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A Comparative Study on Fuzzy Clustering for Cloud Computing. Taking Web Service as a case

A Comparative Study on Fuzzy Clustering for Cloud Computing....

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12th international conference on Ambient Systems, Networks and Technologies (ANT) / 4th international conference on Emerging data and Industry 4.0 (EDI40)

作者： Djellali, Choukri Adda, Mehdi Moutacalli, Mohamed Tarik Univ Quebec Rimouski Dept Comp Sci & Engn Rimouski PQ Canada

this study addresses the increasingly encountered challenge of data clustering. We present a comparative study to data clustering for cloud computing using Fuzzy C-MEANS and Adaptive Resonance theory. To reduce variance and improve generalization ability, we used a resampling method based on 10-fold cross-validation. the typical initialization scheme is applied to improve the convergence speed of training and thus, reach the optimal solution. Experimental results on cloud computing datasets showed that the typical initialization-based Fuzzy Adaptive Resonance theory model is effective and achieves improved accuracy for pattern recognition task compared to Fuzzy C-MEANS. (C) 2021 the Authors. Published by Elsevier B.V.

关键词： data Mining Deep learning Clustering Artificial Neural Network Fuzzy Logic Cross-Validation Cloud Computing Web Service

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Real Time Fake news Detection Web App Enhanced by machine learning Algorithms

Real Time Fake news Detection Web App Enhanced by Machine Le...

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Intelligent Technologies (CONIT), international conference on

作者： V Gunasekaran D Karthikeyan School of Information and Technology Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

In this era, the pervasive proliferation of fabricated news has come to light as a formidable adversary, corroding the credibility of journalistic sources, sowing confusion, and distorting public discourse. To counteract this escalating predicament, this research introduces a pioneering and comprehensive solution-an interactive web application engineered to detect and mitigate the impact of fabricated news. through the strategic integration of sophisticated machine learning algorithms, this platform not only identifies spurious news content but also operates in real time, enabling swift responses to the proclamation of misinformation. the key aim of this study is to elevate the precision and efficiency of fabricated news identification. this is achieved through a meticulously devised amalgamation of advanced natural language processing (NLP) techniques, meticulous feature engineering strategies, and the robust framework of ensemble learning methods. the fusion of these techniques equips the system with an arsenal of tools to scrutinize news story, disentangling the intricate web of linguistic nuances that often obscure deceptive content. the real-time facet of the application is pivotal, ensuring that the detection process transpires in sync with the constant influx of news story across digital platforms. this immediacy minimizes the potential amplification of false narratives, thereby curbing the negative impact of fabricated news proclamation. the heart of the system lies in its fabricated news detection capability, fortified by the intricacies of modern machine learning. By training on vast repositories of verified news data and contrasting it with known instances of false information, the algorithms learn to recognize patterns that distinguish authentic news from falsified narratives. Accessed through standard web browsers, the web application interface offers an intuitive and accessible medium for users to engage with the detection process.

关键词： Training Heart machine learning algorithms Linguistics Real-time systems Natural language processing pattern recognition

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2021 4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

2021 4th International Conference on Pattern Recognition and...

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4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

ISBN: (纸本)9781665413220

the proceedings contain 88 papers. the topics discussed include: using YOLOv5 for garbage classification;ultrasound vector flow mapping in left ventricle: a do it yourself study;a combined data-driven method for electricity theft identification;an unsupervised discriminative random vector functional link network for efficient data clustering;one-dimensional convolutional neural network for detecting internal defects of arc magnets;IV-WA: an information visual tool with WebAR;real-time simulation of first porta treatment based on position based dynamic;image style transfer based on DPN-CycleGAN;and towards automatic Ethiopian endemic animals detection on android using deep learning.

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Review on the Development of Few-Shot Specific Emitter Identification Technology

Review on the Development of Few-Shot Specific Emitter Ident...

引用

Electronic Information Engineering and Computer Science (EIECS), 2021 international conference on

作者： Dian Lv Zhiyong Yu Jiawei Xie Hao Zhang Rocket Force University of Engineering Xi'an China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In the face of a complex and ever-changing electromagnetic environment, the rapid increase in the number and types of radiation sources has made traditional Specific Emitter Identification techniques, which rely on conventional machine learning and statistical features, difficult to apply. In recent years, deep learning models have demonstrated exceptional performance in areas such as image recognition, prompting many researchers to incorporate deep learning into radiation source identification tasks. However, deep learning requires vast amounts of high-quality data, and in practical electromagnetic environments, it is challenging to collect and annotate radiation source signals. As a result, few-shot specific emitter identificatio has become a new focal point of research. this paper first introduces the radiation source identification system and reviews the research on traditional radiation source identification techniques. Next, it provides a detailed overview of few-shot specific emitter identification techniques categorized based on the radiation source identification technology framework, including data augmentation, metric learning, and model-based methods. Finally, the paper analyzes the current challenges in Few-Shot Specific Emitter Identification technology and offers prospects for future research.

关键词： Deep learning Measurement Image recognition Reviews Radar Interference data augmentation data models Robustness Electromagnetics

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Real-Time Water Quality Monitoring via Impedance Spectroscopy and machine learning

Real-Time Water Quality Monitoring via Impedance Spectroscop...

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international conference on Agro-Geoinformatics

作者： Oğuzhan Aybar Zeynep Kara Meriç Yücel Burak Berk Üstündağ Computer and Informatics Department Istanbul Technical University Istanbul Turkiye National Software Certification Research Center Istanbul Technical University Istanbul Turkiye

ISBN: (数字)9798350380606

ISBN: (纸本)9798350380613

Water quality is crucial for plant growth, with factors like salinity levels significantly impacting crop health. Variations in water quality, especially from wells, necessitate regular monitoring. Existing methods are often costly, time-consuming, or unsuitable for continuous monitoring. this study utilizes impedance spectroscopy for real-time water quality monitoring in irrigation systems and machine learning methods for prediction. the proposed method captures spectral features and employs a compact machine-learning model for efficient and accurate pattern recognition, outperforming traditional electric conductivity measurements. Experiments measured various spectral features from water with different concentrations of NaCl, MgSO 4 , and their mixtures, across 1 kHz to 1 MHz using an Analog Discovery 2 device. data from these experiments were used to estimate solute concentrations. machine learning methods, including Random Forest and Multilayer Perceptron, were employed to predict NaCl and MgSO 4 concentrations in mixed samples. Results demonstrate significant improvements over existing methodologies, supporting continuous monitoring. With 55.4 ppm MAE for NaCl and 121.5 ppm MAE for MgSO 4 , the prediction results are promising for real-time water quality monitoring. Implementing farmer-specific devices could enhance agricultural automation by setting thresholds, warnings, and enabling automatic responses.

关键词： Spectroscopy Irrigation Salinity (geophysical) Water quality Multilayer perceptrons Real-time systems pattern recognition

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Use of machine learning in Object Detection for Visually Impaired Person

Use of Machine Learning in Object Detection for Visually Imp...

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Intelligent Technologies (CONIT), international conference on

作者： Tahmina Haque Munia Kazi Abu Taher Department of Information and Communication Technology Bangladesh University of Professionals Dhaka Bangladesh

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

thisreport presents an object detection model for visually impaired persons for object detection, face recognition, navigation and distance measurement. the system model uses a webcam to captures visual images. the images are compressed to a resolution of 300×300 pixels. the MobilenetV3 model is used for object detection which then are classified into human and non-human types. A face detection algorithm is used to recognize human faces. the distance measurement process uses the focal length of the camera and a calibration pattern to calculate the distance of the objects. the proposed object detection model achieved an impressive performance on the test dataset with an average precision of 92.5%, achieving an accuracy of more than 98% for human detection and approximately 91.7% for other objects. the proposed integrated model’s high accuracy and precision make it a promising solution for the visually impaired people

关键词： Visualization Accuracy Navigation Webcams Face recognition Computational modeling Object detection

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Peculiarities of organizing the workspace using machine learning methods 4

Peculiarities of organizing the workspace using machine lear...

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4th international Workshop of Modern machine learning Technologies and data Science, MoMLeT and DS 2022

作者： Basyuk, Taras Vasyliuk, Andrii Demkiv, Lyubomyr Lviv Polytechnic National University Bandera str.12 Lviv79013 Ukraine

the article analyzes the space organization topic, defines its characteristics, and gives the factors that contribute to the conduct of this research. As a result of the research, an analysis of the subject area was carried out, and it was shown that today there is a limited set of tools (libraries) used in the process of pattern recognition, among the most famous are: DeepFace, TensorFlow and OpenCV. A description of the concepts of machine learning and recommender systems is given, which makes it possible to determine the main principles of their construction. A systematic analysis of the subject area was carried out and a tree of goals was built, which showed the subordination and interrelationship of goals in achieving the general goal. A mathematical description of the subject area was carried out using the algebra of algorithms, which provided the means to minimize created models by the number of uniterms. the presented models give a complete idea of the system construction features. the design of the software system was carried out using an object-oriented approach and the display of created diagrams in accordance with the notation of the UML language. the study presents diagrams of use cases and classes, which simplified the understanding of the information system functioning features for organizing the workspace using machine learning methods. the result of the research was the design and implementation of the system using a relational database and the Android Studio environment. the created software product works in prototype mode and implements the described functionality. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Information systems

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the Effective Detection of Alzheimer in the System via ML Algorithm

The Effective Detection of Alzheimer in the System via ML Al...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： R. Kishore Kanna Kapil Joshi Gunjan Chhabra Ambali Pacholi X.S. Blessing NimaSajai Flosy R. Menezes Department of Biomedical Engineering Jerusalem College of Engineering (Autonomous) Chennai India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of CSE Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand School of Nursing Science ITM University Gwalior (MP) India College of Nursing Khamis Mushayt King Khalid University Kingdom of Saudi Arabia Institute of Nursing Education Bambolim(Goa) India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Effective therapy for Alzheimer’s disease depends on early diagnosis. Key results from four research that used machine learning to detect Alzheimer’s disease during its initial level. Early research indicates that combining cognitive and neuropsychological data may enhance diagnosis. the second research interprets neuroimaging data using machine learning methods. Finding the first signs of illness is the objective here. In the third research, risk variables for Alzheimer’s defect conditions are detected using machine learning algorithms. they stress the need of comprehensive data for precise prediction. A novel algorithm is presented in the fourth research. the soft voting classifier method increases recognition accuracy by operating on an averaging mechanism. these papers demonstrate how machine learning may enhance approaches for diagnosing Alzheimer’s disease. To enhance therapeutic results, combine everything from cognitive evaluations to multimodal data.

关键词： Neuroimaging machine learning algorithms Accuracy Sensitivity machine learning Classification algorithms Alzheimer's disease

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Betterment of Classroom and Examination Performance Using Historical data - A Survey 4

Betterment of Classroom and Examination Performance Using Hi...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Rajaraman, Aakash Yashas, S. Ananya, A. Vivek, V. India

ISBN: (纸本)9781665474368

In higher education, understanding, modelling, and forecasting student performance presents considerable hurdles in terms of developing accurate and effective diagnostic models. Several researchers have attempted to construct intelligent classifiers for predicting student accomplishment, but they failed to recognise the necessity of identifying the main components that contribute to the obtained performance. Such recognition is vital for programme directors to evaluate the strengths and limitations of their academic programmes and, as a result, make the required corrections to improve student accomplishment. Consequently, there is room to present a model that improves the accuracy of predicting student academic performance, as measured by future grades in various courses, and, also an optimized multi-label classifier that predicts qualitative values for the influence of various factors on the obtained student performance, primarily using a feed-forward neural network. the goal of the experimental findings are to demonstrate significant gains over single baseline models (e.g., linear regression, matrix factorization), indicating the applicability of the suggested technique in identifying several factors influencing student performance. this study underlines the need of predicting student achievement of learning outcomes in future studies. Along with this, another model that proved to be precise and accurate in predicting the outcome in a similar but not identical scenario, was the KNN classifier, which used data to classify the graduation chances of students. Using the classifiers in the data sets, there are several analyses that can be covered in terms of question pattern, format, and more. this study aims to push a new service that can be established in several organizations and locations to benefit educational professionals and students. the datasets are naturally created in the course of the university's classes. this allows for a lot of flexibility in terms of o

关键词： Students

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the Way of machine learning Based Solicit for Detecting Deceit in Online Based Transaction System with Security

The Way of Machine Learning Based Solicit for Detecting Dece...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Naila Iqbal Qureshi Alba Meça Management Department College of Business Administration Princess Noura Bint Abdul Rahman University Riyadh KSA Università degli Studi di Padova Epoka University

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Detection of fraud still remains one of the most important challenges that continue to stir up research and following implementation of advanced technologies in the field of financial transactions, like machine learning. In this respect, the financial transaction fraud detection seeks to find and compare different approaches in machine learning and understand effectiveness, challenges, and potential directions of future development in a comprehensive way. the paper outlines in the beginning the significance of fraud detection in transactions of finance. It reflects on the general impacts of fraud such as the negative effects on individuals, businesses, and the economy at large due to fraudulent activities. Traditional methods to detect fraud are very valuable; however, they usually fall short against such schemes that are either sophisticated or keep evolving, for which even more sophisticated methods are required. However, with machine learning-based solutions in fraud detection, it is now offering some promise, as these solutions can leverage algorithms processing huge volumes of transactional data to pinpoint any pattern signaling probable fraudulent behavior. Supervised learning techniques, such as logistic regression, decision trees, and support vector machines, have gained great popularity in fraud detection.

关键词： Support vector machines Logistic regression machine learning algorithms Systematics Supervised learning machine learning Fraud

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