Quantum-Inspired Heuristic Algorithm for Secure Healthcare Prediction using Blockchain Technology.

People's health is adversely affected by environmental changes and poor nutritional habits, emphasizing the importance of health awareness. The healthcare system encounters significant challenges, including data insufficiency, threats, errors, and delays. To address these issues and advance medical care, we propose a secure healthcare prediction method, prioritizing patient privacy and data transmission efficiency. The Quantum-inspired heuristic algorithm combined with Kril Herd Optimization (QKHO) is introduced for healthcare prediction, along with a comparison to the Deep Forward Neural Network (DFNN) optimized using Krill Herd Optimization (KHO) and Quantum-inspired heuristic algorithm combined with Kril Herd Optimization. The proposed QKHO model outperforms conventional models and exhibits higher accuracy, precision, recall, and F1-score. Blockchain technology ensures secure data transmission to the server, surpassing the security level of existing RSA and Diffie-Hellman algorithms.

From Nanosystems to a Biosensing Prototype for an Efficient Diagnostic: A Special Issue in Honor of Professor Bansi D. Malhotra.

It has been proven that rapid bioinformatics analysis according to patient health profiles, in addition to biomarker detection at a low level, is emerging as essential to design an analytical diagnostics system to manage health intelligently in a personalized manner. Such objectives need an optimized combination of a nano-enabled sensing prototype, artificial intelligence (AI)-supported predictive analysis, and Internet of Medical Things (IoMT)-based bioinformatics analysis. Such a developed system began with a prototype demonstration of efficient diseases diagnostics performance is the future diseases management approach. To explore these aspects, the Special Issue planned for the nano-and micro-technology section of MDPI's Biosensors journal will honor and acknowledge the contributions of Prof. B.D. Malhotra, Ph.D., FNA, FNASc has made in the field of biosensors.

Automated predictive analytics tool for rainfall forecasting.

Australia faces a dryness disaster whose impact may be mitigated by rainfall prediction. Being an incredibly challenging task, yet accurate prediction of rainfall plays an enormous role in policy making, decision making and organizing sustainable water resource systems. The ability to accurately predict rainfall patterns empowers civilizations. Though short-term rainfall predictions are provided by meteorological systems, long-term prediction of rainfall is challenging and has a lot of factors that lead to uncertainty. Historically, various researchers have experimented with several machine learning techniques in rainfall prediction with given weather conditions. However, in places like Australia where the climate is variable, finding the best method to model the complex rainfall process is a major challenge. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. (b) Develop an optimized neural network and develop a prediction model using the neural network (c) to do a comparative study of new and existing prediction techniques using Australian rainfall data. In this paper, rainfall data collected over a span of ten years from 2007 to 2017, with the input from 26 geographically diverse locations have been used to develop the predictive models. The data was divided into training and testing sets for validation purposes. The results show that both traditional and neural network-based machine learning models can predict rainfall with more precision.

Electrochemical SARS-CoV-2 Sensing at Point-of-Care and Artificial Intelligence for Intelligent COVID-19 Management.

To manage the COVID-19 pandemic, development of rapid, selective, sensitive diagnostic systems for early stage ß-coronavirus severe acute respiratory syndrome (SARS-CoV-2) virus protein detection is emerging as a necessary response to generate the bioinformatics needed for efficient smart diagnostics, optimization of therapy, and investigation of therapies of higher efficacy. The urgent need for such diagnostic systems is recommended by experts in order to achieve the mass and targeted SARS-CoV-2 detection required to manage the COVID-19 pandemic through the understanding of infection progression and timely therapy decisions. To achieve these tasks, there is a scope for developing smart sensors to rapidly and selectively detect SARS-CoV-2 protein at the picomolar level. COVID-19 infection, due to human-to-human transmission, demands diagnostics at the point-of-care (POC) without the need of experienced labor and sophisticated laboratories. Keeping the above-mentioned considerations, we propose to explore the compartmentalization approach by designing and developing nanoenabled miniaturized electrochemical biosensors to detect SARS-CoV-2 virus at the site of the epidemic as the best way to manage the pandemic. Such COVID-19 diagnostics approach based on a POC sensing technology can be interfaced with the Internet of things and artificial intelligence (AI) techniques (such as machine learning and deep learning for diagnostics) for investigating useful informatics via data storage, sharing, and analytics. Keeping COVID-19 management related challenges and aspects under consideration, our work in this review presents a collective approach involving electrochemical SARS-CoV-2 biosensing supported by AI to generate the bioinformatics needed for early stage COVID-19 diagnosis, correlation of viral load with pathogenesis, understanding of pandemic progression, therapy optimization, POC diagnostics, and diseases management in a personalized manner.