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2nd International Conference on Networking, Communications and information Technology, NetCIT 2022

作者： Huang, Mengyuan Yang, Junjie Ou Zhang, Yaolin School of Computing and Information Systems The University of Melbourne Melbourne Australia University of British Columbia Department of Computer Science and Statistics Vancouver Canada School of Mathematical Sciences University College Cork Cork Ireland

ISBN: (纸本)9781665492737

With the development of social media, such as Twitter and MicroBlog, sentiment analysis becomes a quite useful tool for mining the subjective emotion information from the text, which may have important applications in product information feedback, commodity recommendation algorithms, public opinion monitoring, hot event tracking and so on. Though there are many relevant types of research about sentiment analysis, the relevant surveys are not perfect or do not cover the current new methods. Therefore, this survey provides an overview of sentiment analysis to summarize and review the recent methods and resources. © 2022 IEEE.

关键词： Sentiment analysis

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Developing Conceptual Design of Internet Network for Digital Village 8

Developing Conceptual Design of Internet Network for Digital...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Antoni, Darius Syamsuar, Dedy Astuti Pratiwi, Mariska Putri Febrianty Agusman, Andi Universitas Indo Global Mandiri Faculty of Computer Science Palembang Indonesia Bina Nusantara University Information Systems Department Jakarta Indonesia Badan Penelitian dan Pengembangan Daerah Kabupaten Ogan Ilir Ogan Ilir Indonesia Universitas Malahayati Faculty of Management Lampung Indonesia

ISBN: (纸本)9798350342604

The development of information technology (IT) infrastructure currently has a vast influence on the economic growth of cities and districts in Indonesia, especially rural areas. This has resulted in government organizations including village governments building IT infrastructure to provide services and information about villages which is very significant. Digital villages are programs that implement government service systems, community services and community empowerment based on the use of technology. This program aims to develop village potential, marketing and accelerate access and public services. This research focuses on developing digital villages as a means of improving public services using internet infrastructure design approaches, e-business, and village potential information. This study aims to create a digital village concept as a means of building villages in Ogan Ilir district. The data collection technique uses direct interviews with several heads, village secretaries and village communities about their hopes regarding village internet development for the tourism business in their respective areas. After conducting interviews with the head, secretary and village community, the research summarizes them into research results and discussions which will describe the results of interviews and observations. The results of this research are internet network design and the needs of a digital village. Furthermore, this research develops a concept of digital village that is capable of being a guide for each village toward providing better public services. The proposed concept is categorized into six dimensions including village internet design, village internet infrastructure development, village network security, the role of private sectors in developing digital village, the role of internet providers, and the role of local government to distribute intenet access. © 2023 IEEE.

关键词： Conceptual design

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Method for controlling phytoplankton distribution in fresh open water

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International Journal of Environmental Studies 2024年第5期81卷 2130-2147页

作者： Moldasheva, Raushan Ismailova, Aisulu Turmukhanova, Gulnur Mailybayeva, Aiman Yelezhanova, Shynar Zhanuzakova, Zulfiya Zhamangara, Aizhan Department of Information Systems Faculty of Computer Systems and Vocational Education S. Seifullin Kazakh Agrotechnical University Astana Kazakhstan Department of Software Engineering Faculty of Physics Mathematics and Information Technology Atyrau University named after Kh. Dosmukhamedov Atyrau Kazakhstan Department of Computer Science Faculty of Physics Mathematics and Information Technology Atyrau University named after Kh. Dosmukhamedov Atyrau Kazakhstan Laboratory of Palaeobotany Astana Botanical Garden branch of the Institute of Botany and Phytointroduction Astana Kazakhstan

This paper presents a processed phytoplankton dataset from Lake Zerendi and other Kokshetau Upland lakes, Kazakhstan. The study aimed to develop an automated method for controlling the distribution of phytoplankton in fresh open water using a gradient descent approach with the search for a starting point for research. The paper also presents the reconstructed biomass indicators for changes in water temperature, with model calibration based on results from 5 years of surface sampling. A special feature of the proposed computer data processing is the cubic spline interpolation, which allows a higher calculation accuracy. This method reduces the sample size required, providing a comprehensive picture of the condition of the infrastructure and allowing for a more efficient response to emerging ecosystem issues. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Phytoplankton

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Emerging Role of Artificial Intelligence and Internet of Things on Healthcare Management in COVID-19 Pandemic Situation 5th

Emerging Role of Artificial Intelligence and Internet of Thi...

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5th International Conference on Emerging Technologies in computer Engineering: Cognitive Computing and Intelligent IoT, ICETCE 2022

作者： Raghavendra, G.S. Mahesh, Shanthi Chandra Sekhara Rao, M.V.P. CSE RVR & JC College of Engineering Chowdavaram India Information Science and Engineering Atria Institute of Technology Bengaluru India Computer Science and Business Systems RVR & JC College of Engineering Chowdavaram India

ISBN: (纸本)9783031070112

The tremors caused by the COVID-19 epidemic have ushered in a new era of healthcare problems for people all across the globe. Significant problems and challenges have been observed in areas such as victim assistance, remote monitoring, health resources, and healthcare staff, among others. The goal of this study is to provide a comprehensive perspective of digital healthcare during the COVID-19 pandemic, which is now underway. Various efforts such as mobile applications, digital sites, and sophisticated analytics are being used in order to improve early diagnosis and overall healthcare management. While briefly describing the major aspects leading to widespread implementation of e - healthcare concepts, This research also sheds light on some key aspects of artificial intelligence and the Internet of Things, as well as their practical applications such as clinical decision support systems and predictive risk modelling, which are particularly relevant in the context of combating the COVID-19 pandemic’s practical difficulties. The potential uses of artificial intelligence and Internet of Things technology in the battle against the COVID-19 pandemic are thoroughly examined in this paper. The present and future applications of artificial intelligence and the Internet of Things are covered in depth, as well as a thorough assessment of the supporting tools and methodologies. An in-depth analysis. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Health care

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Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions

arXiv

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arXiv 2025年

作者： Fan, Jiani Shar, Lwin Khin Zhang, Ruichen Liu, Ziyao Yang, Wenzhuo Niyato, Dusit Mao, Bomin Lam, Kwok-Yan School of Computer Science and Engineering Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore School of Cybersecurity Northwestern Polytechnical University China

Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated AML investigations. As payment networks become more interconnected, there is an increasing need for efficient real-time detection to process large volumes of transaction data on heterogeneous payment systems by different operators such as digital currencies, cryptocurrencies and account-based payments. Most of these mobile payment networks are supported by connected devices, many of which are considered loT devices in the FinTech space that constantly generate data. Furthermore, the growing complexity and unpredictability of transaction patterns across these networks contribute to a higher incidence of false positives. While machine learning solutions have the potential to enhance detection efficiency, their application in AML faces unique challenges, such as addressing privacy concerns tied to sensitive financial data and managing the real-world constraint of limited data availability due to data regulations. Existing surveys in the AML literature broadly review machine learning approaches for money laundering detection, but they often lack an in-depth exploration of advanced deep learning techniques—an emerging field with significant potential. To address this gap, this paper conducts a comprehensive review of deep learning solutions and the challenges associated with their use in AML. Additionally, we propose a novel framework that applies the least-privilege principle by integrating machine learning techniques, codifying AML red flags, and employing account profiling to provide context for predictions and enable effective fraud detection under limited data availability. Specifically, our approach defines AML-relevant financial profile characteristics and risk indicator

关键词： Deep learning

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Finding the Optimal Features Reduct, a Hybrid Model of Rough Set and Polar Bear Optimization

Finding the Optimal Features Reduct, a Hybrid Model of Rough...

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International Conference on Intelligent and Fuzzy systems, INFUS 2020

作者： Mirkhan, Amer Çelebi, Numan Computer Engineering Department Sakarya University Sakarya Turkey Information Systems Engineering Department Sakarya University Sakarya Turkey

ISBN: (纸本)9783030511555

The target of this research is reducing the size of a dataset which is usually needed before starting the data analysis in scientific research, this can be done by removing attributes that do not affect the accuracy of the dataset, this process will enhance the performance of data analysis and will result in more accurate results and decisions. The rough set theory introduced by Pawlak provides a powerful technique to measure the influence of each attribute in the dataset and the effect of excluding an attribute on the accuracy of the dataset. However, evaluating all possible combinations of features is an NP-Hard problem and is usually not possible when dealing with datasets having large number of attributes. To solve this kind of problems the heuristics algorithms can play a crucial role to avoid scanning all possible combinations. Polar Bear Optimization Algorithm PBO, is a pretty new meta heuristic algorithm has an advantage over other heuristic algorithms of solving such kind of problems using dynamic population with a flexible production and death mechanism, which results in finding optimal solution quickly by keep producing good solutions out of hopeful candidates and keep removing unpromising ones. Our proposed algorithm could find the optimal reduct in better performance comparing to other algorithms in terms of execution time, population size and number of iterations. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Heuristic algorithms

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Malware Detection Based on Stack Ensemble Using Machine Learning

Malware Detection Based on Stack Ensemble Using Machine Lear...

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Computing and Data Analytics (ICCDA), International Conference on

作者： Christopher Ifeanyi Eke Fawaz Ibrahim Olamide Musa Phiri Mulenga Mwenge Faith Fatokun B Liyana Shuib Department of Computer Science Faculty of Computing Federal University of Lafia Nasarawa State Nigeria Department of Computer Science and Information Technology Mulungushi University Kabwe Zambia Malaysian Institute of Information Technology University of Kuala Lumpur Kuala Lumpur Malaysia Department of Information Systems Faculty of Computer Science & IT University of Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

information security remains one of the major challenges faced by organizations and individuals in the current technological era. With the growing popularity of smart devices, the frequency of cyber-attacks targeted at these devices are increasing at a rapid pace. Among these types of attacks on information systems is malicious software also known as malware. A cyber-attack based on malware has the potential to cause loss or manipulation of confidential information. Currently, malware creators who use polymorphic techniques to evade detection can bypass traditional signature-based methods of detecting malware. Thus, various researchers have turned to Machine Learning (ML) techniques to enhance the detection of malware. However, despite the promising results, ML-based classification of malware remains a challenging task that the research community is yet to solve. Therefore, in this study, we propose an ML-based malware detection system that incorporates the AdaBoost, Random Forest, Decision Tree, and K-Nearest Neighbors Algorithm (KNN) algorithms into an ensemble stack with gradient boost as the ensemble boost for the stack meta estimator. We also use Extra Trees Classifier for feature selection. Multiple performance measures are utilized to assess the system's performance while using the Kaggle Malware Dataset. Our findings demonstrate that the proposed method successfully detects malware samples with remarkable accuracy, precision, recall, F1, and area under the curve (AUC) scores of 99.4%, 99.3%, 99.4%, 99.4%, and 99.6%, respectively.

关键词： Machine learning algorithms Nearest neighbor methods Feature extraction Malware Classification algorithms Decision trees Ensemble learning Random forests Cyberattack Tuning

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A Conceptual Deep Learning Model for Real-Time Routin 16

A Conceptual Deep Learning Model for Real-Time Routin

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16th IEEE International Conference on Signal-Image Technology and Internet-Based systems, SITIS 2022

作者： Ikidid, Abdelouafi Fazziki, Abdelaziz El Sadgal, Mohammed Ghazouani, Mohamed El Ichahane, My Youssef Cadi Ayyad University Computer Systems Engineering Laboratory Computer Science Dept Marrakesh Morocco Chouaib Doukkali University Polydisciplinary Faculty of Sidi Bennour El Jadida Morocco Chouaib Doukkali University Information Technologiy Laboratory El Jadida Morocco

ISBN: (纸本)9781665464956

Urban congestion has been a known problem since the first urban revolution throughout the world. Today's major metropolises are synonymous with traffic congestion and complicated urban circulation. This paper introduces a traffic management system based on agent technology and deep learning model. It acts on the phase layouts constituted of sequences and length to reduce average delay time, maximize intersection throughput, and suggest a routing planning scheme to avoid congested intersection. In the optimization model, lights of each intersection are controlled by a group of agents. The system uptake the ability of the agent to communicate, coordinate and collaborate, to decompose the routing process into decentralized architecture. The proposed model was instantiated in the ANYLOGIC simulator. © 2022 IEEE.

关键词： Traffic congestion

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Seeker Optimization with Deep Learning Enabled Sentiment Analysis on Social Media

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computers, Materials & Continua 2022年第12期73卷 5985-5999页

作者： Hanan M.Alghamdi Saadia H.A.Hamza Aisha M.Mashraqi Sayed Abdel-Khalek Department of Computer Science College of Computing Al-QunfidhahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science and HumanitiesSlayelPrince Sattam Bin AbdulAziz UniversitySaudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UnivesityNajran61441Saudi Arabia Department of Mathematics College of ScienceTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohagEgypt

World Wide Web enables its users to connect among themselves through social networks,forums,review sites,and blogs and these interactions produce huge volumes of data in various forms such as emotions,sentiments,views,*** Analysis(SA)is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive,negative,and ***,Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing(NLP).Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array of ***,there is a need exists to develop a proper technique for both identification and classification of sentiments in social *** get rid of these problems,Deep Learning methods and sentiment analysis are consolidated since the former is highly efficient owing to its automatic learning *** current study introduces a Seeker Optimization Algorithm with Deep Learning enabled SA and Classification(SOADL-SAC)for social *** presented SOADL-SAC model involves the proper identification and classification of sentiments in social *** order to attain this,SOADL-SAC model carries out data preprocessing to clean the input *** addition,Glove technique is applied to generate the feature ***,Self-Head Multi-Attention based Gated Recurrent Unit(SHMA-GRU)model is exploited to recognize and classify the ***,Seeker Optimization Algorithm(SOA)is applied to fine-tune the hyperparameters involved in SHMA-GRU model which in turn enhances the classifier *** order to validate the enhanced outcomes of the proposed SOADL-SAC model,various experiments were conducted on benchmark *** experimental results inferred the better performance of SOADLSAC model over recent state-of-the-art approaches.

关键词： Sentiment analysis classification of sentiment social media seeker optimization algorithm glove embedding natural language processing

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A new ontological approach for multilingual scientific research 23

A new ontological approach for multilingual scientific resea...

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Proceedings of the 7th International Conference on Future Networks and Distributed systems

作者： Mohammed Charaf Eddine Meftah Okba Kazar LIAP Laboratory University of El Oued Algeria Smart Computer Science Laboratory (LINFI) Computer Science Department University of Biskra Algeria and Department of Information Systems and Security College of Information Technology United Arab Emirate University United Arab Emirates

ISBN: (纸本)9798400709036

Abstract— Scientific research is all the actions undertaken to produce and develop scientific knowledge. The representation of this knowledge can take various forms: it can be publications, reports, patents, etc. This knowledge can be incorporated into scientific social networks and applications. The scientific social networks in their current form depend on the title, the keywords, and the ontology to compare and link the relationship between different scientific research; there may be different scientific research with the same keywords; the same scientific research may have different titles. In addition, scientific research may be written in different languages, but current scientific social networks do not take into account the multiple languages of researchers and research. they cannot link the relationship and compare scientific research written in different languages. To solve these problems, this paper proposes the use of a multi-lingual ontology to determine and describe scientific research. This work uses ontology in the context of a conceptual indexation, by separating the concept from the term, thus, the result of this work according to the proposed approach will make it possible to express the concept (knowledge extra-linguistic), in different languages, this will allow comparing and linking the relationship between scientific research written in different languages and measuring the percentage of similarity and difference between them. Dealing with multilingualism in scientific research is a very important contribution to this field.

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