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SSRN 2024年

作者： Yong, Kelvin Sheng Chek bin Mohd Illzam Elahee, Muhammad Irfan Elahee Then, Patrick Hang Hui Yong, Sim Kwan Faculty of Engineering Computing & Science Swinburne University of Technology Sarawak Malaysia

Context: In the rapidly evolving digital landscape, the increase of software developers and products has inadvertently increased the risk of software vulnerabilities. This surge in complexity within the software ecosystem has made the task of maintaining code security increasingly difficult. Traditional methods of vulnerability detection, while necessary, often fall short due to time constraints and the potential for oversight, necessitating innovative approaches to address this pressing ***: This research aims to investigate and illuminate the feasibility of using tree/graph-based neural networks in identifying specific vulnerabilities, including buffer overflow, improper input validation, and unrestricted file upload vulnerabilities. By preserving the hierarchical and structural integrity of code, these visual representations of Abstract Syntax Trees (AST), Control Flow Graph (CFG) and Program Dependence Graph (PDG), allow for a nuanced extraction of critical information, enabling the differentiation between vulnerable and non-vulnerable code with unprecedented ***: This study employ a comprehensive approach, by compiling a dataset comprising of normal basic non-vulnerable code, and vulnerable code. The code is then sanitized and cleaned before being converted into the different graph representations. Finally, inputting the graph representation of source codeResults: The results show the CFG does well at detecting buffer overflows and unrestricted file upload, while CFG is effective for improper input validation. This paper underlines the significance of particular representations for accurate and specific vulnerability ***: This paper presents a comparative analysis of each graph type's effectiveness and was evaluated for different vulnerabilities. It can be concluded from this study that different graph representations work best for different code vulnerabilities and highlights the promise of using graph neural networks

关键词： Flow graphs

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Analyzing Code Injection Attacks on Applications of Android Devices and Emulator 9

Analyzing Code Injection Attacks on Applications of Android ...

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9th IEEE International Conference on computing, engineering and Design, ICCED 2023

作者： Erfina, Adhitia Hidayat, Rahmat Rizaldi, Rafli Radya Puput Maulana, Riko Sembiring, Falentino Nusa Putra University Faculty of Engineering Computing and Design Departement of Infomation System Sukabumi Indonesia Budi Luhur University Master of Computer Science Faculty of Information Technology Jakarta Indonesia

ISBN: (纸本)9798350370126

The Android operating system released by Google in 2007 has experienced very significant development. In 2019, the number of Android application users reached 2.5 billion. One of the contributing factors is the nature of the android application, which is very flexible and easy to install. Users can download APK files on the internet. Generally, APK files are stored in archive format, so they are pretty easy to manage. However, APK files may be used to spread malware behind this convenience. This study aims to provide an overview of the process of planting malware/backdoor on Android applications and how to prevent it. This study initiated the injection of malicious code on five android applications, namely WhatsApp, Telegram, DroidCam, Fifa Mobile, and Tokopedia. According to the results, four applications were successfully injected, while one application failed. The failure most likely occurred as a result of the application's source code being obfuscated. Obfuscation is therefore strongly advised for Android applications to prevent code injection that could potentially lead to malware insertion. © 2023 IEEE.

关键词： Android (operating system)

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks 46

Swallowing Assessment using High-Resolution Cervical Auscult...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Anwar, Ayman Khalifa, Yassin Lucatorto, Erin Coyle, James L. Sejdic, Ervin University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh Center for Research Computing and Information Technology Analytics PittsburghPA United States Cairo University Faculty of Engineering Systems and Biomedical Engineering Department Giza Egypt University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient's neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics;specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers' ability to generalize on unseen data. © 2024 IEEE.

关键词： Recurrent neural networks

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Brain Tumor Automated Detection System Based on Hybrid Deep Learning Networks Using MRI Images

Brain Tumor Automated Detection System Based on Hybrid Deep ...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Salama, Gerges M. Ashraf, Shady Bayoumi, Esraa Salah Elwan, Mohammed Abd-Ellah, Mahmoud Khaled Minia University Faculty of Engineering Department of Electrical Engineering Minia6111 Egypt Egyptian Academy for Engineering and Advanced Technology Electrical Engineering Department Egypt College of Computing and Information Technology Arab Academy for Science Smart Village GizaB 2401 Egypt Egyptian Russian university Faculty of Artificial Intelligence Cairo11829 Egypt

ISBN: (纸本)9798350351408

Technology undeniably has a significant impact on human life, particularly in advancing various aspects of healthcare. Artificial intelligence (AI) has greatly contributed to the early and precise detection of tumors, especially in the realm of medical image processing, with a specific focus on brain tumor detection using magnetic resonance imaging (MRI). However, manually analyzing numerous MRI images is a challenging task in clinical settings. Hence, the utilization of a computer-aided diagnostic (CAD) system is imperative for the timely identification of normal and abnormal brain tissues. This study proposes a hybrid detection architecture to determine the presence of brain tumors. The system incorporates five different deep convolutional neural networks (CNNs) for feature extraction, each comprising five models that identify the optimal five layers for feature extraction before employing Support Vector Machine (SVM) for classification. The system undergoes training and evaluation to ascertain the most effective detection outcomes. The study utilizes two datasets to assess the system's performance and functionality. © 2024 IEEE.

关键词： Support vector machines

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Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

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Computer Modeling in engineering & sciences 2024年第6期139卷 3387-3415页

作者： Nazik Alturki Raed Alharthi Muhammad Umer Oumaima Saidani Amal Alshardan Reemah M.Alhebshi Shtwai Alsubai Ali Kashif Bashir Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Engineering University of Hafr Al-BatinHafar Al-BatinSaudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The concept of smart houses has grown in prominence in recent *** challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device *** home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical *** paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in *** have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT *** system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing *** have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache *** feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time *** is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation ***,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber *** trial results support the proposed system and demonstrate its potential for use in everyday life.

关键词： Blockchain Internet of Things(IoT) smart home automation cybersecurity

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Domain Modelling For A Lightweight Convolutional Network Focused On Automated Exudate Detection in Retinal Fundus Images 9

Domain Modelling For A Lightweight Convolutional Network Foc...

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9th International Conference on Information Technology Trends, ITT 2023

作者： Aydin, Burcu Ataer Hussain, Muhammad Hill, Richard Al-Aqrabi, Hussain University of Huddersfield School of Computing and Engineering Huddersfield United Kingdom Higher Colleges of Technology Faculty of Computer Information Science Sharjah United Arab Emirates

ISBN: (纸本)9798350327502

Retinal Fundus images contain important signs implying various stages of diabetic retinopathy. One of the early signs of diabetic retinopathy is exudates. The timely detection of this sign from fundus images can prevent or at least suppress the advancement of this condition. To this effect, this research presents a CNN-based automated exudate detection architecture for the timely detection of this early sign. In order to overcome the challenges of a limited dataset several domain specific augmentations are proposed for improving the generalization capacity of the developed architecture. The lightweight architecture consists of only 6.42 million parameters, compared to Resnet-18 (11.69 million) whilst achieving an overall F1 score of 89% © 2023 IEEE.

关键词： Computer vision

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Design of enterprise worker safety detection algorithm based on YOLO 3

Design of enterprise worker safety detection algorithm based...

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3rd International Symposium on Computer Applications and Information Systems, ISCAIS 2024

作者： Li, Xin Hussin, Norriza Binti Binti Ismail, Nor Alina Faculty of Engineering Built Environment & Information Technology SEGi University Selangor Darul Ehsan 547810 Malaysia Faculty of Data Science and Computing University Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Electrical Engineering Hebei Vocational University of Technology and Engineering Hebei Xingtai054000 China

ISBN: (纸本)9781510681316

Protecting the personal safety of on-site workers is an important task in enterprise production. In order to achieve widespread deployment to edge computing terminals, a lightweight object detection algorithm based on YOLOv5 is used to implement the personal safety detection task for workers. To achieve a lightweight task, PConv is utilized as the convolutional layer to decrease computational complexity, while Bi-Level Routing Attention is incorporated to enhance model accuracy. Furthermore, four detection heads are employed to improve object recognition capabilities. After experimentation, the precision can be improved by 3.4% compared with the baseline model, the parameters are reduced by 1.91MB, and the model size is decreased by 3.2MB. © 2024 SPIE.

关键词： Object detection

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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Toward Anomaly Detection in the Blockchain Using Numerical Association Rule Mining 21

Toward Anomaly Detection in the Blockchain Using Numerical A...

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21st IEEE International Symposium on Intelligent Systems and Informatics, SISY 2023

作者： Kramberger, Renata Fister, Iztok Kamisalic, Aida Zagreb University of Applied Sciences Department of Information Technology and Computing Zagreb Croatia University of Maribor Faculty of Electrical Engineering and Computer Science Maribor Slovenia

ISBN: (纸本)9798350343366

With the global increase in the popularity of cryptocurrencies, the need for anomaly detection and fraudulent behavior is reaching an all-Time high. In our paper, we propose a novel method of anomaly detection with the use of Numerical Association Rule Mining with Differential Evolution. The experiment was conducted by using the Dogecoin blockhain, and the dataset contained all of the transactions from one month. Our results contained 303 rules, with the best fitness function value of 0.8. © 2023 IEEE.

关键词： Association rules

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A Review of Smart Contract Blockchain Based on Multi-Criteria Analysis:Challenges and Motivations

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Computers, Materials & Continua 2023年第5期75卷 2833-2858页

作者： Norah M.Alshahrani M.L.Mat Kiah B.B.Zaidan A.H.Alamoodi Abdu Saif Department of Computer System&Technology Faculty of Computer Science&ITUniversity of MalayaLembah Pantai50603Kuala LumpurMalaysia Department of Computer Science Applied CollageKing Khalid UniversityP.O.Box 62569AbhaSaudi Arabia Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlin64002Taiwan Department of Computing Faculty of ArtsComputing and Creative Industry(FSKIK)Universiti Pendidikan Sultan IdrisTanjung MalimMalaysia Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia

A smart contract is a digital program of transaction protocol(rules of contract)based on the consensus architecture of *** contracts with Blockchain are modern technologies that have gained enormous attention in scientific and practical applications.A smart contract is the central aspect of a blockchain that facilitates blockchain as a platformoutside the cryptocurrency *** development of blockchain technology,with a focus on smart contracts,has advanced significantly in recent ***,research on the smart contract idea has weaknesses in the implementation sectors based on a decentralized network that shares an identical *** paper extensively reviews smart contracts based on multi-criteria analysis,challenges and ***,implementing blockchain in multicriteria research is required to increase the efficiency of interaction between users via supporting information exchange with high *** blockchain in the multi-criteria analysis is necessary to increase the efficiency of interaction between users via supporting information exchange and with high confidence,detecting malfunctioning,helping users with performance issues,reaching a consensus,deploying distributed solutions and allocating plans,tasks and joint *** smart contract with decision-making performance,planning and execution improves the implementation based on efficiency,sustainability and ***,the uncertainty and supply chain performance lead to improved users’confidence in offering new solutions in exchange for problems in smart *** includes code analysis and performance,while development performance can be under development.

关键词： Blockchain smart contract MCDM MADM

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