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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Anand, M. Sheeba, T.M. Princella, A.S.Anshy SRM Institute of Science and Technology Department of Networking and Communications Chennai India SRM Institute of Science and Technology Department of Computer Applications Chennai India SRM Valliammai Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350397635

In biomedicine, disease image classification is a laborious, manual process. However, recent developments in computer vision have made applying artificial intelligence for the detection and categorization of medical images a more practical option. It is essential to analyse retinal illnesses utilising Optical Coherence Tomography (OCT) scans. For patients to survive, these disorders must be accurately detected and classified. The analysis of retinal illnesses is currently done by doctors on a regular basis by looking at the OCT pictures. The manual diagnosing process is laborious, though. In order to aid clinicians in their diagnosis, an automatic detection and classification technique for retinal illnesses has been developed in this work. Utilizing OCT scans, a deep multilayered Artificial Neural Network (ANN) has been employed to identify and categorise the retinal anomalies. On a 150 image open source retinal OCT dataset, the suggested technique has been used. © 2023 IEEE.

关键词： Optical tomography

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Quantum Computing in Wireless communications and Networking: A Tutorial-Cum-Survey

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IEEE communications Surveys and Tutorials 2024年

作者： Zhao, Wei Weng, Tangjie Ruan, Yue Liu, Zhi Wu, Xuangou Zheng, Xiao Kato, Nei Anhui University of Technology School of Computer Science and Technology China Department of Computer and Network Engineering University of Electro-Communications Japan Tohoku University Sendai Japan

Owing to its outstanding parallel computing capabilities, quantum computing (QC) has been a subject of continuous attention. With the gradual maturation of QC platforms, it has increasingly played a significant role in various fields such as transportation, pharmaceuticals, and industrial manufacturing, achieving unprecedented milestones. In modern society, wireless communication stands as an indispensable infrastructure, with its essence lying in optimization. Although artificial intelligence (AI) algorithms such as reinforcement learning (RL) and mathematical optimization have greatly enhanced the performance of wireless communication, the rapid attainment of optimal solutions for wireless communication problems remains an unresolved challenge. QC, however, presents a new alternative. This paper aims to elucidate the fundamentals of QC and explore its applications in wireless communications and networking. First, we provide a tutorial on QC, covering its basics, characteristics, and popular QC algorithms. Next, we introduce the applications of QC in communications and networking, followed by its applications in miscellaneous areas such as security and privacy, localization and tracking, and video streaming. Finally, we discuss remaining open issues before concluding. © 1998-2012 IEEE.

关键词： Quantum computers

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Evaluation of Wireless Sensor Networks Module using IoT Approach 3

Evaluation of Wireless Sensor Networks Module using IoT Appr...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Anand, L. Padmalal, S. Seetha, J. Juliana, R. Naveen Kumar, P.S. Parasa, Gayatri Srm Institute of Science and Technology School of Computing Department of Networking and Communications Kattanguluthur India Mangalam College of Engg Department of Computer Science and Engineering Kerala India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Loyola-ICAM College of Engineering and Technology Department of Information Technology Chennai India St. Ann's College of Engineering and Technology Department of CSE-DATA Science AP Chirala India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9781665462167

Microcomputers and medical devices with signal transceivers that operate on a specific radio display constitute the backbone of wireless sensor networks (WS Ns) that monitor environmental conditions (temperature, pressure, light, vibration levels, location). It is widely used in WAN sensor networks because of its flexible design and low setup fees. The u200b touch network allows for the connection of up to 65,000 devices, while the Intelligent sensors on other wireless networks are used to transfer data ports and assign wireless networks. Since the price of wireless solutions has been decreasing, and their functional capabilities have been growing, they are gradually replacing wired ones in telemetry data gathering systems and long- distance detecting communication. A deep learning model was used in this investigation to prevent the sensor nodes from manipulating data. Sensor nodes include a lot of parameters and estimations. If these projected data values are altered, network performance will suffer, and the node's lifetime will be reduced. Data security became a priority when the sensor nodes were distributed. This new method is 98.82% more efficient than the previous one. © 2023 IEEE.

关键词： Sensor nodes

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Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0

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computers, Materials & Continua 2023年第3期74卷 5349-5367页

作者： Khalid A.Alissa Mohammed Maray Areej A.Malibari Sana Alazwari Hamed Alqahtani Mohamed K.Nour Marwa Obbaya Mohamed A.Shamseldin Mesfer Al Duhayyim SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human *** the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital *** current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 *** suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image ***,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based *** proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover *** secret images,encrypted by signcryption technique,are embedded into cover ***,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax *** integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV *** proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct *** simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

关键词： Unmanned Aerial Vehicles Artificial Intelligence emerging technologies Deep Learning Industry 4.0 image steganography

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Attention-based Phase Shift Control for Multi-RIS-aided Systems

Attention-based Phase Shift Control for Multi-RIS-aided Syst...

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2023 VTS Asia Pacific Wireless communications Symposium, APWCS 2023

作者： Kim, Hyunsoo Jung, Geon-Woong Shim, Byonghyo Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of Advanced Communications Research Center Samsung Research Samsung Electronics Seoul Korea Republic of

ISBN: (纸本)9798350316803

To support extremely high data rates in 6G wireless networks, reconfigurable intelligent surface (RIS) assisted communications have gained much attention in recent years. By controlling the phase shift of each reflecting element, the RIS can proactively modify the wireless propagation environment, thereby improving the signal quality for mmWave/THz systems. One important problem of multi-RIS-aided systems is that accurate channel estimation is difficult due to the heavy pilot overhead. In this paper, we propose a deep learning (DL)-based multi-RIS codeword selection scheme that selects multi-RIS codewords maximizing the sum rate of multi-RIS-aided systems. Specifically, we exploit the attention technique to calculate the impact of each RIS on the UE and emphasize the important reflected channels for multi-RIS codeword selection. From the simulation results, we demonstrate that the proposed scheme outperforms the benchmark schemes by a large margin. © 2023 IEEE.

关键词： Quality control

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Prediction of Stock Market Price using Bi-directional Recurrent Neural Network 1

Prediction of Stock Market Price using Bi-directional Recurr...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Archana Reddy, R. Al-Attabi, Kassem Chandana, Bollampelly Hatem Mohammed, Ibraheem Bhuvaneswary School of Computer Science and Artificial Intelligence Sr University Warangal India The Islamic University Najaf Iraq Hyderabad India Al Rafidain University College Department of Computer Communications Engineering Bagdad Iraq Kalasalingam Academy of Research and Education Department of Ece Krishnankoil India

ISBN: (纸本)9798350316469

Recently, an accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. The advent of artificial intelligence and enhanced processing power has led to the realisation that preprogrammed techniques are more effective at forecasting stock values. The stock market is currently one of the most researched and fastest developing subj ects, and predicting its behaviour is crucial. The primary challenge in this area of research remains improving forecast precision. Strong shareholder returns are made possible by stock price forecasting. Despite this, researchers use these algorithms to predict continuing market trends based on Stock Technical Indicators (STIs), due to recent advancements in machine learning techniques. This study analyses Yahoo Finance data from STIs over a decade to predict stock prices using a Bi- directional Recurrent Neural Network (BI-RNN). Initially, the analysed STIs were used as input for an autoencoder during the dimensionality reduction procedure, which reduced the correlation between the STIs. These STIs and the Yahoo Finance data were then provided to BI-RNN. Next, in order to forecast stock prices, the BI-RNN's outcome attributes were fed into the soft max layer. From the results, it clearly shows that proposed Bi-RNN shown better results in terms of a minimal MAPE value of 0.41, MAE of 4.42, RMSE of 0.10, and MSE of 212.25 in the experiments, the proposed strategy surpassed traditional methods. © 2023 IEEE.

关键词： Autoencoder Bi-directional Recurrent Neural Network Stock market prediction Stock Technical Indicators Yahoo Finance data

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Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

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computer Systems Science & engineering 2023年第7期46卷 303-319页

作者： Imran Ali Zohaib Mushtaq Saad Arif Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Department of Mechanical Engineering National Taiwan University of Science and TechnologyTaipei City106335TaiwanChina Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Mechanical Engineering HITEC University TaxilaTaxila Cantt.47080Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia

Hyperspectral imaging is gaining a significant role in agricultural remote sensing *** data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third *** classification accuracy of hyperspectral images(HSI)increases significantly by employing both spatial and spectral *** this work,the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared(VNIR)range of 400 to 1000 nm wavelength within 180 spectral *** dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each *** data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation *** this study,a unique pixel-based approach was designed to improve the crops'classification accuracy by using the edge-preserving features(EPF)and principal component analysis(PCA)in *** preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired *** the second step,this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral *** resultant feature space(PCA-EPF)demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational *** support vector machines classifier was employed for multiclass classification of target crops using *** classification performance evaluation was measured in terms of individual class accuracy,overall accuracy,average accuracy,and Cohen kappa *** proposed scheme achieved greater than 90%results for all the performance evaluation *** PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR *** proposed scheme

关键词： Hyperspectral imaging visible and near-infrared edge preserving feature dimensionality reduction crop classification

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Performance Comparison of Deep Learning Algorithms in Twitter Bot Detection 5

Performance Comparison of Deep Learning Algorithms in Twitte...

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5th College of Science International Conference on Recent Trends in Information Technology, CSCTIT 2022

作者： Al-Ibadi, Samer Emad Neama Cevik, Mesut Altinbas University Department of Electrical and Computer Engineering Istanbul Turkey Information and Telecom Public Company Ministry of Communications Baghdad Iraq Altinbas University Istanbul Turkey Electrical and Computer Engineering Department Istanbul Turkey

ISBN: (纸本)9798350334920

Difficulty in detecting social media bots - software-controlled accounts that seem as human - has serious consequences. Bots have been used to affect the financial market, political elections, and spreading conspiracy theories. Most approaches detect bots at the account level by studying social media posts and using network structure, system dynamics, content analytics, etc. In this study, we compare the performance of the recurrent neural network (RNN), convolutional neural network (CNN), and feed forward neural network (FNN) in detecting Twitter bots by evaluating the textual content of 10,416,240 tweets from humans and bots. Deep learning models utilize sample data to gauge performance. The CNN model identified recent social bot samples most accurately (97.67 %). In comparison, the RNN model achieved great accuracy (97.04 %) with a longer training time. RNN and CNN algorithms perform well when compared to other deep learning classifiers. © 2022 IEEE.

关键词： Recurrent neural networks

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Verification of Internal Memory Readout by Voltage Fault Attack on Automotive ECUs

Verification of Internal Memory Readout by Voltage Fault Att...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Ryusei Eda Kota Hisafuru Katsuhiko Sato Yuya Adachi Nozomu Togawa Department of Computer Science and Communications Engineering Waseda University 00One Inc.

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

In recent years, the spread of electric vehicles have led to the increasing use of IT devices in vehicle operation control. ECUs (Electronic Control Units) refer to the control devices that control automobiles' operations, and their security issues have emerged as a result of their rapid spread. In this paper, we propose a voltage fault attack execution environment targeting microcontrollers for ECUs. We applied the proposed environment to a microcontroller for ECUs and succeeded in reading out the contents of the read-protected memory in it by the fault attack.

关键词： Microcontrollers Electric vehicles Security Automobiles Consumer electronics Automotive engineering

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An Overview of Information Hiding Techniques Challenges and Applications 7

An Overview of Information Hiding Techniques Challenges and ...

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7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Kareem, Ali Noori Deheyab, Omar Adil Sameen, Aws Zuhair Alwan, Mohammed Hasan Hammadi, Yousif I Kadhum, Mayssam Saad Aziz, Ahmed Ali, Muthana Ali Salem Bilad Alrafidain University College Diyala32001 Iraq Department Of Medical Instrumentation Engineering Techniques College Of Medical Techniques Al-Farahidi University Baghdad10015 Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan Department Of Computer-Aided Design And Engineering Design National University Of Science And Technology 'Misis' Russia

ISBN: (纸本)9798400709036

The internet became a common communication platform for many people. However, information transmission is not secure, and there is a high probability of copying and altering the information easily. In the public network, transmitting sensitive or private information needs some kind of protection. Unless a secure channel is utilized for the transmission. Hiding the information is one of the efficient techniques that provides a solution for transmitting confidential data over communication channels. These techniques should meet various application requirements such as high robustness against attacks, real-time, and high hiding capacity. Generally, information-hiding techniques suffer from many challenges. This paper provides an insight into these techniques, their applications, and challenges to fill the gap of missing such an overview. © 2023 ACM.

关键词： Sensitive data

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