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  • Open Access


    Modeling of Optimal Deep Learning Based Flood Forecasting Model Using Twitter Data

    G. Indra1,*, N. Duraipandian2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1455-1470, 2023, DOI:10.32604/iasc.2023.027703

    Abstract A flood is a significant damaging natural calamity that causes loss of life and property. Earlier work on the construction of flood prediction models intended to reduce risks, suggest policies, reduce mortality, and limit property damage caused by floods. The massive amount of data generated by social media platforms such as Twitter opens the door to flood analysis. Because of the real-time nature of Twitter data, some government agencies and authorities have used it to track natural catastrophe events in order to build a more rapid rescue strategy. However, due to the shorter duration of… More >

  • Open Access


    Intelligent Deep Learning Enabled Wild Forest Fire Detection System

    Ahmed S. Almasoud*

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1485-1498, 2023, DOI:10.32604/csse.2023.025190

    Abstract The latest advancements in computer vision and deep learning (DL) techniques pave the way to design novel tools for the detection and monitoring of forest fires. In this view, this paper presents an intelligent wild forest fire detection and alarming system using deep learning (IWFFDA-DL) model. The proposed IWFFDA-DL technique aims to identify forest fires at earlier stages through integrated sensors. The proposed IWFFDA-DL system includes an Integrated sensor system (ISS) combining an array of sensors that acts as the major input source that helps to forecast the fire. Then, the attention based convolution neural More >

  • Open Access


    Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services

    E. Dhiravidachelvi1, M.Suresh Kumar2, L. D. Vijay Anand3, D. Pritima4, Seifedine Kadry5, Byeong-Gwon Kang6, Yunyoung Nam7,*

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 961-977, 2023, DOI:10.32604/csse.2023.024612

    Abstract Human Activity Recognition (HAR) has been made simple in recent years, thanks to recent advancements made in Artificial Intelligence (AI) techniques. These techniques are applied in several areas like security, surveillance, healthcare, human-robot interaction, and entertainment. Since wearable sensor-based HAR system includes in-built sensors, human activities can be categorized based on sensor values. Further, it can also be employed in other applications such as gait diagnosis, observation of children/adult’s cognitive nature, stroke-patient hospital direction, Epilepsy and Parkinson’s disease examination, etc. Recently-developed Artificial Intelligence (AI) techniques, especially Deep Learning (DL) models can be deployed to accomplish… More >

  • Open Access


    Modeling Metaheuristic Optimization with Deep Learning Software Bug Prediction Model

    M. Sangeetha1,*, S. Malathi2

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1587-1601, 2022, DOI:10.32604/iasc.2022.025192

    Abstract Software testing is an effective means of verifying software stability and trustworthiness. It is essential in the software development process and needs a huge quantity of resources such as labor, money, and time. Automated software testing can be used to save manual work, shorten testing times, and improve testing performance. Recently, Software Bug Prediction (SBP) models have been developed to improve the software quality assurance (SQA) process through the prediction of bug parts. Advanced deep learning (DL) models can be used to classify faults in software parts. Because hyperparameters have a significant impact on the… More >

  • Open Access


    Deep Learning Enabled Computer Aided Diagnosis Model for Lung Cancer using Biomedical CT Images

    Mohammad Alamgeer1, Hanan Abdullah Mengash2, Radwa Marzouk2, Mohamed K Nour3, Anwer Mustafa Hilal4,*, Abdelwahed Motwakel4, Abu Sarwar Zamani4, Mohammed Rizwanullah4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1437-1448, 2022, DOI:10.32604/cmc.2022.027896

    Abstract Early detection of lung cancer can help for improving the survival rate of the patients. Biomedical imaging tools such as computed tomography (CT) image was utilized to the proper identification and positioning of lung cancer. The recently developed deep learning (DL) models can be employed for the effectual identification and classification of diseases. This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image, named DLCADLC-BCT technique. The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT images. The proposed DLCADLC-BCT technique initially uses gray level co-occurrence More >

  • Open Access


    Grid Search for Predicting Coronary Heart Disease by Tuning Hyper-Parameters

    S. Prabu1,*, B. Thiyaneswaran2, M. Sujatha3, C. Nalini4, Sujatha Rajkumar5

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 737-749, 2022, DOI:10.32604/csse.2022.022739

    Abstract Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years. Coronary cardiovascular (CHD) is a kind of heart and blood vascular disease. Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders. Implementing Grid Search Optimization (GSO) machine training models is therefore a useful way to forecast the sickness as soon as possible. The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate. Three models with a cross-validation approach do the… More >

  • Open Access


    Optimized Artificial Neural Network Techniques to Improve Cybersecurity of Higher Education Institution

    Abdullah Saad AL-Malaise AL-Ghamdi1, Mahmoud Ragab2,3,4,*, Maha Farouk S. Sabir1, Ahmed Elhassanein5,6, Ashraf A. Gouda4

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3385-3399, 2022, DOI:10.32604/cmc.2022.026477

    Abstract Education acts as an important part of economic growth and improvement in human welfare. The educational sectors have transformed a lot in recent days, and Information and Communication Technology (ICT) is an effective part of the education field. Almost every action in university and college, right from the process from counselling to admissions and fee deposits has been automated. Attendance records, quiz, evaluation, mark, and grade submissions involved the utilization of the ICT. Therefore, security is essential to accomplish cybersecurity in higher security institutions (HEIs). In this view, this study develops an Automated Outlier Detection… More >

  • Open Access


    Modeling of Hyperparameter Tuned Hybrid CNN and LSTM for Prediction Model

    J. Faritha Banu1,*, S. B. Rajeshwari2, Jagadish S. Kallimani2, S. Vasanthi3, Ahmed Mateen Buttar4, M. Sangeetha5, Sanjay Bhargava6

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1393-1405, 2022, DOI:10.32604/iasc.2022.024176

    Abstract The stock market is an important domain in which the investors are focused to, therefore accurate prediction of stock market trends remains a hot research area among business-people and researchers. Because of the non-stationary features of the stock market, the stock price prediction is considered a challenging task and is affected by several factors. Anticipating stock market trends is a difficult endeavor that requires a lot of attention, because correctly predicting stock prices can lead to significant rewards if the right judgments are made. Due to non-stationary, noisy, and chaotic data, stock market prediction is… More >

  • Open Access


    Attention-Based Deep Learning Model for Early Detection of Parkinson's Disease

    Mohd Sadiq1, Mohd Tauheed Khan2,*, Sarfaraz Masood3

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5183-5200, 2022, DOI:10.32604/cmc.2022.020531

    Abstract Parkinson's disease (PD), classified under the category of a neurological syndrome, affects the brain of a person which leads to the motor and non-motor symptoms. Among motor symptoms, one of the major disabling symptom is Freezing of Gait (FoG) that affects the daily standard of living of PD patients. Available treatments target to improve the symptoms of PD. Detection of PD at the early stages is an arduous task due to being indistinguishable from a healthy individual. This work proposed a novel attention-based model for the detection of FoG events and PD, and measuring the… More >

  • Open Access


    Automatic Human Detection Using Reinforced Faster-RCNN for Electricity Conservation System

    S. Ushasukhanya*, M. Karthikeyan

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1261-1275, 2022, DOI:10.32604/iasc.2022.022654

    Abstract Electricity conservation systems are designed to conserve electricity to manage the bridge between the high raising demand and the production. Such systems have been so far using sensors to detect the necessity which adds an additional cost to the setup. Closed-circuit Television (CCTV) has been installed in almost everywhere around us especially in commercial places. Interpretation of these CCTV images is being carried out for various reasons to elicit the information from it. Hence a framework for electricity conservation that enables the electricity supply only when required, using existing resources would be a cost effective… More >

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