Article
Chemistry, Multidisciplinary
Francisco Laport, Paula M. Castro, Adriana Dapena, Francisco J. Vazquez-Araujo, Oscar Fresnedo
Summary: The study presents a prototype system for identifying eye states using low-cost hardware integration and signal processing algorithms. Experiments compare various parameters to determine a configuration with high accuracy and short response delay.
APPLIED SCIENCES-BASEL
(2021)
Article
Business
Soumya Das, Sarojananda Mishra, ManasRanjan Senapati
Summary: The time series data is predicted with Elephant Herd Optimization (EHO), which has been proven superior in comparison to other methods through experiments. The method shows good performance in feature selection and neural network training.
JOURNAL OF MANAGEMENT ANALYTICS
(2021)
Article
Computer Science, Artificial Intelligence
Ozlem Karabiber Cura, Aydin Akan, Gulce Cosku Yilmaz, Hatice Sabiha Ture
Summary: This study proposes advanced signal processing methods for the detection and follow-up of Alzheimer's dementia (AD) using EEG signals. Various signal decomposition-based approaches and selection procedures are used to classify EEG segments and calculate features. The experimental results show that the proposed methods achieve good classification performances.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Engineering, Biomedical
Mohammad Shahbakhti, Matin Beiramvand, Mojtaba Nazari, Anna Broniec-Wojcik, Piotr Augustyniak, Ana Santos Rodrigues, Michal Wierzchon, Vaidotas Marozas
Summary: This study introduces an algorithm called VME-DWT, which can effectively remove eye blink artifacts in single-channel EEG. The algorithm demonstrates superior performance compared to other methods, making it a promising approach for eye blink removal in low-cost single-channel EEG systems.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2021)
Article
Computer Science, Theory & Methods
Rabi Shaw, Bidyut Kr. Patra
Summary: Flipped learning is an effective teaching method accomplished through pre-loaded lecture videos and in-class activities. However, the inability to monitor students' learning progress during video lectures may impact learning outcomes.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Engineering, Electrical & Electronic
Amer Al-Canaan, Hicham Chakib, Muhammad Uzair, Shuja-uRehman Toor, Amer Al-Khatib, Majid Sultan
Summary: The research focuses on Brain Computer Interface (BCI) system for smart home automation using Electroencephalography (EEG) signals, featuring wavelet features and dual-channel analogue EEG signal acquisition module. By carefully setting deep-learning classifier model parameters, high accuracy results were achieved, with advantages including large bandwidth, low cost, and high classification accuracy.
IET SIGNAL PROCESSING
(2022)
Article
Chemistry, Multidisciplinary
Amer Malki, Abdallah A. Mohamed, Yasser I. Rashwan, Ragab A. El-Sehiemy, Mostafa A. Elhosseini
Summary: The use of EHO technique has successfully estimated optimal parameter values for various solar cell systems, demonstrating higher accuracy and performance compared to other optimization algorithms.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Biomedical
Bethany Gosala, Pappu Dindayal Kapgate, Priyanka Jain, Rameshwar Nath Chaurasia, Manjari Gupta
Summary: Applying AI in healthcare benefits from Bio-signal analysis, particularly the Wavelet Scattering Transform (WST) method, which outperforms traditional ML algorithms (such as logistic regression and support vector machine) in neurological disorder classification. Results indicate that continuous wavelet transform (CWT) and discrete wavelet transform (DWT) yield better feature extraction performance. Decision trees achieve the best results in terms of accuracy, sensitivity, specificity, and Kappa score.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Lokesh Malviya, Sandip Mal
Summary: This study proposes a hybrid deep learning model for detecting stress levels in humans using EEG signals. By utilizing DWT for signal denoising and decomposition, and combining CNN and BLSTM for feature selection and classification, the proposed method achieves high accuracy in detecting stress levels.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Engineering, Electrical & Electronic
R. Ganeshan, Satish Muppidi, D. R. Thirupurasundari, B. Santhosh Kumar
Summary: Copy move forgery is a common form of digital image forgery, but detecting the forgery is complex. This study proposes an effective forgery object detection method based on A-EHO GAN, which extracts LOOP and CNN features, and computes the forgery score using GAN to detect forgery images.
SIGNAL PROCESSING-IMAGE COMMUNICATION
(2022)
Article
Engineering, Electrical & Electronic
R. Ganeshan, Satish Muppidi, D. R. Thirupurasundari, B. Santhosh Kumar
Summary: This paper proposes an effective forgery object detection approach using A-EHO based GAN, which achieves higher performance by extracting features and detecting forged images using the RideNN classifier.
SIGNAL PROCESSING-IMAGE COMMUNICATION
(2022)
Article
Engineering, Biomedical
Matteo Dora, David Holcman
Summary: This paper proposes a new wavelet-based method for removing artifacts from single-channel EEGs. The method adaptively attenuates artifacts of different nature through data-driven renormalization of wavelet components and demonstrates superior performances on different kinds of artifacts and signal-to-noise levels. The proposed method provides a valuable tool to remove artifacts in real-time EEG applications with few electrodes, such as monitoring in special care units.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Engineering, Biomedical
B. Indira Priyadarshini, D. Krishna Reddy
Summary: An optimized Adaptive Neuro Fuzzy Inference System (OANFIS) classifier is proposed in this paper to automatically detect seizures, aiming to increase classifier accuracy while reducing computational complexity. By selecting optimal features using the Binary Particle Swarm Optimization (BPSO) algorithm, the proposed system achieves a classification accuracy of 99.25% and consumes only 2.018 mu W power.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Engineering, Biomedical
I. Velasco, A. Sipols, C. Simon De Blas, L. Pastor, S. Bayona
Summary: This study aims to improve the classification accuracy of EEG signals by using methods based on time series analysis, specifically for EEG motor imagery signals. The proposed method fully captures the relationships among different time series in the EEG data, leading to good classification results. By using a reduced number of variables, the model becomes more interpretable and reduces the risk of overfitting.
BIOMEDICAL ENGINEERING ONLINE
(2023)
Article
Engineering, Biomedical
Syarifah Noor Syakiylla Sayed Daud, Rubita Sudirman
Summary: The focus on evaluating the effect of stimulation on human cognitive function based solely on behavioral data neglects the understanding of physiological responses. This study objectively investigated the correlation between EEG voltage and oscillations pattern during visual learning encoding and recognition stages under various stimulation, combining EEG patterns with behavioral data. The evaluation showed that the most affected EEG channel was Fp2 with a voltage of about 0.1 V, and brain oscillations were higher during recognition than encoding. The study found a correlation between EEG voltage and rhythms patterns during encoding with recognition and behavioral data.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Automation & Control Systems
Rizk M. Rizk-Allah, Aboul Ella Hassanien
Summary: This paper proposes a novel algorithm named EO-PS, based on the hybridization of equilibrium optimizer and pattern search techniques, for accurate and reliable wind farm layout optimization design. The algorithm operates in two phases, utilizing equilibrium optimizer in the first phase to explore the search space and pattern search in the second phase to guide the searching towards better solutions. The algorithm is implemented and tested on irregular land space in Egypt, achieving optimal layout configuration for practical planning trends. The comprehensive results and analyses confirm the competitive performance of EO-PS in terms of solution quality and reliability.
Article
Computer Science, Artificial Intelligence
Mohamed A. Tolba, Essam H. Houssein, Ayman A. Eisa, Fatma A. Hashim
Summary: This study introduces an optimization method to minimize power losses and voltage deviations in electrical distribution networks (EDNs), and validates its effectiveness through validation and demonstration. A comprehensive analysis comparing the method with other optimizers is also conducted, demonstrating its superiority.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Biology
Essam H. Houssein, Awny Sayed
Summary: With the increasing availability of healthcare data, machine learning is becoming more significant in healthcare domains. It is crucial to ensure the integrity and reliability of machine learning models to maintain the quality of healthcare services. Due to privacy and security concerns, healthcare data is often treated as independent sources and limited computational capabilities of wearable healthcare devices hinder traditional machine learning. Federated Learning, which protects data privacy by storing only learned models on a server and advances with data from scattered clients, shows potential to transform healthcare by enabling the development of new machine learning applications.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biology
Marwa M. Emam, Nagwan Abdel Samee, Mona M. Jamjoom, Essam H. Houssein
Summary: Brain tumor, defined as abnormal development of synapses in the brain, is one of the worst diseases. Early detection and classification of brain tumors are crucial for prognosis and treatment. This study proposes an evolved and efficient model based on deep learning and improved metaheuristic algorithms to address the challenges of brain tumor classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biology
Essam H. Houssein, Awny Sayed
Summary: Chronic kidney disease (CKD) is a progressive decrease in kidney function over time, especially in individuals with diabetes and high blood pressure. The INFO algorithm, a metaheuristic algorithm for medical treatment, has been modified to improve its performance by utilizing Opposition-Based Learning (OBL) and Dynamic Candidate Solution (DCS) strategies. The proposed mINFO algorithm outperforms other well-known metaheuristic algorithms in the CEC'22 test suite and achieves a high classification accuracy of 93.17% on two CKD datasets.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Multidisciplinary Sciences
Essam H. Houssein, Rehab E. Mohamed, Abdelmgeid A. Ali
Summary: Heart disease remains a major cause of death, and detecting risk factors in clinical notes can aid in disease progression modeling and clinical decision-making. Previous studies have proposed hybrid systems combining knowledge-driven and data-driven techniques, but none have identified all risk factors. The use of stacked word embeddings has shown significant improvement in identifying risk factors for heart disease.
SCIENTIFIC REPORTS
(2023)
Article
Environmental Sciences
Sanaz Afzali Ahmadabadi, Jafar Jafari-Asl, Elham Banifakhr, Essam H. Houssein, Mohamed El Amine Ben Seghier
Summary: English Summary: In this study, the placement of contamination warning systems (CWSs) in water distribution systems (WDSs) was investigated. A novel optimization model called WOA-SCSO, based on a hybrid algorithm combining whale optimization algorithm (WOA) and sand cat swarm optimization (SCSO), was developed. The effectiveness of the WOA-SCSO algorithm was evaluated using benchmark functions, showing superior performance compared to other algorithms. The results demonstrated that the WOA-SCSO algorithm can effectively optimize the placement of CWSs in WDSs, reducing contamination risks.
Article
Medicine, General & Internal
Essam H. H. Houssein, Hager N. N. Hassan, Nagwan Abdel Samee, Mona M. M. Jamjoom
Summary: Accurately categorizing cancers using microarray data is crucial, and computational intelligence approaches have been employed to analyze gene expression data. Selecting informative genes is believed to be the most difficult part of cancer diagnosis, and the proposed RUN-SVM approach combines the Runge Kutta optimizer with a support vector machine to select significant genes in cancer tissue detection. The approach is tested on different microarray datasets and statistically outperforms competing algorithms due to its innovative search technique.
Article
Medicine, General & Internal
Essam H. Houssein, Gaber M. Mohamed, Nagwan Abdel Samee, Reem Alkanhel, Ibrahim A. Ibrahim, Yaser M. Wazery
Summary: This paper proposes an efficient version of the search and rescue optimization algorithm (mSAR) based on opposition-based learning (OBL) for blood-cell image segmentation and solving multi-level thresholding problems. Experimental results demonstrate that mSAR algorithm outperforms other competing algorithms in terms of segmented image quality and feature conservation.
Article
Mathematics
Hesham Alhumade, Essam H. H. Houssein, Hegazy Rezk, Iqbal Ahmed Moujdin, Saad Al-Shahrani
Summary: Recently, the Artificial Hummingbird Algorithm (AHA) has been proposed as a swarm-based method for optimization problems. In this paper, a modified version of AHA called mAHA is proposed, combining genetic operators. Experimental results demonstrate that mAHA improves convergence speed and search results. mAHA is then used for the first time to find the global maximum power point (MPP) in photovoltaic (PV) systems with shading.
Article
Biology
Essam H. Houssein, Nagwan Abdel Samee, Noha F. Mahmoud, Kashif Hussain
Summary: Medical datasets are often filled with irrelevant and redundant elements, which are not necessary for medical decision-making. Recent research has shown that feature selection can effectively address this issue.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Saroj Kumar Sahoo, Essam H. Houssein, M. Premkumar, Apu Kumar Saha, Marwa M. Emam
Summary: In this study, an upgraded variant of Moth flame optimization algorithm (Es-MFO) is proposed for higher accuracy in classifying COVID-19 CT images. The algorithm is evaluated and compared with other optimization techniques and MFO variants. Its robustness and durability are tested and it is applied to solve the COVID-19 CT image segmentation problem with superior results compared to other algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Civil
Youcef Djenouri, Asma Belhadi, Essam H. Houssein, Gautam Srivastava, Jerry Chun-Wei Lin
Summary: This paper presents a novel intelligent system based on graph convolutional neural networks for road crack detection, which achieves high precision by analyzing image features and training models.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Amir El-Ghamry, Ashraf Darwish, Aboul Ella Hassanien
Summary: Smart farming is an advanced approach to managing a farm, which involves monitoring crop health and productivity using technology and information. The Internet of Things enables smart farming by collecting and storing data, but also exposes it to cyber-attacks. Therefore, an intrusion detection system that can adapt to the challenges of IoT networks in agriculture is crucial.
INTERNET OF THINGS
(2023)
Article
Education & Educational Research
Rana Saeed Al-Maroof, Said A. Salloum, Aboul Ella Hassanien, Khaled Shaalan
Summary: This study examines the impact of fear emotion on the adoption of Google Meet by students and teachers during the COVID-19 pandemic. The findings demonstrate that fear, specifically related to family lockdown, education failure, and loss of social relationships, has a significant effect on the adoption of this educational platform. The study also compares different data analysis techniques and finds that the J48 classifier is the most effective in predicting the dependent variable in most cases.
INTERACTIVE LEARNING ENVIRONMENTS
(2023)
Article
Engineering, Biomedical
Wenwen Wu, Yanqi Huang, Xiaomei Wu
Summary: In this study, a 2D deep learning classification network SRT was proposed to improve automatic ECG analysis. The model structure was enhanced with the CNN and Transformer-encoder modules, and a novel attention module and Dilated Stem structure were introduced to improve feature extraction. Comparative experiments showed that the proposed model outperformed several advanced methods.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Chiheb Jamazi, Ghaith Manita, Amit Chhabra, Houssem Manita, Ouajdi Korbaa
Summary: In this study, a new dynamic and intelligent clustering method for brain tumor segmentation is proposed by combining the improved Aquila Optimizer (AO) and the K-Means algorithm. The proposed MAO-Kmeans approach aims to automatically extract the correct number and location of cluster centers and the number of pixels in each cluster in abnormal MRI images, and the experimental results demonstrate its effectiveness in improving the performance of conventional K-means clustering.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Alberto Hernando, Maria Dolores Pelaez-Coca, Eduardo Gil
Summary: This study applied a new algorithm to decompose the photoplethysmogram (PPG) pulse and identified changes in PPG pulse morphology due to pressure. The results showed that there was an increase in amplitude, width, and area values of the PPG pulse, and a decrease in ratios when pressure increased, indicating vasoconstriction. Furthermore, some parameters were found to be related to the pulse-to-pulse interval.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Jens Moeller, Eveline Popanda, Nuri H. Aydin, Hubert Welp, Iris Tischoff, Carsten Brenner, Kirsten Schmieder, Martin R. Hofmann, Dorothea Miller
Summary: In this study, a method based on texture features is proposed, which can classify healthy gray and white matter against glioma degrees 4 samples with reasonable classification performance using a relatively low number of samples for training. The method achieves high classification performance without the need for large datasets and complex machine learning approaches.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Amrutha Bhaskaran, Manish Arora
Summary: The study evaluates a cyclic repetition frequency-based algorithm for fetal heart rate estimation. The algorithm improves accuracy and reliability for poor-quality signals and performs well for different gestation weeks and clinical settings.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Manan Patel, Harsh Bhatt, Manushi Munshi, Shivani Pandya, Swati Jain, Priyank Thakkar, Sangwon Yoon
Summary: Electroencephalogram (EEG) signals have been effectively used to measure and analyze neurological data and brain-related ailments. Artificial Intelligence (AI) algorithms, specifically the proposed CNN-FEBAC framework, show promising results in studying the EEG signals of autistic patients and predicting their response to stimuli with 91% accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Wencheng Gu, Kexue Sun
Summary: This research proposes an improved version of YOLOv5 (AYOLOv5) based on the attention mechanism to address the issue of low recognition rate in cell detection. Experimental results demonstrate that AYOLOv5 can accurately identify cell targets and improve the quality and recognition performance of cell pictures.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Anita Gade, V. Vijaya Baskar, John Panneerselvam
Summary: Analysis of exhaled breath is an increasingly used diagnostic technique in medicine. This study introduces a new NICBGM-based model that utilizes various features and weight optimization for accurate data interpretation and result optimization.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Arsalan Asemi, Keivan Maghooli, Fereidoun Nowshiravan Rahatabad, Hamid Azadeh
Summary: Biometric authentication systems can perform identity verification with optimal accuracy in various environments and emotional changes, while the performance of signature verification systems can be affected when people are under stress. This study examines the performance of a signature verification system based on muscle synergy patterns as biometric characteristics for stressed individuals. EMG signals from hand and arm muscles were recorded and muscle synergies were extracted using Non-Negative Matrix Factorization. The extracted patterns were classified using Support Vector Machine for authentication of stressed individuals.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tianjiao Guo, Jie Yang, Qi Yu
Summary: This paper proposes a CNN-based approach for segmenting four typical DR lesions simultaneously, achieving competitive performance. This approach is significant for DR lesion segmentation and has potential in other segmentation tasks.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
G. Akilandasowmya, G. Nirmaladevi, S. U. Suganthi, A. Aishwariya
Summary: This study proposes a technique for skin cancer detection and classification using deep hidden features and ensemble classifiers. By optimizing features to reduce data dimensionality and combining ensemble classifiers, the proposed method outperforms in skin cancer classification and improves prediction accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tuuli Uudeberg, Juri Belikov, Laura Paeske, Hiie Hinrikus, Innar Liiv, Maie Bachmann
Summary: This article introduces a novel feature extraction method, the in-phase matrix profile (pMP), specifically adapted for electroencephalographic (EEG) signals, for detecting major depressive disorder (MDD). The results show that pMP outperforms Higuchi's fractal dimension (HFD) in detecting MDD, making it a promising method for future studies and potential clinical use for diagnosing MDD.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
P. Nancy, M. Parameswari, J. Sathya Priya
Summary: Stroke is the third leading cause of mortality worldwide, and early detection is crucial to avoid health risks. Existing research on disease detection using machine learning techniques has limitations, so a new stroke detection system is proposed. The experimental results show that the proposed method achieves a high accuracy rate in stroke detection.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Shimin Liu, Zhiwen Huang, Jianmin Zhu, Baolin Liu, Panyu Zhou
Summary: In this study, a continuous blood pressure (BP) monitoring method based on random forest feature selection (RFFS) and a gray wolf optimization-gradient boosting regression tree (GWO-GBRT) prediction model was developed. The method extracted features from electrocardiogram (ECG) and photoplethysmography (PPG) signals, and employed RFFS to select sensitive features highly correlated with BP. A hybrid prediction model of gray wolf optimization (GWO) technique and gradient boosting regression tree (GBRT) algorithm was established to learn the relationship between BP and sensitive features. Experimental results demonstrated the effectiveness and advancement of the proposed method.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Weijun Gong, Yurong Qian, Weihang Zhou, Hongyong Leng
Summary: The recognition of dynamic facial expressions is challenging due to various factors, and obtaining discriminative expression features has been difficult. Traditional deep learning networks lack understanding of global and temporal expressions. This study proposes an enhanced spatial-temporal learning network to improve dynamic facial expression recognition.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
