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14th International Conference on Signal Processing and Communication Systems, ICSPCS 2020

作者： Lazim Al-Taai, Raghad Yaseen Xiaojun, Wu Zhu, Y. Ministry of Education Shaanxi Normal University SNNU Key Laboratory of Modern Teaching Technology School of Computer Science Shaanxi Normal University Xi'an710062 China

ISBN: (纸本)9781728199726

In many hearing-aids systems, background noise degrades the speech quality and intelligibility. In this paper, we propose a hybrid system for hearing-aids application, which works to separates the target voice from the noisy signal and then enhance the speech based on the user's hearing loss. We achieve this by using two stages: (1) A bandpass filter to filter out the unwanted noise which is followed by (2) composite of two-level of multi-layers deep denoising autoencoder, each which specialized for specific enhancement task of a complete set of tasks. We evaluated the improvement of the speech quality using two typical hearing loss audiograms. For evaluation, hearing-aid speech perception index (HASPI), hearing-aid sound quality index (HASQI), and perceptual evaluation of speech quality (PESQ) used in two types audiograms of high-frequency hearing loss (HFHL). The results for the experiments show that the proposed method achieved significant results compared with the individual deep denoising autoencoder. © 2020 IEEE.

关键词： Speech enhancement

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Phase transitions in swarm optimization algorithms

arXiv

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arXiv 2025年

作者： Vantuch, Tomáš Zelinka, Ivan Adamatzky, Andrew Marwan, Norbert Department of Computer science Technical University of Ostrava Czech Republic Unconventional Computing Lab UWE Bristol United Kingdom Transdisciplinary Concepts and Methods Potsdam Germany

Natural systems often exhibit chaotic behavior in their space-time evolution. Systems transiting between chaos and order manifest a potential to compute, as shown with cellular automata and artificial neural networks. We demonstrate that swarms optimisation algorithms also exhibit transitions from chaos, analogous to motion of gas molecules, when particles explore solution space disorderly, to order, when particles follow a leader, similar to molecules propagating along diffusion gradients in liquid solutions of reagents. We analyse these ‘phase-like’ transitions in swarm optimization algorithms using recurrence quantification analysis and Lempel-Ziv complexity estimation. We demonstrate that converging and non-converging iterations of the optimization algorithms are statistically different in a view of applied chaos, complexity and predictability estimating indicators. © 2025, CC BY-NC-ND.

关键词： Cellular automata

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Comparative analysis of hall current impact on MHD laminar surface tension gradient 3D flow of propylene glycol based tetra hybrid nanofluid with generalized fick's and fourier's perspective

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International Journal of Thermofluids 2025年 26卷

作者： Abbas, Munawar Shomurotova, Shirin Al-Mdallal, Qasem Akgül, Ali Jastaneyah, Zuhair Garalleh, Hakim AL Department of Mathematics The Islamia University of Bahawalpur Bahawalpur63100 Pakistan Department of Chemistry Teaching Methods Tashkent State Pedagogical University named after Nizami Bunyodkor street 27 Tashkent Uzbekistan Department of Mathematical Sciences UAE University P.O. Box 17551 Al-Ain United Arab Emirates Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Siirt University Art and Science Faculty Department of Mathematics Siirt56100 Turkey Department of Computer Engineering Biruni University Istanbul Topkapı34010 Turkey Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 /Mersin 10 Nicosia Turkey Department of Mathematical Science College of Engineering University of Business and Technology Jeddah21361 Saudi Arabia

Examine the significance of the Cattaneo-Christov flux model on the Marangoni convection 3D flow of tetra hybrid nanofluid combined with Hall current in the present study. When exposed to a fluctuating magnetic flux, it demonstrates electrical conductivity over a stretchy sheet. Using the Cattaneo-Christov double diffusion (CCDD) model, the problem is simulated. In this work, the CCDD model is used to analyze the mass and heat transmission tetra hybrid nanofluid. Basic Fourier's and Fick's laws are generalized by their application. A tetra hybrid nanofluid consisting of Molybdenum disulfide (Mos2), copper (Cu), Silicon dioxide (SiO2) and cobalt ferrite (CoFe2o4), propylene glycol (C3H8O2) as the base fluid is used. This model is essential for precisely predicting the behaviors of heat transfer in nanofluid flows since it takes thermal relaxation time into consideration. Its uses include optimizing heat exchanger performance, enhancing cooling systems in electronics, and better thermal management in microfluidic devices. The basic set of equations is resolved employing the numerical technique (bvp4c). The nanofluid, hybrid, trihybrid, and tetra hybrid nanofluid graphs are all compared. The stretching ratio parameter indicates rising trends in the flow distributions, although the opposite performance is observed for thermal and concentration distributions. Rate of heat and mass transmission improve of tetra hybrid, trihybrid, hybrid nanofluids as increase the values of Marangoni convection. © 2025

关键词： Molybdenum disulfide

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Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters Recognition Model

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computer Systems science & Engineering 2024年第5期48卷 1387-1403页

作者： Abdelwahed Motwakel Badriyya B.Al-onazi Jaber S.Alzahrani Ayman Yafoz Mahmoud Othman Abu Sarwar Zamani Ishfaq Yaseen Amgad Atta Abdelmageed Department of Computer and Self Development Prince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Handwritten character recognition becomes one of the challenging research *** studies were presented for recognizing letters of various *** availability of Arabic handwritten characters databases was *** a quarter of a billion people worldwide write and speak *** historical books and files indicate a vital data set for many Arab nationswritten in ***,Arabic handwritten character recognition(AHCR)has grabbed the attention and has become a difficult topic for pattern recognition and computer vision(CV).Therefore,this study develops fireworks optimizationwith the deep learning-based AHCR(FWODL-AHCR)*** intention of the FWODL-AHCR technique is to recognize the distinct handwritten characters in the Arabic *** initially pre-processes the handwritten images to improve their quality of ***,the RetinaNet-based deep convolutional neural network is applied as a feature extractor to produce feature ***,the deep echo state network(DESN)model is utilized to classify handwritten ***,the FWO algorithm is exploited as a hyperparameter tuning strategy to boost recognition *** simulations in series were performed to exhibit the enhanced performance of the FWODL-AHCR *** comparison study portrayed the supremacy of the FWODL-AHCR technique over other approaches,with 99.91%and 98.94%on Hijja and AHCD datasets,respectively.

关键词： Arabic language handwritten character recognition deep learning classification parameter tuning

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Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

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Advances in Manufacturing 2021年第3期9卷 388-402页

作者： Long-Hua Xu Chuan-Zhen Huang Jia-Hui Niu Jun Wang Han-Lian Liu Xiao-Dan Wang Center for Advanced Jet Engineering Technologies Key Laboratory of High-efficiency and Clean Mechanical Manufacture(Ministry of Education)National Experimental Teaching Demonstration Center for Mechanical Engineering(Shandong University)School of Mechanical EngineeringShandong UniversityJinanPeople’s Republic of China School of Mechanical and Manufacturing Engineering The University of New South Wales(UNSW)SydneyNSW2052Australia Department of Computer Science and Software Engineering Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuPeople’s Republic of China

During the actual high-speed machining process,it is necessary to reduce the energy consumption and improve the machined surface ***,the appropriate prediction models and optimal cutting parameters are difficult to obtain in complex machining ***,a novel intelligent system is proposed for prediction and optimization.A novel adaptive neuro-fuzzy inference system(NANFIS)is proposed to predict the energy consumption and surface *** the NANFIS model,the membership functions of the inputs are expanded into:membership superior and membership *** membership functions are varied based on the machining *** inputs of the NANFIS model are cutting parameters,and the outputs are the machining *** optimization,the optimal cutting parameters are obtained using the improved particle swarm optimization(IPSO)algorithm and NANFIS ***,the IPSO algorithm as a learning algorithm is used to train the NANFIS *** proposed intelligent system is applied to the high-speed milling process of compacted graphite *** experimental results show that the predictions of energy consumption and surface roughness by adopting the NANFIS models are up to 91.2%and 93.4%,*** NANFIS models can predict the energy consumption and surface roughness more accurately compared with other intelligent *** on the IPSO algorithm and NANFIS models,the optimal cutting parameters are obtained and validated to reduce both the cutting power and surface roughness and improve the milling *** is demonstrated that the proposed intelligent system is applicable to actual high-speed milling processes,thereby enabling sustainable and intelligent manufacturing.

关键词： Novel adaptive neuro-fuzzy inference system(NANFIS)model Improved particle swarm optimization(IPSO)algorithm Energy consumption Surface roughness Multiobjective optimization

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Smart Healthcare System for Severity Prediction and Critical Tasks Management of COVID-19 Patients in IoT-Fog Computing Environments

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Computational Intelligence and Neuroscience 2022年 2022卷 5012962页

作者： Hameed Abdulkareem, Karrar Awad Mutlag, Ammar Musa Dinar, Ahmed Frnda, Jaroslav Abed Mohammed, Mazin Hasan Zayr, Fawzi Lakhan, Abdullah Kadry, Seifedine Ali Khattak, Hasan Nedoma, Jan College of Agriculture Al-Muthanna University Samawah66001 Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Ministry of Education General Directorate of Curricula Pure Science Department Baghdad Iraq Engineering Department University of Technology- Iraq Baghdad Iraq Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and Communication University of Žilina Žilina Slovakia Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Poruba Ostrava Czech Republic College of Computer Science and Information Technology University of Anbar 11 Ramadi Anbar Iraq Department of Biochemistry College of Medicine University of Wasit Wasit Iraq Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325035 China Noroff University College Kristiansand Norway Islamabad44500 Pakistan

COVID-19 has depleted healthcare systems around the world. Extreme conditions must be defined as soon as possible so that services and treatment can be deployed and intensified. Many biomarkers are being investigated in order to track the patient's condition. Unfortunately, this may interfere with the symptoms of other diseases, making it more difficult for a specialist to diagnose or predict the severity level of the case. This research develops a Smart Healthcare System for Severity Prediction and Critical Tasks Management (SHSSP-CTM) for COVID-19 patients. On the one hand, a machine learning (ML) model is projected to predict the severity of COVID-19 disease. On the other hand, a multi-agent system is proposed to prioritize patients according to the seriousness of the COVID-19 condition and then provide complete network management from the edge to the cloud. Clinical data, including Internet of Medical Things (IoMT) sensors and Electronic Health Record (EHR) data of 78 patients from one hospital in the Wasit Governorate, Iraq, were used in this study. Different data sources are fused to generate new feature pattern. Also, data mining techniques such as normalization and feature selection are applied. Two models, specifically logistic regression (LR) and random forest (RF), are used as baseline severity predictive models. A multi-agent algorithm (MAA), consisting of a personal agent (PA) and fog node agent (FNA), is used to control the prioritization process of COVID-19 patients. The highest prediction result is achieved based on data fusion and selected features, where all examined classifiers observe a significant increase in accuracy. Furthermore, compared with state-of-the-art methods, the RF model showed a high and balanced prediction performance with 86% accuracy, 85.7% F-score, 87.2% precision, and 86% recall. In addition, as compared to the cloud, the MAA showed very significant performance where the resource usage was 66% in the proposed model and 34% in

关键词： COVID-19

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The Power of Mentoring Programs in Retaining Women and Black, Indigenous, and Students of Color in Undergraduate Computing Majors

The Power of Mentoring Programs in Retaining Women and Black...

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Kristina Kramarczuk Kate Atchison Jandelyn Plane Maya Narayanasamy Department of Teaching Learning Policy and Leadership University of Maryland College Park USA Department of Computer Science University of Maryland College Park USA

ISBN: (纸本)9781665458429

This paper outlines the effectiveness of an undergraduate mentoring program offered through the Department of computer science at the University of Maryland in retaining historically marginalized undergraduate students in science, technology, engineering, mathematics, and computer science (STEM-CS) majors. Each undergraduate teacher/mentor (called an Ambassador) is responsible for providing support for current university students or for K-12 computing outreach programs. Enrollment and survey data show that the Ambassador program is successful in retaining undergraduate computing majors in STEM-CS. Ambassadors indicated that they appreciated the community aspect of the Ambassador program and they felt empowered to solve diversity problems in STEM-CS.

关键词： computer science Scientific computing Image color analysis Mathematics Mentoring Monitoring Computational intelligence

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Image Dehazing Algorithm Based on Particle Swarm Optimization for Sky Region Segmentation

Image Dehazing Algorithm Based on Particle Swarm Optimizatio...

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第二十一届分布式计算及其应用国际学术研讨会

作者： Hao Zhou Changjiu Yuan Hongbo Pan Yue Yang Ziyan Wang Xiangyang Chen College of Computer and Information Science Southwest University College of Big Data and Artificial Intelligence Chizhou University College of Big Data and Intelligent Engineering Chongqing College of International Business and Economics Basic Teaching Department Anqing Medical College

The well-known Dark Channel Prior(DCP)dehazing algorithm does not work well in the sky area,thus,we propose an image dehazing method for segmented sky regions based on Particle Swarm Optimization(PSO).First,we apply PSO to the Otsu segmentation method to accurately segment sky and non-sky regions in the hazy ***,the transmission of non-sky regions is derived by DCP.A transmission compensation method is proposed to obtain the transmission for the sky *** transmission is then refined by gradient-domain guided ***,the restored haze-free image is obtained through the Atmospheric Scattering Model(ASM).Quantitative and qualitative experiments show that the proposed method has a better dehazing effect,especially for hazy images with the sky areas.

关键词： image dehazing Ostu sky area segmentation DCP

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Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models

arXiv

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arXiv 2022年

作者： Zhan, Jingtao Xie, Xiaohui Mao, Jiaxin Liu, Yiqun Guo, Jiafeng Zhang, Min Ma, Shaoping Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

A retrieval model should not only interpolate the training data but also extrapolate well to the queries that are different from the training data. While neural retrieval models have demonstrated impressive performance on ad-hoc search benchmarks, we still know little about how they perform in terms of interpolation and extrapolation. In this paper, we demonstrate the importance of separately evaluating the two capabilities of neural retrieval models. Firstly, we examine existing ad-hoc search benchmarks from the two perspectives. We investigate the distribution of training and test data and find a considerable overlap in query entities, query intent, and relevance labels. This finding implies that the evaluation on these test sets is biased toward interpolation and cannot accurately reflect the extrapolation capacity. Secondly, we propose a novel evaluation protocol to separately evaluate the interpolation and extrapolation performance on existing benchmark datasets. It resamples the training and test data based on query similarity and utilizes the resampled dataset for training and evaluation. Finally, we leverage the proposed evaluation protocol to comprehensively revisit a number of widely-adopted neural retrieval models. Results show models perform differently when moving from interpolation to extrapolation. For example, representation-based retrieval models perform almost as well as interaction-based retrieval models in terms of interpolation but not extrapolation. Therefore, it is necessary to separately evaluate both interpolation and extrapolation performance and the proposed resampling method serves as a simple yet effective evaluation tool for future IR studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Extrapolation

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CognoSpeak: an automatic, remote assessment of early cognitive decline in real-world conversational speech

CognoSpeak: an automatic, remote assessment of early cogniti...

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2025 IEEE Symposium on Computational Intelligence in Health and Medicine, CIHM 2025

作者： Pahar, Madhurananda Tao, Fuxiang Mirheidari, Bahman Pevy, Nathan Bright, Rebecca Gadgil, Swapnil Sproson, Lise Braun, Dorota Illingworth, Caitlin Blackburn, Daniel Christensen, Heidi School of Computer Science University of Sheffield SheffieldS1 4DP United Kingdom Therapy Box London United Kingdom NIHR Devices for Dignity HTC Sheffield Teaching Hospitals NHS Foundation Trust SheffieldS10 2JF United Kingdom University of Sheffield SheffieldS10 2HQ United Kingdom

ISBN: (纸本)9798331508333

The early signs of cognitive decline are often noticeable in conversational speech, and identifying those signs is crucial in dealing with later and more serious stages of neurodegenerative diseases. Clinical detection is costly and time-consuming and although there has been recent progress in the automatic detection of speech-based cues, those systems are trained on relatively small databases, lacking detailed metadata and demographic information. This paper presents CognoSpeak and its associated data collection efforts. CognoSpeak asks memory-probing long and short-term questions and administers standard cognitive tasks such as verbal and semantic fluency and picture description using a virtual agent on a mobile or web platform. In addition, it collects multimodal data such as audio and video along with a rich set of metadata from primary and secondary care, memory clinics and remote settings like people’s homes. Here, we present results from 126 subjects whose audio was manually transcribed. Several classic classifiers, as well as large language model-based classifiers, have been investigated and evaluated across the different types of prompts. We demonstrate a high level of performance;in particular, we achieved an F1-score of 0.873 using a DistilBERT model to discriminate people with cognitive impairment (dementia and people with mild cognitive impairment (MCI)) from healthy volunteers using the memory responses, fluency tasks and cookie theft picture description. CognoSpeak is an automatic, remote, low-cost, repeatable, non-invasive and less stressful alternative to existing clinical cognitive assessments. © 2025 IEEE.

关键词： Neurodegenerative diseases

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