1
|
Hamdoun H, Sagheer A, Youness H. Energy time series forecasting-analytical and empirical assessment of conventional and machine learning models. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-201717] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Machine learning methods have been adopted in the literature as contenders to conventional methods to solve the energy time series forecasting (TSF) problems. Recently, deep learning methods have been emerged in the artificial intelligence field attaining astonishing performance in a wide range of applications. Yet, the evidence about their performance in to solve the energy TSF problems, in terms of accuracy and computational requirements, is scanty. Most of the review articles that handle the energy TSF problem are systematic reviews, however, a qualitative and quantitative study for the energy TSF problem is not yet available in the literature. The purpose of this paper is twofold, first it provides a comprehensive analytical assessment for conventional, machine learning, and deep learning methods that can be utilized to solve various energy TSF problems. Second, the paper carries out an empirical assessment for many selected methods through three real-world datasets. These datasets related to electrical energy consumption problem, natural gas problem, and electric power consumption of an individual household problem. The first two problems are univariate TSF and the third problem is a multivariate TSF. Compared to both conventional and machine learning contenders, the deep learning methods attain a significant improvement in terms of accuracy and forecasting horizons examined. In the meantime, their computational requirements are notably greater than other contenders. Eventually, the paper identifies a number of challenges, potential research directions, and recommendations to the research community may serve as a basis for further research in the energy forecasting domain.
Collapse
Affiliation(s)
- Hala Hamdoun
- Center for Artificial Intelligence and Robotics (CAIRO), Faculty of Science, Aswan University, Egypt
- Department of Computers and Systems Engineering, Faculty of Engineering, Minia University, Egypt
| | - Alaa Sagheer
- Center for Artificial Intelligence and Robotics (CAIRO), Faculty of Science, Aswan University, Egypt
- College of Computer Science and Information Technology, King Faisal University, Saudi Arabia
| | - Hassan Youness
- Department of Computers and Systems Engineering, Faculty of Engineering, Minia University, Egypt
| |
Collapse
|
2
|
Nayarisseri A, Khandelwal R, Madhavi M, Selvaraj C, Panwar U, Sharma K, Hussain T, Singh SK. Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation. Curr Top Med Chem 2020; 20:2146-2167. [PMID: 32621718 DOI: 10.2174/1568026620666200704135327] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 03/20/2020] [Accepted: 04/25/2020] [Indexed: 12/17/2022]
Abstract
BACKGROUND The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases have overwhelmed health and public health services. Artificial Intelligence (AI) and Machine Learning (ML) algorithms have extended their major role in tracking disease patterns, and in identifying possible treatments. OBJECTIVE This study aims to identify potential COVID-19 protease inhibitors through shape-based Machine Learning assisted by Molecular Docking and Molecular Dynamics simulations. METHODS 31 Repurposed compounds have been selected targeting the main coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from the 3D shape to the pharmacophoric features of their seed compound. Ligand-Receptor Docking was performed with Optimized Potential for Liquid Simulations (OPLS) algorithms to identify highaffinity compounds from the list of selected candidates for 6LU7, which were subjected to Molecular Dynamic Simulations followed by ADMET studies and other analyses. RESULTS Shape-based Machine learning reported remdesivir, valrubicin, aprepitant, and fulvestrant as the best therapeutic agents with the highest affinity for the target protein. Among the best shape-based compounds, a novel compound identified was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorv-EMBS'. Further, toxicity analysis showed nCorv-EMBS to be suitable for further consideration as the main protease inhibitor in COVID-19. CONCLUSION Effective ACE-II, GAK, AAK1, and protease 3C blockers can serve as a novel therapeutic approach to block the binding and attachment of the main COVID-19 protease (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 receptors in the lung. The novel compound nCorv- EMBS herein proposed stands as a promising inhibitor to be evaluated further for COVID-19 treatment.
Collapse
Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore-452010, Madhya Pradesh, India,Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd., Mahalakshmi Nagar, Indore-452010, Madhya
Pradesh, India,Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King
Saud University, Riyadh, Saudi Arabia,Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
| | - Ravina Khandelwal
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore-452010, Madhya Pradesh, India
| | - Maddala Madhavi
- Department of Zoology, Nizam College, Osmania University, Hyderabad-500001, Telangana State, India
| | - Chandrabose Selvaraj
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
| | - Umesh Panwar
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
| | - Khushboo Sharma
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore-452010, Madhya Pradesh, India
| | - Tajamul Hussain
- Center of Excellence in Biotechnology Research, College of Science, King Saud University, Riyadh, Saudi Arabia,Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King
Saud University, Riyadh, Saudi Arabia
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
| |
Collapse
|
3
|
A Deep Learning Model for Fault Diagnosis with a Deep Neural Network and Feature Fusion on Multi-Channel Sensory Signals. SENSORS 2020; 20:s20154300. [PMID: 32752215 PMCID: PMC7436083 DOI: 10.3390/s20154300] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 07/28/2020] [Accepted: 07/30/2020] [Indexed: 11/16/2022]
Abstract
Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic model based on multiple DNNs (MDNNs) and softmax is constructed with the input of fused deep features. The proposed method is verified in intelligent failure recognition for automobile final drive to evaluate its performance. A set of contrastive analyses of several intelligent models based on the Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM) and the proposed deep architecture with single sensory signal and multi-channel sensory signals is implemented. The proposed deep architecture of feature extraction and feature fusion on multi-channel sensory signals can effectively recognize the fault patterns of final drive with the best diagnostic accuracy of 95.84%. The results confirm that the proposed method is more robust and effective than other comparative methods in the contrastive experiments.
Collapse
|
4
|
Shen T, Jiang J, Lin W, Ge J, Wu P, Zhou Y, Zuo C, Wang J, Yan Z, Shi K. Use of Overlapping Group LASSO Sparse Deep Belief Network to Discriminate Parkinson's Disease and Normal Control. Front Neurosci 2019; 13:396. [PMID: 31110472 PMCID: PMC6501727 DOI: 10.3389/fnins.2019.00396] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 04/08/2019] [Indexed: 12/31/2022] Open
Abstract
As a medical imaging technology which can show the metabolism of the brain, 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) is of great value for the diagnosis of Parkinson's Disease (PD). With the development of pattern recognition technology, analysis of brain images using deep learning are becoming more and more popular. However, existing computer-aided-diagnosis technologies often over fit and have poor generalizability. Therefore, we aimed to improve a framework based on Group Lasso Sparse Deep Belief Network (GLS-DBN) for discriminating PD and normal control (NC) subjects based on FDG-PET imaging. In this study, 225 NC and 125 PD cohorts from Huashan and Wuxi 904 hospitals were selected. They were divided into the training & validation dataset and 2 test datasets. First, in the training & validation set, subjects were randomly partitioned 80:20, with multiple training iterations for the deep learning model. Next, Locally Linear Embedding was used as a dimension reduction algorithm. Then, GLS-DBN was used for feature learning and classification. Different sparse DBN models were used to compare datasets to evaluate the effectiveness of our framework. Accuracy, sensitivity, and specificity were examined to validate the results. Output variables of the network were also correlated with longitudinal changes of rating scales about movement disorders (UPDRS, H&Y). As a result, accuracy of prediction (90% in Test 1, 86% in Test 2) for classification of PD and NC patients outperformed conventional approaches. Output scores of the network were strongly correlated with UPDRS and H&Y (R = 0.705, p < 0.001; R = 0.697, p < 0.001 in Test 1; R = 0.592, p = 0.0018, R = 0.528, p = 0.0067 in Test 2). These results show the GLS-DBN is feasible method for early diagnosis of PD.
Collapse
Affiliation(s)
- Ting Shen
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Jiehui Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.,Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai, China
| | - Wei Lin
- Department of Neurosurgery, 904 Hospital of PLA, Anhui Medical University, Wuxi, China
| | - Jingjie Ge
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Ping Wu
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yongjin Zhou
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China
| | - Jian Wang
- Department of neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhuangzhi Yan
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Kuangyu Shi
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland.,Department of Nuclear Medicine, Technische Universitat Munchen, Munich, Germany
| |
Collapse
|
5
|
DCT–CNN-based classification method for the Gongbi and Xieyi techniques of Chinese ink-wash paintings. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.11.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
6
|
Qiao J, Wang G, Li W, Chen M. An adaptive deep Q-learning strategy for handwritten digit recognition. Neural Netw 2018; 107:61-71. [DOI: 10.1016/j.neunet.2018.02.010] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 02/10/2018] [Accepted: 02/10/2018] [Indexed: 11/25/2022]
|
7
|
Jayadeva, Sharma M, Soman S, Pant H. Ultra-Sparse Classifiers Through Minimizing the VC Dimension in the Empirical Feature Space. Neural Process Lett 2018. [DOI: 10.1007/s11063-018-9793-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
8
|
Jiang X, Pang Y, Li X, Pan J, Xie Y. Deep neural networks with Elastic Rectified Linear Units for object recognition. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.09.056] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
9
|
Arulmozhi P, Abirami S. A comparative study of hash based approximate nearest neighbor learning and its application in image retrieval. Artif Intell Rev 2017. [DOI: 10.1007/s10462-017-9591-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
10
|
Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE. A survey of deep neural network architectures and their applications. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.12.038] [Citation(s) in RCA: 871] [Impact Index Per Article: 124.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
|
11
|
Prieto A, Prieto B, Ortigosa EM, Ros E, Pelayo F, Ortega J, Rojas I. Neural networks: An overview of early research, current frameworks and new challenges. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.06.014] [Citation(s) in RCA: 161] [Impact Index Per Article: 20.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
|