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  • Open Access

    ARTICLE

    An Intelligent Adaptive Dynamic Algorithm for a Smart Traffic System

    Ahmed Alsheikhy1,*, Yahia Said1, Tawfeeq Shawly2

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 1109-1126, 2023, DOI:10.32604/csse.2023.035135

    Abstract Due to excessive car usage, pollution and traffic have increased. In urban cities in Saudi Arabia, such as Riyadh and Jeddah, drivers and air quality suffer from traffic congestion. Although the government has implemented numerous solutions to resolve this issue or reduce its effect on the environment and residents, it still exists and is getting worse. This paper proposes an intelligent, adaptive, practical, and feasible deep learning method for intelligent traffic control. It uses an Internet of Things (IoT) sensor, a camera, and a Convolutional Neural Network (CNN) tool to control traffic in real time. An image segmentation algorithm analyzes… More >

  • Open Access

    ARTICLE

    Multimodal Spatiotemporal Feature Map for Dynamic Gesture Recognition

    Xiaorui Zhang1,2,3,*, Xianglong Zeng1, Wei Sun3,4, Yongjun Ren1,2,3, Tong Xu5

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 671-686, 2023, DOI:10.32604/csse.2023.035119

    Abstract Gesture recognition technology enables machines to read human gestures and has significant application prospects in the fields of human-computer interaction and sign language translation. Existing researches usually use convolutional neural networks to extract features directly from raw gesture data for gesture recognition, but the networks are affected by much interference information in the input data and thus fit to some unimportant features. In this paper, we proposed a novel method for encoding spatio-temporal information, which can enhance the key features required for gesture recognition, such as shape, structure, contour, position and hand motion of gestures, thereby improving the accuracy of… More >

  • Open Access

    ARTICLE

    An Immutable Framework for Smart Healthcare Using Blockchain Technology

    Faneela1, Muazzam A. Khan1, Suliman A. Alsuhibany2,*, Walid El-Shafai3,4, Mujeeb Ur Rehman5, Jawad Ahmad6

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 165-179, 2023, DOI:10.32604/csse.2023.035066

    Abstract The advancements in sensing technologies, information processing, and communication schemes have revolutionized the healthcare sector. Electronic Healthcare Records (EHR) facilitate the patients, doctors, hospitals, and other stakeholders to maintain valuable data and medical records. The traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks. A single attempt of a successful Denial of Service (DoS) attack can compromise the complete healthcare system. This article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things (IoMT) to address the stated challenges. The proposed architecture is on the idea of a lightweight private blockchain-based network that… More >

  • Open Access

    ARTICLE

    Hybrid Watermarking and Encryption Techniques for Securing Medical Images

    Amel Ali Alhussan1,*, Hanaa A. Abdallah2, Sara Alsodairi2, Abdelhamied A. Ateya3

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 403-416, 2023, DOI:10.32604/csse.2023.035048

    Abstract Securing medical data while transmission on the network is required because it is sensitive and life-dependent data. Many methods are used for protection, such as Steganography, Digital Signature, Cryptography, and Watermarking. This paper introduces a novel robust algorithm that combines discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) digital image-watermarking algorithms. The host image is decomposed using a two-dimensional DWT (2D-DWT) to approximate low-frequency sub-bands in the embedding process. Then the sub-band low-high (LH) is decomposed using 2D-DWT to four new sub-bands. The resulting sub-band low-high (LH1) is decomposed using 2D-DWT to four new sub-bands.… More >

  • Open Access

    ARTICLE

    LuNet-LightGBM: An Effective Hybrid Approach for Lesion Segmentation and DR Grading

    Sesikala Bapatla1, J. Harikiran2,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 597-617, 2023, DOI:10.32604/csse.2023.034998

    Abstract Diabetes problems can lead to an eye disease called Diabetic Retinopathy (DR), which permanently damages the blood vessels in the retina. If not treated early, DR becomes a significant reason for blindness. To identify the DR and determine the stages, medical tests are very labor-intensive, expensive, and time-consuming. To address the issue, a hybrid deep and machine learning technique-based autonomous diagnostic system is provided in this paper. Our proposal is based on lesion segmentation of the fundus images based on the LuNet network. Then a Refined Attention Pyramid Network (RAPNet) is used for extracting global and local features. To increase… More >

  • Open Access

    ARTICLE

    Task Offloading Based on Vehicular Edge Computing for Autonomous Platooning

    Sanghyuck Nam1, Suhwan Kwak1, Jaehwan Lee2, Sangoh Park1,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 659-670, 2023, DOI:10.32604/csse.2023.034994

    Abstract Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted actively. The autonomous platooning task generally requires highly complex computations so it is difficult to process only with the vehicle’s processing units. To solve this problem, there are many studies on task offloading technique which transfers complex tasks to their neighboring vehicles or computation nodes. However, the existing task offloading techniques which mainly use learning-based algorithms are difficult to respond to the real-time changing road environment due to their complexity. They are also challenging to process computation tasks within 100 ms which… More >

  • Open Access

    ARTICLE

    A Novel Soft Clustering Method for Detection of Exudates

    Kittipol Wisaeng*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 1039-1058, 2023, DOI:10.32604/csse.2023.034901

    Abstract One of the earliest indications of diabetes consequence is Diabetic Retinopathy (DR), the main contributor to blindness worldwide. Recent studies have proposed that Exudates (EXs) are the hallmark of DR severity. The present study aims to accurately and automatically detect EXs that are difficult to detect in retinal images in the early stages. An improved Fusion of Histogram–Based Fuzzy C–Means Clustering (FHBFCM) by a New Weight Assignment Scheme (NWAS) and a set of four selected features from stages of pre-processing to evolve the detection method is proposed. The features of DR train the optimal parameter of FHBFCM for detecting EXs… More >

  • Open Access

    ARTICLE

    Biometric Verification System Using Hyperparameter Tuned Deep Learning Model

    Mohammad Yamin1, Saleh Bajaba2, Sarah B. Basahel3, E. Laxmi Lydia4,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 321-336, 2023, DOI:10.32604/csse.2023.034849

    Abstract Deep learning (DL) models have been useful in many computer vision, speech recognition, and natural language processing tasks in recent years. These models seem a natural fit to handle the rising number of biometric recognition problems, from cellphone authentication to airport security systems. DL approaches have recently been utilized to improve the efficiency of various biometric recognition systems. Iris recognition was considered the more reliable and accurate biometric detection method accessible. Iris recognition has been an active research region in the last few decades due to its extensive applications, from security in airports to homeland security border control. This article… More >

  • Open Access

    ARTICLE

    An Improved Encoder-Decoder CNN with Region-Based Filtering for Vibrant Colorization

    Mrityunjoy Gain1, Md Arifur Rahman1, Rameswar Debnath1, Mrim M. Alnfiai2, Abdullah Sheikh3, Mehedi Masud3, Anupam Kumar Bairagi1,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 1059-1077, 2023, DOI:10.32604/csse.2023.034809

    Abstract Colorization is the practice of adding appropriate chromatic values to monochrome photographs or videos. A real-valued luminance image can be mapped to a three-dimensional color image. However, it is a severely ill-defined problem and not has a single solution. In this paper, an encoder-decoder Convolutional Neural Network (CNN) model is used for colorizing gray images where the encoder is a Densely Connected Convolutional Network (DenseNet) and the decoder is a conventional CNN. The DenseNet extracts image features from gray images and the conventional CNN outputs a * b * color channels. Due to a large number of desaturated color components compared to saturated… More >

  • Open Access

    ARTICLE

    Image Recognition Based on Deep Learning with Thermal Camera Sensing

    Wen-Tsai Sung1, Chin-Hsuan Lin1, Sung-Jung Hsiao2,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 505-520, 2023, DOI:10.32604/csse.2023.034781

    Abstract As the COVID-19 epidemic spread across the globe, people around the world were advised or mandated to wear masks in public places to prevent its spreading further. In some cases, not wearing a mask could result in a fine. To monitor mask wearing, and to prevent the spread of future epidemics, this study proposes an image recognition system consisting of a camera, an infrared thermal array sensor, and a convolutional neural network trained in mask recognition. The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen. The proposed system reduces the inefficiency… More >

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