A general framework of unsupervised learning for combinatorial optimization (CO) is to train a neural network whose output gives a problem solution by directly optimizing the CO objective. Albeit with some advantages ...
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Due to the clean, effective, but reliable power they supply, substations have growing in popularity. Substations required tankers power in order to use the saved fuel during emergencies or peak loads. Given that dc mi...
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Grey wolf optimizer is a recently developed metaheuristic algorithm that mimics hunting and social behaviour. It has been applied in most of the engineering design problems. Grey wolf optimizer and its variants have b...
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Monkeypox is a recently emerged disease outbreak that has impacted the health of many individuals. This study focuses on developing a skin lesion-based classification system for monkeypox disease. Many recent works su...
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Sri Lankan students, especially those from rural and low-income backgrounds, face significant challenges in accessing higher education, including university applications, career planning, and financial aid. This resea...
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Machine translation requires a vast amount of parallel data in order to generate high-quality translations. Since many Indian languages lack sufficient resources, enhancing translation performance for these language p...
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Skin disease diagnosis is critical for prompt intervention, effective treatment, public health management, and general well-being of people affected by dermatological disorders. Skin disease detection is a challenging...
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This paper focuses on the utilization of hesitant and intuitionistic fuzzy sets (HFS & IFS) in a computational intelligent approach, mainly for decision modelling under complex vague surroundings. Indeed, classica...
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Cardiovascular diseases (CVD) are a prominent contributor to illness and death on a global scale, underscoring the need for precise predictive models to facilitate timely intervention. The present study investigates t...
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ISBN:
(纸本)9789819765805
Cardiovascular diseases (CVD) are a prominent contributor to illness and death on a global scale, underscoring the need for precise predictive models to facilitate timely intervention. The present study investigates the utilization of deep learning methodologies, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM), in the context of predictive modeling of cardiovascular diseases. This study examines the efficacy of three well-known optimization techniques, namely Adam Optimization, RMSprop, and Stochastic Gradient Descent (SGD), within the framework of these neural network architectures. Among the various models based on Convolutional Neural Networks (CNNs), Stochastic Gradient Descent (SGD) has been identified as the optimizer that produces the most favorable outcomes for predicting CVD. The utilization of this optimization technique demonstrated exceptional efficacy in the training of the deep neural network, resulting in superior levels of accuracy, sensitivity, and specificity. On the other hand, it was observed that LSTM-based models exhibited the greatest improvement when utilizing RMSprop optimization. The utilization of RMSprop has been found to have a positive impact on the effectiveness of sequence modeling, resulting in enhanced predictive capabilities for assessing the risk of cardiovascular disease. The efficacy of this technique was demonstrated in its ability to capture temporal dependencies within the dataset, consequently enhancing the predictive capability of the model. The results of this study emphasize the importance of carefully choosing neural network architectures and optimization techniques when constructing predictive models for cardiovascular disease. Customizing the selection of neural network architecture and optimization algorithm according to the unique attributes of the dataset can substantially augment the precision and dependability of CVD risk evaluations. This, in turn, can ultimately lead t
Stroke and related neurological disorders are difficult to classify accurately, hence new computational approaches are needed to enhance diagnosis. In order to transform the categorization of brain strokes, this study...
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