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MoBShield:A Novel XML Approach for Securing Mobile Banking

computers, Materials & Continua 2024年第5期79卷 2123-2149页

作者： Saeed Seraj Ali Safaa Sadiq OmprakashKaiwartya Mohammad Aljaidi Alexandros Konios Mohammed Ali Mohammed Abazeed Department of Computer Science Nottingham Trent UniversityClifton LaneNottinghamNG118NSUK Department of Computer Science Faculty of Information TechnologyZarqa UniversityZarqa13110Jordan Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Mobile banking security has witnessed significant R&D attention from both financial institutions and *** is due to the growing number of mobile baking applications and their reachability and usefulness to ***,these applications are also attractive prey for cybercriminals,who use a variety of malware to steal personal banking *** literature in mobile banking security requiresmany permissions that are not necessary for the application’s intended security *** this context,this paper presents a novel efficient permission identification approach for securing mobile banking(MoBShield)to detect and prevent malware.A permission-based dataset is generated for mobile banking malware detection that consists large number of malicious adware apps and benign apps to use as training *** dataset is generated from 1650 malicious banking apps of the Canadian Institute of Cybersecurity,university of New Brunswick and benign apps from Google Play.A machine learning algorithm is used to determine whether amobile banking application ismalicious based on its permission ***,an eXplainable machine learning(XML)approach is developed to improve trust by explaining the reasoning behind the algorithm’s *** evaluation tests that the approach can effectively and practically identify mobile banking malware with high precision and reduced false ***,the adapted artificial neural networks(ANN),convolutional neural networks(CNN)and XML approaches achieve a higher accuracy of 99.7%and the adapted deep neural networks(DNN)approach achieves 99.6%accuracy in comparison with the state-of-the-art *** promising results position the proposed approach as a potential tool for real-world scenarios,offering a robustmeans of identifying and thwarting malware inmobile-based banking ***,MoBShield has the potential to significantly enhance the security and trustworthiness of

关键词： Security malware detection deep learning convolutional neural networks deep neural networks

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Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

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computer Modeling in Engineering & sciences 2024年第10期141卷 443-458页

作者： Prasanalakshmi Balaji Omar Alqahtani Sangita Babu Mousmi Ajay Chaurasia Shanmugapriya Prakasam Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61451Saudi Arabia College of Science and Arts in Rijal Alma King Khalid UniversityAbha61451Saudi Arabia Department of Information Technology Muffakham Jah College of Engineering and TechnologyHyderabad500034India Department of Computer Science and Business Systems Rajalakshmi Engineering CollegeThandalam602105India

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire *** recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining *** cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival *** analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection *** upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and ***,the histopathology biopsy images are taken from standard data ***,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are ***,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer *** efficacy of the model is evaluated using divergent *** compared with other methods,the proposed work reveals that it offers impressive results for detection.

关键词： Bidirectional long short-term memory breast cancer detection feature extraction histopathology biopsy images multi-scale dilated vision transformer

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Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

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computer Modeling in Engineering & sciences 2024年第5期139卷 1167-1202页

作者： Samina Amin Muhammad Ali Zeb Hani Alshahrani Mohammed Hamdi Mohammad Alsulami Asadullah Shaikh Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information System College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their ***,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by *** paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic *** has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation *** recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM *** paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM ***,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

关键词： Social media epidemic machine learning deep learning health informatics pandemic

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Convex Optimization Techniques for High-Dimensional Data Clustering Analysis: A Review

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第3期5卷 378-398页

作者： Yousif, Ahmed Yacoub Al-Sarray, Basad University of Baghdad College of Science Mathematics Department Baghdad Iraq University of Technology Department of Applied Sciences Baghdad Iraq University of Baghdad College of Science Computer Science Department Iraq

Clustering techniques have been instrumental in discerning patterns and relationships within datasets in data analytics and unsupervised machine learning. Traditional clustering algorithms struggle to handle real-world data analysis problems where the number of clusters is not readily identifiable. Moreover, they face challenges in determining the optimal number of clusters for high-dimensional datasets. Consequently, there is a demand for enhanced, adaptable and efficient techniques. Convex clustering, rooted in a rich mathematical framework, has steadily emerged as a pivotal alternative to traditional techniques. It amalgamates the strengths of conventional approaches while ensuring robustness and guaranteeing globally optimal solutions. This review offers an in-depth exploration of convex clustering, detailing its formulation, challenges and practical applications. It examines synthetic datasets, which serve as foundational platforms for academic exploration, emphasizing their interactions with the semi-smooth Newton augmented Lagrangian (SSNAL) algorithm. Convex clustering provides a robust theoretical foundation, but challenges, including computational limitations with expansive datasets and noise management in high-dimensional contexts, persist. Hence, the paper discusses current challenges and prospective future directions in the domain. This research aims to illuminate the potency and potential of convex clustering in modern data analytics, highlighting its robustness, flexibility and adaptability across diverse datasets and applications. © 2024 College of Education, Al-Iraqia university. All rights reserved.

关键词： Lagrange multipliers

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Implementation of Voice Command-based Object Detection System for Wheeled Humanoid Robot Using Deep Learning

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Journal of Institute of Control, Robotics and Systems 2023年第2期29卷 88-97页

作者： Jeong, Yoon-Jin Han, Ji-Hyeong Department of Computer Science and Engineering Seoul National University of Science and Technology Korea Republic of

The demand for service robots is continuously increasing. Robots that can interact with humans are required not only in factories but also in everyday life. This paper proposes a Human-Robot Interaction (HRI) system based on HUMIC, a wheeled humanoid robot with a Kinect V2 and four mecanum wheels. In the proposed system, when a person issues a voice command to an object to find, the HUMIC detects an object and moves in front of the object. For this purpose, deep learning models, i.e., wav2vec 2.0 and YOLO v3, are used for speech recognition and object detection. Furthermore, the proposed system is configured to select one object by interacting with a human when there is more than one object to find and prevents the object from disappearing from the robot’s view while moving. The experiments are conducted in a real-world environment. Because this study focuses on HRI, the experiments are conducted based on a scenario in which HUMIC detects an object in an environment without obstacles. The scenario was attempted nine times and succeeded eight times to achieve an 88% success rate. © ICROS 2023.

关键词： Human robot interaction

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An Integrated Theoretical Model for Cloud Computing Adoption in Universities of Yemen 1

An Integrated Theoretical Model for Cloud Computing Adoption...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Hashedi, Abdullah Almekhlafi, Nabil Othman, Mohamed Tahar Ben Ghallab, Abdullatif Department of Information Technology University of Science and Technology Sana’a Yemen Department of Computer Science Qassim University Buraydah Saudi Arabia Department of Computer Science University of Science and Technology Sana’a Yemen

ISBN: (纸本)9798331533557

Several studies have been conducted in several sectors to determine the factors influencing the adoption of cloud computing (CC) technology at the individual or organizational level. These studies demonstrated the ability of CC to reduce expenses and provide high flexibility in resource management. However, studies in Yemen and Yemeni higher education institutions (HEIs) are still scarce, and no institution has adopted CC due to security and privacy concerns. This research developed an integrated theoretical model to investigate the factors affecting the adoption of CC in Yemeni HEIs. The developed theoretical model is based on the models of the technology and organization environment (TOE) framework and the diffusion of innovations model. A quantitative approach was used, and partial least squares structural equation modeling (PLS-SEM) was used to analyze and validate the proposed model on the basis of data from 170 participants from academics and IT employees at Yemen universities. The findings revealed that relative advantage, compatibility, security concerns, technological readiness, and senior management support had a significant effect on the adoption of CC in Yemeni HEIs. This study contributes to identifying the most important factors influencing the adoption of CC in HEIs. In addition, I presented a conceptual framework that combines the two theories of DOI-TOE. The results of this research may help HEIs develop a strategy for the adoption of CC by recognizing the key factors in their institutions. © 2024 IEEE.

关键词： Cloud platforms

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Demonstration of high-performance STO-based WORM devices controlled by oxygen-vacancies and metal filament growth

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Journal of Materials Chemistry C 2024年第23期12卷 8418-8425页

作者： Hsu, Chih-Chieh Wen, Xiu-Ming Xiao, Kai-Zer Jhang, Wun-Ciang Joodaki, Mojtaba Department of Electronic Engineering National Yunlin University of Science and Technology Douliu64002 Taiwan Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Douliu64002 Taiwan School of Computer Science and Engineering Constructor University Bremen28759 Germany

Herein, we propose high-performance Ti/STO/n+-Si and Ag/STO/n+-Si write-once-read-many-times devices, where the resistance transition mechanisms are controlled by oxygen vacancies in the STO layer and metal atoms from the electrochemically active Ag electrode, respectively. The as-deposited STO films have a low oxygen-vacancy content of 12.2%. When the Ti electrode is deposited, the formation of a thin TiOx interfacial layer at the Ti/STO interface is achieved, which leads to an increase of oxygen vacancy content in the STO layer to 28.6%. The relatively higher oxygen vacancy density means that the Ti/STO/n+-Si device can provide a voltage-polarity-independent memory function. In contrast, the resistance transition of the Ag/STO/n+-Si device can be limited in the positive voltage region because Ag cations can only be driven into the STO layer using a positive bias. Both devices show high ILRS/IHRS ratios, superior data retention capability, and fast transition speeds. © 2024 The Royal Society of Chemistry.

关键词： Oxygen vacancies

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TRE-DSP:A traceable and revocable CP-ABE based data sharing scheme for IoV with partially hidden policy

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Digital Communications and Networks 2025年第2期11卷 455-464页

作者： Yousheng Zhou Rundong Peng Yuanni Liu Pandi Vijayakumar Brij Gupta College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing 400065China Department of Computer Science and Engineering University College of EngineeringTindivanamMelpakkamTamil nadu 604001India Department of Computer Engineering National Institute of Technology KurukshetraIndia

With the popularity of the Internet of Vehicles(IoV),a large amount of data is being generated every *** to securely share data between the IoV operator and various value-added service providers becomes one of the critical *** to its flexible and efficient fine-grained access control feature,Ciphertext-Policy Attribute-Based Encryption(CP-ABE)is suitable for data sharing in ***,there are many flaws in most existing CP-ABE schemes,such as attribute privacy leakage and key *** paper proposes a Traceable and Revocable CP-ABE-based Data Sharing with Partially hidden policy for IoV(TRE-DSP).A partially hidden access structure is adopted to hide sensitive user attribute values,and attribute categories are sent along with the ciphertext to effectively avoid privacy *** addition,key tracking and malicious user revocation are introduced with broadcast encryption to prevent key *** the main computation task is outsourced to the cloud,the burden of the user side is relatively *** of security and performance demonstrates that TRE-DSP is more secure and practical for data sharing in IoV.

关键词： Privacy-preserving Attribute-based encryption Partially hidden policy Traceability User revocation Internet of vehicles

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Directional emissions from perovskite nanocrystals thin film enabled by metasurface integration through one step spin-coating process

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Nano Research 2023年第5期16卷 7646-7653页

作者： Kexue Li Xuanyu Zhang Peinan Ni Juncheng Liu Fengyuan Lin Xu Man Wenfeng Cai Jianxun Liu Yanjun Liu Rui Chen Zhipeng Wei State Key Laboratory of High Power Semiconductor Laser School of physicsChangchun University of Science and TechnologyChangchun 130022China Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhen 518055China Photonics Laboratory Department of Microtechnology and NanoscienceChalmers University of TechnologyGöteborgSE-41296Sweden

Advances in thin film light-emitting devices have fueled the rapid growth of a new class of solid-state lighting devices,featuring low fabrication cost,high quantum efficiency,broadband spectrum coverage,*** contrast to the conventional inorganic semiconductors that rely on lattice matched high crystalline quality substrate,solution processable thin films eliminate the dependence on the substrate,which is highly desired for the ease and versatility of integrations with foreign *** taking this advantage,this work developed an ultracompact solution to control the directionality of thin film emitters using integrated dielectric metasurface through one step spin-coating *** a proof of concept,directional emissions from perovskite nanocrystal thin film,including collimated light emissions and two-dimensional beam steering,are experimentally ***,our approach,where light emitters were integrated on the back side of substrate after the fabrication of metasurface,judiciously avoids any potential degradation of material optical quality caused by the multi-step ***,it can serve as a generalized scheme to engage the advantageous properties of dielectric metasurface,including the compactness,high efficiency,beam controllability with the emerging thin film light-emitting diodes(LEDs),which is applicable to a wide range of solution processable materials,including organic light-emitting diodes,quantum-dot light emitting diodes,polymer LEDs,and perovskite LEDs,opening up new pathways to develop low-cost and ultra-compact solid state light sources with versatile beams characteristics.

关键词： solid state light source perovskite light-emitting diodes(LEDs) metasurface integrations on-chip beam shaping beam steering

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Leveraging Deep Learning for Sustainable E-commerce: Reducing Opinion Spam in Smart Mobility Platforms 3

Leveraging Deep Learning for Sustainable E-commerce: Reducin...

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3rd International Conference on Sustainable Mobility Applications, Renewables and technology, SMART 2024

作者： Noekhah, Shirin Shamal, Przha Galety, Mohammad Gouse Natarajan, Arul Kumar Abdullaeva, Kamilla Department of Computer Science Samarkand International University of Technology Uzbekistan Department of Computer Science Komar University of Science and Technology Sulaimani Iraq

ISBN: (纸本)9798331532925

During the last decade, online review systems have become vital for people looking for trustworthy information. Online reviews in smart mobility platforms have a deep impact on the sale rates of products but they also cause a strong motivation for spamming activities. The prevalence of opinion spam reduces the legality and utility of such platforms. Therefore, various opinion spam detection techniques including supervised, unsupervised, and semi-supervised have been developed to filter opinionated documents for trustable analysis. However, most of them consider a limited set of features. This study proposes a novel semi-supervised Deep Neural Network model to accurately identify opinion spam in online reviews by considering content-based and meta-data features. The model has been evaluated with state-of-the-art techniques and produces an accuracy of 95% on the crowdsourced Amazon dataset. The experimental results prove that the DNN model can enhance spam detection accuracy by learning complex patterns from large datasets. © 2024 IEEE

关键词： Self-supervised learning

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