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  • Open Access


    A Low-Power 12-Bit SAR ADC for Analog Convolutional Kernel of Mixed-Signal CNN Accelerator

    Jungyeon Lee1, Malik Summair Asghar1,2, HyungWon Kim1,*

    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 >

  • Open Access


    Lightweight Multi-scale Convolutional Neural Network for Rice Leaf Disease Recognition

    Chang Zhang1, Ruiwen Ni1, Ye Mu1,2,3,4, Yu Sun1,2,3,4,*, Thobela Louis Tyasi5

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 983-994, 2023, DOI:10.32604/cmc.2023.027269

    Abstract In the field of agricultural information, the identification and prediction of rice leaf disease have always been the focus of research, and deep learning (DL) technology is currently a hot research topic in the field of pattern recognition. The research and development of high-efficiency, high-quality and low-cost automatic identification methods for rice diseases that can replace humans is an important means of dealing with the current situation from a technical perspective. This paper mainly focuses on the problem of huge parameters of the Convolutional Neural Network (CNN) model and proposes a recognition model that combines a multi-scale convolution module with… More >

  • Open Access


    SF-CNN: Deep Text Classification and Retrieval for Text Documents

    R. Sarasu1,*, K. K. Thyagharajan2, N. R. Shanker3

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1799-1813, 2023, DOI:10.32604/iasc.2023.027429

    Abstract Researchers and scientists need rapid access to text documents such as research papers, source code and dissertations. Many research documents are available on the Internet and need more time to retrieve exact documents based on keywords. An efficient classification algorithm for retrieving documents based on keyword words is required. The traditional algorithm performs less because it never considers words’ polysemy and the relationship between bag-of-words in keywords. To solve the above problem, Semantic Featured Convolution Neural Networks (SF-CNN) is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words for retrieving correct… More >

  • Open Access


    An Optimized and Hybrid Framework for Image Processing Based Network Intrusion Detection System

    Murtaza Ahmed Siddiqi, Wooguil Pak*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3921-3949, 2022, DOI:10.32604/cmc.2022.029541

    Abstract The network infrastructure has evolved rapidly due to the ever-increasing volume of users and data. The massive number of online devices and users has forced the network to transform and facilitate the operational necessities of consumers. Among these necessities, network security is of prime significance. Network intrusion detection systems (NIDS) are among the most suitable approaches to detect anomalies and assaults on a network. However, keeping up with the network security requirements is quite challenging due to the constant mutation in attack patterns by the intruders. This paper presents an effective and prevalent framework for NIDS by merging image processing… More >

  • Open Access


    Development of Mobile App to Support the Mobility of Visually Impaired People

    R. Meenakshi1, R. Ponnusamy1,*, Saleh Alghamdi2, Osama Ibrahim Khalaf3, Youseef Alotaibi4

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3473-3495, 2022, DOI:10.32604/cmc.2022.028540

    Abstract In 2017, it was estimated that the number of persons of all ages visually affected would be two hundred and eighty-five million, of which thirty-nine million are blind. There are several innovative technical solutions available to facilitate the movement of these people. The next big challenge for technical people is to give cost-effective solutions. One of the challenges for people with visual impairments is navigating safely, recognizing obstacles, and moving freely between locations in unfamiliar environments. A new mobile application solution is developed, and the application can be installed in android mobile. The application will visualize the environment with portable… More >

  • Open Access


    An Enhanced Deep Learning Method for Skin Cancer Detection and Classification

    Mohamed W. Abo El-Soud1,2,*, Tarek Gaber2,3, Mohamed Tahoun2, Abdullah Alourani1

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1109-1123, 2022, DOI:10.32604/cmc.2022.028561

    Abstract The prevalence of melanoma skin cancer has increased in recent decades. The greatest risk from melanoma is its ability to broadly spread throughout the body by means of lymphatic vessels and veins. Thus, the early diagnosis of melanoma is a key factor in improving the prognosis of the disease. Deep learning makes it possible to design and develop intelligent systems that can be used in detecting and classifying skin lesions from visible-light images. Such systems can provide early and accurate diagnoses of melanoma and other types of skin diseases. This paper proposes a new method which can be used for… More >

  • Open Access


    Deep Learning Convolutional Neural Network for ECG Signal Classification Aggregated Using IoT

    S. Karthiga*, A. M. Abirami

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 851-866, 2022, DOI:10.32604/csse.2022.021935

    Abstract Much attention has been given to the Internet of Things (IoT) by citizens, industries, governments, and universities for applications like smart buildings, environmental monitoring, health care and so on. With IoT, network connectivity is facilitated between smart devices from anyplace and anytime. IoT-based health monitoring systems are gaining popularity and acceptance for continuous monitoring and detect health abnormalities from the data collected. Electrocardiographic (ECG) signals are widely used for heart diseases detection. A novel method has been proposed in this work for ECG monitoring using IoT techniques. In this work, a two-stage approach is employed. In the first stage, a… More >

  • Open Access


    Improving Date Fruit Classification Using CycleGAN-Generated Dataset

    Dina M. Ibrahim1,2,*, Nada M. Elshennawy2

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 331-348, 2022, DOI:10.32604/cmes.2022.016419

    Abstract Dates are an important part of human nutrition. Dates are high in essential nutrients and provide a number of health benefits. Date fruits are also known to protect against a number of diseases, including cancer and heart disease. Date fruits have several sizes, colors, tastes, and values. There are a lot of challenges facing the date producers. One of the most significant challenges is the classification and sorting of dates. But there is no public dataset for date fruits, which is a major limitation in order to improve the performance of convolutional neural networks (CNN) models and avoid the overfitting… More >

  • Open Access


    Deep Neural Networks for Gun Detection in Public Surveillance

    Erssa Arif1,*, Syed Khuram Shahzad2, Rehman Mustafa1, Muhammad Arfan Jaffar3, Muhammad Waseem Iqbal4

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 909-922, 2022, DOI:10.32604/iasc.2022.021061

    Abstract The conventional surveillance and control system of Closed-Circuit Television (CCTV) cameras require human resource supervision. Almost all the criminal activities take place using weapons mostly handheld gun, revolver, or pistol. Automatic gun detection is a vital requirement now-a-days. The use of real-time object detection system for the improvement of surveillance is a promising application of Convolutional Neural Networks (CNN). We are concerned about the real-time detection of weapons for the surveillance cameras, so we focused on the implementation and comparison of faster approaches such as Region (R-CNN) and Region Fully Convolutional Networks (R-FCN) with feature extractor Visual Geometry Group (VGG)… More >

  • Open Access


    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 manual traditional examination to automated… More >

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