Article
Biochemistry & Molecular Biology
Ramy Hussein, Soojin Lee, Rabab Ward
Summary: In this study, a Transformer-based approach called MViT is introduced for automated learning of spatio-temporal-spectral features in multi-channel EEG data. Extensive experiments demonstrate the superiority of MViT algorithm in seizure prediction.
Article
Computer Science, Artificial Intelligence
Ramy Hussein, Soojin Lee, Rabab Ward, Martin J. McKeown
Summary: This study introduces a novel semi-dilated convolutional neural network architecture that outperforms previous methods in predicting epileptic seizures, achieving an average prediction sensitivity of 98.90% for scalp EEG.
Article
Computer Science, Artificial Intelligence
Kuldeep Singh, Jyoteesh Malhotra
Summary: This paper proposes a two-layer LSTM network model based on spectral features for accurate prediction of epileptic seizures using EEG signals. The model is evaluated on EEG segments of 24 epileptic subjects and found that 30-second segments achieve accurate prediction using the two-layer LSTM model. The performance of the proposed classifier is validated by comparing it with other existing techniques, showing a high classification accuracy.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Engineering, Biomedical
Yifan Wang, Weigang Cui, Tao Yu, Xiaoli Li, Xiaofeng Liao, Yang Li
Summary: This study proposes a novel neural network model for patient-specific seizure prediction. The model captures the temporal, spatial, and spectral features of electroencephalogram (EEG) using a multi-branch feature extractor, a dynamic multi-graph convolution network, and a channel-weighted transformer feature fusion network. The proposed model achieves outstanding prediction performance compared to state-of-the-art methods.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Yankun Xu, Jie Yang, Wenjie Ming, Shuang Wang, Mohamad Sawan
Summary: The authors propose a deep learning framework for shortening the latency of epileptic seizure detection through probabilistic prediction. They convert the seizure detection task from binary classification to probabilistic prediction and introduce a crossing period and soft labeling rule to improve the accuracy. In experiments, they successfully detect seizures with shorter latency compared to previous studies.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Shasha Zhang, Dan Chen, Rajiv Ranjan, Hengjin Ke, Yunbo Tang, Albert Y. Zomaya
Summary: This study proposes a lightweight solution based on synchronization measurement of multivariate EEG to accurately predict seizure onset with low computational overhead, showing potential in brain e-health applications.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Neurosciences
Galya Segal, Noam Keidar, Roy Maor Lotan, Yaniv Romano, Moshe Herskovitz, Yael Yaniv
Summary: The study adopts a risk-controlling prediction calibration method called Learn then Test to reduce false alarm rates in seizure prediction. The method calibrates the output of a black-box model to meet a specified false alarm rate requirement. It was initially validated on synthetic data and subsequently tested on publicly available EEG records from 15 patients with epilepsy using a deep learning model.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Chemistry, Analytical
Gul Hameed Khan, Nadeem Ahmad Khan, Muhammad Awais Bin Altaf, Qammer Abbasi
Summary: This paper proposes a trainable hybrid approach for epileptic seizure detection using a shallow autoencoder (AE) and a conventional classifier. The encoded AE representation is used as a feature vector for classifying EEG signal segments as epileptic or non-epileptic. The algorithm has low computational complexity and can be used in body sensor networks and wearable devices. Experimental results show that the proposed method achieves high accuracy and sensitivity in detecting abnormal seizure activity.
Article
Engineering, Biomedical
Xuyang Zhao, Noboru Yoshida, Tetsuya Ueda, Hidenori Sugano, Toshihisa Tanaka
Summary: This study applies commonly used models such as LeNet, VGG, ResNet, and ViT to the EEG image classification task, and solves the problems of data imbalance and model interpretation through data augmentation and model explanation methods. The models achieve good performance in seizure detection and provide visual and quantitative information for clinical experts in diagnosis.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Telecommunications
Heba M. Emara, Mohamed Elwekeil, Taha E. Taha, Adel S. El-Fishawy, El-Sayed M. El-Rabaie, Turky Alotaiby, Saleh A. Alshebeili, Fathi E. Abd El-Samie
Summary: This paper presents a method for EEG seizure prediction by working on signals in the Hilbert domain, analyzing signal attributes statistically, and using a majority voting strategy for decision-making. Experimental results show good performance in prediction accuracy, false-alarm rate, and prediction time.
WIRELESS PERSONAL COMMUNICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Marcin Kolodziej, Andrzej Majkowski, Andrzej Rysz
Summary: This article explores the possibilities, issues, and challenges associated with utilizing artificial intelligence for seizure detection using the publicly available iEEG database. It presents standard approaches for analyzing iEEG signals and discusses modern deep learning algorithms. The study shows that CNN and LSTM networks yield significantly better results, and the gradient-weighted class activation mapping algorithm can identify important iEEG signal fragments for seizure detection.
APPLIED SCIENCES-BASEL
(2023)
Article
Biology
Syed Muhammad Usman, Shehzad Khalid, Sadaf Bashir
Summary: The research introduces a deep learning-based ensemble learning method that achieves high sensitivity and specificity in predicting epileptic seizures, while also reducing the anticipation time.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Chemistry, Multidisciplinary
Zakareya Lasefr, Khaled Elleithy, Ramasani Rakesh Reddy, Eman Abdelfattah, Miad Faezipour
Summary: This paper studied epileptic seizure detection methods based on EEG signals and proposed an enhanced technique with a mobile application for monitoring the classification of EEG signals. The proposed method achieved high accuracy and outperformed previous studies. It will have significant impacts in the medical field and Human-Computer Interaction fields.
APPLIED SCIENCES-BASEL
(2023)
Article
Clinical Neurology
Una Pale, Tomas Teijeiro, David Atienza
Summary: Long-term monitoring of patients with epilepsy is a challenging problem in engineering. This study proposes a novel semi-supervised learning approach based on multi-centroid HD computing, which shows significantly improved performance in epilepsy detection, especially in cases of data imbalance.
FRONTIERS IN NEUROLOGY
(2022)
Article
Clinical Neurology
Anca A. Arbune, Pirgit Meritam Larsen, Stephan Wustenhagen, Daniella Terney, Elena Gardella, Sandor Beniczky
Summary: The study found that more than one third of epileptiform EEG discharges showed a decrease in spiking patterns during seizures, suggesting a potential anticonvulsive function, while the majority of discharges increased in association with seizures.
CLINICAL NEUROPHYSIOLOGY
(2021)
Article
Multidisciplinary Sciences
Taylan Tugrul, Osman Erogul
JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES
(2020)
Article
Engineering, Electrical & Electronic
Abdullah Irfan Yasar, Fikret Yildiz, Osman Erogul
MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOSYSTEMS-INFORMATION STORAGE AND PROCESSING SYSTEMS
(2020)
Article
Biophysics
Huseyin Nasifoglu, Osman Erogul
Summary: This study compared different deep convolutional neural network models for predicting OSA using ECG, proposing a more effective model that automatically extracts time-frequency features and transforms them. Prediction using scalograms outperformed spectrograms, with overall high accuracy of 91.93% for per-recording classification of OSA events.
PHYSIOLOGICAL MEASUREMENT
(2021)
Article
Chemistry, Multidisciplinary
Hatice Ferda Ozguzar, Ebru Evren, Ahmet Ersin Meydan, Gozde Kabay, Julide Sedef Gocmen, Fatih Buyukserin, Osman Erogul
Summary: The inferior hemocompatibility or antibacterial properties of blood-contacting materials and devices can be improved through a plasma-enhanced modification strategy. This study demonstrates the successful surface tailoring of polypropylene (PP) using plasma etching and amine-rich precursor mediated coating. The heparin-coated PP surfaces obtained through this method show high hydrophilicity, surface activity, and antibacterial properties, as well as decreased blood coagulation and protein adhesion.
ADVANCED MATERIALS INTERFACES
(2023)
Article
Medicine, General & Internal
Ahmet Cankat Ozturk, Hilal Haznedar, Bulent Haznedar, Seyfettin Ilgan, Osman Erogul, Adem Kalinli
Summary: This study proposes an effective method for differentiating thyroid nodules using AI and the innovative ANFIS-GA training method, which outperforms traditional algorithms and DNN methods. Additionally, a novel CAD-based risk stratification system for thyroid nodule ultrasound classification, not found in the literature, is proposed.
Article
Multidisciplinary Sciences
Cansel Ficici, Osman Erogul, Ziya Telatar, Onur Kocak
Summary: This study presents an automated medical decision support system for accurate and immediate detection, segmentation, and volume estimation of brain tumors from MRI. The proposed system does not require user interactions and can perform these tasks by adaptively determining threshold values. The experiments showed high accuracy and Dice similarity coefficient values, indicating the effectiveness of the method.
Article
Medicine, General & Internal
Cansel Ficici, Ziya Telatar, Onur Kocak, Osman Erogul
Summary: Temporal lobe epilepsy is the most common type of focal seizure, accounting for 30-35% of all epilepsies. The detection and localization of epileptic focus are crucial for treatment planning and surgery. A deep learning-based computer-aided diagnosis system was proposed to assist physicians in detecting epileptic focus from EEG recordings.
Proceedings Paper
Computer Science, Artificial Intelligence
Aslan Berk Tuzuner, Osman Erogul, Gokce Kaan Atac, Hale Colakoglu Er
2020 MEDICAL TECHNOLOGIES CONGRESS (TIPTEKNO)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Farhad Nassehi, Basak Erdogdu, Sena Sisman, Yagmur Saglam, Osman Erogul
2020 MEDICAL TECHNOLOGIES CONGRESS (TIPTEKNO)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Mertcan Ozdemir, Erdem Inanc Budak, Osman Erogul
2020 MEDICAL TECHNOLOGIES CONGRESS (TIPTEKNO)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Ugur Sahin, Erdem Inanc Budak, Osman Erogul
2020 MEDICAL TECHNOLOGIES CONGRESS (TIPTEKNO)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Kaya Dagli, Osman Erogul
2020 MEDICAL TECHNOLOGIES CONGRESS (TIPTEKNO)
(2020)
Proceedings Paper
Engineering, Electrical & Electronic
Farhad Nassehi, Mertcan Ozdemir, Osman Erogul
2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU)
(2020)
Article
Oncology
Taylan Tugrul, Osman Erogul
REPORTS OF PRACTICAL ONCOLOGY AND RADIOTHERAPY
(2019)
Article
Computer Science, Information Systems
Memduh Kose, Selcuk Tascioglu, Ziya Telatar
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)