COVID-19 pandemic has changed the lifestyle of all aspects of life. These circumstances have created new patterns in lifestyle that people had to deal with. As such, full and direct dependence on the use of the unsafe Internet network in running all aspects of life. As example, many organizations started officially working through the Internet, students moved to e-education, online shopping increased, and more. These conditions have created a fertile environment for cybercriminals to grow their activity and exploit the pressures that affected human psychology to increase their attack success. The purpose of this paper is to analyze the data collected from global online fraud and cybersecurity service companies to demonstrate on how cybercrimes increased during the COVID-19 epidemic. The significance and value of this research is to highlight by evident on how criminals exploit crisis, and for the need to develop strategies and to enhance user awareness for better detection and prevention of future cybercrimes.

Quantitative methods help farmers plan and make decisions. An apt example of these methods is the linear programming (LP) model. These methods acknowledge the importance of economizing on available resources among them being water supply, labor, and fertilizers. It is through this economizing that farmers maximize their profit. The significance of linear programming is to provide a solution to the existing real-world problems through the evaluation of existing resources and the provision of relevant solutions. This research studies various LP applications including feed mix, crop pattern and rotation plan, irrigation water, and product transformation; that have the main role to enhance various facets of the agriculture sector. The paper will be a review that will probe into the applications of the LP model and it will also highlight the various tools that are central to analyzing LP model results. The review will culminate in a discussion on the different approaches that help optimize agricultural solutions.

The benefits of crowdsourcing are enabled by open environments where multiple external stakeholders contribute to a firm's outcomes. In recent years, crowdsourcing has emerged as a distributed model of problem-solving and market development. Here, model assignments are assigned to networked individuals to complete so that the manufacturing expense of a business can be minimized considerably. The main objective of this research is to develop a methodology which will capture the value generation process in the presence of uncertainties (Risk factors) in crowdsourcing context. This study is designed to make an important contribution to the field of practice and knowledge. Value-Risk Analysis of crowd souring is one of the under studies Worldwide, especially in Pakistan. Provided the need, we have discussed the crowdsourcing as business process and presented an understanding of the risks associated with crowdsourcing use and possible strategies that can be used to maximize the value and minimize the identified risks.
For the better understanding of crowdsourcing practices in Pakistan, three case studies were conducted based on three well reputed organizations of Pakistan and results gathered to help understand its practices, some of the risks associated with it and how they manage those risks.

The users of e-commerce sites are growing rapidly day by day for easy internet access where the performance of web applications plays a key role to satisfy the end-users. The performance of these websites or web applications depends on several parameters such as load time, fully loaded (time), fully loaded (requests), etc. This research tries to investigate and find out the parameters that affects the web performance and it has been tested on e-commerce applications of Bangladesh, where eleven parameters are considered and these are fully loaded (requests), first CPU idle, speed index, start render, load time, fully loaded (time), document complete (time), last painted hero, first contentful paint, and first byte. According to the analysis some applications need to take care of or the developers need to re-modify it. As per the investigation of scanned information, the applications fall under three classes. To start with, the applications do not demonstrate acceptable records to be investigated. The second and third classification applications required medium and high reaction times at the user end separately. Also, the fully loaded (requests)’ and document complete (requests) show the most noteworthy required time at the user end, where maximum values are 347 and 344 seconds individually.

Coffee leaf disease recognition is important as its quality can be affected by the disease like –rust. This paper presents a coffee leaf disease recognition system with the help of gist feature. This research can help coffee producers in diagnosis of coffee plants in initial stage. Rocole coffee leaf dataset is considered in this study. Input image is pre-processed first. Resize and filtering is used as pre-processing work. Gist feature is extracted from pre-processed image. Extracted features are trained with machine learning algorithm. In testing phase, features are extracted and tested with trained ML model. Simulation is done with 10 fold cross validation. Different ML models are used and selected the best among them based on performance. SVM achieved overall 99.8% accuracy in recognizing coffee leaf disease.