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This study aims to examine the research that examines psychological empowerment (PE) and employee engagement (EE) via bibliometric analysis. The study also aims to offer an overview of the present state of research and indicate potential future research topics.

The literature on PE and engagement was reviewed using bibliometric analysis based on publications in the Scopus database. The analysis comprises a three-field plot, theoretical framework examination, thematic analysis and quantitative analysis of the most frequently referenced publications, affiliations, countries and authors.

The study identifies research trends such as the use of the leadership lens, the examination of the different degrees of empowerment, the examination of alternate mechanisms to improve engagement and the impact of supervisor resources on these constructs. The study also suggests areas for future research, such as the influence of leadership and organizational culture on these two factors, the link between PE and EE and the impact of the changing structure of work via the increased use of technology and new work relations like gig work on these concepts.

This study offers a thorough and systematic overview of the state of the research in the area of PE and EE. This study emphasizes the significance of PE and engagement in management by giving a thorough overview of the present state of research and outlining future research possibilities.

The purpose of this paper is to review the nutritional composition, phytochemicals and bioactive compounds of black rice such as flavonoids, phenolic compounds and their health benefits. Black rice has also been used in medicine and for curing diabetes, obesity, cardiovascular diseases and cancer. Green technologies such as microwave-assisted extraction, supercritical fluid extraction and pulse electric field assisted extraction are very useful for the extraction of bioactive compounds as these reduce the use of energy and are environmental friendly. Black rice in different forms can be incorporated in various food products such as bakery, dairy and meat products.

Information and data were collected from different sources such as Google Scholar, Research Gate, online journals available at Banaras Hindu University library, Web of Science and Scopus. A database of more than 80 scientific sources from different sources was made as per the headings and subheadings of the paper.

Black rice is a type of rice species (Oryza sativa L.) and very good source of various nutrients and one of the nutritious varieties of rice. It is a good reservoir of essential amino acids such as lysine, tryptophan, minerals including iron, calcium, phosphorus, zinc and selenium; vitamins such as vitamin B1, vitamin B2 and folic acid. Various recent methods of extraction of bioactive compounds from black rice are suggested.

Researchers and scientists have considered black rice as a “Super Food” because of its nutritional profile. Black rice has antioxidant activity, anti-inflammatory activity, anticancer activity, antihyperlipidemia and antihyperglycemia and anti-allergic activity. There is a need to create awareness among the consumers about its nutritional profile and therapeutic properties.

Femtocells are low-power, inexpensive base stations (BS) used in business enterprises or homes. They could offer higher SNR in a smaller coverage area to enhance the data rates and QoS. Deployment of femtocell is expected to the witness constant development in upcoming years. Despite of all these benefits, there are certain challenges to be resolved that includes management of overlaying MC, interference among femtocells and the resource allocation between 2 tiers.

This work analyses the issues on cross-tier interfering and resource allocation alleviation in “full-duplex (FD) Orthogonal Frequency Division Multiple Access (OFDMA) oriented Heterogeneous Networks (HetNets) that includes macrocell as well as underlying femtocells”. This work concerns on three foremost contributions: portraying a single objective issue including subcarrier allocation, price allocation and power allocation of macrocell–femtocell networks. Moreover, this work introduces a novel Cat Swarm Mated-Lion algorithm (CSM-LA) for solving the defined optimization problem in macrocell–femtocell networks. At last, the supremacy of adopted scheme is proved over traditional models regarding statistical and convergence analysis.

By concerning the cost function, the developed CSM-LA attained 87.5, 60, 93.75 and 93.75% better than LM, WOA, LA and CSO respectively. For utility analysis, it accomplished 70.58% better than LM, 88.23% superior to GWO, 85.88% superior to WOA and 88.23% better than CSO. For statistical analysis, the median performance of developed CSM-LA attained better results, which was 80.52% superior to LA, 80.74% better than GWO, 72% superior to WOA and 48.7% better than LA. Hence, the developed CSM-LA proved its performance in terms of improved results and revealed its betterment over the conventional models.

This paper adopts a latest optimization algorithm called CSM-LA for analyzing the issues on cross-tier interfering and resource allocation alleviation in full-duplex (FD) orthogonal frequency division multiple access (OFDMA) oriented heterogeneous networks (HetNets). This is the first work that utilizes CSM-LA framework that proposes a new CSM-LA model for power control and resource allocation by considering the multi-objectives like price, subcarrier and power as well.

Stock market forecasters are focusing to create a positive approach for predicting the stock price. The fundamental principle of an effective stock market prediction is not only to produce the maximum outcomes but also to reduce the unreliable stock price estimate. In the stock market, sentiment analysis enables people for making educated decisions regarding the investment in a business. Moreover, the stock analysis identifies the business of an organization or a company. In fact, the prediction of stock prices is more complex due to high volatile nature that varies a large range of investor sentiment, economic and political factors, changes in leadership and other factors. This prediction often becomes ineffective, while considering only the historical data or textural information. Attempts are made to make the prediction more precise with the news sentiment along with the stock price information.

This paper introduces a prediction framework via sentiment analysis. Thereby, the stock data and news sentiment data are also considered. From the stock data, technical indicator-based features like moving average convergence divergence (MACD), relative strength index (RSI) and moving average (MA) are extracted. At the same time, the news data are processed to determine the sentiments by certain processes like (1) pre-processing, where keyword extraction and sentiment categorization process takes place; (2) keyword extraction, where WordNet and sentiment categorization process is done; (3) feature extraction, where Proposed holoentropy based features is extracted. (4) Classification, deep neural network is used that returns the sentiment output. To make the system more accurate on predicting the sentiment, the training of NN is carried out by self-improved whale optimization algorithm (SIWOA). Finally, optimized deep belief network (DBN) is used to predict the stock that considers the features of stock data and sentiment results from news data. Here, the weights of DBN are tuned by the new SIWOA.

The performance of the adopted scheme is computed over the existing models in terms of certain measures. The stock dataset includes two companies such as Reliance Communications and Relaxo Footwear. In addition, each company consists of three datasets (a) in daily option, set start day 1-1-2019 and end day 1-12-2020, (b) in monthly option, set start Jan 2000 and end Dec 2020 and (c) in yearly option, set year 2000. Moreover, the adopted NN + DBN + SIWOA model was computed over the traditional classifiers like LSTM, NN + RF, NN + MLP and NN + SVM; also, it was compared over the existing optimization algorithms like NN + DBN + MFO, NN + DBN + CSA, NN + DBN + WOA and NN + DBN + PSO, correspondingly. Further, the performance was calculated based on the learning percentage that ranges from 60, 70, 80 and 90 in terms of certain measures like MAE, MSE and RMSE for six datasets. On observing the graph, the MAE of the adopted NN + DBN + SIWOA model was 91.67, 80, 91.11 and 93.33% superior to the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively for dataset 1. The proposed NN + DBN + SIWOA method holds minimum MAE value of (∼0.21) at learning percentage 80 for dataset 1; whereas, the traditional models holds the value for NN + DBN + CSA (∼1.20), NN + DBN + MFO (∼1.21), NN + DBN + PSO (∼0.23) and NN + DBN + WOA (∼0.25), respectively. From the table, it was clear that the RMSRE of the proposed NN + DBN + SIWOA model was 3.14, 1.08, 1.38 and 15.28% better than the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively, for dataset 6. In addition, he MSE of the adopted NN + DBN + SIWOA method attain lower values (∼54944.41) for dataset 2 than other existing schemes like NN + DBN + CSA(∼9.43), NN + DBN + MFO (∼56728.68), NN + DBN + PSO (∼2.95) and NN + DBN + WOA (∼56767.88), respectively.

This paper has introduced a prediction framework via sentiment analysis. Thereby, along with the stock data and news sentiment data were also considered. From the stock data, technical indicator based features like MACD, RSI and MA are extracted. Therefore, the proposed work was said to be much appropriate for stock market prediction.

Text mining has been used for various knowledge discovery based applications, and thus, a lot of research has been contributed towards it. Latest trending research in the text mining is adopting the incremental learning data, as it is economical while dealing with large volume of information.

The primary intention of this research is to design and develop a technique for incremental text categorization using optimized Support Vector Neural Network (SVNN). The proposed technique involves four major steps, such as pre-processing, feature selection, classification and feature extraction. Initially, the data is pre-processed based on stop word removal and stemming. Then, the feature extraction is done by extracting semantic word-based features and Term Frequency and Inverse Document Frequency (TF-IDF). From the extracted features, the important features are selected using Bhattacharya distance measure and the features are subjected as the input to the proposed classifier. The proposed classifier performs incremental learning using SVNN, wherein the weights are bounded in a limit using rough set theory. Moreover, for the optimal selection of weights in SVNN, Moth Search (MS) algorithm is used. Thus, the proposed classifier, named Rough set MS-SVNN, performs the text categorization for the incremental data, given as the input.

For the experimentation, the 20 News group dataset, and the Reuters dataset are used. Simulation results indicate that the proposed Rough set based MS-SVNN has achieved 0.7743, 0.7774 and 0.7745 for the precision, recall and F-measure, respectively.

In this paper, an online incremental learner is developed for the text categorization. The text categorization is done by developing the Rough set MS-SVNN classifier, which classifies the incoming texts based on the boundary condition evaluated by the Rough set theory, and the optimal weights from the MS. The proposed online text categorization scheme has the basic steps, like pre-processing, feature extraction, feature selection and classification. The pre-processing is carried out to identify the unique words from the dataset, and the features like semantic word-based features and TF-IDF are obtained from the keyword set. Feature selection is done by setting a minimum Bhattacharya distance measure, and the selected features are provided to the proposed Rough set MS-SVNN for the classification.

This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface.

The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on “how exactly the matching process is done for evaluating the document.” Finally, the e-learners are classified based on their grades.

On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively.

This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors’ knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners.

The purpose of this study is to provide a novel portfolio asset prediction by means of the modified deep learning and hybrid meta-heuristic concept. In the past few years, portfolio optimization has appeared as a demanding and fascinating multi-objective problem, in the area of computational finance. Yet, it is accepting the growing attention of fund management companies, researchers and individual investors. The primary issues in portfolio selection are the choice of a subset of assets and its related optimal weights of every chosen asset. The composition of every asset is chosen in a manner such that the total profit or return of the portfolio is improved thereby reducing the risk at the same time.

This paper provides a novel portfolio asset prediction using the modified deep learning concept. For implementing this framework, a set of data involving the portfolio details of different companies for certain duration is selected. The proposed model involves two main phases. One is to predict the future state or profit of every company, and the other is to select the company which is giving maximum profit in the future. In the first phase, a deep learning model called recurrent neural network (RNN) is used for predicting the future condition of the entire companies taken in the data set and thus creates the data library. Once the forecasting of the data is done, the selection of companies for the portfolio is done using a hybrid optimization algorithm by integrating Jaya algorithm (JA) and spotted hyena optimization (SHO) termed as Jaya-based spotted hyena optimization (J-SHO). This optimization model tries to get the optimal solution including which company has to be selected, and optimized RNN helps to predict the future return while using those companies. The main objective model of the J-SHO-based RNN is to maximize the prediction accuracy and J-SHO-based portfolio asset selection is to maximize the profit. Extensive experiments on the benchmark datasets from real-world stock markets with diverse assets in various time periods shows that the developed model outperforms other state-of-the-art strategies proving its efficiency in portfolio optimization.

From the analysis, the profit analysis of proposed J-SHO for predicting after 7 days in next month was 46.15% better than particle swarm optimization (PSO), 18.75% better than grey wolf optimization (GWO), 35.71% better than whale optimization algorithm (WOA), 5.56% superior to JA and 35.71% superior to SHO. Therefore, it can be certified that the proposed J-SHO was effective in providing intelligent portfolio asset selection and prediction when compared with the conventional methods.

This paper presents a technique for providing a novel portfolio asset prediction using J-SHO algorithm. This is the first work uses J-SHO-based optimization for providing a novel portfolio asset prediction using the modified deep learning concept.

The purpose of the research presented in this paper is about an algorithm used for speed limit determination, in cases when there is ambiguity in determining the correct road data for a tracked vehicle. This algorithm resolves the glitch that happens when emitted global positioning system (GPS) data are ambiguous regarding roads that are very near to each other. Furthermore, we give a solution for other difficulties regarding the speed limit involving accuracy of emitted data and lack of information in navigation maps.

Our vehicle tracking system parses all GPS data from different vehicles to a single centralized database. It uses balancers and parsers to parse these data. Balancers use algorithms like round-robin to choose between different parsers. Information gained by the GPS unit is parsed, and then sent to a central server at regular intervals. For the gained data, we try to analyze the speed limit problem when tracking vehicles, analyze the challenges that are linked with speed limit problem, define a solution for the above discussed drawbacks and measure the driving performance.

We have already developed a fully functioning tracking system, which uses the above-described algorithm in tracking of few hundred vehicles, which makes approximately 1,300,000 requests per day, resulting in more than 4,000,000 tracking records gained in six months. The system monitors the motion of different vehicles using the gained GPS data from the first hand. This monitoring is done by developing web and mobile applications for third-party actors. This monitoring regards not only to the raw-produced data but also to new metrics that are derived from the raw one.

To our knowledge, there is not a similar algorithm/technology that can be of help in case of geographical identifier (GID) ambiguity. This research presents a solution to a real problem that we faced and which could not be answered by any of the current algorithms and technologies regarding the speed limit. Therefore, we consider this paper as highly original, which brings value in the field of pervasive computing and machine-to-machine communication.

The growing environmental awareness all through the world has motivated a standard change toward planning and designing better materials having good performance, which are very much suited to the environmental factors. The purpose of this study is to investigate the impact on mechanical, thermal and water absorption properties of sawdust-based composites reinforced by epoxy, and the amount of sawdust in each form.

Manufacturing of the sawdust reinforced epoxy composites is the main area of the research for promoting the green composite by having good mechanical properties, biodegradability or many applications. Throughout this research work, the authors emphasize the importance of explaining the methodology for the evaluation of the mechanical and water absorption properties of the sawdust reinforced epoxy composites used by researchers.

In this paper, a comprehensive review of the mechanical properties of sawdust reinforced epoxy composite is presented. This study is reported about the use of different Wt.% of sawdust composites prepared by different processes and their mechanical, thermal and water absorption properties. It is studied that after optimum filler percentage, mechanical, thermal properties gradually decrease, but water absorption property increases with Wt.% of sawdust. The changes in the microstructure are studied by using scanning electron microscopy.

The novelty of this study lies in its use of a systematic approach that offers a perspective on choosing suitable processing parameters for the fabrication of composite materials for persons from both industry and academia. A study of sawdust reinforced epoxy composites guides new researchers in the fabrication and characterization of the materials.

The purpose of this paper is to show a bibliographical review of the applications of the MTS throughout the time and the different fields.

The Mahalanobis-Taguchi system (MTS) is an analytical method used for the diagnosis and/or pattern recognition of multivariate data for quantitative decision making.

Its scope is very broad, ranging from engineering, medicine, education, and manufacturing, among others. This work presents a classification of the literature in the following areas of the MTS: introduction of the method, cases of study/application, comparison with other methods, integration and development of the MTS with other methods, construction of Mahalanobis space, dimensional reduction and threshold establishment. It realized a wide search of the publications in magazines and congresses.

This paper is a summary of the main applications, contributions and changes to MTS.