Text summarization is a fundamental Natural language processing task that plays a crucial role in efficiently condensing large textual documents into concise and clear summaries for human comprehension. The amount of ...
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With the profound use of digital contents in education and social media, multimedia content have become a prevalent means of communication and with such rapid increase, information security is still a major concern. T...
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Differential Evolution (DE) is a potent stochastic evolutionary optimization algorithm garnering increasing research attention. Over the years, it has been found applicable in solving diverse real-world problems. DE e...
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The finite element method is commonly used to conduct the excavation project and to ensure the safety of the adjacent building. Hence, the analysis method should be rigorous, and the soil parameters used in the analys...
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Over the years, numerous optimization problems have been addressed utilizing meta-heuristic algorithms. Continuing initiatives have always been to create and develop new, practical algorithms. This work proposes a nov...
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In this study, the cloud computing platform is equipped with a hybrid multi-objective meta-heuristic optimization-based load balancing model. Physical Machine (PM) allocates a specific virtual machine (VM) depending o...
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In this study, the cloud computing platform is equipped with a hybrid multi-objective meta-heuristic optimization-based load balancing model. Physical Machine (PM) allocates a specific virtual machine (VM) depending on multiple criteria, such as the amount of memory used, migration expenses, power usage, and the load balancing settings, upon receiving a request to handle a cloud user's duties (‘Response time, Turnaround time, and Server load’). Additionally, the optimal virtual machine (VM) is chosen for efficient load balancing by utilizing the recently proposed hybrid optimization approach. The Cat and Mouse-Based Optimizer (CMBO) and Standard Dingo Optimizer (DXO) are conceptually blended together to get the proposed hybridization method known as Dingo Customized Cat mouse Optimization (DCCO). The developed method achieves the lowest server load in cloud environment 1 is 33.3%, 40%, 42.3%, 40.2%, 36.8%, 42.5%, 50%, 40.2%, 39.2% improved over MOA, ABC, CSO, SSO, SSA, ACSO, SMO, CMBO, BOA, DOX, and FF-PSO, respectively. Finally, the projected DCCO model has been evaluated in terms of makespan, memory usage, migration cost, response time, usage of power server load, turnaround time, throughput, and convergence. ABBREVIATION: CDC, cloud data center;CMODLB, Clustering-based Multiple Objective Dynamic Load Balancing As A Load Balancing;CSP, Cloud service providers;CSSA, Chaotic Squirrel Search Algorithm;DA, Dragonfly Algorithm;ED, Euclidean Distance;EDA-GA, Estimation Of Distribution Algorithm And GA;FF, FireFly algorithm;GA, Genetic Algorithm;HHO, Harris Hawk Optimization;IaaS, Infrastructure-as-a-Service;MGWO, Modified Mean Grey Wolf Optimization Algorithm;MMHHO, Mantaray modified multi-objective Harris Hawk optimization;MRFO, Manta Ray Forging Optimization;PaaS, Platform-as-a-Service;PM, Physical Machine;PSO, Particle Swarm Optimization;SaaS, Software-as-a-Service;SAW, Sample additive weighting;SLA-LB, Service Level Agreement-Based Load Balancing;TBTS, Threshold-Bas
In the realm of education, the pursuit of effective learning outcomes often faces the challenge of limited resources. This paper explores the intersection of maximizing learning outcomes and minimizing costs through a...
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Extreme events jeopardize power network operations, causing beyond-design failures and massive supply interruptions. Existing market designs fail to internalize and systematically assess the risk of extreme and rare e...
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The Internet of Things (IoT) integrates diverse devices into the Internet infrastructure, including sensors, meters, and wearable devices. Designing efficient IoT networks with these heterogeneous devices requires the...
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The Internet of Things (IoT) integrates diverse devices into the Internet infrastructure, including sensors, meters, and wearable devices. Designing efficient IoT networks with these heterogeneous devices requires the selection of appropriate routing protocols, which is crucial for maintaining high Quality of Service (QoS). The Internet engineering Task Force’s Routing Over Low Power and Lossy Networks (IETF ROLL) working group developed the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) to meet these needs. While the initial RPL standard focused on single-metric route selection, ongoing research explores enhancing RPL by incorporating multiple routing metrics and developing new Objective Functions (OFs). This paper introduces a novel Objective Function (OF), the Reliable and Secure Objective Function (RSOF), designed to enhance the reliability and trustworthiness of parent selection at both the node and link levels within IoT and RPL routing protocols. The RSOF employs an adaptive parent node selection mechanism that incorporates multiple metrics, including Residual Energy (RE), Expected Transmission Count (ETX), Extended RPL Node Trustworthiness (ERNT), and a novel metric that measures node failure rate (NFR). In this mechanism, nodes with a high NFR are excluded from the parent selection process to improve network reliability and stability. The proposed RSOF was evaluated using random and grid topologies in the Cooja Simulator, with tests conducted across small, medium, and large-scale networks to examine the impact of varying node densities. The simulation results indicate a significant improvement in network performance, particularly in terms of average latency, packet acknowledgment ratio (PAR), packet delivery ratio (PDR), and Control Message Overhead (CMO), compared to the standard Minimum Rank with Hysteresis Objective Function (MRHOF).
Computational approaches can speed up the drug discovery process by predicting drug-target affinity, otherwise it is time-consuming. In this study, we developed a convolutional neural network (CNN)-based model named S...
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