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

作者： Rahardi, Majid Arsya, Muhammad Rezzha Riamrizal Aminuddin, Afrig Abdulloh, Ferian Fauzi Miah, Mohammad Badrul Alam Pratikno, Heri Universitas Amikom Yogyakarta Faculty of Computer Science Sleman Indonesia Mawlana Bhashani Science and Technology University Faculty of Engineering Tangail Bangladesh Universitas Dinamika Faculty of Technology and Informatics Surabaya Indonesia

ISBN: (纸本)9798350368253

The social media of X has become a focal point for discussions on sensitive issues, including the Israeli-Palestinian conflict. This research analyzes sentiment toward tweets expressing support for Palestine on X, comparing the Support Vector Machine (SVM) and Naïve Bayes methods. Given the global attention to the conflict, sentiment analysis on this topic is crucial. The results reveal that the SVM model outperforms Naïve Bayes, achieving accuracy, precision, recall, and F1-score of 85%, 87%, 85%, and 85% for Naïve Bayes, and 91%, 92%, 90%, and 90% for SVM, respectively. It demonstrates the SVM model's capability to distinguish between positive and negative sentiments. Comparison with previous research indicates that this study surpasses the sentiment analysis results of previous SVM models, while Naïve Bayes excels in specific research. With superior performance, this research provides a new contribution to recognizing and classifying sentiments on a particular dataset related to the Israeli-Palestinian conflict on X social media. © 2024 IEEE.

关键词： Tweets

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High-Performance Flow Classification of Big Data Using Hybrid CPU-GPU Clusters of Cloud Environments

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Tsinghua science and Technology 2024年第4期29卷 1118-1137页

作者： Azam Fazel-Najafabadi Mahdi Abbasi Hani H.Attar Ayman Amer Amir Taherkordi Azad Shokrollahi Mohammad R.Khosravi Ahmed A.Solyman Department of Computer Engineering Faculty of EngineeringBu-Ali Sina UniversityHamedan ***Iran Department of Energy Engineering Zarqa UniversityZarqa 13132Jordan Department of Informatics University of OsloOslo 0316Norway Department of Computer Science MalmöUniversityMalmö20506Sweden Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261100China Department of Electrical and Electronics Engineering NişantaşıUniversityIstanbul 34481742Türkiye

The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.

关键词： OpenMP Compute Unified Device Architecture(CUDA) Message Passing Interface(MPI) packet classification medical data tuple space algorithm Graphics Processing Unit(GPU)cluster

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An approach for measuring spatial similarity among COVID-19 epicenters

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Geo-Spatial Information science 2023年第3期26卷 496-513页

作者： Neda Kaffash Charandabi Abolghasem Sadeghi-Niaraki Soo-Mi Choi Tamer Abuhmed Geomatic Department Marand Technical FacultyUniversity of TabrizTabrizIran Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone Sejong UniversitySeoulSouth Korea College of Computing and Informatics Sungkyunkwan UniversitySuwonSouth Korea

Most countries and territories worldwide are affected by coronavirus disease 2019(COVID-19),and some cities have become known as epicenters owing to high *** of the changeable and unknown nature of the virus,managers of different cities could learn from the experiences of cities that have been successful in controlling COVID-19 instead of wasting time exploring different *** would be even more beneficial if they analyzed the experiences of similar *** similarity of such cities could be examined within a geographic information system based on various *** study investigated the similarities among eight cities-Wuhan,Tehran,Bergamo,Madrid,Paris,Daegu,New York,and Berlin-in terms of the COVID-19 situation(target)in these locations based on proximity factors,weather,and demographic ***,the factor and target layers were prepared,and then similar cities were identified using a similarity model and different distance *** results were aggregated using the Copeland method because of the different outcomes for each *** most similar city was identified for each selected city,and its similarity level was determined based on these *** results suggested the following pairs of similar cities:Wuhan-Berlin,Tehran-Berlin,Daegu-Wuhan,Bergamo-Madrid,Paris-Madrid,and New York-Paris based on COVID-19 related data up to 15 April 2020(target T1),and Daegu-Wuhan,Tehran-Madrid,Bergamo-Paris,Berlin-Paris,and New York-Madrid up to 8 December 2021(target T2)with a minimum and maximum similarity rate of 82.85%and 92.36%,*** similar cities,the most similar factors among the proximity criteria are the distance from bus and metro stations;among weather,the criteria are humidity and pressure;and among demographics,the criteria are male and female population ratios,literacy ratio,and death ratio from asthma and cancer,with a minimum and maximum difference of 0%and 64.94%,*** addition,according to the rando

关键词： COVID-19 GIS spatial similarity environmental factors demographic factors

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IoT and AI-Driven Conceptual Model of Museum Ecosystem

IoT and AI-Driven Conceptual Model of Museum Ecosystem

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2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Puspasari, Shinta Siradjuddin, Indah Agustien Rachmansyah Fahtu Rahman, M. Ade Haversyalapa, Ditho Universitas Indo Global Mandiri Faculty of Computer Science Department of Informatics Palembang Indonesia Universitas Trunojoyo Faculty of Engineering Department of Informatics Madura Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Department of Computer System Palembang Indonesia

ISBN: (纸本)9798350381290

Museum ecosystems need digital transformation to provide value added to museums. This study discusses the digital museum ecosystem conceptual model based on artificial intelligence (AI) technology and the Internet of Things (IoT). The new conceptual model integrates three elements of the museum ecosystem, people, collections & infrastructure, and museum services collaborating and communicating to achieve outputs in supporting the achievement of museum organizational goals through IoT and AI-based digital technology. The conceptual model describes various forms of implementing IoT and AI in the museum ecosystem for museum management along with the value added or results generated for museum management. The development of this ecosystem conceptual model into a physical museum has an impact on optimizing museum performance by providing value added in the form of convenience and prosperity for users and various museum stakeholders, especially in Society 5.0. © 2023 IEEE.

关键词： Internet of things

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Analysis of YouTube Video Comments with NLP Methods 16

Analysis of YouTube Video Comments with NLP Methods

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Mironela, Pirnau Iustin, Priescu Mihaela, Priescu Catalina Claudiu, Pirnau Daniel, Ghiculescu Liviu 'Titu Maiorescu' University Faculty of Informatics Department of Informatics Bucharest Romania National University of Science and Technology Politehnica Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania National University of Science and Technology Politehnica Bucharest Faculty of Industrial Engineering and Robotics Manufacturing Engineering Department Bucharest Romania

ISBN: (纸本)9798350371154

This study proposes methods that can be used to examine and interpret comments that users have made after watching videos on YouTube on a particular topic. YouTube tutorials are very popular among young people. They have become an important pillar in informal education, thus contributing to the rapid acquisition of skills and knowledge. Under these circumstances, we were interested in analysing the YouTube videos comments, knowing that the platform has a continuous increase in popularity, which is also due to the opportunities of sharing them. Since ChatGPT-themed YouTube videos have seen a significant surge in popularity since 2022, we were interested to analyse some videos that approach the topics of 'ChatGPT, AI clone, AI robot and Deep Learning.' © 2024 IEEE.

关键词： Deep learning

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The Association Rule Methods and K-Means Clustering for Optimization Mapping of New Students Admission

The Association Rule Methods and K-Means Clustering for Opti...

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2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

作者： Harsono, Sugi Utami, Ema Yaqin, Ainul Universitas Amikom Yogyakarta Magister of Informatics Engineering Yogyakarta Indonesia Universitas Amikom Yogyakarta Computer Science Faculty Yogyakarta Indonesia

ISBN: (纸本)9798350350524

Educational service providers deliver information about student admission by installing banners, distributing brochures, and providing information directly to prospective new students annually. The target result can be outside expectations because there is no data analysis related to optimizing the support of the system. This study proposes the K-Means algorithm optimized with the Association Rules method to map the interests of prospective students in selecting the study program. The data collection method uses an explanatory research survey and correlational study with primary data from the questionnaires and documents for new students in the year 2022. The accuracy results of this study using the K-Means algorithm process to calculate the silhouette score, elbow methods, cross-combination experiments, and data sharing are 95.00%. The optimization accuracy with mapping relationships between variables using frequent itemset and Association Rules increased to 100%. The most significant factors influencing the student selection of study programs are religion 100%, transportation 85.71%, gender 83.33%, school status 80.95%, regency 73.81%, school origin 71.43%, the district of the school close to student life 66.67%, residence located at the parent's home 64.29%, place of birth 61.90%, and job future ambition 61.90%. © 2024 IEEE.

关键词： Association rules

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A Novel Inception Deep Learning model for Mobile AI Stroke Prediction system: AI Research and Industry Perspectives for Connected Health 6

A Novel Inception Deep Learning model for Mobile AI Stroke P...

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6th International Conference on Computing and informatics, ICCI 2024

作者： Elbagoury, Bassant M. Maskeliuans, Rytis Zaghow, Marwa Kamel, Nabil Faculty of Computer and Information Sciences Ain Shams University Computer Science and Artificial Intelligence Programs Egypt Faculty of Computer Science and Engineering King Salman International University KSIU Egypt Kaunas University of Technology Faculty of Informatics Department of Multimedia Engineering Lithuania Dell Technologies EMEA Egypt

ISBN: (纸本)9798350373875

Artificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep Learning algorithms and developing new models. The main objective is applying artificial Intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. Thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed. © 2024 IEEE.

关键词： Learning systems

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Revolutionizing Recruitment Management: Optimizing Advanced Digital Solutions for Manpower Planning in Higher Education 12

Revolutionizing Recruitment Management: Optimizing Advanced ...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Andrian, Ahmad Mukafi Khamis, Nor Kamaliah Subbiah, Ahgalya Zuhriyah, Halimatuz Faizah, Safira Management and Science University Faculty of Information Sciences and Engineering Section 13 Selangor Shah Alam40100 Malaysia Jakarta Global University Faculty Engineering and Computer Science Department Informatics Engineering Depok16412 Indonesia

ISBN: (纸本)9798350391398

Effective manpower planning is crucial for any organization, including universities, as it directly influences performance, productivity, and overall success. Inefficient recruitment processes can lead to increased costs, higher turnover rates, and suboptimal utilization of human resources, negatively impacting the institution's ability to achieve its goals and maintain a competitive advantage. The objective of this study is to develop a comprehensive digital solution to streamline and enhance recruitment and manpower planning processes, replacing traditional manual methods. This paper presents the Recruitment and Manpower Planning System, an innovative web-based platform designed using Extreme programming (XP) methodologies. The system integrates modern programming tools such as Visual Studio Code, Laragon, Laravel 9, and MySQL, and utilizes Unified Modelling Language (UML) for precise system design. A mixed-methods approach, incorporating quantitative data from structured questionnaires administered to 500 users, informed the system's design. Data analysis indicates a 40% reduction in recruitment time, a 35% increase in successful job placements, and a 90% user satisfaction rate. Rigorous testing phases ensured system accuracy and quality. This digital solution not only optimizes recruitment processes but also provides a robust prototype for future advancements in manpower planning systems. © 2024 IEEE.

关键词： Unified Modeling Language

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A Novel Drone-Station Matching model in Smart Cities based on Strict Preferences

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无人系统（英文） 2023年第3期11卷 261-271页

作者： Debolina Nath Anjan Bandyopadhyay Ankit Rana Tarek Gaber Aboul Ella Hassanien Department of Computer Science and Engineering Amity University Kolkata KolkataWest BengalIndia Department of Computer Science and Engineering Gitam University BangaloreKarnatakaIndia School of Science Engineeringand Environment University of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailiaEgypt Scientific Research Group in Egypt(SRGE) Egypt Faculty of Computer and Artificial Intelligence Cairo UniversityCairoEgypt Scientific Research Group in Egypt(SRGE) Egypt

There has been a considerable increase in the use of drones,or unmanned aerial vehicles(UAVs),in recent times,for a wide variety of purposes such as security,surveillance,delivery,search and rescue operations,penetration of inaccessible or unsafe areas,*** increasing number of drones working in an area poses a challenge to finding a suitable charging or resting station for each drone after completing its task or when it goes low on its *** classical methodology followed by drones is to return to their pre-assigned charging station every time it requires a *** approach is found to be inefficient as it leads to an unnecessary waste of time as well as power,which could be easily saved if the drone is allotted a nearby charging station that is ***,we propose a drone-allocation model based on a preference matching algorithm where the drones will be allotted the nearest available station to land if the station is *** problem is modeled as three entities:Drones,system controllers and charging *** matching algorithm was then used to design a Drone-Station Matching *** simulation results of our proposed model showed that there would be considerably less power consumption and more time saving over the conventional *** would save its travel time and power and ensure more efficient use of the drone.

关键词： Unmanned Aerial Vehicle(UAV) stable matching resource allocation smart city matching algorithm

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Beyond p-y method:A review of artificial intelligence approaches for predicting lateral capacity of drilled shafts in clayey soils

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Journal of Rock Mechanics and Geotechnical engineering 2024年第9期16卷 3812-3840页

作者： M.E.Al-Atroush A.E.Aboelela Ezz El-Din Hemdan Civil and Environmental Engineering Program Engineering Management DepartmentCollege of EngineeringPrince Sultan UniversityRiyadhSaudi Arabia Department of Structural Engineering Faculty of EngineeringAin Shams UniversityCairoEgypt Structure and Materials Research Lab Prince Sultan UniversityRiyadhSaudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityMenoufiaEgypt

In 2023,pivotal advancements in artificial intelligence(AI)have significantly *** that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled *** study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these ***,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter *** extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this *** paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.

关键词： Laterally loaded drilled shaft load transfer and failure mechanisms Physics-informed neural networks(PINNs) P-y curves Artificial intelligence(AI) Dataset

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