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16th International Conference on information Technology and Electrical Engineering, ICITEE 2024

作者： Bachmid, Muhammad Avied Anshari Haq, Muhammad Yasir Mumtaz, Muhammad Rafly Pinandito, Aryo Universitas Brawijaya Faculty of Computer Science Media Game and Mobile Laboratory Department of Informatics Engineering Malang Indonesia Universitas Brawijaya Faculty of Computer Science Malang Indonesia Universitas Brawijaya Faculty of Computer Science Media Game and Mobile Laboratory Department of Information System Malang Indonesia

ISBN: (纸本)9798350375817

Prior study has developed the RouteSegmentation algorithm to identify the perimeter area surrounding a route. In this study, a comparative experiment was carried out to investigate the performance of the RouteSegmentation algorithm implementation in contrast to the RouteBoxer algorithm. Both algorithms play a crucial role in geographical information systems (GIS), particularly in enhancing navigational tools by identifying points of interest along specified routes. Through systematic experimentation using various perimeter distance values and types of route, the trade-offs between processing efficiency and the granularity of the area identified by these algorithms were evaluated. The results demonstrate that RouteSegmentation maintained relatively consistent performance across different perimeter distance values and statistically significantly faster processing time than RouteBoxer. Furthermore, while the RouteBoxer algorithm benefits from larger perimeter distance values by reducing processing time, it compromises the coverage results of the algorithm and potentially undermines its usefulness in practical scenarios. This study not only provides insights into the optimal use of perimeter distance values for each algorithm but also guides users in selecting the appropriate algorithm based on their specific application needs, balancing detailed geographical analysis and runtime efficiency. © 2024 IEEE.

关键词： Transportation routes

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AI Based Automated Food Recognition Using CNN for Fitness Mobile Application

AI Based Automated Food Recognition Using CNN for Fitness Mo...

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2023 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2023

作者： Mahmood, Atif Mahmood, Rafay Azzuhri, Saaidal Razalli Azizul Hasan, Zati Hakim Mat Kiah, Miss Laiha University of Central Punjab Faculty of Information Technology and Computer Science Lahore Pakistan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System & Technology Kuala Lumpur50603 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Artificial Intelligence Kuala Lumpur50603 Malaysia

ISBN: (纸本)9798350347494

Eating a healthy, balanced diet with regular exercise is important for maintaining physical and mental health. Healthy diet and exercise promise a healthier lifestyle. Other than that, people competing in different sports need a customized, strict workout and diet plan. Trainees offer their best at gymnasium by following a strict workout plan recommended by their coaches. However, there is no way till now to determine if the trainees are taking the prescribed diet strictly. Therefore, an optimized application with the feature of real time scanning of the prescribed meal can be helpful to overcome this problem. For this purpose, we have used Convolutional Neural Networks (CNN) along with Image Processing. Using ResNet50 with activation functions of ReLU and Softmax, our trained CNN model and the proposed architecture achieved an accuracy of 82.70 percent. We used CNN pre-trained models such as InceptionV3 with LeakyReLU and Softmax, InceptionV3 with ReLU and Softmax, VGG16 with ReLU and Softmax, VGG16 with ReLU and Softmax, and ResNet50 with ReLU and Softmax, so the achieved accuracy, respectively, is 78%, 71%, 58%, 56%, and 82.7%. Therefore, the proposed model has the potential to achieve high performance on real time food scanning which can be used in online fitness platforms(mobile applications and websites) and fitness gadgets. © 2023 IEEE.

关键词： Convolutional neural networks

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Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

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computers, Materials & Continua 2024年第8期80卷 3315-3332页

作者： Asifa Mehmood Qureshi Abdul Haleem Butt Abdulwahab Alazeb Naif Al Mudawi Mohammad Alonazi Nouf Abdullah Almujally Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

关键词： Semantic segmentation YOLOv5 vehicle detection and tracking Kalman filter SURF

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Challenges of Extended Reality Technology in Higher Education: A Review

International Journal of Emerging Technologies in Learning

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International Journal of Emerging Technologies in Learning 2023年第14期18卷 39-50页

作者： Obeidallah, Randa Al Ahmad, Ayat Qutishat, Duha Department of Computer Information System Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Computer Science and Applications Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan

Extended reality is proposed as a means to deliver qualitative material to students in higher education institutions. The usage of this technology has increased due to Covid-19 lock down. This paper aims to categorize the challenges encountered by literature that deployed extended reality (XR) in higher education during and after the lockdown. However, the number of available articles addressing these challenges is limited, with a total number of 40 articles obtained from dimensions’ dataset. In this study, bibliometric analysis is used to highlight the number of publications, citations, and authors in this field. Thematic analysis with inductive reasoning is applied to extract the primary challenge themes mentioned in 23 articles. After reviewing the eligible articles;we categorized the challenges into three main themes: Teacher, Student, and Technology. © 2023 by the authors of this article. Published under CC-BY.

关键词： Augmented reality

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Building Travel Agent Partnerships in Tourism Industry Using Blockchain Technology to Increase Customer Satisfaction 4

Building Travel Agent Partnerships in Tourism Industry Using...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Inayatulloh Fathurrozi, Ahmad Prio Pamungkas, R. Wisnu Sinlae, Fried Priatna, Wowon Lestari, Tyastuti Sri Yasir, Muhammad Noe'man, Achmad Bina Nusantara University Information Systems Department School of Information system Jakarta11480 Indonesia Bhayangkara Jakarta Raya University Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350353266

Tourist travel is an activity that concern the temporary movement of tourists from their place of origin to a particular destination for recreational purposes. The many parties or agents involved in a tourist trip require coordination between parties to provide the best service for tourists. The negative impact of the lack of coordination and transparency from tourism agents has an impact on tourists. Tourists often receive services that are not as expected because there is no transparency in transactions throughout their tour. Currently, there is no mechanism that unites tourism agents and guarantees transparency and security of information for tourists and stakeholders in the tourism industry. Thus, the aim of this study is to build a blockchain prototype for travel agent partnerships in the tourism industry using blockchain technology. The research method uses a quantitative approach through a literature review by studying the mechanisms of tourist travel and the parties involved in tourist travel, as well as blockchain technology. The research result is a model for adopting blockchain technology for tourism and travel partnerships. The research outcome also show examples of data simulation using the Ganache framework. © 2024 IEEE.

关键词： Customer satisfaction

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Real-time system for Translating American Sign Language to Text Using Robust Techniques

Real-time System for Translating American Sign Language to T...

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2023 International Conference on Recent Advances in science and Engineering Technology, ICRASET 2023

作者： Dabwan, Basel A. Jadhav, Mukti E. Abosaq, Hamad A. Olayah, Fekry A. Al Yami, Mohammed Abdelrahman, Yahya A. AlBaydha University Department of Information System AlBaydha Yemen Shri Shivaji Science & Art College Department of Computer Science India Najran University Computer Science Dep. Saudi Arabia Najran University Kingdom of Saudi Arabia Department of Information System Saudi Arabia Najran University Faculty of Computer Science and Information Systems Department of Information Systems Saudi Arabia Najran University College of Computer Science and Information Systems Department of Information System Saudi Arabia

ISBN: (纸本)9798350306927

Sign language serves as a vital medium of communication for the Deaf and dumb people. We present a real-time system to translate ASL into text, addressing the communication gap between deaf and dumb people and normal people. Our system leverages computer vision and machine learning techniques to accurately recognize ASL gestures. In this study, we introduce a convolutional neural network (CNN) and (LSTM) approaches designed to identify hand gestures associated with human actions as captured through images from cameras. By utilizing the hand's spatial positioning and orientation, we construct the training and testing datasets for the CNN-LSTM model, we were used 4680 skeleton images of the ASL alphabet, from A to Z as a dataset. The hand image undergoes an initial filtering process followed by classification, where a classifier determines the classification of the hand gesture. Subsequently, the refined images are harnessed to train the CNN-LSTM model. Our real-time system provides accurate and reliable results in real-time 99.9%, in addition to generating new datasets that are processed and can be trained on any model. © 2023 IEEE.

关键词： Real time systems

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A Big Data, Bigger Impact: A Comprehensive Review of Machine Learning Advancements 4

A Big Data, Bigger Impact: A Comprehensive Review of Machine...

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4th International Conference on Electrical Engineering and computer science, ICECOS 2024

作者： Dlshad Hamad Ameen, Didar Wahhab Kareem, Shahab Baez Hasan, Sarhad Soran University Faculty of Science Computer Science Department Soran Kurdistan Iraq Engineering Erbil Polytechnic University Erbil Department of Information System Kurdistan Iraq

ISBN: (纸本)9798350368253

The exponential growth of data driven by the internet has necessitated effective extraction of insights, with big data and machine learning standing as pivotal tools. This paper aims to provide insights into the evolving landscape of machine learning and big data analytics through a comprehensive literature review. It seeks to underscore the key advancements and intersections between these domains and their transformative applications across industries. Specifically, the study will delve into the foundational theories that underpin machine learning algorithms, data collection and preprocessing techniques involved in big data analysis, and the prominent challenges associated with extracting insights from large and complex datasets. It will further explore major studies that exemplify the integration of machine learning methodologies and big data technologies for addressing real-world problems. The study will also examine visualization methods that facilitate comprehending insights gleaned from big data. Finally, it will discuss future research directions and highlight best practices for leveraging machine learning and big data analytics in decision-making processes. © 2024 IEEE.

关键词： Adversarial machine learning

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An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity

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computers, Materials & Continua 2022年第2期70卷 3311-3319页

作者： Khattab Alheeti Abdulkareem Alaloosy Haitham Khalaf Abdulkareem Alzahrani Duaa Al_Dosary Department of Computer Networking System College of Computer Sciences and Information TechnologyUniversity of AnbarRamadiIraq College of Medicine University of AnbarRamadiIraq Department of Computer Science Faculty of Computer Science and Information TechnologyAlbaha UniversityAlbahaSaudi Arabia College of Computer Sciences and Information Technology University of AnbarRamadiIraq

Self-driving and semi-self-driving cars play an important role in our daily *** effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital ***,external infrastructures also play significant roles in the transmission and reception of control data,cooperative awareness messages,and caution *** this case,roadside units are considered one of themost important communication *** distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost,bandwidth,connectivity,and radio coverage *** this paper,a new distributed roadside unit is proposed to enhance the performance and connectivity of these ***,this approach is based primarily on k-means to find the optimal location of each roadside *** addition,this approach supports dynamicmobility with a long period of connectivity for each ***,this system can adapt to various locations(e.g.,highways,rural areas,urban environments).The simulation results of the proposed system are reflected in its efficiency and ***,the system can achieve a high connectivity rate with a low error rate while reducing costs.

关键词： Self-driving cars roadside unit autonomous vehicles distributed systems connectivity

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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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Possibility of Profit Gain in Futures Cryptocurrency Trading Plans Using a Patterned Dataset Model under Moon Conditions 10

Possibility of Profit Gain in Futures Cryptocurrency Trading...

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10th IEEE information Technology International Seminar, ITIS 2024

作者： Parlika, Rizky Isnanto, R. Rizal Rahmat, Basuki Universitas Diponegoro Doctoral Programe of Information Systems Semarang Indonesia University of Pembangunan Nasional "Veteran"Jawa Timur Faculty of Computer Science Department of Information System Surabaya Indonesia

ISBN: (纸本)9798331521295

Among the various components of cryptocurrency trading are futures (2-way trading based on short or long contracts) and spot schemes (which operate in the same direction as buying and selling crypto assets). The futures scheme transaction was selected for this study's test since it frequently results in trades with comparatively larger swings than spot scheme deals. If these variations are not correctly anticipated, they may result in losses. Real-time position reading for cryptocurrency trading is made possible by the Patterned Dataset Model. Based on observations of the pattern of influence that Bitcoin price changes have on a variety of other cryptocurrency asset values in a trading market, this model was developed. Three types of data are generated by this model, and they are mined nonstop every few seconds. The entire conditions (a mix of moon and crash conditions), price increase conditions (moon), and price fall conditions (crash) make up the patterned dataset. In this study, the topic of discussion is patterned datasets in rising price (moon) environments. It is possible to demonstrate the possibility of reasonably consistent gains from short contract purchase positions using the Patterned Dataset in Moon Conditions. © 2024 IEEE.

关键词： Marketplaces

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