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9th IEEE International Conference on Adaptive science and Technology, ICAST 2024

作者： Martey, Ezekiel Mensah Abdullah, Abubakar Appiah, Obed Department of Computer Science and Engineering Tarkwa Ghana Department of Computer Science and Informatics Sunyani Ghana

ISBN: (纸本)9798350385403

Machine learning has emerged as one transformative tool in predicting student academic performance. This study evaluates machine learning models regression in predicting student academic performance. It can be found optimised Support Vector Regression (SVR) model accurately predicts student academic performance using a benchmark dataset from Kaggle. The approach incorporated Recursive Feature Elimination (RFE) for optimal feature selection while key parameters of the SVR model are tuned through grid search optimisation to enhance model performance. The optimised SVR model achieves exceptional predictive accuracy with an R-squared value of 0.97, Mean Absolute Error of 2.98, Mean Squared Error of 13.28, Root Mean Squared Error of 3.64, Median Absolute Error of 2.65, and Mean Absolute Percentage Error of 0.23. Comparative analysis demonstrates the superior performance of optimised SVR model against Decision Tree, Random Forest, K-Nearest Neighbors, Bayesian Ridge, and Gradient Boosting models. Experimental results demonstrate the validity of robustness and reliability of our approach, offering valuable insights for educational institutions to develop targeted interventions and support systems. © 2024 IEEE.

关键词： Educational Data Mining Grid Search Optimization Machine Learning Recursive Feature Elimination Student Performance Prediction

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CBIR framework for Diagnosing COVID 19 in Chest X-Ray Images using Deep learning by integrating Photometric Data Augmentation 9

CBIR framework for Diagnosing COVID 19 in Chest X-Ray Images...

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9th IEEE International Conference on Adaptive science and Technology, ICAST 2024

作者： Martey, Ezekiel Mensah Appiah, Obed Sitti, Mensah Nusetor, Foster Delali Department of Computer Science and Engineering Tarkwa Ghana Department of Computer Science and Informatics Sunyani Ghana

ISBN: (纸本)9798350385403

computer-Aided Diagnostic (CAD) systems enhance clinical decision-making, providing more accurate and streamlined processes. CAD systems based on Content-Based Image Retrieval (CBIR) serve as visual tools that strengthen the reliability of clinical decisions. While deep learning-based CAD systems have shown significant progress in extracting insights from X-rays and medical images, they often struggle due to their reliance on large datasets and high computational demands. These challenges are particularly acute in the medical domain, where datasets are frequently limited and imbalanced. This paper presents a framework for diagnosing COVID-19 from X-ray images, combining deep learning with photometric data augmentation. The proposed approach focuses on two core tasks: image retrieval and classification, which depend on robust feature representation. Using Euclidean distance measures, we utilise a modified EfficientNet-B3 model for feature extraction and examine the effects of transfer learning and fine-tuning on retrieval and diagnostic accuracy. Evaluating binary and multiclass datasets reveals that the proposed method performs strongly in classification tasks, with notable effectiveness in binary and multiclass scenarios. Compared to state-of-the-art techniques, our approach demonstrates superior accuracy for binary classification of X-ray images, outperforming most existing models. © 2024 IEEE.

关键词： COVID-19

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Concentric Blocking Techniques for Improved Feature Extraction in Local Binary Pattern (LBP) Systems 9

Concentric Blocking Techniques for Improved Feature Extracti...

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9th IEEE International Conference on Adaptive science and Technology, ICAST 2024

作者： Appiah, Obed Martey, Ezekiel Mensah Department of Computer Science and Informatics Sunyani Ghana Department of Computer Science and Engineering Tarkwa Ghana

ISBN: (纸本)9798350385403

The domain of image analysis has seen rapid advancements, particularly feature extraction techniques, driven by the increasing demand for accurate and efficient results. Among these advancements, Local Binary Patterns (LBP) have gained prominence due to their simplicity and computational efficiency. However, traditional LBP methods often need help with image orientation and scale variations, common in real-world scenarios. To address these challenges, this study explores the potential of concentric blocking methods as an alternative to conventional grid-based blocking techniques. Concentric blocking, which organizes image data into circular or spiral patterns, is hypothesized to enhance the robustness and accuracy of LBP-based systems by better accommodating geometric transformations. The study uses multiple LBP variants to compare traditional grid-based blocking with concentric blocking techniques across various image analysis tasks, including face recognition and texture classification. Experimental results demonstrate that the proposed ring-centric technique significantly outperforms traditional methods in precision and computational efficiency. Precision values for the proposed Ring Centric technique reach up to 90.73% for the basic LBP and 88.47% for Neighboring LBP with Distance (NLBPD), indicating its superior performance in handling variations in orientation and scale. Additionally, the technique demonstrates lower running times, making it a highly efficient and accurate choice for image retrieval tasks. This study highlights the importance of image-blocking strategies in enhancing the performance of LBP-based systems and underscores the potential of concentric blocking methods in overcoming the limitations of traditional approaches. The findings contribute to developing more resilient and efficient image analysis systems, with significant implications for applications in computer vision, medical diagnostics, and security systems. © 2024 IEEE.

关键词： Local binary pattern

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FilterGNN:Image feature matching with cascaded outlier filters and linearattention

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Computational Visual Media 2024年第5期10卷 873-884页

作者： Jun-Xiong Cai Tai-Jiang Mu Yu-Kun Lai Key Laboratory of Pervasive Computing Ministry of EducationDepartment of Computer Science and TechnologyTsinghua UniversityBeijing 100084China School of Computer Science and Informatics Cardiff UniversityWales CF244AGUK

The cross-view matching of local image features is a fundamental task in visual localization and 3D *** study proposes FilterGNN,a transformer-based graph neural network(GNN),aiming to improve the matching efficiency and accuracy of visual *** on high matching sparseness and coarse-to-fine covisible area detection,FilterGNN utilizes cascaded optimal graph-matching filter modules to dynamically reject outlier ***,we successfully adapted linear attention in FilterGNN with post-instance normalization support,which significantly reduces the complexity of complete graph learning from O(N2)to O(N).Experiments show that FilterGNN requires only 6%of the time cost and 33.3%of the memory cost compared with SuperGlue under a large-scale input size and achieves a competitive performance in various tasks,such as pose estimation,visual localization,and sparse 3D reconstruction.

关键词： image matching transformer linear attention visual localization sparse reconstruction

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MusicFace: Music-driven expressive singing face synthesis

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Computational Visual Media 2024年第1期10卷 119-136页

作者： Pengfei Liu Wenjin Deng Hengda Li Jintai Wang Yinglin Zheng Yiwei Ding Xiaohu Guo Ming Zeng School of Informatics Xiamen UniversityXiamen361000China Department of Computer Science The University of Texas at DallasRichardsonTexas75080-3021USA

It remains an interesting and challenging problem to synthesize a vivid and realistic singing face driven by music. In this paper, we present a method for this task with natural motions for the lips, facial expression, head pose, and eyes. Due to the coupling of mixed information for the human voice and backing music in common music audio signals, we design a decouple-and-fuse strategy to tackle the challenge. We first decompose the input music audio into a human voice stream and a backing music stream. Due to the implicit and complicated correlation between the two-stream input signals and the dynamics of the facial expressions, head motions, and eye states, we model their relationship with an attention scheme, where the effects of the two streams are fused seamlessly. Furthermore, to improve the expressivenes of the generated results, we decompose head movement generation in terms of speed and direction, and decompose eye state generation into short-term blinking and long-term eye closing, modeling them separately. We have also built a novel dataset, SingingFace, to support training and evaluation of models for this task, including future work on this topic. Extensive experiments and a user study show that our proposed method is capable of synthesizing vivid singing faces, qualitatively and quantitatively better than the prior state-of-the-art.

关键词： face synthesis singing music generative adversarial network

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UNRAVELING THE EFFECTIVENESS OF THE K-NN ALGORITHM FOR IMBALANCED DATA IN RESEARCH TOPIC RECOMMENDATIONS

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Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University 2024年第1期59卷 271-282页

作者： Muludi, Kurnia Aisyah, Nonik Nama, Gigih Forda Lumbanraja, Favorisen R. Informatics & Business Institute Darmajaya Bandar Lampung35142 Indonesia Computer Science Department Faculty of Science The University of Lampung Bandar Lampung Indonesia Informatics Engineering Department Faculty of Science The University of Lampung Bandar Lampung Indonesia

This study addresses the challenge of selecting research topics for undergraduate students, focusing on computer science, by evaluating a recommendation model based on the k-Nearest Neighbor algorithm (kNN). The objective is to enhance the accuracy of research topic recommendations in the presence of imbalanced data. The methodology involves data cleaning, transformation, and correlation-based feature selection, with a particular focus on addressing missing values and optimizing the feature set. The Synthetic Minority Over-Sampling Technique (SMOTE) is employed to balance the dataset. The model development includes a comprehensive analysis of various k values, leading to the identification of k=3 with Manhattan distance as the optimal configuration, achieving an accuracy of 82%. The experiment explores different training data proportions, revealing that a 90:10 ratio yields the highest accuracy. This study incorporates a Grid Search technique for hyperparameter tuning, highlighting the importance of selecting appropriate distance metrics and nearest neighbor values. The system implementation is presented with user and administrator interfaces. Findings indicate varying model performance across research categories, emphasizing the need for category-specific evaluation metrics. The study concludes with a discussion on the significance of the results, which contribute to the improvement of research topic recommendation systems for students through the integration of advanced machine learning techniques. © 2024 by the authors.

关键词： Recommender systems

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Hybrid zk-STARK and zk-SNARK Framework for Privacy-Preserving Smart Contract Data Feeds

Hybrid zk-STARK and zk-SNARK Framework for Privacy-Preservin...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Nassar, Seif T. Hamdy, Abeer Nagaty, Khaled Department of Computer Networks Faculty of Informatics and Computer Science Cairo Egypt

ISBN: (纸本)9798350367560

Decentralized applications (DApps) are increasingly using off-chain data, yet growing concerns about data privacy hinder their widespread adoption. Zero-knowledge proofs (ZKPs) have emerged as a solution to this problem. This paper proposes a novel hybrid framework that combines Zero-Knowledge Scalable Transparent Arguments of Knowledge (zk-STARKs) and Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs) to deliver an efficient, scalable, quantum-resistant, and privacy-preserving ZKP system. The framework employs zk-STARKs' ability to handle large computations with quantum resistance and zk-SNARKs' succinct proofs and fast verification. The hybrid framework utilizes advanced arithmetization techniques to balance scalability, privacy, and security, including Algebraic Intermediate Representation (AIR) for zk-STARKs and Rank-1 Constraint Systems (RlCS) or PLONKish constraints for zk-SNARKs. The framework uses the Kate-Zaverucha-Goldberg (KZG) polynomial commitment scheme for reliability and transparency and eliminates trusted setup by using Discrete-logarithm-based Argument of Recursive Knowledge (DARK) commitments and Poseidon hashing. This paper details the construction of the hybrid proof system, analyzes its complexity, and explores its potential applications. The framework solves smart contract data feed limitations, security, efficiency, and scalability while prioritizing privacy. © 2024 IEEE.

关键词： Differential privacy

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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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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

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computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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Evolutionary optimization of Yagi–Uda antenna design using grey wolf optimizer

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Neural Computing and Applications 2025年第6期37卷 4155-4183页

作者： Braik, Malik Sheta, Alaa Aljahdali, Sultan El-Hefnawi, Fatma Al-Hiary, Heba Elashmawi, Walaa H. Computer Science Department Al-Balqa Applied University Salt Jordan Department of Computer Science Southern Connecticut State University New HavenCT United States Computer Science Department Taif University Taif Saudi Arabia Microwave Department Electronics Research Institute Giza Egypt Department of Computer Information System Al-Balqa Applied University Salt Jordan Department of Computer Science Faculty of Computers & Informatics Suez Canal University Ismailia Egypt Department of Computer Science Faculty of Computer Science Misr International University Cairo Egypt

Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This research explored the grey wolf optimizer (GWO) to find the ideal configuration for a six-element Yagi–Uda antenna. The GWO algorithm adjusted the lengths of the antenna wires and the spacings between them. The goal was to maximize the antenna’s ability to transmit signals (throughput gain). Optimal antenna selection relies on various parameters, including gain, bandwidth, impedance matching, frequency, side-lobe levels, etc. The optimization of a six-element Yagi–Uda antenna presents a challenging engineering design problem due to its multimodal and nonlinear nature. Achieving optimal performance hinges on the intricate interplay between the lengths of the constituent elements and the spacing configurations. To this end, a multiobjective function was adopted to design this antenna. The performance of several meta-heuristic algorithms, including genetic algorithms, biogeography-based optimization, simulated annealing, and grey wolf optimizer, was compared. The GWO-based approach has performed better than its competitors. This optimized antenna design based on GWO reported a gain of 14.21 decibel. Therefore, the GWO-based method optimizes antennas that can be further investigated for other antenna design problems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Simulated annealing

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