Neural Networks for Handwritten English Alphabet Recognition

This paper demonstrates the use of neural networks for developing a system that can recognize hand-written English alphabets. In this system, each English alphabet is represented by binary values that are used as input to a simple feature extraction system, whose output is fed to our neural network system.Comment: 5 pages, 3 Figure, ISSN:0975 - 888

Present requirements of drawing up necessary changes in our petroleum usage to alleviate the detrimental aftermath of environmental contamination

14-18Climate change is one of the major issues in the current world. Such climate change-related problems have not only problematic for the environment, but also a major issue for the world as a whole. Orderly and properly analysis made in such regards is the need of the time. And in no cases, there is any time left to ignore such necessary steps for mitigating the negative impacts of climate change on our environment. Climate change-related mitigation is all about reducing the overall release of greenhouse gas emissions that are ultimately heating up our world. Mitigation strategies consist of analyzing and taking necessary steps to make the utilization of natural resources more energy efficient. It includes helping cities in creating more feasible transportation systems, such as increasing the uses of electric vehicles and biofuels in place of crude oils; enhancing the utilization of renewable energy sources such as solar power plants, wind energy-related equipment, and small and large hydropower plants for generating the electricity. Mitigation measures include all such functioning that is undertaken to decrease and control the greenhouse emissions outflows. Adaptations of all such measures can support the reduction in the vulnerability factors which have adverse impacts on global climate and results in climate changes. When we considered the transportation sector of the country India, which heavily depends upon fossil fuel usages. And such led to challenges relating to air pollution, energy security, and greenhouse gas emissions, which directly related to the increase in the demand for taking some concrete and mitigation steps by making transportation and energy sectors much more effective and efficient. Petrol and diesel fuel accounts for more than 90% of India's transportation sector's energy utilization. The transportation sector is India's one of the fastest-growing as well as highly energy consuming sector with a growth rate of 6.8 %. Because of such huge demand in the transportation sector, the utilization of petroleum related product increased significantly. As a result of which 7 of the top 10 polluted cities in the whole world are belonging to country India. According to the world air quality report 2018. For instance, in Delhi, vehicle emissions have been considered as the major contributor to rising air pollution compared to all other sources including manufacturing sectors, households, thermal power plants, and the agricultural sector. In India, 2018 alone estimated death of 1.25 million people due to air pollution is about 12.5% of the total deaths. The Government of India has emphasized the target of making the country's energy sector more reliable, secure, and consistent & for achieving the above targets the government had decided to reduce the overall consumption of fossil fuel by 10% by the year 2022

A Novel Approach for Crop Selection and Water Management using Mamdani’s Fuzzy Inference & IOT

In the modern world, technology is always evolving to replace more human labour with artificial intelligence. Moreover, farmers are under constant pressure to irrigate their farms at regular intervals without even a rudimentary grasp of the rainfall pattern and soil humidity, since it is extremely difficult to cultivate any agricultural food in regions with irregular rainfall patterns and high mean temperatures. This paper proposes a crop predictor and smart irrigation system using Mamdani’s fuzzy inference and IoT. The system aims to optimize water usage and crop yield by considering various factors such as soil moisture, temperature, humidity, rainfall, crop type and season. The system consists of three modules: a crop predictor module that uses fuzzy logic to suggest the best crop for a given location and season, an IOT module that collects and transmits the environmental data from sensors to a cloud server, and a smart irrigation module that uses fuzzy logic to control the water flow to the crops based on the data and the crop predictor module. The system is implemented and tested on a NodeMCU and MATLAB platform and shows promising results in terms of water conservation and crop productivity