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Improving Facial Expression Recognition in HCI: Traditional Techniques and Comparative Analysis 4

Improving Facial Expression Recognition in HCI: Traditional ...

4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Alshwihde, Rayhan S Eltarhouni, Wafa I University of Benghazi Faculty of Information Technology Department of computer science Benghazi Libya

ISBN: (纸本)9798350372632

Facial expressions play a vital role in human communication, enabling us to convey a wide range of emotions such as happiness, anger, and sadness. Human-computer Interaction (HCI) is a rapidly growing and highly appealing field. By recognizing facial expressions, HCI can enhance communication by making it more interactive and natural. The proposed methodology is based on traditional techniques for extracting and classifying the features from images. To evaluate its effectiveness, we utilize two datasets: CK+ and JAFFE. First, the facial part of the images is cropped and enhanced using a CLAHE filter. Then, the Histogram of Oriented Gradients (HOG) descriptor extracts features from the images, while Principal Component Analysis (PCA) reduces their dimensionality. Finally, three classifiers Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest are used to classify facial expressions. Our methodology achieved an accuracy of 95.94% for CK+ and 93.75% for JAFFE datasets. © 2024 IEEE.

关键词： Support vector machines

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic Engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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A Deep Learning and Powerful Computational Framework for Brain Cancer MRI Image Recognition

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 1-18页

作者： Kumar, Ankit Shukla, Santosh Kumar Prakash, Navin Yadav, Rakesh Kumar Department of Information Technology Babu Banarasi Das Institute of Technology and Management Lucknow India Department of Computer Science and Engineering Babu Banarasi Das Engineering College Lucknow India Department of Computer Science and Engineering MSOET Maharishi University of Information Technology Lucknow India

Brain tumors are one of the deadliest diseases and require quick and accurate methods of detection. Finding the optimum image for research goals is the first step in optimizing MRI images for pre- and post-processing. As a result, the mean grey-level approach was used to segment the MRI images using a threshold. In the second stage of statistical feature analysis, the picture features were extracted using the spatial gray-level dependency matrix using Hara lick's feature equations. As a consequence, the best features were picked and the tumor was placed in the appropriate location. In the third stage, an automated tool for classifying the photographs under assessment as having a tumor or not was created utilizing supervised learning and an artificially intelligent methodology. An efficient test of the network's performance yielded 97% of the desired results. © 2023, The Institution of Engineers (India).

关键词： Tumors

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Enhancing Plant Leaf Disease Prediction Through Advanced Deep Feature Representations: A Transfer Learning Approach

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Journal of The Institution of Engineers (India): Series B 2024年第3期105卷 469-482页

作者： Naralasetti, Veeranjaneyulu Bodapati, Jyostna Devi Department of Information Technology Vignan’s Foundation for Science Technology and Research Guntur India Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Guntur India

Plant diseases present a considerable threat to the farming industry, causing significant economic losses by reducing crop yields. The emergence of deep neural network models in the realm of computer vision has brought about a groundbreaking transformation in the swift recognition and surveillance of plant diseases. In this study, we introduce an innovative approach for plant disease prediction, leveraging transfer learning techniques to enable accurate and early detection of plant diseases. The proposed leaf disease prediction framework encompasses multiple key stages, including preprocessing, feature extraction, classification, and postprocessing. Notably, our model excels at feature extraction, utilizing the pre-trained convolutional architecture of VGG16 to extract valuable information from leaf images. These features are then fed into a deep neural network model designed for disease prediction. The feature extraction method employed in our approach excels at capturing intricate disease-related details from plant images. When the deep features extracted are utilized for classification using our proposed classifier, the outcome demonstrates a substantial enhancement in disease prediction performance. Based on extensive experimentation with the benchmark plant village dataset, it is evident that our transfer learning-based approach outperforms existing methods, achieving an impressive accuracy rate of 96.56%. Besides accuracy, the proposed approach surpasses F1_score and Kappa scores for the benchmark plant village dataset. © The Institution of Engineers (India) 2024.

关键词： Forecasting

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Assessment of ChatGPT in extracting Arabic morphology: active and passive participles

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International Journal of Speech technology 2025年第1期28卷 85-97页

作者： Alian, Marwah Hussein, Ibtisam Al-Khazaleh, Maisa Computer Science Department Faculty of Information Technology The World Islamic Sciences and Education University Amman Jordan Basic Sciences Department Faculty of Science The Hashemite University Zarqa Jordan Arabic Language Department Faculty of Arts Yarmouk University Irbid Jordan

ChatGPT is a language model that has been developed by Open AI. It has the ability to answer any question raised by a participant in a form of conservation. ChatGPT takes advantages of the effective transformers architecture to generate text discussions that are relevant and perceptive. Although ChatGPT was initially trained on English text data, it has also been fine-tuned on other languages including Spanish, French, Chinese, German and Arabic. ChatGPT has been employed in different NLP domains for English language, but specifically it is still slightly limited in languages other than English. In this research, we assess ChatGPT in extracting active participle (ism al-fa’il) and passive participle (ism al-maf’uul) from Arabic verbs of base forms (alfilu alsahih) and verbs with vowels (alfilu almutal) to help the non-native speakers who find it difficult to extract these participles and try to use ChaTGPT as a learning tool and get inaccurate responses. We experiment ChatGPT’s performance in zero-shot learning then with a wide range of prompt templates as well as few-shot learning. The results show a greet enhancement in the ChatGPT performance when tuning the rule-based prompts with k-shot examples especially for passive participle extraction. ChatGPT performed well in extracting participles from verbs without vowels, but after training with k-shot samples, it improved by 6% for active participles and 25% for passive participles. Additionally, extracting passive participles for verbs with vowels significantly improves performance compared to extracting active participles, especially the case when the vowels exist at two positions, which results in a 100% improvement. The performance of ChatGPT improved in extracting active participle for verbs with no vowels by 6% and passive participles by 25%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Zero-shot learning

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BERT-CNN: A Deep Learning Model for Detecting Emotions from Text

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computers, Materials & Continua 2022年第5期71卷 2943-2961页

作者： Ahmed R.Abas Ibrahim Elhenawy Mahinda Zidan Mahmoud Othman Department of Computer Science Faculty of Computer and InformaticsZagazig UniversityZagazig44519Egypt Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt11835Egypt

Due to the widespread usage of social media in our recent daily lifestyles,sentiment analysis becomes an important field in pattern recognition and Natural Language Processing(NLP).In this field,users’feedback data on a specific issue are evaluated and *** emotions within the text is therefore considered one of the important challenges of the current NLP *** have been widely studied in psychology and behavioral science as they are an integral part of the human *** describe a state of mind of distinct behaviors,feelings,thoughts and *** main objective of this paper is to propose a new model named BERT-CNN to detect emotions from *** model is formed by a combination of the Bidirectional Encoder Representations from Transformer(BERT)and the Convolutional Neural networks(CNN)for textual *** model embraces the BERT to train the word semantic representation language *** to the word context,the semantic vector is dynamically generated and then placed into the CNN to predict the *** of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR *** BERTCNN model achieves an accuracy of 94.7%and an F1-score of 94%for semeval2019 task3 dataset and an accuracy of 75.8%and an F1-score of 76%for ISEAR dataset.

关键词： BERT-CNN deep learning emotion detection semeval2019 text classification

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Machine Learning Approach to Predict Student Performance Based on Previous Records

Machine Learning Approach to Predict Student Performance Bas...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Albohali, Amal Khalifa Alshareef, Almahdi Shantal, Mohammed Sebha University Faculty of information technology Computer science depertment Sebha Libya Sebha University Faculty of information technology Computer science depertment Research Department Lcdmikdr Sebha Libya

ISBN: (纸本)9798350367560

Educational Data Mining (EDM) is an emerging field dedicated to discovering and analyzing meaningful patterns in educational datasets. This paper provides a comparative analysis of seven machine-learning classifiers: Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Neural Network (NN). The primary objective is to evaluate these algorithms, combined with preprocessing techniques, for predicting student performance and identifying the most effective classifier using metrics such as the confusion matrix, accuracy, precision, and sensitivity. The dataset utilized in this study was obtained from Sebha University, Libya, comprising 6,000 student records. It includes 6 attributes from the university's SIS system and 43 attributes derived from completed questionnaires, focusing exclusively on students from scientific departments. The experimental results revealed that the RF algorithm outperformed other classifiers in predicting student performance. Significant improvements in the model's performance were observed after applying data preprocessing and feature selection techniques. These findings offer valuable insights to help new students select suitable academic programs, potentially enhancing their academic performance. Furthermore, the proposed model could serve as an early warning system to identify students at risk of academic failure, enabling timely intervention. © 2024 IEEE.

关键词： Adversarial machine learning

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Integrating Dung Beetle Optimization in Machine Learning for Advanced Zero-Day Attack Detection and Classification 9

Integrating Dung Beetle Optimization in Machine Learning for...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Vanitha, J. Anandababu, P. Annamalai University Faculty of Science Department of Computer and Information Science India

ISBN: (纸本)9798350377972

With the widespread adoption of Internet such as online banking, health systems, e-commerce, and other daily requirements, the risk of becoming revealed to numerous is growing proportionally. Zero-day Attacks targeting unidentified vulnerabilities of a system or software start-up additionally investigate direction in the cyber-attacks fields. Recent techniques both apply machine learning (ML) and anomaly-based approaches to protect against this attack. In the ML umbrella are traditional ML-based models that are familiar with having low detection rates and prediction quality about data that it cannot yet be proficient in. DL-based approaches, particularly convolutional neural networks (CNNs) with regularization techniques, tackle these problems provide a superior prediction quality with unidentified data, and avoid overfitting. Identifying zero-day attacks over this technique miscues various parameters including the frequency of certain byte streams in network traffic and their correlation. This study designs a new Machine Learning with a Dung Beetle Optimization Algorithm using the Zero-Day Attack Detection and Classification (MLDBO-ZDADC) technique. The aim of the MLDBO-ZDADC method is in the automated and effectual detection of zero-day attacks. At first, the MLDBO-ZDADC approach applies min-max normalization to pre-process the input data. Besides, the MLDBO-ZDADC technique exploits the feed-forward neural network (FNN) model for attack detection and its parameter tuning process is performed by using the dung beetle optimization (DBO) model. To ensure better prediction results of the MLDBO-ZDADC approach, a sequence of experiments is applied to benchmark dataset. The experimental validation illustrates the promising accomplishment of the MLDBO-ZDADC approach on the existing methods. © 2024 IEEE.

关键词： Zero-day attack

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Insider Attack Prevention:LAPUP—Lightweight Authentication Protocol Using PUF

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Journal of Communications and information Networks 2024年第2期9卷 192-206页

作者： Siranjeevi Rajamanickam Satyanarayana Vollala N.Ramasubramanian Department of Computer Engineering Government Polytechnic CollegeTiruchirappalli 620022India Department of Computer Science and Engineering Dr.Shyama Prasad Mukherjee International Institute of Information Technology-Naya RaipurChhattisgarh 493661India Department of Computer Science and Engineering National Institute of TechnologyTiruchirappalli 620015India

The incredible progress in technologies has drastically increased the usage of Web *** share their credentials like userid and password or use their smart cards to get authenticated by the application *** cards are handy to use,but they are susceptible to stolen smart card attacks and few other notable security *** prefer to use Web applications that guarantee for security against several security attacks,especially insider attacks,which is *** of several existing schemes prove the security pitfalls of the protocols from preventing security attacks,specifically insider *** paper introduces LAPUP:a novel lightweight authentication protocol using physically unclonable function(PUF)to prevent security attacks,principally insider *** PUFs are used to generate the security keys,challenge-response pair(CRP)and hardware signature for designing the *** transmitted messages are shared as hash values and encrypted by the keys generated by *** messages are devoid of all possible attacks executed by any attacker,including insider *** is also free from stolen verifier attacks,as the databases are secured by using the hardware signature generated by *** analysis of the protocol exhibits the strength of LAPUP in preventing insider attacks and its resistance against several other security *** evaluation results of the communication and computation costs of LAPUP clearly shows that it achieves better performance than existing protocols,despite providing enhanced security.

关键词： physically unclonable function(PUF) insiderattacks authenticationkeymanagementserver(AKMS) security

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Target Detection Algorithm in Foggy Scenes Based on Dual Subnets

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computers, Materials & Continua 2024年第2期78卷 1915-1931页

作者： Yuecheng Yu Liming Cai Anqi Ning Jinlong Shi Xudong Chen Shixin Huang School of Computer Science Jiangsu University of Science and TechnologyZhenjiang212100China Information Department China Merchants Heavy IndustryNantong226116China

Under the influence of air humidity,dust,aerosols,etc.,in real scenes,haze presents an uneven *** this way,the image quality and contrast will *** this case,It is difficult to detect the target in the image by the universal detection ***,a dual subnet based on multi-task collaborative training(DSMCT)is proposed in this ***,in the training phase,the Gated Context Aggregation Network(GCANet)is used as the supervisory network of YOLOX to promote the extraction of clean information in foggy *** the test phase,only the YOLOX branch needs to be activated to ensure the detection speed of the ***,the deformable convolution module is used to improve GCANet to enhance the model’s ability to capture details of non-homogeneous ***,the Coordinate Attention mechanism is introduced into the Vision Transformer and the backbone network of YOLOX is *** this way,the feature extraction ability of the network for deep-level information can be *** experimental results on artificial fog data set FOG_VOC and real fog data set RTTS show that the map value of DSMCT reached 86.56%and 62.39%,respectively,which was 2.27%and 4.41%higher than the current most advanced detection *** DSMCT network has high practicality and effectiveness for target detection in real foggy scenes.

关键词： Target detection fog target detection YOLOX twin network multi-task learning

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