Disaster and Pandemic Management Using Machine Learning: A Survey.

This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.

A Secure IoT-Based Modern Healthcare System With Fault-Tolerant Decision Making Process.

The advent of Internet of Things (IoT) has escalated the information sharing among various smart devices by many folds, irrespective of their geographical locations. Recently, applications like e-healthcare monitoring has attracted wide attention from the research community, where both the security and the effectiveness of the system are greatly imperative. However, to the best of our knowledge none of the existing literature can accomplish both these objectives (e.g., existing systems are not secure against physical attacks). This paper addresses the shortcomings in existing IoT-based healthcare system. We propose an enhanced system by introducing a Physical Unclonable Function (PUF)-based authentication scheme and a data driven fault-tolerant decision-making scheme for designing an IoT-based modern healthcare system. Analyses show that our proposed scheme is more secure and efficient than existing systems. Hence, it will be useful in designing an advanced IoT-based healthcare system.

Role of Flavonoids in Neurodegenerative Diseases: Limitations and Future Perspectives.

BACKGROUND: Flavonoids, a group of natural dietary polyphenols, are known for their beneficial effects on human health. By virtue of their various pharmacological effects, like anti-oxidative, antiinflammatory, anti-carcinogenic and neuroprotective effects, flavonoids have now become an important component of herbal supplements, pharmaceuticals, medicinals and cosmetics. There has been enormous literature supporting neuroprotective effect of flavonoids. Recently their efficacy in various neurodegenerative diseases, like Alzheimer's disease and Parkinson diseases, has received particular attention. OBJECTIVE: The mechanism of flavanoids neuroprotection might include antioxidant, antiapoptotic, antineuroinflammatory and modulation of various cellular and intracellular targets. In in-vivo systems, before reaching to brain, they have to cross barriers like extensive first pass metabolism, intestinal barrier and ultimately blood brain barrier. Different flavonoids have varied pharmacokinetic characteristics, which affect their pharmacodynamic profile. Therefore, brain accessibility of flavonoids is still debatable. METHODS: This review emphasized on current trends of research and development on flavonoids, especially in neurodegenerative diseases, possible challenges and strategies to encounter using novel drug delivery system. RESULTS: Various flavonoids have elicited their therapeutic potential against neurodegenerative diseases, however by using nanotechnology and novel drug delivery systems, the bioavailability of favonoids could be enhanced. CONCLUSION: This study bridges a significant opinion on medicinal chemistry, ethanopharmacology and new drug delivery research regarding use of flavonoids in management of neurodegeneration.

Design of efficient full adder in quantum-dot cellular automata.

Further downscaling of CMOS technology becomes challenging as it faces limitation of feature size reduction. Quantum-dot cellular automata (QCA), a potential alternative to CMOS, promises efficient digital design at nanoscale. Investigations on the reduction of QCA primitives (majority gates and inverters) for various adders are limited, and very few designs exist for reference. As a result, design of adders under QCA framework is gaining its importance in recent research. This work targets developing multi-layered full adder architecture in QCA framework based on five-input majority gate proposed here. A minimum clock zone (2 clock) with high compaction (0.01 µ m(2)) for a full adder around QCA is achieved. Further, the usefulness of such design is established with the synthesis of high-level logic. Experimental results illustrate the significant improvements in design level in terms of circuit area, cell count, and clock compared to that of conventional design approaches.

Design and characterization of cellular automata based associative memory for pattern recognition.

This paper reports a cellular automata (CA) based model of associative memory. The model has been evolved around a special class of CA referred to as generalized multiple attractor cellular automata (GMACA). The GMACA based associative memory is designed to address the problem of pattern recognition. Its storage capacity is found to be better than that of Hopfield network. The GMACA are configured with nonlinear CA rules that are evolved through genetic algorithm (GA). Successive generations of GA select the rules at the edge of chaos. The study confirms the potential of GMACA to perform complex computations like pattern recognition at the edge of chaos.