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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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A Framework for Food Availability Forecasting Using Temporal Knowledge Graph

A Framework for Food Availability Forecasting Using Temporal...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Eltaki, Nada Nagaty, Khaled Elzanfaly, Doaa Maghawry, Noura The British University in Egypt Faculty of Informatics and Computer Science Department of Information System Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Science Department of Computer Science Cairo Egypt Helwan University Faculty of Informatics and Computer Science Department of Information System Cairo Egypt

ISBN: (纸本)9798350367560

The entire world encounters a significant problem which is food availability. 'Zero-hunger' is the second Sustainable Development Goal (SDG) of the goals established by the United Nations in 2015. With the increasing number of people and various factors affecting food availability such as climate and country-based food production rate, food forecasting models have become increasingly essential. However, traditional forecasting techniques are limited in dealing with the data and identifying the relationships between them within a specific timestamp. This paper proposes a framework for an intelligent food availability forecasting model built using deep learning techniques and based on a temporal knowledge graph where different timestamps are taken into consideration. The proposed framework helps in enhancing and overcoming the challenges and limitations of the recent forecasting techniques leading to a dynamic, scalable, intelligent forecasting model. © 2024 IEEE.

关键词： Prediction models

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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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Combining Remote Sensing Dataset, Machine Learning, and Explainable AI for Detailed Human Development Index Monitoring in East Java, Indonesia

Combining Remote Sensing Dataset, Machine Learning, and Expl...

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2024 International Conference on information technology Research and Innovation, ICITRI 2024

作者： Aprianto, Kasiful Saputra, Irwansyah Riana, Dwiza Universitas Nusa Mandiri Master of Computer Science Student Faculty of Information Technology Jakarta Indonesia Universitas Nusa Mandiri Faculty of Information Technology Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350376210

This study integrates satellite imagery, machine learning, and explainable AI to enhance the predictive accuracy and interpretability of the Human Development Index (HDI) in East Java, Indonesia. Using advanced models, particularly Random Forest Regression, the research achieved an R-squared value of 0.8709, explaining approximately 87% of the variance in HDI. Feature importance analysis identified Night Time Light (NTL) as the most influential predictor with an importance score of 0.4910, indicating a strong correlation with urban development and economic activity. Other significant predictors included the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), reflecting the importance of environmental conditions in development metrics. SHAP (SHapley Additive exPlanations) analysis provided insights into individual feature contributions, enhancing model transparency and supporting targeted policy interventions. For instance, higher NTL was associated with increased HDI, highlighting urbanization's role in development, while pollutants like NO2 and SO2 negatively impacted HDI. This study underscores the effectiveness of integrating satellite data and machine learning in socioeconomic analyses and offers a robust framework for future HDI monitoring. © 2024 IEEE.

关键词： Adversarial machine learning

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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Multisource Data Fusion Using MLP for Human Activity Recognition

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computers, Materials & Continua 2025年第2期82卷 2109-2136页

作者： Sujittra Sarakon Wansuree Massagram Kreangsak Tamee Department of Computer Science and Information Technology Faculty of ScienceNaresuan UniversityPhitsanulok65000Thailand Center of Excellence for Innovation and Technology for Detection and Advanced Materials(ITDAM) Faculty of ScienceNaresuan UniversityPhitsanulok65000Thailand Center of Excellence in Nonlinear Analysis and Optimizations Faculty of ScienceNaresuan UniversityPhitsanulok65000Thailand

This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, MotionSense, and PAMAP2—to develop a generalized MLP model for classifying six human activities. Performance analysis of the fused model for each dataset reveals accuracy rates of 95.83 for WISDM, 97 for DaLiAc, 94.65 for MotionSense, and 98.54 for PAMAP2. A comparative evaluation was conducted between the fused MLP model and the individual dataset models, with the latter tested on separate validation sets. The results indicate that the MLP model, trained on the fused dataset, exhibits superior performance relative to the models trained on individual datasets. This finding suggests that multisource data fusion significantly enhances the generalization and accuracy of HAR systems. The improved performance underscores the potential of integrating diverse data sources to create more robust and comprehensive models for activity recognition.

关键词： Multisource data fusion human activity recognition multi-layer perceptron(MLP) artificial intelligent

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Adaptive isomap feature extractive gradient deep belief network classifier for diabetic retinopathy identification

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Multimedia Tools and Applications 2025年第9期84卷 6349-6370页

作者： Singh, Alka Kumar, Rakesh Gandomi, Amir H. Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology Gorakhpur India Faculty of Engineering & Information Technology University of Technology Sydney SydneyNSW Australia Óbuda University Budapest1034 Hungary

Diabetic retinopathy (DR) is an infection that bases eternal visualization loss in patients with diabetes mellitus. With DR, the glucose level in the blood increases, as well as its viscosity, this results in fluid leakage into surrounding tissues in the retina. In other words, DR represents the pathology of capillaries and venules in the retina with leakage effects, being the main acute retinal disorder caused by diabetes. Many DR detection methods have been previously discussed by different researchers;however, accurate DR detection with a reduced execution time has not been achieved by existing methods. The proposed method, the Shape Adaptive box linear filtering-based Gradient Deep Belief network classifier (SAGDEB) Model, is performed to enhance the accuracy of DR detection. The objective of the SAGDEB Model is to perform an efficient DR identification with a higher accuracy and lower execution time. This model comprises three phases: pre-processing, feature extraction, and classification. The shape adaptive box linear filtering image pre-processing is carried out to reduce the image noise without removing significant parts of image content. Then, an isomap geometric feature extraction is performed to compute features of different natures, like shape, texture, and color, from the pre-processed images. After that, the Adaptive gradient Tversky Deep belief network classifier is to perform classification. The deep belief network is probabilistic and generative graphical model that consists of multiple layers such as one input unit, three hidden units, and one output unit. The extracted image featuresare considered in the input layer and these images are sent to hidden layers. Tversky similarity index is applied in hidden layer 1 to analyze the extracted features with testing features. Regarding the similarity value, the sigmoid activation function is determined in hidden layer 2 so different levels of DR can be identified. Finally, the adaptive gradient method is

关键词： Feature extraction

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IoT-CDS:Internet of Things Cyberattack Detecting system Based on Deep Learning Models

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computers, Materials & Continua 2024年第12期81卷 4265-4283页

作者： Monir Abdullah Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

The rapid growth and pervasive presence of the Internet of Things(IoT)have led to an unparalleled increase in IoT devices,thereby intensifying worries over IoT *** learning(DL)-based intrusion detection(ID)has emerged as a vital method for protecting IoT *** rectify the deficiencies of current detection methodologies,we proposed and developed an IoT cyberattacks detection system(IoT-CDS)based on DL models for detecting bot attacks in IoT *** DL models—long short-term memory(LSTM),gated recurrent units(GRUs),and convolutional neural network-LSTM(CNN-LSTM)were suggested to detect and classify IoT *** BoT-IoT dataset was used to examine the proposed IoT-CDS system,and the dataset includes six attacks with normal *** experiments conducted on the BoT-IoT network dataset reveal that the LSTM model attained an impressive accuracy rate of 99.99%.Compared with other internal and external methods using the same dataset,it is observed that the LSTM model achieved higher accuracy *** are more efficient than GRUs and CNN-LSTMs in real-time performance and resource efficiency for cyberattack *** method,without feature selection,demonstrates advantages in training time and detection ***,the proposed approach can be extended to improve the security of various IoT applications,representing a significant contribution to IoT security.

关键词： Cyberattacks intrusion detection system deep learning internet of things

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Dynamic Audio-Visual Biometric Fusion for Person Recognition

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computers, Materials & Continua 2022年第4期71卷 1283-1311页

作者： Najlaa Hindi Alsaedi Emad Sami Jaha Department of Computer Science Faculty of Computer Science and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Biometric recognition refers to the process of recognizing a person’s identity using physiological or behavioral modalities,such as face,voice,fingerprint,gait,*** biometric modalities are mostly used in recognition tasks separately as in unimodal systems,or jointly with two or more as in multimodal ***,multimodal systems can usually enhance the recognition performance over unimodal systems by integrating the biometric data of multiple modalities at different fusion *** this enhancement,in real-life applications some factors degrade multimodal systems’performance,such as occlusion,face poses,and noise in voice *** this paper,we propose two algorithms that effectively apply dynamic fusion at feature level based on the data quality of multimodal *** proposed algorithms attempt to minimize the negative influence of confusing and low-quality features by either exclusion or weight reduction to achieve better recognition *** proposed dynamic fusion was achieved using face and voice biometrics,where face features were extracted using principal component analysis(PCA),and Gabor filters separately,whilst voice features were extracted using Mel-Frequency Cepstral Coefficients(MFCCs).Here,the facial data quality assessment of face images is mainly based on the existence of occlusion,whereas the assessment of voice data quality is substantially based on the calculation of signal to noise ratio(SNR)as per the existence of *** evaluate the performance of the proposed algorithms,several experiments were conducted using two combinations of three different databases,AR database,and the extended Yale Face Database B for face images,in addition to VOiCES database for voice *** obtained results show that both proposed dynamic fusion algorithms attain improved performance and offer more advantages in identification and verification over not only the standard unimodal algorithms but also the multimodal algorithms using stan

关键词： Biometrics dynamic fusion feature fusion identification multimodal biometrics occluded face recognition quality-based recognition verification voice recognition

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A Comparative Study on Deep Learning Models for Stock Price Prediction 5

A Comparative Study on Deep Learning Models for Stock Price ...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： He, Zhexin Zhang, Huan Por, Lip Yee Chongqing College of Architecture and Technology Chongqing401331 China Chongqing Academy of Science and Technology Chongqing401123 China Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia

ISBN: (纸本)9781643685243

With the advancement of deep learning technology, researchers have begun employing deep neural network models such as LSTM, MLP, CNN, and GRU to tackle nonlinear prediction problems in stock markets. This study harnesses these deep learning models, including LSTM, MLP, CNN, and GRU, to investigate the nonlinear forecasting of stock market price indices. The experiment selects globally representative stock indices and, through analyzing short-term, mid-term, and long-term data, discovers that the GRU model demonstrates exceptional performance in both prediction accuracy and generalization capabilities. To further enhance precision, the research constructs a filtering ensemble decision-making system based on multiple GRU models. This strategy reduces the error by at least 0.3% in medium to long-term predictions, significantly outperforming a single standalone GRU model. Consequently, the GRU-filtered ensemble decision model has been empirically proven as an effective tool for predicting stock indices, not only broadening the methods available for stock market prediction but also deepening the understanding and application of the generalization abilities of deep learning models within financial markets. © 2024 The Authors.

关键词： Long short-term memory

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