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The worldwide Covid-19 epidemic while affecting numerous places, has had a profound effect on virtual advertising and advertising and, globally, in the provinces and at the neighborhood level. except, this effect for the most element become positive, in contrast to what has seen in exclusive sectors which include economy, human sources, etc., whilst contamination reasons a variety of incapacity amongst clients and advertisers alike, in phrases of welfare, social work, inflation, business, and many others., the equal shifted conduct goes as a long way as running. , investing strength, getting into self-schooling, adopting new programs from the internet, expanding social and hygiene concerns, retaining distance strategies from complete regions, internet-based media willpower, get right of entry to online sources, etc., and this has greatly impacted the display and endorsed efforts. The moral movement has moved past the PC and digital international, which places open doors for advertisers and products to connect with clients more efficiently than ever before. With the arrival of expanded online media and the call for pc-generated content material, the evolved Media have given advertisers a part of the monetary freedom. At the equal time, this has, in turn, enabling advertisers to be extra proactive and to engage with the public at the same time as appearing excessive excellent demonstration programs. The purpose of this study is to explore, investigate, and recognize the effect of coronavirus on the digital market and businesses.

The purpose of the article is to substantiate the fiscal role of the excise tax by studying its information and functional potential and to model the dynamics of its payment by the brewing industry. Excise tax occupies a special place in a tax system of each state because, in addition to significant fiscal importance, it has a considerable regulatory impact on the production and consumption of certain categories of goods. Based on information systems in the article analyses and monitors the indicators of the excise tax payments on goods produced in Ukraine on the example of a particular enterprise in the brewing industry. By means of the initial data analysis of autocorrelation functions of volumes’ indicators of the accrued excise taxes on beer the expediency of modelling realization of such indicator dynamics on the basis of ARIMA model is proved. The analytical and statistical approaches to the formation of models for the implementation of forecast for the calculation of excise tax on beer of brewing industry enterprises are improved. The proposed approach is based on the values of autocorrelation of balances and partial autocorrelation, as well as methods of analysis of time series with gaps, which allows to use it in the economic activity of enterprises to make forecasts for the calculation and payment of the excise tax. This will produce financial effects for the brewing industry in terms of cost optimization and minimization of the excise tax risks.

Technology innovation affects both the public and private non-financial sectors, the zakat institution (Islamic social finance) included. The institution uses an online system to communicate with zakat payers and zakat recipients to ensure its effectiveness in terms of collection and distribution. This research focuses on the adoption of a technique known as AD/AC and ZP/ZR, which is mainly utilized by partially and non-privatized State zakat institutions in Malaysia. To this end, three (3) techniques pertaining to appraising the performance of Malaysia’s Zakat institutions in managing Zakat funds were established. The analysis also includes the adverse impact of Covid-19 pandemic on the performance of zakat collection of the respective zakat institutions in Malaysia. In this relation, a quantitative approach was adopted using the primary and secondary data collected from JAWHAR and various states’ zakat institutions. By utilizing a technique of online data collection and applying the AD/AC as well as ZP/ZR methods, the performance of Malaysia’s zakat institutions is appraised. Furthermore, with the help two (2) ratios and eight (8) scenarios the performance of the innovative technique of AD/AC and ZP/ZR for zakat collection and disbursement, efficiency and sustainability of zakat institutions in Malaysia were evaluated. The results show that most zakat institutions, particularly those that deal with zakat payments, have begun to use the technique AD/AC and ZP/ZR within their organizations. The performance of zakat institutions in all states in terms of zakat distribution and disbursement do not have a consistent trend for the period 2016-2020. However, there is still a dearth of technology being used for zakat disbursements and fund reporting. From the observed data, with 2016 being the exception, for each remaining year, a state in Malaysia is ranked as the best technology performer or the most efficient. Selangor - fully privatized state (2017), Pulau Pinang - fully privatized state (2018), Federal Territory - partially privatized state (2019) and Negeri Sembilan-partially privatized state (2020) are the most efficient for the respective year. The sustainability of each zakat institution in Malaysia in the presence of Covid-19 pandemic was ascertained as well. Interestingly, contrary to the efficiency result, all non-privatized states (Kedah, Sabah and Kelantan) and one partially privatized state (Negeri Sembilan) were sustainable in coping with Covid-19 pandemic, while all fully privatized states (Selangor and Pulau Pinang) were not. Overall, it was conjectured that the technique AD/AC as well as ZP/ZR would help to improve the operations of zakat institutions to become more efficient and successful in distributing zakat fund to the poor. However, as zakat collecting and distribution involved millions of records, its management is still debatable by all parties.

Co-Saliency object detection is the process of identifying common and repetitive objects from the group of images. Earlier studies have looked over several state-of-art deep neural network methodologies for co-saliency detection approach. The Deep CNN approaches rely heavily on co-saliency detection due to their potent feature extraction capabilities both deep and wide. This article assess the performance of several state-of-art deep learning model (VGG19, Inceptionv3, modifiedResNet, MobileNetV2 and PoolNet) for the purpose of co-saliency detection among images from benchmark datasets. All the models were trained on   70% part of the dataset and remaining were used for testing purpose. Experimental results show that modified ResNetmodel outperforms getting 96.53% accuracy as compared to other state-of-the-art deep neural network models.