Article
Computer Science, Artificial Intelligence
Benteng Ma, Jing Zhang, Yong Xia, Dacheng Tao
Summary: Neural architecture search (NAS) is a popular research topic for identifying better architectures. Recently, differential neural architecture search methods have gained attention for their effectiveness. This paper proposes a novel inter-layer transition NAS method to investigate the dependency between edges in a network.
PATTERN RECOGNITION
(2023)
Review
Computer Science, Interdisciplinary Applications
Yogesh Kumar, Surbhi Gupta
Summary: Artificial intelligence has made significant advancements in healthcare, particularly in the field of ophthalmology. Through deep learning and transfer learning methods, AI is able to predict and identify various eye diseases, improving clinical decision-making and enhancing patient care.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Review
Mathematics
Muhammad Shehrayar Khan, Atif Rizwan, Muhammad Shahzad Faisal, Tahir Ahmad, Muhammad Saleem Khan, Ghada Atteia
Summary: With the increase in users of social media websites such as IMDb and the availability of publicly accessible data, opinion mining has become more accessible. This study explores the categorization of movie reviews, which can be challenging due to the complexity of human language. The use of the Word2Vec model and various features, such as psychological, readability, and linguistic features, were investigated. The results showed that the SVM algorithm with self-trained Word2Vec achieved an F-Measure of 86%, while using a combination of psychological, linguistic, readability features, and Word2Vec features resulted in an F-Measure of 87.93%.
Article
Chemistry, Analytical
Sumit Kumar Singh, Vahid Abolghasemi, Mohammad Hossein Anisi
Summary: This paper proposes a computer-aided diagnosis system for the classification of malignant skin lesions. The system preprocesses, segments, and classifies images, achieving high accuracy through validation on multiple datasets.
Article
Computer Science, Information Systems
Phong Thanh Nguyen, Vy Dang Bich Huynh, Khoa Dang Vo, Phuong Thanh Phan, Eunmok Yang, Gyanendra Prasad Joshi
Summary: The paper introduces an ensemble model EOPSO-CNN based on OPSO algorithm for DR detection and grading, achieving high performance in accuracy, sensitivity, and specificity. Through preprocessing, feature extraction, and classification processes, the proposed model effectively detects and grades DR.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Computer Science, Artificial Intelligence
Yating Huang, Xuechen Li, Siting Zheng, Zhongliang Li, Sihan Li, Linlin Shen, Changen Zhou, Zhihui Lai
Summary: The size and shape of the tongue can reflect different pathological changes of the human body in Traditional Chinese Medicine (TCM). In this work, an efficient deep network, TSCWNet, is proposed for tongue size and shape classification. Experimental results demonstrate that the network achieves better classification performance for tongue diagnosis.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Dawid Polap, Marcin Wozniak
Summary: Modifying existing classifier operation models aims to enhance efficiency and reduce training time. The proposed method can implement various classifiers using federated learning and parallelism. Analysis and selection of the best classifier, as well as augmentation techniques, are important elements to improve operation in federated learning.
Article
Computer Science, Information Systems
Dolly Das, Saroj Kumar Biswas, Sivaji Bandyopadhyay
Summary: Diabetic Retinopathy (DR) is a complication of diabetes that can lead to vision impairment and blindness. This paper proposes a computerized diagnostic system using deep learning algorithms to identify and assess the severity of DR from fundus images. The study evaluates 26 state-of-the-art deep learning models and finds that EfficientNetB4 is the most optimal, efficient, and reliable algorithm for DR detection.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Zhaobin Wang, Minzhe Xu, Yaonan Zhang
Summary: In this paper, a quantum pulse coupled neural network (QPCNN) is proposed based on the pulse coupled neural network (PCNN). Quantum operation modules and quantum image convolution operation are designed to implement quantum image segmentation. Simulation experiments demonstrate the effectiveness of QPCNN, and complexity analysis shows that QPCNN achieves exponential speedup compared to classical PCNN.
Article
Computer Science, Information Systems
Sumod Sundar, S. Sumathy
Summary: Diabetic retinopathy is the leading cause of blindness worldwide, and computer-assisted techniques using a graph convolutional neural network have been proposed to effectively extract essential retinal image features and achieve superior performance compared to other state-of-the-art techniques when evaluated on two datasets.
Article
Computer Science, Information Systems
K. M. Jemshi, G. Sreelekha, P. S. Sathidevi, Poornima Mohanachandran, Anand Vinekar
Summary: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness in low birth weight preterm infants. Plus disease, characterized by abnormal tortuosity and dilation of posterior retinal blood vessels, is used to identify ROP cases requiring treatment. This study proposes an efficient Artificial Neural Network (ANN) architecture with an optimal feature set to achieve zero false negatives in ROP screening. Experimental analysis using 178 retinal fundus images, including 81 (45%) with Plus and 97 (55%) with No Plus, shows that the proposed feature set combining Curvelet transform energy coefficient with vascular features achieves an Accuracy of 96%, Specificity of 93%, and 100% Sensitivity.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jin Li, Shuofeng Li, Bing Li, Bin Liu
Summary: This study proposes a model for detecting the integrity of rice germ, which improves recognition accuracy by modifying existing models and adopting a specific topological structure branch. The model enhances the description of rice germ image features by modifying the convolution operation of the original model and utilizes color image features to identify the location of germ. It further strengthens the training of rice germ detail features.
Article
Engineering, Multidisciplinary
M. Kavitha, R. Gayathri, Kemal Polat, Adi Alhudhaif, Fayadh Alenezi
Summary: This paper introduces an enhanced CNN method for hyperspectral image classification, aiming to improve classification accuracy by merging convolutional layer outputs and using a 1 x 1 convolution layer for feature extraction.
Article
Automation & Control Systems
Dawid Polap, Marta Wlodarczyk-Sielicka, Natalia Wawrzyniak
Summary: This paper presents an alternative approach to automatic image analysis using various artificial intelligence techniques. The system utilizes collaborative learning to continually increase its knowledge during operation, and it benefits from quick implementation and a small database.
Article
Computer Science, Information Systems
Gangyi Shen, Bin Zhang
Summary: Conventional image classification and detection tasks often lack positional information due to the reliance on stacking multiple convolutional layers. With the rise of transformers in computer vision, our proposed centrifugal aggregation module aims to extract useful information from shallow features by transforming the image to the frequency domain and then back to the spatial domain. Experimental results show that adding this module to shallow layers of most convolutional neural networks improves model performance.
Article
Computer Science, Artificial Intelligence
U. Raghavendra, Anjan Gudigar, Yashas Chakole, Praneet Kasula, D. P. Subha, Nahrizul Adib Kadri, Edward J. Ciaccio, U. Rajendra Acharya
Summary: This study proposes a method for diagnosing depression using machine learning and continuous wavelet transform. By extracting and reducing features from electroencephalogram recordings, and labeling them using various classifiers, high diagnostic accuracies were achieved. Additionally, a depression severity index was developed for distinguishing between normal and depressed classes.
Article
Neurosciences
Afshin Shoeibi, Navid Ghassemi, Marjane Khodatars, Parisa Moridian, Abbas Khosravi, Assef Zare, Juan M. Gorriz, Amir Hossein Chale-Chale, Ali Khadem, U. Rajendra Acharya
Summary: This paper presents an intelligent detection method for SZ and ADHD using a new deep learning approach based on rs-fMRI data. The proposed method employs an IT2FR fuzzy method for classification, optimized by the GWO algorithm, achieving satisfactory results.
COGNITIVE NEURODYNAMICS
(2023)
Article
Computer Science, Interdisciplinary Applications
V. Jahmunah, E. Y. K. Ng, Ru-San Tan, Shu Lih Oh, U. Rajendra Acharya
Summary: A Dirichlet DenseNet model was developed to analyze out-of-distribution data and detect misclassification of myocardial infarction (MI) and normal electrocardiogram (ECG) signals. The model was trained with pre-processed MI ECG signals from the PTB database and showed increased confidence in classifying signals with lower levels of noise. The model proved reliable in diagnosing MI.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Biophysics
Mehmet Baygin, Prabal Datta Barua, Subrata Chakraborty, Ilknur Tuncer, Sengul Dogan, Elizabeth Palmer, Turker Tuncer, Aditya P. Kamath, Edward J. Ciaccio, U. Rajendra Acharya
Summary: This study aims to accurately detect schizophrenia using EEG signals by using a handcrafted model based on advanced techniques. The model generates features using a carbon chain pattern and extracts sub-bands from EEG signals using an iterative tunable q-factor wavelet transform. Clinically significant features are selected using iterative neighborhood component analysis, and classification is performed using the k nearest neighbor classifier. The results demonstrate the success of the proposed model in automating SZ detection with high accuracy.
PHYSIOLOGICAL MEASUREMENT
(2023)
Review
Biophysics
Manish Sharma, Ruchit Kumar Patel, Akshat Garg, Ru SanTan, U. Rajendra Acharya
Summary: Schizophrenia is a devastating mental disorder that affects higher brain functions and has a profound impact on individuals. Deep learning models can automatically detect schizophrenia by learning signal data characteristics, without the need for traditional feature engineering. This systematic review explores various deep learning models and methodologies for schizophrenia detection based on EEG signals, structural and functional MRI data from diverse datasets. The study discusses the challenges and future works in using deep learning models for schizophrenia diagnosis.
PHYSIOLOGICAL MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
Manish Sharma, Paresh Makwana, Rajesh Singh Chad, U. Rajendra Acharya
Summary: Nowadays, sleep deprivation due to busy work life has led to sleep-related disorders and adverse physiological conditions. Therefore, sleep study and scoring are crucial for detecting and assessing these disorders. In this study, an automated sleep stage classification model based on the biorthogonal wavelet filter bank's novel least squares (LS) design is proposed. The model achieves high accuracy and can be used in home-based clinical systems and wearable devices.
APPLIED INTELLIGENCE
(2023)
Review
Computer Science, Artificial Intelligence
Joshua Sheehy, Hamish Rutledge, U. Rajendra Acharya, Hui Wen Loh, Raj Gururajan, Xiaohui Tao, Xujuan Zhou, Yuefeng Li, Tiana Gurney, Srinivas Kondalsamy-Chennakesavan
Summary: This systematic review aims to evaluate and critique the methodologies and approaches used in predicting the prognosis of gynecological cancers using machine learning techniques. A total of 139 studies met the inclusion criteria, with varying study quality and inconsistent methodologies, statistical reporting, and outcome measures. This hinders the ability to perform meta-analysis and draw conclusions regarding the superiority of machine learning methods.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2023)
Article
Automation & Control Systems
Manish Sharma, Divyansh Anand, Sarv Verma, U. Rajendra Acharya
Summary: Sleep is crucial for human well-being, and insomnia is a common sleep disorder that affects both physical and mental health. This study proposes a method that uses single-channel EEG signals to automatically identify insomnia, extracting features using a deep convolutional network and developing a model for sleep stages classification.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Irem Tasci, Burak Tasci, Prabal D. Barua, Sengul Dogan, Turker Tuncer, Elizabeth Emma Palmer, Hamido Fujita, U. Rajendra Acharya
Summary: This study presents a large EEG signal dataset and investigates the detection ability of a new hypercube pattern-based framework for epilepsy. A total of 245 feature vectors were extracted and fed to a kNN classifier, achieving high classification accuracy.
INFORMATION FUSION
(2023)
Review
Computer Science, Artificial Intelligence
Smith K. Khare, Sonja March, Prabal Datta Barua, Vikram M. Gadre, U. Rajendra Acharya
Summary: Mental health is essential for a sustainable and developing society. The prevalence and financial burden of mental illness have increased globally, and this paper provides a systematic review of nine developmental and mental disorders in children and adolescents. The paper focuses on the use of physiological signals for automated detection of these disorders, and discusses signal analysis, feature engineering, decision-making, challenges, and future directions in this field. The main findings of the study are presented in the conclusion section.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Moloud Abdar, Arash Mehrzadi, Milad Goudarzi, Farzad Masoudkabir, Leonardo Rundo, Mohammad Mamouei, Evis Sala, Abbas Khosravi, Vladimir Makarenkov, U. Rajendra Acharya, Seyedmohammad Saadatagah, Mohammadreza Naderian, Salvador Garcia, Nizal Sarrafzadegan, Saeid Nahavandi
Summary: In this study, machine learning methods combined with uncertainty quantification were used to accurately classify CSX data from the coronary angiography registry of Tehran's Heart Center. The proposed model reached an accuracy of 85% when applied to the benchmark CSX dataset.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Prabal Datta Barua, Sengul Dogan, Gurkan Kavuran, Turker Tuncer, Ru-San Tan, U. Rajendra Acharya
Summary: In this study, a novel SEM image classification model called NFSDense201 is proposed, which includes several key components. A unique nested patch division approach is introduced to divide each input image into four patches of varying dimensions. DenseNet201 is used to extract deep features from the input image. An iterative neighborhood component analysis function is applied to select the most discriminative features from the merged feature vector, and a standard shallow support vector machine classifier is employed for classification.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Arif Metehan Yildiz, Masayuki Tanabe, Makiko Kobayashi, Ilknur Tuncer, Prabal Datta Barua, Sengul Dogan, Turker Tuncer, Ru-San Tan, U. Rajendra Acharya
Summary: This paper introduces Sound-based Community Emotion Recognition (SCED) as a new challenge in the machine learning domain and proposes the FF-BTP feature engineering model for discerning crowd sentiments. By utilizing a unique dataset and incorporating various techniques for feature extraction and selection, the model achieves impressive classification accuracy on the SCED dataset.
Article
Computer Science, Information Systems
Prabal Datta Barua, Makiko Kobayashi, Masayuki Tanabe, Mehmet Baygin, Jose Kunnel Paul, Thomas Iype, Sengul Dogan, Turker Tuncer, Ru-San Tan, U. Rajendra Acharya
Summary: This study aimed to develop a lightweight machine learning model for diagnosing fibromyalgia using single-lead ECG signals recorded during sleep. The model achieved high accuracy in differentiating the electrocardiographic signatures of fibromyalgia patients from control subjects, demonstrating its efficacy in clinical integration.
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)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
