Support vector neural network based fuzzy hybrid filter for impulse noise identification and removal from gray-scale image by Sagenela Vijaya Kumar, C. Nagaraju

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Development of Anthro-Fitness Model for Evaluating Firefighter Recruits’ Performance Readiness Using Machine Learning by Mohamed Borhanudin Mohd Yusof, Musa Rabiu Muazu, Nazarudin Mohamad Nizam, Abdul Majeed Anwar P. P., Raj Naresh Bhaskar, Razmaan Mohd Azraai Mohd

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Weighted Hybrid Random Forest Model for Significant Feature prediction in Alzheimer’s Disease Stages by M. Rohini, D. Surendran

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A novel similarity-constrained feature selection method for epilepsy detection via EEG signals by Chunlei Shi, Jun Gao, Jian Yu, Lingzhi Zhao, Faxian Jia

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Prioritising explainable AI-driven recommendations with knowledge graphs and reinforcement learning by Neeraj Tiwary, Shahrul Azman Mohd Noah, Fariza Fauzi

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A New hybrid generalized CG- method for non-linear functions by Abbas Al-Bayati, Hamsa Chilmerane

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A Machine Learning Approach for User Behavior Analysis in Developing Websites by Aeesha Shaheen

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Design of a Hybrid Intelligent System for Transitional Bladder Cell Carcinoma Diagnosis by Nada Saleem, Khalid Saleem

“…The proposed system is composed of two main phases, the first phase is the "cell analysis phase" which consists of three main stages including segmentation stage using "Genetic Optimization Based Fuzzy Image Segmentation Algorithm" (GOFISA), morphometric and photometric feature extraction stage and "Neuro-Fuzzy Classifier Model" (NFCM) stage that has been developed and implemented to classify a set of normal and abnormal cells using hybrid intelligent technique that combines the artificial neural network and fuzzy logic. …”
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Deep‐Learning‐Enhanced Electron Microscopy for Earth Material Characterization by Hans vanMelick, Richard Taylor, Oliver Plümper

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An adaptive intelligent thermal-aware routing protocol for wireless body area networks by Abdollah Rahimi, Mehdi Jafari Shahbazzadeh, Amid Khatibi

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Quantum resonant dimensionality reduction by Fan Yang, Furong Wang, Xusheng Xu, Pan Gao, Tao Xin, ShiJie Wei, Guilu Long

“…Therefore, compared with existing algorithms, our algorithm has achieved the optimal performance in terms of time complexity and space complexity. …”
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A Novel Feature Selection Method for Classification of Medical Data Using Filters, Wrappers, and Embedded Approaches by Saba Bashir, Irfan Ullah Khattak, Aihab Khan, Farhan Hassan Khan, Abdullah Gani, Muhammad Shiraz

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Multi-agent deep reinforcement learning for RIS-assisted secure UAV communication by Bojia Zhao, Danyang Qin, Yuhong Chen, Jiaqiang Yang, Huapeng Tang, Lin Ma

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Securing SDN controller placement with MuZero and blockchain-based smart contracts by Ouafae Benoudifa, Abderrahim Ait Wakrime, Redouane Benaini

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Improving parking availability prediction in smart cities with IoT and ensemble-based model by Stéphane Cédric Koumetio Tekouabou, El Arbi Abdellaoui Alaoui, Walid Cherif, Hassan Silkan

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Swarm intelligence based classification rule induction (CRI) framework for qualitative and quantitative approach: An application of bankruptcy prediction and credit risk analysis by J. Uthayakumar, T. Vengattaraman, P. Dhavachelvan

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Full Characterization of the Depth Overhead for Quantum Circuit Compilation with Arbitrary Qubit Connectivity Constraint by Pei Yuan, Shengyu Zhang

“…These results, when combined with existing routing algorithms, give asymptotically optimal compilation for all commonly seen connectivity graphs in quantum computing.…”
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TDMA-based user scheduling policies for federated learning by Meixia TAO, Dong WANG, Rui SUN, Naifu ZHANG

“…To improve the communication efficiency in FL (federated learning), for the scenario with heterogeneous edge user's computing capacity and channel state, a class of time division multiple access (TDMA) based user scheduling policies were proposed for FL.The proposed policies aim to minimize the system delay in each round of model training subject to a given sample size constraint required for computing in each round.In addition, the convergence rate of the proposed scheduling algorithms was analyzed from a theoretical perspective to study the tradeoff between the convergence performance and the total system delay.The selection of the optimal batch size was further analyzed.Simulation results show that the convergence rate of the proposed algorithm is at least 30% higher than all the considered benchmarks.…”
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A Systematic Literature Review of Concept Drift Mitigation in Time-Series Applications by Mujaheed Abdullahi, Hitham Alhussian, Norshakirah Aziz, Said Jadid Abdulkadir, Yahia Baashar, Abdussalam Ahmed Alashhab, Afroza Afrin

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SimulatorOrchestrator: A 6G-Ready Simulator for the Cell-Free/Osmotic Infrastructure by Rohin Gillgallon, Reham Almutairi, Giacomo Bergami, Graham Morgan

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