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Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

Using machine learning algorithms to develop a clinical 2 decision-making tool for COVID-19 inpatients by Vepa, Abhinav, Saleem, Amer, Rakhshan, Kambiz, Daneshkhah, Alireza

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Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

“…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
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Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition by Ali Adlan, Hanan Hassan

“…The RNN was used to detect patterns present in satellite image. A novel feature extraction algorithm was developed to extract the feature vectors. …”
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Analysis of Traffic Accident Patterns Using Association Rule Mining by Yudy, Pranata, Tri Basuki, Kurniawan, Edi Surya, Negara, Ahmad Haidar, Mirza

“…This study also demonstrated the practicality of the apriori algorithm in analyzing extensive datasets to extract actionable insights. …”
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Short-term electricity price forecasting in deregulated electricity market based on enhanced artificial intelligence techniques / Alireza Pourdaryaei by Alireza , Pourdaryaei

“…A multi-objective feature technique is developed in this study to extract the most influential subsets of input variables with the maximum relevancy and minimum redundancy. …”
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Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA by Lee, Lok Hua, Ho, Cyrus Su Hui, Chan, Yee Ling, Tay, Gabrielle Wann Nii, Lu, Cheng-Kai, Tang, Tong Boon

“…To address this, the aim of the current study is to investigate MDD ATR at three response levels using fNIRS and micro-ribonucleic acids (miRNAs). Our proposed algorithm includes a custom inter-subject variability reduction based on the principal component analysis (PCA). …”
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Development Of Semi-Automatic Liver Segmentation Method For Three-Dimensional Computed Tomography Dataset by Chiang, Yi Fan

“…Low contrast of liver boundary with neighbouring organs, high shape variability of liver and presence of various liver pathologies will affect the accuracy of automatic liver segmentation and thus make automatic liver segmentation a challenging task. …”
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Determination of weights for health-related quality of life indicators among ischemic heart disease patients: A fuzzy decision-making method / Siti Hawa Baharuddin by Baharuddin, Siti Hawa

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Spectral and prosodic feature extractions for classical Arabic accents recognition among Malay speakers / Noor Jamaliah Ibrahim by Noor Jamaliah , Ibrahim

“…The variability of speech patterns produced by individuals is unique. …”
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Recommendation System Model For Decision Making in the E-Commerce Application by Mohammed Mahmudur Rahman

Skyline queries on data with uncertain dimensions for efficient computation by Mohd Saad, Nurul Husna

“…The effect of manipulating the independent variables is studied on the dependent variables which are number of pairwise comparisons and processing time. …”
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Development of sorting system for oil palm in vitro shoots using machine vision approach by Al-Ruhaimi, Hamdan Yahya Ahmed

“…Ultimately, the sorting algorithm performance came to be evaluated by support vector machine algorithm. …”
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An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using VEE-YOLO by Qiaoqiao, Xiong, Qipeng, Chen, Saihong, Tang, Yiting, Li

“…Precise integration of spatial structural and channel information further enhanced the model’s overall feature extraction capability. Finally, EIoU Loss replaced CIoU Loss to address bounding box aspect ratio variability and sample imbalance challenges, significantly improving overall detection task performance. …”
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Detection and Classification of Moving Objects for an Automated Surveillance System by Md. Tomari, Mohd Razali

“…Finally, classification algorithm that makes use of the extracted moment values from the detected objects successfully categorize objects into pre-defined classes of human and vehicle with 89.08% of accuracy. …”
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Detection and classification of moving objects for an automated surveillance system by Md Tomari, Mohd Razali

“…Finally, classification algorithm that makes use of the extracted moment values from the detected objects successfully categorize objects into pre-defined classes of human and vehicle with 89.08% of accuracy. …”
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Liver segmentation on CT images using random walkers and fuzzy c-means for treatment planning and monitoring of tumors in liver cancer patients by Moghbel, Mehrdad

“…The proposed method is based on a hybrid method integrating random walkers algorithm with integrated priors and particle swarm optimized spatial fuzzy c-means (FCM) algorithm with level set method and AdaBoost classifier. …”
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Detection and classification of moving objects for an automated surveillance system by Md Tomari, Mohd Razali

“…Finally, classification algorithm that makes use of the extracted moment values from the detected objects successfully categorize objects into pre-defined classes of human and vehicle with 89.08% of accuracy. …”
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Ultrasound-based tissue characterization and classification of fatty liver disease: A screening and diagnostic paradigm by Acharya, U.R., Faust, O., Molinari, F., Sree, S.V., Junnarkar, S.P., Sudarshan, V.

“…We then review the state-of-the-art US-based CAD techniques that utilize a range of image texture based features like entropy, Local Binary Pattern (LBP), Haralick textures and run length matrix in several automated decision making algorithms. These classification algorithms are trained using the features extracted from the patient data in order for them to learn the relationship between the features and the end-result (FLD present or absent). …”
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Development of images segmentation using image thresholder and batch processing technique on the blood smears by Al-Shoukry, Suhad, Zalili, Musa, Amer, Duha, Kareem, Safaa Muhsen

“…A new segmentation technique with each pixel in the image has its own threshold is developed in response to the fact that standard threshold-based segmentation algorithms only establish one or many thresholds, making it difficult to extract the complex information in an image. …”
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