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3rd IEEE International Conference on electronic engineering, ICEEM 2023

作者： Khadra, Shaimaa Abo Ismail, Nabil A. Attiya, Gamal M. Abdulrahman, Salah Eldin S. E. High Institute of Engineering and Technology Dept. Mechatronics Engineerng Al-Mahalla-El-Kobra Egypt Menoufia University Faculty of Electronic En. Dept. Computer Science & Engineering Menouf Egypt

ISBN: (纸本)9798350323511

It is imperative to note that post-quantum cryptography, such as supersingular isogeny Diffie-Hellman (SIDH), is essential for ensuring that Internet of Things (IoT) devices have a restricted amount of resources. The primary challenges in assuring the security of IoT devices are addressed by this work's analysis of SIDH implementations designed for field programmable gate array (FPGA) architecture. Extensive efforts towards implementing the architecture for a rapid and constant-time FPGA implementation of SIDH. This quantum-resistant cryptographic primitive is a crucial component for adhering to NIST's PQC standardization. Our goal with this paper is to demonstrate how FPGA architectures can enhance the parallelism of SIDH, making it a more practical option for securing resource-limited IoT devices. Our focus is on providing a reliable speed record for SIDH, which is crucial for ensuring the security of IoT devices. The design for isogeny computation was built over p434 in Xilinx Virtex 6 and produced 45ms for public key generation in addition to 35ms for secret key generation. © 2023 IEEE.

关键词： Internet of things

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Security and Reliability Performance Analysis of Cognitive NOMA Network Under Outage Constraint of Multiple Primary Users 23

Security and Reliability Performance Analysis of Cognitive N...

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12th International Symposium on Information and Communication Technology, SOICT 2023

作者： Pham Huu, Tung Tran, Hung Tran, Duc-Tan Dang, Viet-Hung Nhan Vo, Van Nguyen Dinh, Viet Faculty of Information Technology Hanoi University of Civil Engineering Hanoi Viet Nam Faculty of Computer Science Phenikaa Univerity Hanoi Viet Nam Faculty of Electrical and Electronic Engineering Phenikaa Univerity Hanoi Viet Nam Faculty of Information Technology Duy Tan University Da Nang Viet Nam Faculty of Information Technology Vnu University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9798400708916

In this paper, we conduct an investigation into the security and reliability aspects of a cognitive non-orthogonal multiple access (NOMA) network operating under outage constraints imposed by multiple primary users (PUs). Specifically, we examine a scenario where secondary users (SUs) have the capability to operate in NOMA mode, allowing them to harness the licensed spectrum band of multiple PUs, provided that the interference generated by SUs remains below a predefined threshold. Given that context, both SUs and PUs are susceptible in this spectrum-sharing environment, a potential threat arises in the form of an eavesdropper who seeks to exploit the secrecy of messages exchanged among SUs. Given this setting, we derive a power allocation policy for the SUs, formulate closed-form expressions for outage probability (OP) and intercept probability (IP) to assess system performance, and analyze the trade-off between reliability and security. Finally, we supplement our findings with numerical examples and discussions to illustrate the proposed concepts. © 2023 ACM.

关键词： Economic and social effects

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Thalassemia Screening by Sentiment Analysis on Social Media Platform Twitter

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computers, Materials & Continua 2023年第7期76卷 665-686页

作者： Wadhah Mohammed M.Aqlan Ghassan Ahmed Ali Khairan Rajab Adel Rajab Asadullah Shaikh Fekry Olayah Shehab Abdulhabib Saeed Alzaeemi Kim Gaik Tay Mohd Adib Omar Ernest Mangantig School of Computer Sciences Universiti Sains MalaysiaUSM11800PenangMalaysia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia86400JohorMalaysia IPPT Universiti Sains MalaysiaUSM11800PenangMalaysia

Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production,resulting in a drop in the size of red blood *** severe forms,it can lead to *** genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for ***,controlling thalassemia is extremely important and is made by promoting screening to the general population,particularly among thalassemia *** Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like people’s health conditions and major public health *** individuals’sentiments in these tweets helps the research centers to formulate strategies to promote thalassemia screening to the *** effective Lexiconbased approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning(VADER).In this study applied twitter intelligence tool(TWINT),Natural Language Toolkit(NLTK),and VADER constitute the three main *** represents a gold-standard sentiment lexicon,which is basically tailored to attitudes that are communicated by using social *** contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier calledVADERto analyze the sentiment of the general population,particularly among thalassemia carriers on the social media platform *** this study,the results showed that the proposed approach achieved 0.829,0.816,and 0.818 regarding precision,recall,together with F-score,*** tweets were crawled using the search keywords,“thalassemia screening,”thalassemia test,“and thalassemia diagnosis”.Finally,results showed that India and Pakistan ranked the highest in mentions in tweets by the public’s conversations on thalassemia screening with 181 and 164 tweets,respectively.

关键词： Social media platform Twitter screening thalassemia lexicon-based VADER

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Efficient Frameworks for EEG Epileptic Seizure Detection and Prediction

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Annals of Data Science 2022年第2期9卷 393-428页

作者： Emara, Heba M. Elwekeil, Mohamed Taha, Taha E. El-Fishawy, Adel S. El-Rabaie, El-Sayed M. El-Shafai, Walid El Banby, Ghada M. Alotaiby, Turky Alshebeili, Saleh A. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt KACST Riyadh Saudi Arabia King Saud University Riyadh Saudi Arabia Department of Electrical Engineering King Saud University Riyadh11421 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh21974 Saudi Arabia

Seizure detection and prediction are a very hot topics in medical signal processing due to their importance in automatic medical diagnosis. This paper presents three efficient frameworks for applications related to electroencephalogram (EEG) signal processing. The first one is an automatic seizure detection framework based on the utilization of scale-invariant feature transform (SIFT) as an extraction tool. The second one depends on the utilization of the fast Fourier transform (FFT) and an artificial neural network for epileptic seizure prediction. Finally, an automated patient-specific framework for channel selection and seizure prediction is presented based on FFT. The simulation results show the success of the proposed frameworks for automated medical diagnosis. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Fast Fourier transforms

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Portable AI-powered spice recognition system using an eNose based on metal oxide gas sensors

Portable AI-powered spice recognition system using an eNose ...

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2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023

作者： Ramadan, Montaser N. A. Alkhedher, Mohammad Tevfik Akgun, Bekir Alp, Sina Istanbul Okan University Computer Engineering Department Istanbul Turkey Abu Dhabi University Mechanical and Industrial Engineering Department Abu Dhabi United Arab Emirates Faculty Yeditepe University Computer and Information Sciences Istanbul Turkey Istanbul Okan University Electrical and Electronic Engineering Department Istanbul Turkey

ISBN: (纸本)9798350302523

In our daily lives, we use spices and herbs. There are thousands of different sorts of spices that surround words. And occasionally it's difficult to distinguish between them. Furthermore, without specialized knowledge it is impossible to determine whether they are fresh or not. A challenging algorithm and highly sensitive sensors are needed to predict the labels and freshness of spices and herbs based primarily on their smell. In this paper, we present AI-powered spice recognition system (AISRS), which is made up of an array of 8 inexpensive BME688 digital tiny sensors are exploited to classify four different types of herbs and spices: clove, cinnamon, anise, and chamomile. The proposed eNose measures temperature, humidity, pressure, and gas concentrations for various types of spices and condiments. For every sort of class, we keep track of more than 10,000 readings. Through the use of assessment indexes at each level, we were able to determine whether or not algorithms such as k-NN, Random Forest, SVM, MLP, DT, and AdaBoost were successful. The Random Forest instantaneous classification algorithm performed the best among others where the success rate for predicting and differentiating between the four classes was better than 97 percent according to the validation data. These validation findings plus the eNose's low power consumption (0.05 W) make it possible for it to be improved and used in portable and battery-operated applications in the future. © 2023 IEEE.

关键词： Adaptive boosting

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Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

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computers, Materials & Continua 2024年第6期79卷 4963-4977页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Zulfiqar Ali Awais Ahmad Shafiq Ur Rehman Arfan Jaffar Faculty of Computer Sciences&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Information System Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Lung cancer is a leading cause of global mortality *** detection of pulmonary tumors can significantly enhance the survival rate of ***,various computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high ***,the existing method-ologies cannot obtain a high level of specificity and *** present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet *** LCSC model comprises two distinct *** first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung ***,an improved AlexNet architecture is employed to classify lung *** the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate *** suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or *** proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

关键词： Lung cancer segmentation AlexNet U-Net classification

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Deep learning approach for credit card fraud detection 2

Deep learning approach for credit card fraud detection

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2nd IEEE International Conference on electronic engineering, ICEEM 2021

作者： El Naby, Aya Abd El-Din Hemdan, Ezz El-Sayed, Ayman MSA University Department of Computer Science and Engineering Faculty of Electronic Engineering Egypt Menoufia University Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia Egypt

ISBN: (纸本)9781665418423

As technology evolves rapidly, the world is using credit cards instead of cash in its everyday lives, opening up a new way for fraudulent people to abuse them. Credit card fraud losses reached approximately $28.65 billion in 2019, according to Nilsson's report, and global card fraud is expected to reach around $32.96 billion by 2023. Providers should therefore develop an efficient model to detect and prevent fraud early. In this paper, we used deep learning techniques as an effective way to detect fraudsters in credit card transactions. Therefore, we present a model for predicting legitimate transactions or fraud on Kaggle's credit card dataset. The proposed model is OSCNN (Over Sampling with Convolution Neural Network) which is based on over-sampling preprocessing and CNN (convolution neural network). The MLP (Multi-layer perceptron) was also applied to the dataset. Comparing the MLP-OSCNN results, they proved that the proposed model achieved better results with 98% accuracy. © 2021 IEEE.

关键词： Deep learning

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An Optimal Algorithm for Resource Allocation in D2D Communication

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computers, Materials & Continua 2023年第4期75卷 531-546页

作者： Shahad Alyousif Mohammed Dauwed Rafal Nader Mohammed Hasan Ali Mustafa Musa Jabar Ahmed Alkhayyat Research Centre University of MashreqBaghdadIraq College of Engineering Department of Electrical&Electronic EngineeringGulf UniversityAlmasnadKingdom of Bahrain Medical Instrumentation Techniques Engineering Dijlah University CollegeBaghdadIraq Department of Pharmacy Al-Mustaqbal University CollegeHilla51001Iraq Computer Techniques Engineering Department Faculty of Information TechnologyImam Ja’afar Al-Sadiq UniversityNajaf10023Iraq Department of Medical Instruments Engineering Techniques Al-Turath University CollegeBaghdad10021Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi UniversityBaghdad10021Iraq College of Technical Engineering The Islamic UniversityNajafIraq

The number of mobile devices accessing wireless networks isskyrocketing due to the rapid advancement of sensors and wireless communicationtechnology. In the upcoming years, it is anticipated that mobile datatraffic would rise even more. The development of a new cellular networkparadigm is being driven by the Internet of Things, smart homes, and moresophisticated applications with greater data rates and latency *** are being used up quickly due to the steady growth of smartphonedevices andmultimedia apps. Computation offloading to either several distantclouds or close mobile devices has consistently improved the performance ofmobile devices. The computation latency can also be decreased by offloadingcomputing duties to edge servers with a specific level of computing ***-to-device (D2D) collaboration can assist in processing small-scaleactivities that are time-sensitive in order to further reduce task delays. The taskoffloading performance is drastically reduced due to the variation of differentperformance capabilities of edge nodes. Therefore, this paper addressed thisproblem and proposed a new method for D2D communication. In thismethod, the time delay is reduced by enabling the edge nodes to exchangedata samples. Simulation results show that the proposed algorithm has betterperformance than traditional algorithm.

关键词： D2D communication resource allocation latency optimization

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Time Series Forecasting of COVID-19 Mortality in SADC region with Facebook Prophet Model

Time Series Forecasting of COVID-19 Mortality in SADC region...

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2022 International Conference on Smart Applications, Communications and Networking, SmartNets 2022

作者： Mphale, Ofaletse Raffing, Neo Sheikh, Sajid M. Balasubramanian, Lavanya University of Botswana Faculty of Science Department of Computer Science Gaborone Botswana University of Botswana Faculty of Electrical Engineering Department of Electrical Engineering Gaborone Botswana New Era College Faculty of Electrical Engineering Department of Electrical and Electronic Engineering Gaborone Botswana

ISBN: (纸本)9781665487580

This study proposes Facebook-Prophet model for understanding and forecasting of corona virus (COVID-19) mortality in Southern African Development Community (SADC) region over a 90-day time period. Findings showed that COVID-19 mortality in SADC region is expected to degrade in the near future. Model performance metrics were used to compute prediction performance. These results implied that the selected model was satisfactory and reliable. Findings of the study are expected to raise situational awareness into better understanding of the pandemic and to provide support for strategic health decisions for better management of COVID-19 disease. Moreover, the study provides a series of recommendations to be followed in order to decline COVID-19 related deaths in SADC countries. © 2022 IEEE.

关键词： COVID-19

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On the System Performance of Mobile Edge Computing in an Uplink NOMA WSN With a Multiantenna Access Point Over Nakagami-m Fading

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 668-685页

作者： Van-Truong Truong Van Nhan Vo Dac-Binh Ha Chakchai So-In the Faculty of Electrical-Electronic Engineering Duy Tan University the Institute of Research and Development Duy Tan University the Faculty of Information Technology Duy Tan University IEEE the Applied Network Technology(ANT)Laboratory Department of Computer ScienceCollege of ComputingKhon Kaen University

In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA).Due to limited computation and energy resources,the cluster heads(CHs)offload their tasks to a multiantenna AP over Nakagami-m *** proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining(SC)or maximal ratio combining(MRC)and each cluster selects a CH to participate in the communication process by employing the sensor node(SN)*** derive the closed-form exact expressions of the successful computation probability(SCP)to evaluate the system performance with the latency and energy consumption constraints of the considered *** results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas,number of SNs in each cluster,task length,working frequency,offloading ratio,and transmit power ***,to determine the optimal resource parameters,i.e.,the offloading ratio,power allocation of the two CHs,and MEC AP resources,we proposed two algorithms to achieve the best system *** approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar *** use Monte Carlo simulations to confirm the validity of our analysis.

关键词： Mobile edge computing(MEC) Nakagami-m fading offloading selection combining(SC)/maximal ratio combining(MRC) successful computation probability(SCP) uplink nonortho-gonal multiple access(NOMA) wireless sensor networks(WSNs)

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