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There are various system techniques or models which are used for access control by performing cryptographic operations and characterizing to provide an efficient cloud and in Internet of Things (IoT) access control. Particularly in cloud computing environment, there is a large-scale distribution of these traditional symmetric cryptographic techniques. These symmetric cryptographic techniques use the same key for encryption and decryption processes. However, during the execution of these phases, they are under the problems of key distribution and management. The purpose of this study is to provide efficient key management and key distribution in cloud computing environment.

This paper uses the Cipher text-Policy Attribute-Based Encryption (CP-ABE) technique with proper access control policy which is used to provide the data owner’s control and share the data through encryption process in Cloud and IoT environment. The data are shared with the the help of cloud storage, even in presence of authorized users. The main method used in this research is Enhanced CP-ABE Serialization (E-CP-ABES) approach.

The results are measured by means of encryption, completion and decryption time that showed better results when compared with the existing CP-ABE technique. The comparative analysis has showed that the proposed E-CP-ABES has obtained better results of 2373 ms for completion time for 256 key lengths, whereas the existing CP-ABE has obtained 3129 ms of completion time. In addition to this, the existing Advanced Encryption Standard (AES) scheme showed 3449 ms of completion time.

The proposed research work uses an E-CP-ABES access control technique that verifies the hidden attributes having a very sensitive dataset constraint and provides solution to the key management problem and access control mechanism existing in IOT and cloud computing environment. The novelty of the research is that the proposed E-CP-ABES incorporates extensible, partially hidden constraint policy by using a process known as serialization procedure and it serializes to a byte stream. Redundant residue number system is considered to remove errors that occur during the processing of bits or data obtained from the serialization. The data stream is recovered using the Deserialization process.

The provision of fair and remunerative prices to farmers through government intervention is one of the key debates to address the farmers' distress in India. This article identifies how different marketing channels are responsible for higher price realization over the officially announced minimum support price (MSP).

The study uses the NSSO-SAS, 2012–13 and NSSO-SAS, 2018–19 for Aggregate level data and Unit Level Data on the Situation Assessment Survey of Farmers' households. It uses logit regression to determine the factors responsible for better price realization.

Our major findings indicate that two factors importantly determine better price realization than MSP. Firstly, government agencies provide better prices for crops covered by MSP, such as paddy, wheat and cotton. However, the probability of receiving higher prices increases for some crops if the farmers belong to the upper land size classes and upper social category. Secondly, jowar, bajra, maize and ragi, other important crops that don't benefit from government agencies, may require higher levels of procurement at the state level.

The present study only analyzes selected major crops. Distance is an important factor in choosing a marketing channel that is not incorporated due to unavailability in NSS Data.

The study is based on the latest original empirical evidence and sheds light on the variation in price realization in different agricultural marketing channels in India.

It has become an ongoing debate whether Indian commodity futures markets can accommodate farmers. The purpose of this paper is to examine whether Indian commodity futures markets help rationalize farmers’ price expectation. The study starts with questions on the efficiency and other roles of commodity futures markets.

From a sectoral standpoint and economic importance, the study considers pepper, coffee, and natural rubber (NR) futures and spot markets. The efficiency of futures markets, divergence/convergence and causality between futures and spot markets have been studied by employing co-integrations, error correction and causality models. The sample period of the data are taken from the inception of futures trading. These three commodities are also compared on the basis of trading at the futures markets vs spot markets.

Analysis shows that though pepper futures market is informationally efficient in price discovery, while coffee and NR spot markets do the process faster. Pepper and coffee futures and spot prices exhibit the convergence; NR shows a sign of divergence. Unidirectional causality from pepper futures to spot market is observed wherein the former was weakly exogenous to the latter and while, bidirectional causality is observed in coffee and rubber. Coffee spot appears weakly exogenous while this remains inconclusive in the case of NR.

The authors analyzed the futures markets in rationalizing the spot market price in three plantation crops in India. In order to make the study more generalizable, further research is warranted in other commodities including those prices of which are government regulated.

The paper is unique in terms of understanding the interaction or interrelationship between futures markets and spot markets and drawing inferences about the role of futures markets in price formation in plantation commodities like pepper, coffee and NR.

In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.

To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.

The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.

To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.

Although there are many studies addressing the learning styles of business students as well as students of other disciplines, there are few studies which address the learning style preferences of statistics students. The purpose of this study is to explore the learning style preferences of statistics students at a United Arab Emirates University (UAEU). Furthermore, it investigates whether there are statistically significant differences along the four dimensions of learning styles due to students’ demographic and academic characteristics.

Questionnaires were distributed to the whole population which included 79 undergraduate statistics students at the UAEU, of which 69 returned the questionnaire. Descriptive statistics such as frequencies and percentages were used to present the main characteristics of respondents and the results of the study. Additionally, a chi-square test was used to find out if there were significant differences along the four dimensions of learning style preferences due to students’ demographic and academic characteristics.

The results indicate that UAEU undergraduate statistics students have balanced preferences along the four dimensions of learning styles. Results also suggest that there are no statistically significant differences along the four dimensions of learning styles due to students’ demographic and academic characteristics, except in the active-reflective and sensing-intuitive dimensions with respect to high school type (private vs public).

There are a number of limitations associated with this study. First, the findings of the study are based on data from only one university. Second, the sample was small and limited to undergraduate statistics students and, therefore, it excluded graduate students who might have had different experiences. Third, the results are based on a self-reported questionnaire and this, in turn, might have affected the reliability of the results On the other hand, it has a number of implications for educators and students. Educators will benefit from the results of this study in the sense that they will adopt teaching styles and strategies that match learning styles of the majority of their students. Students themselves will benefit from knowing their own learning style.

The present study is the first attempt to explore learning styles preference of undergraduate students not only in the UAE setting but also in the developing country setting.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

Green Lean Six Sigma (GLSS) is a comprehensive approach that aims to reduce waste, emissions and non-value added activities, thus mark an effective impact on sustainability of firms. Despite the numerous benefits of GLSS implementation, it is evident that Micro Small and Medium Enterprises (MSMEs) are still struggling to understand, integrate and implement this strategy. This research work provides a comprehensive analysis of GLSS within MSMEs and proposes a conceptual integration framework of GLSS for improving economic and environmental dimensions of sustainability MSMEs.

A systematic literature review (SLR) methodology was planned to assess and analyse the research articles from 2007 to 2022. Different key elements of GLSS such as barriers, enablers and tool sets have been thoroughly reviewed and analysed for MSME organisations to understand their behaviour and effectively adopt the GLSS approach in their operations.

This study provides the analysis of different perspectives of GLSS and this will contribute to improve different metrics related to emissions and quality in MSMEs. It provides MSMEs industrial managers with a comprehensive knowledge base of GLSS elements, enabling effective deployment.

The present study provides a significant knowledge base and know-how of GLSS to researchers that will assist in deploying this sustainable approach in different industrial domains. The study also assists industrial managers by providing a systematic framework of GLSS for MSMEs. Further, the study also supports society by proving pathway to improve the environmental dynamics through the execution GLSS.

The study is of the first kind that review different facets of GLSS related to MSME. The study not only enhances theoretical know how of GLSS but also guides practitioners how to implement comprehensive GLSS program for improved environmental sustainability.

Recent years have documented the growing interest in using WhatsApp in higher education. However, the determinants of students’ satisfaction and loyalty towards WhatsApp groups have received less attention. This study aims to extend the Delone and McLean information system success model by incorporating social and emotional factors to investigate the drivers of satisfaction.

Data were collected through questionnaires completed by 308 undergraduate students. The partial least squares technique was used for data analysis.

The findings reveal that information quality, trust in members and social usefulness play crucial roles in shaping students’ satisfaction and loyalty to WhatsApp groups. System quality has no significant effect on satisfaction. Furthermore, emotional connection negatively moderates the relationship between social usefulness and satisfaction.

The findings of this study will be useful for educators and practitioners seeking to integrate WhatsApp into their pedagogical repertoire. The results demonstrate the importance of considering the social and emotional needs of students in addition to the quality of the information provided.

To the best of the authors’ knowledge, this study is the first attempt to integrate system characteristics, particularly with social and emotional factors. Furthermore, this study extends the literature on WhatsApp use in higher education by testing the drivers of students’ satisfaction and loyalty.