Search Results - (( evolution location allocation algorithm ) OR ( java implication based algorithm ))

Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: Automotive green supply chain empirical evidence by Doolun, Ian Shivraj, Ponnambalam, S. G., Subramanian, Nachiappan, Kanagaraj, G.

“…Data driven hybrid evolutionary analytical approach is proposed by integrating Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) to handle multiple objectives into Differential Evolution (DE) algorithm. …”
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A New Hybrid Approach Based On Discrete Differential Evolution Algorithm To Enhancement Solutions Of Quadratic Assignment Problem by Asaad Shakir, Hameed, Mohd Aboobaider, Burhanuddin, Mutar, Modhi Lafta, Ngo, Hea Choon

“…The primary aim of this study is to propose a hybrid approach which combines Discrete Differential Evolution (DDE) algorithm and Tabu Search (TS) algorithm to enhance solutions of QAP model, to reduce the distances between the locations by finding the best distribution of N facilities to N locations, and to implement hybrid approach based on discrete differential evolution (HDDETS) on many instances of QAP from the benchmark. …”
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Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage by Huy P.D., Ramachandaramurthy V.K., Yong J.Y., Tan K.M., Ekanayake J.B.

Performance analyses of adaptive handover decision algorithm using spectrum aggregation in long term evolution - advanced network by Usman, I. H., Nordin, N. K., Omizegba, E. E., Sali, A., Rasid, M. F. A., Hashim, F.

“…In this work, LTE-A network building and deployment as well as configuration management through radio resources allocation and system level performance evaluation for the Proposed TVWS, MIF and CONV handover decision algorithms were successfully carried out. …”
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Exp-bet packet scheduling algorithm for multiple traffic services in long term evolution (LTE) system / Ku Siti Syahidah Ku Mohd Noh by Ku Mohd Noh, Ku Siti Syahidah

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A review on object detection algorithms based deep learning methods / Wan Xing ... [et al.] by Wan Xing, Sultan Mohd, Mohd Rizman, Johari, Juliana, Ahmat Ruslan, Fazlina

“…The training process for deep learning-based object identification involves several key steps, thoroughly exploring the data preprocessing, neural network design, prediction, label allocation, and loss calculation. Deep learning-based object detection algorithms can be categorized into three main types: end-to-end algorithms, two-stage algorithms, and one-stage algorithms. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

LTE-based passive radar for ground target detection and tracking by Salah, Asem Ahmad Mohamad

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