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Optimizing Hammerstein-Wiener Model for Forecasting Confirmed Cases of Covid-19

IAENG International Journal of applied Mathematics 2022年第1期52卷 1页

作者： Abdullahi, Sunusi Bala Ibrahim, Abdulkarim Hassan Abubakar, Auwal Bala Kambheera, Abhiwat General Investigation Zonal Criminal Investigation Section Force Criminal Investigation and Intelligence Department Nigeria Police Abuja900211 Nigeria Department of Applied Mathematics King Mongkut’s University of Technology Thonburi Bangkok Thailand Department of Mathematical Sciences Faculty of Physical Sciences Bayero University Kano700241 Nigeria Mathematics and Computing Science Program Faculty of Science and Technology Phetchabun Rajabhat University Phetchabun 67000 Thailand

Noise poses challenge to nonlinear Hammerstein-Wiener (HW) subsystem model application, because HW subsystem need large number of parameter interactions. However, flexibility, soft computing, and automatic adjustment to dynamic observation for best model fitting make it potential for forecasting nonlinear data. In this article, we adopted improved HW inference from Levenberg-Marquardt optimization algorithm to optimize HW subsystem and to select best model parameters. Therefore, the adopted model is tested on COVID-19 confirmed reported cases, to estimate transmission rate of COVID-19 virus for period from 15th March 2020 to 29th April 2020. Model validation is carried out on small dataset, which outperforms some existing models. The adopted model is further evaluated using statistical metrics and reported best accuracy of 0.127 and 0.998 for Mean Absolute percentage error (MAPE) and coefficient of determination (R2) respectively, with best model complexity of 1.86. The obtained results are promising enough in predicting spread of COVID-19 virus and may inspire as guidance to relax lockdown restriction policies. © 2022, IAENG International Journal of applied Mathematics. All Rights Reserved.

关键词： COVID-19

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Enhancing drug-target interaction predictions in context of neurodegenerative diseases using bidirectional long short-term memory in male Swiss albino mice pharmaco-EEG analysis

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Heliyon 2024年第21期10卷 e39279页

作者： Qureshi, Shahnawaz Iqbal, Syed Muhammad Zeeshan Ameer, Asif Karrila, Seppo Ghadi, Yazeed Yasin Shah, Syed Aziz Intelligent Biomedical Application Lab Sino-Pak center for Artificial Intelligence School of Computing Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Mang Haripur 22620 Pakistan Research and Development BrightWare LLC Riyadh 13323 Saudi Arabia Department of Computer Science National University of Computing and Emerging Sciences Faisalabad 38000 Pakistan Faculty of Science and Industrial Technology Prince of Songkla University Surat Thani Campus Muang Surat Thani 84000 Thailand Department of Computer Science Al Ain University Abu Dhab Al Ain United Arab Emirates Healthcare Sensing Technology Faculty Research Centre for Intelligent Healthcare Coventry University Coventry United Kingdom

Background and Objective: Emerging diseases like Parkinson or Alzheimer's, which are not curable, endanger human mental health and are challenging to research. Drug target interactions (DTI) are pivotal in the screening of candidate drugs and focus on a small pool of drug targets. Electroencephalogram shows the responses to psychotropic medicines in the brain bioelectric activity. Synaptic activity can be analyzed by using Local Field Potential recordings obtained from micro-electrodes implanted in the brain. The aim is to evaluate the effects of drug on brain bioelectric activity and increase the drug classification accuracy. The ultimate goal is to advance our understanding of how drugs affect synaptic activity and open the door to more focused treatment for neurodegenerative diseases. Methods: In this study, Pharmaco-EEG recordings are processed using Advanced neural network models, particularly Convolutional Neural Networks, to assess the effects of medications. The five different medicines used in this study are Ephedrine, Fluoxetine, Kratom, Morphine, and Saline. The signals observed are local field potential signals. To overcome some limits of DTI prediction, we propose Bidirectional Long Short-Term Memory (LSTM) for the categorization of intracranial EEG (i-EEG) data, departing from standard approaches. Similar EEG patterns are presumably caused by drugs that work by homologous pharmacological pathways, producing similar psychotropic effects. To improve accuracy and reduce training loss, our study introduces a bidirectional LSTM model for classification along with Bayesian optimization Results: High recall, precision, and F1-Scores, particularly a 95% F1-Score for morphine, ephedrine, fluoxetine, and saline, suggest good performance in predicting these drug classes. Kratom produces a somewhat lower recall of 94%, but a high F1-Score of 97% and perfect precision of 1.00. The weighted average F1-Score, macro average, and overall accuracy are all consistently h

关键词： Convolutional neural network Drug target interaction Intracranial EEG Local field potential Long short-term memory

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IoT-based Smart Parking System using Android Application

IoT-based Smart Parking System using Android Application

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2020 International Conference on Emerging Technology in computing, Communication and Electronics, ETCCE 2020

作者： Warif, Nor Bakiah Abd Azman, Mohd Izzat Syahmi Saiful Ismail, Nor-Syahidatul N Remli, Muhammad Akmal Faculty of Computer Science Information Technology Universiti Tun Hussien Onn Malaysia Department of Information Security and Web Technology Batu Pahat Johor Malaysia Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pekan Pahang Malaysia Kota Bharu Kelantan Malaysia

ISBN: (纸本)9781665419628

Since all people used vehicles as a necessity, the growing number of vehicles is increasing day by day. With the increasing number of vehicles on the road, the requirement of parking lot will also be restricted. Thus, it become a problem for the people to find a free parking space in the busy building especially in shopping complex. Internet of Things (IoT) is an interesting technology that can be applied in this paper because this technology has a capability to transfer the data over a network without requiring human intervention in almost all applications in today's society. In this paper, a user-friendly mobile application, named as Android-based Car Parking Monitoring System (ACPMS) is built to aid in locating a particular parking place. ACPMS can provide a user with the ability to check vacant parking space and locate the nearest parking lot. ACPMS obtained the parking location from the current user's position with the sensor located in the shopping complex's parking lot. ACPMS is testing in a realistic environment for the purpose of movement detection and location service to notify user using the mobile application. By considering seven test case scenarios, the combination of the ACPMS mobile application with a parking prototype kit shows that the proposed work able to solve the parking problem. © 2020 IEEE.

关键词： Internet of things

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Cognitive-inspired Post-processing of optical character recognition for Swedish addresses

Cognitive-inspired Post-processing of optical character reco...

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IEEE International Conference on Cognitive Informatics

作者： Moa Andersson Summrina Kanwal Faiza Khan School of Electrical Engineering and Computer Science Kth Royal Institute of Technology in Stockholm Sweden School of Electrical Engineering and Computer Science Kth Royal Institute of Technology in Stockholm Centre for Applied Intelligent Systems Research Halmstad University Sweden Faculty of Computing Riphah International University Islamabad Pakistan

Optical character recognition (OCR) has many ap-plications, such as digitizing historical documents, automating processes, and helping visually impaired people read. However, extracting text from images into a digital format is not an easy problem to solve, and the outputs from the OCR frameworks often include errors. The complexity comes from the many variations in (digital) fonts, handwriting, lighting, etc. To tackle this problem, this thesis investigates two different methods for correcting the errors in OCR output. The used dataset consists of Swedish addresses. The methods are therefore applied to postal automation to investigate the usage of these methods for further automating postal work by automatically reading addresses on parcels using OCR. The first method, the lexical implementation, uses a dataset of Swedish addresses so that any valid address should be in this dataset (hence there is a known and limited vocabulary), and misspelled addresses are corrected to the address in the lexicon with the smallest Levenshtein distance. The second approach uses the same dataset, but with artificial errors, or artificial noise, added. The addresses with this artificial noise are then used together with their correct spelling to train a machine learning model based on Neural machine translation (NMT) to automatically correct errors in OCR read addresses. The results from this study could contribute by defining in what direction future work connected to OCR and postal addresses should go. The results were that the lexical implementation outperformed the NMT model. However, more experiments including real data would be required to draw definitive conclusions as to how the methods would work in real-life applications.

关键词： Vocabulary Automation Computational modeling Optical character recognition Lighting Machine learning Complexity theory

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Image super resolution via multi-regularization combining hybrid Tikhonov-TV prior and deep denoiser prior

Image super resolution via multi-regularization combining hy...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Jiahao Zhang Shengrong Zhao Hu Liang Changchun Wen Chen Liang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

In a real scenario, the image is often corrupted by complex degradation, and a lot of useful information is lost, which makes super-resolution (SR) reconstruction seriously ill-posed. To effectively solve such a problem, it is crucial to correctly exploit image prior knowledge. Although existing deep learning-based methods can obtain excellent results, they cannot deal with the complex degradation effectively, which would lead to the loss of texture details and the destruction of edge details. In this paper, an efficient multi-regularization method for SR is proposed, which can simultaneously exploit both internal and external image priors within a unified framework. The hybrid Tikhonov-TV prior and deep denoiser prior are introduced to constrain the reconstruction process. That is, the proposed model combines the superiority of the piecewise-smooth prior and deep prior. Moreover, an adaptive weight parameter is employed to make the hybrid component more detail-preserving. Experimental demonstrate that the proposed method achieves better performance in image detail protection than advanced methods.

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Internet of Things in construction industry: A bibliometric analysis

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AIP Conference Proceedings 2023年第1期2608卷

作者： Mohd Affendi Ahmad Pozin Suraiya Ibrahim J. A. Bamgbade Faculty of Applied and Human Sciences Universiti Malaysia Perlis Perlis Malaysia Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Malaysia

The Internet of Things (IoT) aims to provide benefit and value to the industry, government, society, and academic and research groups in this digital era. This research gives a bibliometric analysis of IoT research articles from the Google Scholar database from 2015 to 2021. Based on the keywords used in the paper title and abstract, a total of 850 documents from the Google Scholar database were discovered. We employ several tools and techniques such as Microsoft Excel, VOSviewer for data visualization, and Harzing’s Publish or Perish for citation metrics and analysis. The results of this study are reported using standard bibliometric indicators such as publishing trends, authorship patterns, co-occurrences, and top-cited articles. According to our data, since2015, the number of publications on IoT research has been steadily increasing. The top google Scholar cited per year belongs to the paper entitled “Prefabricated construction enabled by the Internet-of-Things”. The findings of this study may contribute to the body of knowledge by providing a complete trend analysis of IoT in the Construction Industry papers published in the Google Scholar database between 2015 and 2021.

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference

Multilingual Knowledge Graph Completion via Multimodal Decou...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Xiaojing Liu Xuesong Jiang Fengge Yi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance.

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Classical and Bayesian Inferences for Three-Parameter Burr-XIIDistribution Based on Joint Progressive Type-II Censoring Scheme

SSRN

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SSRN 2023年

作者： Ghazal, Mohamed G.M. Hasaballah, Mustafa M. EL-Sagheer, Rashad M. Balogun, Oluwafemi Samson Alshangiti, Arwa M. Bakr, M.E. Mathematics Department Faculty of Science Minia University Minia61519 Egypt Department of Mathematics College of Education University of Technology and Applied Sciences Al-Rustaq329 Oman Marg Higher Institute for Engineering and Modern Technology Cairo Egypt Mathematics Department Faculty of Science Al-Azhar University Nasr City Cairo11884 Egypt School of Computing University of Eastern Finland FI-70211 Finland Department of Statistics and Operation Research College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia

The joint censoring technique is a practical and valuable approach for comparing the life tests of items from different units within a single facility. In this study, the joint Progressive Type-II censoring scheme was utilized to calculate point and interval estimates for the three-parameter Burr-XII distribution. Several methodologies, including maximum likelihood, Bayes, and parametric bootstrap, were employed to estimate the two distribution parameters. The study also obtained approximate confidence intervals, four bootstrap confidence intervals, and credible intervals. To compute the results of Bayes estimators under squared error loss and linear exponential loss functions, the researchers applied the Markov chain Monte Carlo method. The Gibbs within the Metropolis-Hastings algorithm was employed to generate Markov chain Monte Carlo samples from posterior density functions. A real data set was used to illustrate their proposed methods, and a simulation study was conducted to compare their performance. Finally, based on the results, it was found that the Bayes estimates for the parameters outperformed the Maximum likelihood estimators. © 2023, The Authors. All rights reserved.

关键词： Parameter estimation

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Next Generation Image Watermarking via Combined DWT-SVD Technique

Next Generation Image Watermarking via Combined DWT-SVD Tech...

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Cyber Resilience (ICCR), International Conference on

作者： Qais Al-Na'amneh Ammar Almomani Ahmad Nasayreh Khalid M.O. Nahar Hasan Gharaibeh Rabia Emhamed Al Mamlook Mohammad Alauthman Department of Cyber Security Applied Science Private University Amman Jordan School of Computing Skyline University College Sharjah United Arab Emirates I. T. department AlHuson University College AlBalqa Applied University Irbid Jordan Department of Computer Sciences Yarmouk University Irbid Jordan Faculty of Information Technology Computer Sciences Yarmouk University Irbid Jordan Dep. of Business Administration Trine University Indiana USA Dep.of Mechanical and Industrial Engineering University of Zawia Tripoli Libya Department of Information Security Faculty of Information Technology University of Petra Amman Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Personal computers and the Internet are used in different areas and are easier to use. Most data is easy to transmit and duplicate in digital format, and being tampered with and stolen easily leads to issues for content owners and service providers. The Paper describes a secure (tampering-resistant) method for watermarking images for protection and authentication and digital media like audio, video, and multimedia data, and a combination of singular value decomposition (SVD) and Discrete wavelet transform (DWT) for insertion watermark by MATLAB to increase accuracy, simplicity, and flexibility Readability, secure robustness, and imperceptibility the visibility of the image. The proposed algorithm consists of three main steps: Firstly, we decomposed the original image into four sub-bands and the original watermark. Secondly, embed the watermark in the LL sub-band by replacing the singular value for the watermark. Finally, embed a watermark in the other three sub-bands by adding LL values for a watermark in each sub-band in the original image.

关键词： Resistance PSNR Watermarking Transforms Streaming media Robustness Real-time systems

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LDVNet: Lightweight and Detail-Aware Vision Network for Image Recognition Tasks in Resource-Constrained Environments

LDVNet: Lightweight and Detail-Aware Vision Network for Imag...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yuchen Liu Hu Liang Ran Qiu Shengrong Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network’s lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments.

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