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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Wijaya, Santo Fernandi Wiratama, Jansen Kuswanto, Verri School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Universitas Multimedia Nusantara Faculty of Engineering and Informatics Information Systems Department Tangerang11480 Indonesia Buddhi Dharma University Faculty of Science and Technology Information Systems Department Tangerang11480 Indonesia

ISBN: (纸本)9798350326093

The digital changes business patterns that require companies by using an integrated system for strategic decision-making. ERP system is one technological innovation that enable to have a competitive advantage. But the fact is that only a few companies have succeeded in implementing the ERP system. The objective of this research is to integrate the ERP system with business strategy in implementing ERP systems. The research focuses on developing an ERP system integration model for implementing the SAP ERP success. The methodology is based on previous research for the ERP implementation. This research examines quantitative data to determine indicators using entropy and conducts case studies in the industry. The research results prove that the management support indicator is an essential factor in integrating ERP systems to develop the company's strategic business. The output research is the design of the integration model development of the ERP system. © 2023 IEEE.

关键词： Decision making

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A Knowledge Extraction Pipeline between Supervised and Unsupervised Machine Learning Using Gaussian Mixture Models for Anomaly Detection

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Journal of Computing Science and engineering 2021年第1期15卷 1-17页

作者： Chefira, Reda Rakrak, Said Laboratory of Engineering Informatics and Systems at Faculty of Sciences and Techniques Cadi Ayyad University Marrakech Morocco

This paper presents a new approach to design a decision support model with suitability across various contexts, and in particular for the Internet of Things. It provides an anomaly detection-learning model that is adapted to the patient's medical condition. A highly balanced artificial intelligence based on a Gaussian mixture model and association rules leverages the knowledge acquired through cross-referencing supervised and unsupervised machine learning. This process ensures an unsupervised cluster-based model, to accurately classify medical inputs according to their risk level and provide a knowledge extraction bridge between the supervised and unsupervised aspects of the data, thereby enhancing the medical decision-making process to be data-driven and therefore case-specific. © 2021 The Korean Institute of Information Scientistsand Engineers. All Rights Reserved.

关键词： Swarm intelligence

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Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

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Computers, Materials & Continua 2024年第9期80卷 4997-5027页

作者： Milos Dobrojevic Luka Jovanovic Lepa Babic Miroslav Cajic Tamara Zivkovic Miodrag Zivkovic Suresh Muthusamy Milos Antonijevic Nebojsa Bacanin Technical Faculty Singidunum UniversityBelgrade11000Serbia Informatics and Computing Singidunum UniversityBelgrade11000Serbia Business Economics Singidunum UniversityBelgrade11000Serbia Computing and Informatics Sinergija UniversityBijeljina76300Bosnia and Herzegovina Department for Information Systems and Technologies University“Union Nikola Tesla”Cara DusanaBelgrade11080Serbia Department for Computer Science and Informatics School of Electrical EngineeringUniversity of BelgradeBelgrade11000Serbia Department of Electrical and Electronics Engineering Kongu Engineering College(Autonomous)PerunduraiErode638060India Department of Mathematics Saveetha School of Engineering(Deemed to be University)SIMATS ThandalamChennai602105India MEU Research Unit Middle East UniversityAmman11831Jordan

Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or *** can occur through various channels,such as social media,text messages,online forums,or gaming *** involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and ***,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this ***,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this *** algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or ***,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by *** this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost ***,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was ***,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.

关键词： Coyote optimization algorithm NLP TF-IDF BERT XGBoost online harassment and cyberbullying metaheuristics

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Recent Progress of Motionless Optical Scanning Holography

Recent Progress of Motionless Optical Scanning Holography

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2022 JSAP-Optica Joint Symposia, JSAP 2022

作者： Yoneda, Naru Matoba, Osamu Saita, Yusuke Nomura, Takanori Graduate School of System Informatics Kobe University Japan Faculty of Systems Engineering Wakayama University Japan

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Energy Quality Analysis of Electrical Drive systems with Induction Machine 17

Energy Quality Analysis of Electrical Drive Systems with Ind...

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17th International Conference on engineering of Modern Electric systems, EMES 2023

作者： Silaghi, Helga Spoiala, Viorica Costea, Claudiu Magdoiu, Liliana Gamec, Jan Silaghi, Alexandru Marius University of Oradea Control Systems Engineering and Management Department Oradea Romania Technical University of Kosice Faculty of Electrical Engineering and Informatics Kosice Slovakia University of Oradea Electrical Engineering Department Oradea Romania

ISBN: (纸本)9798350310634

Inthis paper many improvements in dealing with energy quality issues that could arise in electrical drive systems using asynchronous machines are presented. A series active power filter is used in electrical drive systems with induction motors to reduce harmonic current and voltage. We also propose a variety of active power filters with hysteresis current value control. Lastly, some experimental results for an electrical drive system utilizing an induction motor and a PWM converter are provided. These findings are followed by a discussion of the system's energy quality issues. © 2023 IEEE.

关键词： Induction machine

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Integrating Textual and Financial Time Series Data for Enhanced Forecasting∗ 16

Integrating Textual and Financial Time Series Data for Enhan...

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16th IIAI International Congress on Advanced Applied informatics, IIAI-AAI 2024

作者： Mou, Shangyang Xue, Qiang Zhang, Wenting Kinkyo, Takuji Hamori, Shigeyuki Chen, Jinhui Takiguchi, Tetsuya Ariki, Yasuo Graduate School of System Informatic Kobe University Kobe Japan Graduate School of Economics Kobe University Kobe Japan Socio-Informatics Wakayama University Faculty of Systems Engineering Wakayama Japan

ISBN: (纸本)9798350377903

Time series forecasting is crucial in various real-world dynamic systems, with machine learning techniques increasingly applied for this purpose. Despite extensive research, there is still a lack of specialized deep learning-based forecasting models in the financial domain. Financial time series, such as stock prices and gold prices, present significant predictive challenges due to their complex short-term and long-term temporal dependencies. Furthermore, they are influenced by underlying fundamentals often reflected in diverse news sources. Current financial time series prediction methods typically concentrate on numerical data, often overlooking the potential insights offered by textual information. Even when considering textual data, the prevailing approach is to utilize sentiment information extracted from news sources as features. This paper introduces an innovative methodology that integrates relevant financial news content directly with corresponding time series data, aiming to explore the synergistic effects of numerical and textual information to enhance the accuracy of financial time series forecasting. Our experimental results demonstrate a substantial improvement in predictive accuracy when considering related textual data, offering a promising avenue for more nuanced and comprehensive economic market analysis. © 2024 IEEE.

关键词： Decentralized finance

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The Impact of Social Media in Marketing on Brand Awareness and Customer Trust in Increasing Purchase Intention

The Impact of Social Media in Marketing on Brand Awareness a...

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2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Fernando, Erick Prabowo, Yulius Denny Winanti Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Information Systems Tangerang Indonesia Binus Online Learning Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Universitas Insan Pembangunan Department of Information Systems Indonesia

ISBN: (纸本)9798350376210

In the current digital era, marketing has developed using technology through media to reach consumers. One prominent feature is influencer marketing, where individuals with significant social media influence promote products or services to their audiences. This strategy has become dominant because of its potential to expand brand reach and boost sales. However, there are challenges in ensuring the effectiveness of influencer marketing, particularly in delivering content with sufficient information and entertainment value. Overcoming these challenges is critical to maximizing customer purchase intent. This research explores the influence of information value, entertainment value, brand awareness, and customer trust on customer purchase intentions in influencer marketing. This research used an online survey (google form) with 398 valid respondents used for analysis PLS-SEM with SMART PLS 3.0. The indicator test results are valid and reliable with loading value for each item with a value >0.5, AVE value ⩾ 0.50, Cronbach's alpha value ⩾ 0.70 and Composite Reliability (CR) value ⩾ 0.7. The findings show that these factors have a significant effect on purchase intention. In emphasizing the importance of influencers delivering educational and entertaining content to increase Brand Awareness in building customer trust, with the consistency of influencers. Overall, providing valuable insights for companies looking to optimize their influencer marketing strategy to increase brand awareness and customer trust, ultimately driving customer purchase intent. © 2024 IEEE.

关键词： Sales

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Breast Cancer Detection on X-Tray Mammogram Images 3

Breast Cancer Detection on X-Tray Mammogram Images

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Azmi, Muhammad Arrif Amirul Mustafa, Wan Azani Alquran, Hiam Aziz, Azwa Abdul Mansor, Muhammad Naufal Alzubaidi, Laith H. Hussein, A.H.A. Universiti Malaysia Perlis Faculty of Electrical Engineering Technology UniCITI Alam Campus Sungai Chuchuh Padang Besar Perlis Malaysia Yarmouk University Department of Biomedical Systems and Informatics Engineering Irbid Jordan Faculty of Informatics & Computing Terengganu Malaysia The Islamic University Faculty of Engineering Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

Breast cancer (BC) is a common cancer affecting women everywhere in world. Mammography is identified and efficient technique to detect primary BC. The aim of project is for detect BC on mammogram in order to categorize disease by image processing when comparing with a previous technique. Through utilizing conventional methods, it creates it complex for radiology to detect cancer from patient's breast. In addition, there are environmental disturbances and technical problems if using the old method. Image processing techniques was separated to numerous elements. The elements are input, pre-processing, segmentation, morphological, object classification and classification. First pre-processing was done through Weiner and Median filter. Then, thresholding method on segmentation and finally, morphological will eliminate limitations at a segmentation. The image classified into 2 classes like normal and tumor. Both type of images analyzed based on elements. Additionally, it comprises a building of Graphical User Interface (GUI) which is utilized to generate the system as user-friendly. The developed model attain accuracy of 93.71 %, specificity of 82.53 % sensitivity of 94.36% for tumorous images. © 2023 IEEE.

关键词： Breast Cancer Classification Graphical User Interface Image Processing MATLAB

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Application of Parallel Signal Processing Using Reverse Blockchain Technology in Conditions of Network Instability 4

Application of Parallel Signal Processing Using Reverse Bloc...

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4th International Conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Khilenko, Volodymyr Zinchenko, Andrii Stepanov, Oleksii Kotuliak, Ivan Ries, Michal Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia Institute of Transport Systems and Technologies National Academy of Sciences of Ukraine Dnipro Ukraine National University of Life and Environmental Sciences of Ukraine Kyiv Department of Information Technologies Ukraine Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia

ISBN: (纸本)9798350391183

The theses is in line with works aimed at studying the possibility of using mathematical algorithms for parallel processing of information in reverse blockchain technology. The paper examines the use of parallel signal processing algorithms to improve the speed and quality of control systems for autonomous dynamic objects using reverse blockchain technology. The use of reverse blockchain technology is due to the possibility of loss of control signals and, at the same time, the inadmissibility of loss of control in the event of changes in the conditions and parameters of the operation of the obj ect. The purpose of the work is to analyze the implementation of solutions for parallel information processing for various models of industrially produced equipment and to develop solutions to increase the efficiency of reverse blockchain technology when using parallel data processing. © 2024 IEEE.

关键词： blockchain dynamic objects and technological processes improving the quality of control systems instability of communication channels loss of information and control signals reverse blockchain technology

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SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

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Computers, Materials & Continua 2023年第1期74卷 995-1010页

作者： Khadem Ullah Nasru Minallah Durre Nayab Ishtiaque Ahmed Jaroslav Frnda Jan Nedoma Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25000Pakistan National Centre in Big Data and Cloud Computing University of Engineering and Technology Peshawar(NCBC-UETP)Peshawar25000Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstrava-PorubaCzech Republic

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia *** Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)*** article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video *** advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon *** codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel *** RSC encoders may be operating on the same or different architectures and code *** proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video ***,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−*** Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

关键词： H.265 RSC turbo codes SCC SP-DSTS BP-CNN BER PSNR

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