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International Conference on Computer Applications and Industrial Electronics (ICCAIE)

作者： I’ zzul Labiqah Mohamad Azmi Murizah Kassim Norakmar Arbain Sulaiman Suhaili Beeran Kutty Marina Yusoff School of Electrical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor Malaysia Faculty of Computer and Mathematical Science Universiti Teknologi MARA Shah Alam Selangor Malaysia

Pediatric patients are defined based on the age of 21 years old or younger during the time of diagnosis or treatment. Patients that are traumatized by the unfortunate incident do not have a companion in the ward once admitted. Medical procedures may become invasive that could cause distress for the patients causing them to develop stress and anxiety before the upcoming procedure. The research has developed and tested an interactive robot to assist pediatric patients to undergo medical procedures. Speech recognition and body gestures was built in the robot to interact with the patients. The humanoid robot is programmed to maintain receiving responses from samples of pediatric patients with artificial intelligence (AI) which acted as social skills such as giving feedback and giving mutual gaze while communicating. An Arduino microcontroller was built into the robot to control the movements of the robot via a Bluetooth module. DC motors are used for robot movements that are connected to motor drivers due to the extra power supply required to operate. AI was created for communication skills with patients. The result presents an analysis of the communication performance of the robot and AI. The robot movement was presented, and AI answers communication was listed. Research has presented its significance in that the robot helps and assisted patients to entertain and interact with pediatric patients during hospital stay which motivates and cheer them.

关键词： Service robots Shape Friction Anxiety disorders Speech recognition DC motors artificial intelligence

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Swin Transformer with Spatial and Local Context Augmentation for Enhanced Semantic Segmentation of Remote Sensing Images

IEEE Open Journal of Signal Processing

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IEEE Open Journal of Signal Processing 2025年 6卷 608-620页

作者： Ding, Rong-Xing Xu, Yi-Han Yu, Gang Zhou, Wen Zhou, Ding Nanjing Forestry University College of Information Science and Technology Artificial Intelligence Nanjing210037 China Qinghai University School of Computer Technology and Applications Xining810016 China The University of Sheffield Department of Electronic and Electrical Engineering SheffieldS10 2TN United Kingdom University of Surrey 5G/6G Innovation Centre SurreyGU2 7XH United Kingdom Guangzhou Maritime University College of Low Altitude Equipment and Intelligent Control Guangzhou510725 China University of Malaya Faculty of Engineering Kuala Lumpur50603 Malaysia

Semantic segmentation of remote sensing images is extensively used in crop cover and type analysis, and environmental monitoring. In the semantic segmentation of remote sensing images, owning to the specificity of remote sensing images, not only the local context is required, but also the global context information makes an important role in it. Inspired by the powerful global modelling capability of Swin Transformer, we propose the LSENet network, which follows the encoder-decoder architecture of the UNet network. In encoding phase, we propose spatial enhancement module (SEM), which helps Swin Transformer further enhance feature extraction by encoding spatial information. In decoding stage, we propose local enhancement module (LEM), which is embedded in the Swin Transformer to improve the Swin Transformer to assist the network to obtain more local semantic information so as to classify pixels more accurately, especially in the edge region, the adding of LEM enables to obtain smoother edges. The experimental results on the Vaihingen and Potsdam datasets demonstrate the effectiveness of our proposed method. Specifically, the mIoU metric is 78.58% on the Potsdam dataset, 72.59% on the Vaihingen dataset and 64.49% on the OpenEarthMap dataset. © 2020 IEEE.

关键词： Semantic Segmentation

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Real-Time Signal Processing using AI Integrated Framework for Color and Drawing in Gesture Recognition

Real-Time Signal Processing using AI Integrated Framework fo...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Geerija Lavania Vivek Arya Neha Sharma Mamoon Rashid Shaik Vaseem Akram Artificial Intelligence & Data Science Department JECRC Jaipur Rajasthan India Dept of Electronics and Communication Engineering Gurukula Kangri (Deemed to be University) Haridwar India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Department of Computer Engineering Vishwakarma University Pune India Uttaranchal Institute of Technology Uttaranchal University Dehradun India

In the realm of image processing and pattern recognition, writing with gestures in the air has grown in popularity and difficulty over the past few years. It contributes significantly to the creation of automation processes and can improve human-machine communication in a range of systems. Many studies focus on cutting-edge methods and techniques that will expedite processing while preserving a high level of identification accuracy. Internally, the area of computer vision considers object tracking to be crucial. Object tracking techniques are credited to modern computers, easy access to good-looking, affordable video cameras, and automated video analytics requirements. The video analysis technique generally consists of three fundamental steps: first, recognizing the object, second, tracking its movement from frame to frame, and third, assessing the behavior of that object. Each of the four takes into account a separate issue: choosing the best item representation, choosing the tracking function, purchasing the item, and tracking the item. Numerous real-world applications, such as autonomous monitoring, video identification, and vehicle navigation, depend heavily on object-tracking algorithms. This area is utilized by the research, which focuses on it to create a prospective text-to-text converter transition. This Research occasionally works as a freelance journalist. Your fingerprint will be tracked by a computer. The created text can be copied and used for a variety of things, including emails and SMS. For the deaf, it will be a potent means to establish this connection. By removing the need for writing, an efficient communication system decreases the use of the phone and laptop.

关键词： Portable computers Navigation Visual analytics Signal processing algorithms Writing Signal processing Real-time systems

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Optimum Location of Field Hospitals for COVID-19: A Nonlinear Binary Metaheuristic Algorithm

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Computers, Materials & Continua 2021年第7期68卷 1183-1202页

作者： Said Ali Hassan Khalid Alnowibet Prachi Agrawal Ali Wagdy Mohamed Department of Operations Research and Decision Support Faculty of Computers and Artificial IntelligenceCairo UniversityGiza12613Egypt College of Science Department of Statistics and Operations ResearchKing Saud UniversityRiyadhSaudi Arabia Department of Mathematics and Scientific Computing National Institute of Technology HamirpurHimachal Pradesh177005India Department of Operations Research Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt

Determining the optimum location of facilities is critical in many fields,particularly in *** study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019(COVID-19)*** used model is the most appropriate among the three most common location models utilized to solve healthcare problems(the set covering model,the maximal covering model,and the P-median model).The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear *** model is used to determine the optimum location of field hospitals for COVID-19 risk *** designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper *** this case study,a discrete binary gaining–sharing knowledge-based optimization(DBGSK)algorithm is *** DBGSK algorithm is based on how humans acquire and share knowledge throughout their *** DBGSK algorithm mainly depends on two junior and senior binary *** two stages enable DBGSK to explore and exploit the search space efficiently and effectively,and thus it can solve problems in binary space.

关键词： Facility location nonlinear binary model field hospitals for COVID-19 gaining-sharing knowledge-based metaheuristic algorithm

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Prediction of crack quantity of carbon fiber composites based on Sparrow BP Neural Network

Prediction of crack quantity of carbon fiber composites base...

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Far East Forum on Nondestructive Evaluation/Testing: New technology & Application (FENDT)

作者： Danhong Wang Bo Ye Qiming Duan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Provincial Key Laboratory of Artificial Intelligence Kunming China

ISBN: (纸本)9781665401722

Carbon fiber composites was affected by complex alternating load, environment and other factors in the process of service, led to the emergence and development of tiny crack, once the crack reaches a certain degree, composites fractures, serious safety accidents happen, so structural health monitoring of carbon fiber composites is necessary, predicting the quantity of cracks is helpful to monitor the health status of carbon fiber composites better. Common damage prediction methods include Back Propagation Neural Network, Logistic Regression, etc., BP Neural Network has higher accuracy in damage prediction. However, BP Neural Network has the risk of falling into local extreme value. Therefore, using Sparrow BP Neural Network, which is combined by Sparrow Search Algorithm (SSA) with strong global optimization ability and BP Neural Network, to predict the crack quantity of composites. The prediction results by using Sparrow BP Neural Network are closer to the real crack quantity, which can predict the crack quantity of carbon fiber composites more accurately, solve the problem that the internal cracks of the composites are difficult to predict. It helps to promote the widespread application of intelligent algorithms to predict the cracks of composites.

关键词： Backpropagation Neural networks Prediction methods Optical fiber networks Prediction algorithms Safety Carbon

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Research on reconstruction of signals for carbon fiber composite materials structural health monitoring based on compressed sensing

Research on reconstruction of signals for carbon fiber compo...

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Far East Forum on Nondestructive Evaluation/Testing: New technology & Application (FENDT)

作者： QiMing Duan Bo Ye DanHong Wang JunLin OuYang Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Key Laboratory of Artificial Intelligence of Yunnan Province Kunming China

ISBN: (纸本)9781665401722

The structural health monitoring technology (SHM) can use Lamb waves to monitor the health of structural parts online and in real time, so as to carry out safety assessment and life prediction of structural parts. As an important structural component of aviation, transportation and other fields, carbon fiber composite materials are prone to damage such as delamination, cracks, and fiber breaks during service. Therefore, it is necessary to use piezoelectric sensor arrays to excite Lamb waves to monitor carbon fiber composite materials actively to ensure the full operation of these important structural parts. In the process of monitoring, a higher sampling rate is usually used for data collection, which leads to a decrease in the speed of data transmission, storage, and processing. Therefore, it is necessary to compress the original data to reduce the amount of collected data. In this study, based on the compressed sensing technology, Gaussian random matrix is used to project the damage signal of Lamb wave for carbon fiber composite material into low-dimensional space, so as to obtain linear measurement value of sparse sampling and achieve the compressed sampling of signals. Finally, the reconstruction algorithm is used to realize the reconstruction of signals. Experiments show that the method of compressed sensing can compress and reconstruct Lamb wave signals, and it has good noise resistance. The absolute error of reconstruction is within [−0.3V, 0.3V], compressive sensing not only saves data storage space and improves data transmission speed, but also guarantees the accuracy of the reconstructed signal.

关键词： Resistance Optical fiber sensors Composite materials Array signal processing Reconstruction algorithms data communication Carbon

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A novel binary modified beluga whale optimization algorithm using ring crossover and probabilistic state mutation for enhanced bladder cancer diagnosis

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Informatics in Medicine Unlocked 2024年 50卷

作者： Gharaibeh, Hasan Alawad, Noor Aldeen Nasayreh, Ahmad Al Mamlook, Rabia Emhamed Makhadmeh, Sharif Naser Bashkami, Ayah Abualigah, Laith Ezugwu, Absalom E. Computer Science Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid 21163 Jordan Department of Business Administration Trine University IN United States Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libya Department of Information Technology King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan Department Medical Laboratory Sciences – Faculty of Science Al Balqa Applied University Salt 19117 Jordan Department of Cyber Security and Cloud Computing Faculty of Information Technology Applied Science Private University Amman 11937 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq 25113 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Artificial Intelligence Research Center (AIRC) College of Engineering and Information Technology Ajman University Ajman P.O. Box 346 United Arab Emirates

Bladder cancer (BC) remains a significant global health challenge, requiring the development of accurate predictive models for diagnosis. In this study, a new Binary Modified White Whale Optimization (B-MBWO) algorithm is proposed to address the BC problem. The proposed method utilizes circular transitivity optimization and the Probabilistic State Mutation Algorithm (PSMA) to enhance its optimization performance. The new method is called the BBWORCPS algorithm. High-dimensional and complex medical datasets pose challenges to the original optimization algorithms in addressing the BC problem, motivating the proposed modifications to the original Beluga Whale Optimization algorithm. These enhancements, including quantum-inspired mutation and circular crossing, aim to improve solution space exploration and enhance the algorithm's effectiveness in handling intricate feature spaces. Through comprehensive experiments on BC datasets, the superiority of the BBWORCPS algorithm in terms of feature selection accuracy and computational efficiency is demonstrated compared to existing optimization methods. The obtained findings suggest that BBWORCPS offers a promising approach for developing more precise and reliable predictive models for bladder cancer analysis. © 2024 The Author(s)

关键词： Binary modified beluga whale optimization Bladder cancer Feature selection Machine learning Predictive modeling

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Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics

arXiv

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arXiv 2022年

作者： Panja, Madhurima Chakraborty, Tanujit Kumar, Uttam Liu, Nan Spatial Computing Laboratory Center for Data Sciences International Institute of Information Technology Bangalore India Department of Science and Engineering Sorbonne University Abu Dhabi United Arab Emirates Sorbonne Center for Artificial Intelligence Paris Abu Dhabi United Arab Emirates

Infectious diseases remain among the top contributors to human illness and death worldwide, among which many diseases produce epidemic waves of infection. The lack of specific drugs and ready-to-use vaccines to prevent most of these epidemics worsens the situation. These force public health officials and policymakers to rely on early warning systems generated by accurate and reliable epidemic forecasters. Accurate forecasts of epidemics can assist stakeholders in tailoring countermeasures, such as vaccination campaigns, staff scheduling, and resource allocation, to the situation at hand, which could translate to reductions in the impact of a disease. Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics. We analyze various epidemic time series datasets using a maximal overlap discrete wavelet transform (MODWT) based autoregressive neural network and call it Ensemble Wavelet Neural Network (EWNet) model. MODWT techniques effectively characterize non-stationary behavior and seasonal dependencies in the epidemic time series and improve the nonlinear forecasting scheme of the autoregressive neural network in the proposed ensemble wavelet network framework. From a nonlinear time series viewpoint, we explore the asymptotic stationarity of the proposed EWNet model to show the asymptotic behavior of the associated Markov Chain. We also theoretically investigate the effect of learning stability and the choice of hidden neurons in the proposal. From a practical perspective, we compare our proposed EWNet framework with twenty-two statistical, machine learning, and deep learning models for fifteen real-world epidemic datasets with three test horizons using four key performance indicators. Experimental results show that the proposed EWNet is highly competitive compared to the state-of-the-art epidemic forecasting methods. © 2022

关键词： Diseases

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Adaptive Convolutional Neural Network for Image Super-resolution

arXiv

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arXiv 2024年

作者： Tian, Chunwei Zhang, Xuanyu Wang, Tao Zhang, Yongjun Zhu, Qi Lin, Chia-Wen The School of Software Northwestern Polytechnical University Xi’an710129 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China The Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang215400 China The School of Computer Science Northwestern Polytechnical University Xi’an710129 China The College of Computer Science and Technology Guizhou University Guiyang550025 China The School of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing210016 China The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Taiwan

Convolutional neural networks can automatically learn features via deep network architectures and given input samples. However, the robustness of obtained models may face challenges in varying scenes. Bigger differences in network architecture are beneficial to extract more diversified structural information to strengthen the robustness of an obtained superresolution model. In this paper, we proposed a adaptive convolutional neural network for image super-resolution (ADSRNet). To capture more information, ADSRNet is implemented by a heterogeneous parallel network. The upper network can enhance relation of context information, salient information relation of a kernel mapping and relations of shallow and deep layers to improve performance of image super-resolution. That can strengthen adaptability of an obtained super-resolution model for different scenes. The lower network utilizes a symmetric architecture to enhance relations of different layers to mine more structural information, which is complementary with a upper network for image super-resolution. The relevant experimental results show that the proposed ADSRNet is effective to deal with image resolving. Codes are obtained at https://***/hellloxiaotian/ADSRNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Comparative Analysis of Different Feature Extraction Techniques for Motor Imagery Based BCI System 1st

A Comparative Analysis of Different Feature Extraction Techn...

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1st International Conference on artificial intelligence and Computer Visions, AICV 2020

作者： Selim, Sahar Tantawi, Manal Shedeed, Howida Badr, Amr Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Faculty of Computer and Information Science Ain Shams University Cairo Egypt Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt

ISBN: (纸本)9783030442880

Brain Computer Interface (BCI) system based on Motor Imagery (MI) signals is one among the most prospering systems in the field of BCI. Motor imagination does not involve motor output from the human. This can be used to help motor disabled people to accomplish elementary tasks by themselves. One of the primary difficulties facing MI-based BCIs is extracting discriminative features from the EEG signal. This study seeks to minimize the number of features input to classifier by finding out the best feature extraction techniques that can achieve high system accuracy with minimum computation cost. To achieve this purpose, this study compares five different feature extraction techniques;Root Mean Square (RMS), Renyi entropy, Shannon entropy, Katz fractal dimension and Common Spatial Patterns (CSP), regarding accuracy and execution time. To ensure that these techniques are reliable;they are applied on three benchmark datasets;BCI Competition III datasets IVa and IIIa, and BCI Competition IV dataset IIa. The extracted features are examined with two classifiers;Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). Experimental results showed that CSP is the best feature extraction technique compared to other examined techniques, yet Renyi entropy has the least computation time which is a critical issue for an online system. © 2020, Springer Nature Switzerland AG.

关键词： Feature extraction

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