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The purpose of this research study is to explore simple collaborative technique for teaching mathematics and thus improving the problem solving skills of the students. Better pedagogic activities are required to motivate the students to perceive mathematics as a subject that stimulates problem-solving skills required for engineers.

This paper presents a research study on the application of technology-supported collaborative learning in improving the problem-solving skills of first-year engineering students in a mathematics course. The experiment was conducted in a mathematics course “Engineering Calculus” with 286 first-year engineering students in two groups: experimental group (N = 60) and controlled group (N = 226). The academic performance of the students was measured and analyzed with statistical techniques.

From the results obtained, it was found that the academic performance of the experimental group was better than the controlled group. Also, interest shown by the students in the topic that dealt with collaborative learning was more than in other topics taught using conventional teaching methods.

The teachers are required to find effective pedagogical activities to improve the problem-solving skills in mathematics. The research work proposes a collaborative method in mathematics for attaining higher cognitive level in an entry level engineering course. The limitation of this study lies in group formation techniques and the grading policy which deals only with individual assessment scores.

Practice of collaborative learning is made easy with simple technology. A clear strategy for the conduct of collaborative learning sessions has been presented. The solutions recommended are practically feasible and does not require any special infrastructure or specific training.

Using technology in mathematics teaching may not be very easy for all teachers. Especially, for an undergraduate engineering fresher, mathematics may not be a very easy task. This work shall bridge the gap with simple technology-assisted collaborative learning. The teachers need not spend too much time and effort in learning technology for mathematics teaching. The effect of this learning is significant in terms of the performance and satisfaction evaluation.

This work presents a systematic implementation of collaborative learning that shall result in improved problem-solving skills and engaging learning sessions. The statistical analysis methods and the visualization of obtained results shall help in understanding the implications of the presented work. Practice of collaborative learning is made easy with simple technology. The solutions recommended are practically feasible and does not require any special infrastructure or specific training.

The purpose of this paper is to empirically investigate the impact of board characteristics such as size, independence, busyness and duality on the extent of discretionary disclosures of listed Indian firms.

A disclosure index with 110 items was constructed to assess the discretionary disclosures in the annual reports of listed firms. The study measured disclosure using 1,024 firm-year observations over 8 years from 2009 to 2016. Board characteristics such as size, independence, busyness and duality have been used in the study as indicators of corporate governance.

The results indicate that while the proportion of independent directors positively impacts the extent of discretionary disclosures, boards with duality and the busyness of the director have a negative impact. The size of the board does not significantly impact the extent of disclosures.

This study examines the discretionary disclosures made only in the annual reports. Future studies could examine information disclosed in other media. Moreover, this study uses an un-weighted self-constructed disclosure index, which is subject to its inherent limitations.

This study has examined the impact of the “busyness” of the director on the extent of disclosures. This variable has not been explored in prior studies. The significance of the variable indicates that the number of directorships held impacts the efficiency with which a director performs his/her role in the board. The study reiterates the need for firms and policymakers to focus on improving board independence and to move away from leadership structures with duality.

To augment the Insolvency and Bankruptcy Code, the Indian Government introduced the pre-packaged insolvency process exclusively for small and medium firms. This paper aims to critically review some of the key features of the process and also identifies potential glitches imminent in the initial years of implementation.

This study is descriptive and based on secondary data. The provisions of the pre-pack scheme, Insolvency and Bankruptcy Code and reports on the progress of insolvency resolution in India are used to substantiate the observations.

This study shows that pre-packs would certainly help enhance the small and medium enterprise insolvency resolution process in India. However, the ambitious time frame can be adhered to only if the institutional framework for bankruptcy is strengthened.

This paper is based on the initial regulatory provisions of the pre-pack process. Subsequent changes in regulations may affect the findings.

Some of the concerns in the process and the changes required to facilitate a smooth, speedy and efficient resolution process have been highlighted in this study.

Pre-packs are a very recent introduction to the insolvency regime in India. This paper makes a fervent attempt to explain the pre-pack process and the outcomes that can be expected in the early years after its rollout.

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.

This research aims to reveal the challenges faced by female entrepreneurs in Kosovo, emphasizing the challenge of work–life balance (WLB). As deliberated by many authors, concerning the term work–life balance, it is defined as the opportunity of balancing between personal life on the one hand and work on the other. The three research questions of this chapter were: do women entrepreneurs face difficulties on work–life balancing? do they involve other family members in business operations in order to help work–life balancing? and is their marital status a factor on WLB? This issue is more or less studied in the context of Kosovo. This is the reason why this research is conducted in Kosovo, with the sample of 24 female entrepreneurs. 21 questions of interviews are designed accordingly to the three research questions. The findings indicated that the majority of female entrepreneurs face difficulties on work–life balancing, but to overcome this challenge, they engage their family members on business activities and also, women who are married, especially those with more than one child have more difficulties on this aspect than those unmarried.

Fulfilling the gaps relied by previous studies and recommendations to female entrepreneurs in terms of better management of life and work simultaneously, it is considered the greatest contribution of this investigation.

Affective states in learning have gained immense attention in education. The precise affective-states prediction can increase the learning gain by adapting targeted interventions that can adjust the changes in individual affective states of students. Several techniques are devised for predicting the affective states considering audio, video and biosensors. Still, the system that relies on analyzing audio and video cannot certify anonymity and is subjected to privacy problems.

A new strategy, termed rider squirrel search algorithm-based deep long short-term memory (RiderSSA-based deep LSTM) is devised for affective-state prediction. The deep LSTM training is done by the proposed RiderSSA. Here, RiderSSA-based deep LSTM effectively predicts the affective states like confusion, engagement, frustration, anger, happiness, disgust, boredom, surprise and so on. In addition, the learning styles are predicted based on the extracted features using rider neural network (RideNN), for which the Felder–Silverman learning-style model (FSLSM) is considered. Here, the RideNN classifies the learners. Finally, the course ID, student ID, affective state, learning style, exam score and course completion are taken as output data to determine the correlative study.

The proposed RiderSSA-based deep LSTM provided enhanced efficiency with elevated accuracy of 0.962 and the highest correlation of 0.406.

The proposed method based on affective prediction obtained maximal accuracy and the highest correlation. Thus, the method can be applied to the course recommendation system based on affect prediction.

This study aims to examine the link between boards and audit committees and firm performance in Kuwaiti listed firms in the context of recent and extensive corporate governance regulatory reform.

Panel data regression analysis with fixed effects and clustered standard errors of firm performance for 61–97 listed industrial and services firms in Kuwait over a seven-year period. The dependent variables are the returns on assets and equity, the debt-to-equity ratio and leverage and Tobin’s Q and the independent variables comprise board of directors and audit committee characteristics, including size, the number of meetings and the numbers of independent and outside board and expert committee members. Firm size, subsidiary status and cash flow serve as control variables.

Mixed results with respect to the characteristics of the board of directors. Board size and independent and outsider board members positively relate only to Tobin’s Q and insiders only to debt to equity. For audit committee characteristics, committee size, independence and expertise positively relate to the return on equity and committee size and expertise only to Tobin’s Q. Of the five performance measures considered, board and audit committee characteristics together best determine Tobin’s Q.

Data from a single country limits generalisability and control variables necessarily limited in a developing market context. Need for qualitative insights into corporate governance reform as a complement to conventional quantitative analysis. In combining accounting and market information, Tobin’s Q appears best able to recognise the performance benefits of good corporate governance in terms of internal organisational change.

The recent corporate governance code and guidelines reforms exert a mixed impact on firm performance, with audit committees, not boards, of most influence. But recent reforms implied most change to boards of directors. One suggestion is that non-market reform may have been unneeded given existing market pressure on listed firms and firms anticipating regulatory change.

Kuwait’s corporate governance reforms codified corporate governance practices already in place among many of its firms in pursuit of organisational legitimacy, and while invoking substantial change to audit committees, involved minor change to firm performance, at least in the short term. Some firms may also have delisted in expectation of stronger corporate governance requirements. Regardless, these direct and indirect processes both improved the overall quality of listed firm corporate governance and performance in Kuwait.

Seminal analysis of corporate governance reforms in Kuwait, which have rapidly progressed from no corporate governance code and guidelines to an initially voluntary and then compulsory regime. Only known analysis to incorporate both board of directors and audit committee characteristics. Reveals studies of the corporate governance–firm performance relationship may face difficulty in model specification, and empirical significance, given the complexity of corporate governance codes and guidelines, leads in changing firm behaviour and self-selection of firms into and out of regulated markets.

Majority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism. This research aims to propose the optimization of makespan, energy consumption and data transfer time (DTT) by considering the priority tasks. The research work is concentrated on the multi-objective approach based on the genetic algorithm (GA) and energy aware model to increase the efficiency of the task scheduling.

Cloud computing is the recent advancement of the distributed and cluster computing. Cloud computing offers different services to the clients based on their requirements, and it works on the environment of virtualization. Cloud environment contains the number of data centers which are distributed geographically. Major challenges faced by the cloud environment are energy consumption of the data centers. Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan. This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm. This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines. The energy model is developed for picking the task based on the fitness function. The simulation results show the performance of the multi-objective model with respect to makespan, DTT and energy consumption.

The energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine. The directed acyclic graph is used to represent the task dependencies. The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms. The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.

This paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm. The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing. The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection. The energy model is used as fitness function to the GA for selecting the tasks to perform the scheduling.

The purpose of this paper is to investigate the impact of board composition on the level of corporate internet reporting (CIR) practices.

This study uses content analysis to examine the CIR practices of 140 Indian companies selected from the Bombay Stock Exchange 200 index for the year 2015. CIR was measured on a comprehensive internet disclosure index of 136 items capturing both content and presentation dimensions. Regression analysis was used to explore the impact of board composition (board size, board independence, frequency of board meetings, CEO duality and family members on the board) and audit committee characteristics on CIR while controlling the impact of variables firm size, leverage, profitability and industry type.

The findings reveal that larger boards, boards with less family members and audit committees that meet more frequently are more likely to engage in CIR practices. In addition, larger firms and firms that make less use of debt tend to disclose more information on their websites.

The focus of the study has been on one aspect of corporate governance mechanisms i.e. board characteristics. Future studies can explore the impact of ownership structure on CIR practices.

This study extends the prior CIR research by demonstrating the effectiveness of corporate governance mechanisms in particular board characteristics in adopting internet reporting practices for Indian companies. The examination of the relationship between corporate reporting on the internet and corporate governance aids regulators in evaluating and enhancing the effectiveness of the boards.

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.