We explore the fair distribution of a set of m indivisible chores among n agents, where each agent’s costs are evaluated using a monotone cost function. Our focus lies on two fairness criteria: envy-freeness up to an...
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Contact tracing has shown to be an effective tool in limiting the spread of transmittable diseases in countries where it is widely adopted. During the COVID-19 pandemic, contact tracing app adoption in the United Stat...
The medium access control (MAC) protocol plays a crucial role in wireless communications, influencing key performance metrics, such as energy consumption, delay, collision, and throughput. This paper introduces the Co...
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Wireless sensor networks (WSN) have acquired great importance from industries owing to their wide range of applications. In system security, intrusion detection systems (IDS) play a significant role. In WSN, data aggr...
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Wireless sensor networks (WSN) have acquired great importance from industries owing to their wide range of applications. In system security, intrusion detection systems (IDS) play a significant role. In WSN, data aggregation protocols are utilized to maximize the network’s life span and minimize every sensor node’s communication weight as well as energy consumption. However, WSN faces energy and resource conservation conflicts. Furthermore, assuming uniform initial energy levels among sensors in existing algorithms is impractical for real-world applications. This variation in initial energy levels has a significant effect on data aggregation within sensor networks. To mitigate these issues, an innovative approach named ShuffleNet Fuzzy Zeiler and Fergus network (Shuffle-F-ZFNet) for data aggregation in WSN is proposed. Primarily, the system model for dynamic cluster WSN is simulated, by considering energy, mobility, link lifetime (LLT) and trust model. Thereafter, energy prediction is conducted utilizing the Deep Neuro-Fuzzy Network (DNFN). Then, dynamic cluster (DC) computation is carried out utilizing the Adaptive Genetic Fuzzy System (AGFS) by considering objectives like distance, throughput, trust factors, LLT, delay, residual and predicted energy. Here, AGFS is trained using Fractional Flamingo Jellyfish Search Optimization (FFJSO), which combines Fractional Calculus (FC) with Flamingo Jellyfish Search Optimization (FJSO). FJSO integrates Flamingo Search Algorithm (FSA) and Jellyfish Search Optimization (JSO). Next, the routing process is executed utilizing FFJSO with the above-mentioned objectives as fitness parameters. At Base Station (BS), feature selection is performed utilizing Angular Separation Distance (ASD) by getting input log data. Then, intrusion attack detection is carried out utilizing a Convolutional Deep Q Learning Network (CDQ-LN), which is designed by integrating a Deep Q-learning Network (DQN) and Convolutional Neural Network (CNN), where lay
Microservice deployment in cloud computing is a challenging combinatorial optimization problem due to the complex dependencies among microservices and the intricate trade-offs among different QoS requirements, e.g., m...
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Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techn...
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Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techniques, categorizing them into three primary approaches: deterministic methods, probabilistic models, and machine learning algorithms. Traditional techniques, including mean or mode imputation, regression imputation, and last observation carried forward, are evaluated alongside more contemporary methods such as multiple imputation, expectation-maximization, and deep learning strategies. The strengths and limitations of each approach are outlined. Key considerations for selecting appropriate methods, based on data characteristics and research objectives, are discussed. The importance of evaluating imputation’s impact on subsequent analyses is emphasized. This synthesis of recent advancements and best practices provides researchers with a robust framework for effectively handling missing data, thereby improving the reliability of empirical findings across diverse disciplines.
Due to the convergence of healthcare and smart cities, information and technology are employed in health and medical procedures worldwide. Residents of smart cities now enjoy better lives and healthier bodies because ...
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Can current robotic technologies truly replicate the full scope and intricacies of human labour?In practice,the adoption of robots remains limited,especially in open,unstructured environments commonly encountered in e...
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Can current robotic technologies truly replicate the full scope and intricacies of human labour?In practice,the adoption of robots remains limited,especially in open,unstructured environments commonly encountered in everyday scenarios such as services,healthcare,agriculture,construction,and numerous other *** the perspective of general robotic manipulation,the challenges arise from three factors.(1)High operational barriers:human operators are obliged to master specialized robotic programming languages and gain a deep understanding of the tasks at *** tasks need to be broken down into action-level robotic programs,which results in high labour costs.(2)Limited autonomous task execution:robots lack the capability to independently plan and execute actions required to achieve the target *** limitation renders them unsuitable for deployment in open,unstructured environments that demand sophisticated interaction and seamless collaboration with humans.
This paper deals with the problem of detecting the malware by using emulation approach. Modern malware include various avoid techniques, to hide its anomaly actions. Advantages of using sandbox and emulation technolog...
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This study compares the effectiveness of various Generative Adversarial Network architectures, including WGAN and WGAN-GP, in data clustering using the Iris dataset. Performance was evaluated with metrics such as Silh...
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