Search Results - (( intelligence model learning algorithm ) OR ( intelligence based view algorithm ))

Overhead view based person counting using deep learning by Kaw, Chee Zhao

“…Hence, the overall performance of the YOLOv4-tiny model integrated with DeepSORT algorithm outperforms the other tracking algorithms.…”
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Comparative performance of machine learning algorithms for cryptocurrency forecasting by Hitam, Nor Azizah, Ismail, Amelia Ritahani

“…Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. …”
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Supervised deep learning algorithms for process fault detection and diagnosis under different temporal subsequence length of process data by Terence Chia Yi Kai, Agus Saptoro, Zulfan Adi Putra, King Hann Lim, Wan Sieng Yeo, Jaka Sunarso

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Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System by Tawafan, Adnan, Sulaiman , Marizan, Ibrahim, Zulkifilie

“…This paper proposes an intelligent algorithm using an adaptive neural- Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault. …”
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Application of machine learning and artificial intelligence in detecting SQL injection attacks by Md Sultan, Abu Bakar, Agiliga, Nwabudike Augustine, Osman, Mohd Hafeez Bin, Sharif, Khaironi Yatim

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Chaos Search in Fourier Amplitude Sensitivity Test by Koda, Masato

“…This paper explores the characterization of learning functions involved in FAST and derives the underlying dynamical relationships with chaos search, which can provide new learning algorithms. …”
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The framework of inventory prediction model using ABC analysis for inventory management / Umi Safierah Masdiki and Rozita Kadar by Masdiki, Umi Safierah, Kadar, Rozita

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A comparative study and simulation of object tracking algorithms by Ji, Yuanfa, Yin, Pan, Sun, Xiyan, Kamarul Hawari, Ghazali, Guo, Ning

“…This article introduces the popular object tracking algorithms, from common problems in object tracking to the classification of algorithms: Early classic trackingalgorithms, tracking algorithms based on kernel correlation filtering, and tracking algorithms based on deep learning. …”
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Computer vision that can ‘see’ in the dark by Wong, Yan Chiew, Ahmad Radzi, Syafeeza, Goh, Shi Yong, Sarban Singh, Ranjit Singh

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Innovating communication adaptation on social media using AI-based decision support systems by Zakaria, Noor Azura, Adriana, Dadi, Budi, Danang Surya, Mardiana, Mardiana, Rafika, Ageng Setiani, Asmawati, Ari

“…By integrating data riven insights and machine learning models, the system supports users in crafting adaptive and persuasive communication strategies that resonate with their network. …”
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Smart agriculture: precision farming through sensor-based crop monitoring and control system by Mohamad Hakhrani, Asyful Azhim, Abdul Hamid, Syamsul Bahrin

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Reimagining English language learning: a systematic review of AI integration in classroom practice (2019–2024) by Ibrahim Brian, Muhammad Shyazzwan

“…This Systematic Literature Review (SLR) examines the integration of Artificial Intelligence (AI) in English Language Learning (ELL) with the aim of reimagining classrooms as creative, inclusive, and technology-enhanced spaces. …”
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Identifying the correct articulation point of a Quranic letters of the throat (al-halqu) makhraj by Othman, Ahmad Al Baqir, Ahmad, Salmiah, Badron, Khairayu, Altalmas, Tareq M. K.

“…Data was trained using an improved deep learning Convolutional Neural Network (CNN) classification model. …”
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EDITORIAL: INTEGRATION OF HYDROLOGICAL MODELS AND MACHINE LEARNING TECHNIQUES FOR WATER RESOURCES MANAGEMENT by Ren Jie, Chin, Sai Hin, Lai

“…Advancements in computational power and data availability enable machine learning to complement traditional models. Algorithms such as ANNs, SVMs, and RF enhance hydrological forecasting, while deep learning methods (LSTMs, CNNs) improve spatio-temporal predictions. …”
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Predicting Market Trends : A Stock Prices Forecasting with Artificial Neural Network for Apple Inc. and Microsoft Corp. by Nur Fazliana, Rahim, Farah Liyana, Azizan, Nur’azra Alia Nisa, Zulpakar

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Feature extraction and supervised learning for volatile organic compounds gas recognition by Mohd Tombel, Nor Syahira, Mohd Zaki, Hasan Firdaus, Mohd Fadglullah, Hanna Farihin

“…This research project aims to investigate effective feature extraction techniques that can be employed as discriminative features for machine learning algorithms. A preliminary dataset was used to predict VOC classification through the application of five supervised machine learning algorithms: k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), and Artificial Neural Networks (ANN). …”
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Machine learning-based enhanced deep packet inspection for IP packet priority classification with differentiated services code point for advance network management by Khan, Fazeel Ahmed, Abubakar, Adamu

“…This study presents an approach to enhance intelligent packet forwarding priority classification on Differentiated Services Code Point (DSCP), leveraging classifiers from machine learning algorithms for Deep Packet Inspection (DPI). …”
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Modelling of clinical risk groups (CRGs) classification using FAM by Mohd. Asi, Salina, Saad, Puteh

“…FAM is a fast learning algorithm and used less epoch training [4]. …”
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InnoSAT Attitude Control System Based on Adaptive Neuro-Controller by Sharun, Siti Maryam, Mashor, Mohd Yusoff, Mohd Nazid, Norhayati, Yaacob, Sazali, Wan Jaafar, Wan Nurhadani

“…The Weighted Recursive Least Square (WRLS) algorithm will adjust the controller parameters to minimize error between the plant output and the model reference output. …”
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Adaptive security architecture for protecting RESTful web services in enterprise computing environment by Beer, M.I., Hassan, M.F.

“…The proposed security architecture is constructed as an adaptive way-forward Internet-of-Things (IoT) friendly security solution that is comprised of three cyclic parts: learn, predict and prevent. A novel security component named â��intelligent security engineâ�� is introduced which learns the possible occurrences of security threats on SOA using artificial neural networks learning algorithms, then it predicts the potential attacks on SOA based on obtained results by the developed theoretical security model, and the written algorithms as part of security solution prevent the SOA attacks. …”
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