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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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IoT Based Smart Refrigerator Using Machine Learning 2

IoT Based Smart Refrigerator Using Machine Learning

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Devanath, B. Dharunish, A.P. Mohana Bharathi, S. Suganth, R. Banupriya, V. Ramesh, M. M Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India M Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

The fridge is the most constantly employed menage/ kitchen electrical device encyclopedically for conserving food. The leading sectors of smart widgets include refrigerators in the kitchen. The Web of Effects (IoT) denotes the tools and systems employed to link the factors deduced from the detectors and selectors. Primarily this contrivance is employed for different purposes such as storing flora, fruits, etc. This design transforms any pre-existing fridge into an intelligent, budget-friendly outfit with exercise sensors. The proposed design is educated and worked grounded on the medium of learning machines, and it contrasts the condition of the aliment, for case, Mass, quantum, etc. The significance of this undertaking will involve barring food decay, dwindling affections, and promoting a more restorative way of life for contemporary mortal beings. The arrangement of dish recommendations grounded on the veggies being in the caddy is carried out through an abecedarian algorithm of machine literacy, which categorizes the vegetables grounded on the tinges, latterly suggesting a specific form. It'll be canny enough to warn the stoner IoT about the present condition of food papers through an Android operation on our cellular device. © 2024 IEEE.

关键词： Refrigerators

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Advancements in Fake News Detection: A Comprehensive Machine Learning Approach Across Varied Datasets

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SN computer science 2024年第5期5卷 1-14页

作者： Aslam, Adeel Abid, Fazeel Rasheed, Jawad Shabbir, Anza Murtaza, Manahil Alsubai, Shtwai Elkiran, Harun National College of Business Administration and Economics Lahore Pakistan Department of Information Systems University of Management and Technology Lahore Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul Turkey Department of Computer Science College of Computer Engineering and Sciences in Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj 11942 Saudi Arabia

Fake news has become a major social problem in the current period, controlled by modern technology and the unrestricted flow of information across digital platforms. The deliberate spread of inaccurate or misleading information jeopardizes the public's ability to make educated decisions and seriously threatens the credibility of news sources. This study thoroughly examines the intricate terrain of identifying false news, utilizing state-of-the-art tools and creative approaches to tackle this crucial problem at the nexus of information sharing and technology. The study uses advanced machine learning (ML) models comprising multinomial Naive Bayes (MNB), linear support vector classifiers (SVC), random forests (RF), logistic regression (LR), gradient boosting (GB), decision trees (DT), and to discern and identify instances of fake news. The research shows remarkable performance using publicly available datasets, achieving 94% accuracy on the first dataset and 84% on the second. These results underscore the model's efficacy in reliably detecting fake news, thereby contributing substantially to the ongoing discourse on countering misinformation in the digital age. The research not only delves into the technical intricacies of employing diverse ML models but also emphasizes the broader societal implications of mitigating the impact of fake news on public discourse. The findings highlight the pressing need for proactive measures in developing robust systems capable of effectively identifying and addressing the propagation of false information. As technology evolves, the insights derived from this research serve as a foundation for advancing strategies to uphold the integrity of information sources and safeguard the public's ability to make well-informed decisions in an increasingly digitalized world. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision making Digital platforms Social media Societal implications

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Practical analysis on the algorithm of the Cross-Site Scripting Attacks 29

Practical analysis on the algorithm of the Cross-Site Script...

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29th International Conference on systems, Signals and Image Processing, IWSSIP 2022

作者： Abazi, Blerton Hajrizi, Edmond University for Business and Technology Ubt Faculty of Computer Science and Engineering Prishtina Serbia

ISBN: (数字)9781665495783

ISBN: (纸本)9781665495783

With the change of information technology over the years, it has become necessary to re-evaluate security risks, various possible errors and responses to them. Well-informed and uninformed internet users who click on the wrong links or even use outdated versions of technologies are the most common causes of various errors resulting in security breaches. There is a growing need to educate users of these technologies to prevent or avoid various errors that pose a high risk of security breaches. Web applications suffer from various security vulnerabilities that can be exploited by hackers to cause harm in different ways. Private data are those which can be directly endangered by these errors, which in one way or another can cause us great personal and financial problems. The various infectious scripts are invisible to the visitor, but they determine the website behavior in response to certain clicks sent by the user. In addition to the World Wide Web, various online scripts are also used to automate processes on a local computer. Each script represents a text containing instructions to be executed by a program or script manager in order to achieve the automated action which is desired. Cross Site Scripting is the attack in which the attacker attempts to create scripts in order to extract data from the victim and utilizing this data for various purposes all to the detriment of the victim. Cross Site Scripting Attack can even rewrite the contents of an HTML page. In this paper we will focus on the attack in question by displaying various details and examples, as well as the attack submission algorithm. © 2022 IEEE.

关键词： Errors

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The Impact of Trendy Topics on Information Diffusion 23

The Impact of Trendy Topics on Information Diffusion

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Proceedings of the 2023 12th International Conference on Software and Information Engineering

作者： Sara G Fahmy Rana Khalil Hebat Allah Adel Moukhtar Doaa S Elzanfaly Khaled M Abdelgaber Department of Information Systems Faculty of Informatics & Computer Science The British University in Egypt Egypt Department of information systems Faculty of business information systems The Canadian International College Egypt Department of Information Systems Faculty of Computer & Artificial Intelligence Helwan University Egypt Faculty of Informatics & Computer Science The British University in Egypt Egypt

ISBN: (纸本)9798400708107

This paper presents a new information diffusion model that studies the impact of trendy topics on the diffusion process based on content analysis. The suggested model (Math 1) extends the SIR model by adding the topic weight (tw) that mirrors the topic significance reflected on the infection rate. In which, the new infection rate (λtw ) will be calculated by multiplying the normal infection rate (λ) with the topic weight (tw). An experimental study has been conducted on trendy and non-trendy topics and the results are compared to the classical SIR model. The simulation results of the Math 2 model using Mathematica show the early diffusion of the trendy topics. It has been concluded that the number of infected accounts will accelerate within a shorter time due to the impact of the trendy topics. Therefore, the proposed model (Math 3) better reflects the diffusion process in the presence of trendy topics.

关键词： Information Diffusion SIR model SI_{t_w}R model Social Networks Structure of Network Textual Content Topic Identification Trend Extraction Trendy Topics

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Heart Disease Detection Using Classification and Prediction Models

Heart Disease Detection Using Classification and Prediction ...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Shanthi, P.A. Padmapriea, S. Sachet, K. Livinesh, L. Tharun, A.K. Sri Sai Ram Engineering College Department Of Mathematics Chennai India Sri Sai Ram Engineering College Department Of Computer Science And Business Systems Chennai India

ISBN: (纸本)9798350352689

Cardiovascular diseases persist as a prominent global cause of mortality, emphasizing the importance of precise prediction techniques for timely identification and intervention. This research explores the efficacy of three machine learning models - Logistic Regression, K-Nearest Neighbor (KNN) Classifier, and Gaussian Naive Bayes (Gaussian NB) - in predicting heart disease using datasets of varying sizes and test proportions. Leveraging demographic, clinical, and physiological variables such as age, gender, blood pressure, cholesterol levels, fasting blood sugar levels, electrocardiogram results, and maximum heart rate, predictive models were trained and evaluated. Across all dataset with different sizes and test proportions, Gaussian NB consistently demonstrated superior performance, characterized by high accuracy, precision, recall, and F1-score. Logistic Regression also exhibited commendable performance, particularly in terms of simplicity and interpretability, slightly lower than Gaussian NB. In contrast, KNN Classifier displayed varied performance across datasets with different sizes and test proportions, indicating sensitivity to these factors. These findings underscore the potential of machine learning models, particularly Gaussian NB and Logistic Regression, in enhancing heart disease prediction accuracy, thus enabling targeted interventions and personalized treatment strategies for improved patient outcomes. Further optimization and consideration of dataset characteristics are warranted to enhance the performance of KNN Classifier in heart disease prediction tasks. © 2024 IEEE.

关键词： Statistical tests

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Artificial Intelligence-Based System 'SiMoniK' for MSMEs 9

Artificial Intelligence-Based System 'SiMoniK' for MSMEs

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9th IEEE Information Technology International Seminar, ITIS 2023

作者： Lestari, Sri Yulmaini Aswin Sulyono Nul Fikri, Ruki Rizal Informatics Engineering Study Program Faculty of Computer Science Bandar Lampung Indonesia Management Study Program Faculty of Economics Business Bandar Lampung Indonesia

ISBN: (纸本)9798350306835

Artificial intelligence was recently implemented in various fields. One of these applied artificial intelligence systems was the Performance Monitoring System "SiMoniK"for MSMEs. This system was designed through the C4.5 algorithm and the Porat-Rank method. The C4.5 algorithm was applied to classify the MSMEs into three classifications, e.g. the micro enterprise classification, the small enterprise classification, and the medium enterprise classification. Moreover, the Porat-Rank method was also applied to distribute the solving-problem recommendations and to improve the independence of MSMEs. The result of this study was that the C4.5 algorithm could classify the MSME data correctly in the form of an accuracy value of 99.2%. Besides, the Porat-Rank method could also provide recommendations in the form of outstanding quality with an NDCG mean value of 0.87. © 2023 IEEE.

关键词： Artificial intelligence

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Protecting machine learning systems using blockchain: solutions, challenges and future prospects

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Multimedia Tools and Applications 2024年 1-28页

作者： Hajlaoui, Rejab Dhahri, Salah Mahfoudhi, Sami Moulahi, Tarek Alotibi, Gaseb Department of Computer and Informtion Sciences College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Laboratory of Advanced Multifunctional Materials and Technological Applications Faculty of Science and Technology of Sidi Bouzid University of Kairouan Sidi Bouzid Tunisia Department of Information and Technology Management Mediterranean School of Business South Mediterranean University Tunis Tunisia Department of Information Technology College of Computer Qassim University Buraidah Saudi Arabia Department of Computers and Information Technology Alwajah College University of Tabuk Tabuk Saudi Arabia

Machine learning-based systems have emerged as the primary means for achieving the highest levels of productivity and efficiency. They have become the most influential competitive factor for many technologies and business companies such as Cloud AI Companies (Google, Facebook…) and Health Care AI Companies (Tempus, Nanox …). However, privacy and security issues have become the biggest challenge facing all ML applications. These threats are known as Adversarial Machine Learning (AML). Their main goal is to maliciously manipulate training data and model parameters to obtain misleading results. To overcome these challenges, several research studies have proven that Blockchain, which relies on cryptographic technologies, constitutes a promising solution for securing ML applications. It provides high guarantees to make the system scalable, reliable and more secure. In this survey, we provide an overview of ML applications and the most hostile attacks they face. Then, we define the blockchain technique and its distinctive features. After that, we conduct a comprehensive and detailed survey of the latest and best blockchain-based research to address security issues in ML applications (integrity, confidentiality and availability). We analyze, compare and comment on all the proposed solutions. In the end, we discuss in detail the various challenges that hinder the adoption of blockchain technology and we extract the most important scientific trends that will serve as guidance and support for researchers in their future works. © The Author(s), under exclusive licence to Springer science+business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Contemporary Impact of Cyberbullying on Youngsters: An Appraisal of Psychological, Emotional, Physical Health, and Academic Implications 19

Contemporary Impact of Cyberbullying on Youngsters: An Appra...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Faith, Fatokun B. Long, Zalizah Awang Hamid, Suraya Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia Department of Information Systems Faculty of Computer Science IT Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

Cyberbullying is now a serious issue in contemporary times. The impact of cyberbullying on youngsters is of enormous concern in the public health sector as well as in the cybersecurity field. Several researchers have discussed the impact of cyberbullying from diverse perspectives, however, there is still a large gap in finding the paramount impact of cyberbullying in contemporary times, as well as providing solutions to eradicate this menace, especially as it deals with the negative effects of technology which is emerging daily. This study reviews via a scoping evidence approach suggesting the impact of cyberbullying on youngsters, focusing on the psychological, emotional, physical health, and academic implications. Findings from studies revealed that most youngsters face more of the psychological and emotional consequences of cyberbullying as compared to others discussed in the literature. Interestingly, there were also revelations of how cyberbullying had negative influences on the physical health and academic achievements of youngsters. These findings thus instigate the need for novel approaches that can help curb the rampancy of cyberbullying as well as ensure that decision-makers and stakeholders in the respective institutions put these impacts of cyberbullying into consideration when designing cybersecurity policies to ensure that victims of cyberbullying are assisted to recover, put in place technological solutions that can track and punish offenders and also reduce the rate of cyberbullying victimization. © 2025 IEEE.

关键词： Electronic health record

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Sector-Based Incremental Clustering and Scalable Deletion for Real-Time Big Data Streaming Application 2nd

Sector-Based Incremental Clustering and Scalable Deletion fo...

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Ananthi, M. Mangayarkarasi, T. Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai India Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai India

ISBN: (纸本)9783031689079

Massive streaming data is the most current technology for storing and manipulating large quantities of data. Processing the substantial amount of streaming data is still a challenging issue. The speed and throughput can be improved while extracting this huge amount of data by using a dynamic query processing technique. The sector-based incremental clustering indexing and scalable deletion approach is used in this paper to improve the system performance. Stream processing can be more effective and flexible by applying dynamic query processing technique. Incremental clustering using sector-based method and scalable deletion which removes unwanted, spurious data is proposed in this work in order to process incoming streaming data effectively. Incremental clustering is one of the clustering methods used for processing dynamic information. It is not necessary to plan the entire dataset ahead of time in this method which is suitable for live streaming data, because it processes one instance at a time. So, less space and time is used. Various queries with different types of datasets executed using sector based incremental clustering and scalable deletion methods. The proposed system achieves the highest results for more than 90% accuracy with less space and more speed when the execution time and data retrieval are empirically increased compared with conventional approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Data streams

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