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  • Open Access


    Moth Flame Optimization Based FCNN for Prediction of Bugs in Software

    C. Anjali*, Julia Punitha Malar Dhas, J. Amar Pratap Singh

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1241-1256, 2023, DOI:10.32604/iasc.2023.029678

    Abstract The software engineering technique makes it possible to create high-quality software. One of the most significant qualities of good software is that it is devoid of bugs. One of the most time-consuming and costly software procedures is finding and fixing bugs. Although it is impossible to eradicate all bugs, it is feasible to reduce the number of bugs and their negative effects. To broaden the scope of bug prediction techniques and increase software quality, numerous causes of software problems must be identified, and successful bug prediction models must be implemented. This study employs a hybrid of Faster Convolution Neural Network… More >

  • Open Access


    Healthcare Monitoring Using Ensemble Classifiers in Fog Computing Framework

    P. M. Arunkumar1, Mehedi Masud2, Sultan Aljahdali2, Mohamed Abouhawwash3,4,*

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2265-2280, 2023, DOI:10.32604/csse.2023.032571

    Abstract Nowadays, the cloud environment faces numerous issues like synchronizing information before the switch over the data migration. The requirement for a centralized internet of things (IoT)-based system has been restricted to some extent. Due to low scalability on security considerations, the cloud seems uninteresting. Since healthcare networks demand computer operations on large amounts of data, the sensitivity of device latency evolved among health networks is a challenging issue. In comparison to cloud domains, the new paradigms of fog computing give fresh alternatives by bringing resources closer to users by providing low latency and energy-efficient data processing solutions. Previous fog computing… More >

  • Open Access


    Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy

    S. Sudha*, A. Srinivasan, T. Gayathri Devi

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1985-2000, 2023, DOI:10.32604/csse.2023.030960

    Abstract The substantial vision loss due to Diabetic Retinopathy (DR) mainly damages the blood vessels of the retina. These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage, if this problem doesn’t exhibit initially, that leads to permanent blindness. So, this type of disorder can be only screened and identified through the processing of fundus images. The different stages in DR are Micro aneurysms (Ma), Hemorrhages (HE), and Exudates, and the stages in lesion show the chance of DR. For the advancement of early detection of DR in the eye we have… More >

  • Open Access


    Detection of Omicron Caused Pneumonia from Radiology Images Using Convolution Neural Network (CNN)

    Arfat Ahmad Khan1, Malik Muhammad Ali Shahid2, Rab Nawaz Bashir2, Salman Iqbal2, Arshad Shehzad Ahmad Shahid3, Javeria Maqbool4, Chitapong Wechtaisong5,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3743-3761, 2023, DOI:10.32604/cmc.2023.033924

    Abstract COVID-19 disease caused by the SARS-CoV-2 virus has created social and economic disruption across the world. The ability of the COVID-19 virus to quickly mutate and transfer has created serious concerns across the world. It is essential to detect COVID-19 infection caused by different variants to take preventive measures accordingly. The existing method of detection of infections caused by COVID-19 and its variants is costly and time-consuming. The impacts of the COVID-19 pandemic in developing countries are very drastic due to the unavailability of medical facilities and infrastructure to handle the pandemic. Pneumonia is the major symptom of COVID-19 infection.… More >

  • Open Access


    A Hyperparameter Optimization for Galaxy Classification

    Fatih Ahmet Şenel*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4587-4600, 2023, DOI:10.32604/cmc.2023.033155

    Abstract In this study, the morphological galaxy classification process was carried out with a hybrid approach. Since the Galaxy classification process may contain detailed information about the universe’s formation, it remains the current research topic. Researchers divided more than 100 billion galaxies into ten different classes. It is not always possible to understand which class the galaxy types belong. However, Artificial Intelligence (AI) can be used for successful classification. There are studies on the automatic classification of galaxies into a small number of classes. As the number of classes increases, the success of the used methods decreases. Based on the literature,… More >

  • Open Access


    Grey Wolf Optimizer Based Deep Learning for Pancreatic Nodule Detection

    T. Thanya1,*, S. Wilfred Franklin2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 97-112, 2023, DOI:10.32604/iasc.2023.029675

    Abstract At an early point, the diagnosis of pancreatic cancer is mediocre, since the radiologist is skill deficient. Serious threats have been posed due to the above reasons, hence became mandatory for the need of skilled technicians. However, it also became a time-consuming process. Hence the need for automated diagnosis became mandatory. In order to identify the tumor accurately, this research proposes a novel Convolution Neural Network (CNN) based superior image classification technique. The proposed deep learning classification strategy has a precision of 97.7%, allowing for more effective usage of the automatically executed feature extraction technique to diagnose cancer cells. Comparative… 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


    Rotation, Translation and Scale Invariant Sign Word Recognition Using Deep Learning

    Abu Saleh Musa Miah1, Jungpil Shin1,*, Md. Al Mehedi Hasan1, Md Abdur Rahim2, Yuichi Okuyama1

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2521-2536, 2023, DOI:10.32604/csse.2023.029336

    Abstract Communication between people with disabilities and people who do not understand sign language is a growing social need and can be a tedious task. One of the main functions of sign language is to communicate with each other through hand gestures. Recognition of hand gestures has become an important challenge for the recognition of sign language. There are many existing models that can produce a good accuracy, but if the model test with rotated or translated images, they may face some difficulties to make good performance accuracy. To resolve these challenges of hand gesture recognition, we proposed a Rotation, Translation… More >

  • Open Access


    Early Skin Disease Identification Using eep Neural Network

    Vinay Gautam1, Naresh Kumar Trivedi1, Abhineet Anand1, Rajeev Tiwari2,*, Atef Zaguia3, Deepika Koundal4, Sachin Jain5

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2259-2275, 2023, DOI:10.32604/csse.2023.026358

    Abstract Skin lesions detection and classification is a prominent issue and difficult even for extremely skilled dermatologists and pathologists. Skin disease is the most common disorder triggered by fungus, viruses, bacteria, allergies, etc. Skin diseases are most dangerous and may be the cause of serious damage. Therefore, it requires to diagnose it at an earlier stage, but the diagnosis therapy itself is complex and needs advanced laser and photonic therapy. This advance therapy involves financial burden and some other ill effects. Therefore, it must use artificial intelligence techniques to detect and diagnose it accurately at an earlier stage. Several techniques have… More >

  • Open Access


    Identification and Acknowledgment of Programmed Traffic Sign Utilizing Profound Convolutional Neural Organization

    P. Vigneshwaran1,*, N. Prasath1, M. Islabudeen2, A. Arun1, A. K. Sampath2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1527-1543, 2023, DOI:10.32604/iasc.2023.028444

    Abstract Traffic signs are basic security workplaces making the rounds, which expects a huge part in coordinating busy time gridlock direct, ensuring the prosperity of the road and dealing with the smooth segment of vehicles and individuals by walking, etc. As a segment of the clever transportation structure, the acknowledgment of traffic signs is basic for the driving assistance system, traffic sign upkeep, self-administering driving, and various spaces. There are different assessments turns out achieved for traffic sign acknowledgment in the world. However, most of the works are only for explicit arrangements of traffic signs, for example, beyond what many would… More >

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