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Stock Market Price Prediction Using Machine Learning and Deep Learning
This project aims to address the long-standing challenge of stock market prediction by leveraging the power of machine learning. The stock market is influenced by a multitude of factors, making it difficult for investors to accurately predict trends and make informed decisions. To mitigate this risk, we have chosen to analyse NSE real-time series data using the ARIMA model, a popular forecasting method. Our ultimate goal is to develop a user-friendly web application using Flask, which will allow investors to select a specific firm and receive a forecast for its future stock price. This application will utilize web scraping techniques to retrieve real-time data from the NSE, and the ARIMA model will be applied to predict future outcomes. By combining these powerful tools, we hope to significantly reduce the potential danger associated with stock market prediction and enable investors to make more informed decisions.
Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System
Movies are an important part of our daily entertainment, with the global movie industry experiencing significant growth and capturing the attention of people of all ages. However, only a few movies achieve success, leading to pressure on movie production stakeholders. Therefore, researchers and moviemakers require expert systems that can accurately predict the probability of a movie's success prior to its production. Most research on predicting movie popularity has focused on post-production stages, but it's essential to predict a movie's success at an early stage to enable necessary changes to be made. To this end, a content-based movie recommendation system (RS) has been proposed that uses preliminary movie features such as genre, cast, director, keywords, and movie description. This RS output and movie rating and voting information are then used to create a new feature set, which is input into a CNN deep learning (DL) model to build a multiclass movie popularity prediction system. The study also proposes a system to predict the popularity of the upcoming movie among different age groups, dividing the audience into four categories: junior, teenage, mid-age, and senior. The study uses publicly available data from the Internet Movie Database (IMDb) and The Movie Database (TMDb). The multiclass classification model implemented in this study achieved 96.8% accuracy, outperforming all benchmark models. Overall, this study highlights the potential of predictive and prescriptive data analytics in information systems to support industry decision- making.
Utilization of UGV in Mining Sectors
In various sectors such as defense and mining, existing Unmanned Ground Vehicle (UGV) applications face specific limitations, including the inability to achieve wide- range control and operate in dark environments. To mitigate human risks and reduce human involvement in bomb disposal operations, the need for UGVs has become crucial. In this context, we propose a model/framework that utilizes gas and temperature sensors to detect environmental conditions. Our framework aims to enhance UGV applications by incorporating wide-range Wi-Fi enabled control devices and night mode camera functionality. This enables rapid object detection over a greater distance and emphasizes remote control through a mobile application. As a result, the proposed system automates the process of bomb disposal, thereby reducing human risks involved in such operations.
METAVERSE
The Metaverse is a term that refers to a virtual universe where users can interact with each other and the environment through immersive technologies such as virtual reality (VR) and augmented reality (AR). It is a network of interconnected virtual spaces where users can participate in a range of activities such as gaming, socializing, shopping, and education. VR allows users to enter a completely simulated world, while AR overlays digital content onto the real world. These technologies enable users to experience a sense of presence in the Metaverse and interact with it as they would in the real world. As technology continues to advance, the Metaverse is becoming increasingly realistic and accessible. It has the potential to transform how we work, learn, and play, and create new opportunities for social and economic growth. However, it also raises ethical concerns around privacy, security, and the potential for addiction. Overall, the Metaverse represents an exciting and rapidly evolving field that has the potential to revolutionize how we interact with technology and each other. As such, it is an area of research and development that is likely to attract significant attention and investment in the coming years. As the Metaverse continues to evolve and expand, it will have a profound impact on how people interact with each other and with digital content. It will provide a new level of immersion and engagement, and it will transform the way we work, play, and learn.
Fuzzy Logic
Fuzzy logic is a mathematical framework for reasoning about ambiguous or inaccurate information. It is founded on the idea that truth can be stated as a degree of membership in a fuzzy set rather than as a binary value of true or untrue. Fuzzy logic is used in control systems, artificial intelligence, and decision-making. This paper defines fuzzy logic and discusses its key concepts, mathematical underpinnings, and applications. We look at the benefits and drawbacks.
ALTERNATE FUEL BIODIESEL
The rapid growth of industrialization and transportation has led to a significant increase in greenhouse gas emissions and environmental degradation. To mitigate these challenges, there is an urgent need for sustainable energy solutions that can reduce dependence on fossil fuels and minimize environmental impact. Bio-diesel, a renewable and cleaner-burning fuel derived from organic sources, has emerged as a promising alternative to traditional petroleum-based diesel. This abstract provides an overview of bio-diesel, its production process, properties, and its environmental benefits. Bio-diesel is typically produced from feedstocks such as vegetable oils, animal fats, and waste cooking oils through a transesterification process that converts triglycerides into esters. The resulting bio-diesel exhibits similar properties to petroleum diesel, making it compatible with existing diesel engines and infrastructure. One of the significant advantages of bio-diesel is its reduced carbon footprint. It has a significantly lower lifecycle greenhouse gas emissions compared to petroleum diesel, primarily due to the absorption of carbon dioxide during the growth of the feedstock plants. Bio-diesel also contributes to a reduction in air pollutants, such as sulfur oxides and particulate matter, resulting in improved air quality and human health benefits. Additionally, bio-diesel offers economic benefits by promoting local agriculture and creating new job opportunities in the biofuel industry. The utilization of waste cooking oils and animal fats as feedstocks also contributes to waste reduction and efficient resource utilization. Despite its numerous benefits, challenges remain in the widespread adoption of bio-diesel. These challenges include feedstock availability, land use competition with food production, and the need for consistent quality standards. Ongoing research and development efforts are focused on optimizing the production process, exploring alternative feedstocks, and improving the overall sustainability of bio-diesel production. In conclusion, bio-diesel holds great potential as a sustainable alternative to petroleum diesel, offering environmental, economic, and societal benefits. Its reduced carbon footprint and compatibility with existing infrastructure make it a viable option for transitioning towards a greener future. Further advancements in technology and policies supporting bio-diesel production and utilization will play a crucial role in realizing its full potential and achieving a more sustainable energy landscape
Dynamic Cloud Computing Platform in E-Healthcare System
Cloud computing technology is the delivery of computing services or IT services through the internet it means instead of buying and maintaining computer products and services, you can pay to use a cloud computing service. The advantage is that it reduces you the time, cost and effort. This cloud computing technology is more profitable in the healthcare system because it has the ability to store an enormous number of medical health information. We all know that the healthcare system plays a major role in all nations. Using cloud computing technology we can able to save numerous Electronic Medical Records (EMR) every day and also it ensures patient data privacy in the cloud. The information will be more secure and safe so that no one can access it illegally. By using this technology the healthcare management can access the patient information very easily. So it will be the best technology for e- health care system.
Feature Extraction Based on Cellular Particle Swarm Optimization Algorithm for American Sign Language Images
Image Recognition is becoming a critical task and many problem-solving systems and approaches for image detection, analysis and classification are introduced by many modern researcher. The techniques should be user friendly and ease to interpret for both normal people and special people, it they should be analysed. The sign language act as a mode to transfer and exchange the message, information, knowledge and ideas from deaf to common people. The gaining information and responses to the pattern or gesture is called as sign. Sign Language is only mode to communication between the hearing-impaired people and common people. In this method each individual gesture is called as sign. In this system the American Sign Language recognition system which is used for visually impaired people to access and educate the special children’s. American Sign Language (ASL) images of a special people is collected with some constraints are taken as the dataset. The objective of the research work is done with three different Phase. Phase1 is pre-processing, Phase 2 is segmentation and Phase 3 is feature extraction for static hand gestures with the maximum possible accuracy rate. A novel approach for the proposed system is analysed with its structural feature extraction and compared with its parameters. The novel feature extraction is made with 8 different structural features they are Bounding Box, Area, Perimeter, Centroid, Roundness, EquiDiameter, Number of Boundaries and Angle. F-Measure, Recall and Precision based on this Accuracy, Sensitivity and Specificity are measured for features are extracted images. The accuracy of the new Phase has been found significantly higher.
Utilization of Unmanned Aerial Vehicle In Smart Agriculture
Unmanned aerial vehicles (UAVs) with vertical take-off and landing (VTOL) are known as drones. The World Health Organisation estimates that there are millions of chemical cases each year. More than one large integer death is caused year by human handling and use of pesticides, mainly in developing countries. In addition to cancer, endocrine disruption, issues with reproduction and craniums from chemical exposure, respiratory illnesses, allergies, and hypersensitivity are among the detrimental effects of pesticides on human health. Other insecticides may cause inflammation of the skin and eyes. There are other pesticides with highly dangerous poisons. Other pesticides also have an effect on the body's internal secretions and systems. Low exposure levels during spraying may be harmful to your health. A wide range of health effects on the body brought on by chemical exposure include changed or uncontrollable mood, behavior, loss of coordination, slower response times, decreased eye sight, and state of mind. To save time and labour, crop fertilizer is sprayed using a drone. The primary goal of this project is to design a framework for the drone that sprays fertiliser over farms. Include a description of the creation and design of the quadcopter system's spaying mechanism.
Malevolent WEBSITE identification using Machine Learning Techniques
Threats to network information security are currently increasing both in number and seriousness. Today's hackers are mostly focused on attacking end-to-end systems and finding weak points in people. These strategies include pharming, phishing, and social engineering. These assaults include a stage where malicious Uniform Resource Locators (URLs) are used to trick visitors. Consequently, harmful URL detection is growing in popularity. The use of machine learning and deep learning algorithms to identify malicious URLs has been proven in several academic articles. In this study, we propose a malicious URL detection system that makes use of machine learning approaches based on proposed URL behaviors and attributes. Bigdata technology is additionally being utilized to enhance the detection of risky URLs based on peculiar behavior. A novel set of URL traits and behaviors, a machine learning algorithm, and big data technologies make up the suggested detection technique. The trials' findings imply that the suggested URL characteristics and behavior can significantly improve the ability to identify potentially harmful URLs. This shows that the suggested method might be regarded as the best and most convenient way to find counterfeit URLs.
A Study on the importance of EMR Data Used in Business Analytics for Decision making Using Open Source tools
EMR is used in healthcare system for the managing the day-to-day activities, by providing quality of care and improving the performances of healthcare services. It has been understood by the Healthcare Professionals, students, patients that EMR can be used to solve the operational issues, provide the data to the patient and other facilities through the nationally accepted interoperable standards.
Crop Health Monitoring Using Rasberry Pi and IOT Things
Our main objective is using the IoT sensors provide the real-time status of environmental parameters impacting the crop, and using Raspberry Pi provide the multispectral data used for generating Vegetation Indices (Vis) such as Normalized Difference Vegetation Index (NDVI) to monitor the crop health and environment.
Advanced Cascaded H-Bridge & Modular Multilevel Converter Applied to a Wind Energy Generation
As the number of renewable energy sources increases worldwide, wind energy is one of themwho stand out from the crowd. With the development of technology, wind turbine projects have had a significant impact increased their capacity, reaching a higher capacity mainly for offshore installations. One of the most important challenges are power Converter, specifically semiconductor components that have limited voltage and current characteristics. This gave birth to the concept of multilevel Converter , which increased the voltage level and thus carried a higher power level. Apart from the application In multilevel converter , it is possible to increase the voltage and power level by using an generator. This context was investigated in this work the use of a multi-level converter (three-level cascaded H-bridge in series), which controlsan open coil of Generator Analysis of the proposed system was developed dynamicallySimulation of 1.71 MW WECS using PLECS software including basic system modeling components: generator, power converters, system control, filter and grid connection. The score show that the goal of achieving 5-level behavior of the output voltage is achieved by using OEW connection. In addition, a low harmonic concentration is achieved in the machine flow as such current supplied to the grid. In addition, it is possible to control the distribution of power between converter, indicating that lower power converter can be connected in a higher direction Wind power
Security Measures of Textual Data
As the volume and importance of textual data in data science continues to grow, combined with advancements in its techniques, it has created numerous opportunities for extracting valuable insights from textual information. However, privacy and the security of private data are issues that are brought up by the analysis of text data. This paper presents an in-depth analysis of security measures specifically designed for protecting textual data in data science applications. It explores various techniques and strategies to safeguard the confidentiality, integrity, and availability of textual data throughout its lifecycle. The objective is to provide data scientists and practitioners with a comprehensive understanding of the security challenges and best practices associated with textual data in the context of data science.
Smart Self Monitoring Eco Friendly EV Charging Station
Due to their low expenses for operation and commitment to the environment, electric cars (EVs) are growing in popularity. However, the inadequate charging infrastructure limits their popularity. To keep their vehicles always ready for use, EV owners need access to convenient and dependable charging stations. Thus, promoting the adoption of EVs requires the installation of electric charging stations. A specialized infrastructure called an electric charging station is created to supply electricity to electric vehicles. It is made up of a unit for charging, one for distributing power, and one for communicating. The charging unit is in charge of feeding electricity from the grid to an electric vehicle's battery. The electric car receives power from the charging unit through the power distribution unit. To make sure that the charging process is secure, dependable, and effective, the communication unit is in charge of maintaining communication between the charging station and the electric car. There are two categories of electric charging stations: DC rapid charging stations and AC charging stations. While DC fast charging stations offer a faster charging rate of up to 350 kW, AC charging stations offer a slower rate of up to 22 kW. Although installing DC fast charging stations costs more money, they are essential for long- distance travel and in places where there is a high demand for quick charging.
Food Freshness Detection Using IOT and ML
In today's modern world, the issue of food spoilage poses a significant challenge, as consuming spoiled meals can be hazardous to consumers' health. Our objective is to develop a solution for detecting spoiled food by employing suitable sensors that monitor gasses released by various food items. By utilizing a microcontroller equipped with Internet of Things (IoT) capabilities, an alert can be issued, prompting appropriate action to be taken. This technology has the potential for widespread application in the food industry, where manual food detection is currently employed. Our plan is to create devices that can learn and adapt to this detection process, enabling us to estimate the likelihood of food spoilage and predict the timeframe within which it may occur, especially when sourced from specific suppliers. By implementing this system, we aim to foster increased competition among retailers, encouraging them to prioritize the sale of healthier and fresher food products. Ultimately, this will contribute to the creation of a safer environment for all consumers. To achieve our goals, we propose the integration of Internet of Things (IoT) and Machine Learning (ML) technologies in food freshness detection. This system is designed to assist food suppliers and retailers in monitoring the freshness of their food products. The proposed system employs a combination of IoT devices and ML algorithms to identify changes in the freshness of food items. IoT-enabled sensors are utilized to gather data from the surrounding environment, which is then processed by ML algorithms to detect any deviations in the freshness of the food products. In instances where the freshness begins to deteriorate, the system promptly notifies the food suppliers and retailers, enabling them to take immediate action. We have conducted thorough testing of the proposed system through a case study, and the results demonstrate its effectiveness in detecting changes in the freshness of food products. The integration of a microcontroller equipped with IoT capabilities ensures timely alerts, facilitating the necessary actions to prevent the sale or consumption of spoiled food. This technology has the potential to revolutionize the food industry by automating and improving the detection of food spoilage, thereby safeguarding the health and well-being of consumers. In summary, our proposed solution utilizes IoT and ML to detect changes in food freshness. By leveraging IoT-enabled sensors and ML algorithms, we can monitor and predict the likelihood of food spoilage, enabling suppliers and retailers to take appropriate measures. This approach not only fosters competition among retailers to provide healthier and fresher food but also ensures a safer and more secure food supply chain for consumers worldwide.
Diabetes Prediction Using Machine Learning Algorithm
Diabetes is a prevalent chronic illness affecting a vast population worldwide. The accurate prediction of diabetes presents considerable challenges due to the scarcity of labeled data and the existence of outliers (missing values) within the dataset. Early detection and effective management of diabetes are crucial in preventing severe complications that can lead to significant health issues. In this project, we aim to investigate the application of machine learning algorithms in predicting diabetes among patients based on their clinical data. Our dataset comprises diverse sources, including electronic health records, medical databases, and surveys. Rigorous preprocessing techniques have been employed to handle data quality, while feature engineering methodologies have been implemented to extract pertinent information. By undertaking these steps, we strive to produce original work without any instances of plagiarism.
Smart Agriculture with IoT
Precision farming, often known as smart agriculture, is a management strategy that makes use of IoT technologies to raise the caliber and output of agricultural goods. Agricultural productivity has been hampered by the climate, for example, creating difficulties. Soil moisture, changes, and a lack of resources. It has been researched to use wireless sensors to incorporate proper crop management and soil moisture monitoring in order to overcome these difficulties. Precision farming enables real-time data collection and analysis on elements including weather patterns, soil moisture, and crop health. With better efficiency, less waste, and higher crop yields as a result, farmers are able to make educated decisions about pest control, irrigation, and fertilization. IoT technology's promise is illustrated through the application of precision farming in agriculture.
A Method to Improve Human Heart Disease Prediction Using Machine Learning Algorithms
There is great diversity in the field of medical science due to computational power and technical innovation, especially in identifying human heart disease. Today it is one of the deadliest human heart diseases in the world and have very serious effects on human life. Accurate and timely identification of heart disease in humans can be very helpful in prevent heart failure in its early stages and will improve patient survival. Manual method for determining the heart disease is biased and can vary between researchers. In this regard, efficient and reliable machine learning algorithms resources for detecting and classifying people with heart disease and those who are healthy. According to suggestion in our study, we identified and predicted heart disease in humans using a variety of machine learning algorithms and using heart disease dataset to evaluate its performance using various measures, such as sensitivity, specificity, F-measure, and classifier accuracy. For this purpose, we used nine machine learning classifiers for the final dataset before and after hyper parameter tuning of machine learning classifiers, such as AB, LR, ET, MNB, CART, SVM, LDA, RF, and XGB. In addition, we verify their accuracy on a standard heart disease dataset by performing several standardized, pre-processing procedures of the data set and hyper parameter tuning. In addition, to train and validate machine learning algorithms, we implemented standard K-fold cross-validation technique. Finally, the experimental results show that the accuracy of the predictive classifiers with improved hyper parameter tuning and achieved remarkable results with data normalization and hyper parameter tuning of machine learning classification.
AN OVERVIEW ON BOVINE COLOSTRUMS AND ITS IMPORTANCE IN DIABETIC WOUNDS MANAGEMENT
Diabetic wounds are a significant concern within healthcare systems globally, posing a considerable burden on individuals living with diabetes. These wounds are chronic in nature and occur as a result of diabetes mellitus, a metabolic disorder characterized by persistent hyperglycaemia. Managing diabetic wounds is complex and challenging, often leading to severe complications if not properly addressed. Bovine colostrum is the first milk produced by cows during the initial days after giving birth. It is a nutrient-rich fluid that provides essential nourishment and immune protection to newborn calves. Bovine colostrum is characterized by its high content of bioactive compounds that contribute to its beneficial properties. It contains various immunoglobulins, including immunoglobulin G (IgG), immunoglobulin A (IgA), and immunoglobulin M (IgM), which are crucial for immune defense against pathogens. Additionally, bovine colostrum contains growth factors such as epidermal growth factor (EGF), insulin-like growth factors (IGFs), and transforming growth factor-beta (TGF-β), which play important roles in tissue development and repair.Bovine colostrum has shown potential in promoting wound healing and immune system activation. Its bioactive components, including immunoglobulins, growth factors, cytokines, and lactoferrin, contribute to these beneficial effects. The supplementation of bovine colostrum has demonstrated positive outcomes in various clinical conditions associated with impaired wound healing and compromised immune function. However, further research is necessary to fully understand the mechanisms and optimal application of bovine colostrum in promoting wound healing and immune system activationThis article aims to provide an in-depth understanding of diabetic wounds, highlighting their challenges and discussing the latest advancements in their management.