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Major depressive disorder (MDD) is the most common mental disorder in the present day as all individuals' lives, irrespective of being employed or unemployed, is going through the depression phase at least once in their lifetime. In simple terms, it is a mood disturbance that can persist for an individual for more than a few weeks to months. In MDD, in most cases, the individuals do not consult a professional, and even if being consulted, the results are not significant as the individuals find it challenging to identify whether they are depressed or not. Depression, most of the time, cooccurs with anxiety and leads to suicide in few cases, among the employees, who are about to handle the pressure at work and home and mostly unnoticing such problems. This is why this work aims to analyze the IT employees who are mostly working with targets. The artificial neural network, which is modeled loosely like the brain, has proved in recent days that it can perform better than most of the classification algorithms. This study has implemented the multilayered neural perceptron and experimented with the backpropagation technique over the data samples collected from IT professionals. This study aims to develop a model that can classify depressed individuals from those who are not depressed effectively with the data collected from them manually and through sensors. The results show that deep-MLP with backpropagation outperforms other machine learning-based models for effective classification., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2021 P. M. Durai Raj Vincent et al.)

Analyzing community response has always played an important role in marketing and development of various products ranging from services to manufacturing. Analyzing the responses and interpreting them helps improve the quality of the product. This task is traditionally done by community managers using surveys and focus groups. One other hands-off solution would be using the technology to analyze sentiments from users' digital interactions in various online communities. Sentiment Analysis is the use of Natural Language Processing to process text to identify and retrieve sentiments behind the textual data. Sentiment Analysis can be approached either by supervised classification where a machine learning model is trained using labeled data or lexicon-based unsupervised analysis where a sentiment dictionary is used to find the overall sentiment. The idea is to build a system which presents community sentiment results on discussion threads from reddit on demand with the end user not to concern about data processing. We propose a streamlined pipeline architecture for a web application with several closely connected components in the backend that allows users to simply send the reddit discussion thread's URL, which the backend receives and scrapes the comments using the Reddit API, creates a dataset, cleans it and performs sentiment analysis using a RNNs. We used a Long Short-Term Memory (LSTM) a variant of RNN like Bidirectional RNNs and Multi-layered RNNs are also used to get better results. [ABSTRACT FROM AUTHOR]

Aim: The research is about the Novel Deep Belief Network (NDBN) approach for Retail Store Sales Prediction during peak demand seasons and its performance comparison over K-Nearest Neighbour Technique (KNN). Materials and Methods: Deep Belief Network (N=10) and K-Nearest Neighbour algorithm (N=10) samples were considered based on the clinc calc online sample size calculator for predicting the accidents that happened in terms of accuracy. Two sample groups are taken into consideration and tested, G-power is the calculation that contains two different groups, alpha (0.05), power (80%), and environment ratio. Results: The Novel Deep Belief Network algorithm achieved 84.53% accuracy and K-Nearest Neighbour has 74.24%. This NDBN appears to have significance of p equal to 0.02 for the K-Nearest Neighbour, that is p less than 0.05 using independent sample T-test analysis. From the result, it proves that the Deep Belief Network approaches predict the retail sales store prediction. [ABSTRACT FROM AUTHOR]

Aim: The aim of the study is to analyze the death ratio of covid patients using Novel Multiple Logistic Regression and linear regression which comes under supervised learning. Materials and Method: Accuracy is analyzed for a covid dataset of size 239 places. Analyzingthe death ratio of covid patients is performed by a Novel Multiple Logistic Regression of sample size (N=35) and Linear Regression of sample size (N=35), obtained using the G-power value of 80%. These are supervised learning algorithms. Result: Novel Multiple Logistic Regression accuracy is 96% which is comparatively higher than LR with an accuracy of 86%. The significance value is determined as p=0.030 (p<0.05) for accuracy. Conclusion: Novel Multiple Logistic Regression performs better in finding accuracy when compared to Linear Regression. [ABSTRACT FROM AUTHOR]

Expert Systems are interactive and reliable computer-based decisionmaking systems that use both facts and heuristics for solving complex decision-making problems. Generally, the cyclic voltammetry (CV) experiments are executed a random number of times (cycles) to get a stable production of power. However, presently there are not many algorithms or models for predicting the power generation stable criteria in microbial fuel cells. For stability analysis of Medicinal herbs' CV profiles, an expert system driven by the augmented K-means clustering algorithm is proposed. Our approach requires a dataset that contains voltage-current relationships from CV experiments on the related subjects (plants/herbs). This new approach uses feature engineering and augmented K-means clustering techniques to determine the cycle number beyond which the CV curve stabilizes. We obtain an excellent estimate of the required CV cycles for getting a stable Voltage versus Current curve in this approach. Moreover, this expert system would reduce the time needed and the money spent on running additional and superfluous CV experiments cycles. Thus, it would streamline the process of Bacterial Fuel Cells production using the CV of medicinal herbs. [ABSTRACT FROM AUTHOR]

Summary: The global demand for electricity has visualized high growth with the rapid growth in population and economy. It thus becomes necessary to efficiently distribute electricity to households and industries in order to reduce power loss. Smart Grids (SG) have the potential to reduce such power losses during power distribution. Machine learning and artificial intelligence techniques have been successfully implemented on SGs to achieve enhanced accuracy in customer demand prediction. There exists a dire need to analyze and evaluate the various machine learning algorithms, thereby identify the most suitable one to be applied to SGs. In the present work, several state‐of‐the‐art machine learning algorithms, namely Support Vector Machines (SVM), K‐Nearest Neighbor (KNN), Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the SG. The SG dataset used in the study is publicly available collected from UC Irvine (UCI) machine learning repository. The experimentation results highlighted the superiority of the Decision Tree classification algorithm, which outperformed the other state of the art algorithms yielding 100% precision, 99.9% recall, 100% F1 score, and 99.96% accuracy. [ABSTRACT FROM AUTHOR]

Summary: The global demand for electricity has visualized high growth with the rapid growth in population and economy. It thus becomes necessary to efficiently distribute electricity to households and industries in order to reduce power loss. Smart Grids (SG) have the potential to reduce such power losses during power distribution. Machine learning and artificial intelligence techniques have been successfully implemented on SGs to achieve enhanced accuracy in customer demand prediction. There exists a dire need to analyze and evaluate the various machine learning algorithms, thereby identify the most suitable one to be applied to SGs. In the present work, several state‐of‐the‐art machine learning algorithms, namely Support Vector Machines (SVM), K‐Nearest Neighbor (KNN), Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the SG. The SG dataset used in the study is publicly available collected from UC Irvine (UCI) machine learning repository. The experimentation results highlighted the superiority of the Decision Tree classification algorithm, which outperformed the other state of the art algorithms yielding 100% precision, 99.9% recall, 100% F1 score, and 99.96% accuracy. [ABSTRACT FROM AUTHOR]