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2021 International Conference on Artificial Intelligence and Big Data Analytics, ICAIBDA 2021

作者： Gultom, Reinaldo Yosafat Zulkarnaen, Fahri Izzuddin Nurhasanah, Yahma Sholahuddin, Asep Universitas Padjadjaran Research Center for Artificial Intelligence and Big Data Bandung Indonesia Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Informatics Engineering Department UPN Veteran Jawa Timur Surabaya Indonesia Department of Statistics Universitas Padjadjaran Bandung Indonesia Department of Computer Science Universitas Padjadjaran Bandung Indonesia

ISBN: (纸本)9781665408905

Twitter is one of the famous social media applications in Indonesia. The study will classify data to detect hate speech on Twitter into two classes: abusive and non-abusive. The abusive class will contain tweets in the form of words that contain negative elements or hate speech, while the non-abusive class contains tweets in the form of words that do not contain negative elements. The desired purpose of this research is to help those who need this research to be able to use it to supervise others from hate speech and find the perpetrators who do it. The data used is secondary data taken from the Kaggle website. The data contains tweets that have been classified based on connotations of abusive or non-abusive. Several processes are carried out to achieve the desired goal, including data acquisition, preprocessing, classification, evaluation, and content detection. The algorithms used in the study were LSTM and CNN. Both algorithms produce the same performance relatively. It's just that CNN is slightly superior. The accuracy value of CNN is 0.9131 or 91.31%, the precision is 0.9131 or 91.31%, the recall is 0.9131 or 91.31%, and the F1-Score of 0.9131 or 91.31%. Meanwhile, for LSTM, the accuracy value is 0.9104 or 91.04%, the precision is 0.9104 or 91.04%, the recall is 0.9104 or 91.04%, and the F1-Score is 0.9104 or 91.04% © 2021 IEEE.

关键词： Social networking (online)

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Recent Advances of Chimp Optimization Algorithm:Variants and Applications

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Journal of Bionic engineering 2023年第6期20卷 2840-2862页

作者： Mohammad Sh.Daoud Mohammad Shehab Laith Abualigah Mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva Mohammad AAlia Raed Abu Zitar College of Engineering Al Ain University112612Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Haj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio'n Basada en la Informacio'n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500Petaling JayaMalaysia

Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence *** has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial ***,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable ***,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short ***,in this review paper,several research publications using ChOA have been overviewed and ***,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual *** main operations of ChOA are procedurally discussed,and the theoretical foundation is ***,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled *** main applications of ChOA are also thoroughly *** applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,*** of ChOA is also *** review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and *** well,it is wealthy in research on health,environment,and public ***,it will aid those who are interested by providing them with potential future research.

关键词： Artificial intelligence Nature-inspired optimization algorithms Chimp optimization algorithm Optimization problems

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Blockchain Solution For SMEs Cybersecurity Threats In E-Commerce

Blockchain Solution For SMEs Cybersecurity Threats In E-Comm...

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computer Science and Emerging Technologies (CSET), International Conference on

作者： Firas H. Zawaideh Waheeb Abu-Ulbeh Salameh A. Mjlae Yousef A. Baker El-Ebiary Yazeed Al Moaiad Sunanda Das Department of Cyber Security Faculty of Science and Informaiton Technology Irbid National University Irbid Jordan Cybersecurity Department Faculty of Administrative sciences and Informatics Al-Istiqlal University Palestine CS Department Prince Abdullah bin Ghazi Faculty of Information Technology Al-Balqa Applied University (BAU) Jordan Faculty of Informatics and Computing UniSZA Malaysia Faculty of Computer and Information Technology MEDIU Malaysia School of Computer Science and Engineering Jain University Bangalore India

The rapid growth of electronic commerce (e-commerce) has revolutionized the way Small and Medium-sized Enterprises (SMEs) conduct business, enabling them to reach global markets and unlock new growth opportunities. However, this digital transformation has also exposed SMEs to an escalating array of cybersecurity threats that could compromise their data, financial integrity, and reputation. Blockchain technology, known for its decentralized and immutable nature, has emerged as a potential solution to enhance the security and resilience of e-commerce operations for SMEs. This research paper aims to investigate the current state of cybersecurity preparedness among SMEs engaged in e-commerce and identify the prevalent threats they face in the digital landscape. To achieve this goal, the study incorporates systematic literature review (SLR) method to examine the best technological solution to address the cybersecurity issues faced by e-commerce organizations in this paper of SMEs operating in e-commerce to gather data on their security practices, incident history, and perceptions of cybersecurity risks. In the context of this research, blockchain technology will be explored as a potential mechanism to enhance the security of e-commerce operations for SMEs. The inherent features of blockchain, such as decentralization, transparency, and immutability, could help protect sensitive data and thwart cyberattacks. By exploring the integration of blockchain into e-commerce systems, the research aims to shed light on the potential benefits and challenges associated with its adoption by SMEs. The research findings will not only shed light on the prevailing cybersecurity practices within SMEs and highlight their vulnerabilities but will also explore how blockchain technology can be leveraged to address these challenges. By understanding these patterns and exploring the feasibility of blockchain integration, the research aims to provide comprehensive recommendations for enhancing cy

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An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools

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Sensors 2024年第8期24卷 2531页

作者： Kristiani, Endah Wang, Lu-Yan Liu, Jung-Chun Huang, Cheng-Kai Wei, Shih-Jie Yang, Chao-Tung Department of Computer Science Tunghai University Taichung City407224 Taiwan Department of Informatics Krida Wacana Christian University Jakarta11470 Indonesia Industrial Technology Research Institute Hsinchu310401 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung City411030 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung City407224 Taiwan

This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi. © 2024 by the authors.

关键词： Edge computing

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Energy Preservation in MANET Using Self-Adaptive CH Selection with Advanced Genetic Algorithm 1

Energy Preservation in MANET Using Self-Adaptive CH Selectio...

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1st International Conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Indriyono, Bonifacius Vicky Pamungkas, Natalinda Abbas, Fatima Hashim Mintorini, Ery Almohamadi, Mohammed Mahmood, Sarmad Nozad Alani, Sameer Alkhayyat, Ahmed H. Rachmawanto, Eko Hari University of Dian Nuswantoro Information System Kediri Indonesia Al-Mustaqbal University College Medical Laboratories Techniques Department Hillah51001 Iraq University of Dian Nuswantoro Visual Communication Design Kediri Indonesia College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna66002 Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Iraq University of Mashreq Research Center Medical Laboratories Techniques Baghdad Iraq College of Technical Engineering University of Mashreq the Islamic University Najaf Iraq University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia

ISBN: (纸本)9798350306484

Mobile Adhoc Networks (MANETs) is an emerging technology in both the industrial and academic research. The major drawback in MANETs is improving the battery capacity. MANETs are dynamic in nature therefore during communication it consumes more energy that reduces the overall energy efficiency of the network. Many past and present researches are concern about this problem. In this paper, Energy Preservation in MANETs using Self-Adaptive Cluster Head Selection with Advanced Genetic Algorithm (EPMSA-CHAG) approach is proposed where the CH selection is performed using two segments;they are initial parameter based on CH selection and Advanced Genetic Algorithm (AGA) based CH selection. At the initial stage the parameters which are considered for the CH selection are node degree, node stability, distance, residual energy, and speed and delivery rate. Using these parameters are the best fit for CH selection is chosen then in order to find the optimal best fit from the best fit calculation, Advanced Genetic Algorithm (AGA). The proposed EPMSA-CHAG approach is simulated using NS2 and the parameters which are considered for the performance analysis are packet delivery rate, energy efficiency, end to end delay, routing overhead and throughput. The methods that are taken for the comparative analysis are HLSPM-CHSR and HAMBO-CHLD. From the results calculated it is proven that the proposed EPMSA-CHAG approach achieved high packet delivery rate, energy efficiency and throughput as well as lower end to end delay and routing overhead when compared with the earlier methods HLSPM-CHSR and HAMBO-CHLD. © 2023 IEEE.

关键词： Energy efficiency

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Intelligent Filtering of Graph Shells in the Problem of Influence Maximization Based on the Independent Cascade Model 7

Intelligent Filtering of Graph Shells in the Problem of Infl...

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7th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2021

作者： Kazemzadeh, Farzaneh Karian, Amir Safaei, Ali Asghar Mirzarezaee, Mitra Science and Research Branch Islamic Azad University Department of Computer Engineering Tehran Iran Islamic Azad University Central Tehran Branch Department of Industrial Design Iran Tarbiat Modares University Faculty of Medical Sciences Department of Medical Informatics Tehran Iran

ISBN: (纸本)9781665409384

In social networks, the problem of influence maximization seeks for a solution to find individuals or nodes in different communities so that they can diffuse information influence among a wide range of other nodes. The proposed algorithms for influence maximization problem have many drawbacks. For example, the computational overhead is very high and also the seed nodes is not selected optimally. For this reason, the influence does not spread totally in the social *** solving the problem, This paper provides the SFIM algorithm and uses the idea of layering community nodes and identifying valuable layers to limit the search space. The operation is continued only on nodes of valuable layers, which significantly reduces the algorithm's runtime. Then, the best set of influential nodes with the highest accuracy is found by considering the main criteria of centrality topology such as harmonic and degree. Accuracy in selecting a node is one of the most important needs of the problem that is best answered. Moreover, different experiments and datasets indicate that this algorithm can provide the best efficiency required to solve the problem compared to other algorithms. © 2021 IEEE.

关键词： Topology

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A Review of 5G Technology: Architecture and Challenges 8

A Review of 5G Technology: Architecture and Challenges

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8th IEEE International Conference on Computing, engineering and Design, ICCED 2022

作者： Attarbashi, Zainab S. Altaleb, Abdulazeez E. Mohd Aman, Azana Hafizah Hassan Abdalla Hashim, Aisha Eker, Sebahattin International Islamic University Malaysia Information Communication Technology Kuala Lumpur Malaysia Informatics Institute Istanbul Technical University Communication Systems Department Istanbul Turkey University Kebangsaan Malaysia Faculty of Information Science Technology Bangi Malaysia International Islamic University Malaysia Computer Information Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9781665453899

5G Networks are considered as a new evolution since it aims to increase the wireless capacity 1000 times the previous generations. By 2025, it is expected that 5G will be connecting around 3 billion people and 41.6 billion devices enabling the Internet of Things (IoT) and the Internet of Everything (IoE) while increasing energy efficiency 90% more than 4G and reducing 20% of carbon emissions. In this paper, the deployment requirements and challenges of 5G Networks are reviewed. Starting with the overall network architecture and network slicing framework including the service-type slicing and the infrastructure slicing are investigated. Later, the spectrum and multiple access techniques are explained. Finally, the challenges and the available solutions have been discussed. © 2022 IEEE.

关键词： 5G mobile communication systems

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Hybrid Methodologies for Segmentation and Classification of Skin Diseases: A Study

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Journal of computer and Communications 2021年第4期9卷 67-84页

作者： Md. Al Mamun Mohammad Shorif Uddin Department of Public Health and Informatics Jahangirnagar University Savar Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Savar Dhaka Bangladesh

Skin disorders are a serious global health problem for humans. These disorders become dangerous when they grow into the malignant stage. Hence, it is necessary to detect these diseases at their early stage. A mobile-based automated skin disease detection system is vital for detecting skin diseases. This system also offers cure or treatment plans to the affected person through the short message service (SMS) or electronic mail (e-mail). An effective skin disease detection system consists of three processes: segmentation, feature extraction, and classification. Several hybrid methodologies are already developed for the above-mentioned processes for detecting skin diseases at the initial stage. This research gives a standard hybrid framework for detecting skin diseases and highlights some design requirements for achieving high accuracy. Existing state-of-the-art hybrid methods of three processes for detecting skin diseases along with their limitations are also summarized. It also identifies the challenges for developing an effective skin disease detection system and gives future research directions.

关键词： Segmentation Feature Extraction Classification Machine Learning

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When data disappear: public health pays as US policy strays

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The Lancet Digital Health 2025年 100874页

作者： McAndrew, Thomas Lover, Andrew A Hoyt, Garrik Majumder, Maimuna S Department of Biostatistics and Health Data Science College of Health Lehigh University BethlehemPA United States Department of Biostatistics and Epidemiology School of Public Health and Health Sciences University of Massachusetts Amherst AmherstMA United States Department of Computer Science and Engineering PC Rossin College of Engineering and Applied Sciences Lehigh University BethlehemPA United States Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children's Hospital BostonMA United States

Presidential actions on Jan 20, 2025, by President Donald Trump, including executive orders, have delayed access to or led to the removal of crucial public health data sources in the USA. The continuous collection and maintenance of health data support public health, safety, and security associated with diseases such as seasonal influenza. To show how public health data surveillance enhances public health practice, we analysed data from seven US Government-maintained sources associated with seasonal influenza. We fit two models that forecast the number of national incident influenza hospitalisations in the USA: (1) a data-rich model incorporating data from all seven Government data sources;and (2) a data-poor model built using a single Government hospitalisation data source, representing the minimal required information to produce a forecast of influenza hospitalisations. The data-rich model generated reliable forecasts useful for public health decision making, whereas the predictions using the data-poor model were highly uncertain, rendering them impractical. Thus, health data can serve as a transparent and standardised foundation to improve domestic and global health. Therefore, a plan should be developed to safeguard public health data as a public good. © 2025 The Author(s)

关键词： Health insurance

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Traffic flow prediction based on BP neural network

Traffic flow prediction based on BP neural network

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2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021

作者： Wang, Tuo Zhang, Beibei Wei, Wei Damasevicius, Robertas Scherer, Rafal School of Computer Science and Engineering Xi'an University of Technology Xi'an710048 China Kaunas University of Technology Faculty of Informatics Multimedia Engineering Department Kaunas Lithuania Czestochowa University of Technology Al. ArmiiKrajowej 36 Czestochowa42-200 Poland

ISBN: (纸本)9780738110837

With the rise of artificial intelligence, artificial neural networks have also begun to develop rapidly. An artificial neural network consists of many artificial neurons, which is a structure that imitates the neurons created by the human brain. BPneural network is a classic algorithm in artificial neural network. Because the BP neural network has a simple structure, is easy to implement and can deal with nonlinear problems, it is widely used in pattern recognition, signal processing, and transportation systems. In this paper, the BP neural network, one of the classic artificial neural networks, is applied to the traffic flow prediction. The data used in this article is the traffic situation on the M4 highway from 2020-1-1 0:14:00 to 2020-1-2 23:59:00. Record the traffic data once every 15 minutes. There are a total of 192 groups of traffic data. From these 192 sets of data, 19 sets of random data are selected as test data, and the remaining 173 sets of data are used as training data. The BP neural network input layer is set to four variables: date, time, the number of cars in this period, the number of cars in this period, and the average speed value, and the output layer is set to the total traffic volume in this period. Use matlab2016a for simulation. The simulation results show that the BP neural network can accurately complete the traffic flow prediction problem. Because the initial weights and thresholds of the BP neural network have a great influence on the prediction accuracy, this paper uses particle swarm optimization to optimize the initial weights and thresholds of the BP neural network. Simulation results show that the optimized BP neural network is more stable than the mean square error before optimization, and the average relative error is smaller. © 2021 IEEE.

关键词： Mean square error

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