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.

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.

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.

: Ransomware, a form of malicious software originating from cryptovirology, poses a serious threat by coercing victims to pay a ransom under the risk of exposing their data or permanently restricting access. While basic ransomware may lock a system without causing harm to files, more sophisticated variants utilize cryptoviral extortion techniques. The danger of ransomware is significant, with ongoing discoveries of new strains and families on the internet and dark web. Recovering from ransomware infections is challenging due to the complex encryption schemes employed. The exploration of machine learning and deep learning methods for ransomware detection is crucial, as these technologies can identify zero-day threats. This survey delves into research contributions on the detection of ransomware using deep learning algorithms. With deep learning gaining prominence in cybersecurity, we aimed to explore techniques for ransomware detection, assess weaknesses in existing deep learning approaches, and propose enhancements using those deep learning algorithms. Machine learning algorithms can be employed to tackle worldwide computer security challenges, encompassing the detection of malware, recognition of ransomware, detection of fraud, and identification of spoofing attempts. Machine learning algorithms play a crucial role in assessing prevalent forms of cyber security risks. They are instrumental in identifying and mitigating attacks, conducting vulnerability scans, and evaluating the risks associated with the public internet. By leveraging machine learning, computer defense mechanisms can effectively identify and respond to various cyber threats. These techniques aid in fortifying systems against potential vulnerabilities and enhance the overall security posture. Research in this field investigates the utilization of cyber training in both defensive and offensive contexts, offering insights into the intersection of cyber threats and machine learning techniques.