Generating financial reports from a piece of news is a challenging task due to the lack of sufficient background knowledge to effectively generate long financial reports. To address this issue, this article proposes a...
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Post Quantum Cryptography has received an increasing amount of active research. This has been made prominent by the ever-growing field of quantum computing which poses a formidable threat to the modern cybersecurity l...
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With the development of the sixth-generation network, Digital Twin (DT) is driving the explosive growth of Internet-of-Vehicles (IoVs). The rapid proliferation of highly mobile IoVs, coupled with advanced applications...
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With the development of the sixth-generation network, Digital Twin (DT) is driving the explosive growth of Internet-of-Vehicles (IoVs). The rapid proliferation of highly mobile IoVs, coupled with advanced applications, resulted in rigorous demands for quality of experience (QoE) and intricate task caching. The diverse requirements of on-vehicle applications, as well as the freshness of dynamic cached information, provide significant challenges for edge servers in efficiently fulfilling energy and latency demands. This work studies a freshness-aware caching-aided offloading-based task allocation problem (FCAOP) in DT-enabled IoV (DTIoV) with Intelligent Reflective Surfaces (IRS) and edge computing. DT is used to accumulate real-time data and digitally depict the physical objects of the IoV to enhance decision-making. A quantum-inspired differential evolution (QDE) algorithm is proposed to reduce the overall delay and energy consumption in DTIoV (QDE-DTIoV). The quantum vector (QV) is encoded to represent a complete solution to the FCAOP. The decoding of the QVs is done using a one-time hashing algorithm. The fitness function is derived by considering delay, energy consumption, and freshness of the tasks. Extensive simulations demonstrate the superiority of QDE-DTIoV over other benchmark algorithms, showing an average latency improvement of 23%-26% and a reduction in energy consumption ranging from 22% to 33%. IEEE
Background: The main objective of the Internet of Things (IoT) has significantly influenced and altered technology, such as interconnection, interoperability, and sensor devices. To ensure seamless healthcare faciliti...
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Task scheduling for virtual machines (VMs) has shown to be essential for the effective development of cloud computing at the lowest cost and fastest turnaround time. A number of research gaps about job schedule optimi...
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Traffic rerouting is a technique used to optimize traffic flow and reduce congestion by redirecting vehicles to alternate routes. The work done in this research focuses on a specific case scenario covering a 25 k...
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With the development of the Internet, the use of social media has increased dramatically over time and has emerged as the most powerful networking tool of the twenty-first century. From youngsters of ten years to seni...
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To address the privacy concerns that arise from centralizing model training on a large number of IoT devices, a revolutionary new distributed learning framework called federated learning has been developed. This setup...
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Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social interaction, communication difficulties, repetitive behaviors, and a range of strengths and differences in...
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Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social interaction, communication difficulties, repetitive behaviors, and a range of strengths and differences in cognitive abilities. Early ASD diagnosis using machine learning and deep learning techniques is crucial for preventing its severity and long-term effects. The articles published in this area have only applied different machine learning algorithms, and a notable gap observed is the absence of an in-depth analysis in terms of hyperparameter tuning and the type of dataset used in this context. This study investigated predictive modeling for ASD traits by leveraging two distinct datasets: (i) a raw CSV dataset with tabular data and (ii) an image dataset with facial expression. This study aims to conduct an in-depth analysis of ASD trait prediction in adults and toddlers by doing hyper optimized and interpreting the result through explainable AI. In the CSV dataset, a comprehensive exploration of machine learning and deep learning algorithms, including decision trees, Naive Bayes, random forests, support vector machines (SVM), k-nearest neighbors (KNN), logistic regression, XGBoost, and ANN, was conducted. XGBoost emerged as the most effective machine learning algorithm, achieving an accuracy of 96.13%. The deep learning ANN model outperformed the traditional machine learning algorithms with an accuracy of 99%. Additionally, an ensemble model combining a decision tree, random forest, SVM, KNN, and logistic regression demonstrated superior performance, yielding an accuracy of 96.67%. The XGBoost model, utilized in hyperparameter optimization for CSV data, exhibited a substantial accuracy increase, reaching 98%. For the image dataset, advanced deep learning models, such as ResNet50, VGG16, Boosting, and Bagging, were employed. The bagging model outperformed the others, achieving an impressive accuracy of 99%. Subsequent hyperparameter optimization was conduct
Lung disorders are medical conditions that disrupt the lungs and their capacity to function normally. One fatal lung disease is a collapsed lung where the lung collapses partially or fully due to diseases like pneumot...
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