Article
Biology
Mate Hires, Matej Gazda, Peter Drotar, Nemuel Daniel Pah, Mohammod Abdul Motin, Dinesh Kant Kumar
Summary: The computerized detection of Parkinson's disease (PD) using an ensemble of convolutional neural networks (CNNs) achieved excellent results in distinguishing the voices of people with PD and those of healthy people for all vowels. This method has the potential for use in clinical practice for the screening, diagnosis, and monitoring of PD.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Physics, Multidisciplinary
Jiu-Cheng Xie, Yanyan Gan, Ping Liang, Rushi Lan, Hao Gao
Summary: This article proposes a Parkinson's auxiliary diagnosis system based on human speech, which can adaptively build a suitable deep neural network based on sound features. Experimental results show that this method improves the accuracy of voice-based Parkinson's disease detection to some extent.
FRONTIERS IN PHYSICS
(2022)
Article
Chemistry, Analytical
Giovanni Costantini, Valerio Cesarini, Pietro Di Leo, Federica Amato, Antonio Suppa, Francesco Asci, Antonio Pisani, Alessandra Calculli, Giovanni Saggio
Summary: This study analyzed the voice characteristics of Parkinson's disease patients using machine learning techniques, and compared different feature selection and classification algorithms. The results showed that both feature-based machine learning and deep learning achieved comparable results in terms of classification, with KNN, SVM, and naive Bayes classifiers performing similarly. The superiority of CFS as the best feature selector was more evident, and the selected features acted as relevant vocal biomarkers capable of differentiating healthy subjects, early untreated PD patients, and mid-advanced L-Dopa treated patients.
Article
Clinical Neurology
Antonio Suppa, Giovanni Costantini, Francesco Asci, Pietro Di Leo, Mohammad Sami Al-Wardat, Giulia Di Lazzaro, Simona Scalise, Antonio Pisani, Giovanni Saggio
Summary: Voice changes in Parkinson's disease (PD) were investigated using machine learning algorithms in a large cohort of patients at different disease stages. The results showed abnormal voice in early-stage PD, progressive degradation in mid-advanced-stage PD, and improvement but not restoration of voice with L-Dopa therapy. Machine learning analysis allowed for tracking disease severity and quantifying the effect of L-Dopa on voice parameters with high accuracy, potentially serving as a new biomarker for PD.
FRONTIERS IN NEUROLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Cagatay Berke Erdas, Emre Sumer
Summary: Three-dimensional magnetic resonance imaging has been proven effective in detecting and predicting the severity of Parkinson's disease. Pre-processing with neuroimaging methods, including FLIRT image registration and BET non-brain tissue scraper, plays a vital role in post-processing for these disorders. Deep learning methods, such as convolutional neural networks (CNN), have been successfully applied to magnetic resonance imaging (MRI) due to technological advancements. The results of this study demonstrate promising performance in the detection and prediction of Parkinson's disease using 2D and 3D CNN with pre-processed T1-weighted MRIs.
PEERJ COMPUTER SCIENCE
(2023)
Article
Engineering, Biomedical
Mounira Chaiani, Sid Ahmed Selouani, Malika Boudraa, Mohammed Sidi Yakoub
Summary: This paper proposes a two-stage framework for accurately classifying different voice pathologies. The first stage involves speech enhancement processing that treats impaired voice as a noisy signal, using the noise lestral harmonic-to-noise ratio (CHNR). The second stage utilizes a convolutional neural network with long short-term memory (CNN-LSTM) architecture to learn complex features from spectrograms of the enhanced signals in the first stage. Experimental results show that using the minimum mean square error (MMSE) based signal enhancer and the sinusoidal rectified unit (SinRU) as the activation function improves the automatic classification of voice pathologies and dysarthria severity levels.
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
(2022)
Article
Clinical Neurology
Laetitia Jeancolas, Graziella Mangone, Dijana Petrovska-Delacretaz, Habib Benali, Badr-Eddine Benkelfat, Isabelle Arnulf, Jean-Christophe Corvol, Marie Vidailhet, Stephane Lehericy
Summary: This study characterized PD voice signature using automated acoustic analysis and compared male and female patients from the prodromal stage to early PD. The results showed PD-related impairments in prosody, pause durations, and rhythmic abilities, with these alterations being more pronounced in men than in women. Early PD detection achieved high accuracy in males and moderate accuracy in females. The study highlights the importance of including automated voice analysis in future diagnostic procedures for prodromal PD.
PARKINSONISM & RELATED DISORDERS
(2022)
Article
Health Care Sciences & Services
Irving Luna-Ortiz, Mario Aldape-Perez, Abril Valeria Uriarte-Arcia, Alejandro Rodriguez-Molina, Antonio Alarcon-Paredes, Elias Ventura-Molina
Summary: Parkinson's disease (PD) is a chronic neurological disorder that worsens over time and poses challenges for diagnosis. Algorithms based on associative memory have been used to diagnose PD using voice samples, but they lack a component to identify and remove irrelevant features, limiting their classification performance. In this study, an improved algorithm called ISNDAM was proposed, which showed superior performance compared to other well-known algorithms for PD diagnosis.
Article
Computer Science, Interdisciplinary Applications
Quoc Cuong Ngo, Mohammod Abdul Motin, Nemuel Daniel Pah, Peter Drotar, Peter Kempster, Dinesh Kumar
Summary: This study summarizes the literature on speech and voice in detecting and assessing the severity of Parkinson's disease (PD). Speech and voice may be valuable markers for PD, but there are differences between datasets and limitations in analysis methods.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Rania Khaskhoussy, Yassine Ben Ayed
Summary: Parkinson's disease (PD) is one of the most common neurological diseases in the world, characterized by motor, cognitive, and language disorders. Changes in voice have been identified as important clinical signs for the diagnosis and assessment of PD. This research paper introduces a new approach based on speech signal analysis to automatically detect PD.
PATTERN RECOGNITION LETTERS
(2023)
Article
Automation & Control Systems
B. Vidya, P. Sasikumar
Summary: Diagnosing Parkinson's disease is a complex and challenging task that involves evaluating various motor and non-motor symptoms. This study aims to design a gait analysis based classifier model using a hybrid convolutional neural network-long short term memory network to predict the severity rating of PD.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Onur Karaman, Hakan Cakin, Adi Alhudhaif, Kemal Polat
Summary: This study aimed to develop deep convolutional neural networks for automated identification of Parkinson's disease based on voice signals. Results showed that the model successfully identified PD, providing a new approach for pre-diagnosis methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Medicine, General & Internal
Siddharth Arora, Athanasios Tsanas
Summary: This study evaluated the potential of using voice as a population-based PD screening tool and successfully differentiated PD participants from controls using dysphonia measures with 67.43% sensitivity and 67.25% specificity. These findings could contribute to the development of an inexpensive and reliable diagnostic support tool for PD.
Article
Medicine, General & Internal
Ankit Kurmi, Shreya Biswas, Shibaprasad Sen, Aleksandr Sinitca, Dmitrii Kaplun, Ram Sarkar
Summary: Parkinson's Disease is a progressive disorder caused by neural degeneration in the brain. This study proposes an ensemble of Deep Learning models and a fuzzy fusion logic-based approach to enhance Parkinson's disease classification. The results show that the proposed method outperforms other state-of-the-art methods, and a GUI-based software tool has been developed for real-time disease detection.
Article
Clinical Neurology
Thomas Payne, Toby Burgess, Stephen Bradley, Sarah Roscoe, Matilde Sassani, Mark J. Dunning, Dena Hernandez, Sonja Scholz, Alisdair McNeill, Rosie Taylor, Li Su, Iain Wilkinson, Thomas Jenkins, Heather Mortiboys, Oliver Bandmann
Summary: This study characterized bioenergetic dysfunction in Parkinson's disease using a multimodal approach, and found impaired mitophagy and mitochondrial uncoupling in patient-derived fibroblasts. The study also revealed abnormal phosphocreatine levels and implicated a link between impaired mitophagy and impaired striatal energy homeostasis in early Parkinson's disease.
Article
Computer Science, Artificial Intelligence
Jinee Goyal, Padmavati Khandnor, Trilok Chand Aseri
Summary: Parkinson's Disease, the second most common neurogenerative disease, requires early diagnosis for improved quality of life. This paper compares various classification techniques and explores the use of feature selection methods in reducing dataset dimensionality.
INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS
(2021)
Article
Automation & Control Systems
Jinee Goyal, Padmavati Khandnor, Trilok Chand Aseri
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2020)
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)