Article
Mathematics, Interdisciplinary Applications
Muhammad Tariq Sadiq, Hesam Akbari, Siuly Siuly, Yan Li, Peng Wen Paul
Summary: Alcoholism is a severe disorder that affects the brain and leads to cognitive, emotional, and behavioral impairments. This article proposes a novel framework for automatically detecting alcoholism using electroencephalogram (EEG) signals. The framework explores the chaotic nature and complexity of EEG signals, decodes the chaotic behavior using graphical features, and utilizes feature selection and machine learning classifiers to develop an efficient detection system. The experimental results show high classification performance, and the proposed system provides visual biomarkers and indexes for alcoholic detection.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Engineering, Biomedical
Hesam Akbari, Sedigheh Ghofrani, Pejman Zakalvand, Muhammad Tariq Sadiq
Summary: The study proposed a novel framework using EEG signals' phase space dynamic to automatically diagnose schizophrenia disorders, with steps including feature extraction and classifier testing, determining that the KNN classifier and GRNN classifier performed the best. Results suggested that the PSD shape of the Cz channel could serve as a biomarker for schizophrenia, and that the frontal and parietal lobes reflected the effects of schizophrenia disorder better.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Computer Science, Information Systems
Cun Ji, Mingsen Du, Yanxuan Wei, Yupeng Hu, Shijun Liu, Li Pan, Xiangwei Zheng
Summary: Time series classification is widely used in various domains, including EEG/ECG classification, device anomaly detection, and speaker authentication. Despite the existence of many methods, selecting intuitive temporal features for accurate classification remains a challenge. Therefore, this paper proposes a new method called TSC-RTF, which utilizes random temporal features, and shows that it can compete with state-of-the-art methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Acoustics
Jederson S. Luz, Myllena C. Oliveira, Flavio H. D. Araujo, Deborah M. Magalhaes
Summary: This paper proposes a representation method for urban sound classification based on the combination of deep and handcrafted features, which outperforms most state-of-the-art CNN models in terms of classification accuracy.
Article
Computer Science, Theory & Methods
Yishu Liu, Guifang Fu
Summary: This paper introduces a method for human emotion recognition using multi-channel features learned from EEG signal and textual features, improving emotion classification accuracy by fusing different statistical features in the time domain.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Physics, Multidisciplinary
Egils Avots, Klavs Jermakovs, Maie Bachmann, Laura Paeske, Cagri Ozcinar, Gholamreza Anbarjafari
Summary: This research aims to determine the long-lasting effects of depression through EEG signals. After comparing several classifiers and feature selection methods, the results show that the EEG features used for classifying ongoing depression also work for classifying the long-lasting effects of depression.
Article
Computer Science, Artificial Intelligence
Mostafa Khojastehnazhand, Mozaffar Roostaei
Summary: This study used a machine vision system and texture feature extraction methods to classify seven varieties of wheat in the East Azerbaijan Province of Iran. By utilizing unsupervised and supervised methods, along with feature extraction, the different wheat varieties were identified with over 95% accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Hesam Akbari, Muhammad Tariq Sadiq, Malih Payan, Somayeh Saraf Esmaili, Hourieh Baghri, Hamed Bagheri
Summary: This research introduces a novel strategy for diagnosing depression based on geometric features derived from EEG signal shape, utilizing Binary Particle Swarm Optimization for feature selection and support vector machine and K-nearest neighbor classifiers for signal identification. The proposed system achieves an average classification accuracy of 98.79% in a study involving 22 normal and 22 depressed subjects.
TRAITEMENT DU SIGNAL
(2021)
Article
Computer Science, Information Systems
Ones Sidhom, Haythem Ghazouani, Walid Barhoumi
Summary: Facial Expression Recognition (FER) is widely used in various fields, and feature extraction and selection are crucial for designing efficient FER systems. Previous studies mainly focused on static feature selection methods, neglecting the individual differences in facial emotion display. To address this, the proposed dynamic feature selection method based on facial features outperforms existing techniques in terms of accuracy on the CK+ and DISFA datasets.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Mathematics
Sang-Ha Sung, Sangjin Kim, Byung-Kwon Park, Do-Young Kang, Sunhae Sul, Jaehyun Jeong, Sung-Phil Kim
Summary: Research shows that feature selection using EEG data in BCI technology can effectively predict whether individuals correctly detect facial expression changes, with specific EEG features largely influencing the detection of expression changes. Various feature selection methods and machine learning techniques were used to achieve high classification accuracy.
Article
Neurosciences
Kai Yang, Li Tong, Ying Zeng, Runnan Lu, Rongkai Zhang, Yuanlong Gao, Bin Yan
Summary: Recent studies have shown that recognizing and monitoring different valence emotions can effectively prevent human errors caused by cognitive decline. This study explores effective electroencephalography (EEG) features for recognizing different valence emotions. The results show that first-order difference, second-order difference, high-frequency power, and high-frequency differential entropy features perform better in emotion recognition. Time-domain features, especially first-order difference and second-order difference features, have shorter computing time, making them suitable for real-time emotion recognition applications. Features extracted from the frontal, temporal, and occipital lobes are more effective in recognizing different valence emotions. Furthermore, when the number of electrodes is reduced by 3/4, using features from 16 electrodes located in these brain regions achieves a classification accuracy of 91.8%, only about 2% lower than using all electrodes. These findings provide important guidance for feature extraction and selection in EEG-based emotion recognition.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Ayan Seal, Rishabh Bajpai, Mohan Karnati, Jagriti Agnihotri, Anis Yazidi, Enrique Herrera-Viedma, Ondrej Krejcar
Summary: This study presents a dataset that includes EEG data and Patient Health Questionnaire scores for the diagnosis and classification of depression. The results demonstrate the effectiveness of traditional supervised machine learning algorithms and feature selection methods in distinguishing healthy subjects from depressed individuals.
APPLIED INTELLIGENCE
(2023)
Article
Chemistry, Multidisciplinary
Maritza Mera-Gaona, Diego M. Lopez, Rubiel Vargas-Canas
Summary: The study utilized an ensemble feature selection method to improve the precision of distinguishing normal and abnormal EEG signals, demonstrating the stability and effectiveness of the approach. Evaluation results indicated that classifiers trained with the EFS features showed enhanced performance and achieved high accuracy, sensitivity, and specificity.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Victor Hugo da Silva Muniz, Joao Baptista de Oliveira e Souza Filho
Summary: This paper discusses the importance of music genre in music recommendations and presents a method to improve system performance through the generation of new handcrafted features and feature selection.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Yuanjin Xu, Ming Wei, M. M. Kamruzzaman
Summary: Classification, recognition, and quality assessment of aerial images depend on detecting and identifying discriminative visual features. A novel method is proposed to explore quality-related and topological cues to mitigate the challenges posed by image quality and topological structures. This method shows effective prediction of aerial image categories and outperforms other state-of-the-art algorithms.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Engineering, Electrical & Electronic
Muhammad Abu Bakar Siddique, Adeel Asad, Rao M. Asif, Ateeq Ur Rehman, Muhammad Tariq Sadiq, Inam Ullah
Summary: This paper presents a rapid, effective, and linear incremental conductance (IC) algorithm for chasing the maximum power point (MPP) of the grid-tied photovoltaic (PV) array. A technique has been proposed to find the optimum values of the design parameters of the PV array. This methodology enables the prime matching of the PV system with the DC/AC converter configuration by calculating the optimal values of the inductor, capacitor, and the duty cycle of the boost converter. Analysis of different techniques for maximum power point tracking (MPPT) is presented and the comprehensive design of the PV converter system is also incorporated. The number of switches has been reduced for optimal power generation. Similarly, a technical inter-comparison has been made with the existing topologies. MATLAB/Simulink is used to characterize solar PV characteristics at various irradiance and temperature profiles. Furthermore, the simulation results agree well with the designed version in terms of PV panel efficiency.
IETE JOURNAL OF RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Farah Deeba, Fayaz A. Dharejo, Muhammad Zawish, Fida H. Memon, Kapal Dev, Rizwan A. Naqvi, Yuanchun Zhou, Yi Du
Summary: The study introduces a progressive two-stage image dehazing network that utilizes edges and colors as significant components to enhance image clarity and realism. The new dehazing framework includes multiscale image feature extraction and color correction model, with the introduction of dense residual attention units to handle diverse features and pixels, ultimately surpassing existing algorithms in both visual and quantitative aspects.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Anatomy & Morphology
Marriam Nawaz, Zahid Mehmood, Tahira Nazir, Rizwan Ali Naqvi, Amjad Rehman, Munwar Iqbal, Tanzila Saba
Summary: This study presents a fully automated method for segmenting melanoma skin cancer using deep learning and fuzzy k-means clustering, aiming to aid in the early diagnosis and treatment of this disease. Evaluation on three standard datasets shows that the method outperforms state-of-the-art approaches in skin lesion recognition and segmentation, demonstrating robustness.
MICROSCOPY RESEARCH AND TECHNIQUE
(2022)
Article
Environmental Sciences
Syed Raza Mehdi, Kazim Raza, Hui Huang, Rizwan Ali Naqvi, Amjad Ali, Hong Song
Summary: This paper explores the potential of deep learning and single-spectrum ultraviolet imaging (UV) for detecting hazardous and noxious substances spills. A modified deep learning model achieved high accuracy and swift detection on UV and RGB images.
Article
Chemistry, Analytical
Hassaan Malik, Ahmad Naeem, Rizwan Ali Naqvi, Woong-Kee Loh
Summary: COVID-19 is still a threat to global health and safety in 2019. Deep learning is considered the most effective way to detect COVID-19 and other chest diseases. Federated learning allows clients to train models together without sharing source data, enhancing privacy. The DMFL_Net model achieves high accuracy in classifying COVID-19 and protects data privacy.
Article
Mathematics
Mehdi Hosseinzadeh, Rizwan Ali Naqvi, Masoumeh Safkhani, Lilia Tightiz, Raja Majid Mehmood
Summary: Authenticated key agreement is a process of sharing a secret session key over a public channel for encrypting subsequent communications. LLAKEP, an authenticated key agreement protocol for EIoT applications, lacks clarity in its security level. A security analysis reveals vulnerabilities in LLAKEP, including traceability, dictionary, stolen smart glass, known session-specific temporary information, and key compromise impersonation attacks. A proposed protocol, LLAKEP+, overcomes these vulnerabilities and demonstrates resistance to various threats with acceptable overhead.
Article
Mathematics
Ali Raza, Sharjeel Adnan, Muhammad Ishaq, Hyung Seok Kim, Rizwan Ali Naqvi, Seung-Won Lee
Summary: The increasing prevalence of retinal diseases, specifically glaucoma, demands efficient automated methods for diagnosis. Current artificial intelligence-based approaches for glaucoma diagnosis have limitations in accuracy and efficiency due to challenges in segmentation caused by various factors. To address these issues, a novel feature excitation-based dense segmentation network (FEDS-Net) was developed. FEDS-Net utilizes feature excitation and information aggregation mechanisms to enhance the segmentation performance of optic cup (OC) and optic disc (OD). The proposed method achieved superior segmentation performance and computational efficiency compared to state-of-the-art methods.
Article
Mathematics
S. M. A. Sharif, Rizwan Ali Naqvi, Zahid Mehmood, Jamil Hussain, Ahsan Ali, Seung-Won Lee
Summary: This study proposes an end-to-end scale-recurrent deep network for learning deblurring from multi-modal medical images. The proposed network includes a novel residual dense block and a module with spatial-asymmetric attention to recover salient information while learning medical image deblurring. Experimental results demonstrate that the proposed method can remove blur from medical images without visually disturbing artifacts and outperforms existing deep deblurring methods. The applicability of the proposed method has been verified by incorporating it into various medical image analysis tasks.
Article
Chemistry, Analytical
Hamza Nadeem, Kashif Javed, Zain Nadeem, Muhammad Jawad Khan, Saddaf Rubab, Dong Keon Yon, Rizwan Ali Naqvi
Summary: Hundreds of people are injured or killed in road accidents due to factors like driver inattentiveness. This study proposes a computer vision-based solution using deep learning models to detect and recognize road features such as traffic types and signs. The models achieved state-of-the-art results, providing a benchmark for improving traffic situations and enabling future technological advances.
Article
Oncology
Maryam Tahir, Ahmad Naeem, Hassaan Malik, Jawad Tanveer, Rizwan Ali Naqvi, Seung-Won Lee
Summary: This paper proposes a deep learning-based skin cancer classification network (DSCC_Net) that achieves high accuracy in categorizing four distinct types of skin cancer diseases. The proposed model outperforms baseline models such as ResNet-152, Vgg-19, MobileNet, Vgg-16, EfficientNet-B0, and Inception-V3. The results show the potential of the DSCC_Net model to assist dermatologists and health experts in diagnosing skin cancer.
Article
Mathematics
Muhammad Ishaq, Salman Raza, Hunza Rehar, Shan e Zain ul Abadeen, Dildar Hussain, Rizwan Ali Naqvi, Seung-Won Lee
Summary: The application of deep learning methods in the in vitro fertilization (IVF) process is crucial for improving success rates and efficiency. This study proposes a novel feature-supplementation-based blastocyst segmentation network (FSBS-Net) that accurately detects the composition of blastocyst components with higher accuracy and lower computational overhead. The experimental results demonstrate that this method is very helpful in assisting embryologists in the morphological assessment of human blastocyst components.
Article
Medical Informatics
Muhammad Tariq Sadiq, Siuly Siuly, Ahmad Almogren, Yan Li, Paul Wen
Summary: This study aims to automatically identify alcoholism using EEG signals, overcoming the drawbacks of traditional assessment methods. By utilizing the fast Walsh-Hadamard transform and linear/nonlinear features, a high-performance classification system was successfully designed. The results show that the system achieves 97.5% accuracy and 96.7% sensitivity, providing valuable assistance in clinical and commercial applications.
HEALTH INFORMATION SCIENCE AND SYSTEMS
(2023)
Review
Chemistry, Analytical
Shafia Riaz, Ahmad Naeem, Hassaan Malik, Rizwan Ali Naqvi, Woong-Kee Loh
Summary: Skin cancer is a dangerous type of cancer with a high mortality rate worldwide. This article discusses the challenges of manual skin cancer diagnosis and explores the effectiveness of deep learning and transfer learning in diagnosing the disease. The study presents various federated learning and transfer learning techniques used in classifying skin cancer, and evaluates their performance using different metrics. The authors conducted a systematic literature review of well-reputed studies published between January 2018 and July 2023, and compiled a total of 86 articles. The results of this review highlight the limitations and challenges of current research and provide insights for future work in automating the classification of melanoma and nonmelanoma skin cancers.
Article
Mathematics
Mudassir Khalil, Ahmad Naeem, Rizwan Ali Naqvi, Kiran Zahra, Syed Atif Muqarib, Seung-Won Lee
Summary: This study developed a deep learning-based system to automatically classify abrasion foot sores (AFS) and ischemic diabetic foot sores (DFS). The proposed model combined convolutional neural network (CNN) capabilities with Vgg-19 and used data augmentation and image segmentation techniques. The model achieved high accuracy and excellent performance in identifying foot ulcers, providing valuable assistance to medical professionals.
Article
Medicine, General & Internal
Hesam Akbari, Muhammad Tariq Sadiq, Nastaran Jafari, Jingwei Too, Nasser Mikaeilvand, Antonio Cicone, Stefano Serra-Capizzano
Summary: This paper proposes new geometrical features for the classification of seizure and seizure-free EEG signals using the Poincare pattern of discrete wavelet transform coefficients. Binary particle swarm optimization and k-nearest neighbor, support vector machine classifiers are used for feature selection and classification. The results show that the proposed method achieves high classification accuracy.
BRATISLAVA MEDICAL JOURNAL-BRATISLAVSKE LEKARSKE LISTY
(2023)
Article
Acoustics
Cailiang Zhang, Zhihui Lai, Zhisheng Tu, Hanqiu Liu, Yong Chen, Ronghua Zhu
Summary: This paper proposes two single-parameter-adjusting SR models to optimize the output performance of SR systems. The effects of the proposed models on SR output under different parameters and signals are investigated through numerical simulations, and their feasibility is verified through experimental results. The research results are of great significance for guiding the design of tri-stable SR models and the application of SR-based signal processing in the context of big data.
Article
Acoustics
Shaoqiong Yang, Hao Chang, Yanhui Wang, Ming Yang, Tongshuai Sun
Summary: In this study, a suspension system based on phononic crystals is designed for vibration isolation of acoustic loads in underwater gliders. The vibration properties of the phononic crystals and the effects of physical parameters on the underwater attenuation zones are investigated. Vibration tests show that the phononic crystal suspension system has a stable vibration isolation effect in the frequency range of 120-5000 Hz.
Article
Acoustics
Xuebin Zhang, Jun Zhang, Tao Liu, Ning Hu
Summary: This study proposes a tunable metamaterial beam to isolate flexural waves. A genetic algorithm-based size optimization is used to obtain a broad low-frequency bandgap. The tunability of the beam is achieved by attaching different numbers of permanent magnets to change the mass of the resonators. Additionally, ultra-broadband flexural wave attenuation is achieved by forming a gradient metamaterial beam based on the rainbow effect. Numerical and experimental results confirm the good flexural wave attenuation ability of the proposed beam.
Article
Acoustics
Luca Rapino, Francesco Ripamonti, Samanta Dallasta, Simone Baro, Roberto Corradi
Summary: This paper presents a method for simulating tyre/road noise using equivalent monopoles, including the synthesis of monopoles through an inverse problem approach and the use of an ISO 10844 road replica for laboratory testing. The method combines acoustic finite element models and numerical simulations of vehicles, and the results are validated by comparing them with measured data.
Article
Acoustics
Xiaoyan Zhu, Tin Oberman, Francesco Aletta
Summary: This paper explores the definition of acoustical heritage and proposes a multidimensional definition based on interviews with experts and detailed analysis of the data.
Article
Acoustics
Faeez Masurkar, Saurabh Aggarwal, Zi Wen Tham, Lei Zhang, Feng Yang, Fangsen Cui
Summary: This research focuses on estimating the elastic constants of orthotropic laminates using ultrasonic guided waves and inverse machine learning models. The results show that this approach has the potential to accurately predict the elastic constants of a material and reduce computational time.
Article
Acoustics
Feng Xiao, Haiquan Liu, Jia Lu
Summary: Diagnostic methods for cardiovascular disease based on heart sound classification have been widely studied due to their noninvasiveness, low-cost, and high efficiency. However, existing research often faces challenges such as the nonstationarity and complexity of heart sound signals, leading to limited capability of neural networks to extract discriminative features. To address these issues, this study proposes a novel convolutional neural network that combines 1D convolution and 2D convolution, and introduces an attention mechanism to enhance feature extraction capability. The study also explores the advantages and disadvantages of combining deep learning features with manual features, and adopts an evolving fuzzy system for decision-making interpretability.
Article
Acoustics
Hong Xu, Zhengyao He, Qiang Shi, Yushi Wang, Bo Zhang
Summary: This paper presents the development of a directional segmented ring transmitting transducer that can radiate sound waves in any horizontal region. The study focuses on the structure of the segmented ring transducer, its radiation sound field characteristics, and the beam pattern control method based on modal synthesis. The authors propose orthogonal beam pattern functions for adjusting steering angles and establish a three-dimensional finite element model to simulate the transmitting beam patterns. Experimental measurements and tests validate the effectiveness of the proposed transducer, showcasing its ability to steer the beam patterns to different directions.
Article
Acoustics
Jirui Yang, Shefeng Yan, Di Zeng, Gang Tan
Summary: This paper proposes an improved domain adaptation framework, self-supervised learning minimax entropy, to enhance the recognition performance of underwater target recognition models. The experimental results demonstrate that applying domain adaptation methods can effectively improve the recognition accuracy of the models under various marine conditions.
Article
Acoustics
Zonghan Sun, Jie Tian, Yuhang Zheng, Xiaocheng Zhu, Zhaohui Du, Hua Ouyang
Summary: This paper analyzes the noise reduction method of installing a sinusoidal-shaped inlet duct on a cooling fan through theoretical and experimental analysis of the acoustic mode modulation. The study establishes the correlation between the free field noise and acoustic mode of the fan rotor and the unsteady forces on the rotor blade surface. The results show that the sinusoidal-shaped inlet duct achieves greater noise reduction compared to a straight duct, especially at the blade passing frequency and its first harmonic.
Article
Acoustics
Min Li, Rumei Han, Hui Xie, Ruining Zhang, Haochen Guo, Yuan Zhang, Jian Kang
Summary: This study is part of a global collaboration to translate and standardise soundscape research. A reliable questionnaire for soundscape characterisation in Mandarin Chinese was developed and validated. The study found that salient sound sources become the focus of attention for individuals in urban open spaces, and the perception is also influenced by the acoustic characteristics of the soundscape. Certain types of sound sources play a more important role in soundscape perception.
Article
Acoustics
Arezoo Talebzadeh, Dick Botteldooren, Timothy Van Renterghem, Pieter Thomas, Dominique Van de Velde, Patricia De Vriendt, Tara Vander Mynsbrugge, Yuanbo Hou, Paul Devos
Summary: This study proposes a sound selection methodology to enhance the soundscape in nursing homes and reduce BPSD by analyzing sound characteristics and recognition methods. The results highlight the sound characteristics that lead to positive responses, while also pointing out the need for further studies to understand which sounds are most suitable for people with dementia.
Article
Acoustics
Yang Yang, Yongxin Yang, Zhigang Chu
Summary: This paper introduces a grid-free compressive beamforming method compatible with arbitrary linear microphone arrays, and demonstrates the correctness and superiority of the proposed method through examples. Monte Carlo simulations are performed to reveal the effects of source coherence, source separation, noise, and number of snapshots.
Article
Acoustics
Sukru Selim Calik, Ayhan Kucukmanisa, Zeynep Hilal Kilimci
Summary: Computer-Aided Language Learning (CALL) is growing rapidly due to the importance of acquiring proficiency in multiple languages for effective communication. In the field of CALL, the detection of mispronunciations is vital for non-native speakers. This research introduces a novel framework using audio-centric transformer models to detect mispronunciations in Arabic phonemes. The results demonstrate that the UNI-SPEECH transformer model yields notable classification outcomes in Arabic phoneme mispronunciation detection. The comprehensive comparison of these transformer models provides valuable insights and guidance for future investigations in this domain.
Article
Acoustics
Yi-Yang Ni, Fei-Yun Wu, Hui-Zhong Yang, Kunde Yang
Summary: This paper proposes an improved method for compressive sensing by introducing a self training dictionary scheme and a CS reconstruction method based on A*OLS, which enhances the sparse representation performance of propeller signals.