Indian Currency Denomination Recognition and Fake Currency Identification

The progression of shading printing innovation has expanded the rate of Fake currency copying notes on a large scale. Albeit electronic monetary exchanges are turning out to be more popular and the utilization of paper cash has been diminishing as of late, banknotes still remain in distribution attributable to their dependability and straight forwardness in use. Few years ago, the printing should be possible in a printing-houses, yet presently anybody can print a money paper with most extreme exactness utilizing a straightforward laser printer. As an outcome, the issue of duplicate currency rather than the authentic ones has been increases generally. India had reviled the problems like defilement and dark cash and fake of money notes is likewise a big issue to it. To handle this problem, a deep learning-based framework is proposed to identify the fake Indian currency. The MATLAB tool has been used to identify the fake currency. The outcome will classify whether the Indian currency note is Real or Fake.

Identification of Fake Indian Currency using Convolutional Neural Network

The objective of this research work is to categorize and identify various plant diseases at their earliest stages. Due to the late 20th century’s dramatic expansion in human population, there is an enormous demand for crops, fruits, and vegetables that much exceeds availability. Due to plant disease and pests, nearly more than 30% of crops are lost. Pests have a direct negative influence on crops because they eat the plants, spread illnesses, and open the door for bacterial, viral, and fungal growth. The illnesses and pests can be located by professionals using a visual check. However, it is quite expensive to continually consult professionals to treat sickness. Advance discovery of pests can help in reducing disease transmission to crops nearby, which is advantageous given the wastage factor. Artificial intelligence and algorithms of machine learning can be utilized for early detection. The identifying and categorization of various plant ailments may be done using deep learning techniques like Convolution Neural Network and Resnet. To identify and categorize the plant illness at an early stage, the suggested approach employs an object detection and picture recognition model. For the locally prevalent plant diseases brought on by pests, this model has been specifically trained to operate more accurately. This study assists in developing a creative solution to the problem and lessens harm to crops, vegetables, and fruits by identifying and eradicating pests early on. 

Performance Analysis of ResNet50 Architecture based Pest Detection System

The COVID-19 pandemic has affected healthcare in several ways. Some patients were unable to make it to appointments due to curfews, transportation restrictions, and stay-at-home directives, while less urgent procedures were postponed or cancelled. Others steered clear of hospitals out of fear of contracting an infection. With the use of a conversational artificial intelligence-based program, the Talking Health Care Bot (THCB) could be useful during the pandemic by allowing patients to receive supportive care without physically visiting a hospital. Therefore, the THCB will drastically and quickly change in-person care to patient consultation through the internet. To give patients free primary healthcare and to narrow the supply-demand gap for human healthcare professionals, this work created a conversational bot based on artificial intelligence and machine learning. The study proposes a revolutionary computer program that serves as a patient's personal virtual doctor. The program was carefully created and thoroughly trained to communicate with patients as if they were real people. Based on a serverless architecture, this application predicts the disease based on the symptoms of the patients. A Talking Healthcare chatbot confronts several challenges, but the user's accent is by far the most challenging. This study has then evaluated the proposed model by using one hundred different voices and symptoms, achieving an accuracy rate of 77%. 

Speech Recognition and Neural Networks based Talking Health Care Bot (THCB): Medibot

The COVID-19 pandemic has affected healthcare in several ways. Some patients were unable to make it to appointments due to curfews, transportation restrictions, and stay-at-home directives, while less urgent procedures were postponed or cancelled. Others steered clear of hospitals out of fear of contracting an infection. With the use of a conversational artificial intelligence-based program, the Talking Health Care Bot (THCB) could be useful during the pandemic by allowing patients to receive supportive care without physically visiting a hospital. Therefore, the THCB will drastically and quickly change in-person care to patient consultation through the internet. To give patients free primary healthcare and to narrow the supply-demand gap for human healthcare professionals, this work created a conversational bot based on artificial intelligence and machine learning. The study proposes a revolutionary computer program that serves as a patient's personal virtual doctor. The program was carefully created and thoroughly trained to communicate with patients as if they were real people. Based on a serverless architecture, this application predicts the disease based on the symptoms of the patients. A Talking Healthcare chatbot confronts several challenges, but the user's accent is by far the most challenging. This study has then evaluated the proposed model by using one hundred different voices and symptoms, achieving an accuracy rate of 77%.

Currency is used to carry out not only business but also for various other transactions to get access to various services and commodities. There are a total of 7 denominations for the Indian currency each with unique features to distinguish them from each other and with various and distinct security features to prevent them from fraudulent copying. However,with the evolution of technology, there is also an increase in the ways in which fake forms of these currencies are created. These fake or counterfeit notes have various ill-effect on society. The proposed system will be used to check the genuine Indian currency notes and to find the denomination of the currency note. Comparative study for various image processing algorithms was conducted to identify and select the one which will be able to extract more prominent features,and is also better in terms of processing time,outlier rejection, efficiency in computation and in feature matching. After which a real time system is created for currency detection in real time.

/pdf/original-vs-counterfeit-indian-currency-detection-vohqrwia01.pdf

Original vs Counterfeit Indian Currency Detection

Identification of plant disease is tough in agribusiness arena. If it is inaccurate, there occurs a tremendous damage in the production and economical price. Leaf Disease detection requires huge amount of work, knowledge, processing time in plant disease. The most used and edible vegetable all over the world is from cucurbitaceous family. The crops under this family have great economic value in the food industry and its production is done in large scale. This family consists of 965 species. If any of these plants catch disease then there would be a tremendous loss in the production of this field yields. Thus, treating them at early stage is best way to prevent such losses. Hence, Deep Learning Algorithm like CNN can be used to detect the diseases of the plants. The leaves of the plants would be used as primary material for identification of the disease, as they are much more visible on the leaves.

/pdf/leaf-disease-detection-of-cucurbits-using-cnn-21flnnzt7g.pdf

Leaf disease detection of cucurbits using cnn

In today’s digital world Presentation using a slideshow is an effective and attractive way that helps speakers to convey information and convince the audience. There are ways to control slides with devices like mouse, keyboard, or laser pointer, etc. The drawback is one should have previous knowledge about the devices in order to manage them. Gesture recognition has acquired importance a couple of years prior and are utilized to control applications like media players, robot control, gaming. The hand gesture recognition system builds the use of gloves, markers and so on However, the utilization of such gloves or markers expands the expense of the system. In this proposed system, Artificial intelligence-based hand gesture detection methodology is proposed. Users will be able to change the slides of the presentation in both forward and backward directions by just doing hand gestures. Use of hand gestures cause connection simple, helpful, and doesn’t need any additional gadget. The suggested method is to help speakers for a productive presentation with natural improved communication with the computer. Specifically, the proposed system is more viable than utilizing a laser pointer since the hand is more apparent and thus can better grab the attention of the audience.

Automated Digital Presentation Control using Hand Gesture Technique

Even today talking about women health is big taboo in India. The society is unbiased about women especially about their health issues hence they face disparity in the society. Women feel hesitant to talk about or speak about their health issues or problems openly. Thus, the goal of this chatbot is to help women to find information and remedial solutions about their health. Query is processed by the bot and response will be displayed on web application. This chatbot will provide helpful information instantly. It will also provide prediction about the disease that the women might be suffering.So this bot will help to make Right decision and give right advice to women on 24/7 basis. It will act as an helping hand for working women to keep check on their health in their busy routine. At the same time this will also help women in rural areas who are apprehensive to talk about their health issues publicly.

Intelligent Medical Chatbot System For Women’s Healthcare

A big challenge in the majority of cities all over India is that of waste management. The key issue in waste management is that the garbage bins at public places get overflowed well in advance before initiating the next cleaning process. Improper management of waste can be a major factor for environmental pollution resulting in several health issues. The traditional way of manually monitoring garbage bins is a cumbersome process and utilizes more human effort, time and cost which can easily be avoided with present technologies. In order to handle such issues, we propose an IOT based smart waste management system which checks the waste level over the dustbins by using sensors. The ultrasonic sensors are interfaced with Arduino Uno 3 Microcontroller to obtain and send the level of garbage to the server of concerned authority after waste is filled up to a particular level. Then authority can take required action by employing a worker to empty out the bins. So through this system, the real-time status of waste is monitored. Therefore one can ensure proper management of waste and this can be a step towards a quality environment which ultimately bodes well for human beings and other life-forms.

/pdf/iot-based-garbage-monitoring-system-2nubam4596.pdf

IoT based garbage monitoring system

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