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The purpose of this paper is to initially perform Senti-WordNet (SWN)- and point wise mutual information (PMI)-based polarity computation and based polarity updation. When the SWN polarity and polarity mismatched, the vote flipping algorithm (VFA) is employed.

Recently, in domains like social media(SM), healthcare, hotel, car, product data, etc., research on sentiment analysis (SA) has massively increased. In addition, there is no approach for analyzing the positive or negative orientations of every single aspect in a document (a tweet, a review, as well as a piece of news, among others). For SA as well as polarity classification, several researchers have used SWN as a lexical resource. Nevertheless, these lexicons show lower-level performance for sentiment classification (SC) than domain-specific lexicons (DSL). Likewise, in some scenarios, the same term is utilized differently between domain and general knowledge lexicons. While concerning different domains, most words have one sentiment class in SWN, and in the annotated data set, their occurrence signifies a strong inclination with the other sentiment class. Hence, this paper chiefly concentrates on the drawbacks of adapting domain-dependent sentiment lexicon (DDSL) from a collection of labeled user reviews and domain-independent lexicon (DIL) for proposing a framework centered on the information theory that could predict the correct polarity of the words (positive, neutral and negative). The proposed work initially performs SWN- and PMI-based polarity computation and based polarity updation. When the SWN polarity and polarity mismatched, the vote flipping algorithm (VFA) is employed. Finally, the predicted polarity is inputted to the mtf-idf-based SVM-NN classifier for the SC of reviews. The outcomes are examined and contrasted to the other existing techniques to verify that the proposed work has predicted the class of the reviews more effectually for different datasets.

There is no approach for analyzing the positive or negative orientations of every single aspect in a document (a tweet, a review, as well as a piece of news, among others). For SA as well as polarity classification, several researchers have used SWN as a lexical resource. Nevertheless, these lexicons show lower-level performance for sentiment classification (SC) than domain-specific lexicons (DSL). Likewise, in some scenarios, the same term is utilized differently between domain and general knowledge lexicons. While concerning different domains, most words have one sentiment class in SWN, and in the annotated data set their occurrence signifies a strong inclination with the other sentiment class.

The proposed work initially performs SWN- and PMI-based polarity computation, and based polarity updation. When the SWN polarity and polarity mismatched, the vote flipping algorithm (VFA) is employed.

Development of thin film sensors with pH function for noninvasive real-time monitoring of spoilage of packed seafood such as fish, crab and shrimp are described in this study. It is also the purpose of this study to enhance the leaching resistance of the sensors by using a suitable strategy and to quantitatively correlate the sensor’s halochromism with the total volatile amine.

To prepare halochromic sensors with better leaching resistance, biocompatible materials such as starch, agar, polyvinyl alcohol and cellulose acetate along with a halochromic dye were used to prepare the thin film sensors. These thin films were evaluated for monitoring the spoilage of packed seafood at room temperature, 4°C and −2°C up to 30 days. The halochromic sensors were characterized using UV-visible and FT-IR spectroscopy.

CIELab analyses of the halochromism of the thin film sensors revealed that the color changes exhibited by the sensors in response to the spoilage of seafood are visually distinguishable. Further, the halochromic response of the thin films was directly proportional to the amount of total volatile base nitrogen that evolved from the packed seafood. Excellent leaching resistance was observed for the developed thin film sensors. The halochromic property of the sensors is reversible and thus the sensors are recyclable. Besides, the thin film sensors exhibited significant biodegradability.

This study provides insights for use of different biocompatible polymers for obtaining enhanced leaching resistance in halochromic sensors. Further, the color changes exhibited by the sensors are in line with the total volatile amines evolved from the packed seafood. These results highlight the importance of the developed halochromic thin film sensors for real-time monitoring of the spoilage of packed seafood.

This study aims to investigate the feasibility of employing a multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation. The formulated objectives are the minimisation of the total allocation cost of the anti-fraud capacities and the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots.

From the literature survey conducted and primary qualitative data gathered from the 17 licenced banks in South Africa on fraud investigators, the suggested fraud investigators are the organisation’s finance department, the internal audit committee, the external risk manager, accountants and forensic accountants. These five human resource capacities were considered for the formulation of the multi-objectives integer programming (MOIP) model. The MOIP model is employed for the optimisation of the employed capacities for cyberfraud mitigation to ensure the effective allocation and utilisation of human resources. Thus, the MOIP model is validated by a genetic algorithm (GA) solver to obtain the Pareto-optimum solution without the violation of the identified constraints.

The formulated objective functions are optimised simultaneously. The Pareto front for the two objectives of the MOIP model comprises the set of optimal solutions, which are not dominated by any other feasible solution. These are the feasible choices, which indicate the suitability of the MOIP to achieve the set objectives.

The results obtained indicate the feasibility of simultaneously achieving the minimisation of the total allocation cost of the anti-fraud capacities, or the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots – or the trade-off between them, if they cannot be reached simultaneously. This study recommends the use of an iterative MOIP framework for decision-makers which may aid decision-making with respect to the allocation and utilisation of human resources.

The originality of this work lies in the development of multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation.

This study aims to extend the driving range by on-board charging with use of photovoltaic (PV) source, avoiding the dependency on the grid supply and energy storage system in addition to that reduce the conversion complexity influenced on converter section of electric vehicle (EV) system.

This paper proposed a PV fed integrated converter topology called integrated single-input multi-output (I-SIMO) converter with enriched error tolerant fuzzy logic controller (EET-FLC) based control technique to regulate the speed of brushless direct current motor drive. I-SIMO converter provides both direct current (DC) and alternating current (AC) outputs from a single DC input source depending on the operation mode. It comprises two modes of operation, act as DC–DC converter in vehicle standby mode and DC–AC converter in vehicles driving mode.

The use of PV panels in the vehicle helps to reduce dependence of grid supply as well as vehicle’s batteries. The proposed topology has to remove the multiple power conversion stages in EV system, reduce components count and provide dual outputs for enhancement of performance of EV system.

The proposed topology leads to reduction of switching losses and stresses across the components of the converter and provides reduction in system complexity and overall expenditure. So, it enhances the converter reliability and also improves the efficiency. The converter provides ripple-free output voltage under dynamic load condition. The performance of EET-FLC is studied by taking various performance measures such as rise time, peak time, settling time and peak overshoot and compared with conventional control designs.

This study aims to examine the relationship between operational CRM technologies, human CRM resources, organizational CRM resources and customer satisfaction toward Islamic-based hotels in Indonesia. The role of customer knowledge is examined as the moderating variable in each relationship, intertwining both independent and dependent variables.

This study adopts a quantitative deductive approach. Descriptive and other statistical analyses, namely, confirmatory factor analysis and structural equation model of Smart partial least squares, are used. The questionnaires are disseminated to employees of 24 Islamic-based hotels located in Indonesia. The selection of 136 respondents is made using the stratified sampling technique.

The results established that the three variables of technological resources, human resources and organizational resources have a positive and significant effect on customer satisfaction. Next, customer knowledge moderates the effect of technological resources, human resources and organizational resources on customer satisfaction insignificantly.

This study highlighted the role of the newly adopted customer knowledge as a moderating factor in the relationship between the three components of CRM and customer satisfaction in a vulnerable industry (hotels, especially the Islamic-based one) in a developing country.

The COVID-19 pandemic virus has affected the largest economies around the world, especially Group 8 and Group 20. The increasing numbers of confirmed and deceased cases of the COVID-19 pandemic worldwide are causing instability in stock indices every day. These changes resulted in the G8 suffering major losses due to the spread of the pandemic. This paper aims to study the impact of COVID-19 events using country lockdown announcement on the most important stock indices in G8 by using seven lockdown variables. To find the impact of the COVID-19 virus on G8, a correlation analysis and an artificial neural network model are adopted.

In this study, a Pearson correlation is used to study the strength of lockdown variables on international indices, where neural network is used to build a prediction model that can estimate the movement of stock markets independently. The neural network used two performance metrics including R² and mean square error (MSE).

The results of stock indices prediction showed that R² values of all G8 are between 0.979 and 0.990, where MSE values are between 54 and 604. The results showed that the COVID-19 events had a strong negative impact on stock movement, with the lowest point on the March of all G8 indices. Besides, the US lockdown and interest rate changes are the most affected by the G8 stock trading, followed by Germany, France and the UK.

The study has used artificial intelligent neural network to study the impact of US lockdown, decrease the interest rate in the USA and the announce of lockdown in different G8 countries.

The purpose of this paper is to carry out phytochemical investigations of different extracts of Eucalyptus globulus bark such as aqueous, methanolic and supercritical carbon dioxide fluid extract (SCFE) with ethyl acetate as entrainer. Three fractions (Eu 8, 9 and 10) containing steroidal δ‐lactone were isolated from SCF extract and the structure of Eu‐10 was earlier determined on the basis of NMR, HPLC‐MS, X‐Ray crystallography.

Column chromatography led to the isolation of flavonoids, tannins, steroids, etc. in different solvent systems. Isolated steroidal lactone (Eu‐8,9&10) of Withanolide series were tested for the presence of total phenolic content, total flavonoid content and the results were expressed as gGAE/100 g (TPC), and gQE/100 g (TFC), respectively. The antioxidant capacity was evaluated based on their ability to scavenge free radicals generated from ABTS, DPPH, FRAP and H₂O₂ by spectrophotometric method.

The result of the present study showed that different extracts of E. globulus bark and the isolated fractions, exhibited different antioxidant activity. This was due to the fact that they contained different amounts of flavonoid and phenolic compounds as per their ability to solubilize these compounds; the high scavenging property of E. globulus may be attributed to hydroxyl groups existing in the phenolic compounds. All the samples exhibited different extent of antioxidant activity (AOA) and showed higher potency when compared with BHT in scavenging action of DPPH free radical. Comparative data analysis showed SCF extract to be better than methanolic and aqueous extracts, both in terms of yield and AOA, while Eu‐10 was the best amongst purified fractions.

The present research has serious implications on identification of natural antioxidants from E. globulus. Natural antioxidants with better structure‐activity relationship are under investigation. Isolation of withanolide from Eucalyptus bark has opened newer horizon for its use.

Collection of Eucalyptus bark from the forest (a forest waste) by women folk can be a source of revenue generation and thus has social implication as well. It is an important agro product.

The steroidal lactone (Eu‐10) showed highest radical scavenging effect even at IC50, thus the isolated lactone proved to be the best potential scavenger of free radicals amongst all crude extracts and the isolated fractions.

This paper aims to study the evolution of definitions of internet of things (IoT) through time, critically assess the knowledge these definitions contain and facilitate sensemaking by providing those unfamiliar with IoT with a theoretical definition and an extended framework.

164 articles published between 2005 and 2019 are collected using snowball sampling. Further, 100 unique definitions are identified in the sample. Definitions are examined using content analysis and applying a theoretical framework of five knowledge dimensions.

In declarative/relational dimensions of knowledge, increasing levels of agreement are observed in the sample. Sources of tautological reasoning are identified. In conditional and causal dimensions, definitions of IoT remain underdeveloped. In the former, potential limitations of IoT related to resource scarcity, privacy and security are overlooked. In the latter, three main loci of agreement are identified.

This study does not cover all published definitions of IoT. Some narratives may be omitted by our selection criteria and process.

This study supports sensemaking of IoT. Main loci of agreement in definitions of IoT are identified. Avenues for further clarification and consensus are explored. A new framework that can facilitate further investigation and agreement is introduced.

This is, to the authors’ knowledge, the first study that examines the historical evolution of definitions of IoT vis-à-vis its technological features. This study introduces an updated framework to critically assess and compare definitions, identify ambiguities and resolve conflicts among different interpretations. The framework can be used to compare past and future definitions and help actors unfamiliar with IoT to make sense of it in a way to reduce adoption costs. It can also support researchers in studying early discussions of IoT.