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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

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Dictionary Attention-Weighted Cross-Domain Contrastive Learning for Remote Sensing Image Change Detection

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Data Intelligence 2024年第4期6卷 893-908页

作者： Tian Wei Youfa Liu Rui Zhao School of Computer Science Wuhan UniversityWuhan430072China Faculty of Electrical Engineering and Computer Science Ningbo University315211China

Significant progress has been made in remote sensing image change detection due to the rapid development of Deep Learning techniques. Convolutional neural networks(CNNs) have become foundational models in this field. Previous works on remote sensing image change detection has utilized domain adaptation methods, achieving promising predictive performance. However, the transferable knowledge between source and target domain has not been fully exploited. In this paper, we propose a novel cross-domain contrastive learning approach for remote sensing image change detection, which correlates source and target domain using contrastive principles. Specifically, we introduce a transferable cross-domain Dictionary Learning scheme where a shared dictionary between the source and target domains generates sparse representations. Based on these representations, we compute attention weights and propose an attention-weighted contrastive loss to enhance knowledge transfer between source and target domains. Experiments demonstrate the effectiveness of the proposed methods on public remote sensing image change detection datasets.

关键词： Change detection Domain adaptation Contrastive learning Dictionary Learning Attention mechanism

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An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder

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computer Modeling in engineering & sciences 2023年第2期134卷 909-926页

作者： Passent El-kafrawy Maie Aboghazalah Abdelmoty M.Ahmed Hanaa Torkey Ayman El-Sayed School of Information Technology and Computer Science Nile UniversityGiza12677Egypt Math and Computer Science Department Faculty of ScienceMenoufia UniversityMenoufia32511Egypt Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common *** ofmedical images is very important to secure patient *** these images consumes a lot of time onedge computing;therefore,theuse of anauto-encoder for compressionbefore encodingwill solve such a *** this paper,we use an auto-encoder to compress amedical image before encryption,and an encryption output(vector)is sent out over the *** the other hand,a decoder was used to reproduce the original image back after the vector was received and *** convolutional neural networks were conducted to evaluate our proposed approach:The first one is the auto-encoder,which is utilized to compress and encrypt the images,and the other assesses the classification accuracy of the image after decryption and *** hyperparameters of the encoder were tested,followed by the classification of the image to verify that no critical information was lost,to test the encryption and encoding *** this approach,sixteen hyperparameter permutations are utilized,but this research discusses three main cases in *** first case shows that the combination of Mean Square Logarithmic Error(MSLE),ADAgrad,two layers for the auto-encoder,and ReLU had the best auto-encoder results with a Mean Absolute Error(MAE)=0.221 after 50 epochs and 75%classification with the best result for the classification *** second case shows the reflection of auto-encoder results on the classification results which is a combination ofMean Square Error(MSE),RMSprop,three layers for the auto-encoder,and ReLU,which had the best classification accuracy of 65%,the auto-encoder gives MAE=0.31 after 50 *** third case is the worst,which is the combination of the hinge,RMSprop,three layers for the auto-encoder,and ReLU,providing accuracy of 20%and MAE=0.485.

关键词： Auto-encoder cloud image encryption IoT healthcare

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Number Plate Recognition Using OpenCV 6

Number Plate Recognition Using OpenCV

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Roopa Chandrika, R. Vanitha, K. Thahaseen, Aafiya Chandramma, R. Neerugatti, Vikram Mahesh, T.R. Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Al-Ameen Engineering College Department of Computer Science and Engineering Erode India Bangalore India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350306613

The advancement of automated number plate recognition (ANPR) systems has garnered noteworthy attention in recent times owing to their diverse applications across multiple domains, including traffic management, parking management, and law enforcement. This paper presents an innovative ANPR method using OpenCV, an open-source computer vision library. Developing ANPR systems is a great fit for OpenCV since it offers a flexible platform for image processing and computer vision tasks. The suggested method effectively detects and recognizes number plates from images and video streams by utilizing OpenCV's capabilities for feature extraction, image preprocessing, and machine learning. Character segmentation, optical character recognitlon (OCR), and licence plate localization are important stages in the process. The ANPR system's accuracy and effectiveness are increased by using these strategies. A variety of real-world datasets, including different lighting conditions, camera angles, and vehicle types, are used in extensive experiments to assess the system's performance. The outcomes show how well the OpenCV-based ANPR system performs in tasks requiring license plate recognition, attaining high accuracy and resilience. Additionally, covered in the paper are possible uses for the ANPR system in security systems, vehicle tracking, and traffic monitoring. To make sure the system is workable in a variety of situations, scalability and real-time implementation are also taken into account. © 2024 IEEE.

关键词： machine learning number plate OpenCV recognition

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Explainable AI model for PDFMal detection based on gradient boosting model

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Neural Computing and Applications 2024年第34期36卷 21607-21622页

作者： Elattar, Mona Younes, Ahmed Gad, Ibrahim Elkabani, Islam Department of Computer Science Faculty of Science Tanta University Tanta31527 Egypt Department of Mathematics and Computer Science Faculty of Science Alexandria University Alexandria21511 Egypt Faculty of Computer Science and Engineering Al Alamein International University Alamein51718 Egypt

Portable document formats (PDFs) are widely used for document exchange due to their widespread usage and versatility. However, PDFs are highly vulnerable to malware attacks, which pose significant security risks. Existing defense mechanisms often struggle to effectively detect and mitigate these threats, highlighting the need for more robust solutions. This paper introduces a robust framework that uses advanced tree-based ensemble models to detect malicious PDFs using the Evasive-PDFMal2022 dataset. The proposed model achieves a recall rate of 100%, an accuracy rate of 99.95%, and a fast inference time of 0.1723 s. Furthermore, the framework exhibits minimal false positive and false negative rates, ensuring a high level of precision in distinguishing between malicious and benign PDFs. Shapley additive explanations are used to improve the interpretability and reliability of the model’s predictions. The results highlight the effectiveness of the proposed model in improving PDF document security and addressing the challenges posed by malware attacks. © The Author(s) 2024.

关键词： Explainable artificial intelligence (XAI) Malicious PDF Malware detection Tree-based ensemble models

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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Comprehensive Overview of Gas and Bio-Sensors Enabling Artificial Intelligence in Healthcare 2

Comprehensive Overview of Gas and Bio-Sensors Enabling Artif...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Raut, Yashasvi Chaudhri, Shiv Nath Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra India Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra India

ISBN: (纸本)9798331528713

Gas and biosensors are crucial in the modern healthcare system, enabling non-invasive monitoring and diagnosis of various medical conditions. These sensors are used in various applications, including smart home health monitoring devices and critical care units. Common non-imaging sensor types include gas or VOC sensors, diagnostic ECG sensors, blood glucose sensors, pulse oximeters, blood pressure sensors, body temperature sensors, EEG sensors, accelerometers, and imaging sensors like X-rays, MRIs, CT scans, and ultrasounds. Healthcare-related sensors offer numerous advantages, including diagnosing and treating medical conditions, monitoring vital signs, enabling remote patient monitoring, personalized healthcare, and early detection and prevention. However, issues like data accessibility, privacy, comfort, security, and accuracy must be considered when installing sensors in homes. Efficient data sharing and analysis require devices, sensors, and platforms to communicate effectively. Real-time data processing is essential for prompt diagnosis and intervention. In this article, we have put it briefly: medical sensors are transforming the way we track, identify, and manage adverse health conditions. © 2024 IEEE.

关键词： Glucose sensors

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The Mass Growth Factor - Snowball Effects in Aircraft Design 83

The Mass Growth Factor - Snowball Effects in Aircraft Design

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83rd SAWE International Conference on Mass Properties engineering 2024

作者： Cheema, John Singh Faculty of Engineering and Computer Science Department of Automotive and Aeronautical Engineering

ISBN: (纸本)9781713898436

Purpose - This project work shows a literature survey, clearly defines the mass growth factor, shows a mass growth iteration, and derives an equation for a direct calculation of the factor (without iteration). Definite values of the factor seem to be missing in literature. To change this, mass growth factors are being calculated for as many of the prominent passenger aircraft as to cover 90% of the passenger aircraft flying today. The dependence of the mass gain factor on requirements and technology is examined and the relation to Direct Operating Costs (DOC) is pointed out. Methodology - Calculations start from first principles. Publically available data is used to calculate a list of mass growth factors for many passenger aircraft. Using equations and the resulting relationships, new knowledge and dependencies are gained. Findings - The mass growth factor is larger for aircraft with larger operating empty mass ratio, smaller payload ratio, larger specific fuel consumption (SFC), and smaller glide ratio. The mass growth factor increases much with increasing range. The factor depends on an increase in the fixed mass, so this is the same for the payload and empty mass. The mass growth factor for subsonic passenger aircraft is on average 4.2, for narrow body aircraft 3.9 and for wide body aircraft (that tend to fly longer distance) 4.9. In contrast supersonic passenger aircraft show a factor of about 14. Practical implications - The mass growth factor has been revisited in order to fully embrace the concept of mass growth and may lead to a better general understanding of aircraft design. Social implications - A detailed discussion of flight and aircraft costs as well as aircraft development requires detailed knowledge of the aircraft. By understanding the mass growth factor, consumers can have this discussion with industry at eye level. Originality/value - The derivation of the equation for the direct calculation of the mass growth factor and the determination of the

关键词： Iterative methods

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DeepLeaf: an optimized deep learning approach for automated recognition of grapevine leaf diseases

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Neural Computing and Applications 2025年第15期37卷 8799-8823页

作者： Talaat, Fatma M. Shams, Mahmoud Y. Gamel, Samah A. ZainEldin, Hanaa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Faculty of Engineering Horus University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Plant diseases can cause severe losses in agricultural production, impacting food security and safety. Early detection of plant diseases is crucial to minimize crop damage and ensure agricultural sustainability. Manual monitoring is often impractical due to the complexity and time involved, making automated disease recognition essential. This study presents a new Plant Disease Detection Algorithm (PDDA) called DeepLeaf focused on identifying four common grapevine diseases: leaf blight, black rot, stable, and black measles. The PDDA integrates three key modules: an Image Preprocessing Module, a Feature Extraction Module, and an Optimized Convolutional Neural Network (OCNN)-based Classification Module. The OCNN forms the core of the classification system, with its hyperparameters fine-tuned using fuzzy optimization to enhance performance. Preprocessing techniques are applied to analyze diseased leaves, and a logistic regression algorithm is used to downsample the features for better analysis. The CNN is trained on images from the Plant Village dataset, allowing it to detect and classify grapevine leaf diseases accurately. The proposed model's efficiency in the automated diagnosis of grapevine diseases is demonstrated by its remarkable 99.7% accuracy rate. This high accuracy indicates that the PDDA may help with more effective and scalable plant disease monitoring, which will ultimately allow better agricultural practices. © The Author(s) 2025.

关键词： CNN Detection of leaf disease Extracting features Plant disease

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Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

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computers, Materials & Continua 2023年第3期74卷 5705-5721页

作者： Zulqar Nain B.Shahana Shehzad Ashraf Chaudhry P.Viswanathan M.S.Mekala Sung Won Kim Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siKorea Department of Computer Science and Engineering KLEFIndia Department of Computer Engineering Faculty of Engineering and ArchitectureIstanbul Gelisim UniversityIstanbul34310Turkey School of Computer Science and Engineering VIT UniversityVelloreIndia

Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System(CPS)*** devices enable limited computational capacity and energy availability that hamper end user *** designed a novel performance measurement index to gauge a device’s resource *** examination addresses the offloading mechanism issues,where the end user(EU)offloads a part of its workload to a nearby edge server(ES).Sometimes,the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources(such as storage and computation).The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided *** this regard,an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float ***,the development of a reinforcement learning-based flexible x-scheduling(RFXS)approach resolves the service offloading issues,where x=service/resource for producing the low latency and high performance of the *** approach to the theoretical bound and computational complexity is derived by formulating the system efficiency.A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set ofmeasurements,as well as the behavioural association rate and adulation *** system managed an average 0.89%of the service offloading rate,with 39 ms of delay over complex scenarios(using three servers with a 50%service arrival rate).The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty,and is suitable for simulating heterogeneous CPS frameworks.

关键词： Fog computing task allocation measurement models feasible node selection methods performance metrics

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