimal joint policy due to overfitting to suboptimal behaviors of other *** methods have been proposed for addressing RO in multi-agent policy gradient (MAPG) methods although these methods produce state-of-the-art *** address this gap, we propose a general, yet simple, framework to enable optimistic updates in MAPG methods that alleviate the RO *** approach involves clipping the advantage to eliminate negative values, thereby facilitating optimistic updates in *** optimism prevents individual agents from quickly converging to a local ***, we provide a formal analysis to show that the proposed method retains optimality at a fixed *** extensive evaluations on a diverse set of tasks including the Multi-agent MuJoCo and Overcooked benchmarks, our method outperforms strong baselines on 13 out of 19 tested tasks and matches the performance on the rest. Copyright 2024 by the author(s)
Ransomware, a form of malicious software, encrypts victim data and renders it inaccessible until a ransom is paid. Different ransomware variants employ various tactics to avoid detection during their attacks. Understa...
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Introduction: Pathologists are majorly concerned with detecting the diseases and helping the patients in their healthcare and well-being. The present method used by pathologists for this purpose is manually viewing th...
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Introduction: Pathologists are majorly concerned with detecting the diseases and helping the patients in their healthcare and well-being. The present method used by pathologists for this purpose is manually viewing the slides using a microscope and other instruments. However, this method has a number of limitations such as there is no standard way of diagnosis, there are certain chances of human errors and besides, it burdenizes the laboratory personnel to diagnose a large number of slides on a daily basis. Methods: The slide viewing method is widely used and converted into digital form to produce high resolution images. This enables the area of deep learning and machine learning to get an insight into this field of medical sciences. In the present study, a neural based network has been proposed for classification of blood cells images into various categories. When an input image is passed through the proposed architecture and all the hyper parameters and dropout ratio values are applied in accordance with the proposed algorithm, then the model classifies the blood images with an accuracy of 95.24%. Result: After training the models on 20 epochs. The plots of training accuracy, testing accuracy and corresponding training loss, and testing loss for the proposed model is plotted using matplotlib and trends. Discussion: The performance of the proposed model is better than the existing standard architectures and other works done by various researchers. Thus, the proposed model enables the development of pathological system which will reduce human errors and daily load on laboratory personnel. . This can also in turn help the pathologists in carrying out their work more efficiently and effectively. Conclusion: In the present study, a neural based network has been proposed for classification of blood cells images into various categories. These categories have significance in the medical sciences. When input image is passed through the proposed architecture and all the hyp
Background: The biggest challenge in our technologically advanced society is the healthy being of aging individuals and differently-abled people in our society. The leading cause for signifi-cant injuries and early de...
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