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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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Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles 10

Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles

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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Purnama, Iwan Pane, Rahmadani Sooai, Adri Gabriel Edi, Firman Chamid, Ahmad Abdul Adi, Puput Dani Prasetyo Universitas Labuhanbatu Faculty of Science and Technology Department of Information Technology Labuhanbatu Indonesia Universitas Katolik Widya Mandira Faculty of Computer Science Department of Computer Engineering NTT Kupang Indonesia Batam Institute of Technology Faculty of Industrial Technology Department of Engineering Management Batam City Indonesia Muria Kudus University Department of Informatics Engineering Kudus Indonesia Department of Telecommunication Bandung Indonesia

ISBN: (纸本)9798331521295

This article presents a simulation study on the use of LoRaWAN technology in autonomous vehicles. The research focuses on developing an Auto-Handover Gateway for Autonomous Vehicles, utilizing LoRaWAN parameters such as bit rates, spreading factor (SF), packet loss, and received signal strength. These parameters are tested in simulations of autonomous vehicle movements, which include both regular and random motion patterns. The aim is to assess the performance of LoRaWAN in different scenarios and its impact on communication in autonomous vehicle systems. The simulation results show several key trends. As the vehicle moves further, the spreading factor (SF) increases, and bit rates tend to decrease, indicating a drop in communication performance over longer distances. The research also identifies that the gateway with the closest proximity to the vehicle is selected, both in random and non-random movement scenarios. Additionally, packet loss increases as the distance between the vehicle and the gateway grows, highlighting the challenges of maintaining reliable communication over long distances. These findings provide insights into optimizing LoRaWAN parameters for real-time communication in autonomous vehicles, contributing to the development of intelligent vehicle networks. © 2024 IEEE.

关键词： Packet loss

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Mobile Edge Computing for Multi-Services Digital Twin-Enabled IoT Heterogeneous Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1845-1853页

作者： Liu, Weiqi Hossain, Mohammad Arif Ansari, Nirwan Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States Middle Tennessee State University Department of Engineering Technology MurfreesboroTN37132 United States New Jersey Institute of Technology Advanced Networking Laboratory Department of Electrical and Computing Engineering NewarkNJ07102 United States

A scheme for edge computing-enabled offloading in a digital twin (DT) enabled heterogeneous network (HetNet) of multi-services IoT devices (IDs) is proposed. This scheme optimizes the association and handover of IDs, offloading ratio, and resource allocation considering the number of IDs, deadline requirements, and resource capacities. The objective is to enhance future generation networks by considering the ID movement, diverse ID requests, and network heterogeneity. We formulate the problem as Joint ID assOciatIon, offloadiNg ratio, Wireless bandwidth and computIng reSource allocation, and digital twin placEment (JOINWISE), aiming to minimize the task completion time of all IDs while considering ID movement. Since JOINWISE is a mixed-integer nonlinear problem, we decompose it into two sub-problems: the ID Association (IDA) problem and the offloading Ratio, DT plAcement, bandwiDth and computIng resource allOcation (RADIO) problem. IDA can be solved by mapping it to a multi-dimensional multiple knapsacks problem. Due to the non-convexity, high dimension of decision variables, and dynamic HetNet environment of RADIO, we propose a deep deterministic policy gradient (DDPG) based reinforcement learning method to iteratively solve the two sub-problems. Simulation results have confirmed the effectiveness of our proposed scheme in tackling the JOINWISE problem. © 2015 IEEE.

关键词： Mobile edge computing

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Neuroimaging Precision: Crafting a Stacked VGG19-Logistic Fusion for Breakthrough Early Detection of Alzheimer's 9

Neuroimaging Precision: Crafting a Stacked VGG19-Logistic Fu...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Selvi, G. Anbu Karthick, T. Srmist Department of Computer Science And Engineering Chennai Kattankulathur India School of Computing Srmist Department of Data Science And Business Systems Chennai Kattankulathur India

ISBN: (纸本)9798350365092

Several medical investigations have demonstrated that Alzheimer's disease (AD) manifests itself long before the formal diagnosis of dementia. These studies have led to the identification of numerous ideal biomarkers associated with Alzheimer's symptoms. Consequently, the imperative for early diagnosis has become evident, necessitating a high-performance computational tool capable of efficiently processing vast amounts of data. Early detection of AD not only offers valuable insights into the condition's progression but also presents a significant opportunity to benefit from timely and effective treatment. This ground-breaking study pioneers an innovative approach to the early detection and diagnosis of AD by crafting a sophisticated model SVGGL, which blends the power of stacked VGG19 neural networks with the interpretability of logistic regression. Leveraging neuro-imaging data, specifically the insights provided MRI, and our model aims to discern subtle structural and functional alterations in the brains of individuals at risk for or in the early stages of AD. The combination of feature extraction from stacked VGG19 models and subsequent logistic regression classification yields a precise and efficient classifier with an outstanding accuracy of 98.24%. With such a high degree of accuracy, the model can reliably differentiate among healthy people and those with Alzheimer's, opening the door to potential early intervention and individualized treatment plans. The study not only sheds light on the intricate neuro pathological landscape of AD but also propels us toward a future where cutting-edge technology and data-driven insights, exemplified by our high-accuracy model, converge to tackle the challenges posed by this prevalent neurodegenerative disorder. © 2024 IEEE.

关键词： Neuroimaging

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A Deep Learning-Based Chatbot to Enhance Maternal Health Education 8

A Deep Learning-Based Chatbot to Enhance Maternal Health Edu...

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8th Conference on Information Communication Technology and Society, ICTAS 2024

作者： Batani, John Mbunge, Elliot Leokana, Lipuo Botho University Faculty of Engineering & Technology Maseru Lesotho University of Eswatini Department of Computer Science Bambane Swaziland

ISBN: (纸本)9798350314892

Maternal mortality remains a global concern, with resource-constrained countries disproportionately affected due to inherent challenges in such countries, like underfunding, distant health facilities, lack of access to maternal health education and inequitable access to maternal health services. Though medical chatbots are gaining popularity, resource-constrained countries lag, and there is a dearth of chatbots specific to maternal health education in local languages. Therefore, this study utilised natural language processing to develop a maternal health education chatbot using a feedforward deep neural network. The model was trained using three local African languages (Sesotho, Shona and Ndebele) and English, and the chatbot was deployed using the Flask server through a web app to present a friendly interface to users. The training and evaluation losses reached zero, while the training and evaluation accuracies reached 100%. © 2024 IEEE.

关键词： Deep neural networks

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EEG Signal-Based Autism Spectrum Disorder Detection Through Normalized Mutual Information and Convolutional Neural Network 2nd

EEG Signal-Based Autism Spectrum Disorder Detection Through ...

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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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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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Challenges in Mobile Network Capacity Monitoring & Solutions to Improve Capacity Problems Using Various Parameters 4

Challenges in Mobile Network Capacity Monitoring & Solutions...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Gupta, Vikas Chakraverti, Ashish Kumar Kumar, Manoj School of Engineering and Technology Sharda University Department of Computer Science and Engineering Uttar Pradesh Greater Noida201310 India University of Wollongong Faculty of Engineering and Information Science Dubai United Arab Emirates

ISBN: (纸本)9798350387490

This paper has an overview on study of traffic capacity of Mobile network parameter that affect the network utilization and network experience in Mobile network era., This capacity study & analytics provide a conception of how the various networks function, capacity parameters & constraints of different domain of Mobile Network like RAN (Radio access Network),CN (Core Network), IP & TX (Internet Protocol & Transport Network) & their associated capacity parameter have correlations in network utilization. It also has the access of Mobile Network KPIs detail, for readers to easily retrieve a variety of capacity parameters for effective capacity management. In order to manage end to end capacity requirement in telecom domain & can help for solving capacity problem with the help of various network parameters. These various capacity parameters can be used to generate systematic, rule-based decision which are standardized, more flexible, and faster to take decision on capacity expansion well in advance through the parametric behaviour. furthermore, the review concludes the published work their gaps and future work in the direction of improving Mobile network capacity management. © 2024 IEEE.

关键词： Internet protocols

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Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

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computers, Materials & Continua 2022年第9期72卷 4249-4265页

作者： Muhammad Irfan Ali Raza Faisal Althobiani Nasir Ayub Muhammad Idrees Zain Ali Kashif Rizwan Abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Saifur Rahman Omar Alshorman Samar Alqhtani Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadWah Campus22060Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Federal Urdu University of ArtsScience and TechnologyIslamabad44000Pakistan School of Electrical Engineering and Computer Science National University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science and Engineering University of Engineering and TechnologyNarowal CampusLahore54000Pakistan Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial ***,electricity demand and price forecasting play a significant role and can help in terms of reliability and *** to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG *** this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as *** minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant *** this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the ***,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test *** evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark *** proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.

关键词： Smart grid deep neural networks consumer demand big data analytics load forecasting price forecasting

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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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