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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Peya, Zahrul Jannat Maria, Mahfuza Akter Akhand, M.A.H. Siddique, Nazmul Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna9203 Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Northern Ireland Londonderry United Kingdom

ISBN: (纸本)9789819989362

Autism Spectrum Disorder (ASD) is a diverse neurological problem with several contributing factors involving both genetic and environmental variables. The diagnosis of ASD based on neural activity analysis of various signals from the brain, especially electroencephalography (EEG), has drawn attention. A technique for capturing an electrogram of the brain's spontaneous electrical activity is called EEG which is simple to use and non-intrusive. The aim of this study is to classify people with ASD and controls using the most efficient band by examining different sub-bands (such as alpha, beta, and gamma) of the EEG data. Normalized Mutual Information (NMI) is used to create Connectivity Feature Maps (CFMs). Classification is performed through Convolutional Neural Network (CNN). Gamma band is found effective for ASD detection with an accuracy of 96%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electroencephalography

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A New Intelligent Approach for Deaf/Dumb People based on Deep Learning

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computers, Materials & Continua 2022年第9期72卷 6045-6060页

作者： Haitham Elwahsh Ahmed Elkhouly Emad Abouel Nasr Ali K.Kamrani Engy El-shafeiy Computer Science Department Faculty of Computers and InformationKafrelsheikh UniversityKafrelsheikhEgypt Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh 11421Saudi Arabia Industrial Engineering Department College of EngineeringUniversity of HoustonHoustonTX 77204-4008USA Department of Computer Science Faculty of Computers and Artificial IntelligenceUniversity of Sadat CitySadat CityEgypt

People who are deaf or have difficulty speaking use sign language,which consists of hand gestures with particular motions that symbolize the“language”they are communicating.A gesture in a sign language is a particular movement of the hands with a specific shape from the fingers and whole *** this paper,we present an Intelligent for Deaf/Dumb People approach in real time based on Deep Learning using Gloves(IDLG).The approach IDLG offers scientific contributions based deep-learning,a multimode command techniques,real-time,and effective use,and high accuracy *** this purpose,smart gloves working in real time were *** data obtained from the gloves was processed using deep-learning-based approaches and classified multi-mode commands that allow dumb people to speak with regular people via their smart ***,the glove has five flex sensors and an accelerometer using to achieve Low-Cost Control *** flex sensor generates a proportional change in resistance for each individual *** processing of these hand gestures is in Atmega32A Microcontroller which is an advance version of the microcontroller and the lab view *** compares the input signal to memory-stored specified voltage *** performance of the IDLG approach was verified on a dataset created using different hand gestures from 20 different *** the test using the IDLG approach on 10,000 data points,process time performance of milliseconds was achieved with 97%accuracy.

关键词： Deep learning flex sensor labview Atmega32A low-cost control system accelerometer intelligent glove

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Bluetooth Vulnerabilities and Security 7

Bluetooth Vulnerabilities and Security

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7th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2025

作者： Mareeswari, V. Vijayan, R. Shukla, Ashish Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India

ISBN: (纸本)9798331515683

Bluetooth technology, which facilitates wireless communication between Billions of devices including smartphones, tablets, laptops, and Internet of Thing (IoT) devices, is a cornerstone of modern connectivity. Its importance lies in its ability to enable seamless data exchange and interaction across a wide range of applications, from personal gadgets to complex industrial systems. Despite its widespread adoption, Bluetooth is not immune to critical security vulnerabilities. Issues like Bluetooth Low Energy (BLE) vulnerabilities and denial-of-service (DoS) attacks can overwhelm devices with excessive traffic, while BLE Forced Connection can lead to unauthorized access, and eavesdropping exposes sensitive data being exchanged. We have discussed these vulnerabilities in detail, examining their mechanisms, potential impacts, and various implementation aspects. Additionally, we have added demonstrations to illustrate how these attacks can be executed and the potential consequences. To prevent these threats, we explored essential measures, including regular firmware updates, secure pairing protocols, and careful management of Bluetooth settings. Adopting best practices like disabling Bluetooth when not in use and monitoring connected devices can further enhance security, ensuring the reliable and safe operation of the vast and growing ecosystem of Bluetooth-enabled devices. © 2025 IEEE.

关键词： Bluetooth Low Energy (BLE) Bluetooth Vulnerabilities Denial of Service (DoS) PIN Bypass Security Risks

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Early-Stage Segmentation and Characterization of Brain Tumor

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computers, Materials & Continua 2022年第10期73卷 1001-1017页

作者： Syed Nauyan Rashid Muhammad Hanif Usman Habib Akhtar Khalil Omair Inam Hafeez Ur Rehman Department of Computer Science National University of Computer and Emerging Sciences IslamabadPeshawar CampusPakistan Faculty of Computer Science and Engineering Ghulam Ishaq Khan(GIK)Institute of Engineering Sciences and TechnologyTopiPakistan IFahja Pvt Limited PeshawarPakistan Department of Electrical and Computer Engineering COMSATS University IslamabadPakistan

Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain *** life expectancy of patients diagnosed with gliomas decreases *** gliomas are diagnosed in later stages,resulting in imminent *** average,patients do not survive 14 months after *** only way to minimize the impact of this inevitable disease is through early *** Magnetic Resonance Imaging(MRI)scans,because of their better tissue contrast,are most frequently used to assess the brain *** manual classification of MRI scans takes a reasonable amount of time to classify brain *** this,dealing with MRI scans manually is also cumbersome,thus affects the classification *** eradicate this problem,researchers have come up with automatic and semiautomatic methods that help in the automation of brain tumor classification ***,many techniques have been devised to address this issue,the existing methods still struggle to characterize the enhancing *** is because of low variance in enhancing region which give poor contrast in MRI *** this study,we propose a novel deep learning based method consisting of a series of steps,namely:data pre-processing,patch extraction,patch pre-processing,and a deep learning model with tuned hyper-parameters to classify all types of gliomas with a focus on enhancing *** trained model achieved better results for all glioma classes including the enhancing *** improved performance of our technique can be attributed to several ***,the non-local mean filter in the pre-processing step,improved the image detail while removing irrelevant ***,the architecture we employ can capture the non-linearity of all classes including the enhancing ***,the segmentation scores achieved on the Dice Similarity Coefficient(DSC)metric for normal,necrosis,edema,enhancing and non-enhancing tumor classes are 0.95,0.97,0.91,0.93,0.95;respectively.

关键词： Segmentation CNN characterization brain tumor MRI

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Innovative Data Encryption Techniques using AI for Wireless Sensor Actuator Network Security 2

Innovative Data Encryption Techniques using AI for Wireless ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Dharmateja, M. Rama, Prabha K.P. Asha, N. Nithya, P. Lalitha, S. Manojkumar, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Arunai Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvannamalai India Bannari Amman Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Sathyamangalam India

ISBN: (纸本)9798350382952

Wireless Sensor Actuator Networks (WSANs) are sensitive to cyber threats due to present static encryption techniques. Adaptive security methods are required in WSAN settings to reduce evolving dangers and maintain data integrity. The paper introduces a novel method for encrypting WSAN data using AI and machine learning. Long Short-Term Memory (LSTM) networks process data, Genetic Algorithms (GAs) optimize encryption, and Isolation Forests detect threats in real-time. The proposed system achieved remarkable performance, with scores of 98% data integrity, 96% error repair, and 95% threat detection accuracy. Encryption rates reach their max at 200 Mbps, with optimal resource usage (30% CPU, 25% RAM) and a 5% false positive rate. The proposed AI-driven security architecture improves data integrity, encryption speed, and overall security efficacy in WSANs, surpassing existing methods. The paper overcomes the limitations of static encryption methods and proposes a proactive protection mechanism against developing cyber threats, making a substantial contribution to WSAN security improvement. © 2024 IEEE.

关键词： Cryptography

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Improving bot detection of fake followers on Twitter via a hybrid B-HC optimisation algorithm 5

Improving bot detection of fake followers on Twitter via a h...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Alzeyadi, Haider Duran, Fecir Informatics Institute Department of Computer Science Gazi University Ankara Turkey Gazi University Engineering Faculty of Technology Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9781665493604

Sophisticated cyber threats are seen on Online Social Networks (OSNs) social media accounts automated to imitate human behaviours has an impactful effect on distorting public thoughts and opinions. OSNs are weaponized to diffuse deception, misinformation, and malicious activities, that forms a serious threat to society. The deceptive nature of imitating human behaviour has become a challenging and crucial task to detect automated accounts (socialbots). This research, however, proposes a hybrid metaheuristic optimisation algorithm for socialbot detection. Specifically, a hybrid B-Hill Climbing (B-HC) optimisation algorithm works in tandem with a k-NN nearest neighbour classifier to accurately select a relevant feature subset. It is applied to be tested for fake followers account on Twitter data. Experimental results showed that the proposed method is better than the traditional and the latest feature selection techniques as well as the rule-set methods. The B-HC alongside with k-NN method achieved promising results using only relevant feature subset. © 2023 IEEE.

关键词： Feature Selection

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An Algorithmic Approach to Intrusion Detection in Ad Hoc Wireless Networks Based on Artificial Intelligence

An Algorithmic Approach to Intrusion Detection in Ad Hoc Wir...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Sonekar, Shrikant V. Kshirsagar, Pravin R. Unhelkar, Bhuvan Chakrabarti, Prasun Maram, Balajee Upreti, Kamal J D College of Engineering & Management Department of Computer Science & Engineering Nagpur India J D College of Engineering & Management Department of Electronics & Telecommunication Engineering Nagpur India Muma College of Business University of South Florida Department of Infornation Systems and Decision Sciences Sarasata United States Sir Padampat Singhania University Department of Computer Science & Engineering Rajasthan Udaipur3313606 India Sr University Department of Computer Science and Artificial Intelligence Telangana Warangal506371 India Christ University Department of Computer Science Delhi NCR Ghaziabad India

ISBN: (纸本)9798331527495

The self-configured, autonomous, and framework-free modes of communication that mobile adhoc networks (MANETs) offer have revolutionized our culture. As a result, efforts have been made to explore ways to maximize the potential of MANETs through increased and improved utilization. Standards for AI have been developed thanks to the most recent release of new machine learning technologies. Different security-related issues from malware assaults affect mobile ad hoc networks (MANETs). Any node operates as a router to move data without centralized control, making nodes more vulnerable to threats from other nodes or attackers because of their brief existence. Because of this, MANET needs particular security policies to detect the incorrect entrance of misbehaving nodes. If all nodes are self-assured and correctly collaborate, the networks function better. The paper presents a practical artificial intelligence algorithm-based security system that uses AdaBoost and DT algorithms to recognize and identify packet falling nodes, classify information packets as normal or abnormal, and detect insider threats in real-time. The results showed that DT performed better than AdaBoost, with a 98% accurate prediction rate. Consequently, DT is better able to recognize damaging attacks in MANETs. © 2025 IEEE.

关键词： computer viruses

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Decisioning-Based Approach for Optimising Control engineering Tools Using Digital Twin Capabilities and Other Cyber-Physical Metaverse Manufacturing System Components

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3212-3221页

作者： Mourad, Nahia Alsattar, Hassan A. Qahtan, Sarah Zaidan, Aws Alaa Deveci, Muhammet Sangaiah, Arun Kumar Pedrycz, Witold British University in Dubai Faculty of Engineering and IT Dubai United Arab Emirates University of Mashreq College of Administrative Science Department of Business Administration Baghdad10067 Iraq Middle Technical University College of Health and Medical Technology-Baghdad Department of Computer Center Baghdad10047 Iraq SP Jain School of Global Management SydneyNSW2141 Australia National Defence University Turkish Naval Academy Department of Industrial Engineering Istanbul34040 Turkey Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon International Graduate Institute of AI National Yunlin University of Science and Technology Yunlin64002 Taiwan University of Alberta Faculty of Engineering Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul34010 Turkey

The optimisation of control engineering tools based on digital twin capabilities and other cyber-physical metaverse manufacturing system (CPMMS) components are crucial for the successful performance. This study proposes a model for optimising control engineering tools using digital twin capabilities and other CPMMS components to solve the open issues. The main contributions and novelty aspects of the methodological process are outlined as follows: Formulated and developed is a decision matrix based on a utility procedure for 10 control engineering tools with digital twin capabilities and other three CPMMS components (Programmable-Logic-Controller and Human-Machine-Interface, Internet of Things connectivity and cybersecurity features). This matrix accounts for the uncertainty associated with tool assessment and transformation evaluation issue;formulated and develop an integrating fuzzy weighted with zero-inconsistency-interval-valued spherical fuzzy rough sets (IvSFRS-FWZIC) and combined compromise solution (CoCoSo) methods. The IvSFRS-FWZIC method is utilised to assign importance degrees to the digital twin capabilities and other CPMMS components. The applicability and robustness of the proposed approach are validated and evaluated through conducting sensitivity, correlation, and comparative analyses. The proposed approach can assist managers in analysing and selecting the most suitable tool for developing CPMMS. © 1975-2011 IEEE.

关键词： Internet of things

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A Study on Purchasing Behavior Analysis Method by Comparing Difference in Latent Class Distributions between Membership Stages

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Journal of Japan Industrial Management Association 2022年第2期73卷 54-69页

作者： Yang, Tianxiang Yamashita, Haruka Goto, Masayuki Major of Industrial and Management Systems Engineering Graduate School of Creative Science and Engineering Waseda University Japan Department of Information and Communication Sciences Faculty of Science and Technology Sophia University Japan Department of Industrial and Management Systems Engineering School of Creative Science and Engineering Waseda University Japan

A marketing policy called the"Membership Stage System" is widely used in retail business. A membership stage provides benefits to customers such as shopping points when a customer's annual cumulative purchase amount exceeds a certain threshold and the customer's stage is raised a level. As a result, the company is not only able to promote the customer's willingness to purchase, but it can also obtain the purchasing history data, thereby enabling high-quality customer analysis. The most fundamental analysis is to infer the difference of purchasing characteristics between member stages and to construct different clustering models for each member stage. However, when the clustering models are learned independently for each membership stage, it is not possible to compare the obtained clusters between membership stages. In this study, we propose a new analytical method and its learning algorithm to analyze differences in cluster distribution between membership stages. Through demonstrating the proposed model applied to an actual data set of purchasing history data on a membership stage system, the effectiveness of our proposal is clarified. © 2022 Japan Industrial Management Association. All rights reserved.

关键词： Sales

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Characterization of Traffic in the Internet of Vehicles Using Microscopic Modelling

Characterization of Traffic in the Internet of Vehicles Usin...

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2024 IEEE International Conference on Advanced Telecommunication and Networking Technologies, ATNT 2024

作者： Obeng, Kobby Asare Ariffin, Sharifah H.S. Muhammad, Nor Aishah Baharudin, Muhammad Ariff Universiti Teknologi Malaysia Faculty of Electrical Engineering Malaysia Koforidua Technical University Department of Computer Science Ghana Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Malaysia

ISBN: (纸本)9798350353501

The emergence of the Internet of Vehicles (IoV) as a driver for the new age of Intelligent Transportation systems (ITS) provides an opportunity for a wide range of services and applications driven by the various interactions that exist between a vehicle and its environment. However, the nature of road networks affects real-time vehicular movements with respect to the departure, delay, speed and arrival of vehicles. In this paper, we present a data-driven, traffic theory and microscopic traffic simulation approach that can be used in modelling the underlying vehicular network of the IoV. Using the traffic status from the A4 highway dataset in Luxembourg, we obtain vehicular volumes from the vehicle flow rate, define thresholds for traffic status using data scaling and employ microscopic modeling to create traffic scenarios. The analysis from the results of the traffic scenarios shows unique vehicular patterns due to the nature of the road network providing insights that ultimately affect the design and implementation of IoV-based services and applications. © 2024 IEEE.

关键词： Internet of things

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