Intelligent smart farming and crop visualization

Agriculture is a backbone of the economy for any country. Being a part of primary sector, all the other major sectors and industries depend on it for their raw materials. It satisfies the basic needs of human like food, clothing and shelter. However, due to climate change and other related problems, it is becoming increasingly difficult for farmers to keep pace with rising demands. As per estimate by Food and Agricultural Organization of United Nations, around 55 percent of India’s total land area is used for agricultural produce. India is also a leading producer and exporter of some of the major crops. Still there are concerns regarding food security in India by United Nations. For overcoming the natural hurdles, involvement of technology is required for better analysis and decision-making. Through this paper, we plan to propose a visualization technique, which can help farmers to make better decision regarding crop selection. The study proposes a novel framework where farmers can get detailed information about the crops grown in any particular district and also area, production and productivity of any particular crop. This web-based agri solution will help farmers to take smart farming decision by resource optimization and smart planning

An analysis of mobile pass-codes in case of criminal investigations through social network data

In today’s modern world, mobile has turned out to be one of the essentials for all the people irrespective of their status and profession. With the help of this device, all the data about a person can be tracked down (i.e. from the diet, diseases to contacts and transactions). In case of criminal investigations, inspectors need to collect information about a victim or accused. For this, the individual’s mobile phone plays a vital role. However, it is very difficult to access a device without its owner’s permission. Suppose, if the victim is dead or not willing to expose the information, the cyber police should perform many complex tasks to retrieve the data. Thus, there is a great need to analyze this task and make it feasible to find out pass-codes in order to access the mobile device. This paper explains how passwords can be cracked with ease with the help of survey and training of large dataset. We all know that people these days are very active on social media, which makes it easy to track them. In this work, we analyze a few passcodes and patterns and try to test that data by giving the queries. Here, we can analyze and retrieve the data from an individual’s social media such as date of birth, name, personal information and try to predict the passcode in very few attempts as it turns out that majority of the time, the passcode is generally predictable based on some key characteristics identified in this paper

Comparative Analysis of Machine Learning Algorithms for Stock Market Prediction During COVID-19 Outbreak

In the current period of time, when there is a havoc across the world due to COVID-19 virus outbreak, it becomes very important to foresee the impact of this pandemic on the world economy. This has attracted us to analyze and predict the stock market prices of some international IT international companies which provide employment to thousands of people and create revenue for many countries namely Google, Microsoft, Apple and Amazon. In this study, we have implemented algorithms such as SVM and LSTM on stock market data to see if major IT companies see a rise or fall during the COVID-19 pandemic. We have also used ARIMA forecasting method to predict the stocks of above mentioned 4 companies. This paper provides a simple but original statistical analysis of the impact of the COVID-19 pandemic on stock market risk for 4 major IT companies of the world. Results revealed that while some businesses like personal computers from Microsoft, I phone handsets, sale of luxury and fashion goods at Amazon has declined during the pandemic, thus leading to fall of stocks. However, some prominent other segments like online shopping, cloud computing and streaming video from Amazon, oversees Office, Dynamics, Skype, LinkedIn Intelligent Cloud from Microsoft, Google’s ad sales during the crisis and issue of cheap bonds by Apple came out to be the winning corporate strategies to fight the negative economic effect of COVID-19 and to stabilize the situation of stocks in coming months. This study may help investors and companies to sustain the tide of economic fall.</p

Analysis of geolocation dataset and fertiliser availability to farmers at minimum cost

According to World Bank’s survey, India has the largest area of arable land of approximately 156.4 million hectares which is about 57% of total land in the country. The demand for agricultural products is high in India which corresponds to high use of fertilisers. Both natural and synthetic fertilisers are equally predominant. The primary issue with fertilisers is how to buy the fertilisers with lesser cost where cost of travelling and transportation plays a major role. The poor market research and unawareness of the farmers makes them vulnerable while buying fertilisers. They levy unnecessary costs on fertilisers and increase their expenditure for the yield. We analysed this growing concern for a vast agricultural country like India and came up with proper insights and solution. Our solution involves analysis of distances between geolocations or places where fertilisers are available (e.g. fertiliser shop or retail store for fertiliser) and allowing the farmers to go select amongst those available storesthe nearest and best place to purchase the fertilisers

Digital innovations: Implications for African agribusinesses

Despite the agribusiness industry's potential, it is still characterised by a high level of conventional means of production, denying it numerous opportunities along the value chain in Africa. Meanwhile, adopting modern means of production using digital innovations can boost farmers' efficiency. Given the relevance of digital innovations within the agribusiness industry, the current paper gives an overview of digital innovations in the agribusiness industry, particularly in Africa, by synthesising existing literature on digital innovations to consolidate scattered ideas and give recommendations for practice and future studies. The PRISMA technique was adopted to synthesise 47 existing relevant articles from the Scopus database. Bibliometric tools such as VOSviewer and R-Package-Bibliometrix were used to analyse the data. From the results, most papers on African digital innovations emanated from a journal with a broader scope—Sustainability Journal (Switzerland). Most of the funding for digital innovation studies in agribusiness was sponsored by foreign donors outside the African Continent, with the Consortium of International Agricultural Research Centers in France sponsoring the most research. Based on the publication synthesis and the trend of publications, five themes, namely, digital innovations in the agricultural value chain, training and skills development through digital innovations, digital finance innovations in agribusiness, precision digital innovations: agribusiness survival, and drivers of digital innovation adoption in agribusiness were identified. From the results, digital agribusiness innovations in marketing, production, and finance have been profound in Africa, though they are skewed towards a few South, West, and North African countries. Meanwhile, the drivers and challenges of digital innovations in Africa have been mainly social, economic, political, and institutional factors. Given the results, it is recommended that, since most of the studies were primarily sponsored by foreign governments, thus betraying the commitment of the African continent's readiness to transform the agribusiness sector via innovation and technology, African Governments, first, and second, non-governmental organisations and other agricultural donors should prioritise the digitalisation of the agriculture sector by integrating them into economic development plans and making relevant resources available to all actors from upstream to downstream