Faculty Publications
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Item Tea leaf disease prediction using texture-based image processing(Springer, 2020) Srivastava, A.R.; Venkatesan, M.Nowadays, Tea is commonly used in India as well as in all over the world. Tea is produced in many states of India, i.e., Assam, West Bengal, Tamil Nadu, Karnataka, and so on. But, production of tea is heavily affected by various diseases and pests. There are various kinds of diseases in tea leaves and various pests that can damage the tea crop and affect the tea production. Tea crop damage is reduced by recognizing the tea leaf diseases in an early stage. After detection of the kind of tea leaf diseases, suitable preventive method can be used to reduce the tea crop damage. For the detection of tea leaves diseases, there are different classification methods. Some classification techniques are random forest classifier, k-nearest neighbor classifier, support vector machine classifier, neural network, etc. After training the dataset with classifier, the image of tea leaf is given as an input, the best possible match is found by the classifier system, and diseases are recognized by the classifier system. This project is going to use various classification techniques to recognize and predict the tea leaves disease which helps us to improve the tea production of India. © Springer Nature Singapore Pte Ltd 2020.Item Optimized diet plan using unbounded knapsack Algorithm(Institute of Electrical and Electronics Engineers Inc., 2020) Bobade, P.; Kumar, P.; Chandrasekaran, K.; Divakarla, D.Cholesterol, hypertension and diabetes are the three major chronic diseases from which most of the people suffers and these peoples often use search engines to acquire related information about these problems. But, almost every information related to diet on the internet isn't suitable for people to gather information about the diet suggestions. A system for diet suggestion which can advocate a prudent diet for such peoples is suggested in this paper. We designed a system that recommends a proper diet which has the adequate knowledge of three above mentioned highly chronic diseases. We propose a solution to the menu recommending problem using the optimization algorithm known as unbounded knapsack. We designed a model which satisfies the nutritional requirements of individuals while imposing the 'Laws of Nutrition', a set of hypothesis used by almost all Latin America's nutrition scientists. This prototype corresponds to a numerical optimization problem with constraints. We design a menu items generator application model to set up a convenient menu for a user with different properties. © 2020 IEEE.Item Trustworthiness of COVID-19 News and Guidelines(Springer, 2023) Singh, S.; Nagar, L.; Lal, A.; Chandavarkar, B.R.COVID-19 pandemic is a serious health concern issue over the past couple of years. It spreads mostly due to bio-contacts, which leads people to follow social distancing and stay away from social gatherings. It leads the people to bound themselves to stay with their family members at their home only, being at home, staying idle, or following work from home schedule by working online through the Internet over the electronic gadgets such as mobiles, laptops, desktops, etc. It leads the people to attach to online activities more for spending their time at their home, which enormously increases people interest in social media platforms such as Twitter, Facebook, etc. As it was a major pandemic period, it created panic and a fearful situation in society. It makes the people believe any news and guidelines spreading through social media platforms irrespective of checking their trustworthiness and truthiness of it. This pandemic period created a seriously bad impact on society’s emotional, physical, and mental health that is a great loss to a country even all over the world. Under this, many unwanted messages are spreading for one’s interest or a group to polarize their interest. In a panic situation, it is highly required of a solution that prevents the spread of these negative vibes to maintain the overall health of society. This chapter tries to implement an optimal solution using various kinds of layers and different optimization functions. It particularly gives better performance in the case of sequential data using machine learning (ML) and deep learning (DL) frameworks trained with the dataset for identifying the fake news and guidelines spread over on COVID-19. To train the model, a dataset was taken from the Twitter Application Programming Interface (API). Finally, the truthiness detection technique with social interaction is completed using Twitter dataset. The efficacy of the suggested method is demonstrated by the obtained results on a Twitter dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item AI Technology for NoC Performance Evaluation(Institute of Electrical and Electronics Engineers Inc., 2021) Bhowmik, B.; Hazarika, P.; Kale, P.; Jain, S.An on-chip network has become a powerful platform for solving complex and large-scale computation problems in the present decade. However, the performance of bus-based architectures, including an increasing number of IP cores in systems-on-chip (SoCs), does not meet the requirements of lower latencies and higher bandwidth for many applications. A network-on-chip (NoC) has become a prevalent solution to overcome the limitations. Performance analysis of NoC's is essential for its architectural design. NoC simulators traditionally investigate performance despite they are slow with varying architectural sizes. This work proposes a machine learning-based framework that evaluates NoC performance quickly. The proposed framework uses the linear regression method to predict different performance metrics by learning the trained dataset speedily and accurately. Varying architectural parameters conduct thorough experiments on a set of mesh NoCs. The experiments' highlights include the network latency, hop count, maximum switch, and channel power consumption as 30-80 cycles, 2-11, $25\mu \text{W}$ , and $240\mu \text{W}$ , respectively. Further, the proposed framework achieves accuracy up to 94% and speedup of up to $2228\times $. © 2004-2012 IEEE.