Stock Market Prediction Using Machine Learning published in ijirt.org

Research paper published by

Mr.Tejas Pandav, Mr.Chetan Shinde, Mr.Malharrao Shelar, Mr.Shubham Shelar, Mr.Saurabh Kodre & Prof. Punam Chavan

Department of Information Technology

Zeal College of Engineering and Research, Narhe, Pune,
Maharashtra, India

Read full paper on ijirt.org PAPERID 160289, Volume 10 Issue 1

The subject of stock forecasting is currently
quite popular. Only a select few individuals had access to
the study in the beginning due to a number of factors
including the device’s limitations. Since science and
technology have advanced so quickly, more and more
people are now interested in studying prediction, and it is
getting simpler and simpler for us to predict stocks using
various techniques like machine learning, deep learning,
and others. In this essay, we’ll use LSTM (Long and Short
Term Memory) to predict stocks and incentives for the
following day. We can improve pre-processing methods
to remove noise from data so that subsequent operations
like categorization and prediction doesn’t make any
impact.

Citywide Real time Heatmap Generation | Research paper published on ijirt Journal Volume 6 Issue 12

Published by

Prof. Varsha Dange, Amol Pawar, Sagar Bhandge, Siddharth Prajapati, Riya Daryanani, Vaishnavi Javalkar

The premise is to collect the numerical subjective data produced by individual end users, the device’s location, as well as universal data such as Time of the year, time of the day, and apply a machine learning predictive algorithm to generate a heatmap overlapping the city or any other geographical entity simulating a predictive threat/safety level of a given area or route. Based on the data provided to the algorithm, the algorithm can suggest statistically safest routes for travelling as well as visualize the route onto the map.

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HATE SPEECH DETECTION

As online content continues to grow, so does the spread of hate speech. We identify and examine challenges faced by online automatic approaches for hate speech detection in text. The increasing propagation on social media and the urgent need for effective countermeasures have drawn significant investment from governments, companies, and researchers. Machine Learning and predictive analytics now help companies to focus on important areas, anticipating problems before they happen, reducing costs, and increasing revenue.

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Smart City Waste Monitoring system using IOT | Research Paper published on ijirt Volume 6 Issue 7 December 2019

As the world is in a phase of upgradation, there is a stinky problem that we have to deal with. Garbage! In our daily lives, we see that garbage bins are overflowing and all the garbage has spread out. A large number of insects and mosquitoes breed on it. A major challenge not only in India but for most of the countries in the world is solid waste management. Therefore, a system must be built that can eliminate this problem or at least reduce it to a minimum level.

Published by Vikaskumar M Dane, Mrs.M. Paralikar, Naitik A Soni, Omkar D Joshi, Pavan V Baile

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MULTIPURPOSE AGRI ROBOT Research Paper published on ijirt

In Modern World, Autonomous robot is used in many of the fields such as defense, surveillance, medical field, industries, agricultural and so on. In this project, the robot system is used to develop agricultural processes without the use of manpower. our proposed system aims is to reduce manpower, time, and increase the productivity rate. It also check humidity with the help of humidity sensors. The main component of our proposed system is the Advanced Virtual RISC (AVR) at mega microcontroller that supervises the entire process. For manual control, the robot uses the Bluetooth connection application as a control device and helps in the navigation of the robot inside the field. This is especially important for the safety and health of the workers. Automation is the ideal solution to overcome all the shortcomings by creating machines that perform all operations and automating it to increase yield on a large scale.

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Machine Learning Approaches to Multi-Class Human Skin Disease Detection published on ijirt

Human Cancer is a standout among the most perilous sickness which is fundamentally brought about by hereditary shakiness of various sub-atomic modifications. Among numerous types of human malignant growth, skin disease is the most widely recognized one. To distinguish skin disease at a beginning time we will ponder and break down them through different procedures named as division and highlight extraction. Here, we center harmful melanoma skin disease, (because of the high centralization of Melanoma-Hier we offer our skin, in the dermis layer of the skin) identification. In this, we utilized our ABCD rule dermoscopy innovation for dangerous melanoma skin disease identification. In this framework diverse advance for skin injury portrayal i.e, first the Image Acquisition Technique, pre-handling, division, characterize include for skin Feature Selection decides sore portrayal, characterization strategies. In the Feature extraction by computerized picture handling technique incorporates GLCM and ABCDE highlights and furthermore we utilized DRLBP. Here we proposed the Recurrent Neural Network to group the kind of ailment.

Big Data Analysis by Classification Algorithm Using Flight Data Set

In this work, we proposed a technique with broad application which is used to classify each item in a set of data into a set of predefined classes or groups .Big Data concept comes into existence .This is a tedious works for user to identify accurate data from huge unstructured data .As the years go on the web is overloaded with lots of information .Where Classification is a process of generalizing the data according to different instances .Several major kinds of classification algorithm C4.5 ,Decision Tree ,J48, ID3, includes Naive Baye’s algorithm This paper provides Flight dataset related queries. System is learn that is capable of predicting the number of aircraft in certain region of the airspace at a given time with greater accuracy than similar Model. The Naive Baye’s Classifier on the data set on different size for different cluster configuration provides the potential data as well as aspects that affect its performance.

Indian students have created a Device with Emotion Detection Enabled Automation and Telegram App for Home Safety “Auto Secure” – research paper published on ijirt volume 6 issue 5

Advancement in IOT based application has become the state-of-the art technology among the researcher due to the availability of Internet everywhere. To make the application more user friendly, web based and android based technologies have gained their importance in this cutting edge technology. In this paper, device with emotion detection enable automation using telegram app user can manage their home security. This system is very useful for reducing the cost of monitoring the movement from outside. In this paper, a real time recognition system is proposed which will handle images very quickly. The main purpose of this paper is to protect home, office by recognizing people. The proposed system is real-time, fast and has low computational cost.

Computer vision can present more security system in the IoT platform for smart houses. It has abilities to recognize a person in the incorrect area and at the wrong time because this person may be a malicious one for the environment. It has an assortment of large applications in the ranges: public security, access control, credit card verification, criminal identification, law enforcement commerce, information security, human computer intelligent interaction, and digital libraries. The face is the most important part of human’s body. So, it can reflect many emotions of a person. Long year ago, humans were using the non-living things like smart cards, plastic cards, PINS, tokens and keys for authentication, and to get grant access in restricted areas like ISRO, NASA and DRDO. The system will fall into two categories; face detection and face recognition. Facial recognition is a way of perceiving a human face through technology. A facial recognition system used to map facial features from a photograph or video. It compares the information with a database of faces. Facial recognition can help to verify unique identity of human face. Using face detection, we can get the information we need to perform tasks like exaggerate selfies and portraits. There are a few techniques for fetching the most important features to implement face recognition. HAAR-CASCADE, and Local Binary Patterns (LBP) along with feature extraction techniques and are used for classification of emotion for the said automation system. To get fast discriminatory performance and good results, these techniques are chosen for face recognition. OpenCV is the most widely used library for computer vision. It uses machine learning algorithms to search for faces within a picture. Patterns and features that must be matched. The algorithms break the task of identifying the face into smaller, bite-sized tasks, each of which is easy to solve. These tasks are also called classifiers. In this paper, Raspberry Pi 3B+ is utilized and Raspberry Pi camera is connected to it. The system will take an image when ultrasonic sensor detects any movement. Then, computer vision is applied to the captured images. Subsequently, the system sends the images to a smartphone via the Internet.

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How Movie recommendation works?

With the frequent growth of Internet technology, information overload is place a major problem. By this huge data unable to fetch the useful information, to eradicate this issue search engines are came into existence but still the problem is not completely solve. Because of these issues Recommender systems are came into existence and they are use filtering technique, in this approach implement the Movie recommender system by using a cocept of traditional User based Collaborative filtering algorithm (UserCF). Here we get the user preferences and recommend the finest movie to the user. Meanwhile here we use a Neo4j Grpah database because of its huge advantage in dealing with the complex interconnected data and also handle a Bigdata.

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Forest Preventive System by using Micro-controller and GPS System

Nowadays smuggling of trees is a big problem. Our project is deals saving the important and precious trees like sandal, sagwan etc from being harvested or smuggled. As name suggests here we are saving trees by using accelerometer sensor, GPS and GSM module. These trees are very costly as well as less available in the world. These are used in the medical sciences as well as cosmetics. Because of huge amount money involved in selling of such trees woods lots of Karnataka and Tamil nadu notorious smuggler “Virrappan” did the smuggling of such trees for so many years. To restrict such smuggling and to save the forest around the globe some preventive measures need to be deployed. We are developing such a system which can be used to restrict this smuggling. In our project, every tree will be equipped with small electronic unit consisting of a micro controller, accelerometer sensor and GPS and GSM module. The data of different tree units can be controlled by this Unit. The unit will send the data in the form of text message to concern forest authority with the help of GPS and GSM module. This data can be used by concerned forest authorities to take preventive action

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