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A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa Ahmed Mohammed Alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme

关键词： Software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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Multiattribute Deep CNN-Based Approach for Detecting Medicinal Plants and Their Use for Skin Diseases

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 710-724页

作者： Dalvi, Prachi Kalbande, Dhananjay R. Rathod, Surendra Singh Dalvi, Harshal Agarwal, Amey Bharatiya Vidya Bhavans Sardar Patel Institute of Technology Department of Computer Engineering Mumbai400058 India Fr. Conceicao Rodrigues College of Engineering Department of Electronics and Computer Science Mumbai400050 India Dwarkadas J Sanghvi College of Engineering Department of Information Technology Mumbai400056 India

Skin health is a critical concern for humans, especially in geographical areas where environmental conditions and lifestyle factors adversely affect their condition, leading to a prevalence of skin diseases. This issue is exacerbated in rural regions, like parts of India, where a notable dermatologist shortage exists, leading to overlooked skin diseases. In response, the use of medicinal plants for dermatological purposes has been a longstanding tradition. However, traditional plant identification often relies on a single attribute, such as leaves or flowers, which can be unreliable due to seasonal variations. This article introduces a novel approach for accurately identifying medicinal plants using a multiattribute deep convolutional neural network. This approach aims to bridge the gap in healthcare access by empowering individuals to recognize and utilize these plants effectively. Our objective is to develop a robust deep CNN model trained on a diverse dataset of images encompassing leaves, trunks, and seeds of medicinal plants associated with skin health. Our findings demonstrate that the model achieves high accuracy in plant identification, effectively addressing the limitations of single-attribute methods. This research not only contributes to the field of medicinal plant classification but also empowers individuals to make informed decisions about their skin health while preserving valuable traditional knowledge. © 2024 IEEE.

关键词： Medicinal chemistry

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An Optimal Weighted Ensemble of 3D CNNs for Early Diagnosis of Alzheimer’s Disease

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SN computer science 2024年第2期5卷 252页

作者： Dharwada, Sriram Tembhurne, Jitendra Diwan, Tausif Department of Computer Science and Automation Indian Institute of Science Bangalore India Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur 441108 India

Alzheimer disease (AD) is a chronic neurological disorder in which the loss of brain cells causes dementia. Early and accurate diagnosis of AD will lead to better treatment of the disease before irreversible brain damage has been occurred. This paper proposes the classification of Alzheimer's disease using 3D structural Magnetic Resonance Imaging (sMRI) images through 3D convolutional neural networks (CNNs). Most existing methods utilizing 3D subject-level CNNs for Alzheimer's disease classification design a single model which relies on a very large training dataset for improved generalization. Herein, we address this issue through 3D transfer learning which makes use of knowledge gained from a pre-trained task. We train 3D versions of five classical 2D image classification architectures—ResNet, ResNeXt, SE-ResNet, SE-ResNeXt, and SE-Net—by initializing each model with pre-trained weights from their 2D counterparts, and combine their predictions through a weighted average method. The weights assigned to each model of the ensemble are optimized to achieve a performance better than any single 3D CNN model. With a relatively smaller training dataset, the proposed model obtains 97.27%, 82.33%, 90.41%, 84.22%, 84.26%, and 77.1% accuracies for the Alzheimer’s disease (AD) versus cognitively normal (CN), early mild cognitive impairment (EMCI) versus CN, late mild cognitive impairment (LMCI) versus CN, EMCI versus AD, LMCI versus AD, and EMCI versus LMCI classification tasks, outperforming current state-of-the-art methods, and indicating the effectiveness of our proposed model. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Alzheimer’s disease Convolutional neural networks Ensemble learning Transfer learning

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SciAR Labs: Exploring science Through Augmented Reality

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SN computer science 2025年第5期6卷 1-14页

作者： Sondagar, Bhavisha Sheth, Dhruvi Kachare, Kaustubh Pawar, Renuka Kalbande, Dhananjay Department of Information Technology Sardar Patel Institute of Technology Mumbai India Department of Computer Science and Engineering Sardar Patel Institute of Technology Mumbai India

SciAR Labs presents a science education Augmented Reality (AR) application in response to the changing landscape of educational technologies. Thorough studies of the research demonstrate how AR can improve student motivation, engagement, and performance. Research highlights the benefits of augmented reality (AR) in social learning environments, special education, and distant learning, but it also notes drawbacks, including expensive hardware, poor content quality, and teacher shortages. In order to overcome these issues, SciAR Labs is developing an engaging and user-friendly augmented reality experience for science investigations. The initiative aims to break through obstacles in conventional learning methods by utilizing AR’s advantages. It’s leading the way in integrating technology into education with the goal of improving student learning and adding to the conversation about augmented reality’s potential in the classroom. By combining scientific material with user-friendly interfaces, it hopes to make an influence on science education and encourage students’ interest and understanding. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Augmented reality (AR) Education science labs Unity User experience Virtual labs Vuforia

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MA-MFIF: When misaligned multi-focus Image fusion meets deep homography estimation

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Multimedia Tools and Applications 2025年第12期84卷 10877-10898页

作者： Zhao, Baojun Luo, Fei Fuentes, Joel Ding, Weichao Gu, Chunhua School of Information Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Department of Computer Science and Information Technologies Universidad del Bio-Bio Chillán3780000 Chile

Multi-focus image fusion is a technique that combines multiple out-of-focus images to enhance the overall image quality. It has gained significant attention in recent years, thanks to the advancements in deep learning. However, one of the persistent challenges in this field is the processing of misaligned data, which can negatively impact the fusion results. To overcome this problem, a novel fusion framework with pre-registration is proposed for the fusion of misaligned multi-focus images. For pre-registration, content-aware deep homography estimation is used, which performs transfer learning on a real multi-focus image dataset to adapt to registration under defocused conditions. For fusion, a fusion module with dual-branch feature interaction is utilized to avoid invalid feature fusion and trained on real light field dataset to achieve better fusion performance. Qualitative and quantitative experimental results show that the proposed method has a 2-3 percentage point improvement in multiple evaluation metrics compared to existing advanced registration and fusion methods, and a maximum improvement of 4.83 percentage points in fusion performance when tested independently on the Lytro dataset. Additionally, We find that the value of the Qcv metric is greatly influenced by the alignment status of the input images, leading to its inability to reflect the fusion quality of aligned images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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An Enhanced Aspect-Based Sentiment Analysis Model Based on RoBERTa For Text Sentiment Analysis

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 193-202页

作者： Chauhan, Amit Sharma, Aman Mohana, Rajni Jaypee University of Information Technology HP Solan173234 India Department of Computer Science Amity School of Engineering and Technology Amity University Punjab Punjab Mohali140306 India

Using an aspect-based sentiment analysis task, sentiment polarity towards specific aspect phrases within the same sentence or document is to be identified. The process of mechanically determining the underlying attitude or opinion indicated in the text is known as sentiment analysis. One of the most important aspects of natural language processing is sentiment analysis. The RoBERTa transformer model was pretrained in a self-supervised manner using a substantial corpus of English data. This means it was pretrained solely with raw texts and an algorithmic process to generate inputs and labels from those texts. No human labelling was involved, allowing it to utilise a vast amount of publicly available data. The authors of this work provide a thorough investigation of aspect-based sentiment analysis with RoBERTa. The RoBERTa model and its salient characteristics are outlined in this work, followed by an analysis of the model's optimisation by the authors for aspect-based sentiment analysis. The authors compare the RoBERTa model with other state-of-the-art models and evaluate its performance on multiple benchmark datasets. Our experimental results show that the RoBERTa model is effective for this important natural language processing task, outperforming competing models on sentiment analysis tasks. Based on the SemEval-2014 variant benchmarking datasets, the restaurant and laptop domains have the highest accuracy, scoring 92.35 % and 82.33 %, respectively. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Natural language processing systems

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MAIDS: malicious agent identification-based data security model for cloud environments

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Cluster Computing 2024年第5期27卷 6167-6184页

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Singh, Ashutosh Kumar Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan School of Computer Science and Engineering The University of Aizu Aizuwakamatsu965-0006 Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland Department of Computer Applications SRM Institute of Science & ampTechnology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad201204 India Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent’s behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system’s sec

关键词： Forecasting

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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Elevating IoT healthcare security using ProSRN and ICOM methodologies for effective threat management

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International Journal of information technology (Singapore) 2025年 1-19页

作者： Sowjanya, Yemineni Gopalakrishnan, S. Kumar, R. Dinesh Department of Computer Science and Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai 603103 India Department of Information Technology Hindustan Institute of Technology and Science Tamil Nadu Chennai 603103 India Department of Computer Science and Engineering Bhoj Reddy Engineering College for Women Telangana Hyderabad 500059 India

IoT healthcare security is increasingly important, as the interconnectedness of medical devices in itself introduces a major vulnerability, given the impact on patient safety and data integrity. Past works in this domain have been afflicted by persistent issues of high false-positive rates, resource utilization inefficiency, and failure to adapt to rapidly changing threat spaces. The paper presents a novel framework that incorporates ProSRN, which provides the robustness in threat detection, and ICOM for dynamic weight estimation. ProSRN adopts a residual-connected stacking approach to reach high accuracy of detection while reducing the complexity in training and enhancing responsiveness in real time. It is further complemented by ICOM, which will then dynamically optimize network parameters to yield an adaptive IoT health security model with the capability of addressing evolving threats. Together, ProSRN and ICOM advance both detection and optimization significantly, enhancing the adaptiveness, efficiency, and security in IoT healthcare systems. This creates a concrete basis for developing resilient frameworks that can protect sensitive healthcare data against emerging threats. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： And deep learning Attack detection Classification Healthcare systems Internet of things (IoT) Optimization Security

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White-Box Penetration Testing for Hash Collision Attack on Web Applications 2nd

White-Box Penetration Testing for Hash Collision Attack on W...

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2nd International Conference on Security and information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Chou, Chao-Lung Chiang, Hung-Wei Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan Department of Computer Science and Information Engineering Chung Cheng Institute of Technology National Defense University Taichung Taiwan

ISBN: (纸本)9789819777853

Web applications have become essential tools for communication, commerce, and information dissemination in an era where the internet is pivotal in our daily lives. However, as the digital landscape continues to evolve, so do the threats that target web applications. Web application security ensures the integrity, availability, and confidentiality of online services. Hash collisions, though often considered a theoretical concern, can threaten web application security under specific circumstances. A hash collision occurs when two different inputs produce the same hash value when hashed. This paper utilizes a white-box penetration testing approach specifically targeting hash collision vulnerabilities in web applications. Using PHP as our example language, we create an experimental website to demonstrate privilege escalation by exploiting hash collision vulnerabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Black-box testing

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