M. Adimoolam¹, K. Maithili², N. M. Balamurugan³, R. Rajkumar⁴, S. Leelavathy⁵, Raju Kannadasan⁶, Mohd Anul Haq^7,*, Ilyas Khan⁸, ElSayed M. Tag El Din⁹, Arfat Ahmad Khan¹⁰

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 33-55, 2024, DOI:10.32604/iasc.2024.039009

Abstract At present, the prediction of brain tumors is performed using Machine Learning (ML) and Deep Learning (DL) algorithms. Although various ML and DL algorithms are adapted to predict brain tumors to some range, some concerns still need enhancement, particularly accuracy, sensitivity, false positive and false negative, to improve the brain tumor prediction system symmetrically. Therefore, this work proposed an Extended Deep Learning Algorithm (EDLA) to measure performance parameters such as accuracy, sensitivity, and false positive and false negative rates. In addition, these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network (CNN) way further using… More >

Zhuo Chen^1,*, Ningning Wang², Wenbo Jin³, Dui Li¹

Energy Engineering, Vol.121, No.4, pp. 1007-1026, 2024, DOI:10.32604/ee.2023.045270

Abstract A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines. To ensure the safe operation of crude oil pipelines, an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines. Aiming at the shortcomings of the ENN prediction model, which easily falls into the local minimum value and weak generalization ability in the implementation process, an optimized ENN prediction model based on the IRSA is proposed. The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition… More > Graphic Abstract

Abdullah Alsaleh^1,2,*

Computer Systems Science and Engineering, Vol.48, No.2, pp. 431-449, 2024, DOI:10.32604/csse.2023.043107

Abstract With the increasing number of connected devices in the Internet of Things (IoT) era, the number of intrusions is also increasing. An intrusion detection system (IDS) is a secondary intelligent system for monitoring, detecting and alerting against malicious activity. IDS is important in developing advanced security models. This study reviews the importance of various techniques, tools, and methods used in IoT detection and/or prevention systems. Specifically, it focuses on machine learning (ML) and deep learning (DL) techniques for IDS. This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the Internet of Vehicles. To speed… More >

Anas Bilal¹, Azhar Imran², Talha Imtiaz Baig^3,4, Xiaowen Liu^1,*, Haixia Long¹, Abdulkareem Alzahrani⁵, Muhammad Shafiq⁶

Computer Systems Science and Engineering, Vol.48, No.2, pp. 511-528, 2024, DOI:10.32604/csse.2023.039672

Abstract Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which is a leading cause of visual impairment and blindness worldwide. However, previous automated VTDR detection methods have mainly relied on manual feature extraction and classification, leading to errors. This paper proposes a novel VTDR detection and classification model that combines different models through majority voting. Our proposed methodology involves preprocessing, data augmentation, feature extraction, and classification stages. We use a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model for feature extraction and selection and an improved SVM-RBF with a Decision Tree (DT) and K-Nearest Neighbor (KNN)… More >

Abdelrahman Maharek^1,2,*, Amr Abozeid^2,3, Rasha Orban¹, Kamal ElDahshan²

Computer Systems Science and Engineering, Vol.48, No.2, pp. 305-320, 2024, DOI:10.32604/csse.2024.039436

Abstract What causes object detection in video to be less accurate than it is in still images? Because some video frames have degraded in appearance from fast movement, out-of-focus camera shots, and changes in posture. These reasons have made video object detection (VID) a growing area of research in recent years. Video object detection can be used for various healthcare applications, such as detecting and tracking tumors in medical imaging, monitoring the movement of patients in hospitals and long-term care facilities, and analyzing videos of surgeries to improve technique and training. Additionally, it can be used in telemedicine to help diagnose… More >

Amr Ismail¹, Walid Hamdy^1,2, Aya M. Al-Zoghby³, Wael A. Awad³, Ahmed Ismail Ebada³, Yunyoung Nam⁴, Byeong-Gwon Kang^4,*, Mohamed Abouhawwash^5,6

Computer Systems Science and Engineering, Vol.48, No.2, pp. 273-285, 2024, DOI:10.32604/csse.2023.038192

Abstract Deep learning (DL) plays a critical role in processing and converting data into knowledge and decisions. DL technologies have been applied in a variety of applications, including image, video, and genome sequence analysis. In deep learning the most widely utilized architecture is Convolutional Neural Networks (CNN) are taught discriminatory traits in a supervised environment. In comparison to other classic neural networks, CNN makes use of a limited number of artificial neurons, therefore it is ideal for the recognition and processing of wheat gene sequences. Wheat is an essential crop of cereals for people around the world. Wheat Genotypes identification has… More >

Arpit Jain¹, Nageswara Rao Moparthi¹, A. Swathi², Yogesh Kumar Sharma¹, Nitin Mittal³, Ahmed Alhussen⁴, Zamil S. Alzamil^5,*, MohdAnul Haq⁵

Computer Systems Science and Engineering, Vol.48, No.2, pp. 341-362, 2024, DOI:10.32604/csse.2023.036973

Abstract Recently, the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the economy. World Health Organization (WHO) and many others advised controlling Corona Virus Disease in 2019. The limited treatment resources, medical resources, and unawareness of immunity is an essential horizon to unfold. Among all resources, wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) droplets. All countries made masks mandatory to prevent infection. For such enforcement, automatic and effective face detection systems are crucial. This study presents a face… More >

Kuosheng Jiang¹, Chengrui Han¹, Yasheng Chang^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3329-3352, 2024, DOI:10.32604/cmes.2024.046729

Abstract Angular contact ball bearings have been widely used in machine tool spindles, and the bearing preload plays an important role in the performance of the spindle. In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties, a roller bearing preload test method based on the improved D-S evidence theory multi-sensor fusion method was proposed. First, a novel controllable preload system is proposed and evaluated. Subsequently, multiple sensors are employed to collect data on the bearing parameters during preload application. Finally, a multisensor fusion algorithm is used to make predictions, and a… More >

Jinbo Yang¹, Hai Huang¹, Lailai Yin², Jiaxing Qu³, Wanjuan Xie^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3085-3099, 2024, DOI:10.32604/cmes.2023.045417

Abstract Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources, including clinical symptoms, physical signs, biochemical test results, imaging findings, pathological examination data, and even genetic data. When applying machine learning modeling to predict and diagnose multi-stage diseases, several challenges need to be addressed. Firstly, the model needs to handle multimodal data, as the data used by doctors for diagnosis includes image data, natural language data, and structured data. Secondly, privacy of patients’ data needs to be protected, as these data contain the most sensitive and private information. Lastly, considering the practicality of the model, the computational requirements should… More >

Wangchen Yan^1,*, Jinbao Yang¹, Xin Luo²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2507-2524, 2024, DOI:10.32604/cmes.2023.044709

Abstract Transfer learning could reduce the time and resources required by the training of new models and be therefore important for generalized applications of the trained machine learning algorithms. In this study, a transfer learning-enhanced convolutional neural network (CNN) was proposed to identify the gross weight and the axle weight of moving vehicles on the bridge. The proposed transfer learning-enhanced CNN model was expected to weigh different bridges based on a small amount of training datasets and provide high identification accuracy. First of all, a CNN algorithm for bridge weigh-in-motion (B-WIM) technology was proposed to identify the axle weight and the… More >