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A Survey of Blockchain Based systems: Scalability Issues and Solutions, Applications and Future Challenges

IEEE Access 2024年 12卷 79626-79651页

作者： Alghamdi, Turki Ali Khalid, Rabiya Javaid, Nadeem Umm Al-Qura University College of Computer and Information Systems Department of Computer Science Makkah21955 Saudi Arabia University of Leeds School of Computing LeedsLS2 9JT United Kingdom COMSATS University Islamabad Department of Computer Science Islamabad44000 Pakistan National Yunlin University of Science and Technology International Graduate School of Artificial Intelligence Douliu64002 Taiwan

Blockchain technology originated alongside Bitcoin as a novel method of conducting financial transactions. It has garnered significant attention from both industry and academia in recent years, emerging as a prominent area of research. It is a decentralized record-keeping system that holds transactional information. The scale of a blockchain network expands according to the growth in the number of nodes and transactions, resulting in issues related to storage capacity, data processing speed, and time delay. These issues have a direct impact on the scalability of a blockchain network. Currently, scalability is one of the prominent concerns in the field of blockchain technology and an active research area. This study conducts a comprehensive survey of the scalability challenges faced by blockchain technology in several sectors. It also examines potential solutions based on consensus mechanisms, smart contracts and directed acyclic graph (DAG). It is observed that the proposed scalability solutions target enhancing system throughput, reducing costs, and improving blockchain efficiency. Therefore, we examine, compare, and evaluate the literature using these specific criteria. Moreover, a survey of existing blockchain based survey papers is presented. A comparative analysis of these survey papers is presented along with their recency score, which is determined by the number of recent publications reviewed in a survey paper. By 'recent,' we mean the current year (or the publication year of a survey paper) and the three years prior to it. Additionally, this paper offers an elaborate discussion on the forthcoming open research challenges and applications of blockchain. © 2013 IEEE.

关键词： Smart contract

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection systems

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computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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Deep Learning Based Classification of Wrist Cracks from X-ray Imaging

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computers, Materials & Continua 2022年第10期73卷 1827-1844页

作者： Jahangir Jabbar Muzammil Hussain Hassaan Malik Abdullah Gani Ali Haider Khan Muhammad Shiraz Department of Computer Science National College of Business Administration&Economics LahoreMultan60000Pakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Faculty of Computing and Informatics University of Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Department of Computer Science Federal Urdu University of ArtsScience and TechnologyIslamabadPakistan

Wrist cracks are the most common sort of cracks with an excessive occurrence *** the routine detection of wrist cracks,conventional radiography(X-ray medical imaging)is used but periodically issues are presented by crack *** cracks often appear in the human arbitrary bone due to accidental injuries such as ***,many hospitals lack experienced clinicians to diagnose wrist ***,an automated system is required to reduce the burden on clinicians and identify *** this study,we have designed a novel residual network-based convolutional neural network(CNN)for the crack detection of the *** the classification of wrist cracks medical imaging,the diagnostics accuracy of the RN-21CNN model is compared with four well-known transfer learning(TL)models such as Inception V3,Vgg16,ResNet-50,and Vgg19,to assist the medical imaging technologist in identifying the cracks that occur due to wrist *** RN-21CNN model achieved an accuracy of 0.97 which is much better than its competitor`s *** results reveal that implementing a correct generalization that a computer-aided recognition system precisely designed for the assistance of clinician would limit the number of incorrect diagnoses and also saves a lot of time.

关键词： Wrist cracks fracture deep learning X-rays CNN

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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Coupling Mobile Brain Imaging and Virtual Reality Omnidirectional Treadmill to Explore Attenuated Situational Awareness during Distracted Walking

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IEEE Transactions on Cognitive and Developmental systems 2024年第3期16卷 1063-1076页

作者： Chuang, Chun-Hsiang Chiu, Tsai-Feng Hsu, Hao-Che Lin, Shih-Syun National Tsing Hua University Research Center for Education and Mind Sciences College of Education The Institute of Information Systems and Applications College of Electrical Engineering and Computer Science Hsinchu300 Taiwan National Tsing Hua University Research Center for Education and Mind Sciences Hsinchu300 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300193 Taiwan National Taiwan Ocean University Department of Computer Science and Engineering Keelung20224 Taiwan

Walking while engaging in distracting activities affects our perception of changes happening on the road, leading to exposure to hazardous conditions. This study aimed to evidence that pedestrians who become distracted when absorbed in their smartphones fail to maintain situational awareness. Thirty-six recruited participants performed a psychomotor vigilance task and a wayfinding task designed to simulate a multitasking scenario;meanwhile, an auditory oddball paradigm presented to participants tested their situational awareness of an external stimulus. The experiment was conducted in virtual reality (VR) environment with an omnidirectional treadmill. Participant's behavioral and electroencephalographic (EEG) responses were measured and analyzed using a newly proposed brain network measure, phase locking distance (PLD), to capture multitasking-associated couplings between brain regions. The current study supports the notion that multitasking can lead to diminished behavioral performance and changes in walking patterns. Despite the increased interregional brain connectivity, which may be an adaptive response to heightened cognitive demands, the associated reduction in event-related EEG activity suggests competition for neural resources during multitasking. Moreover, the observed EEG activities could indicate additional information processing capacity limitations and provide evidence of decreased situational awareness during multitasking, thereby increasing susceptibility to potentially hazardous situations. © 2016 IEEE.

关键词： Electroencephalography

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Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

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computers, Materials & Continua 2023年第1期74卷 881-895页

作者： Nitin Sharma Mohd Anul Haq Pawan Kumar Dahiya B.R.Marwah Reema Lalit Nitin Mittal Ismail Keshta Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India Department of Computer Science College of Computer and Information SciencesMajmaah University11952Al-MajmaahSaudi Arabia Department of Electronics&Communication Engineering DCRUST Murthal131039India Department of Transportation Engineering IITKanpur208016Uttar PradeshIndia Department of Computer Science Applications PIETSmalkha132102India Skill Faculty of Science and Technology Shri Vishwakarma Skill UniversityPalwal121102India Computer Science and Information Systems Department College of Applied SciencesAlMaarefa UniversityRiyadhSaudi Arabia

Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile *** is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight *** the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency *** Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic *** study is based on a comprehensive and detailed literature evaluation in the field of *** major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car *** research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for *** properties of characters are employed to recognise the Indian licence plate in this *** licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were *** comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.

关键词： Intelligent transportation system automatic license plate recognition system neural network random forest convolutional neural network support vector machine

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Usability Evaluation of Open Data of Government Website: A Case Study Approach 9

Usability Evaluation of Open Data of Government Website: A C...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Kurniawan, Yohannes Luhukay, Devyano Prasetya, Erdi Meiryani Philip, Samuel Anwar, Norizan School of Information Systems Bina Nusantara University Information Systems Department Jakarta Indonesia Bina Nusantara University Accounting Department School of Accounting Jakarta Indonesia Bina Nusantara University Computer Science Department School of Computer Science Jakarta Indonesia School of Information Science College of Computing Informatics and Media Universiti Teknologi Mara Shah Alam Malaysia

ISBN: (纸本)9798350383010

Open data is closely related to government, supporting transparency and accountability. The paper focus on DKI Jakarta government's open data website, which this website provides an accurate, open, centralized, and integrated database for e-government. This research will use three tools to help collect information for evaluation purpose. First is the Web Accessibility Evaluation tool (WAVE) which will help identify accessibility errors and the web content accessibility guideline. Then the second is the Webpage Test to analyze the website network and speed of loading when accessing the website. And the third use VisualEyes to see how attractive and clear the website is. The aim of this research is to identify the errors and malfunctions that occur and do not affect user comfort when accessing and improving the quality of the site in future. From the results of this research using WAVE, there is still a color contrast between the background and text, and some small errors were found. For now, the website's loading is quite fast, only 8.40 seconds, and the results from VisualEyes are quite high that is 77%, where the website is sufficient to be clear and aesthetically appealing. © 2024 IEEE.

关键词： accessibility aesthetics open data quality assurance usability user experience website

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Optimized Resource Allocation and Queue Management for Traffic Control in MANET

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computer systems science & Engineering 2023年第5期45卷 1323-1342页

作者： I.Ambika Surbhi Bhatia Shakila Basheer Pankaj Dadheech Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia Department of Information College of Computer Science and Information TechnologyKing Faisal University36362Saudi Arabia Department of Information Systems College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&Gramothan(SKIT)Jaipur302017RajasthanIndia

A set of mobile devices that employs wireless transmission for communication is termed Mobile Ad hoc Networks(MANETs).Offering better communication services among the users in a centralized organization is the primary objective of the *** to the features of MANET,this can directly End-to-End Delay(EED)the Quality of Service(QoS).Hence,the implementation of resource management becomes an essential issue in *** paper focuses on the efficient Resource Allocation(RA)for many types of Traffic Flows(TF)in *** Mobile Ad hoc Networks environments,the main objective of Resource Allocation(RA)is to process consistently available resources among terminals required to address the service requirements of the *** three categories improve performance metrics by varying transmission rates and simulation *** solving that problem,the proposed work is divided into Queue Management(QM),Admission Control(AC)and *** effective QM,this paper develops a QM model for elastic(EL)and inelastic(IEL)Traffic *** research paper presents an AC mechanism for multiple TF for effective *** work presents a Resource Allocation Using Tokens(RAUT)for various priority TF for effective ***,nodes have three cycles which are:Non-Critical Section(NCS),Entry Section(ES)and Critical Section(CS).When a node requires any resources,it sends Resource Request Message(RRM)to the *** and inelastic TF priority is determined using Fuzzy Logic(FL).The token holder selects the node from the inelastic queue with high priority for allocating the *** Network Simulator-2(NS-2),simulations demonstrate that the proposed design increases Packet Delivery Ratio(PDR),decrease Packet Loss Ratio(PLR),minimise the Fairness and reduce the EED.

关键词： MANET resource allocation end-to-end delay fuzzy logic QoS

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Identification of Age, Plumage, and Sex of Bird Species Using Pre-Trained Deep Convolutional Neural Network 2

Identification of Age, Plumage, and Sex of Bird Species Usin...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Banerjee, Sourik Gupta, Richa Pandian, J. Arun Saranya, S. Mariappan, L. Thanga Vellore Institute of Technology Department of Computer Application Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350306415

The research aims to identify the age, plumage, and sex of bird species using standard Pre-trained Deep Convolutional Neural Networks (Pre-DCNNs). The proposed work involves collecting various bird images, which are then used for training and validating the pre-trained DCNNs. The dataset contains 200 images of four bird species. The dataset was split into twelve labeled classes of images based on age, plumage, and sex. The standard image manipulation techniques such as cropping, flipping, mirroring, rotating, shearing, and skewing were used as image augmentation techniques to increase the size of the proposed dataset. The proposed data is preprocessed, including feature extraction and data cleaning, to ensure the performance of the Pre-Trained DCNNs. The Pre-Trained DCNNs were trained on 90% of the dataset to recognize patterns in bird images. The accuracy of the pre-trained DCNNs was evaluated using metrics such as classification accuracy. The research acknowledged the advantages and limitations of the standard Pre-Trained DCNNs on the age, plumage, and sex of birds using the images. Also, it compares the performance of the Pre-Trained DCNNs on the proposed bird species image dataset. © 2024 IEEE.

关键词： Bird Plumage Identification Bird Species Identification Deep Convolutional Neural Networks Image Augmentation Pre-trained networks

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EkoHate: Abusive Language and Hate Speech Detection for Code-switched Political Discussions on Nigerian Twitter 8

EkoHate: Abusive Language and Hate Speech Detection for Code...

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8th Workshop on Online Abuse and Harms, WOAH 2024 - co-located with NAACL 2024

作者： Ilevbare, Comfort Eseohen Alabi, Jesujoba Oluwadara Adelani, David Ifeoluwa Bakare, Firdous Damilola Abiola, Oluwatoyin Bunmi Adeyemo, Oluwaseyi Adesina Department of Computer Science Afe Babalola University Ado-Ekiti Nigeria Spoken Language Systems Saarland University Saarland Informatics Campus Germany University College London United Kingdom

ISBN: (纸本)9798891761056

Nigerians have a notable online presence and actively discuss political and topical matters. This was particularly evident throughout the 2023 general election, where Twitter was used for campaigning, fact-checking and verification, and even positive and negative discourse. However, little or none has been done in the detection of abusive language and hate speech in Nigeria. In this paper, we curated code-switched Twitter data directed at three musketeers of the governorship election on the most populous and economically vibrant state in Nigeria;Lagos state, with the view to detect offensive speech in political discussions. We developed EKOHATE—an abusive language and hate speech dataset for political discussions between the three candidates and their followers using a binary (normal vs offensive) and fine-grained four-label annotation scheme. We anal-ysed our dataset and provided an empirical evaluation of state-of-the-art methods across both supervised and cross-lingual transfer learning settings. In the supervised setting, our evaluation results in both binary and four-label annotation schemes show that we can achieve 95.1 and 70.3 F1 points respectively. Furthermore, we show that our dataset adequately transfers very well to three publicly available offensive datasets (OLID, HateUS2020, and FountaHate), generalizing to political discussions in other regions like the US. © 2024 Association for Computational Linguistics.

关键词： Tweets

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