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  • Open Access

    REVIEW

    IoMT-Based Healthcare Systems: A Review

    Tahir Abbas1,*, Ali Haider Khan2, Khadija Kanwal3, Ali Daud4,*, Muhammad Irfan5, Amal Bukhari6, Riad Alharbey6

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 871-895, 2024, DOI:10.32604/csse.2024.049026

    Abstract The integration of the Internet of Medical Things (IoMT) and the Internet of Things (IoT), which has revolutionized patient care through features like remote critical care and real-time therapy, is examined in this study in response to the changing healthcare landscape. Even with these improvements, security threats are associated with the increased connectivity of medical equipment, which calls for a thorough assessment. With a primary focus on addressing security and performance enhancement challenges, the research classifies current IoT communication devices, examines their applications in IoMT, and investigates important aspects of IoMT devices in healthcare. The More >

  • Open Access

    ARTICLE

    Reducing the Encrypted Data Size: Healthcare with IoT-Cloud Computing Applications

    Romaissa Kebache1, Abdelkader Laouid1,*, Ahcene Bounceur2, Mostefa Kara1,3, Konstantinos Karampidis4, Giorgos Papadourakis4, Mohammad Hammoudeh2

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 1055-1072, 2024, DOI:10.32604/csse.2024.048738

    Abstract Internet cloud services come at a price, especially when they provide top-tier security measures. The cost incurred by cloud utilization is directly proportional to the storage requirements. Companies are always looking to increase profits and reduce costs while preserving the security of their data by encrypting them. One of the offered solutions is to find an efficient encryption method that can store data in a much smaller space than traditional encryption techniques. This article introduces a novel encryption approach centered on consolidating information into a single ciphertext by implementing Multi-Key Embedded Encryption (MKEE). The effectiveness… More >

  • Open Access

    ARTICLE

    Enhancing Multi-Modality Medical Imaging: A Novel Approach with Laplacian Filter + Discrete Fourier Transform Pre-Processing and Stationary Wavelet Transform Fusion

    Mian Muhammad Danyal1,2, Sarwar Shah Khan3,4,*, Rahim Shah Khan5, Saifullah Jan2, Naeem ur Rahman6

    Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 35-53, 2024, DOI:10.32604/jimh.2024.051340

    Abstract Multi-modality medical images are essential in healthcare as they provide valuable insights for disease diagnosis and treatment. To harness the complementary data provided by various modalities, these images are amalgamated to create a single, more informative image. This fusion process enhances the overall quality and comprehensiveness of the medical imagery, aiding healthcare professionals in making accurate diagnoses and informed treatment decisions. In this study, we propose a new hybrid pre-processing approach, Laplacian Filter + Discrete Fourier Transform (LF+DFT), to enhance medical images before fusion. The LF+DFT approach highlights key details, captures small information, and sharpens… More >

  • Open Access

    ARTICLE

    LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes

    Brij B. Gupta1,2,3,*, Akshat Gaurav4, Razaz Waheeb Attar5, Varsha Arya6,7, Ahmed Alhomoud8, Kwok Tai Chui9

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2689-2706, 2024, DOI:10.32604/cmes.2024.050825

    Abstract This study introduces a long-short-term memory (LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes, focusing on the critical application of elderly fall detection. It balances the dataset using the Synthetic Minority Over-sampling Technique (SMOTE), effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks. The proposed LSTM model is trained on the enriched dataset, capturing the temporal dependencies essential for anomaly recognition. The model demonstrated a significant improvement in anomaly detection, with an accuracy of 84%. The results, detailed in the comprehensive classification and confusion More >

  • Open Access

    REVIEW

    A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

    Deepak Upreti1, Eunmok Yang2, Hyunil Kim3,*, Changho Seo1,4

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2239-2274, 2024, DOI:10.32604/cmes.2024.048932

    Abstract Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security. It involves constructing machine learning models using datasets spread across several data centers, including medical facilities, clinical research facilities, Internet of Things devices, and even mobile devices. The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information, reducing the risk of data loss, privacy breaches, or data exposure. The application of federated learning in the healthcare industry holds significant promise More >

  • Open Access

    ARTICLE

    The Association between Fear of COVID-19, Obsession with COVID-19, and Post Traumatic Stress Disorder in Korean Emergency Rescue Firefighters: A Cross-Sectional Study

    Yun-Jung Choi1, Heewon Song2,*

    International Journal of Mental Health Promotion, Vol.26, No.6, pp. 475-480, 2024, DOI:10.32604/ijmhp.2024.050824

    Abstract During the rapid spread of COVID-19, first responders are at risk of being exposed to COVID-19 due to their role in providing first aid and responding to an unspecified number of people. This uncertainty can have adverse mental health effects, such as increased anxiety and fear. This study aimed to investigate the degree of association between fear of COVID-19, obsession with COVID-19, and post-traumatic stress disorder (PTSD) in emergency rescue firefighters. The participants were 150 emergency rescue firefighters working in Region S, Korea. They filled out self-report questionnaires: The data obtained through the Fear of… More >

  • Open Access

    ARTICLE

    Citizens’ Mental Health Issues and Psychological Trauma Experience due to a Crowd-Crush Disaster in Korea

    Yun-Jung Choi1,#,*, Jae-Won Kwak2,#, Hae-In Namgung3

    International Journal of Mental Health Promotion, Vol.26, No.6, pp. 439-447, 2024, DOI:10.32604/ijmhp.2024.050458

    Abstract This study evaluated the state of anxiety, depression, post-traumatic stress disorder, general mental health, and mental well-being among citizens after a crowd-crush disaster in Korea. Individuals who experienced the crowd crush had significantly higher anxiety, depression, and post-traumatic stress disorder (PTSD) scores than those who did not (p < 0.001). Additionally, people who avoided the disaster area had significantly higher depression and PTSD scores than those who did not avoid the area (p < 0.001). Those who directly witnessed the Seoul Halloween crowd crush had a significant difference in PTSD levels in either group than those More >

  • Open Access

    ARTICLE

    Scarcity and Mental Health—Multiple Mediators of Sleep Quality and Life Satisfaction

    Na Liu1, Yan Zhang2, Junxiu Wang3,4,*

    International Journal of Mental Health Promotion, Vol.26, No.6, pp. 449-462, 2024, DOI:10.32604/ijmhp.2024.049334

    Abstract Background: In the current social environment, scarcity, as a universally present objective state, profoundly impacts individuals’ decision-making and health through the subjective feeling it induces, known as a “scarcity mindset.” Particularly, the feeling of scarcity related to money and sleep time is not only widespread but also directly linked to an individual’s mental health. Purpose: This study aims to delve into the relationship between the feeling of scarcity and mental health, with a specific focus on the relationship between the feeling of money scarcity or sleep time scarcity and mental health, as well as the… More >

  • Open Access

    ARTICLE

    Vector Dominance with Threshold Searchable Encryption (VDTSE) for the Internet of Things

    Jingjing Nie1,*, Zhenhua Chen2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4763-4779, 2024, DOI:10.32604/cmc.2024.051181

    Abstract The Internet of Medical Things (IoMT) is an application of the Internet of Things (IoT) in the medical field. It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems, which is essential in smart healthcare. However, Personal Health Records (PHRs) are normally kept in public cloud servers controlled by IoMT service providers, so privacy and security incidents may be frequent. Fortunately, Searchable Encryption (SE), which can be used to execute queries on encrypted data, can address the issue above. Nevertheless, most existing SE schemes cannot solve the vector dominance threshold… More >

  • Open Access

    REVIEW

    Does young feces make the elderly live better? Application of fecal microbiota transplantation in healthy aging

    YUANYUAN LIAO1,2,3, XINSI LI2,3, QIAN LI2,3, YIZHONG WANG4, XIUJUN TAN1,2,3, TING GONG2,3,5,*

    BIOCELL, Vol.48, No.6, pp. 873-887, 2024, DOI:10.32604/biocell.2024.050324

    Abstract As we are facing an aging society, anti-aging strategies have been pursued to reduce the negative impacts of aging and increase the health span of human beings. Gut microbiota has become a key factor in the anti-aging process. Modulation of gut microbiota by fecal microbiota transplantation (FMT) to prevent frailty and unhealthy aging has been a hot topic of research. This narrative review summarizes the benefits of FMT for health span and lifespan, brains, eyes, productive systems, bones, and others. The mechanisms of FMT in improving healthy aging are discussed. The increased beneficial bacteria and More >

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