Aiping Xu^1,2, Xuan Zou³, Chao Wang^2,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1043-1059, 2023, DOI:10.32604/csse.2023.039645

Abstract To protect the environment, the discharged sewage’s quality must meet the state’s discharge standards. There are many water quality indicators, and the pH (Potential of Hydrogen) value is one of them. The natural water’s pH value is 6.0–8.5. The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater’s pH value will exceed the standard. This paper aims to study the deep learning prediction model of wastewater’s pH. Firstly, the research uses the random forest method to select the data features and then, based on the sliding window, convert the data set into… More >

Zhen Zhen¹, Jian Gao^1,2,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 955-973, 2023, DOI:10.32604/csse.2023.039525

Abstract Telecommunication fraud has run rampant recently worldwide. However, previous studies depend highly on expert knowledge-based feature engineering to extract behavior information, which cannot adapt to the fast-changing modes of fraudulent subscribers. Therefore, we propose a new taxonomy that needs no hand-designed features but directly takes raw Call Detail Records (CDR) data as input for the classifier. Concretely, we proposed a fraud detection method using a convolutional neural network (CNN) by taking CDR data as images and applying computer vision techniques like image augmentation. Comprehensive experiments on the real-world dataset from the 2020 Digital Sichuan Innovation Competition show that our proposed… More >

Zhihan Liu¹, Xiang Li¹, Siqi Liu², Wei Li^1,*, Xiangxu Meng¹, Jing Jia³

Computer Systems Science and Engineering, Vol.47, No.1, pp. 937-954, 2023, DOI:10.32604/csse.2023.039417

Abstract The COVID-19 virus is usually spread by small droplets when talking, coughing and sneezing, so maintaining physical distance between people is necessary to slow the spread of the virus. The World Health Organization (WHO) recommends maintaining a social distance of at least six feet. In this paper, we developed a real-time pedestrian social distance risk alert system for COVID-19, which monitors the distance between people in real-time via video streaming and provides risk alerts to the person in charge, thus avoiding the problem of too close social distance between pedestrians in public places. We design a lightweight convolutional neural network… More >

K. Ravishankar^*, C. Jothikumar

Computer Systems Science and Engineering, Vol.47, No.1, pp. 613-635, 2023, DOI:10.32604/csse.2023.038343

Abstract Since COVID-19 infections are increasing all over the world, there is a need for developing solutions for its early and accurate diagnosis is a must. Detection methods for COVID-19 include screening methods like Chest X-rays and Computed Tomography (CT) scans. More work must be done on preprocessing the datasets, such as eliminating the diaphragm portions, enhancing the image intensity, and minimizing noise. In addition to the detection of COVID-19, the severity of the infection needs to be estimated. The HSDC model is proposed to solve these problems, which will detect and classify the severity of COVID-19 from X-ray and CT-scan… More >

Rui Wen^1,*, Jie Zhou², Zhongliang Shen¹, Xiaorui Zhang^2,3,4, Sunil Kumar Jha⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 187-204, 2023, DOI:10.32604/csse.2023.037889

Abstract Convolution Neural Networks (CNN) can quickly diagnose COVID-19 patients by analyzing computed tomography (CT) images of the lung, thereby effectively preventing the spread of COVID-19. However, the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population. Which reduces the model’s classification sensitivity, resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people. To solve the problem, this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image… More >

Kai Yang^*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 317-332, 2023, DOI:10.32604/csse.2023.037266

Abstract With the rapid development of sports, the number of sports images has increased dramatically. Intelligent and automatic processing and analysis of moving images are significant, which can not only facilitate users to quickly search and access moving images but also facilitate staff to store and manage moving image data and contribute to the intellectual development of the sports industry. In this paper, a method of table tennis identification and positioning based on a convolutional neural network is proposed, which solves the problem that the identification and positioning method based on color features and contour features is not adaptable in various… More >

Nadeem Jabbar Chaudhry^1,*, M. Bilal Khan², M. Javaid Iqbal¹, Siddiqui Muhammad Yasir³

Journal on Artificial Intelligence, Vol.4, No.4, pp. 245-259, 2022, DOI:10.32604/jai.2022.038875

Abstract Recently, outstanding identification rates in image classification tasks were achieved by convolutional neural networks (CNNs). to use such skills, selective CNNs trained on a dataset of well-known images of metal surface defects captured with an RGB camera. Defects must be detected early to take timely corrective action due to production concerns. For image classification up till now, a model-based method has been utilized, which indicated the predicted reflection characteristics of surface defects in comparison to flaw-free surfaces. The problem of detecting steel surface defects has grown in importance as a result of the vast range of steel applications in end-product… More >

S. Eliza Femi Sherley^1,*, J. M. Karthikeyan¹, N. Bharath Raj¹, R. Prabakaran², A. Abinaya¹, S. V. V. Lakshmi³

Journal on Artificial Intelligence, Vol.4, No.4, pp. 215-227, 2022, DOI:10.32604/jai.2022.034931

Abstract Semantic change detection is extension of change detection task in which it is not only used to identify the changed regions but also to analyze the land area semantic (labels/categories) details before and after the timelines are analyzed. Periodical land change analysis is used for many real time applications for valuation purposes. Majority of the research works are focused on Convolutional Neural Networks (CNN) which tries to analyze changes alone. Semantic information of changes appears to be missing, there by absence of communication between the different semantic timelines and changes detected over the region happens. To overcome this limitation, a… More >

Jianbo Lu^1,2,*, Ruxin Shi², Jin Tong³, Wenqi Cheng⁴, Xiaoya Ma^1,3, Xiaobin Liu²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 525-544, 2023, DOI:10.32604/iasc.2023.038287

Abstract In the deep learning approach for identifying plant diseases, the high complexity of the network model, the large number of parameters, and great computational effort make it challenging to deploy the model on terminal devices with limited computational resources. In this study, a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is proposed. In the proposed model, the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions; the efficient channel attention module is added into the ShuffleNetV2 model network structure… More >

Wenbo Li, Qi Wang^*, Shang Gao

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 921-938, 2023, DOI:10.32604/iasc.2023.038257

Abstract Infrared target detection models are more required than ever before to be deployed on embedded platforms, which requires models with less memory consumption and better real-time performance while considering accuracy. To address the above challenges, we propose a modified You Only Look Once (YOLO) algorithm PF-YOLOv4-Tiny. The algorithm incorporates spatial pyramidal pooling (SPP) and squeeze-and-excitation (SE) visual attention modules to enhance the target localization capability. The PANet-based-feature pyramid networks (P-FPN) are proposed to transfer semantic information and location information simultaneously to ameliorate detection accuracy. To lighten the network, the standard convolutions other than the backbone network are replaced with depthwise… More >