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  • Open Access

    ARTICLE

    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

    ARTICLE

    The Laplacian Energy of Hesitancy Fuzzy Graphs in Decision-Making Problems

    N. Rajagopal Reddy1, Mohammad Zubair Khan2, S. Sharief Basha3, Abdulrahman Alahmadi2, Ahmed H. Alahmadi2, Chiranji Lal Chowdhary4,*

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2637-2653, 2023, DOI:10.32604/csse.2023.029255

    Abstract Decision-making (DM) is a process in which several persons concurrently engage, examine the problems, evaluate potential alternatives, and select an appropriate option to the problem. Technique for determining order preference by similarity to the ideal solution (TOPSIS) is an established DM process. The objective of this report happens to broaden the approach of TOPSIS to solve the DM issues designed with Hesitancy fuzzy data, in which evaluation evidence given by the experts on possible solutions is presents as Hesitancy fuzzy decision matrices, each of which is defined by Hesitancy fuzzy numbers. Findings: we represent analytical results, such as designing a… More >

  • Open Access

    ARTICLE

    SA-MSVM: Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter

    C. P. Thamil Selvi1,*, R. PushpaLakshmi2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2439-2456, 2023, DOI:10.32604/csse.2023.029254

    Abstract One of the drastically growing and emerging research areas used in most information technology industries is Bigdata analytics. Bigdata is created from social websites like Facebook, WhatsApp, Twitter, etc. Opinions about products, persons, initiatives, political issues, research achievements, and entertainment are discussed on social websites. The unique data analytics method cannot be applied to various social websites since the data formats are different. Several approaches, techniques, and tools have been used for big data analytics, opinion mining, or sentiment analysis, but the accuracy is yet to be improved. The proposed work is motivated to do sentiment analysis on Twitter data… More >

  • Open Access

    ARTICLE

    Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

    K. Selvasheela1,*, A. M. Abirami2, Abdul Khader Askarunisa3

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2537-2552, 2023, DOI:10.32604/csse.2023.029148

    Abstract Nowadays, commercial transactions and customer reviews are part of human life and various business applications. The technologies create a great impact on online user reviews and activities, affecting the business process. Customer reviews and ratings are more helpful to the new customer to purchase the product, but the fake reviews completely affect the business. The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information. Therefore, in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity. Here, Amazon Product Kaggle dataset information is utilized for investigating the customer review.… More >

  • Open Access

    ARTICLE

    Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

    B. Murugeshwari1,*, S. Rajalakshmi1, K. Sudharson2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2293-2307, 2023, DOI:10.32604/csse.2023.029074

    Abstract Imagine numerous clients, each with personal data; individual inputs are severely corrupt, and a server only concerns the collective, statistically essential facets of this data. In several data mining methods, privacy has become highly critical. As a result, various privacy-preserving data analysis technologies have emerged. Hence, we use the randomization process to reconstruct composite data attributes accurately. Also, we use privacy measures to estimate how much deception is required to guarantee privacy. There are several viable privacy protections; however, determining which one is the best is still a work in progress. This paper discusses the difficulty of measuring privacy while… More >

  • Open Access

    ARTICLE

    Tracking Pedestrians Under Occlusion in Parking Space

    Zhengshu Zhou1,*, Shunya Yamada2, Yousuke Watanabe2, Hiroaki Takada1,2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2109-2127, 2023, DOI:10.32604/csse.2023.029005

    Abstract Many traffic accidents occur in parking lots. One of the serious safety risks is vehicle-pedestrian conflict. Moreover, with the increasing development of automatic driving and parking technology, parking safety has received significant attention from vehicle safety analysts. However, pedestrian protection in parking lots still faces many challenges. For example, the physical structure of a parking lot may be complex, and dead corners would occur when the vehicle density is high. These lead to pedestrians’ sudden appearance in the vehicle’s path from an unexpected position, resulting in collision accidents in the parking lot. We advocate that besides vehicular sensing data, high-precision… More >

  • Open Access

    ARTICLE

    Optimal Deep Belief Network Enabled Cybersecurity Phishing Email Classification

    Ashit Kumar Dutta1,*, T. Meyyappan2, Basit Qureshi3, Majed Alsanea4, Anas Waleed Abulfaraj5, Manal M. Al Faraj1, Abdul Rahaman Wahab Sait6

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2701-2713, 2023, DOI:10.32604/csse.2023.028984

    Abstract Recently, developments of Internet and cloud technologies have resulted in a considerable rise in utilization of online media for day to day lives. It results in illegal access to users’ private data and compromises it. Phishing is a popular attack which tricked the user into accessing malicious data and gaining the data. Proper identification of phishing emails can be treated as an essential process in the domain of cybersecurity. This article focuses on the design of biogeography based optimization with deep learning for Phishing Email detection and classification (BBODL-PEDC) model. The major intention of the BBODL-PEDC model is to distinguish… More >

  • Open Access

    ARTICLE

    An Enhanced Graphical Authentication Scheme Using Multiple-Image Steganography

    Khalil Hamdi Ateyeh Al-Shqeerat*

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2095-2107, 2023, DOI:10.32604/csse.2023.028975

    Abstract Most remote systems require user authentication to access resources. Text-based passwords are still widely used as a standard method of user authentication. Although conventional text-based passwords are rather hard to remember, users often write their passwords down in order to compromise security. One of the most complex challenges users may face is posting sensitive data on external data centers that are accessible to others and do not be controlled directly by users. Graphical user authentication methods have recently been proposed to verify the user identity. However, the fundamental limitation of a graphical password is that it must have a colorful… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Model for Defect Detection and Localization on Photovoltaic Panels

    S. Prabhakaran1,*, R. Annie Uthra1, J. Preetharoselyn2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2683-2700, 2023, DOI:10.32604/csse.2023.028898

    Abstract The Problem of Photovoltaic (PV) defects detection and classification has been well studied. Several techniques exist in identifying the defects and localizing them in PV panels that use various features, but suffer to achieve higher performance. An efficient Real-Time Multi Variant Deep learning Model (RMVDM) is presented in this article to handle this issue. The method considers different defects like a spotlight, crack, dust, and micro-cracks to detect the defects as well as localizes the defects. The image data set given has been preprocessed by applying the Region-Based Histogram Approximation (RHA) algorithm. The preprocessed images are applied with Gray Scale… More >

  • Open Access

    ARTICLE

    Cooperative Relay Networks Based on the OAM Technique for 5G Applications

    Mohammad Alkhawatrah, Ahmad Alamayreh, Nidal Qasem*

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 1911-1919, 2023, DOI:10.32604/csse.2023.028614

    Abstract Orbital Angular Momentum (OAM) is an intrinsic property of electromagnetic waves. Great research has been witnessed in the last decades aiming at exploiting the OAM wave property in different areas in radio and optics. One promising area of particular interest is to enhance the efficiency of the available communications spectrum. However, adopting OAM-based solutions is not priceless as these suffer from wave divergence especially when the OAM order is high. This shall limit the practical communications distance, especially in the radio regime. In this paper, we propose a cooperative OAM relaying system consisting of a source, relay, and destination. Relays… More >

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