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8th International Conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Yang, Dapeng Cao, Longhan Pan, Lifang Xie, Shutong School of Computer Engineering Jimei University Fujian Xiamen China School of Science Jimei University Fujian Xiamen China

ISBN: (纸本)9798400710094

In recent years, blended learning models combining online and offline instructional methods have been widely implemented in higher education institutions. Online learning platforms accumulate a large number of fine-grained records of students’ learning behavior, offering an excellent opportunity to analyze students’ learning behavior. Firstly, a year’s worth of student behavior log data was collected from the "Java Language Programming" blended learning course at a college. Secondly, we propose an interpretable grade prediction method based on the error correcting output code (ECOC) with Shapley theory. This approach employs Recursive Feature Elimination (RFE) to select features from the original features, and creates an ECOC classification matrix to predict student performance. Finally, the Shapley values for each feature were used to conduct an explainable analysis of the prediction results. Experimental results show that the proposed method outperforms other classical classification algorithms. This study provides a visual and educational analysis of the major factors affecting academic performance, thereby offering educational administrators more intuitive decision support. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Rethinking Random Walk in Graph Representation Learning 48

Rethinking Random Walk in Graph Representation Learning

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zeng, Dingyi Chen, Wenyu Liu, Wanlong Zhou, Li Qu, Hong University of Electronic Science and Technology of China School of Computer Science and Engineering China

ISBN: (纸本)9781728163277

With the help of deep learning, Graph Neural Networks (GNNs) have achieved remarkable progress in various fields. However, due to the limitation of the message passing mechanism of GNNs, there exists an upper limit on its expressiveness. Some high-order GNNs have achieved good results in expressiveness, but they also have shortcomings in complexity and real-world performance. In this paper, we attempt to provide a graph neural network architecture that simultaneously addresses expressiveness, complexity and real-world performance. To this end, we propose Spatially constrained Random walk diffusion structural Encoding (SRE) to encode structural information and can be used for any GNN under our architecture. Our extensive and diverse experiments on datasets of different types and sizes demonstrate the superior expressiveness and state-of-the-art performance of our architecture on real-world tasks. © 2023 IEEE.

关键词： Graph neural networks

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Challenges of Using Pre-trained Models: The Practitioners' Perspective 31

Challenges of Using Pre-trained Models: The Practitioners' P...

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Tan, Xin Li, Taichuan Chen, Ruohe Liu, Fang Zhang, Li School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Economics and Management Beihang University Beijing China

ISBN: (纸本)9798350330663

The challenges associated with using pre-trained models (PTMs) have not been specifically investigated, which hampers their effective utilization. To address this knowledge gap, we collected and analyzed a dataset of 5,896 PTM-related questions on Stack Overflow. We first analyze the popularity and difficulty trends of PTM-related questions. We find that PTM-related questions are becoming more and more popular over time. However, it is noteworthy that PTM-related questions not only have a lower response rate but also exhibit a longer response time compared to many well-researched topics in software engineering. This observation emphasizes the significant difficulty and complexity associated with the practical application of PTMs. To delve into the specific challenges, we manually annotate 430 PTM-related questions, categorizing them into a hierarchical taxonomy of 42 codes (i.e., leaf nodes) and three categories. This taxonomy encompasses many PTM prominent challenges such as fine-tuning, output understanding, and prompt customization, which reflects the gaps between current techniques and practical needs. We discuss the implications of our study for PTM practitioners, vendors, and educators, and suggest possible directions and solutions for future research. © 2024 IEEE.

关键词： Taxonomies

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Reliability and Incentive of Performance Assessment for Decentralized Clouds

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Journal of computer science & Technology 2022年第5期37卷 1176-1199页

作者： Jiu-Chen Shi Xiao-Qing Cai Wen-Li Zheng Quan Chen De-Ze Zeng Tatsuhiro Tsuchiya Min-Yi Guo Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Computer Science China University of GeosciencesWuhan 430074China Graduate School of Information Science and Technology Osaka UniversityOsaka 565-0871Japan

Decentralized cloud platforms have emerged as a promising paradigm to exploit the idle computing resources across the Internet to catch up with the ever-increasing cloud computing *** any user or enterprise can be the cloud provider in the decentralized cloud,the performance assessment of the heterogeneous computing resources is of vital ***,with the consideration of the untrustworthiness of the participants and the lack of unified performance assessment metric,the performance monitoring reliability and the incentive for cloud providers to offer real and stable performance together constitute the computational performance assessment problem in the decentralized *** this paper,we present a robust performance assessment solution RODE to solve this *** mainly consists of a performance monitoring mechanism and an assessment of the claimed performance(AoCP)*** performance monitoring mechanism first generates reliable and verifiable performance monitoring results for the workloads executed by untrusted cloud *** on the performance monitoring results,the AoCP mechanism forms a unified performance assessment metric to incentivize cloud providers to offer performance as *** extensive experiments,we show RODE can accurately monitor the performance of cloud providers on the premise of reliability,and incentivize cloud providers to honestly present the performance information and maintain the performance stability.

关键词： decentralized cloud computing robust performance assessment trusted execution environment(TEE)

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Comparative Study of Different Optical Character Recognition Models on Handwritten and Printed Medical Reports

Comparative Study of Different Optical Character Recognition...

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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Kumar, Anand Singh, Pardeep Lata, Kusum National Institute of Technology Hamirpur Department of Computer Science and Engineering Hamirpur India Sharda University Department of Computer Science and Engineering School of Engineering and Technology India

ISBN: (纸本)9798350397208

Text may be extracted from an image using a method called optical character recognition (OCR). An OCR is mostly used to translate existing paper documents or image data into editable documents. To create an OCR, a large variety of algorithms are needed, and it essentially functions in two stages, such as character and word detection. To retain a document's structure while using a more advanced technique, an OCR additionally focuses. The advancement in OCR has been very promising lately. Different tools have arrived in the market ranging from open-source tools PyTesseract to big established companies releasing their own OCR tools like Amazon's AWS Textract and Google's Google Vision. The accuracy of modern OCR techniques like AWS Textract, Google Vision, and updated versions of Pytesseract haven't been analyzed for different datasets. For complex datasets like a CBC report, there are various parameters and fields in a tabular and unstructured fashion. Every algorithm has a different approach to extracting the text from the image, so it becomes difficult to determine how accurate these models are on such datasets. Based on 2 different datasets, one handwritten and the other printed CBC images, this study performed analysis using the above 3 models by building an end-to-end pipeline to retrieve the necessary fields, thus making it feasible to access the accuracy of these models. Upon testing these models' accuracy, the study concluded that the AWS Textract model was the highest-performing model for both datasets, achieving an accuracy of 95% for the printed dataset and 89.42% for the handwritten IAM dataset after preprocessing. © 2023 IEEE.

关键词： Blood

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Transfer Learning based Analysis of Chest X-rays for Accurate Lung Disease Detection and Interpretation 3

Transfer Learning based Analysis of Chest X-rays for Accurat...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Perla, Someswari Veledendi, Sudhakar Jaiswal, Pooja Muppidi, Satish Mandala, Jyothi Maram, Balajee Gmr Institute of Technology Department of Cse - Ai&ml Andhra Pradesh Rajam India Sr University School of Computer Science and Artificial Intelligence Warangal India Galgotias University India Gmr Institute of Technology Departnent of Computer Science and Engineering Rajam India University Department of Computer Science and Engineering Banglore India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798331515911

This is a research paper based on a transfer learning approach with a primary aim at the analysis of chest Xrays for accurate detection and interpretation of lung diseases. The proposed method relies heavily on the use of pretrained deep learning models to enhance diagnostic accuracy and reduce the time and computational resources taken during training. Applying transfer learning to a large chest X-ray dataset, the model successfully detects key patterns associated with common lung diseases, such as pneumonia and tuberculosis. The manuscript encompasses data preprocessing, model finetuning, and performance evaluation and demonstrates huge improvements over the traditional methods both in terms of accuracy and interpretability. It has been experimentally proven that the model is competent enough to provide localization of disease areas, as it can be visualized through heatmaps obtained from predictions, which might further help the radiologists perform their diagnosis tasks. This work advocates for medical imaging automation for the early and efficient detection of lung disease. © 2025 IEEE.

关键词： Lung cancer

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An Enhanced Equilibrium Optimizer for Solving Optimization Tasks

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computers, Materials & Continua 2023年第11期77卷 2385-2406页

作者： Yuting Liu Hongwei Ding Zongshan Wang Gaurav Dhiman Zhijun Yang Peng Hu School of Information Science and Engineering Yunnan UniversityKunming650500China Department of Electrical and Computer Engineering Lebanese American UniversityByblos1401Lebanon University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali140413India Department of Computer Science and Engineering Graphic Era(Deemed to be University)Dehradun248002India Research and Development Department Youbei Technology Co.Ltd.Kunming650500China

The equilibrium optimizer(EO)represents a new,physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass *** its innovative foundation,the EO exhibits certain limitations,including imbalances between exploration and exploitation,the tendency to local optima,and the susceptibility to loss of population *** alleviate these drawbacks,this paper introduces an improved EO that adopts three strategies:adaptive inertia weight,Cauchy mutation,and adaptive sine cosine mechanism,called ***,a new update formula is conceived by incorporating an adaptive inertia weight to reach an appropriate balance between exploration and ***,an adaptive sine cosine mechanism is embedded to boost the global exploratory ***,the Cauchy mutation is utilized to prevent the loss of population diversity during *** validate the efficacy of the proposed SCEO,a comprehensive evaluation is conducted on 15 classical benchmark functions and the CEC2017 test *** outcomes are subsequently benchmarked against both the conventional EO,its variants,and other cutting-edge metaheuristic *** comparisons reveal that the SCEO method provides significantly superior results against the standard EO and other *** addition,the developed SCEO is implemented to deal with a mobile robot path planning(MRPP)task,and compared to some classical metaheuristic *** analysis results demonstrate that the SCEO approach provides the best performance and is a prospective tool for MRPP.

关键词： Metaheuristic algorithms equilibrium optimizer Cauchy mutation robot path planning

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A Review on Geo-location-Based Authentication with Various Lossless Compression Techniques 2nd

A Review on Geo-location-Based Authentication with Various L...

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2nd International Conference on Information Technology, InCITe 2022

作者： Kumar, Vivek Singh, Gursharan Singh, Iqbal School of Computer Science and Engineering Lovely Professional University Jalandhar Punjab Phagwara India School of Computer Science and Engineering Modern Group of Colleges Punjab Mukerian India

ISBN: (纸本)9789811973451

Data transactions on the Internet are rising every day, posing a significant security concern. Consequently, the development of techniques such as steganography and cryptography were required. These approaches change the raw data into a different format or into an unreadable format so that it can be safeguarded from attack. The attackers, on the other hand, are keeping up with the times. An old technique of geo-location is also a potent defender against attack of real-time information. It helps in providing a safe delivery of data at remote location. As a result, for increased security, numerous techniques combining these notions have been created. A review of several compression techniques and combinations of these and geo-location approaches is presented here. The complexity may degrade the quality of result for which Huffman is used which reduce the redundancy. Public-key cryptography algorithm can be used through which we can share key with the receiver. Algorithms like RSA and elliptical curve cryptosystem are such potent types. The review founds that a combined algorithm with authentication can make much composite and strong way to protect the data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Steganography

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ViT-SENet-Tom: machine learning-based novel hybrid squeeze–excitation network and vision transformer framework for tomato fruits classification

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Neural Computing and Applications 2025年第9期37卷 6583-6600页

作者： Swapno, S M Masfequier Rahman Nobel, S. M. Nuruzzaman Islam, Md Babul Bhattacharya, Pronaya Mattar, Ebrahim A. Department of CSE Bangladesh University of Business and Technology Dhaka1216 Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University West Bengal Kolkata India Robotics - Cybernetic College of Engineering University of Bahrain Manama Bahrain

Tomatoes are essential fruits in numerous nations for their vast demand. It is very important to maintain the freshness of tomatoes. One of the primary challenges in the recent culinary landscape is accurately identifying healthy tomatoes while effectively eliminating damaged or rejected ones. Existing approaches employ various strategies for categorizing tomato fruit, but they often suffer from inaccuracies, slow detection, and suboptimal performance. Thus, motivated by this gap, in this paper, we propose a novel machine learning (ML) framework, ViT-SENet-Tom, which is a hybrid vision transformer (ViT) model with squeeze and excitation (SENet) block network for fast, accurate, and efficient tomato fruit classification. The framework works on three tomato classes, respectively, the ripe, unripe, and reject. In developing the proposed model, we utilized advanced and newly designed layers and functions. This integration created a more complex and sophisticated neural network, significantly enhancing efficiency and contributing to the model’s novelty. Our chosen dataset was small initially, but we implemented augmentation techniques to increase its size. This approach made our system more reliable, efficient, and effective. The hybrid ViT-SENet framework employs encoders and self-attention networks with squeeze and excitation channel functions to allow precise, robust, fast, and efficient tomato classification. In simulation, the framework achieves a training accuracy of 99.87% and validation accuracy of 93.87%, indicating the precise classification of tomatoes. Besides, this work tests accuracy using fivefold cross-validation. The highest accuracy seen at fold-5 is 99.90%. These testing results demonstrate the efficacy of the proposed framework in real-deployment scenarios. The implementation has the potential to provide enhanced and more sustainable food security and safety in future. © The Author(s) 2025.

关键词： Fruits

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Leveraging EfficientNet-B3 with Advanced Fine-Tuning for Precise Breast Cancer Classification 16

Leveraging EfficientNet-B3 with Advanced Fine-Tuning for Pre...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： George, Geomol Anusuya, S. Simats School of Engineering Department of Electronics and Communication Chennai India Simats School of Engineering Department of Computer Science Chennai India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505264

This study explores the use of the pre-trained EfficientNet-B3 model for classifying breast cancer images into benign, malignant, and normal categories. The model was finetuned with layer-specific adjustments, advanced regularization techniques, and comprehensive layer fine-tuning to enhance its ability to detect complex patterns. The results demonstrated exceptional performance, with benign cases achieving a precision of 98%, recall of 94%, and F1 score of 96%;malignant cases with precision of 90%, recall of 96%, and F1 score of 93%;and normal cases showing precision of 94%, recall of 96%, and F1 score of 95%. The model also achieved 99.84% training accuracy, 0.2964 training loss, and 95.25% test accuracy with a loss of 0.4333. These findings underscore the potential of this advanced EfficientNet-B3-based method to improve diagnostic accuracy and support timely, reliable treatment decisions in breast cancer care. © 2024 IEEE.

关键词： Ultrasonic imaging

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