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  • Open Access

    ARTICLE

    Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model

    Mohamed Ibrahim Waly1, Mohamed Yacin Sikkandar1, Mohamed Abdelkader Aboamer1, Seifedine Kadry2, Orawit Thinnukool3,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3295-3309, 2022, DOI:10.32604/cmc.2022.020713

    Abstract Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases. An important kind of biomedical image is Pap smear image that is widely employed for cervical cancer diagnosis. Cervical cancer is a vital reason for increased women’s mortality rate. Proper screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical cancer. Computer-aided systems for cancerous cell detection need to be developed using deep learning (DL) approaches. This study introduces an intelligent deep convolutional neural network for cervical cancer detection and… More >

  • Open Access

    ARTICLE

    CNN Based Driver Drowsiness Detection System Using Emotion Analysis

    H. Varun Chand*, J. Karthikeyan

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 717-728, 2022, DOI:10.32604/iasc.2022.020008

    Abstract

    The drowsiness of the driver and rash driving are the major causes of road accidents, which result in loss of valuable life, and deteriorate the safety in the road traffic. Reliable and precise driver drowsiness systems are required to prevent road accidents and to improve road traffic safety. Various driver drowsiness detection systems have been designed with different technologies which have an affinity towards the unique parameter of detecting the drowsiness of the driver. This paper proposes a novel model of multi-level distribution of detecting the driver drowsiness using the Convolution Neural Networks (CNN) followed

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  • Open Access

    ARTICLE

    Defect Detection in Printed Circuit Boards with Pre-Trained Feature Extraction Methodology with Convolution Neural Networks

    Mohammed A. Alghassab*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 637-652, 2022, DOI:10.32604/cmc.2022.019527

    Abstract Printed Circuit Boards (PCBs) are very important for proper functioning of any electronic device. PCBs are installed in almost all the electronic device and their functionality is dependent on the perfection of PCBs. If PCBs do not function properly then the whole electric machine might fail. So, keeping this in mind researchers are working in this field to develop error free PCBs. Initially these PCBs were examined by the human beings manually, but the human error did not give good results as sometime defected PCBs were categorized as non-defective. So, researchers and experts transformed this… More >

  • Open Access

    ARTICLE

    Plant Disease Classification Using Deep Bilinear CNN

    D. Srinivasa Rao1, Ramesh Babu Ch2, V. Sravan Kiran1, N. Rajasekhar3,*, Kalyanapu Srinivas4, P. Shilhora Akshay1, G. Sai Mohan1, B. Lalith Bharadwaj1

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 161-176, 2022, DOI:10.32604/iasc.2022.017706

    Abstract

    Plant diseases have become a major threat in farming and provision of food. Various plant diseases have affected the natural growth of the plants and the infected plants are the leading factors for loss of crop production. The manual detection and identification of the plant diseases require a careful and observative examination through expertise. To overcome manual testing procedures an automated identification and detection can be implied which provides faster, scalable and precisive solutions. In this research, the contributions of our work are threefold. Firstly, a bi-linear convolution neural network (Bi-CNNs) for plant leaf disease

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  • Open Access

    ARTICLE

    Few-Shot Learning for Discovering Anomalous Behaviors in Edge Networks

    Merna Gamal1, Hala M. Abbas2, Nour Moustafa3,*, Elena Sitnikova3, Rowayda A. Sadek1

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1823-1837, 2021, DOI:10.32604/cmc.2021.012877

    Abstract Intrusion Detection Systems (IDSs) have a great interest these days to discover complex attack events and protect the critical infrastructures of the Internet of Things (IoT) networks. Existing IDSs based on shallow and deep network architectures demand high computational resources and high volumes of data to establish an adaptive detection engine that discovers new families of attacks from the edge of IoT networks. However, attackers exploit network gateways at the edge using new attacking scenarios (i.e., zero-day attacks), such as ransomware and Distributed Denial of Service (DDoS) attacks. This paper proposes new IDS based on… More >

  • Open Access

    ARTICLE

    Breast Cancer Classification Using Deep Convolution Neural Network with Transfer Learning

    Hanan A. Hosni Mahmoud*, Amal H. Alharbi, Doaa S. Khafga

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 803-814, 2021, DOI:10.32604/iasc.2021.018607

    Abstract In this paper, we aim to apply deep learning convolution neural network (Deep-CNN) technology to classify breast masses in mammograms. We develop a Deep-CNN combined with multi-feature extraction and transfer learning to detect breast cancer. The Deep-CNN is utilized to extract features from mammograms. A support vector machine (SVM) is then trained on the Deep-CNN features to classify normal, benign, and cancer cases. The scoring features from the Deep-CNN are coupled with texture features and used as inputs to the final classifier. Two texture features are included: texture features of spatial dependency and gradient-based histograms.… More >

  • Open Access

    ARTICLE

    Intelligent Multiclass Skin Cancer Detection Using Convolution Neural Networks

    Reham Alabduljabbar*, Hala Alshamlan

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 831-847, 2021, DOI:10.32604/cmc.2021.018402

    Abstract The worldwide mortality rate due to cancer is second only to cardiovascular diseases. The discovery of image processing, latest artificial intelligence techniques, and upcoming algorithms can be used to effectively diagnose and prognose cancer faster and reduce the mortality rate. Efficiently applying these latest techniques has increased the survival chances during recent years. The research community is making significant continuous progress in developing automated tools to assist dermatologists in decision making. The datasets used for the experimentation and analysis are ISBI 2016, ISBI 2017, and HAM 10000. In this work pertained models are used to… More >

  • Open Access

    ARTICLE

    Non-contact Real-time Monitoring of Driver’s Physiological Parameters under Ambient Light Condition

    Zhengzheng Li1, Jiancheng Zou2,*, Peizhou Yan1, Don Hong3

    Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 811-822, 2021, DOI:10.32604/iasc.2021.016516

    Abstract Real-time and effective monitoring of a driver’s physiological parameters and psychological states can provide early warnings and help avoid traffic accidents. In this paper, we propose a non-contact real-time monitoring algorithm for physiological parameters of drivers under ambient light conditions. First, video sequences of the driver’s head are obtained by an ordinary USB camera and the AdaBoost algorithm is used to locate the driver’s facial region. Second, a face expression recognition algorithm based on an improved convolutional neural network (CNN) is proposed to recognize the driver’s facial expression. The forehead region is divided into three… More >

  • Open Access

    ARTICLE

    An Enhanced Convolutional Neural Network for COVID-19 Detection

    Sameer I. Ali Al-Janabi1, Belal Al-Khateeb2,*, Maha Mahmood2, Begonya Garcia-Zapirain3

    Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 293-303, 2021, DOI:10.32604/iasc.2021.014419

    Abstract The recent novel coronavirus (COVID-19, as the World Health Organization has called it) has proven to be a source of risk for global public health. The virus, which causes an acute respiratory disease in persons, spreads rapidly and is now threatening more than 150 countries around the world. One of the essential procedures that patients with COVID-19 need is an accurate and rapid screening process. In this research, utilizing the features of deep learning methods, we present a method for detecting COVID-19 and a screening model that uses pulmonary computed tomography images to differentiate COVID-19 More >

  • Open Access

    ARTICLE

    Detecting Driver Distraction Using Deep-Learning Approach

    Khalid A. AlShalfan1, Mohammed Zakariah2,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 689-704, 2021, DOI:10.32604/cmc.2021.015989

    Abstract Currently, distracted driving is among the most important causes of traffic accidents. Consequently, intelligent vehicle driving systems have become increasingly important. Recently, interest in driver-assistance systems that detect driver actions and help them drive safely has increased. In these studies, although some distinct data types, such as the physical conditions of the driver, audio and visual features, and vehicle information, are used, the primary data source is images of the driver that include the face, arms, and hands taken with a camera inside the car. In this study, an architecture based on a convolution neural More >

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