Article
Engineering, Electrical & Electronic
Jaeu Park, Jinwoong Jeong, Minseok Kang, Nagwade Pritish, Youngjun Cho, Jeongdae Ha, Junwoo Yea, Kyung-In Jang, Hyojin Kim, Jumin Hwang, Byungchae Kim, Sungjoon Min, Hoijun Kim, Soonchul Kwon, ChangSik John Pak, HyunSuk Peter Suh, Joon Pio Hong, Sanghoon Lee
Summary: This paper presents tailored sEMG sensors for amputees wearing sockets, prioritizing breathability, durability, and reliable recording performance. The sensors utilize porous PDMS and Silbione substrates, optimized serpentine electrode pattern, and achieve exceptional permeability and adhesive properties. The wireless control of robotic legs for amputees demonstrates the practical feasibility of the proposed sensors, driving forward neuro-prosthetic control in clinical research.
NPJ FLEXIBLE ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
R. Rajapriya, K. Rajeswari, Deepak Joshi, S. J. Thiruvengadam
Summary: This study aims to overcome limitations in existing methods through a novel feature set extracted from electromyography's wavelet bispectrum. The proposed method shows independence when dealing with multiple dynamic factors and outperforms conventional feature sets in terms of classification accuracy.
IEEE SENSORS JOURNAL
(2021)
Article
Engineering, Biomedical
Zhen Liu, Bingyu Zhu, Manfeng Hu, Zhaohong Deng, Jingxiang Zhang
Summary: This paper proposes a revised tunable Q-factor wavelet transform (RTQWT) to overcome the limitations of traditional methods and improve the adaptability to nonstationary EEG signals. Classification experiments using the extracted features show that RTQWT can effectively extract detailed features and improve the classification accuracy of EEG signals.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Engineering, Biomedical
Chandan Kumar Jha, Maheshkumar H. Kolekar
Summary: The paper presents a novel ECG data compression technique based on the tunable Q-wavelet transform, achieving good compression performance by compacting signal energy and discarding small valued transform coefficients. Experimental results show that the proposed technique performs well in cardiac arrhythmia classification.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Neurosciences
Keun-Tae Kim, Sangsoo Park, Tae-Hyun Lim, Song Joo Lee
Summary: Recently, myoelectric interfaces using surface electromyogram (EMG) signals have been developed to assist individuals with physical disabilities, particularly for controlling robotic hands or arms. This study aimed to investigate the possibility of classifying reaching-to-grasping tasks based on EMG signals from the upper arm and upper body, excluding wrist muscles, for prosthetic users. Results showed that the PCA-CNN method outperformed traditional methods in accurately decoding tasks for healthy subjects, although slightly lower performance was observed for amputees.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Engineering, Biomedical
Younggeol Cho, Pyungkang Kim
Summary: This study proposes a method for real-time estimation of muscle unit activation and a robust finger force intention estimation model. The proposed model achieves high performance compared to previous studies.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Robotics
Nayan M. Kakoty, Lakhyajit Gohain, Juri Borborua Saikia, Amlan Jyoti Kalita, Satyajit Borah
Summary: This paper presents a real-time EMG-based embedded controller for prosthetic hands. The experiments show that the controller enables the prosthetic hand to perform grasping operations in a time comparable to human hands, while maintaining a high level of accuracy.
INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS
(2022)
Article
Robotics
Lakhyajit Gohain, Krishna Sarma, Amlan Jyoti Kalita, Nayan M. Kakoty, Shyamanta M. Hazarika
Summary: This paper reports a prosthetic hand performing 16 grasp types in real-time using a single channel EMG customized with an Android application called Graspy.
INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS
(2022)
Article
Engineering, Biomedical
Smith K. Khare, Varun Bajaj, U. Rajendra Acharya
Summary: Deep brain simulations are important for studying physiological and neuronal behavior in Parkinson's disease. EEG signals can accurately reflect changes in the brain during PD, but manual analysis of these signals is time-consuming due to their complex nature. An automated tunable Q wavelet transform (A-TQWT) was proposed for automatic decomposition of EEG signals, achieving high accuracy in classifying healthy controls and PD patients with and without medication.
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
(2021)
Article
Biology
Sezer Ulukaya, Gorkem Serbes, Yasemin P. Kahya
Summary: The proposed resonance-based decomposition method in this study shows significant advantages in separating crackles and wheezes in lung sounds and successfully preserving discriminative information. Compared to previous methods, this approach is able to automatically and simultaneously decompose with smaller root mean square error and higher accuracy.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Engineering, Biomedical
Mingkan Shen, Peng Wen, Bo Song, Yan Li
Summary: This paper proposes an EEG based real-time approach using tunable-Q wavelet transform and convolutional neural network (CNN) to detect epilepsy seizures. Statistical moments and spectral band power are used to extract features from EEG and are fed into CNN as imaged-like data. The proposed approach achieves 97.57% accuracy, 98.90% sensitivity, 2.13% false positive rate and a delay of 10.46 seconds. It is also suitable for real-time implementation and can be applied to clinical seizure detection.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Acoustics
Mittapalle Kiran Reddy, Yagnavajjula Madhu Keerthana, Paavo Alku
Summary: This study proposes a method for classifying functional dysphonia using acoustic voice signals, and the combination of TQWT and glottal features achieves the highest classification accuracy.
SPEECH COMMUNICATION
(2023)
Article
Engineering, Electrical & Electronic
Soumyendu Banerjee, Girish Kumar Singh
Summary: The proposed algorithm combines TQWT and AFD to develop a new single-channel ECG signal compression method, achieving efficient compression by improving TQWT and AFD parameter selection and introducing a new AFD transformation. The algorithm performed well in fidelity and compression ratio during testing, making it suitable for medical applications.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Energy & Fuels
Lin Yang, Linming Guo, Wenhai Zhang, Xiaomei Yang
Summary: This article proposes a novel method to identify and classify multiple PQ disturbances by integrating improved TQWT with XGBoost algorithm. Experimental results show that the method is noise-resistant and achieves high classification accuracy.
Article
Mathematics
Pampa Sinha, Kaushik Paul, Chidurala Saiprakash, Almoataz Y. Abdelaziz, Ahmed I. Omar, Chun-Lien Su, Mahmoud Elsisi
Summary: This article focuses on the unusual fault conditions of transmission lines in an electricity system, particularly those that pass through wooded areas. The study utilizes the Tunable Q Wavelet Transform (TQWT) to extract signal characteristics associated with cross-country faults (CCFs) and high-impedance fault (HIF) syndrome. An adaptive TQWT-based feature-extraction approach is presented, which demonstrates effective results in signal processing.
Article
Engineering, Electrical & Electronic
Ali Abbasian Ardakani, Afshin Mohammadi, Fariborz Faeghi, U. Rajendra Acharya
Summary: This study aims to evaluate 67 denoising filters and select the best one for ultrasound image denoising. A new filter evaluation method, Rank Analysis, was introduced and utilized. The best filter identified was the Spatial correlation (SCorr) filter.
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
(2023)
Article
Medicine, General & Internal
Soumya Ranjan Nayak, Deepak Ranjan Nayak, Utkarsh Sinha, Vaibhav Arora, Ram Bilas Pachori
Summary: The research community is interested in developing automated systems for COVID-19 detection using deep learning approaches and chest radiography images. However, current deep learning techniques require more parameters and memory, making them unsuitable for real-time diagnosis. This paper proposes a lightweight CNN model called LW-CORONet, which extracts meaningful features from chest X-ray images with only five learnable layers. The proposed model achieves high classification accuracy on large datasets and can assist radiologists in COVID-19 diagnosis.
Article
Biology
Smith K. Khare, U. Rajendra Acharya
Summary: The study explores a method called VMD-HT, which combines variational mode decomposition (VMD) and Hilbert transform (HT), to extract hidden information from EEG signals for the detection of ADHD. The results show that the explainable model has high accuracy and reliability in automatically detecting ADHD.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Automation & Control Systems
Tugce Keles, Arif Metehan Yildiz, Prabal Datta Barua, Sengul Dogan, Mehmet Baygin, Turker Tuncer, Caner Feyzi Demir, Edward J. Ciaccio, U. Rajendra Acharya
Summary: This article proposes an automated EEG signal classification model that extracts low-level and high-level features, utilizes statistical and textural features, selects informative features, and calculates channel-wise results using a shallow classifier. The proposed model achieved high classification accuracy.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Amanpreet Singh, Ali Abbasian Ardakani, Hui Wen Loh, P. V. Anamika, U. Rajendra Acharya, Sidharth Kamath, Anil K. Bhat
Summary: The objective of this study was to develop a high-performing deep-learning model using only plain wrist radiographs to detect apparent and non-apparent occult scaphoid fractures. A CNN-based model was developed and achieved good performance in two-class and three-class classification, with high sensitivity, specificity, accuracy, and AUC values. The model also utilized gradient-weighted class activation mapping for fracture localization.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Biomedical
Bharti Jogi Dakhale, Manish Sharma, Mohammad Arif, Kushagra Asthana, Ankit A. Bhurane, Ashwin G. Kothari, U. Rajendra Acharya
Summary: Healthy sleep is important for physical and mental well-being, but factors like work schedules and medical complications can lead to sleep disorders. This study proposes a new method for automated sleep stage classification using machine learning and EEG signals. By analyzing data from 453 subjects, the developed model achieved a classification accuracy of 81.3%.
MEDICAL ENGINEERING & PHYSICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Hui Wen Loh, Chui Ping Ooi, Shu Lih Oh, Prabal Datta Barua, Yi Ren Tan, Filippo Molinari, Sonja March, U. Rajendra Acharya, Daniel Shuen Sheng Fung
Summary: This study aims to create the first explainable deep learning model for objective ECG-based ADHD/CD diagnosis, achieving high classification accuracy and providing vital temporal data for clinicians to make wise medical judgments. The Grad-CAM function highlights important ECG characteristics impacting classification scores, potentially encouraging larger-scale research with biosignal datasets for implementing biosignal-based computer-aided diagnostic tools in healthcare settings.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Computer Science, Interdisciplinary Applications
Shuting Xu, Ravinesh Deo, Jeffrey Soar, Prabal Datta Barua, Oliver Faust, Nusrat Homaira, Adam Jaffe, Arm Luthful Kabir, U. Rajendra Acharya
Summary: This study investigates the role of automated detection of obstructive airway diseases in reducing cost and improving diagnostic quality. Medical imaging, genetics, and physiological signals are the main sources used for disease detection. Machine Learning is more prevalent than Deep Learning in the field, with Convolutional Neural Network being a common DL classifier and Support Vector Machine being widely used in ML.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Biology
U. Raghavendra, Anjan Gudigar, Aritra Paul, T. S. Goutham, Mahesh Anil Inamdar, Ajay Hegde, Aruna Devi, Chui Ping Ooi, Ravinesh C. Deo, Prabal Datta Barua, Filippo Molinari, Edward J. Ciaccio, U. Rajendra Acharya
Summary: A brain tumor is an abnormal mass inside the skull that can lead to significant health problems by putting pressure on the brain. Early detection of these tumors is crucial as malignant brain tumors grow rapidly and can result in higher mortality rates. Computer-aided diagnostic systems, combined with artificial intelligence techniques, play a vital role in the early detection of this disorder. This review highlights the challenges faced by CAD systems based on different modalities, current requirements in this field, and future prospects in research.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Engineering, Biomedical
Kamlesh Kumar, Kapil Gupta, Manish Sharma, Varun Bajaj, U. Rajendra Acharya
Summary: This study successfully developed a model that integrates electrocardiogram with a convolutional neural network to accurately measure sleep quality for identifying insomnia. By employing continuous wavelet transform, 1-D time domain ECG signals were converted into 2-D scalograms, which were then fed to different neural networks for automated detection of insomnia. The proposed system showed high accuracy and performance in insomnia detection based on the validation experiments.
MEDICAL ENGINEERING & PHYSICS
(2023)
Article
Chemistry, Analytical
Orhan Atila, Erkan Deniz, Ali Ari, Abdulkadir Sengur, Subrata Chakraborty, Prabal Datta Barua, U. Rajendra Acharya
Summary: This paper introduces a novel approach (LSGP-USFNet) for the early diagnosis of attention deficit hyperactivity disorder (ADHD) using electroencephalogram (EEG) signals. The approach involves filtering and segmenting the EEG signals, applying continuous wavelet transform for time-frequency analysis, extracting features based on Ulam's spiral and Sophia Germain's prime numbers, and using a support vector machine classifier for classification. The proposed approach achieved an accuracy of 97.46% in automatically detecting ADHD.
Article
Computer Science, Information Systems
Neethu Mohan, S. Sachin Kumar, K. P. Soman, V. G. Sujadevi, Prabaharan Poornachandran, U. Rajendra Acharya
Summary: Voltage fluctuations are caused by various factors such as variations in renewable energy sources, increased usage of nonlinear loads, and high reactive power consumption. Traditional techniques are unable to accurately monitor these fluctuations due to their uncharacteristic variations. This paper proposes a data-driven hybrid method for monitoring voltage fluctuations, using low-rank approximation of dynamic mode decomposition (DMD) and flat-top finite impulse response (FIR) filter. The results show that this methodology can accurately identify parameters and monitor voltage fluctuations in smart grid scenarios, as well as detect higher-order harmonics in distribution grids.
Article
Radiology, Nuclear Medicine & Medical Imaging
Taha Muezzinoglu, Nursena Baygin, Ilknur Tuncer, Prabal Datta Barua, Mehmet Baygin, Sengul Dogan, Turker Tuncer, Elizabeth Emma Palmer, Kang Hao Cheong, U. Rajendra Acharya
Summary: This study proposes a patch-based deep feature engineering model called PatchResNet, which utilizes different sized patches, feature extractors, and selectors to achieve high classification accuracy for brain tumor images.
JOURNAL OF DIGITAL IMAGING
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Suat Kamil Sut, Mustafa Koc, Gokhan Zorlu, Ihsan Serhatlioglu, Prabal Datta Barua, Sengul Dogan, Mehmet Baygin, Turker Tuncer, Ru-San Tan, U. Rajendra Acharya
Summary: A new handcrafted machine learning method has been developed for the automated and accurate classification of adrenal gland CT images. The method analyzed a dataset of 759 CT image slices from 96 subjects, and achieved high accuracy in classification using k-nearest neighbor, support vector machine, and neural network classifiers.
JOURNAL OF DIGITAL IMAGING
(2023)
Article
Computer Science, Artificial Intelligence
Sinan Tatli, Gulay Macin, Irem Tasci, Burak Tasci, Prabal Datta Barua, Mehmet Baygin, Turker Tuncer, Sengul Dogan, Edward J. Ciaccio, U. Rajendra Acharya
Summary: This study aims to propose a new algorithm for early diagnosis of multiple sclerosis (MS) using machine learning. The algorithm utilizes transfer learning and hybrid feature engineering, and calculates feature vectors through multiple layers of neural networks, resulting in high classification accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Editorial Material
Computer Science, Theory & Methods
Kiho Lim, Christian Esposito, Tian Wang, Chang Choi
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Jesus Carretero, Dagmar Krefting
Summary: Computational methods play a crucial role in bioinformatics and biomedicine, especially in managing large-scale data and simulating complex models. This special issue focuses on security and performance aspects in infrastructure, optimization for popular applications, and the integration of machine learning and data processing platforms to improve the efficiency and accuracy of bioinformatics.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Renhao Lu, Weizhe Zhang, Qiong Li, Hui He, Xiaoxiong Zhong, Hongwei Yang, Desheng Wang, Zenglin Xu, Mamoun Alazab
Summary: Federated Learning allows collaborative training of AI models with local data, and our proposed FedAAM scheme improves convergence speed and training efficiency through an adaptive weight allocation strategy and asynchronous global update rules.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Qiangqiang Jiang, Xu Xin, Libo Yao, Bo Chen
Summary: This paper proposes a multi-objective energy-efficient task scheduling technique (METSM) for edge heterogeneous multiprocessor systems. A mathematical model is established for the task scheduling problem, and a problem-specific algorithm (IMO) is designed for optimizing task scheduling and resource allocation. Experimental results show that the proposed algorithm can achieve optimal Pareto fronts and significantly save time and power consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Weimin Li, Lu Liu, Kevin I. K. Wang, Qun Jin
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Mohammed Riyadh Abdmeziem, Amina Ahmed Nacer, Nawfel Moundji Deroues
Summary: Internet of Things (IoT) devices have become ubiquitous and brought the need for group communications. However, security in group communications is challenging due to the asynchronous nature of IoT devices. This paper introduces an innovative approach using blockchain technology and smart contracts to ensure secure and scalable group communications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Robert Sajina, Nikola Tankovic, Ivo Ipsic
Summary: This paper presents and evaluates a novel approach that utilizes an encoder-only transformer model to enable collaboration between agents learning two distinct NLP tasks. The evaluation results demonstrate that collaboration among agents, even when working towards separate objectives, can result in mutual benefits.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Hebert Cabane, Kleinner Farias
Summary: Event-driven architecture has been widely adopted in the software industry for its benefits in software modularity and performance. However, there is a lack of empirical evidence to support its impact on performance. This study compares the performance of an event-driven application with a monolithic application and finds that the monolithic architecture consumes fewer computational resources and has better response times.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Javier Del Ser, Khan Muhammad
Summary: Wireless capsule endoscopy (WCE) is a revolutionary diagnostic method for small bowel pathology. However, the manual analysis of WCE videos is cumbersome and the privacy concerns of WCE data hinder the adoption of AI-based diagnoses. This study proposes a federated learning framework for collaborative learning from multiple data centers, demonstrating improved anomaly classification performance while preserving data privacy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Maruf Monem, Md Tamjid Hossain, Md. Golam Rabiul Alam, Md. Shirajum Munir, Md. Mahbubur Rahman, Salman A. AlQahtani, Samah Almutlaq, Mohammad Mehedi Hassan
Summary: Bitcoin, the largest cryptocurrency, faces challenges in broader adaption due to long verification times and high transaction fees. To tackle these issues, researchers propose a learning framework that uses machine learning to predict the ideal block size in each block generation cycle. This model significantly improves the block size, transaction fees, and transaction approval rate of Bitcoin, addressing the long wait time and broader adaption problem.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
Summary: This paper introduces the importance of user interfaces for digital twins and presents a technique called ADD for modeling requirements of Human-DT interaction. A study is conducted to assess the feasibility and utility of ADD in designing user interfaces, using the virtualization of a natural space as a case study.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
Summary: This article proposes a novel multiclass multi-pool analytical model for optimizing the quality of composite service applications deployed in the cloud. By considering embarrassingly parallel services and using differentiated parallel processing mechanisms, the model provides accurate prediction results and significantly reduces job response time.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
Summary: In this paper, a novel MEV detection model called ArbiNet is proposed, which offers a low-cost and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sacheendra Talluri, Nikolas Herbst, Cristina Abad, Tiziano De Matteis, Alexandru Iosup
Summary: Serverless computing is increasingly used in data-processing applications. This paper presents ExDe, a framework for systematically exploring the design space of scheduling architectures and mechanisms, to help system designers tackle complexity.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Summary: This paper introduces a Federated Learning framework called FedBnR to address the issue of potential data heterogeneity in distributed entities. By breaking up the original task into multiple subtasks and reconstructing the representation using feature extractors, the framework improves the learning performance on heterogeneous datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
