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from detection » drone detection (Expand Search), ion detection (Expand Search), fire detection (Expand Search)
from detection » drone detection (Expand Search), ion detection (Expand Search), fire detection (Expand Search)
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Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks
Published 2024“…With this intent, this work proposes a “Discrete Wavelet Transform with Deep Neural Network (DWT-DNN)” for detecting and classifying the various faults that occurred in hybrid energy-based multi-area grid-connected microgrid clusters. …”
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MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection
Published 2023“…Hence, an optimized deep neural network (DNN) for pedestrian lane detection is required. Designing a DNN from scratch is a laborious task that requires significant experience and time. …”
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YOLO-DefXpert: An Advanced Defect Detection on PCB Surfaces Using Improved YOLOv11 Algorithm
Published 2025“…This study introduces an improved PCB defect detection model, YOLO-DefXpert, using the YOLOv11 algorithm to address the low accuracy and efficiency challenges in detecting tiny-sized defects on PCBs. …”
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NOVEL STACKING CLASSIFICATION AND PREDICTION ALGORITHM BASED AMBIENT ASSISTED LIVING FOR ELDERLY
Published 2022“…Therefore, this thesis proposed a Novel Stacking Classification and Prediction (NSCP) algorithm based on AAL for the older people with Multi-strategy Combination based Feature Selection (MCFS) and Novel Clustering Aggregation (NCA) algorithms. …”
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A Novel Genetic Algorithm Optimized Adversarial Attack in Federated Learning for Android-Based Mobile Systems
Published 2025“…The experimental results on the CICMalDroid 2020, KronoDroid, and AndroZoo Android malware detection datasets demonstrate a significant attack success rate, with a reduction of accuracy from 96–97% down to 24–29%, which surpasses the traditional FGSM and PGD variants. …”
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A Fusion-Based Approach for Skin Cancer Detection Combining Clinical Images, Dermoscopic Images, and Metadata
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VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems
Published 2019“…<p dir="ltr">The Cypher Physical Power Systems (CPPS) became vital targets for intruders because of the large volume of high speed heterogeneous data provided from the Wide Area Measurement Systems (WAMS). The Nonnested Generalized Exemplars (NNGE) algorithm is one of the most accurate classification techniques that can work with such data of CPPS. …”
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A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city security
Published 2023“…Moreover, the Reconciliate Multi-Agent Markov Learning (RMML) based classification algorithm is used to predict the intrusion with its appropriate classes. …”
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Exposure of Botnets in Cloud Environment by Expending Trust Model with CANFES Classification Approach
Published 2022“…The purpose of this study was to create a multi-layered architecture that could detect a variety of existing and emerging botnets. …”
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CEAP
Published 2016“…To reduce the overhead of the proposed detection model and make it feasible for the resource-constrained nodes, we reduce the size of the training dataset by (1) restricting the data collection, storage, and analysis to concern only a set of specialized nodes (i.e., Multi-Point Relays) that are responsible for forwarding packets on behalf of their clusters; and (2) migrating only few tuples (i.e., support vectors) from one detection iteration to another. …”
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CNN and HEVC Video Coding Features for Static Video Summarization
Published 2022“…The proposed solutions are compared with existing works based on an SIFT flow algorithm that uses CNN features. Subsequently, an optional dimensionality reduction based on stepwise regression was applied to the feature vectors prior to detecting key frames. …”
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Automatic Video Summarization Using HEVC and CNN Features
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Benchmark on a large cohort for sleep-wake classification with machine learning techniques
Published 2019“…We also implemented and compared state-of-the-art methods to score sleep-wake stages, which range from the widely used traditional algorithms to recent machine learning approaches. …”
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