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electronics and Sustainable communication Systems (ICESC), 2020 International Conference on

作者： Sudhir K. Routray Mahesh K. Jha M. Pappa K. P. Sharmila Aritri Debnath Department of Computer Science and Engineering CMR University Bangalore India Department of Electronics and Communication Engineering CMR Institute of Technology Bangalore India

The emerging ubiquitous applications of Internet of things (IoT) and the recent popularity of the immersive technologies have created an aura for new services. These services can be delivered with new way of presentation and better customer experiences. These advances in IoT and immersive technologies have opened the roads for metaverse and its new applications in many different areas. Metaverse certainly has the potentials for large scale disruptions. However, it is not yet the time for those disruptions to be real. In this study, the main features and concepts of metaverse are analyzed. How both IoT and the immersive technologies can be instrumental in its widespread applications are looked into. The study also presents some of the popular applications of the metaverse which are clearly visible from the recent developments.

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Expectation maximization—vector approximate message passing based generalized linear model for channel estimation in intelligent reflecting surface-assisted millimeter multi-user multiple-input multiple-output systems

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PeerJ computer Science 2025年 11卷 e2582-e2582页

作者： Ali, Shoukath K. Philip, Sajan P. Khan, Arfat Ahmad Moses, Leeban Cengiz, Korhan Akleylek, Sedat Ivković, Nikola Department of Electronics and Communication Engineering Presidency University Karnataka Bengaluru India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu Erode India Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Department of Electrical-Electronics Engineering Istinye University Istanbul Turkey Institute of Computer Science University of Tartu Tartu Estonia Faculty of Organization and Informatics University of Zagreb Pavlinska Varaždin Croatia

Channel estimation poses a main challenge in intelligent reflecting surface (IRS)assisted millimeter wave (mmWave) multi-user multiple-input multiple-output (MIMO) systems due to the substantial number of antennas at the base station (BS) and the passive reflective elements within the IRS lacking sufficient signal processing capabilities. This article addresses this challenge by proposing a channel estimation technique for IRS-assisted mmWave MIMO systems. The problem of channel estimation is normally taken as a compressed sensing (CS) problem, typically addressed through algorithms such as Orthogonal Matching Pursuit (OMP), Generalized Approximate Message Passing (GAMP), and Vector Approximate Message Passing with Expectation-Maximization (EM-VAMP). EM-VAMP demonstrates better performance only when a Gaussian mixture (GM) distribution is chosen as the prior for the sparse channel, especially at high signal-to-noise ratios (SNRs). To address this, the article introduces the application of generalized linear models (GLMs), extensions of standard linear models, providing increased flexibility in modeling data that deviates from Gaussian distribution. Numerical results unveil that the proposed Its EM-VAMP-GLM is much more robust to the existing OMP, GAMP and EM-LAMP algorithms. Copyright 2025 K et al. Distributed under Creative Commons CC-BY 4.0

关键词： Gaussian distribution

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Highly Accurate Crow Search Optimized CNN Classifier for Cyclone Prediction

Highly Accurate Crow Search Optimized CNN Classifier for Cyc...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Ramya Kavin K. S R. Anuja A. T. R. Krishna Priya D. Karthikeyan Kiruba Thangam Raja Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Tamilnadu India Department of Electrical and Electronics Engineering Government College of Engineering Tirunelveli Tamilnadu India Department of Computer Science and Engineering Rohini College of Engineering and Technology Kanyakumari Tamilnadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) VIT University Vellore

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Accurate and timely prediction of cyclone events is essential for effective disaster management and risk mitigation. This study introduces a highly precise cyclone prediction model that combines Convolutional Neural Networks (CNNs) with the optimization capabilities of the Crow Search Algorithm (CSA). The resulting model, called Crow Search Optimized CNN (CSO-CNN), harnesses the feature extraction and classification strengths of CNNs, while the CSA is used to optimize the hyperparameters of the CNN architecture for optimal performance. The model is trained and validated on a comprehensive dataset that includes satellite imagery and meteorological data from a wide range of cyclone events. The CSO-CNN achieves remarkable classification accuracy, surpassing traditional CNN models and other leading techniques. Its ability to quickly and accurately assess the likelihood of cyclone formation provides crucial decision support for disaster management authorities, enabling timely and effective response strategies. By integrating the CSA optimization technique with the CNN framework, this study presents a novel approach to cyclone prediction. It highlights the potential of combining advanced Machine Learning (ML) algorithms to tackle complex environmental challenges and deepen our understanding of intricate atmospheric phenomena.

关键词： Accuracy Atmospheric modeling Disaster management Predictive models Prediction algorithms Feature extraction Cyclones Satellite images Convolutional neural networks Optimization

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Design and Analysis of a Dual-band Microstrip Patch Antenna for Energy Harvesting

Design and Analysis of a Dual-band Microstrip Patch Antenna ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Md Shahariar Islam Shanto Md Mahidur Rahman Nayan Sarker Liton Chandra Paul Md Ashraful Islam Romel Tithi Rani M. Rubaiyat Hossain Mondal Electrical and Electronic Engineering Jatiya Kabi Kazi Nazrul Islam University Mymensingh Bangladesh Electrical Electronic and Communication Engineering Pabna University of Science and Technology Pabna Bangladesh Electrical and Computer Engineering North Dakota State University Fargo ND USA Electronics and Telecommunication Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Energy harvesting is becoming an ongoing focus in many technical and scientific fields. For energy harvesting, the design of rectennas is crucial. In this paper, two distinct rectenna systems are proposed using an optimized dual band microstrip patch antenna at 5.41 GHz and 7.36 GHz using computer simulation technology (CST). The 5.41 GHz band was especially chosen for energy harvesting in this study because of its widespread usage in 5G, satellite communication, and radar applications. The simulation results of the designed antenna at 5.41 GHz provide satisfactory performances in terms of return loss, gain, and radiation efficiency, which are $-30.04 \mathrm{~dB}, 7.64 \mathrm{~dB}$, and 93.55%, respectively. To design rectenna systems, a voltage doubler and a three-stage Dickson rectifier are used at 5.41 GHz for RF power harvesting. Advance design software (ADS) is used to create the equivalent RLC circuit of the proposed antenna, and LTspice is used to simulate the suggested rectenna systems. Using LTspice, the performance of the proposed rectennas is assessed in terms of input and output voltage, load resistance, and output power. The output power levels of the voltage doubler rectifier and the three-stage Dickson rectifier are 11.14 dBm and 11.98 dBm, respectively. According to this research, the voltage doubler yields lower output power than the three-stage Dickson rectifier at the same resistive load of $6.5 \mathrm{~K} \Omega$ and the same input voltage of 250 mV.

关键词： Radio frequency Resistance Rectennas Patch antennas Rectifiers Dual band Voltage Microstrip antennas Energy harvesting Power generation

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A Wavelet Features and Machine Learning Founded Error Analysis of Sound and Trembling Signal

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SN computer Science 2023年第6期4卷 717页

作者： Shukla, Prashant Kumar Roy, Vandana Chandanan, Amit Kumar Sarathe, Vivek Kumar Mishra, Pankaj Kumar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur 522302 India Department of Electronics Communication Gyan Ganga Institute of Technology and Sciences MP Jabalpur India Department of CSIT Guru Ghasidas Vishvavidyalay CG Bilaspur India Department of Applied Physics Amity School of Engineering and Technology Amity University Madhya Pradesh Maharajpura Dang Gwalior India

The sound and vibration signals coming from the spinning machinery are non-stationary signals, and to analyse them, we require information on both their time and frequency components. The signal in the time domain is converted by the Fourier transform into one in the frequency domain, which does not include any information about the passage of time Wavelet transforms, can represent non-stationary signals in time and frequency. It can breakdown source signals of different time and frequency resolutions, which are characteristic of rotating machine fault mechanisms. Rotating the machine achieves this. Wavelet can filter raw waveforms and compress massive amounts of data without losing information. Thus, eliminating background noise improves sound-based defect diagnosis. Sound and vibration data recovered wavelet energy features and wavelet energy to entropy characteristics. Eight decomposition stages extracted wavelet features. Wavelet characteristics have nine coefficients: one approximate and eight detailed. Decision trees were used to select the best wavelet features. Wavelet energy features outperform wavelet energy-to-entropy ratio features. The classification algorithm's mean classification efficiency percentage for vibration signals of class 24 is 94.23 and for sound signals, 96.73. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Frequency Noise Vibration signal Wavelet

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A Novel Automatic Text Document Classification Using Learning based Text Classification(LbTC) Approach

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Procedia computer Science 2025年 258卷 4279-4290页

作者： Avinash N J Krishnaraj Rao Rama Moorthy H Raviprakash B Raghunadan K R Vasudeva Venkatadri M Department of Electronics and Communication Engineering Mangalore Institute of Technology and Engineering Moodabidre 574225 Karnataka India Department of Information Science and Engineering NMAM Institute of Technology Nitte (Deemed to be University) Karkala 574110 Karnataka India Department of Computer Applications Nitte Institute of Professional Education Nitte (Deemed to be University) Mangalore 575007 Karnataka India Department of Computer Science and Engineering NMAM Institute of Technology Nitte (Deemed to be University) Karkala 574110 Karnataka India Associate Dean MPSTME Shirpur Campus NMIMS (Deemed-to-be-University) Shirpur Maharashtra India

There are practical uses for text document classification. In actuality, it is crucial to categorize and parse documents with natural language. Utilizing applications like bogus news identification, query tagging, sentiment classification, and spam filtering needs this sort of study. However, because of their ambiguity, open-ended nature, and vastness, text documents provide a difficult Classification challenge. Machine learning (ML) methods became valuable alongside the growth of artificial intelligence (AI) due to their data-driven learning techniques. These methods are seen as highly effective at handling and analyzing vast data sets in depth. Topic segmentation, text categorization, entity identification, machine translation, and text summarization, to name a few, are just a few of the issues that may be resolved with ML approaches. In this study, we introduced an automatic Text Classification system (ATCF), a system that uses shallow and deep neural networks to classify text documents. To implement our system, we proposed an approach called Learning based Text Classification (LbTC). We investigated the performance of several models in comparison. Based on the training provided to ML models, the suggested framework assists in categorizing any type of document. It is compatible with practical applications where the categorization of documents is essential.

关键词： Machine learning shallow learning deep learning text classification document Classification

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Adaptive Sparse Channel Estimation for RIS-Assisted Mmwave Massive MIMO Systems

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IEEE Transactions on Vehicular Technology 2025年

作者： Shao, Shuying Lv, Tiejun Du, Siying Zeng, Jie Tan, Fangqing Lin, Zhipeng School of Information and Communication Engineering Beijing100876 China Shandong Institute of Petroleum and Chemical Technology Department of Computer Science and Engineering Dongying257061 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China Guilin University of Electronic Technology School of Information and Communication Guilin541004 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China

The integration of reconfigurable intelligent surfaces (RISs) with millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems holds significant promise for future wireless communication methods aiming to achieve high-speed data transmission and expand communication coverage. However, channel estimation (CE) in RIS-assisted wireless communication systems faces many challenges due to the limited signal processing capability of the RIS. Existing methods often fail to fully capture the non-uniform sparsity of mmWave channels. To address this limitation, we propose an adaptive sparse CE scheme involving two progressive algorithms, i.e., normalized least mean squares–log sum (NLMS-LOG) and normalized least mean squares–normalized least mean fourth–log sum (NLMS-NLMF-LOG), for RIS-assisted mmWave massive MIMO systems. The proposed scheme selectively adjusts the channel coefficients to improve the accuracy of the estimation, enabling more efficient exploitation of the inherent non-uniform sparsity in mmWave channels. The NLMS-LOG algorithm innovatively incorporates a log-sum penalty term into the cost function of the NLMS algorithm, thereby increasing the convergence rate. Building upon NLMS-LOG, the NLMS-NLMF-LOG algorithm employs a mixed error function to achieve superior performance across different SNR conditions. The simulation results demonstrate that the proposed scheme outperforms existing methods, such as the NLMS, sparse exponential forgetting window least mean square, and sparse hybrid adaptive filtering algorithms, in terms of CE accuracy and convergence speed. © 1967-2012 IEEE.

关键词： Cost functions

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Dual-Model Approach for Lung Disease Classification Using Convolutional Neural Networks and Support Vector Machines

Dual-Model Approach for Lung Disease Classification Using Co...

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Integrated Intelligence and communication Systems (ICIICS), International Conference on

作者： Mohammed Ali Shaik Afshan Fatima Masrath Parveen A Soumya Rani Akheel Mohammad Abdul Rahim School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computer Science & Engineering CMR Institute of Technology Hyderabad India Department of Information Technology Vidya Jyothi Institute of Technology Hyderabad India Department of Computer Science and Engineering MallaReddy Engineering College(Autonomous) Hyderabad India Department of Computer Science & Engineering Dr. Vrk women's College of Engineering and Technology Hyderabad India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Secunderabad India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

This paper proposes a solution of integrating the use of Support Vector Machine (SVM) and Convolutional Neural Networks (CNNs) for categorizing lung conditions of people with and without lung disease. The proposed model has achieved a high F1-Score of 97.4 %, a high Precision of 93.17% and high Recall of 93. 63 for the Healthy class. The F1 Scores of this study for the Infected Lung class were as follows: the accuracy of F1 Score was 97.4%; Precision was 94.12%; and Recall was 92.06%. The combining of SVM and CNN model was quite accurate with the accuracy of 97% and the model was accurate in multiple domains of assessment. These results suggest that the application of CNNs and SVMs to the task of medical image analysis constitutes a promising and accurate approach for diagnosing lung diseases.

关键词： Support vector machines Measurement Adaptation models Accuracy Lungs Pulmonary diseases Medical services Vectors Convolutional neural networks Medical diagnostic imaging

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An efficient Manuka honey purity classification technique with FSVM

An efficient Manuka honey purity classification technique wi...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Shafia Nur Huma Sabrina Akter Subrina Akter Nuren Nafisa M. Jamshed Alam Patwary Department of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh Institute of Information and Communication Technology Chittagong University of Engineering & Technology Chittagong Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Manuka honey is renowned for its exceptional medicinal properties in healing wound infections and other conditions. Due to its high cost, this honey is a common target for fraud. Several machine-learning techniques are available for classifying the quality of Manuka honey. However, existing methods often struggle with accurate categorization. Imprecise data, high dimensionality, imbalances in various classes, and lengthy processing times are the root causes of misclassification. This research addresses these issues by providing a viable honey impurity classification method using Fuzzy Support Vector Machine (FSVM). The experiments utilize a freely available pre-processed Manuka honey dataset containing 3300 data points and 128 features. FSVM enhances the classification process by assigning fuzzy membership functions to each class, thereby handling imprecise data and resolving class imbalance problems. Furthermore, this study aims to minimize time and space complexity by reducing dimensions. For this purpose, four feature reduction techniques have been applied to the dataset, and the results have revealed that applying FSVM along with Class Embodiment Autoencoder (CEAE) has yielded 97% accuracy and F1 score. Lastly, a comparative analysis has been performed with other existing SVM methods, and it has been found that the proposed technique has outperformed previous SVMs in terms of accuracy and time and space complexity. The previous method takes (O(N!)) time complexity whereas the proposed method’s time complexity is (O(N 2 )), where N represents the number of data points in the dataset. This significant reduction provides a clear picture of the feasibility of the suggested approach and its potential to enhance the procedures involved in the classification and authentication of honey.

关键词： Support vector machines Accuracy Impurities Autoencoders Machine learning Wounds Robustness Complexity theory Time complexity Optimization

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Disassembly Sequence Planning:A Survey

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1308-1324页

作者： Xiwang Guo MengChu Zhou Abdullah Abusorrah Fahad Alsokhiry Khaled Sedraoui the College of Computer and Communication Engineering Liaoning Shihua UniversityFushun 113001China the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia the Department of Electrical and Computer Engineering Faculty of Engineeringand the Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be *** contain reusable resource that can be recycled and used to generate desired economic ***,performing their efficient disassembly is highly important in green manufacturing and sustainable economic *** typical examples are electronic appliances and electromechanical/mechanical *** paper presents a survey on the state of the art of disassembly sequence *** can help new researchers or decision makers to search for the right solution for optimal disassembly *** reviews the disassembly theory and methods that are applied for the processing,repair,and maintenance of obsolete/discarded *** paper discusses the recent progress of disassembly sequencing planning in four major aspects:product disassembly modeling methods,mathematical programming methods,artificial intelligence methods,and uncertainty *** survey should stimulate readers to be engaged in the research,development and applications of disassembly and remanufacturing methodologies in the Industry 4.0 era.

关键词： Artificial intelligence(AI) disassembly sequence planning(DSP) intelligent optimization modeling methods multiobjective optimization single-objective optimization uncertainty.

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