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3rd International Conference on Creative Communication and Innovative technology, ICCIT 2024

作者： Siswanto, Joko Hendry Rahardja, Untung Sembiring, Irwan Lisangan, Erick Alfons Hakim, Muhammad Iman Nur Wibowo, Feri Polytechnic of Road Transportation Safety Road Transportation System Engineering Tegal Indonesia Satya Wacana Christian University Faculty of Information Technology Indonesia University of Raharja Faculty of Science and Technology Tangerang Indonesia Satya Wacana Christian University Faculty of Computer Science Indonesia The University of Western Australia Computer Science and Software Engineering Perth Australia Polytechnic of Road Transportation Safety Automotive Engineering Technology Tegal Indonesia Muhammadiyah University Purwokerto Faculty of Engineering and Science Banyumas Indonesia

ISBN: (纸本)9798350367492

A crucial role in the BRT transportation system's planning, development, and operation is the prediction of passenger numbers. Using time-series data, it is necessary to develop careful prediction models, appropriate techniques, and an indepth understanding of the number of BRT Transjakarta passengers. A prediction model is sought based on comparing combined LSTM and BiLSTM experiments using three evaluation matrices and time. Historical data used from daily passenger data for 13 BRT Transjakarta corridors (1/01/2021-31/12/2023). The best prediction model was obtained from the BiLSTM-CNN combination with the lowest MSLE (0.0425), MAPE (0.1598), and SMAPE (15.9387) evaluation matrix values. However, it required a longer time (00:02:14). Predictions of passenger numbers on the 13 Transjakarta BRT corridors for the next 12 months can be made simultaneously by predicting fluctuations occurring simultaneously. The strongest positive correlation is in corridor 9-6, while the strongest negative correlation is in corridor 12-5. The prediction results must be understood by stakeholders, managers, and technopreneurs to develop and support appropriate strategies to increase the number of BRT passengers. © 2024 IEEE.

关键词： Prediction models

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STDP-Driven Automated Retinal Circuit with 7nm FinFET for Motion and Looming Detection: A Hybrid Model with Image Analysis

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IEEE Access 2025年 13卷 95594-95608页

作者： Rabeya, Rubina Akter Islam, Md Turiqul Uddin, Shah Muhammad Imtiyaj Al-Shidaifat, Alaaddin Song, Hanjung Choi, Heung-Kook Kim, Hee-Cheol Department of Digital Anti-Aging Healthcare Inje university Gimhae50834 Korea Republic of Department of Nanoscience and Engineering Centre for Nano Manufacturing Inje university Gimhae50834 Korea Republic of Middle East University Faculty of Information Technology Computer Science Department Amman11831 Jordan Department of Computer Engineering Inje university Gimhae50834 Korea Republic of

This paper presents a spike-timing-dependent plasticity (STDP) driven silicon retina designed using 7nm FinFET technology for detecting object motion and looming threats inspired by the biological visual system. The proposed neuromorphic architecture integrates an automated motion spiking circuit with STDP and advanced image analysis, mimicking natural retinal processing to identify potential hazards. Two specialized circuits are introduced to generate bipolar spiking signals for object motion and looming detection. These spikes are processed by an STDP-based spiking neural network, qualifying real-time distance estimation of approaching objects. Model Results demonstrate accurate collision time prognosis based on object dynamics. YOLOv5 and MiDaS enhance object recognition and depth perception, improving situational awareness. The proposed model uses a hybrid strategy combining object detection and depth estimation to identify objects with different spatial distances and confidence levels accurately. Experimental results show a motion detection accuracy of 93.2% and an average latency of around 15 ms, 66 frames per second, which proves this is suitable for time-sensitive applications. Because the 7nm FinFET technology improves system efficiency and responsiveness, it can be used in real-time applications where high-precision motion detection is necessary in dynamic conditions. However, potential challenges include scalability for large-scale deployment and hardware constraints associated with real-time processing on FinFET-based circuits. The proposed system can be used for robotic vision systems, autonomous vehicles, and other contexts where real-time visual processing and threat detection are needed. © 2013 IEEE.

关键词： Motion estimation

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Car Price Prediction using Machine Learning 9

Car Price Prediction using Machine Learning

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Ahmad, Muhammad Farooq, Muhammad Ali Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Hussain, Usman Khan, Zohaib Ahmed Javaid, Arslan University Lahore Department of Computer Engineering Information Technology Punjab Pakistan Bahria University Lahore Campus Zunnurain Dept. of Computer Science Pakistan University of Central Punjab Lahore Faculty of Information Technology Department of Computer Science Pakistan National College of Business Administration And Economics Department of Computer Science Lahore Pakistan Information Technology University Lahore Pakistan University of Home Economics Pakistan

ISBN: (纸本)9798350394474

Predicting car prices involves determining a vehicle's market worth based on various attributes like brand, model, year of manufacture, mileage, and overall state. This prediction holds significant value for the auto industry, aiding both prospective buyers and sellers in understanding pricing and making educated decisions regarding vehicle transactions. Machine learning techniques, trained on historical datasets sourced from online automotive marketplaces, dealerships, and auction platforms, are adept at forecasting the prices of both new and pre-owned cars, using their specifications. Noteworthy machine learning models applied include Linear Regression, AdaBoostRegressor, Lasso Regression, and Ridge Regression. Integrating more diverse data points, such as customer reviews, prevailing market dynamics, and regional factors, can further refine the accuracy of these prediction models. In recent trials with the provided dataset, the Lasso Regression and Ridge Regression models stood out, delivering an impressive accuracy rate nearing 90%. © 2024 IEEE.

关键词： Forecasting

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Assessing the Effects of Blackhole Attacks on MANET Reliability and Security

Assessing the Effects of Blackhole Attacks on MANET Reliabil...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Fadhil, Saif Aamer Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad10011 Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Mobile Ad Hoc Networks (MANETs) are characterized by some important attributes, including infrastructure, mobile, and dynamic nature, which makes them have vast applications in different areas of computer networks. The black hole attack falls among the strongly criticized security threats within the framework of the MANETs. This deceptive behavior captures data packets from a source destined for the malicious node and then declines them when they are received. This paper comprehensively reviews prior studies done to analyze and mitigate black hole attacks in MANETs. Besides this, we will conduct a comparative analysis of the research above projects. The present paper extends prior work by actually performing the black hole assault simulations in a MANETs environment. This is done with the aid of the NS2 simulator and the Ad Hoc On-Demands Distance Vector (AODV) routing protocol. In this study, the black hole attack is analyzed and the consequences of the attack are assessed in terms of the system performance and security. We analyze crucial parameters like end-to-end latency, PDR, packet drop, and throughput to evaluate the consequences of this assault. From our results derived from the analysis of our simulations, we aim to present substantial points to do with the impacts of blackhole attacks on MANETs. In this way, our goal is to participate in the perpetually evolving efforts aimed at enhancing the security and reliability of such dynamic networks. © 2024 IEEE.

关键词： Mobile ad hoc networks

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Genome-wide exploratory analysis for NARAC dataset with preparation for haplotype block partitioning through minor allele frequency quality control viewpoint

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Iran Journal of computer science 2023年第4期6卷 387-396页

作者： Saad, Mohamed N. Zareef, Galena W. Ibrahim, Fatma S. Said, Ashraf M. Hamed, Hisham F. A. Biomedical Engineering Department Faculty of Engineering Minia University Minia Egypt Biomedical Engineering Department Misr University for Science and Technology 6th of October City Egypt Bioinformatics Department Faculty of Computer and Information Minia University Minia Egypt Computer and Software Engineering Department Misr University for Science and Technology 6th of October City Egypt Department of Electrical Engineering Faculty of Engineering Egyptian-Russian University Cairo Egypt Electrical and Electronics Engineering Department Faculty of Engineering Minia University Minia Egypt

This article provides a detailed description, analysis, and visualization of a case–control genome-wide genotypic dataset from the North American Rheumatoid Arthritis Consortium (NARAC). The data is presented in terms of the number of females and males in both cases and controls, as well as the percentage of missing data. The number of alleles and genotypes is also counted, and the minor allele frequency (MAF) is calculated for each single nucleotide polymorphism (SNP). The data is further classified into four categories based on the SNP's MAF, namely, very rare, rare, low frequency, and common SNPs. The regions of these categories in the chromosome are investigated to determine the proportion of SNPs in coding locations and other regions. It is observed that each category has a different proportion in each region of consequence annotation. The data composition in terms of alleles and genotypes is found to be greatly disproportionate. The results present clear insights into the data and its MAF, which can be compared with other datasets. These findings can aid researchers in gaining a comprehensive understanding of such case–control datasets and bring accurate insights into the data. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Genome-wide association study Genome-wide exploratory analysis Minor allele frequency Single nucleotide polymorphism Variant annotation

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Virtual Reality Semiconductor Laboratory: Usability Testing to Improve User Experience (UX) in Virtual Environments 3

Virtual Reality Semiconductor Laboratory: Usability Testing ...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Ahmed Alnagrat, Ahmed Jamah Ismail Alkhallas, Muhannad Imhmed Mohamed, Abdalslam S Che Ismail, Rizalafande Syed Idrus, Syed Zulkarnain Salem Saed, Halifah Department of Computer Science and Information Technology Wadi al-Shati Fezzan Libya Perlis Kangar01000 Malaysia Faculty of Engineering Technology in Zuwarah Department of Software Engineering Zuwarah Libya Aljufra University Faculty of Information Technology Aljufra Libya Arau02600 Malaysia Department of Communication Perlis Kangar01000 Malaysia Higher Institute of Science and Technology Wadi Al-Shati Department of Computer Science and Information Technology Wadi al-Shati Fezzan Libya

ISBN: (纸本)9798350319897

The Virtual Reality (VR) Semiconductor Laboratory, a computer-based virtual lab designed for training engineering students in semiconductor concepts, was evaluated to enhance user experience (UX) in virtual environments. A sample of 97 participants, including undergraduate, Master's, and PhD students, participated in the study. The User Experience Questionnaire (UEQ) was developed to collect data from users to evaluate their experience with the VR lab. The results of the study showed that the participants had a positive experience with the VR learning environment. They found the experience to be enjoyable, easy to understand, and helpful in completing learning tasks. The majority of participants rated the experience as very easy or easy in terms of learning and understanding the material, enjoyment, ease of using the interface, comfort with body movement during learning, and assistance with tasks. Additionally, most participants rated the experience as very easy in terms of task completion and comfort with the VR experiment. The study found high reliability in terms of usability, indicating that the UEQ is a valid and reliable tool for assessing virtual laboratory usability. These findings suggest that virtual laboratories can be effective learning and training tools in various fields, such as science and engineering. This research highlights the potential of VR technology in improving the training and learning experience for students. © 2023 IEEE.

关键词： Virtual reality

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Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

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computers, Materials & Continua 2024年第6期79卷 3549-3568页

作者： Reem Ibrahim Alkanhel Doaa Sami Khafaga Ahmed Mohamed Zaki Marwa M.Eid Abdyalaziz A.Al-Mooneam Abdelhameed Ibrahim S.K.Towfek 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 Computer Science and Intelligent Systems Research Center BlacksburgVirginia24060USA Delta Higher Institute for Engineering and Technology Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Artificial Intelligence and Computational Intelligence Research Center MeridenConnecticut06450USA School of ICT Faculty of EngineeringDesign and Information&Communications Technology(EDICT)Bahrain PolytechnicP.O.Box 33349Isa TownBahrain

The networks of wireless sensors provide the ground for a range of applications,including environmental moni-toring and industrial *** the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network *** infrastructure planning should be focused on increasing performance,and it should be affected by the detailed data about node *** work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location,which will contribute to better network planning and *** using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization,our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and *** implementing adaptive protocols according to varying environments and sensor constraints,our study aspires to improve overall network *** compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization,Particle Swarm Optimization,Genetic Algorithms,and Whale Optimization about performance on real-world *** the most efficient in the optimization process,Biruni displays the lowest error rate at *** two other statistical techniques,like ANOVA,are also useful in discovering the factors influencing the nature of sensor data and network-specific *** to the multi-faceted support the comprehensive approach promotes,there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be *** delivering better performance and reliability for various in-situ applications,this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

关键词： Wireless sensor networks optimization ARIMA model BER algorithm metaheuristic algorithms

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Providing an Intelligent Hybrid Routing Method in Wireless Sensor Networks 23th

Providing an Intelligent Hybrid Routing Method in Wireless S...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Hosseinabadi, Ali Asghar Rahmani Mirkamali, Seyedsaeid Hajiabadi, Mahdi Rohani Abraham, Ajith Department of Computer Science University of Regina Regina Canada Department of Computer Engineering and IT Payame Noor University Tehran Iran Faculty of Computer and Information Technology Engineering Islamic Azad University Qazvin Qazvin Iran School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

ISBN: (纸本)9783031647758

Optimal energy consumption in Wireless Sensor Networks (WSNs) is important. Previous research has shown that by organizing network nodes into a number of clusters, one can achieve greater energy efficiency leading to longer network life. The WSN is made up of a very large number of sensor nodes that communicate with each other to perform specific tasks. These nodes have limited energy, processing power, and memory. Because the lifetime of the network depends on the brightness of the nodes, the energy consumption of each node is very important. Therefore, we need to find solutions to reduce the energy consumption of the nodes. One way to reduce energy consumption is clustering, many algorithms have been introduced in this field. In this paper, we optimize Particle Swarm Optimization (PSO) algorithm based on fuzzy logic and PSO algorithm. The fitting criterion will be based on the minimum energy consumed by the nodes of the network during each operation period, It leads to a balance in the energy consumption of the headers and, as a consequence, a lengthening of the life of the network. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Fuzzy logic

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GIRP: Energy-Efficient QoS-Oriented Microservice Resource Provisioning via Multi-Objective Multi-Task Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5793-5807页

作者： Yuan, Honggang Wang, Ting Fu, Min Shi, Yuanming East China Normal University MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Microservice architecture has revolutionized web service development by facilitating loosely coupled and independently developable components distributed as containers or virtual machines. While existing studies emphasize end-to-end latency, this paper investigates energy-efficient quality-of-service (QoS)-oriented microservice provisioning, focusing on both QoS satisfaction and power consumption (PC) conservation. We propose the Green and Intelligent Resource Provision (GIRP) architecture, integrating a data-driven energy-latency-aware resource allocation and scheduling manager to balance latency and PC. To reconcile the trade-offs involved, a dual-objective optimization problem is formulated to minimize latency and energy use by selecting proper servers, allocating CPU cores, and determining service replicas. To address challenges with discrete variables, dual objectives, and implicit mappings, we leverage a model-free deep deterministic policy gradient-based reinforcement learning algorithm. Specifically, we develop a multi-task agent via the Multi-gate Mixture-of-Experts model to simultaneously make two separate actions regarding CPU core numbers and service replica numbers, followed by a single-task agent to determine service scheduling. Extensive experiments on the DeathStarBenchmark testbed validate GIRP's effectiveness, demonstrating approximately 52% resource savings and a 43% reduction in PC compared to leading methods like Sinan, Firm, and heuristic-based algorithms. These results highlight GIRP's capability to optimize microservice orchestration by balancing end-to-end latency and power efficiency. © 2025 IEEE. All rights reserved.

关键词： Quality of service

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A Combined Approach of Principal Component Analysis and Support Vector Machine for Early Development Phase Modeling of Ohrid Trout(Salmo Letnica)

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computer Modeling in engineering & sciences 2021年第3期126卷 991-1009页

作者： Sunil Kr.Jha Ivan Uzunov Xiaorui Zhang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Faculty of Computer Science and Engineering University of Information Science and TechnologyOhrid6000Republic of Macedonia

Ohrid trout(Salamo letnica)is an endemic species of fish found in Lake Ohrid in the Former Yugoslav Republic of Macedonia(FYROM).The growth of Ohrid trout was examined in a controlled environment for a certain period,thereafter released into the lake to grow their natural *** external features of the fish were measured regularly during the cultivation period in the laboratory to monitor their *** data mining methods-based computational model can be used for fast,accurate,reliable,automatic,and improved growth monitoring procedures and classification of Ohrid *** this motivation,a combined approach of principal component analysis(PCA)and support vectormachine(SVM)has been implemented for the visual discrimination and quantitative classification of Ohrid trout of the experimental and natural breeding and their growth *** PCA results in better discrimination of breeding categories of Ohrid trout at different development phases while the maximum classification accuracy of 98.33% was achieved using the combination of PCA and *** classification performance of the combination of PCA and SVM has been compared to combinations of PCA and other classification methods(multilayer perceptron,naive Bayes,randomcommittee,decision stump,random forest,and random tree).Besides,the classification accuracy of multilayer perceptron using the original features has been studied.

关键词： Salamo letnica growth phase modeling PCA SVM

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