The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.

Internet of Things (IoT) is producing an extraordinary volume of data daily, and it is possible that the data may become useless while on its way to the cloud, due to long distances. Fog/edge computing is a new model for analysing and acting on time-sensitive data, adjacent to where it is produced. Further, cloud services provided by large companies such as Google, can also be localised to improve response time and service agility. This is accomplished through deploying small-scale datacentres in various locations, where needed in proximity of users; and connected to a centralised cloud that establish a multi-access edge computing (MEC). The MEC setup involves three parties, i.e. service-providers (IaaS), application-providers (SaaS), network-providers (NaaS); which might have different goals, therefore, making resource management difficult. Unlike existing literature, we consider resource management with-respect-to all parties; and suggest game-theoretic resource management techniques to minimise infrastructure energy consumption and costs while ensuring applications' performance. Our empirical evaluation, using Google's workload traces, suggests that our approach could reduce up to 11.95% energy consumption, and ~17.86% user costs with negligible loss in performance. Moreover, IaaS can reduce up-to 20.27% energy bills and NaaS can increase their costs-savings up-to 18.52% as compared to other methods.

Neuropeptides (NPs) are a kind of neuromodulator/ neurotransmitter that works as signaling molecules in the central nervous system, and perform major roles in physiological and hormone regulation activities. Recently, machine learning-based therapeutic agents have gained the attention of researchers due to their high and reliable prediction results. However, the unsatisfactory performance of the existing predictors is due to their high execution cost and minimum predictive results. Therefore, the development of a reliable prediction is highly indispensable for scientists to effectively predict NPs. In this study, we presented an automatic and computationally effective model for identifying of NPs. The evolutionary information is formulated using a bigram position-specific scoring matrix (Bi-PSSM) and K-spaced bigram (KSB). Moreover, for noise reduction, a discrete wavelet transform (DWT) is utilized to form Bi-PSSM_DWT and KSB_DWT based high discriminative vectors. In addition, one-hot encoding is also employed to collect sequential features from peptide samples. Finally, a multi-perspective feature set of sequential and embedded evolutionary information. The optimum features are chosen from the extracted features via Shapley Additive exPlanations (SHAP) by evaluating the contribution of the extracted features. The optimal features are trained via six classification models i.e., XGB, ETC, SVM, ADA, FKNN, and LGBM. The predicted labels of these learners are then provided to a genetic algorithm to form an ensemble classification approach. Hence, our model achieved a higher predictive accuracy of 94.47% and 92.55% using training sequences and independent sequences, respectively. Which is ~3% highest predictive accuracy than present methods. It is suggested that our presented tool will be beneficial and may execute a substantial role in drug development and research academia. The source code and all datasets are publicly available at https://github.com/shahidawkum/Target-ensC_NP.

Amyloid proteins (AMYs) are usually an aggregate of insoluble fibrous that have major pathogenic effects on various tissues. However, its abnormal deposition may lead to several diseases i.e., Parkinson’s, Alzheimer’s, and type 2 diabetes. In addition, AMYs form amyloid aggregates when they are in a misfolded state. Therefore, it is crucial to accurately predict AMYs and their pathogenic characteristics. Various computational predictors have been presented for the accurate prediction of AMYs. Although, the effectiveness of these predictors is unsatisfactory due to their low generalization abilities and high training cost. In this attempt, we proposed an intelligent computational predictor for the accurate prediction of AMYs. The novel embedded evolutionary features are gathered using K-separated bigrams, and the Filter method into the evolutionary descriptors. Moreover, DDE-based enhanced frequency coupling information are gathered from the Amyloid sequences. Additionally, a multi-model vector is obtained by combining the features of the applied formulation techniques. To reduce the computational cost of the proposed model, the eXtreme Gradient Boosting-Recursive Feature Elimination (XGB-RFE) based high-ranked features are selected from the heterogeneous vector. In the next part, the optimal features are evaluated via several learners, i.e., XGBoost (XGB), Light Gradient Boosted Machine (LGBM), Support Vector Machine (SVM), Adaboost (ada), and Extra Trees classifier (ETC),. The proposed model reported an improved predictive prediction accuracy of 93.10% using training sequences and 89.67% using independent sequences, respectively. Which is ~4% higher training accuracy than existing predictors. It is anticipated that our predictive approach will be useful for scientists and might play a key role in drug development and academic research.

IoT-based remote health monitoring is a promising technology to support patients who are unable to travel to medical facilities. Due to the sensitivity of health data, it is important to secure it against all possible threats. While a great deal of work has been done to secure IoT device-cloud communication and health records on the cloud, insider attacks remain a significant challenge. Malicious insiders may tamper, steal or change patients’ health data, which results in a loss of patient trust in these systems. Audit logs in the cloud, which may point to illegal data access, may also be erased or forged by malicious insiders as they tend to have technical knowledge and privileged access to the system. Thus, in this work, we propose a Cloud Access Security Broker (CASB) model that (a) logs every action performed on user data and (b) secures those logs by placing them in a private blockchain that is viewable by the data owners (i.e., patients). Patients can query the blockchain, track their data’s movement, and be alerted if their data has been accessed by an administrator or moved outside the cloud storage. In this work, we practically implement a web application that receives health data from patients, a CASB that securely stores the records in the cloud, and integrate a private blockchain that immediately logs all actions happening in the backend of the web application and CASB. We evaluate the system’s security and performance under varying numbers of patients and actions.