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3-Multi ranked encryption with enhanced security in cloud computing

Digital Communications and Networks 2023年第2期9卷 313-326页

作者： YeEun Kim Junggab Son Reza M.Parizi Gautam Srivastava Heekuck Oh Major in Bio Artificial Intelligence Dept.of Computer Science and EngineeringHanyang UniversityAnsanRepublic of Korea Department of Computer Science Kennesaw State UniversityMariettaUSA Department of Software Engineering Kennesaw State UniversityMariettaUSA Department of Mathematics and Computer Science Brandon UniversityBrandonCanada Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Dept.of Computer Science and Engineering Hanyang UniversityAnsanRepublic of Korea

Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when *** refer to a model that satisfies all of the conditions a 3-multi ranked search ***,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation *** is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real *** this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security *** proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same ***,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different *** evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.

关键词： Searchable encryption Multi-keyword search Multiple data owners/users Ranked search Enhanced security

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Beyond Agile: NLP-Driven Quality Attributes Retrieval Using ChatGPT in software Development Strategies

Beyond Agile: NLP-Driven Quality Attributes Retrieval Using ...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Aamana Ain, Qurat Ul Nisa, Sardar Un NUML Department of Software Engineering Rawalpindi Pakistan NUML Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350349719

This study explores Agile software Development (ASD) methodologies' effectiveness in achieving quality software products. It investigates the underexplored application of ChatGPT as a Quality Attribute Extractor in ASD. Utilizing hard prompt techniques, ChatGPT extracts quality attributes from user stories without fine-Tuning, enhancing understanding through few-shot and zero-shot Prompt engineering. This innovative approach bridges NLP advancements with agile methodologies, facilitating efficient quality assessment in software development processes. © 2024 IEEE.

关键词： Bridge approaches

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Bias analysis of AI models for undergraduate student admissions

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Neural Computing and Applications 2025年第12期37卷 7785-7795页

作者： Van Busum, Kelly Fang, Shiaofen Department of Computer Science and Software Engineering Butler University IndianapolisIN United States Computing and Engineering Indiana University Luddy School of Informatics Indiana University Indianapolis IndianapolisIN United States

Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors Van Busum and Fang (Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 2023) to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a given student would be directly admitted into the School of science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed AI models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We also extended our analysis to show that the biases detected were persistent. Finally, we included several fairness metrics in our analysis and discussed the uses and limitations of these metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Students

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Visual News Ticker Surveillance Approach from Arabic Broadcast Streams

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computers, Materials & Continua 2023年第3期74卷 6177-6193页

作者： Moeen Tayyab Ayyaz Hussain Usama Mir M.Aqeel Iqbal Muhammad Haneef Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science Quaid-i-Azam UniversityIslamabad44000Pakistan Department of Computer Science Senior Member IEEEUniversity of WindsorN9B 3P4Canada Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan Department of Electrical Engineering Foundation University IslamabadIslamabad44000Pakistan

The news ticker is a common feature of many different news networks that display headlines and other *** ticker recognition applications are highly valuable in e-business and news surveillance for media regulatory *** this paper,we focus on the automatic Arabic Ticker Recognition system for the Al-Ekhbariya news *** primary emphasis of this research is on ticker recognition methods and storage *** that end,the research is aimed at character-wise explicit segmentation using a semantic segmentation technique and words identification *** proposed learning architecture considers the grouping of homogeneousshaped *** incorporates linguistic taxonomy in a unified manner to address the imbalance in data distribution which leads to individual ***,experiments with a novel ArabicNews Ticker(Al-ENT)dataset that provides accurate character-level and character components-level labeling to evaluate the effectiveness of the suggested *** proposed method attains 96.5%,outperforming the current state-of-the-art technique by 8.5%.The study reveals that our strategy improves the performance of lowrepresentation correlated character classes.

关键词： Arabic text recognition optical character recognition deep convolutional network SegNet LeNet

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Designing dataless neural networks for kidney exchange variants

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Neural Computing and Applications 2024年第35期36卷 22265-22275页

作者： Jena, Sangram K. Subramani, K. Velasquez, Alvaro Department of Computer Science and Software Engineering Miami University OxfordOH United States LDCSEE West Virginia University MorgantownWV United States Department of Computer Science University of Colorado Boulder BoulderCO United States

Kidney transplantation is vital for treating end-stage renal disease, impacting roughly one in a thousand Europeans. The search for a suitable deceased donor often leads to prolonged and uncertain wait times, making living donor transplants a viable alternative. However, approximately 40% of living donors are incompatible with their intended recipients. Therefore, many countries have established kidney exchange programs, allowing patients with incompatible donors to participate in "swap" arrangements, exchanging donors with other patients in similar situations. Several variants of the vertex-disjoint cycle cover problem model the above problem, which deals with different aspects of kidney exchange as required. This paper discusses several specific vertex-disjoint cycle cover variants and deals with finding the exact solution. We employ the dataless neural networks framework to establish single differentiable functions for each variant. Recent research highlights the framework’s effectiveness in representing several combinatorial optimization problems. Inspired by these findings, we propose customized dataless neural networks for vertex-disjoint cycle cover variants. We derive a differentiable function for each variant and prove that the function will attain its minimum value if an exact solution is found for the corresponding problem variant. We also provide proof of the correctness of our approach. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Combinatorial optimization

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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Recently emerging trends in big data analytic methods for modeling and combating climate change effects

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Energy Informatics 2024年第1期7卷 1-28页

作者： Ikegwu, Anayo Chukwu Nweke, Henry Friday Mkpojiogu, Emmanuel Anikwe, Chioma Virginia Igwe, Sylvester Agbo Alo, Uzoma Rita Software Engineering Department Veritas University Abuja Nigeria Computer Science Department David Umahi Federal University of Health Sciences Ebonyi State Uburu Nigeria Computer Science Department Alex Ekwueme Federal University Ebonyi Ndufu-Alike Nigeria Computer Science Department Ebonyi State University Ebonyi State Abakaliki Nigeria

Big climate change data have become a pressing issue that organizations face with methods to analyze data generated from various data types. Moreover, storage, processing, and analysis of data generated from climate change activities are becoming very massive, and are challenging for the current algorithms to handle. Therefore, big data analytics methods are designed for significantly large amounts of data required to enhance seasonal change monitoring and understand and ascertain the health risks of climate change. In addition, analysis of climate change data would improve the allocation, and utilisation of natural resources. This paper provides an extensive discussion of big data analytic methods for climate data analysis and investigates how climate change and sustainability issues can be analyzed through these approaches. We further present the big data analytic methods, strengths, and weaknesses, and the essence of analyzing big climate change using these methods. The common datasets, implementation frameworks for climate change modeling, and future research directions were also presented to enhance the clarity of these compelling climate change analysis challenges. This big data analytics method is well-timed to solve the inherent issues of data analysis and easy realization of sustainable development goals. © The Author(s) 2024.

关键词： Data Analytics

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PDC-ViT: source camera identification using pixel difference convolution and vision transformer

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Neural Computing and Applications 2025年第9期37卷 6933-6949页

作者： Elharrouss, Omar Akbari, Younes Almadeed, Noor Al-Maadeed, Somaya Khelifi, Fouad Bouridane, Ahmed Department of Computer Science and Software Engineering United Arab Emirates University Al Ain United Arab Emirates Department of Computer Science and Engineering Qatar University Doha Qatar Northumbria University Newcastle Newcastle upon Tyne United Kingdom University of Sharjah Sharjah United Arab Emirates

Source camera identification has emerged as a vital solution to unlock incidents involving critical cases like terrorism, violence, and other criminal activities. The ability to trace the origin of an image/video can aid law enforcement agencies in gathering evidence and constructing the timeline of events. Moreover, identifying the owner of a certain device narrows down the area of search in a criminal investigation where smartphone devices are involved. This paper proposes a new pixel-based method for source camera identification, integrating Pixel Difference Convolution (PDC) with a Vision Transformer network (ViT), and named PDC-ViT. While the PDC acts as the backbone for feature extraction by exploiting Angular PDC (APDC) and Radial PDC (RPDC). These techniques enhance the capability to capture subtle variations in pixel information, which are crucial for distinguishing between different source cameras. The second part of the methodology focuses on classification, which is based on a Vision Transformer network. Unlike traditional methods that utilize image patches directly for training the classification network, the proposed approach uniquely inputs PDC features into the Vision Transformer network. To demonstrate the effectiveness of the PDC-ViT approach, it has been assessed on five different datasets, which include various image contents and video scenes. The method has also been compared with state-of-the-art source camera identification methods. Experimental results demonstrate the effectiveness and superiority of the proposed system in terms of accuracy and robustness when compared to its competitors. For example, our proposed PDC-ViT has achieved an accuracy of 94.30%, 84%, 94.22% and 92.29% using the Vision dataset, Daxing dataset, Socrates dataset and QUFVD dataset, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolution

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ACDD: Automated COVID Detection using Deep Neural Networks

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IEIE Transactions on Smart Processing and Computing 2023年第6期12卷 518-525页

作者： Raza, Ghulam Musa Shoaib, Muhammad Kim, Byung-Seo Department of Software and Communications Engineering Hongik University Sejong Korea Republic of School of Electrical Engineering and Computer Science National University of Science and Technology Islamabad Pakistan

December 2019 witnessed the outbreak of a novel coronavirus, thought to have started in the Chinese city of Wuhan. The situation worsened owing to its quick spread across the globe, leading to a worldwide pandemic that became known as COVID-19. To suppress the pandemic, early detection of positive COVID-19 patients has become highly important. There is a lack of precise automated tool kits available for use in diagnosing medical conditions, so auxiliary diagnostic tools are in high demand. Important information about this virus can be extracted from X-ray images, which can be used in conjunction with advanced artificial intelligence. This study addresses the unavailability of physicians in remote areas, and the complex algorithm proposed in this study can find potential matches for patients in rural areas who need care. This could help to improve access to care for those who need it most. The purpose of this study is to develop a novel model that can automatically detect COVID-19 by utilizing chest X-ray images. The proposed model incorporates binary and multi-class classification and can be employed by radiologists for timely detection of the COVID-19 virus in an effective manner. © 2023 The Institute of Electronics and Information Engineers.

关键词： COVID-19

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Enhancing Waste Management Sustainability Through Smart and Incentivized Sorting 15

Enhancing Waste Management Sustainability Through Smart and ...

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15th Annual Undergraduate Research Conference on Applied Computing, URC 2024

作者： Woga, Muluberhan Negash, Rediet Bekera, Ahmedin Alemu, Nathanael Zaki, Nazar Department of Computer Science and Software Engineering Al Ain15551 United Arab Emirates

ISBN: (纸本)9798331527341

This paper presents an intelligent waste sorting system that utilizes computer vision and deep learning to accurately categorize waste items. Moreover, the system incentivizes proper waste disposal through a rewards scheme. Testing indicated over 90% accuracy in classifying waste into multiple categories. This sustainable solution has strong potential to address critical waste management challenges. © 2024 IEEE.

关键词： Deep learning

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