Novel coronavirus (COVID-19 or 2019-nCoV) pandemic has neither clinically proven vaccine nor drugs; however, its patients are recovering with the aid of antibiotic medications, anti-viral drugs, and chloroquine as well as vitamin C supplementation. It is now evident that the world needs a speedy and quicker solution to contain and tackle the further spread of COVID-19 across the world with the aid of non-clinical approaches such as data mining approaches, augmented intelligence and other artificial intelligence techniques so as to mitigate the huge burden on the healthcare system while providing the best possible means for patients' diagnosis and prognosis of the 2019-nCoV pandemic effectively. In this study, data mining models were developed for the prediction of COVID-19 infected patients' recovery using epidemiological dataset of COVID-19 patients of South Korea. The decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor algorithms were applied directly on the dataset using python programming language to develop the models. The model predicted a minimum and maximum number of days for COVID-19 patients to recover from the virus, the age group of patients who are of high risk not to recover from the COVID-19 pandemic, those who are likely to recover and those who might be likely to recover quickly from COVID-19 pandemic. The results of the present study have shown that the model developed with decision tree data mining algorithm is more efficient to predict the possibility of recovery of the infected patients from COVID-19 pandemic with the overall accuracy of 99.85% which stands to be the best model developed among the models developed with other algorithms including support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor.

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Meta-heuristic algorithms inspired by biological species have become very popular in recent years. Collective intelligence of various social insects such as ants, bees, wasps, termites, birds, fish, has been investigated to develop a number of meta-heuristic algorithms in the general domain of swarm intelligence (SI). The developed SI algorithms are found effective in solving different optimization tasks. Traveling Salesman Problem (TSP) is the combinatorial optimization problem where a salesman starting from a home city travels all the other cities and returns to home city in the shortest possible path. TSP is a popular problem due to the fact that the instances of TSP can be applied to solve real-world problems, implication of which turns TSP into a standard test bench for performance evaluation of new algorithms. Spider Monkey Optimization (SMO) is a recent addition to SI algorithms based on the social behavior of spider monkeys. SMO implicitly adopts grouping and regrouping for the interactions to improve solutions; such multi-population approach is the motivation of this study to develop an effective method for TSP. This paper presents an effective variant of SMO to solve TSP called discrete SMO (DSMO). In DSMO, every spider monkey represents a TSP solution where Swap Sequence (SS) and Swap Operator (SO) based operations are employed, which enables interaction among monkeys in obtaining the optimal TSP solution. The SOs are generated using the experience of a specific spider monkey as well as the experience of other members (local leader, global leader, or a randomly selected spider monkey) of the group. The performance and effectiveness of the proposed method have been verified on a large set of TSP instances and the outcomes are compared to other well-known methods. Experimental results demonstrate the effectiveness of the proposed DSMO for solving TSP.

Wearable technology plays a significant role in our daily life as well as in the healthcare industry. The recent coronavirus pandemic has taken the world’s healthcare systems by surprise. Although trials of possible vaccines are underway, it would take a long time before the vaccines are permitted for public use. Most of the government efforts are currently geared towards preventing the spread of the coronavirus and predicting probable hot zones. The essential and healthcare workers are the most vulnerable towards coronavirus infections due to their required proximity to potential coronavirus patients. Wearable technology can potentially assist in these regards by providing real-time remote monitoring, symptoms prediction, contact tracing, etc. The goal of this paper is to discuss the different existing wearable monitoring devices (respiration rate, heart rate, temperature, and oxygen saturation) and respiratory support systems (ventilators, CPAP devices, and oxygen therapy) which are frequently used to assist the coronavirus affected people. The devices are described based on the services they provide, their working procedures as well as comparative analysis of their merits and demerits with cost. A comparative discussion with probable future trends is also drawn to select the best technology for COVID-19 infected patients. It is envisaged that wearable technology is only capable of providing initial treatment that can reduce the spread of this pandemic.

Nowadays, Diabetes is one of the most common and severe diseases in Bangladesh as well as all over the world. It is not only harmful to the blood but also causes different kinds of diseases like blindness, renal disease, kidney problem, heart diseases etc. that causes a lot of death per year. So, it badly needs to develop a system that can effectively diagnose the diabetes patients using medical details. We propose a strategy for the diagnosis of diabetes using deep neural network by training its attributes in five-fold and tenfold crossvalidation fashion. The Pima Indian Diabetes (PID) data set is retrieved from the UCI machine learning repository database. The results on PID dataset demonstrate that deep learning approach design an auspicious system for the prediction of diabetes with prediction accuracy of 98.35%, F1 score of 98, and MCC of 97 for five-fold cross-validation. Additionally, accuracy of 97.11%, sensitivity of 96.25%, and specificity of 98.80% are obtained for tenfold cross-validation. The experimental results exhibit that the proposed system provides promising results in case of five-fold cross-validation.

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Generally, crimes influence organisations as it starts occurring frequently in society. Because of having many dimensions of crime data, it is difficult to mine the available information using off the shelf or statistical data analysis tools. Improving this process will aid the police as well as crime protection agencies to solve the crime rate in a faster period. Also, criminals can often be identified based on crime data. Data mining includes strategies at the convergence of machine learning and database frameworks. Using this concept, we can extract previously unknown useful information and their patterns of occurrence from unstructured data. The sole purpose of this paper is to give an idea of how data mining can be utilised by crime investigation agencies to discover relevant precautionary measures from prediction rates. Data sets are analysed by some supervised classification algorithms, namely decision tree, K-nearest neighbours (KNN) and random forest algorithms. Crime forecasting is done for frequently occurring crimes like robbery, assault, theft, etc. Specifically, the results indicate the superiority of the random forest algorithm in test accuracy.