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Evaluation the Impact Potentials of Materials and Systems with Specific Criteria under Neutrosophic Set

Neutrosophic Sets and Systems 2023年 57卷 342-355页

作者： Abdel-aziem, Ayman H. Soliman, Tamer H. M. Ali, Ahmed M. Abdelhafeez, Ahmed Faculty of Information Systems and Computer Science October 6th University Cairo Egypt Faculty of Computers and Informatics Zagazig University Sharqiyah Zagazig44519 Egypt

Energy, healthcare, electronics, transportation, ecology, and infrastructure are just a few of the areas that might greatly benefit from the use of new materials and systems. This study delves into the factors that should be taken into account when determining the possible effect of a certain substance or system. This analysis takes into account ecological, monetary, technical, health and safety, regulatory, commercial, and cooperative factors. Stakeholders may make more well-informed decisions and ensure that materials and systems are used to their maximum capacity if they take these into account. This paper used the concept of multi-criteria decision-making (MCDM) to deal with various criteria and factors. The VIKOR method is used as an MCDM method to rank the materials according to various criteria. The VIKOR method is integrated with the single-valued neutrosophic set to overcome uncertain information. This paper used eight criteria and ten materials to select the best one. The results show the cost criteria is the height weight in all criteria. © (2023). All Rights Reserved.

关键词： Ecology

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Assessment Software Reliability in Component-Based Software Systems: A Comprehensive Review 6

Assessment Software Reliability in Component-Based Software ...

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6th International Conference on Computing and informatics, ICCI 2024

作者： Tagen, Ibrahim Ayman Wassif, Khaled T. Sayed, Sabah Moawad, Ramadan Department of Computer Science Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt Department of Computer Science Faculty of Computers and Information Technology Future University in Egypt New Cairo Egypt

ISBN: (纸本)9798350373875

The reliability of Component-Based Software Systems (CBSS) is a critical factor in the success and longevity of software applications across various domains. This comprehensive review synthesizes methodologies and findings from thirteen key studies, offering a panoramic view of the landscape of CBSS reliability assessment. We explore a spectrum of approaches, from traditional statistical models to advanced computational intelligence techniques, evaluating their effectiveness, challenges, and contexts of applicability. Our review reveals a trend towards the integration of machine learning and fuzzy logic in reliability estimation, reflecting the complexity and dynamic nature of CBSS. Moreover, we identify a significant emphasis on predictive models that cater to early detection of reliability issues, underscoring the proactive aspect of modern software reliability engineering. The findings suggest a need for more adaptable and context-sensitive methods that can accommodate the evolving nature of software components and their interactions. This review not only charts the current state of CBSS reliability assessment but also highlights future directions for research, including the integration of real-time data and the development of more robust models for uncertainty management. Our study serves as a crucial resource for researchers and practitioners seeking to enhance the reliability of CBSS, ultimately contributing to the development of more dependable software systems. © 2024 IEEE.

关键词： Machine learning

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A secure data hiding approach based on least-significant-bit and nature-inspired optimization techniques

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4639-4657页

作者： Hameed, Mohamed Abdel Abdel-Aleem, Omar A. Hassaballah, M. Department of Computer Science Faculty of Computers and Information Luxor University Luxor Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena83523 Egypt

With remarkable information technology development, information security has become a major concern in the communication environment, where security must be performed for the multimedia messages exchanged between the sender and the intended recipient. Digital multimedia steganography techniques have been developed to attain a security for covert communication and secure data. This paper proposes an approach for image steganography using the Least Significant Bit Substitution (LSB) and Nature-Inspired Harris Hawks Optimization (HHO) algorithm for efficient concealing of the secret data inside a cover image;thus providing high confidentiality. The HHO based data encoding operation uses the PSNR visual quality metric as an objective function. The objective function is used to determine the ideal encoding vector to convert the secret message to its encoded form. The proposed approach performs better than other state-of-the-art methods in terms of standard measures of visual quality with maintaining high embedding capacity. Comparisons with existing LSB or multi-directional PVD embedding methods demonstrate that the proposed method has more optimized and higher embedding capacity with maintaining visual quality. Besides, the proposed approach achieves high security against statistical StegoExpose analysis, ALASKA2 deep learning steganalysis, and image processing attacks. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Steganography

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COVID19 Outbreak:A Hierarchical Framework for User Sentiment Analysis

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computers, Materials & Continua 2022年第2期70卷 2507-2524页

作者： Ahmed F.Ibrahim M.Hassaballah Abdelmgeid A.Ali Yunyoung Nam Ibrahim A.Ibrahim Department of Computer Science Faculty of Computer ScienceNahda UniversityBanisuefEgypt Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt Faculty of Computers and Information Minia UniversityAlMiniaEgypt Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Social networking sites in the most modernized world are flooded with large data *** the sentiment polarity of important aspects is necessary;as it helps to determine people’s opinions through what they *** Coronavirus pandemic has invaded the world and been given a mention in the social media on a large *** a very short period of time,tweets indicate unpredicted increase of *** reflect people’s opinions and thoughts with regard to coronavirus and its impact on *** research community has been interested in discovering the hidden relationships from short texts such as Twitter and Weiboa;due to their shortness and *** this paper,a hierarchical twitter sentiment model(HTSM)is proposed to show people’s opinions in short *** proposed HTSM has two main features as follows:constructing a hierarchical tree of important aspects from short texts without a predefined hierarchy depth and width,as well as analyzing the extracted opinions to discover the sentiment polarity on those important aspects by applying a valence aware dictionary for sentiment reasoner(VADER)sentiment *** tweets for each extracted important aspect can be categorized as follows:strongly positive,positive,neutral,strongly negative,or *** quality of the proposed model is validated by applying it to a popular product and a widespread *** results show that the proposed model outperforms the state-of-the-art methods used in analyzing people’s opinions in short text effectively.

关键词： COVID19 COVID data sentiment analysis hierarchical clustering sentiment tree

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Developing a method to detect driver drowsiness based on a single EEG channel and discriminated features

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International Journal of Intelligent Systems Technologies and Applications 2024年第1期22卷 29-40页

作者： Hussein, Raed Mohammed George, Loay E. Miften, Firas Sabar Iraqi Commission for Computers and Informatics Informatics Institute of Postgraduate Studies Baghdad Iraq Computer Sciences Department University of Information Technology and Communication Baghdad Iraq Computer Sciences Department College of Education for Pure Science University of Thi-Qar Thi-Qar Iraq

Driver drowsiness is one of the leading causes of road deaths and transportation industry dangers. Due to its direct evaluation of neurophysiological brain activity, electroencephalography (EEG) has been regarded as one of the most reliable physiological indicators for identifying driver drowsiness. This study proposes a straightforward, cost-effective method for detecting driver drowsiness using a single channel. The contribution of this research is the discovery of drowsiness using discriminated features [moments features (M1, M2, M3, M4), roughness features (R1, R2, R3, R4), zero crossing rate (ZCR), sample entropy (SE) and median absolute deviation (MAD)] from publicly available datasets. A novel model was introduced in this study, which involved the fusion of wavelet transform Daubechies order 4 (WTDB4) and residue decomposition (RD) techniques for feature extraction. Various classification algorithms, including the least-square support vector machine (LSSVM) and ensemble models were compared in terms of their performance metrics. The algorithm that exhibited superior accuracy with reduced computational time was chosen to classify the driver's status into two groups: awake and drowsy. Notably, the proposed model achieved an impressive accuracy of 97.95%. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Electroencephalography

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Exploring the frontiers of image super-resolution: a review of modern techniques and emerging applications

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Neural Computing and Applications 2025年 1-49页

作者： Hassan, Esraa El-Rashidy, Nora Elbedwehy, Samar Abd El-Hafeez, Tarek Saber, Abeer Shams, Mahmoud Y. Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Computer Science Unit Deraya University Minia61765 Egypt Department of Computer Science Faculty of Science Minia University Minia61519 Egypt Department of Information Technology Faculty of Computers and Artificial Intelligence Damietta University New Damietta Egypt

Super-resolution (SR) aims to reconstruct high-resolution images from low-resolution inputs, with deep learning advancements driving substantial improvements in SR performance. This paper presents a comprehensive review of single- and multi-image SR techniques, analyzing findings from 12,873 research papers published between 2015 and 2025 in the computer science field. Key insights are derived from fifteen summary tables covering various SR tasks, including natural, medical, video, burst, depth map, and underwater image SR. The analysis highlights several major findings: (1) the integration of specialized modules, such as attention mechanisms, has led to consistent yearly improvements in performance metrics like PSNR and SSIM;(2) domain-specific architectures often outperform general models, particularly in medical and underwater SR applications;(3) while benchmark datasets enable objective comparisons, real-world validation remains limited, reducing the generalizability of current approaches;(4) inconsistent metric reporting across studies hampers reproducibility and fair evaluation;and (5) practical deployment considerations, including computational efficiency and real-time processing, are rarely addressed. Despite significant progress, challenges such as the need for more diverse training datasets, robust validation, and better interpretability persist. This review synthesizes these critical findings, offering an updated perspective on SR advancements, emerging trends, and future research directions. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Medical computing

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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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Feature-Driven Human Action Recognition from Mobile Sensor Data using Deep Learning Networks 8

Feature-Driven Human Action Recognition from Mobile Sensor D...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Abdullah, Muthana Hmod Ahmed, M.A. Al-Qaysi, Z.T. Abdulateef, S.K. Tikrit University Faculty of Computers & Mathematics Department of Computer Science Tikrit Iraq

ISBN: (纸本)9798350354423

Human activity recognition is essential for comprehending behavior patterns and forecasting the physical movements of humans, which has ramifications for domains including healthcare, sports, and security. The objective of this study was to recognize 18 primary human activities that were recorded using sensors on smartphones. This study focuses on extracting statistical features from time-domain sensory data collected by accelerometers and gyroscopes. Various deep learning techniques are utilized for activity recognition, such as 1D-Convolutional Neural Networks (1DCNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). The LSTM model achieved an accuracy of 64 percent, while the GRU model achieved an accuracy of 70 percent. The proposed 1D CNN attained a maximum accuracy of 97%. The study concludes that leveraging statistical features yields promising accuracy, with 1D CNN Network. Future research should work on fixing the problem of miss labeling in some classes to enhance accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

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computers, Materials & Continua 2023年第1期74卷 2133-2148页

作者： Ahmed Y.Hamed M.Kh.Elnahary Faisal S.Alsubaei Hamdy H.El-Sayed Department of Computer Science Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia

Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the *** a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud *** primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence *** and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor *** diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing *** a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the *** research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing *** basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal *** assess our algorithm’s performance by running it through three scenarios with varying numbers of *** findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.

关键词： Heterogeneous processors cooperation search algorithm task scheduling cloud computing

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A System for Registering and Selecting Courses and Teaching Staff for Postgraduate Students 8

A System for Registering and Selecting Courses and Teaching ...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Yousif, Ali Atalah Thalij, Saadi Hamad Tikrit University Faculty of Computers & Mathematics Department of Computer Science Tikrit Iraq

ISBN: (纸本)9798350354423

As modern technology grows and new networking and communication methods spread human resources management and data organization have expanded into many areas. These changes make statistical operations and organizational and financial management easier in various state educational and service institutions. Universities and educational institutions now need well-designed data management systems for student registration platforms that use multimedia applications. These systems offer key services to organize and manage data, save time and effort, and help institutions work more. Paper-based systems for managing this information have become too difficult to use. They often lead to duplicate, wrong, or useless data that's hard to find and not secure. Hence, effective management and error free management of the information. In this dissertation, a mechanism and techniques will be proposed that ought to mean how graduate students register (automatically) for the subjects assigned to them as well as select the professor (the subject teacher) and the topic they wish to study in this course, but also choose the right lecture time while the learning process begins at Tikrit University. To achieve these requirements, JavaScript and SQL Server will be used according to the recommendation. © 2024 IEEE.

关键词： Students

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