A bibliography of parasites and diseases of marine and freshwater fishes of India

With the increasing demand for fish as human food, aquaculture both in freshwater and salt water is rapidly developing over the world. In the developing countries, fishes are being raised as food. In many countries fish farming is a very important economic activity. The most recent branch, mariculture, has shown advances in raising fishes in brackish, estuarine and bay waters, in which marine, anadromous and catadromous fishes have successfully been grown and maintained

IOT based solar energy prophecy using RNN architecture

It is the 21st century and scientists say that by the end of this century, resources will be replenished and the only way the future generations can access energy is through renewable resources— those which are inexhaustible. One such source is sunlight, which has a guaranteed stay in the long run. The energy thus given is termed as solar energy. In the present paper it is tried to solve the issue of limited resources and their adverse effects. Since the power generated from solar energy systems is highly variable, due to its dependence on meteorological conditions, an efficient method of usage of this fluctuating but precious energy source has to come in picture. This requires the scope of reliable forecast information as the development of predictive control algorithms for efficient energy management and monitoring for residential grid connected photovoltaic systems. The paper has given an overview of different applications and models for solar irradiance and photovoltaic power prediction, including time series models based on live measured data from rooftop solar power plant located at 17.5203° N, 78.3674° E. For experimentation, data collected over four years from the solar power plant was used in order to the train machine and understand the characteristics of the solar power plant and gives the predicted energy as the result. The use of Deep Learning is done where LSTM is used for the training and keras and tensorflow are used for obtaining the result. The mean square error thus obtained is 0.015

Natural Disaster Discernment and Vigilance

Natural Disasters like cyclones and Earthquakes have a huge impact on the lives of people, results in damage to infrastructure, and lead to injuries and deaths. IoT Based detection systems are utilized for detecting disasters and performing subsequent rescue operations. The challenge with these IoT Based systems is that collecting data from sensors might be failed due to communication breakages or network congestions. To address this issue, this paper has come up with an idea of implementing Disaster Detection using Convolutional Neural Networks and sending SMS to people for making people alert. This paper aims to particularly detect Cyclones and Earthquakes. Data sets were collected from Kaggle. Convolutional Neural Network is a deep learning algorithm that takes an image as input, assigns weights/biases to a variety of aspects in the image for differentiating one from another image. Applications of this work includes disaster preparedness such as forecasts, warnings and predictions, disaster management and disaster relief operations. A comparative study has been performed on CNN and its variants

A Comparative Study using Feature Selection to Predict the Behaviour of Bank Customers

Though banks hold an abundance of data on their customers in general, it is not unusual for them to track the actions of the creditors regularly to improve the services they offer to them and understand why a lot of them choose to exit and shift to other banks. Analyzing customer behavior can be highly beneficial to the banks as they can reach out to their customers on a personal level and develop a business model that will improve the pricing structure, communication, advertising, and benefits for their customers and themselves. Features like the amount a customer credits every month, his salary per annum, the gender of the customer, etc. are used to classify them using machine learning algorithms like K Neighbors Classifier and Random Forest Classifier. On classifying the customers, banks can get an idea of who will be continuing with them and who will be leaving them in the near future. Our study determines to remove the features that are independent but are not influential to determine the status of the customers in the future without the loss of accuracy and to improve the model to see if this will also increase the accuracy of the results

Smart Parking for Smart Cities using IoT

The idea of smart cities has gained immense popularity in real-time, Internet of Things (IoT) is not just about connecting people and about connecting things. Billions of devices will connect to the internet through sensors they talk to each other. Searching for parking is a routine activity for many peoples in the city around the world. There are many solutions some of the existing solutions are inductive loops, image processing at shopping malls, universities, airports, also, the thought behinds advancement of IoT. With this IoT there is numerous wide scope of utilizations in our day by day life, not many of them are keen urban communities, smart waste administration, smart health, brilliant security, and smart industry applications. This paper provides a novel solution for the people to minimize the waiting time and maximize productivity and help to find the space where the vehicles can be left to decrease the traffic in the leaving region. Internet of Things and distributed computing are two unique parts of innovations. IoT is an arrangement of interrelated gadgets and things with unique identifiers associated with the web can speak with one another. The cloud gives everything as administration i. e. pays for what we use. The obstacle sensor senses the obstacle status when the car is being parked in the parking area and status is sent to the cloud. Utilizing the IoT framework it gives the accessible free stopping spaces, Entry and exit likewise gives payment process through RFID card to diminish the bottleneck of the payment procedure and approve at the passage/leave the entrance of the shopping area

AI enabled legal assistance system: A case study

Given a Case, finding the related prior cases and their judgements is a time consuming job of a Lawyer. The lawyer has to go through huge volumes of law books and prepare his case.  An automated tool that retrieves the relevant past cases and their judgments is a very useful application for lawyers.  It is a complex task especially in Indian context, the cases and their judgments are un-structured, and there is no standard format of case and judgement presentation.  It is understandable for a lawyer but, a most difficult task for a machine. The work here presents a case study to retrieve judgments given in the past for a given factual description. The dataset considered for this work is selected from FIRE-2019, AILA track.  The previously developed models showed best average precision of 0.149 using BM25, which itself demonstrates the challenging aspect of the given task.   In this work LDA, a probabilistic algorithm for Topic Modelling is explored and studied. The proposed method has shown improved precision

Sarcasm Discernment on Social Media Platform

Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag-based supervision but such datasets are noisy in terms of labels and language. To overcome the limitations related to noise in Twitter datasets, this News Headlines dataset for Sarcasm Detection is collected from two news website. TheOnion aims at producing sarcastic versions of current events and we collected all the headlines from News in Brief and News in Photos categories (which are sarcastic). We collect real (and non-sarcastic) news headlines from Huff Post. Sarcasm Detection on social media platform. The dataset is collected from two news websites, theonion.com and huffingtonpost.com. Since news headlines are written by professionals in a formal manner, there are no spelling mistakes and informal usage. This reduces the sparsity and also increases the chance of finding pre-trained embeddings. Furthermore, since the sole purpose of TheOnion is to publish sarcastic news, we get high-quality labels with much less noise as compared to Twitter datasets. Unlike tweets that reply to other tweets, the news headlines obtained are self-contained

Anomaly Detection in Solar Modules with Infrared Imagery

Image classification is a machine learning task that involves assigning a label or class to an input image. In the context of the Infrared Solar Modules dataset, image classification can be used to identify anomalies in solar panel imagery. To achieve this goal, A convolutional neural network (CNN) model trained from scratch and fine-tuned on the Infrared Solar Modules dataset from ai4earthscience. Model includes techniques such as dropout and image data generation to enhance its accuracy on this specific dataset. With these methods, Model can achieve high accuracy in identifying solar panel anomalies even with low-size images

Abstracts of National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020

This book presents the abstracts of the papers presented to the Online National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020 (RDMPMC-2020) held on 26th and 27th August 2020 organized by the Department of Metallurgical and Materials Science in Association with the Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, India. Conference Title: National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020Conference Acronym: RDMPMC-2020Conference Date: 26–27 August 2020Conference Location: Online (Virtual Mode)Conference Organizer: Department of Metallurgical and Materials Engineering, National Institute of Technology JamshedpurCo-organizer: Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, IndiaConference Sponsor: TEQIP-