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The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.

Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.

The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.

The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

The present world is crippled with the pandemic coronavirus (Covid-19). The pandemic that originated in Wuhan city of China has sent every country in the world in an unprecedented situation that has social and economic impacts. This paper aims to explore whether epidemics and pandemics have any impact on consumption patterns among rural and urban consumers in India. Taking pandemic Covid-19 as a case study, it was explored how this pandemic impacted the consumption pattern of consumers in India; what are the similarities and/or differences between rural and urban consumers that are found in their consumption habits in the wake of Covid-19 pandemic.

The required data was collected through questionnaires sent to respondents. Approximately 500 respondents were contacted through the mail to fill in the survey questionnaire. Despite the sincere efforts, a total of 175 complete survey questionnaires were filled in by respondents. The study used SPSS Statistics version 25 software for the analysis of data.

It was found that epidemics and pandemics have a profound impact on the pattern of consumption in India. The study reveals that consumers resort to panic buying in the initial stages of epidemics and pandemics. It was found that consumption habits of consumers went a sea change and they were spending largely on essentials only. The study also reveals that the majority of consumers would like to continue in the same consumption habits as that of during COVID-19. The consumption pattern of urban consumers witnessed more change than the consumption pattern of rural consumers. It is due to the closure of eateries and restaurants, shopping malls, movie theatres, etc., in urban areas that forced the change in the consumption pattern of urban consumers.

The research has a limitation of using a less sample size. For the generalizations, more robust studies can be conducted with more data.

The findings of the study will give marketers an insight for framing their policies in the wake of epidemics and pandemics.

The research adds to the existing body of knowledge. There are plenty of studies on the behaviour of consumers. However, there are no major studies that focus on the behaviour of consumers during the outbreak of a pandemic. So, this study fills this gap in the existing body of knowledge.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

This paper focuses on two examples of constitutional corruption in India where the constitution is used for questionable political reasons by the Bharatiya Janata Party under the leadership of Prime Minister Narendra Modi.

The paper relies on public documents and media reports to analyse Prime Minister Modi's handling of the purchase of Rafale jet fighters from France and the revocation of Articles 370 and 35A which resulted in the division of the State of Jammu and Kashmir.

Constitutional and democratic norms were violated in both cases, but the Supreme Court did not find any irregularities in the sale of the Rafale jet fighters. The second case is under challenge in the Supreme Court. The analysis reveals how the Modi government has undermined democratic values and used constitutional provisions to pursue its partisan and ideological agenda.

The paper focuses attention on the often neglected topic of constitutional corruption in India.

The purpose of this paper is to determine the role of service quality (SQ), trust and commitment to customer loyalty (CL) for telecom service users. Further, the moderating role of gender, marital status and connection type within the model was tested.

A measurement model was created based on valid 615 responses from Indian TSUs for SQ, trust, commitment and loyalty with the help of partial least squares structural equation modeling (PLS-SEM). Multi-group analysis (MGA) was conducted to understand the moderating effect of marital status, gender and connection type within the model.

The results suggest that, out of five dimensions of SQ, only responsiveness, assurance and empathy have a significant positive relationship with both commitment and trust. Tangibility has a significant positive relationship with trust only. Both commitment and trust have a significant impact on loyalty. It was noticed that both commitment and trust act as mediators between three SQ dimensions (assurance, empathy and responsiveness) and CL. MGA revealed that empathy and responsiveness positively induce trust in telecom users who are single. Whereas, assurance increases commitment toward telecom service providers in married users. Assurance and empathy significantly contribute toward commitment and trust, respectively, in male users as compared to females. Empathy was found important for postpaid users for trust-building, whereas trust was found to be more important for prepaid users to stay loyal to the service provider.

This article contributes toward understanding the role of SQ, trust and commitment to CL moderated by marital status, gender and connection type in an integrated model concerning telecom service.

Many problems occur when assigning tasks to work centres, especially in determining the required number of workstations for line balancing which requires a minimum theoretical number of workstations. The most common problem is bottleneck. In this paper, a method is proposed to solve floating tasks problem in single-model line when the actual required number of workstations exceeds the minimum theoretical number, and the standard time of the floating task (work center) exceeds the cycle time. The floating task will represent a critical bottleneck activity in line. The proposed method depends on minimizing the standard time of critical bottleneck and non-critical activities by a minimum free-floating time depends on the average of slack times of the non-critical activities, and it will increase the line efficiency from (77%) to (88%), and balance delay is minimized from (23%) to (12%).

On 20 March 2020, the four adult convicts of the 2012 Delhi rape case were executed after a long debate regarding the punishment for their crime. The Delhi rape case, unlike others, was also given to the fast track court because of the worldwide outrage India received in its aftermath. Otherwise, most rape survivors rarely speak out and if they do, their lives are often endangered and threatened, depending on the severity of the case itself and the perpetrator's rank in the society. Through the analysis of Aniruddha Roy Chowdhury's, 2016 film Pink, and Ajay Bahl's film Section 375 (2019), this chapter explores the different ways in which mainstream Hindi cinema deals with such questions, especially in its depictions of courts. Both these films foreground India's contemporary cultural systems of fear that silence the rape survivors. They also imply that in the court cases, unless the specific court case faces intense global publicity, as was the case of the Delhi gang rape, rape survivors will never want to speak out. Moreover, the rape survivors will also hesitate to file a First Information Report (FIR) – a document that records crimes by the police against their perpetrators – limiting any possibility for justice for them. The laws surrounding rape cases are obscure and complex and finding justice for a rape victim (unless it is on a global level) is not an easy venture in India. At the time of the #metoo movement, the rape laws in India are not designed in such a way to arguably encourage victim-survivors to speak up. Instead, if rape survivors do decide to confront their perpetrators, they not only face ostracisation from society but also the danger of losing loved ones and endanger their lives as well.

Social media networks like Twitter, Facebook, WhatsApp etc. are most commonly used medium for sharing news, opinions and to stay in touch with peers. Messages on twitter are limited to 140 characters. This led users to create their own novel syntax in tweets to express more in lesser words. Free writing style, use of URLs, markup syntax, inappropriate punctuations, ungrammatical structures, abbreviations etc. makes it harder to mine useful information from them. For each tweet, we can get an explicit time stamp, the name of the user, the social network the user belongs to, or even the GPS coordinates if the tweet is created with a GPS-enabled mobile device. With these features, Twitter is, in nature, a good resource for detecting and analyzing the real time events happening around the world. By using the speed and coverage of Twitter, we can detect events, a sequence of important keywords being talked, in a timely manner which can be used in different applications like natural calamity relief support, earthquake relief support, product launches, suspicious activity detection etc. The keyword detection process from Twitter can be seen as a two step process: detection of keyword in the raw text form (words as posted by the users) and keyword normalization process (reforming the users’ unstructured words in the complete meaningful English language words). In this paper a keyword detection technique based upon the graph, spanning tree and Page Rank algorithm is proposed. A text normalization technique based upon hybrid approach using Levenshtein distance, demetaphone algorithm and dictionary mapping is proposed to work upon the unstructured keywords as produced by the proposed keyword detector. The proposed normalization technique is validated using the standard lexnorm 1.2 dataset. The proposed system is used to detect the keywords from Twiter text being posted at real time. The detected and normalized keywords are further validated from the search engine results at later time for detection of events.