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Öğe A grasshopper optimizer approach for feature selection and optimizing SVM parameters utilizing real biomedical data sets(Springer London Ltd, 2019) Ibrahim, Hadeel Tariq; Mazher, Wamidh Jalil; Uçan, Osman Nuri; Bayat, OğuzSupport vector machines (SVM) are one of the important techniques used to solve classifications problems efficiently. Setting support vector machine kernel factors affects the classification performance. Feature selection is a powerful technique to solve dimensionality problems. In this paper, we optimized SVM factors and chose features using a Grasshopper Optimization Algorithm (GOA). GOA is a new heuristic optimization algorithm inspired by grasshoppers searching for food. It approved its ability to solve real-world problems with anonymous search space. We applied the proposed GOA + SVM approach on biomedical data sets for Iraqi cancer patients in 2010-2012 and for University of California Irvine data sets.Öğe AIoT in healthcare: a systematic mapping study(Institute of Electrical and Electronics Engineers Inc., 2022) Ibrahim, Hadeel Tariq; Mazher, Wamidh Jalil; Uçan, Osman Nurihe Internet of Things (IoT) infrastructure and artificial intelligence (AI) technologies are combined to create artificial intelligence of things (AIoT). AIoT in healthcare is a key factor in enabling physicians' offices and hospitals as well as giving patients access to superior scientific facilities. To this end, we conduct a Systematic Mapping Study (SMS) to provide critical information about various applications of AIoT in healthcare. The primary goals of this research are to provide an overview of AIoT research in healthcare and to categorize AIoT research based on annual number of publications, publication venues, and journal distribution. We present some AIoT techniques used in healthcare fields in nine well-known online libraries (IEEE, Springer, Elsevier, MDPI, Taylor and Francis, Hindawi, Wiley online lib., ACM and Google). Initially, we used a number of research questions related to the issue, and when we applied them, 71 studies were produced.Öğe Boosting Multiverse Optimizer by Simulated Annealing for Dimensionality Reduction(Airlangga University, 2025) Mutlag, Wamidh K.; Mazher, Wamidh Jalil; Ibrahim, Hadeel Tariq; Uçan, Osman NuriBackground: Because of The Multi-Verse Optimizer (MVO) has gained popularity in feature selection due to its strong global and local search capabilities. However, its effectiveness diminishes when tackling high-dimensional datasets due to the exponential growth of the search space and a tendency for premature convergence. Objective: This study aims to enhance MVO’s performance by integrating it with the Simulated Annealing Algorithm (SAA), creating a hybrid model that improves search convergence and optimizes feature selection efficiency. Methods: A High-level Relay Hybrid (HRH) architecture is proposed, where MVO identifies promising regions of the feature space and passes them to SAA for local refinement. The resulting MVOSA-FS model was evaluated on ten high-dimensional benchmark datasets from the Arizona State University (ASU) repository. Support Vector Machine (SVM) classifiers were used to assess the classification accuracy. MVOSA-FS achieved superior performance compared to six state-of-the-art feature selection algorithms: Atom Search Optimization (ASO), Equilibrium Optimizer (EO), Emperor Penguin Optimizer (EPO), Monarch Butterfly Optimization (MBO), Satin Bowerbird Optimizer (SBO), and Sine Cosine Algorithm (SCA). Results: The proposed model yielded the lowest average classification error rate (1.45%), smallest standard deviation (0.008), and most compact feature subset (0.91%). The hybrid MVOSA-FS model effectively balances exploration and exploitation, delivering robust and scalable performance in feature selection for high-dimensional data. Conclusion: This hybridization approach demonstrates improved classification accuracy and reduced computational burden.Öğe Feature selection using salp swarm algorithm for real biomedical datasets(Int Journal Computer Science & Network Security-Ijcsns, 2017) Ibrahim, Hadeel Tariq; Mazher, Wamidh Jalil; Uçan, Osman Nuri; Bayat, Oğuz; Uçan, Osman NuriThe main objective of this paper is to develop a new powerful heuristic optimization algorithm to be used in feature selection. Here, the use of Salp Swarm Algorithm in feature selection (SSA-FS) is proposed for the first time in literature. SSA-FS has been compared with Particle Swarm Optimization and Differential Evolution performance with criteria of accuracy and runtime. In this paper, real datasets obtained from Iraqi hospitals for breast, bladder and colon cancers and synthetic datasets for evaluation. We have found that SSA-FS has been achieved the highest accuracies with less runtime in comparison with other selected algorithms for both real and synthetic datasets.Öğe Optical onboard double decoding/forward performance with long codes for optical routing(Spie-Soc Photo-Optical Instrumentation Engineers, 2019) Mazher, Wamidh Jalil; Ibrahim, Hadeel Tariq; Mathboob, Yaareb M.; Uçan, Osman NuriThe optical decoding and forward (ODF) performance with long systematic Hamming distance-4 (LSD_4) codes is suggested. Hereby, the LSD_4 codes are produced via generating 224 codewords of 15 bits length. The LSD_4 codes length is extremely long (equal to 4096 bits); accordingly, the encoding/decoding by the LSD_4 codes becomes more compatible with optical speed and consistency. Apparently, increasing the length of coding is concurrent with the ODF speed. Our model is grounded on hiring the LSD_4 codes in the encoding/decoding process of the onboard ODF for optical routing serial. The net gain of plugging the planned LSD_4 codes in optical routing is obtained within 5- to 15-dB range for diverse optical routing forms at code length 4096 bits with Q-ary pulse-position modulation. The numeric and simulated results confirmed the significant improvements of the planned LSD_4 codes performance with ODF over the non-LSD_4 codes ODF counterparts. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)
