Tahir Abbas^1,*, Ali Haider Khan², Khadija Kanwal³, Ali Daud^4,*, 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 >

Romaissa Kebache¹, Abdelkader Laouid^1,*, Ahcene Bounceur², Mostefa Kara^1,3, 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 >

Mian Muhammad Danyal^1,2, Sarwar Shah Khan^3,4,*, 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 >

Brij B. Gupta^1,2,3,*, Akshat Gaurav⁴, Razaz Waheeb Attar⁵, Varsha Arya^6,7, 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 >

Deepak Upreti¹, Eunmok Yang², Hyunil Kim^3,*, Changho Seo^1,4

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 >

Jingjing Nie^1,*, 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 >

Umi Salma Basha¹, Shashi Kant Gupta², Wedad Alawad³, SeongKi Kim^4,*, Salil Bharany^5,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3397-3416, 2024, DOI:10.32604/cmc.2024.046265

Abstract A new era of data access and management has begun with the use of cloud computing in the healthcare industry. Despite the efficiency and scalability that the cloud provides, the security of private patient data is still a major concern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentiality and integrity of healthcare data in the cloud. The computational overhead of encryption technologies could lead to delays in data access and processing rates. To address these challenges, we introduced the Enhanced Parallel Multi-Key Encryption Algorithm (EPM-KEA), aiming to bolster… More >

Muhammad Armghan Latif¹, Zohaib Mushtaq², Saad Arif³, Sara Rehman⁴, Muhammad Farrukh Qureshi⁵, Nagwan Abdel Samee⁶, Maali Alabdulhafith^6,*, Yeong Hyeon Gu⁷, Mohammed A. Al-masni⁷

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4225-4241, 2024, DOI:10.32604/cmc.2024.047621

Abstract Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland. Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care. This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques. Sequential forward feature selection, sequential backward feature elimination, and bidirectional feature elimination are investigated in this study. In ensemble learning, random forest, adaptive boosting, and bagging classifiers are employed. The effectiveness of… More >

Rabia Abid¹, Muhammad Rizwan², Abdulatif Alabdulatif^3,*, Abdullah Alnajim⁴, Meznah Alamro⁵, Mourade Azrour⁶

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3413-3429, 2024, DOI:10.32604/cmc.2024.046880

Abstract Explainable Artificial Intelligence (XAI) has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning (ML) and Deep Learning (DL) based algorithms. In this paper, we chose e-healthcare systems for efficient decision-making and data classification, especially in data security, data handling, diagnostics, laboratories, and decision-making. Federated Machine Learning (FML) is a new and advanced technology that helps to maintain privacy for Personal Health Records (PHR) and handle a large amount of medical data effectively. In this context, XAI, along with FML, increases efficiency and improves the More >

Cheryl Dickson^1,2,4, Danielle Osborn¹, David Baker^1,4, Judith Fethney³, David S. Celermajer^1,4, Rachael Cordina^1,4,*

Congenital Heart Disease, Vol.19, No.1, pp. 49-63, 2024, DOI:10.32604/chd.2023.044874

Abstract Background: Much has been written about the loss to follow-up in the transition between pediatric and adult Congenital Heart Disease (CHD) care centers. Much less is understood about the loss to follow-up (LTF) after a successful transition. This is critical too, as patients lost to specialised care are more likely to experience morbidity and premature mortality. Aims: To understand the prevalence and reasons for loss to follow-up (LTF) at a large Australian Adult Congenital Heart Disease (ACHD) centre. Methods: Patients with moderate or highly complex CHD and gaps in care of >3 years (defined as LTF)… More >