Search Results - (( model evaluation method algorithm ) OR ( feature detection method algorithm ))

Extremal region detection and selection with fuzzy encoding for food recognition by Razali @ Ghazali, Mohd Norhisham

“…Three algorithms were used to accomplish the task of feature representation. …”
Get full text
Get full text

Temporal video segmentation using squared form of Krawtchouk-Tchebichef moments by Abdulhussain, Sadiq H.

“…TVS algorithm design is still challenging because most of the recent methods are unable to achieve robust detection for different types of transitions: hard transition (HT) and soft transition (ST). …”
Get full text
Get full text
Get full text

An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

“…In comparison to different single algorithms for feature selection,experimental results show that the proposed ensemble method is able to reduce dimensionality, the number of irrelevant features and produce reasonable classifier accuracy. …”
Get full text
Get full text

Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

“…Genetic algorithm GA had been adopted to perform features selection method; however, this method could not deliver an acceptable detection rate, lower accuracy, and higher false alarm rates. …”
Get full text
Get full text

Fast temporal video segmentation based on Krawtchouk-Tchebichef moments by Abdulhussain, Sadiq H., Al-Haddad, Syed Abdul Rahman, Saripan, M. Iqbal, Mahmood, Basheera M., Hussien, Aseel

“…Specifically, the hard transitions are detected by the trained SVM model. The proposed algorithm has been evaluated on four datasets. …”
Get full text
Get full text
Get full text

An intelligent DDoS attack detection tree-based model using Gini index feature selection method by Bouke, Mohamed Aly, Abdullah, Azizol, ALshatebi, Sameer Hamoud, Abdullah, Mohd Taufik, Atigh, Hayate El

“…This paper proposes a novel intelligent DDoS attack detection model based on a Decision Tee (DT) algorithm and an enhanced Gini index feature selection method. …”
Get full text
Get full text

A guided hybrid k-means and genetic algorithm models for children handwriting legibility performance assessment / Norzehan Sakamat by Sakamat, Norzehan

“…Size was extracted using Extreme Point Detection algorithm and Hit or Miss Transformation method was used to extract the stroke formation pattern. …”
Get full text
Get full text

Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning by Mohammed, Badiea Abdulkarem, Senan, Ebrahim Mohammed, Al-Mekhlafi, Zeyad Ghaleb, Rassem, Taha Hussein, Makbol, Nasrin M., Alanazi, Adwan Alownie, Almurayziq, Tariq S., Ghaleb, Fuad A., Sallam, Amer A.

“…The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. …”
Get full text
Get full text
Get full text

Identifying melanoma characteristics using directional imaging algorithm and convolutional neural network on dermoscopic images / Mohammad Asaduzzaman Rasel by Mohammad Asaduzzaman , Rasel

“…Multiple deep-learning models are proposed for segmentation. Several imaging, computer vision, and pattern recognition algorithms are employed to describe five dermoscopic features. …”
Get full text
Get full text
Get full text

An efficient anomaly intrusion detection method with feature selection and evolutionary neural network by Sarvari, Samira, Mohd Sani, Nor Fazlida, Mohd Hanapi, Zurina, Abdullah @ Selimun, Mohd Taufik

“…This research designed an anomaly-based detection, by adopting the modified Cuckoo Search Algorithm (CSA), called Mutation Cuckoo Fuzzy (MCF) for feature selection and Evolutionary Neural Network (ENN) for classification. …”
Get full text
Get full text
Get full text

Deep learning-based colorectal cancer classification using augmented and normalised gut microbiome data / Mwenge Mulenga by Mwenge , Mulenga

“…First, to investigate the methods used to address limitations associated with microbiome-based datasets in colorectal cancer identification using deep neural network algorithms. …”
Get full text
Get full text
Get full text

An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA by Bian, Hui

“…Using the NSL-KDD dataset for evaluation, the proposed method demonstrates superior performance compared to conventional algorithms and related deep learning techniques, achieving higher precision, recall, F1 scores and overall accuracy in both binary and multi-class classification tasks. …”
Get full text
Get full text
Get full text

Object detection utilizing modified auto encoder and convolutional neural networks by Khiarak, Jalil Nourmohammadi, Mazaheri, Samaneh, Tayebi, Rohollah Moosavi, Noorbakhsh-Devlagh, Hamid

Get full text
Get full text

Heart disease prediction using artificial neural network with ADAM optimization and harmony search algorithm by Alyaa Ghazi Mohammed, Mohd Zakree Ahmad Nazri

“…Drawing from an extensive review of existing predictive models and cardiovascular health risk factors, this research proposes an enhanced ADAM optimization algorithm, integrated with advanced data processing and feature selection methodologies, to identify and refine key predictors for improved model performance. …”
Get full text
Get full text
Get full text

Enhancing Efficientnet-Yolov4 For Integrated Circuit Detection On Printed Circuit Boards by Tay, Shiek Chi

“…This thesis addresses these intricacies by proposing an improved algorithm, efficientnet-yolov4. The research methodology combines the high-performance feature extraction capabilities of efficientnet as the backbone network with the precise object localisation capabilities of yolov4, a dual advantage unique compared to other methods that may rely on less sophisticated localisation algorithms. …”
Get full text
Get full text

Concrete surface inspection by using Unmanned Aerial Vehicle (UAVs) and deep learning algorithms Yolov7 by Rusdinaidi, Saffa Nasuha, Hashim, Khairil Afendy, Ahmad Dahlan, Zaki

“…This research aims to evaluate the detection and analysis of cracks on concrete surfaces by utilizing Uavs and Yolo algorithms. …”
Get full text
Get full text

AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites by Alsariera, Y.A., Adeyemo, V.E., Balogun, A.O., Alazzawi, A.K.

Get full text
Get full text

Sketch-based 3D modeling of symmetric objects from wireframe sketches on paper by Yahya, Zahrah

“…Feature extraction is a preliminary step before 3D reconstruction and therefore, a junction detection algorithm is proposed. …”
Get full text
Get full text

Metaheuristic based ids using multi-objective wrapper feature selection and neural network classification by Ghanem, W.A.H.M, El-Ebiary, Y.A.B., Abdulnab, M., Tubishat, M., Alduais, N.A.M., Nasser, A.B., Abdullah, N., Al-wesabi, O.A.

“…Due to the significant ongoing expansion of computer networks in our lives nowadays, the demand for network security and protection from cyber-attacks has never been more imperative to either clients or businesses alike, which signifies the key role of cyber intrusion detection systems in network security. This article proposes a cyber-intrusion detecting system classification with MLP trained by a hybrid metaheuristic algorithm and feature selection based on multi-objective wrapper method. …”
Get full text
Get full text

Concrete surface inspection by using unmanned aerial vehicle (UAV) and deep learning algorithms YOLOv7 / Saffa Nasuha Rusdinadi by Rusdinadi, Saffa Nasuha

“…These images are then processed using yolov7, a state-of-the-art object detection algorithm, to accurately identify and classify surface cracks. the study involves the collection of a comprehensive dataset of concrete surfaces with varying crack patterns, pre-processed using Roboflow and Opencv tools to enhance crack features. the annotated dataset is utilized to train and validate the yolov7 model, ensuring high precision which is 96.8% and 90.1% recall in crack detection. the performance of the model is evaluated through metrics such as precision, recall, and f1-score, demonstrating its robustness and reliability in detecting both fine and prominent cracks. …”
Get full text
Get full text