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6 results for “Sadegh Aminifar”
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Research ArticleOpen accessIn the author namesEnhancing Indoor Asset Tracking: IoT Integration and Machine Learning Approaches for Optimized PerformanceRafiq Hamadamin Maulud, Sadegh Abdollah AminifarIndoor asset tracking entails the surveillance and governance of the position and movement of tangible assets within enclosed spaces, including warehouses, hospitals, and workplaces. Indoor asset tracking systems employ technologies such as Radio Frequency Identification (RFID), Bluetooth Low Energy (BLE), Wi-Fi, and UWB (Ultra-Wideband) to deliver real-time visibility and precise placement of goods. This research introduces indoor asset tracking with … …Journal of Computer Science, Vol. 21, Issue 7 · 7 Jul 2025 · 3,742 accessesView PDFCite
Research ArticleOpen accessIn the author namesMeasuring Uncertainty to Extract Fuzzy Membership Functions in Recommender SystemsHeersh Azeez Khorsheed, Sadegh AminifarNowadays, due to the high volume of choices for customers which causes confusion, the use of recommender systems is strongly growing. Of course, existing systems have two problems, one is complexity and the other is failure to consider uncertainty. In this article, we have reduced the complexity of the system by using a fuzzy innovative system and solved the problem … …Journal of Computer Science, Vol. 19, Issue 11 · 15 Oct 2023 · 5,497 accessesView PDFCite
Research ArticleOpen accessIn the author namesHigh Accurate Multicriteria Cluster-Based Collaborative Filtering Recommender SystemZhila Yaseen Taha, Sadegh Abdollah AminifarA Recommender System (RS) is one of the appropriate answers by researchers for alleviating the problem of customers' overload with information from an internet source. In RS, users' evaluation of the items assists in quickly determining the user's preferences and removing the choosing overhead for the user the next time they search. However, using user ratings for an item based … …Journal of Computer Science, Vol. 18, Issue 12 · 10 Dec 2022 · 5,918 accessesView PDFCite
Research ArticleOpen accessIn the author namesIntelligent Digital Signal Modulation Recognition using Machine LearningMudhafar Haji Mala Abd, Sadegh AminifarInrecent years, modulation type recognition has received a lot of attentionacross the board. There are several methods to detect modulation types, butthere are only a few effective methods to handle signals with a higher level ofnoise. This study introduces an approach to verify the ability of differentmachine learning algorithms to automatically manage noise in detecting digitalmodulations. This research examines two … …Journal of Computer Science, Vol. 18, Issue 10 · 26 Sep 2022 · 6,098 accessesView PDFCite