The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach. The proposed approach effectively detects the intrusion within the IoT network. Initially, the privacy-preserving technology was implemented using a blockchain-based methodology. Security of patient health records (PHR) is the most critical aspect of cryptography over the Internet due to its value and importance, preferably in the Internet of Medical Things (IoMT). Search keywords access mechanism is one of the typical approaches used to access PHR from a database, but it is susceptible to various security vulnerabilities. Although blockchain-enabled healthcare systems provide security, it may lead to some loopholes in the existing state of the art. In literature, blockchain-enabled frameworks have been presented to resolve those issues. However, these methods have primarily focused on data storage and blockchain is used as a database. In this paper, blockchain as a distributed database is proposed with a homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Additionally, the proposed approach provides a secure key revocation mechanism and updates various policies accordingly. As a result, a secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Hence, our proposed approach provides more security, efficiency, and transparency with cost-effectiveness. We performed our simulations based on the blockchain-based tool Hyperledger Fabric and OrigionLab for analysis and evaluation. We compared our proposed results with the benchmark models, respectively. Our comparative analysis justifies that our proposed framework provides better security and searchable mechanism for the healthcare system.
Due to the value and importance of patient health records (PHR), security is the most critical feature of encryption over the Internet. Users that perform keyword searches to gain access to the PHR stored in the database are more susceptible to security risks. Although a blockchain-based healthcare system can guarantee security, present schemes have several flaws. Existing techniques have concentrated exclusively on data storage and have utilized blockchain as a storage database. In this research, we developed a unique deep-learning-based secure search-able blockchain as a distributed database using homomorphic encryption to enable users to securely access data via search. Our suggested study will increasingly include secure key revocation and update policies. An IoT dataset was used in this research to evaluate our suggested access control strategies and compare them to benchmark models. The proposed algorithms are implemented using smart contracts in the hyperledger tool. The suggested strategy is evaluated in comparison to existing ones. Our suggested approach significantly improves security, anonymity, and monitoring of user behavior, resulting in a more efficient blockchain-based IoT system as compared to benchmark models.
The rising interest in integrative approach has shifted gene selection from purely data-centric to incorporating additional biological knowledge. Integrative gene selection is viewed as a promising approach in microarray data classification that took into consideration the complex relationships among genes. However, in most of the existing methods, the selection of genes is still based on expression values alone and biological knowledge is integrated at the end of analysis to verify experimental results or to gain biological insights. Thus, this paper proposed an integrative gene selection based on filter method and association analysis for selecting genes that are not only differentially expressed but also informative for classification. Association analysis is employed to integrate microarray data with multiple types of biological knowledge simultaneously, and to identify groups of genes that are frequently co-occurred in target samples. It has been tested on four cancer-related datasets, and two types of biological knowledge are incorporated, namely Gene Ontology (GO) and KEGG Pathways (KEGG). The experimental results show that the recommended GO based models, KEGG based models, and GO-KEGG based models outperformed the expression-only models by attaining better classification accuracies with lesser number of genes. The performance of the integrative models verified the efficiency and scalability of association analysis in mining microarray data.
The discovery of reliable cancer biomarkers is crucial for accurate early detection and clinical diagnosis. One of the strategies is by identifying expression-based cancer biomarkers through integrative microarray data analysis. Microarray is a powerful high-throughput technology, which allows a genome-wide analysis of human genes with various biological information. Nevertheless, more studies are needed on improving the predictability of the discovered gene biomarkers, as well as their reproducibility and interpretability, to qualify them for clinical use. This paper proposes an informative top-k class associative rule (iTCAR) method in an integrative framework for identifying candidate genes of specific cancers. iTCAR introduces an enhanced associative classification algorithm that integrates microarray data with biological information from gene ontology, KEGG pathways, and protein-protein interactions to generate informative class associative rules. A new interestingness measurement is used to rank and select class associative rules for building accurate classifiers. The experimental results show that iTCAR has excellent predictability by achieving the average classification accuracy above 90% and the average area under the curve above 0.8. Besides, iTCAR has significant reproducibility and interpretability through functional enrichment analysis and retrieval of meaningful cancer terms. These promising results suggest the proposed method has great potential in identifying candidate genes, which can be further investigated as biomarkers for cancer diseases.
This paper presents a prototype that can convert sign language into text. A Leap Motion controller was utilised as an interface for hand motion tracking without the need of wearing any external instruments. Three recognition techniques were employed to measure the performance of the prototype, namely the Geometric Template Matching, Artificial Neural Network and Cross Correlation. 26 alphabets from American Sign Language were chosen for training and testing the proposed prototype. The experimental results showed that Geometric Template Matching achieved the highest recognition accuracy compared to the other recognition techniques.
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