Search Results - (((( pattern research algorithm ) OR ( pattern _ algorithm ))) OR ( deep learning algorithm ))
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Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023)
Published 2025Subjects:Review -
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Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system
Published 2022“…In future, the HW-DBN algorithm can be proposed as an integrated deep Learning for the classification performance of attack detection models.…”
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Thesis -
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DeepIoT.IDS: Hybrid deep learning for enhancing IoT network intrusion detection
Published 2021“…With an increasing number of services connected to the internet, including cloud computing and Internet of Things (IoT) systems, the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points. Recently, researchers have suggested deep learning (DL) algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks. …”
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Deep learning in face recognition for attendance system: an exploratory study / Mochamad Azkal Azkiya Aziz, Shahrinaz Ismail and Noormadinah Allias
Published 2022“…There are various algorithms for face recognition, such as Local Binary Pattern Histogram (LBPH), Local Binary Pattern Network (LBPn), Haar Cascade, and Convolutional Neural Network. …”
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Concrete surface inspection by using unmanned aerial vehicle (UAV) and deep learning algorithms YOLOv7 / Saffa Nasuha Rusdinadi
Published 2024“…The results indicate that the combined use of Uavs and yolov7 significantly improves the efficiency of concrete surface inspections, providing a scalable and automated solution for infrastructure monitoring. This research contributes to the field of automated infrastructure inspection by integrating Uav technology with advanced deep learning algorithms, presenting a novel approach that reduces manual effort and enhances the accuracy of concrete surface assessments. …”
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Student Project -
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Optimize and deploy machine learning algorithms on embedded devices for manufacturing applications
Published 2025“…In recent studies, we seen developers and researchers proposing solutions on deep learning algorithms like YOLO, EfficientNet, CNN, MobileNet etc. …”
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Final Year Project / Dissertation / Thesis -
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Concrete surface inspection by using Unmanned Aerial Vehicle (UAVs) and deep learning algorithms Yolov7
Published 2024“…The results indicate that the combined use of Uavs and Yolov7 significantly improves the efficiency of concrete surface inspections, providing a scalable and automated solution for infrastructure monitoring. This research contributes to the field of automated infrastructure inspection by integrating Uav technology with advanced deep learning algorithms, presenting a novel approach that reduces manual effort and enhances the accuracy of concrete surface assessments. …”
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Conference or Workshop Item -
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Underwater Image Recognition using Machine Learning
Published 2024“…It encompasses the procedure for feeding algorithms information to create the algorithms realize patterns in the data and then increase the performance of the algorithms. …”
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Analysis of banana plant health using machine learning techniques
Published 2024“…Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. …”
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Glass break detection system using deep auto encoders with fuzzy rules induction algorithm
Published 2019“…This paper proposes a new design of a glass break detection algorithm based on Fuzzy Deep Auto-encoder Neural Network. …”
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An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA
Published 2025“…However, these algorithms often fall short in consistently detecting and classifying network intrusions, particularly when distinctions between classes are subtle or when facing evolving attack patterns. …”
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Identifying melanoma characteristics using directional imaging algorithm and convolutional neural network on dermoscopic images / Mohammad Asaduzzaman Rasel
Published 2024“…Several imaging, computer vision, and pattern recognition algorithms are employed to describe five dermoscopic features. …”
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Enhancement of text representation for Indonesian document summarization with deep sequential pattern mining
Published 2023“…Second, the proposed SPM approach is combined with Deep Belief Network (DBN), called DeepSPM, based on the unsupervised Deep Learning method. …”
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Classification of chest radiographs using novel anomalous saliency map and deep convolutional neural network
Published 2021“…The rapid advancement in pattern recognition via the deep learning method has made it possible to develop an autonomous medical image classification system. …”
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Assessment of crops healthiness via deep learning approach: Python / Mohamad Amirul Asyraf Mohd Ramli
Published 2023“…By leveraging image processing techniques, statistical analysis and machine learning algorithms, Python enables the extraction of relevant features and patterns from data. …”
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Student Project -
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Cabbage disease detection system using k-NN algorithm
Published 2022“…Plant disease study means the study of disease patterns that can be visually seen on plants. The main objective of this research is to develop a prototype system with the help of machine learning to detect cabbage diseases which are Alternaria Leaf Spot disease, Mosaic Virus disease, Downy Fungus disease, Bacterial Soft Rot disease, and Black Rot disease . …”
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Academic Exercise -
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Deep plant: A deep learning approach for plant classification / Lee Sue Han
Published 2018“…Hitherto, the majority of computer vision approaches have been focused on designing sophisticated algorithms to achieve a robust feature representation for plant data. …”
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Advances in materials informatics: A review
Published 2024“…Progress of MI depends on the strength of database and artificial intelligence protocols comprising machine learning (ML) and deep learning (DL) frameworks. Conventional ML models are simple and interpretable, relying on statistical techniques and algorithms to learn patterns and make predictions with limited data. …”
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