COVID-19, a brand-new coronavirus that causes pneumonia, recently appeared.Since then, COVID-19 has been responsible for several infections and fatalities across the globe.One way to stop the spread of this virus is to isolate those who are afflicted.Radiologists can diagnose COVID-19 in hospitals with the help of X-rays, which give radiologists a precise image of the lungs.However, a person's X-rays typically contain hundreds of slides, and using these scans to diagnose COVID-19 Pneumonia can cause delays in hospitals.A thorough investigation is required on the effectiveness of an automated diagnosis system for identifying COVID-19 patients to stop the coronavirus from spreading.By using four different pre-trained architectures, including VGG16, ResNet50, InceptionV3, and CNN-11, which were trained and tested on X-ray images of Normal, and Viral Pneumonia, this proposal seeks to address this issue.The outcomes from the three classes (Normal, COVID-19, and Pneumonia) were presented.
One of the biggest problems faced by Indian farmers is the lack of effective irrigation and monitoring techniques.Manual disease monitoring techniques require the full attention of farmers day and night and may lead to the detection of disease at a stage beyond reversal.Conventional irrigation methods require immense effort and lead to the misutilization of water.It also results in a decrease in crop yield as a result of water being given as an approximate quantity, irrespective of the type of the crop.The proposed system is a fully automated farming system, which includes a smart irrigation system using IoT and a deep learning model to detect leaf diseases.Currently, this model pertains to the potato crop, however it can be easily modified according to one's cropping needs.The crop's water requirements are taken care of by the Smart Irrigation system, in which a soil moisture sensor detects the moisture content in the soil, and based on the threshold, turns on and off the pump to ensure adequate water supply to the crop.Alongside the irrigation system, a deep learning disease classifier model is integrated, in which the images of the crop is taken automatically in regular intervals.These images are used as the test data to identify the current health status of the plant, by classifying it as either healthy or unhealthy.
A WHO study states that insufficient and unbalanced food intake causes about 9% of heart attack deaths, about 11% of coronary heart disease deaths and 14% of gastrointestinal cancer deaths worldwide.In addition, about 0.25 billion children suffer from various types of deficiency ranging from vitamin A to vitamin K deficiency, 0.2 billion people suffer from iron deficiency (anemia) and 0.7 billion people suffer from iodine deficiency.The main goal of this work is to recommend a menu to different individuals.The Diet recommendation system deals with the large volume of information present from the dataset.In this project, a custom data set is prepared based on different high and low values of vitamins from (vitamin A, B, C, D, E, K ) and the features are divided from normal and abnormal states of vitamins and the labels are divided into 0 and 1 as normal and abnormal.Another data set is prepared based on a combination of different vitamins and their deficiency and recommended foods according to which vitamin is deficient.In this project, multiple classifier algorithms (KNN, decision tree, random forest, logistic regression, 36 K. Sneha Reddy et al voting classifier) are used and ensemble algorithm is used to combine multiple algorithms and train a new algorithm.The prediction is displayed using a flask web application that detects vitamin deficiency and recommends the type of food to be consumed in different combinations.
Stuttering is a speech disorder characterized by repetition of sounds, syllables, or words;prolongation of sounds;An individual who stutters exactly knows what he or she would like to say but has trouble producing a normal flow of speech.This project aims to use machine learning algorithms to detect stuttering behavior in Telugu language speech samples.Stuttering is a speech disorder that affects the flow of speech, making it difficult to communicate with others.The study will collect voice samples from individuals reading the same Telugu script and generate 8KHz and 16KHz samples in .wavfiles.The data will be annotated and coded on a 0-8 scale and will be generated as a data set.Machine Learning algorithms will be used to build a model for detecting stuttering patterns in the audio files.Finally the model will be integrated with a user interface.I.
It is clear to every Indian that agriculture is the backbone of our country.It is indeed the case that the created, immature and non-industrial nations derive their food from agriculture.Therefore, individuals cannot overlook the importance of agriculture, on the contrary.It is believed that development in agriculture brings a great change to a country.Along with the advancements in innovation, there are a lot of advancements in agriculture and other fields.Nowadays, new strategies and innovations are carried out for the advanced development of agriculture.With the help of agriculture, we can support the economy of our country.Agriculture in India is carried out with ordinary strategies.The way the majority of our farmers need suitable information makes them much more unpredictable.A great deal of cultivation and farming depends on forecasts, which occasionally fail to materialize.Farmers have to cope with colossal misfortunes, and now and then it happens that they give up everything.Knowing the benefits of adequate soil moisture and quality, air quality and irrigation for yield development, such limitations cannot be ignored.In this paper, a novel idea of precision farming, drones, soil management and livestock management using IOT is presented.The proposed work will have great benefits in the field of agriculture due to its reliability and remote observation.This idea seeks to digitize agriculture and farming activities so that farmers can check crop requirements and accurately predict their growth.This idea will undoubtedly accelerate their business to reach new scales and furthermore be more productive.The implementation of this research work generally depends on the mindfulness of farmers as it brings
