Predictive Analytics of Energy Usage by IoT-Based Smart Home Appliances for Green Urban Development

Green IoT primarily focuses on increasing IoT sustainability by reducing the large amount of energy required by IoT devices. Whether increasing the efficiency of these devices or conserving energy, predictive analytics is the cornerstone for creating value and insight from large IoT data. This work aims at providing predictive models driven by data collected from various sensors to model the energy usage of appliances in an IoT-based smart home environment. Specifically, we address the prediction problem from two perspectives. Firstly, an overall energy consumption model is developed using both linear and non-linear regression techniques to identify the most relevant features in predicting the energy consumption of appliances. The performances of the proposed models are assessed using a publicly available dataset comprising historical measurements from various humidity and temperature sensors, along with total energy consumption data from appliances in an IoT-based smart home setup. The prediction results comparison show that LSTM regression outperforms other linear and ensemble regression models by showing high variability ( R 2 ) with the training (96.2%) and test (96.1%) data for selected features. Secondly, we develop a multi-step time-series model using the auto regressive integrated moving average (ARIMA) technique to effectively forecast future energy consumption based on past energy usage history. Overall, the proposed predictive models will enable consumers to minimize the energy usage of home appliances and the energy providers to better plan and forecast future energy demand to facilitate green urban development.

Influence of AI and Machine Learning in Insurance Sector

The Aim of this research is to identify influence, usage, and the benefits of AI (Artificial Intelligence) and ML (Machine learning) using big data analytics in Insurance sector. Insurance sector is the most volatile industry since multiple natural influences like Brexit, pandemic, covid 19, Climate changes, Volcano interruptions. This research paper will be used to explore potential scope and use cases for AI, ML and Big data processing in Insurance sector for Automate claim processing, fraud prevention, predictive analytics, and trend analysis towards possible cause for business losses or benefits. Empirical quantitative research method is used to verify the model with the sample of UK insurance sector analysis. This research will conclude some practical insights for Insurance companies using AI, ML, Big data processing and Cloud computing for the better client satisfaction, predictive analysis, and trending.

Can HRM predict mental health crises? Using HR analytics to unpack the link between employment and suicidal thoughts and behaviors

PurposeThe aim of this research is to determine the extent to which the human resource (HR) function can screen and potentially predict suicidal employees and offer preventative mental health assistance.Design/methodology/approachDrawing from the 2019 National Survey of Drug Use and Health (N = 56,136), this paper employs multivariate binary logistic regression to model the work-related predictors of suicidal ideation, planning and attempts.FindingsThe results indicate that known periods of joblessness, the total number of sick days and absenteeism over the last 12 months are significantly associated with various suicidal outcomes while controlling for key psychosocial correlates. The results also indicate that employee assistance programs are associated with a significantly reduced likelihood of suicidal ideation. These findings are consistent with conservation of resources theory.Research limitations/implicationsThis research demonstrates preliminarily that the HR function can unobtrusively detect employee mental health crises by collecting data on key predictors.Originality/valueIn the era of COVID-19, employers have a duty of care to safeguard employee mental health. To this end, the authors offer an innovative way through which the HR function can employ predictive analytics to address mental health crises before they result in tragedy.

An AI-Enabled Predictive Analytics Dashboard for Acute Neurosurgical Referrals

Healthcare dashboards make key information about service and clinical outcomes available to staff in an easy-to-understand format. Most dashboards are limited to providing insights based on group-level inference, rather than individual prediction. Here, we evaluate a dashboard which could analyze and forecast acute neurosurgical referrals based on 10,033 referrals made to a large volume tertiary neurosciences center in central London, U.K., from the start of the Covid-19 pandemic lockdown period until October 2021. As anticipated, referral volumes significantly increased in this period, largely due to an increase in spinal referrals. Applying a range of validated time-series forecasting methods, we found that referrals were projected to increase beyond this time-point. Using a mixed-methods approach, we determined that the dashboard was usable, feasible, and acceptable to key stakeholders. Dashboards provide an effective way of visualizing acute surgical referral data and for predicting future volume without the need for data-science expertise.

Price Bubbles in the Real Estate Markets - Analysis and Prediction

The article concerns the issue of price bubbles on the markets, with particular emphasis on the specificity of the real estate market. Up till now, more than a decade after the subprime crisis, there is no accurate enough method to predict price movements, their culmination and, eventually, the burst of price and speculative bubbles on the markets. Hence, the main goal of the article is to present the possibility of early detection of price bubbles and their consequences from the point of view of the surveyed managers. The following research hypothesis was verified: price bubbles on the real estate market cannot be excluded, therefore constant monitoring and predictive analytics of this market are needed. In addition to standard research methods (desk research or statistical analysis), the authors conducted their own survey on a group of randomly selected managers from Portugal and Poland in the context of their attitude to crises and price bubbles. The obtained results allowed us to conclude that managers in both analysed countries are different relating the effects of price bubbles to the activities of their own companies but are similar (about 40% of respondents) expecting quick detection and deactivation of emerging bubbles by the government or by central bank. Nearly 40% of Polish and Portuguese managers claimed that the consequences of crises must include an increased responsibility of managers for their decisions, especially those leading to failures.

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine Learning techniques has been proposed for different states of India. The proposed classification model is applied to the recent COVID-19 dataset for India, and a performance evaluation of various state-of-the-art classifiers to the proposed model is performed. The classifiers forecasted the patients’ infection status in different regions to better plan resources and response care systems. The appropriate classification of the output class based on the extracted input features is essential to achieve accurate results of classifiers. The experimental outcome exhibits that the proposed Hybrid Model reached a maximum F1-score of 94% compared to Ensembles and other classifiers like Support Vector Machine, Decision Trees, and Gaussian Naïve Bayes on a dataset of 5004 instances through 10-fold cross-validation for predicting the right class. The feasibility of automated prediction for COVID-19 infection cure and death rates in the Indian states was demonstrated.

Unlocking Drivers for Employee Engagement Through Human Resource Analytics

The authors have discussed in detail the meaning of employee engagement and its relevance for the organizations in the present scenario. The authors also highlighted the various factors that predict the employee engagement of the employees in the varied organizations. The authors have emphasized on the role that HR analytics can play to identify the reasons for low level of engagement among employees and suggesting ways to improve the same using predictive analytics. The authors have also advocated the benefits that organizations can reap by making use of HR analytics in measuring the engagement levels of the employees and improving the engagement levels of diverse workforce in the existing organizations. The authors have also proposed the future perspectives of the proposed study that help the organizations and officials from the top management to tap the benefits of analytics in the function of human resource management and to address the upcoming issues related to employee behavior.