The examination of historical crime data using advances in molecular biology and genetics, such as TERT (Telomerase Reverse Transcriptase), opens up new avenues in forensic research. This research investigates the pos...
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Cardiac ischemia, a prevalent cause of heart failure, remains the leading cause of death in Iran. Early diagnosis of this condition is crucial, and electrocardiogram (ECG) signal processing techniques offer valuable i...
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In today's data-driven world, the intersection of artificial intelligence, natural language processing(NLP) and computer Vision(CV) has given rise to innovative solutions in various domains. One intriguing applica...
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This research proposes a novel cross-modal approach to sentiment analysis that integrates textual, audio, and visual modalities to enhance the accuracy and depth of emotion recognition. By combining textual features (...
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Object identification is a well-known research subject in the field of computer vision, with various applications like surveillance, autonomous driving, and robotics. The integration of machine learning with cloud com...
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ISBN:
(数字)9789819998111
ISBN:
(纸本)9789819998104
Object identification is a well-known research subject in the field of computer vision, with various applications like surveillance, autonomous driving, and robotics. The integration of machine learning with cloud computing has enabled organizations to automate many procedures and tasks, cut costs, and boost efficiency. With the help of a wide range of machine learning (ML) services offered by cloud computing platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), organizations may take advantage of ML’s potential without the need for specialized equipment or costly staff. A cloud-based ML service called Amazon Rekognition offered by Amazon Web Services is a powerful tool for object identification. Through this paper, the authors offer a study on the application of Amazon Rekognition for object detection and recognition. The idea is to detect objects in the provided images using machine learning and deep learning algorithms provided by Amazon Rekognition. The effectiveness of Amazon Rekognition in recognizing objects in images is precisely examined by the authors, who compare the discovered objects with state-of-the-art object detection algorithms and then provide the result with a corresponding confidence percentage. Experimental results show that Amazon Rekognition handles object detection tasks well, achieving a good balance between accuracy and speed. It is an effective tool for object detection with high average precision and recall values for many object categories. However, accuracy may vary depending on the complexity of the objects in the image, the lighting conditions, and other factors. Amazon Rekognition is a managed service that makes use of encryption, access control, compliance, monitoring, and logging. While the infrastructure and security are handled by AWS, it’s crucial to incorporate security best practices within the application for maximum security. It is important for developers to carefully evaluate the perf
The advent of Web 3.0 has opened up new possibilities for the development of secure, decentralized digital solutions. Having a safe and user-friendly cryptocurrency wallet is essential in this new era where digital as...
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Low latency TCP congestion control (CC) is a key enabler of delay-sensitive applications such as cloud gaming, remote driving, and virtual/augmented reality(VR/AR). In recent years, TCP BBR has emerged as a popular ch...
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Fog computing is an emerging paradigm that extends cloud computing (CC) by providing computation, communication, and storage services at the edge of a network, closer to end devices. It has gained significance due to ...
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Fog computing is an emerging paradigm that extends cloud computing (CC) by providing computation, communication, and storage services at the edge of a network, closer to end devices. It has gained significance due to the rapid development of IoT devices, which generate various types of tasks. Processing these tasks in the cloud can strain its infrastructure and lead to delays in time-sensitive requests. To address this limitation, fog computing (FC) concepts were introduced in 2012 by Cisco. FC is not meant to replace CC but rather to complement and extend its capabilities. One of the challenges in FC is efficiently assigning tasks to appropriate resources to minimize makespan, energy consumption (EC), and increase the number of deadline-satisfied tasks. In this work, the improvement of semi-greedy algorithm has been done by incorporating fuzzy logic (FL). By leveraging FL, the aim is to enhance the algorithm's decision-making process and make it more adaptive to varying conditions and uncertainties in the fog environment. The use of FL allows more nuanced and flexible task scheduling (TS) decisions based on fuzzy sets and fuzzy rules. The simulation experiments demonstrate that the proposed algorithm outperforms PSG (Priority-aware Semi-Greedy) and PSG-M (PSG with multistart), which were identified as the best scheduling algorithms (Algos) in the literature review. The algorithm exhibits better performance in terms of reducing makespan, EC, and increasing the percentage of deadline-satisfied tasks compared to PSG and PSG-M. The inclusion of FL further enhances the algorithm's effectiveness in handling complex scheduling scenarios in a FC environment. To evaluate the performance of the proposed algorithm, different simulation experiments have been conducted using a selected simulator after a systematic review of existing simulators. The experiments involved 300 and 500 random and static tasks, as well as 60 fog nodes in the fog environment. All simulations were impl
Over the last decade, Bitcoin and Ethereum have become cryptocurrencies that have attracted the attention of the financial world with their potential for business transactions and the use of new blockchain technology....
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Breast cancer remains the primary cause of death for women worldwide, which emphasises how important it is to detect the disease early and accurately in order to increase survival rates. Conventional approaches encoun...
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