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Machine Learning for Data Fusion:A Fuzzy AHP Approach for Open Issues

computers, Materials & Continua 2023年第12期77卷 2899-2914页

作者： Vinay Kukreja Asha Abraham K.Kalaiselvi K.Deepa Thilak Shanmugasundaram Hariharan Shih-Yu Chen Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India Department of Networking and Communications SRM Institute of Science and TechnologyKattankulathurChengalpattuTamil Nadu603203India Department of Networking and Communications Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamil Nadu603203India Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabad501218India Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Data fusion generates fused data by combining multiple sources,resulting in information that is more consistent,accurate,and useful than any individual source and more reliable and consistent than the raw original data,which are often imperfect,inconsistent,complex,and *** data fusion methods like probabilistic fusion,set-based fusion,and evidential belief reasoning fusion methods are computationally complex and require accurate classification and proper handling of raw *** fusion is the process of integrating multiple data *** filtering means examining a dataset to exclude,rearrange,or apportion data according to the *** sensors generate a large amount of data,requiring the development of machine learning(ML)algorithms to overcome the challenges of traditional *** advancement in hardware acceleration and the abundance of data from various sensors have led to the development of machine learning(ML)algorithms,expected to address the limitations of traditional ***,many open issues still exist as machine learning algorithms are used for data *** the literature,nine issues have been identified irrespective of any *** decision-makers should pay attention to these issues as data fusion becomes more applicable and successful.A fuzzy analytical hierarchical process(FAHP)enables us to handle these *** helps to get the weights for each corresponding issue and rank issues based on these calculated *** most significant issue identified is the lack of deep learning models used for data fusion that improve accuracy and learning quality weighted *** least significant one is the cross-domain multimodal data fusion weighted 0.076 because the whole semantic knowledge for multimodal data cannot be captured.

关键词： Signal level fusion feature level fusion decision level fusion fuzzy hierarchical process machine learning

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Three-Level Unipolar/Bipolar Buck-Boost AC-AC Converters with Less Active Switches and No Commutation Issues

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IEEE Transactions on Transportation Electrification 2024年第2期10卷 4184-4200页

作者： Ahmed, Hafiz Furqan Zaabi, Omar Al El Moursi, Mohamed Shawky Hosani, Khalifa Al National Sun Yat-sen University Department of Electrical Engineering Kaohsiung80424 Taiwan Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Mansoura University Faculty of Engineering Mansoura35516 Egypt

This article proposes three-level (TL) buck-boost direct ac-ac converters based on switching-cell configuration with coupled magnetics. The proposed converters use only six active switches and can produce noninverting buck (NIBu), noninverting boost (NIBo), versatile noninverting buck-boost (VNIBB), and inverting buck-boost (IBB) output voltages. Unlike the conventional ac-ac converters, pulsewidth modulation (PWM) dead and overlap times are allowed in the proposed converters without switch voltage and current overshoots, respectively, making it free of commutation issues without requiring extra circuitry and control. Moreover, high-speed power MOSFETs can be utilized without the conduction of their body diodes and related poor reverse recovery issues. The proposed converters generate TL input and output voltages, increasing the voltage levels of input-output inductors and reducing their voltage stresses. In addition, a phase shift PWM control is utilized, which enhances the actual frequency of input and output voltages by twice the operating frequency of the switching devices. Consequently, input and output inductors can be designed much smaller, while reducing input-output current ripples. The proposed circuits also feature shared-ground point between ac source and load and provide continuous input and output currents. Two circuit topologies are proposed: Type-I is a noninverting (unipolar) buck-boost converter with less magnetic components and diodes and Type-II is a bipolar buck-boost converter. The experimental test results on a 400-W laboratory circuit are given to verify the operation of the proposed converter. © 2015 IEEE.

关键词： Buck-Boost converter

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The Deep Learning Approach For American Sign Language Detection

The Deep Learning Approach For American Sign Language Detect...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Pangestu, Gusti Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Bina Nusantara University School of Computer Science Computer Science Department Binus Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9781665468763

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising. © 2022 IEEE.

关键词： Convolutional neural networks

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On a Linear Fractional Differential Equation Involving Liouville Derivative

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IFAC-PapersOnLine 2024年第12期58卷 226-230页

作者： Ivan Matychyn Viktoriia Onyshchenko Faculty of Mathematics and Computer Science University of Warmia and Mazury in Olsztyn Olsztyn Poland

The paper investigates linear fractional differential equations involving the Liouville derivative. Solution to these equations under a non-local boundary condition are derived in explicit form, their uniqueness is es... 详细信息

关键词： Fractional systems Linear systems Optimal control theory Time-invariant systems Analytic design

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Estimation of the Impact of Dimensionality Reduction of the Feature Space on the Efficiency of Movements Classification Based on Surface Electromyography 5

Estimation of the Impact of Dimensionality Reduction of the ...

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5th International Conference on Neural Networks and Neurotechnologies, NeuroNT 2024

作者： Kuznetsov, Ivan V. Ponomarchuk, Yulia V. Far Eastern State Transport University Department of Computer Science and Computer Graphics Khabarovsk Russia Far Eastern State Transport University Faculty of Control Automation and Telecommunications Khabarovsk Russia

ISBN: (纸本)9798350363739

One of the actual tasks of movement classification based on electromyography is the choice of the most informative set of features characterizing the movements. Increasing the number of features may decrease the learning performance of classifiers and reduce the efficiency of gesture classification. This paper is devoted to study the possibilities of improving the movement's classification efficiency by using the feature space dimensionality reduction methods. Classifiers based on Support Vector Machines (SVM)., Decision Trees (DT)., Random Forest (RF) and K-nearest neighbors (KNN) were considered., and Principal Component Analysis method (PCA) was used to dimensionality reduction. 3% improvement in classification performance was achieved using PCA and KNN. © 2024 IEEE.

关键词： Support vector machines

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Review of dehazing techniques: challenges and future trends

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Multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

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Granular or Long: Influence of the Content Structure on Student Interaction with Learning Materials 53

Granular or Long: Influence of the Content Structure on Stud...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Marciniak, Jacek Wójtowicz, Andrzej Kolodziejczak, Barbara Szczepański, Marcin Marciniak, Dorota Stachowiak, Anna Adam Mickiewicz University Faculty of Mathematics and Computer Science Poznań Poland Swps University of Social Sciences and Humanities Faculty of Psychology and Law Poznań Poland

ISBN: (纸本)9798350336429

Learning materials provided to students during remote learning are characterized by different technical forms and interactivity levels. The structure of the materials is often enforced by the tools used for their creation, it could also result from the preferences and experiences of their authors. This article presents the effect of the structure of learning materials on the student's performance as well as the dynamics of all interactions of students with such materials. The experiment used learning materials provided to students for self-study as part of classes from a 15-week database course. The classes were held in the form of weekly lectures and teaching labs. The course comprised several larger sections, beginning with the introduction to the SQL SELECT command. Each learning materials module for this part of the course contained approx. 25 programming tasks which were assessed automatically using the Jobe/CodeRunner environment. It ended with a quiz with random questions of single- or multiple-choice, fill-in the gap, or calculate the result. Before the course started, 88 students were randomly divided into two groups, each numbering 44 students. The first group received learning materials with a granular structure, which were divided into small, thematically cohesive chunks of knowledge. The second group received the same learning materials organized in the manner webpages are constructed (long form), i.e., all the material content from one class was presented on one webpage. This is a solution known, for example, from Jupyter Notebook documents. The deadline for completing one module and taking the quiz at the end of it was one week and was the same for both groups. The self-learning materials replaced traditional weekly teaching labs, but students were allowed to contact the teachers remotely to obtain help or additional explanations. The results show that the structure of the materials did not have a significant effect on the performance of the students,

关键词： asynchronous distance learning learning materials structure programming relational databases student assessment

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Enhanced Lung Cancer Classification and Prediction based on Hybrid Neural Network Approach 8

Enhanced Lung Cancer Classification and Prediction based on ...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Gopinath, A. Gowthaman, P. Gopal, Lisa Abul Ala Walid, Md. Manju Priya, M. Keshav Kumar, K. Drbccc Hindu College Department of Electronics and Communication Science Pattabiram Tamilnadu Chennai India Erode Arts and Science College Department of Electronics Tamilnadu Erode India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Bangladesh Anna Adarsh College for Women Department of Computer Science Shift Ii Tamilnadu Chennai India Department of Humanities and Mathematics Telangana Hyderabad India

ISBN: (纸本)9798350396638

Cancer is the leading killer on a global scale. As the leading cause of cancer-related mortality, lung cancer is among the most prevalent forms of the disease. Uncontrolled cell growth in the lung tissues is the hallmark of lung cancer, a potentially fatal malignancy. This uncontrolled growth can spread to nearby tissues and other sections of the body if the disease is not treated in its early stages. Because there are few or no signs in the early stages of this disease, most cancer cases are typically detected in the later stages, making early detection particularly challenging. Survival rates can be increased via early detection and treatment of lung cancer. And early detection of lung cancer is a crucial step in this direction. Greyscale conversion, image scaling, and noise removal are all part of preprocessing once the input image has been provided. The gaussian filter is used to improve the quality of an image. Following segmentation using the watershed threshold method, features are extracted using area, perimeter, and eccentricity, and the model is trained using an enhanced GAN-mask region-based CNN. In this proposed approached a novel model the GAN-R-CNN model that combines the GAN with the CNN technique. © 2023 IEEE.

关键词： Diseases

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A Comprehensive Systematic Review of Deep Learning Techniques for Anomaly Detection in Urban Video Surveillance

A Comprehensive Systematic Review of Deep Learning Technique...

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Innovative Research in Applied science, Engineering and Technology (IRASET),Conference on

作者： Asmae Baala Hanoune Mostafa Bentaib Mohssine Department of Mathematics and Computer Science Faculty of Science Ben M’Scik Hassan II University Casablanca Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

The rapid evolution of urban surveillance systems has created an urgent need for advanced anomaly detection methods capable of interpreting complex public environments. This study employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to evaluate deep learning's (DL) role in video-based anomaly detection. It contrasts conventional approaches with cutting-edge architectures like spatiotemporal convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based models. Our analysis demonstrates DL's superior performance over traditional methods across multiple benchmarks while revealing significant implementation challenges in real-world deployment, including computational complexity, cross-domain generalization, and ethical constraints. The study provides a comprehensive taxonomy of anomaly types, examines key evaluation metrics for operational systems, and identifies emerging solutions like edge-compatible lightweight models and privacy-preserving federated learning. By synthesizing a decade of research progress and practical limitations, this work offers actionable insights for developing robust, efficient, and socially responsible surveillance systems. The study proposes future directions focused on self-supervised learning, multimodal sensor fusion, and explainable AI frameworks to address critical gaps in urban security applications.

关键词： Deep learning Ethics Adaptation models Computational modeling Scalability Benchmark testing Video surveillance Transformers Anomaly detection Systematic literature review

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Implementing Machine Learning Adoption and Blockchain in the Health Care System

Implementing Machine Learning Adoption and Blockchain in the...

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2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Sohil, C. Siva Subramaniam, D. Gowtham, R. Sundareswari, K. Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Insider attacks on the electronic healthcare device category can result in a deceptive inspection of patients' fitness information, ensuing in unaccountability of records intake along with huge low-budget prices as an outcome of facts fissures in the Digital-Healthcare device without an effective detection technique. As a result, several health centers have confronted prison and reputational implications. A green technique must be proposed to address this challenge, especially for eHealth structures inside the cloud surroundings, in which activities are now carried out via cloud be cloud-based as a result of it. Even as anticipating such options proposed, health records may be centered, which may additionally bring about poorly affected person remedy thanks to disinformation, and accordingly individual mortality. This research is being driven by way of this necessity. This paper proposes a brand-new context for figuring out insider threats upon the usage of extraction in watermarking and detection in logging strategies which is primarily based on Cloud-based health care systems. The method generated a file that included numerous acts carried out through users as well as an audit path of criminal and un legal entries into the device. The technique displayed an immoderate degree of precision, recall, and accuracy inside the assessment finishedat the cease of the studies, indicating that its performance is extremely good to undertake. © 2022 IEEE.

关键词： Budget control

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