Muhammad Hameed Siddiqi^1,*, Asfandyar Khan², Muhammad Bilal Khan², Abdullah Khan², Madallah Alruwaili¹, Saad Alanazi¹

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2219-2239, 2023, DOI:10.32604/csse.2023.040212

Abstract The use of the internet is increasing all over the world on a daily basis in the last two decades. The increase in the internet causes many sexual crimes, such as sexual misuse, domestic violence, and child pornography. Various research has been done for pornographic image detection and classification. Most of the used models used machine learning techniques and deep learning models which show less accuracy, while the deep learning model ware used for classification and detection performed better as compared to machine learning. Therefore, this research evaluates the performance analysis of intelligent neural-based deep learning models which are based… More >

Fawad Ali Shah¹, Habib Akbar¹, Abid Ali^2,3, Parveen Amna⁴, Maha Aljohani⁵, Eman A. Aldhahri⁶, Harun Jamil^7,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1385-1413, 2023, DOI:10.32604/csse.2023.036144

Abstract The detection of rice leaf disease is significant because, as an agricultural and rice exporter country, Pakistan needs to advance in production and lower the risk of diseases. In this rapid globalization era, information technology has increased. A sensing system is mandatory to detect rice diseases using Artificial Intelligence (AI). It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases. Deep Neural Network (DNN) is a novel technique that will help detect disease present on a rice leave because DNN is also considered… More >

Song Yu^*, Aiping Luo, Xiang Wang

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1877-1893, 2023, DOI:10.32604/iasc.2023.039132

Abstract Railway passenger flow forecasting can help to develop sensible railway schedules, make full use of railway resources, and meet the travel demand of passengers. The structure of passenger flow in railway networks and the spatiotemporal relationship of passenger flow among stations are two distinctive features of railway passenger flow. Most of the previous studies used only a single feature for prediction and lacked correlations, resulting in suboptimal performance. To address the above-mentioned problem, we proposed the railway passenger flow prediction model called Flow-Similarity Attention Graph Convolutional Network (F-SAGCN). First, we constructed the passenger flow relations graph (RG) based on the… More >

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 >

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 >

R. D. Dhaniya¹, K. M. Umamaheswari^2,*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1129-1143, 2023, DOI:10.32604/iasc.2023.035905

Abstract Current revelations in medical imaging have seen a slew of computer-aided diagnostic (CAD) tools for radiologists developed. Brain tumor classification is essential for radiologists to fully support and better interpret magnetic resonance imaging (MRI). In this work, we reported on new observations based on binary brain tumor categorization using HYBRID CNN-LSTM. Initially, the collected image is pre-processed and augmented using the following steps such as rotation, cropping, zooming, CLAHE (Contrast Limited Adaptive Histogram Equalization), and Random Rotation with panoramic stitching (RRPS). Then, a method called particle swarm optimization (PSO) is used to segment tumor regions in an MR image. After… More >

Yalong Yang^1,2,3, Wenjing Xu^1,2,3, Yinfeng Zhu⁴, Liangliang Su^1,2,3,*, Gongquan Zhang^1,2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 761-773, 2023, DOI:10.32604/cmes.2023.027010

Abstract As a current popular method, intelligent detection of cracks is of great significance to road safety, so deep learning has gradually attracted attention in the field of crack image detection. The nonlinear structure, low contrast and discontinuity of cracks bring great challenges to existing crack detection methods based on deep learning. Therefore, an end-to-end deep convolutional neural network (AttentionCrack) is proposed for automatic crack detection to overcome the inaccuracy of boundary location between crack and non-crack pixels. The AttentionCrack network is built on U-Net based encoder-decoder architecture, and an attention mechanism is incorporated into the multi-scale convolutional feature to enhance… More >

Amarendra Reddy Panyala^1,2, M. Baskar^3,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3883-3899, 2023, DOI:10.32604/csse.2023.037050

Abstract The deep learning models are identified as having a significant impact on various problems. The same can be adapted to the problem of brain tumor classification. However, several deep learning models are presented earlier, but they need better classification accuracy. An efficient Multi-Feature Approximation Based Convolution Neural Network (CNN) model (MFA-CNN) is proposed to handle this issue. The method reads the input 3D Magnetic Resonance Imaging (MRI) images and applies Gabor filters at multiple levels. The noise-removed image has been equalized for its quality by using histogram equalization. Further, the features like white mass, grey mass, texture, and shape are… More >

Yang Zhou, Chunjiao Fu, Yuting Zhai, Jian Li, Ziqi Jin, Yanlei Xu^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4501-4517, 2023, DOI:10.32604/cmc.2023.038446

Abstract Accurate identification of rice diseases is crucial for controlling diseases and improving rice yield. To improve the classification accuracy of rice diseases, this paper proposed a classification and identification method based on an improved ShuffleNet V2 (GE-ShuffleNet) model. Firstly, the Ghost module is used to replace the convolution in the two basic unit modules of ShuffleNet V2, and the unimportant convolution is deleted from the two basic unit modules of ShuffleNet V2. The Hardswish activation function is applied to replace the ReLU activation function to improve the identification accuracy of the model. Secondly, an effective channel attention (ECA) module is… More >

Jungyeon Lee¹, Malik Summair Asghar^1,2, HyungWon Kim^1,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4357-4375, 2023, DOI:10.32604/cmc.2023.031372

Abstract As deep learning techniques such as Convolutional Neural Networks (CNNs) are widely adopted, the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip (SoC). Although conventional CNN accelerators can reduce the computational time of learning and inference tasks, they tend to occupy large chip areas due to many multiply-and-accumulate (MAC) operators when implemented in complex digital circuits, incurring excessive power consumption. To overcome these drawbacks, this work implements an analog convolutional filter consisting of an analog multiply-and-accumulate arithmetic circuit along with an analog-to-digital converter (ADC). This paper introduces the architecture of an analog convolutional… More >