Walaa H. Elashmawi^1,3, Ahmad Akram², Mohammed Yasser², Menna Hisham², Manar Mohammed², Noha Ihab², Ahmed Ali^4,5,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3637-3656, 2023, DOI:10.32604/iasc.2023.035605 - 15 March 2023

Abstract Parking space is usually very limited in major cities, especially Cairo, leading to traffic congestion, air pollution, and driver frustration. Existing car parking systems tend to tackle parking issues in a non-digitized manner. These systems require the drivers to search for an empty parking space with no guarantee of finding any wasting time, resources, and causing unnecessary congestion. To address these issues, this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of parking availability. User authentication More >

J. Prabakaran^1,*, P. Selvaraj²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2141-2157, 2023, DOI:10.32604/csse.2023.034210 - 09 February 2023

Abstract In healthcare systems, the Internet of Things (IoT) innovation and development approached new ways to evaluate patient data. A cloud-based platform tends to process data generated by IoT medical devices instead of high storage, and computational hardware. In this paper, an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography (CT) images of patients with pneumonia, Covid-19, tuberculosis (TB), and cancer. Firstly, the CT images are captured and transmitted to the fog node through IoT devices. In the fog node, the image gets modified into… More >

Murat Dener^*, Sercan Gulburun

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1235-1251, 2023, DOI:10.32604/cmc.2023.036357 - 06 February 2023

Abstract One of the most common types of threats to the digital world is malicious software. It is of great importance to detect and prevent existing and new malware before it damages information assets. Machine learning approaches are used effectively for this purpose. In this study, we present a model in which supervised and unsupervised learning algorithms are used together. Clustering is used to enhance the prediction performance of the supervised classifiers. The aim of the proposed model is to make predictions in the shortest possible time with high accuracy and f1 score. In the first… More >

Mehwish Shaikh¹, Isma Farah Siddiqui¹, Qasim Arain¹, Jahwan Koo^2,*, Mukhtiar Ali Unar³, Nawab Muhammad Faseeh Qureshi^4,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 287-302, 2023, DOI:10.32604/csse.2023.035311 - 20 January 2023

Abstract Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful; thus, catching it early is crucial. Medical physicians’ time is limited in outdoor situations due to many patients; therefore, automated systems can be a rescue. The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’ experience. Therefore, radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays. In medical classifications, deep convolution neural networks are commonly used. This research aims to use deep pre-trained transfer learning models to… More >

Ahmed Fathalla¹, Zakaria Alameer², Mohamed Abbas³, Ahmed Ali^4,5,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 929-950, 2023, DOI:10.32604/csse.2023.035255 - 20 January 2023

Abstract The oil industries are an important part of a country’s economy. The crude oil’s price is influenced by a wide range of variables. Therefore, how accurately can countries predict its behavior and what predictors to employ are two main questions. In this view, we propose utilizing deep learning and ensemble learning techniques to boost crude oil’s price forecasting performance. The suggested method is based on a deep learning snapshot ensemble method of the Transformer model. To examine the superiority of the proposed model, this paper compares the proposed deep learning ensemble model against different machine More >

Nga Nguyen Thi Thanh, Quang H. Nguyen^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 491-504, 2023, DOI:10.32604/csse.2023.032107 - 20 January 2023

Abstract Nowadays, web systems and servers are constantly at great risk from cyberattacks. This paper proposes a novel approach to detecting abnormal network traffic using a bidirectional long short-term memory (LSTM) network in combination with the ensemble learning technique. First, the binary classification module was used to detect the current abnormal flow. Then, the abnormal flows were fed into the multilayer classification module to identify the specific type of flow. In this research, a deep learning bidirectional LSTM model, in combination with the convolutional neural network and attention technique, was deployed to identify a specific attack. More >

Yunlong Ma¹, Junwei Niu^2,*, Bo Xu³, Xingtao Song², Wei Huang², Guoqiang Sun²

Energy Engineering, Vol.120, No.3, pp. 681-700, 2023, DOI:10.32604/ee.2023.024719 - 03 January 2023

Abstract In the power distribution system, the missing or incorrect file of users-transformer relationship (UTR) in low-voltage station area (LVSA) will affect the lean management of the LVSA, and the operation and maintenance of the distribution network. To effectively improve the lean management of LVSA, the paper proposes an identification method for the UTR based on Local Selective Combination in Parallel Outlier Ensembles algorithm (LSCP). Firstly, the voltage data is reconstructed based on the information entropy to highlight the differences in between. Then, the LSCP algorithm combines four base outlier detection algorithms, namely Isolation Forest (I-Forest),… More >

Rukiye Karakis^1,2,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4649-4666, 2023, DOI:10.32604/cmc.2023.035881 - 28 December 2022

Abstract Medical image steganography aims to increase data security by concealing patient-personal information as well as diagnostic and therapeutic data in the spatial or frequency domain of radiological images. On the other hand, the discipline of image steganalysis generally provides a classification based on whether an image has hidden data or not. Inspired by previous studies on image steganalysis, this study proposes a deep ensemble learning model for medical image steganalysis to detect malicious hidden data in medical images and develop medical image steganography methods aimed at securing personal information. With this purpose in mind, a… More >

Umair Muneer Butt^1,2,*, Hadiqa Aman Ullah², Sukumar Letchmunan¹, Iqra Tariq², Fadratul Hafinaz Hassan¹, Tieng Wei Koh³

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5017-5033, 2023, DOI:10.32604/cmc.2023.035512 - 28 December 2022

Abstract Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences. Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions. This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications. It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network. Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification… More >

Qaiser Abbas¹, Turki Alghamdi¹, Yazed Alsaawy¹, Tahir Alyas^2,*, Ali Alzahrani¹, Khawar Iqbal Malik³, Saira Bibi⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4261-4276, 2023, DOI:10.32604/cmc.2023.034482 - 31 October 2022

Abstract The emergence of deep fake videos in recent years has made image falsification a real danger. A person’s face and emotions are deep-faked in a video or speech and are substituted with a different face or voice employing deep learning to analyze speech or emotional content. Because of how clever these videos are frequently, Manipulation is challenging to spot. Social media are the most frequent and dangerous targets since they are weak outlets that are open to extortion or slander a human. In earlier times, it was not so easy to alter the videos, which… More >