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MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network

MSEMG: Surface Electromyography Denoising with a Mamba-based...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Yu-Tung Wang, Kuan-Chen Chao, Rong Siniscalchi, Sabato Marco Yeh, Ping-Cheng Tsao, Yu Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan University of Palermo Italy Research Center for Information Technology Innovation Academic Sinica Taiwan

ISBN: (纸本)9798350368741

Surface electromyography (sEMG) recordings can be contaminated by electrocardiogram (ECG) signals when the monitored muscle is closed to the heart. Traditional signal processing-based approaches, such as high-pass filtering and template subtraction, have been used to remove ECG interference but are often limited in their effectiveness. Recently, neural network-based methods have shown greater promise for sEMG denoising, but they still struggle to balance both efficiency and effectiveness. In this study, we introduce MSEMG, a novel system that integrates the Mamba state space model with a convolutional neural network to serve as a lightweight sEMG denoising model. We evaluated MSEMG using sEMG data from the Non-Invasive Adaptive Prosthetics database and ECG signals from the MIT-BIH Normal Sinus Rhythm Database. The results show that MSEMG outperforms existing methods, generating higher-quality sEMG signals using fewer parameters. © 2025 IEEE.

关键词： Deep neural network ECG interference removal State space model Surface electromyography

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A Feature engineering Approach for Detecting Cyberbullying in Bangla Text Using Machine Learning

A Feature Engineering Approach for Detecting Cyberbullying i...

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2022 International Conference on Recent Progresses in Science, engineering and Technology, ICRPSET 2022

作者： Rahman, Maqsudur Nur, Shafayet Ahmed, Md. Tofael Das, Dipankar Islam, Azm Touhidul Comilla University Department of Information and Communication Technology Bangladesh Port City International Unviersity Department of Computer Science and Engineering Bangladesh University of Rajshahi Department of Information and Communication Engineering Bangladesh University of Rajshahi Department of Electrical and Electronics Engineering Bangladesh

ISBN: (纸本)9798350332704

Cyberbullying has become an alarming issue with the prodigious rise of different social media platforms. The consequences of cyberbullying are uncountable and thus it should be detected and prevented immediately. Previous research has demonstrated great accuracy in detecting textual cyberbullying data. But the detection of the severity level of cyberbullying specially in Bangla text is yet to be performed. To mitigate this gap, this research is conducted to assess the severity level of Bangla cyberbullying text using feature engineering approach. We performed feature engineering to introduce new features such as PMI-SO (Pointwise Mutual information-Semantic Orientation) and domain specific features to perform the detection. Two datasets were created in order to detect the severity level and to generate the features. The datasets contained 9721 and 33736 Bangla texts respectively. The Severity Dataset was annotated as low, medium, high and non-bullying. Final performance analysis showed that XG Boost classifier was the most accurate classifier with 87 % accuracy. The performance of SVM was followed by Logistic Regression, SVM and Naive Bayes classifiers. These classifiers obtained 85%, 81 % and 77% accuracy respectively. © 2022 IEEE.

关键词： Feature extraction

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Transfer Learning-Based Classification of Radiation Induced Lung Injury in Breast Cancer Patients Using PET Images 38

Transfer Learning-Based Classification of Radiation Induced ...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Jayanthi, K.B.A. Rajasekaran, C.B. Praveenkumar, S.C. Dhanalakshmi, R.D. Ramasamy, E. Suresh Kumar K.S. Rangasamy College of Technology Department of Electronics and Communication Engineering Namakkal India Indian Institute of Information Technology Department of Computer Science Engineering Tiruchirappalli India Indian Cancer Centre Tirupur India

ISBN: (纸本)9798350302196

Radiation-induced lung injury (RILI) is a serious concern for patients affected by breast cancer. Preventing lung injury is impossible since radiotherapy is very effective for breast cancer when applied in the mammary glands close to lungs. Starting early treatment for preventing lung injury can improve the quality of life of the patients. This paper proposes artificial intelligence-based automation for classification of lung injury in retrospective patients. 1692 Position Emission Tomography images of injured lungs are taken and clustered with K-means clustering, since the data set is unlabeled. The entire dataset groups into two clusters-radiation pneumonitis and radiation fibrosis. Silhouette score is 0.1619 when applied on PET images of the lungs. VGG 16 based transfer learning is applied on the k-means clustering algorithm to improve the classification accuracy. The silhouette score is increased to 0.806836. © 2023 IEEE.

关键词： K-means clustering

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A Bioinformatics Approach to Identify Hub Genes Across Schizophrenia, Anxiety, Bipolar Disorder, and Depressive Disorder for Network-Based Drug Discovery

A Bioinformatics Approach to Identify Hub Genes Across Schiz...

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information and communication Technology (ICICT), International Conference on

作者： Md. Emran Biswas MD Galib Hasan Md. Jobare Hossain Md. Abul Basar Md. Selim Hossain Department of Electronics and Communication Engineering HSTU Bangladesh Department of Computer Science and Engineering Engineering Uttara University Bangladesh Department of Information and Communication Technology MBSTU Bangladesh

ISBN: (数字)9798331508227

ISBN: (纸本)9798331508234

Mental health disorders represent a significant challenge in society, requiring immediate attention and comprehensive knowledge to mitigate their widespread impacts. Although considerable research has focused on hub gene identification for one or two illnesses, there is a lack of literature examining multiple common disorders and their implications for drug discovery. This study utilizes a bioinformatics methodology to identify shared and critical genes linked to anxiety disorders, bipolar disorders, schizophrenia, and depressive disorders, along with potential therapeutic targets for these conditions. We methodically collected gene-related data from the National Center for Biotechnology information (NCBI) for these disorders. Through protein-protein interaction analysis, we discovered hub genes, including GSK3B, MAPT, NR3C1, ESR1, TNF, SOD1, APP, CREB1, NOS1, APOE, GNB3, DISC1, SIRT1, CACNA1C, and BDNF. Additionally, we identified potential therapeutic agents targeting these hub genes, including Kaempferol, Sim-vastatin (CTD 00007319), Luteolin, Clozapine (CTD 00005693), Curcumin, Resveratrol, Melatonin (CTD 00006260), Felodipine, Apigenin, Docosahexaenoic Acid, Diazepam, and Alprazolam, using the DSigDB database. This study seeks to elucidate the genetic connections across mental health diseases and to find therapeutic compounds that may provide common treatment advantages.

关键词： Proteins Biotechnology Databases Anxiety disorders Mental health Machine learning Schizophrenia Drug discovery Compounds Bioinformatics

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Investigating the Effects of AI-Driven Systems on Data Science Automation 15

Investigating the Effects of AI-Driven Systems on Data Scien...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Senthilkumar, K.K. Mehta, Pradnya S. Pandey, Megha Rao, Panduranga M.V. Singh, Baldev Khurana, Saniya Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karnataka Bangalore India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (纸本)9798350370249

This article investigates the effect of AI-driven structures on the automation of information science. AI-pushed structures and automation of records technology have ended up an increasing number of essential because of the growing complexity of information and the numerous and ever-increasing data resources in today's digital international. This text explores the approaches in which AI-driven structures have converted how records technology is being computerized and the way it has impacted the method. Moreover, the article looks at the benefits and shortcomings of AI-driven structures in terms of automation of data science as well as the various regions which are still open for research that allows you to, in addition, improve the automation of information technology. Finally, the article proposes future study guidelines in this field, allowing the continued development of AI-driven records science automation. ©2024 IEEE.

关键词： automated information proposes shortcomings transformed

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A Low-Profile Circularly Polarized End-Fire Antenna Based on Substrate-Integrated Waveguide Horn

A Low-Profile Circularly Polarized End-Fire Antenna Based on...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Liu, Zhiheng Lin, Shu Li, Pingyu Wang, Yingqiao Zhang, Xingqi Zhang, Xinyue School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Jilin University Department of Communication Engineering Changchun China University of Alberta Department of Electrical and Computer Engineering Edmonton Canada Maynooth University Department of Electronic Engineering Maynooth Ireland

ISBN: (纸本)9781733509657

This paper presents a novel circularly polarized end-fire antenna operating in the L-band, designed specifically for personal mobile communication terminals. The antenna utilizes a substrate-integrated waveguide (SIW) horn to achieve end-fire radiation. To achieve left-handed circular polarization (LHCP), slots are introduced on the metallic frame of the device. Through simulations, we demonstrate that the designed antenna can achieve LHCP end-fire radiation within the frequency range of 1560-1610 MHz, with an in-band reflection loss better than 6 dB and an axial ratio (AR) better than 3.6 dB. The circularly polarized end-fire antenna exhibits extremely low-profile characteristics and holds great potential for applications in personal mobile communication terminals. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Substrate integrated waveguides

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Hierarchical Deep Learning Framework for Multi-Scale Energy Modeling in Smart Building Environments 7

Hierarchical Deep Learning Framework for Multi-Scale Energy ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Raj, J. Relin Francis Nikshya, J. Ebens Vinothini, S. Umesh, R. Krishnan, R. Santhana Gopikumar, S. Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Electronics And Communication Engineering Chennai India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India Psna College of Engineering And Technology Department of Computer Applications Dindigul India Velammal College of Engineering And Technology Department of Information Technology Madurai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology Department of Civil Engineering Chennai Avadi India

ISBN: (纸本)9798350359299

In contemporary urban landscapes, the quest for sustainable energy consumption has propelled the evolution of smart building technologies. These structures are equipped with advanced sensors, actuators, and control systems to optimize energy utilization while ensuring occupant comfort and operational efficiency. Central to this paradigm is energy modeling, a process that entails the analysis and prediction of energy consumption patterns within building environments. Efficient energy modeling holds paramount significance for devising effective strategies to mitigate energy wastage, reduce operational costs, and minimize environmental footprints. This research presents a novel approach, the Hierarchical Deep Learning Framework for Multi-scale Energy Modeling in Smart Building Environments, aimed at enhancing energy modeling efficiency. The Deep Energy Fusion (DEF) algorithm is introduced by integrating Deep Belief Networks (DBNs) with a hierarchical energy modeling architecture. DEF leverages DBNs' capability to learn complex hierarchical representations of energy data across various scales, facilitating a comprehensive understanding of energy consumption patterns. To evaluate DEF's performance, the simulation analysis are conducted by comparing it with existing algorithms. Metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R^2) are employed to assess prediction accuracy. Our results demonstrate that DEF outperforms traditional algorithms in capturing both local and global dependencies within the energy data, thereby improving energy modeling accuracy. Moreover, DEF showcases superior performance in terms of computational efficiency and scalability. This research contributes to advancing energy modeling techniques in smart buildings, offering a robust framework for optimizing energy management strategies © 2024 IEEE.

关键词： Energy efficiency

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Cotton Leaf Disease Classification Using Deep Learning based Novel Approach

Cotton Leaf Disease Classification Using Deep Learning based...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Pandey, Bihari Nandan Singh, Raghvendra Pratap Pandey, Mahima Shanker Jain, Sachin Ajay Kumar Garg Engineering College Department of Computer Science and Engineering UP Ghaziabad India Ajay Kumar Garg Engineering College Department of Electronics and Communication Engineering UP Ghaziabad India Bbditm Department of Information Technology UP Lucknow India

ISBN: (纸本)9798350323887

In India, one of the essential professions in agriculture. Cotton is the best crop out of several and is very important to the nation's agricultural economy. Six million farmers in India depend directly on cotton, while 40-50 million people are employed in the cotton trade and cotton processing. Over the past few decades, the cotton leaf disease has become a significant issue, causing losses in harvests, agricultural activities, and financial resources. Farmers may find it challenging to find a solution if they cannot accurately identify the characteristics of the crop's actual disorder at the outset. This study intended to develop a model to aid in the detection of cotton leaf pests and diseases. In this study, we propose a hybrid strategy based on deep learning. On a data set of 1710 images, the presented model accurately discovers Leaf Curl Disease and Fusarium wilt in cotton plants with an accuracy of 98.9%. © 2023 IEEE.

关键词： Cotton

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A Miniaturized Fan Beam Antenna Based on Spherical Luneburg Lens 11

A Miniaturized Fan Beam Antenna Based on Spherical Luneburg ...

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11th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Liu, Zhiheng Lin, Shu Li, Pingyu Wang, Yingqiao Zhang, Xinyue Zhang, Xingqi Harbin Institute of Technology School of Electronics and Information Engineering Harbin China Jilin University Department of Communication Engineering Changchun China Maynooth University Department of Electronic Engineering Maynooth Ireland University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798350326277

A beamforming antenna based on a spherical Luneburg lens operating at 3-4 GHz is proposed. The antenna consists of a spherical lens with a radius of 0.8 %0 (free-space wavelength of the central operating frequency) and five double-ridged waveguides. An approximation to an ideal Luneburg lens is realized by controlling the air-filling rate of a series of hollow dielectric cones with common vertices. By adopting the double ridge waveguide as the feed source, the requirement of the fan beam for the smaller aperture feed source is realized. The simulation results show that the aperture efficiency of the designed lens can reach 60 %, the synthetic beam is a fan beam with a width of 145°, and the gain flatness is better than 0.5 dB within ± 60°. The antenna has the advantages of small lens size, high aperture efficiency, and wide synthetic beam, and can be used for the deployment of 5G communication base stations. © 2023 IEEE.

关键词： 5G communication Aperture efficiency Double-ridged waveguides Fan beam Luneburg lens

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Combining Machine Learning and Handcrafted Image Processing to Identify Common Weeds in Cotton Fields 2

Combining Machine Learning and Handcrafted Image Processing ...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Priyanka Singh, Ram Krishna Gupta, Vikas Pawar, Janardan Devi Illa, Uma Aruna Sharma, Meenakshi Chandigarh University Gharuan Mohali India Ims Engineering College Department of Computer Science Ghaziabad India Ghaziabad India Indira College of Commerce and Science Department of Computer Science Pune India Swarnandhra College of Engineering and Technology Department of Information Technology Andhra Pradesh Narsapur India Inderprastha Engineering College Department of Electronics & Communication Engineering Ghaziabad India

ISBN: (纸本)9798350371055

Weed management is a critical aspect of agriculture, with weed infestations posing significant challenges to crop yields. Conventional manual methods for weed identification have limitations, emphasizing the need for innovative solutions. This research focuses on the integration of machine learning and handcrafted image processing to identify common weeds in cotton fields, with a specific emphasis on Southern India. The targeted weed species include Palmer Amaranth (Amaranthus palmeri), Pigweed (Amaranthus species), Johnsongrass (Sorghum halepense), and Yellow Nutsedge (Cyperus esculentus), which are prevalent in cotton fields. Through the use of an RGB camera and a developed ImageJ plugin for shape feature extraction, 39 shape features were extracted for each weed species. Three machine learning models, k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Machine (SVM), were employed, and the results were compelling. RF exhibited the highest accuracy at 96.1%, demonstrating remarkable precision, recall, and F1- Score values, particularly for Palmer Amaranth. The practical implications of this methodology extend to farmers, producers, and other stakeholders in cotton production. The open-source nature of the tools used makes this approach accessible, cost-effective, and user-friendly. It offers a valuable resource for precise weed identification, timely intervention, resource optimization, and ultimately, improved crop yields. This research underscores the potential for advanced machine learning models in weed management, offering a practical solution for enhancing agriculture's productivity and sustainability. © 2024 IEEE.

关键词： Agriculture Cotton fields Image processing Machine learning Weed identification

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