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  • Open Access

    ARTICLE

    Deep Learning-Based Classification of Rotten Fruits and Identification of Shelf Life

    S. Sofana Reka1, Ankita Bagelikar2, Prakash Venugopal2,*, V. Ravi2, Harimurugan Devarajan3

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 781-794, 2024, DOI:10.32604/cmc.2023.043369

    Abstract The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality, flavor and nutritional value. The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers. The impact of rotten fruits can foster harmful bacteria, molds and other microorganisms that can cause food poisoning and other illnesses to the consumers. The overall purpose of the study is to classify rotten fruits, which can affect the taste, texture, and appearance of other fresh fruits, thereby reducing their shelf life. The agriculture and food industries… More >

  • Open Access

    ARTICLE

    Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

    Ch Avais Hanif1, Muhammad Ali Mughal1, Muhammad Attique Khan2,3,*, Nouf Abdullah Almujally4, Taerang Kim5, Jae-Hyuk Cha5

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 357-374, 2024, DOI:10.32604/cmc.2023.043061

    Abstract The demand for a non-contact biometric approach for candidate identification has grown over the past ten years. Based on the most important biometric application, human gait analysis is a significant research topic in computer vision. Researchers have paid a lot of attention to gait recognition, specifically the identification of people based on their walking patterns, due to its potential to correctly identify people far away. Gait recognition systems have been used in a variety of applications, including security, medical examinations, identity management, and access control. These systems require a complex combination of technical, operational, and definitional considerations. The employment of… More >

  • Open Access

    ARTICLE

    Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks

    Aysha Naseer1, Nouf Abdullah Almujally2, Saud S. Alotaibi3, Abdulwahab Alazeb4, Jeongmin Park5,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1381-1398, 2024, DOI:10.32604/cmc.2023.042963

    Abstract Object segmentation and recognition is an imperative area of computer vision and machine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their features. The proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks (ANNs). The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label them based on their characteristics. Then, two distinct kinds of features are obtained from the segmented images to help identify the objects… More >

  • Open Access

    ARTICLE

    Deep Learning Approach for Hand Gesture Recognition: Applications in Deaf Communication and Healthcare

    Khursheed Aurangzeb1, Khalid Javeed2, Musaed Alhussein1, Imad Rida3, Syed Irtaza Haider1, Anubha Parashar4,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 127-144, 2024, DOI:10.32604/cmc.2023.042886

    Abstract Hand gestures have been used as a significant mode of communication since the advent of human civilization. By facilitating human-computer interaction (HCI), hand gesture recognition (HGRoc) technology is crucial for seamless and error-free HCI. HGRoc technology is pivotal in healthcare and communication for the deaf community. Despite significant advancements in computer vision-based gesture recognition for language understanding, two considerable challenges persist in this field: (a) limited and common gestures are considered, (b) processing multiple channels of information across a network takes huge computational time during discriminative feature extraction. Therefore, a novel hand vision-based convolutional neural network (CNN) model named (HVCNNM)… More >

  • Open Access

    ARTICLE

    Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

    Sangeeta Yadav1, Preeti Gulia1,*, Nasib Singh Gill1,*, Piyush Kumar Shukla2, Arfat Ahmad Khan3, Sultan Alharby4, Ahmed Alhussen4, Mohd Anul Haq5

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1253-1274, 2024, DOI:10.32604/cmc.2023.042869

    Abstract Inpatient falls from beds in hospitals are a common problem. Such falls may result in severe injuries. This problem can be addressed by continuous monitoring of patients using cameras. Recent advancements in deep learning-based video analytics have made this task of fall detection more effective and efficient. Along with fall detection, monitoring of different activities of the patients is also of significant concern to assess the improvement in their health. High computation-intensive models are required to monitor every action of the patient precisely. This requirement limits the applicability of such networks. Hence, to keep the model lightweight, the already designed… More >

  • Open Access

    ARTICLE

    Image Splicing Forgery Detection Using Feature-Based of Sonine Functions and Deep Features

    Ala’a R. Al-Shamasneh1, Rabha W. Ibrahim2,3,4,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 795-810, 2024, DOI:10.32604/cmc.2023.042755

    Abstract The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic. One of the most popular methods for manipulating digital images is image splicing, which involves copying a specific area from one image and pasting it into another. Attempts were made to mitigate the effects of image splicing, which continues to be a significant research challenge. This study proposes a new splicing detection model, combining Sonine functions-derived convex-based features and deep features. Two stages make up the proposed method. The first step entails feature extraction, then classification using the “support vector… More >

  • Open Access

    ARTICLE

    Novel Rifle Number Recognition Based on Improved YOLO in Military Environment

    Hyun Kwon1,*, Sanghyun Lee2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 249-263, 2024, DOI:10.32604/cmc.2023.042466

    Abstract Deep neural networks perform well in image recognition, object recognition, pattern analysis, and speech recognition. In military applications, deep neural networks can detect equipment and recognize objects. In military equipment, it is necessary to detect and recognize rifle management, which is an important piece of equipment, using deep neural networks. There have been no previous studies on the detection of real rifle numbers using real rifle image datasets. In this study, we propose a method for detecting and recognizing rifle numbers when rifle image data are insufficient. The proposed method was designed to improve the recognition rate of a specific… More >

  • Open Access

    ARTICLE

    An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

    Syeda Wajiha Zahra1, Muhammad Nadeem2, Ali Arshad3,*, Saman Riaz3, Muhammad Abu Bakr4, Ashit Kumar Dutta5, Zaid Alzaid6, Badr Almutairi7, Sultan Almotairi8

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 589-616, 2024, DOI:10.32604/cmc.2023.042386

    Abstract Various organizations store data online rather than on physical servers. As the number of user’s data stored in cloud servers increases, the attack rate to access data from cloud servers also increases. Different researchers worked on different algorithms to protect cloud data from replay attacks. None of the papers used a technique that simultaneously detects a full-message and partial-message replay attack. This study presents the development of a TKN (Text, Key and Name) cryptographic algorithm aimed at protecting data from replay attacks. The program employs distinct ways to encrypt plain text [P], a user-defined Key [K], and a Secret Code… More >

  • Open Access

    REVIEW

    Fuzzing: Progress, Challenges, and Perspectives

    Zhenhua Yu1, Zhengqi Liu1, Xuya Cong1,*, Xiaobo Li2, Li Yin3

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1-29, 2024, DOI:10.32604/cmc.2023.042361

    Abstract As one of the most effective techniques for finding software vulnerabilities, fuzzing has become a hot topic in software security. It feeds potentially syntactically or semantically malformed test data to a target program to mine vulnerabilities and crash the system. In recent years, considerable efforts have been dedicated by researchers and practitioners towards improving fuzzing, so there are more and more methods and forms, which make it difficult to have a comprehensive understanding of the technique. This paper conducts a thorough survey of fuzzing, focusing on its general process, classification, common application scenarios, and some state-of-the-art techniques that have been… More >

  • Open Access

    ARTICLE

    Multimodal Sentiment Analysis Based on a Cross-Modal Multihead Attention Mechanism

    Lujuan Deng, Boyi Liu*, Zuhe Li

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1157-1170, 2024, DOI:10.32604/cmc.2023.042150

    Abstract Multimodal sentiment analysis aims to understand people’s emotions and opinions from diverse data. Concatenating or multiplying various modalities is a traditional multi-modal sentiment analysis fusion method. This fusion method does not utilize the correlation information between modalities. To solve this problem, this paper proposes a model based on a multi-head attention mechanism. First, after preprocessing the original data. Then, the feature representation is converted into a sequence of word vectors and positional encoding is introduced to better understand the semantic and sequential information in the input sequence. Next, the input coding sequence is fed into the transformer model for further… More >

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