Newspapers are an important source for job postings, admission notices, tender announcements, sales and promotions, and many other advertisements. However, there are no category-wise search capabilities for advertisem...
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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
Alzheimer's disease (AD) can cause a lapse in cognitive capacity along with forgetfulness, and addressing this issue is vital for forthcoming treatment efforts. This study offers another approach to investigating ...
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This research paper has focused on the integration of promising stock market indicators such as the relative strength index (RSI) and different versions of the exponential moving average (EMA) (i.e., 50-day, 100-day, ...
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Data quality is of paramount importance to the training of any machine learning model. Recently proposed approaches for noisy learning focus on detecting noisy labeled data instances by using a fixed loss value thresh...
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As the cultural heritage sector increasingly adopts technologies like Retrieval-Augmented Generation (RAG) to provide more person-alised search experiences and enable conversations with collections data, the demand fo...
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With the use of Machine Learning Algorithms, a fundamental task in Human-computer Interaction, Speech Recognition has advanced significantly. The state-of-the-art in machine learning-based voice recognition is summari...
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Agriculture, a foundation of India's economy, faces challenges in maximizing yield and resource efficiency. This research presents a machine learning-based system to recommend optimal crops and fertilizers based o...
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With the continuous advancement of satellite technology, remote sensing images has been increasingly applied in fields such as urban planning, environmental monitoring, and disaster response. However, remote sensing i...
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With the continuous advancement of satellite technology, remote sensing images has been increasingly applied in fields such as urban planning, environmental monitoring, and disaster response. However, remote sensing images often feature small target sizes and complex backgrounds, posing significant computational challenges for object detection tasks. To address this issue, this paper proposes a lightweight remote sensing images object detection algorithm based on YOLOv9. The proposed algorithm incorporates the SimRMB module, which effectively reduces computational complexity while improving the efficiency and accuracy of feature extraction. Through a dynamic attention mechanism, SimRMB is capable of focusing on important regions while minimizing background interference, and by integrating residual learning and skip connections, it ensures the stability of deep networks. To further enhance detection performance, the FasterRepNCSPELAN4 module is introduced, which employs PConv operations to reduce computational load and memory usage. It also utilizes dilated convolutions and DFC attention mechanisms to strengthen feature extraction, thereby increasing the efficiency and accuracy of object detection. Additionally, this study integrates the GhostModuleV2 module, which generates core feature maps and employs lightweight operations to create redundant features, greatly reducing the computational complexity of *** results show that on the SIMD dataset, the improved YOLOv9 model has a parameter size of 167.88 MB and GFLOPs of 208.6. Compared to the baseline YOLOv9 model (parameter size: 194.57 MB, GFLOPs: 239.0), the parameter size is reduced by 13.71%, GFLOPs are reduced by 12.72%, and detection accuracy is improved by 1.4%. These results demonstrate that the proposed lightweight YOLOv9 model effectively reduces computational overhead while maintaining excellent detection performance, providing an efficient solution for object detection tasks in resou
Vehicular Ad-Hoc Networks (VANETs) are studied wireless networks that enable communication among vehicles and roadside infrastructure. The role a vital play in improving on-road safety, efficacy, and convenience by en...
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