Search Results - (( based detection model algorithm ) OR ( using optimization based algorithm ))
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1
Neural network algorithm-based fall detection modelling
Published 2020“…However, the improvement of model accuracy is still needed. This article presents results of modelling for fall detection system by using nonlinear autoregression neural network NARnet algorithm. …”
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2
Optimised content-social based features for fake news detection in social media using text clustering approach
Published 2025“…In general, the process of fake news detection was conducted in two different phases, the topic detection phase using a graph-based unsupervised clustering method based on HFPA and Markov Clustering Algorithm (MCL) called (HFPA-MCL) and the fake news detection phase using an unsupervised clustering method based on K-means algorithm. …”
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3
Flock optimization algorithm-based deep learning model for diabetic disease detection improvement
Published 2024“…These issues affect the system's performance and reduce diabetic disease detection accuracy. Hence, the research objective is to create an improved diabetic disease detection system using a Flock Optimization Algorithm-Based Deep Learning Model (FOADLM) feature modeling approach that leverages the PIMA Indian dataset to predict and classify diabetic disease cases. …”
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4
Overhead view based person counting using deep learning
Published 2022“…Third, the accurate tracking of each detected person is performed using the deep learning based tracking framework, known as DeepSORT. …”
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Final Year Project / Dissertation / Thesis -
5
Dingle's Model-based EEG Peak Detection using a Rule-based Classifier
Published 2015“…In this study, the performances of four different peak models of time domain approach which are Dumpala's, Acir's, Liu's, and Dingle's peak models are evaluated for electroencephalogram (EEG) signal peak detection algorithm. …”
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Conference or Workshop Item -
6
Development of lung cancer prediction system using meta-heuristic optimized deep learning model
Published 2023“…After that cancer-affected region in the lung is segmented with the help of the proposed Butterfly Optimization Algorithm-based K-Means Clustering (BOAKMC) algorithm. …”
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7
Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System
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8
Optimization of RFID network planning for monitoring railway mechanical defects based on gradient-based Cuckoo search algorithm
Published 2020“…The Gradient-Based Cuckoo Search (GBCS) algorithm was used to achieve the final objective. …”
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9
Designing a new model for Trojan horse detection using sequential minimal optimization
Published 2024Conference Paper -
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Improved Malware detection model with Apriori Association rule and particle swarm optimization
Published 2019“…These rule models are used together with extraction algorithm to classify and detect malicious android application. …”
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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things
Published 2022“…Here, the memory consumption can be reduced by enabling a feature selection algorithm that excludes nonrelevant features and preserves the relevant ones. the algorithm is developed based on the variable length of the PSO. …”
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12
Vehicle detection for vision-based intelligent transportation systems using convolutional neural network algorithm
Published 2022“…Software based solutions using traditional algorithms such as Histogram of Gradients (HOG) and Gaussian Mixed Model (GMM) are computationally slow and not suitable for real-time traffic detection. )erefore, the paper will review and evaluate different vehicle detection methods. …”
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Optimizing in-car-abandoned children’s sounds detection using deep learning algorithms / Nur Atiqah Izzati Md Fisol
Published 2023“…To address this problem, an optimized in-car-abandoned children's sounds detection model using deep learning algorithms is proposed. …”
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Student Project -
14
Fault Detection Relevant, Neural Network and Evolutionary Algorithm based Model for a Single-shaft Industrial Gas Turbine
Published 2009“…Analytical redundancy based fault detection system generally requires an accurate model. …”
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15
Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi
Published 2019“…In fact a data clustering method is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on our proposed algorithm; which is Enhanced Binary Particle swarm Optimization (EBPSO), (ii) To mine data using various data chunks (windows) and overcome a failure of single clustering. …”
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16
Designing a New Model for Trojan Horse Detection Using Sequential Minimal Optimization
Published 2024“…Nowadays the malwares attack especially by the trojan horse is becoming more sophisticated and intelligent, makes it is harder to be detected than before. Therefore, in this research paper, a new model called Efficient Trojan Detection Model (ETDMo) is built to detect trojan horse attacks more efficiently. …”
Proceedings Paper -
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Cloud Resource Management Framework Using Monarch Butterfly Harmony Search And Case Based Reasoning
Published 2017“…The rop framework consists of two main components: global resource optimizer (gro) and resource identifier (ri). This research enhances the butterfly optimization algorithm and plugs the resulting algorithm into the rop as a gro. …”
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18
Model-based fault detection and diagnosis optimization for process control rig
Published 2013“…In this paper, fuzzy logic based model using genetic algorithm for optimizing the membership function is used in the development of fault detection and diagnosis of a process control rig. …”
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Conference or Workshop Item -
19
Fuzzy modelling using butterfly optimization algorithm for phishing detection
Published 2020“…Fuzzy system is a rule-based system by using human experts which hold the truth values or membership values to make a particular decision while fuzzy modelling is a process of identifying the fuzzy parameter. …”
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A hybrid deep learning-based unsupervised anomaly detection in high dimensional data
Published 2022“…However, Adamax optimization algorithm showed the best results when employed to train the DANN model. …”
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