International Journal of Engineering and Management Research
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Multiple Disease Prediction System Using ML
Get PDFMachine Learning and Artificial Intelligence have become integral components of numerous industries. From self-driving cars to medical fields, we can find them everywhere. In the medical industry, the abundance of patient data presents an opportunity for leveraging machine learning techniques to enhance disease detection and diagnosis. In this project, we present a comprehensive Prediction System capable of detecting multiple diseases simultaneously, addressing the limitations of existing systems that often offer lower accuracy and focus on individual diseases. Our system currently focuses on five major diseases: Heart, Liver, Diabetes, Lung Cancer, and Parkinson\u27s disease, with the potential for expansion to include more diseases in the future.
By incorporating various parameters specific to each disease, users can input their data and receive reliable predictions regarding disease presence. The implications of this project are significant, as it enables individuals to monitor their health conditions and take proactive measures, ultimately leading to improved life expectancy. By harnessing the power of machine learning, we aim to contribute to the well-being of countless individuals, providing accurate disease predictions that can potentially save lives
The Future of Cryptocurrency
Get PDFCryptocurrency is a peer-to-peer network for exchange. It is digital and also known as virtual currency that uses cryptography a security related coding system. Most of the governments across the world are in the dilemma of making cryptocurrecy as legal. The regulation of official digital currency proposed to introduce in centralized way by various banks in an around the world. Unlike equity, this currency has no regulation regarding the trade in decentralized market due to no underlining in fundamentals and financial values. This money has a good future in terms of technological advancement in circulation and controlling of money with centralized controlling system. Central based digital currency (CBDC) is continuously doing a pilot survey from Dec 1st 2022 in major cities to exactly know the channelization if this currency.
Major dialogues are going on as decentralized currency is not safe for any country’s economic system. Digital money can either be centralized, where there is a central point of control over the money supply by a central bank or decentralized where the control over the money supply is predetermined or agreed upon self-governing. The Mining of crypto currency is done through cryptography in which specialized computers are used for recording the transactions. The young Indian especially in tier 2 and tire 3 cities are inspired through this currency. Financial literacy and awareness are of paramount importance in order to have a healthy skin in the crypto game. The trading amounts to gambling might not hold much steam because success in any market depends on informed entries, caution risk-taking and strategic exits.
Bitcoin is the first and most prevalent Cryptocurrency till the date. Now in the information technology world in which we can interact with each other for exchange without a regulatory body or centralized system, Cryptocurrency is an example of digital change accepted by the people. In this paper we have analyzed the effects of crypto currency and its impact during pandemic. The whole study of this currency is divided in 7 continents and bifurcated according to legal, illegal, banned, prohibited and restricted countries according to the use. And also the initiative by the Indian government regulation related to crypto currency
Accident Severity Detection Using Machine Learning
Get PDFRoad accidents are one of the most regrettable hazards in this hectic world. Each year, traffic accidents cause a large number of casualties, illnesses, and deaths in addition to suffering huge financial losses.There are numerous things that cause traffic accidents, especially those related to the environment, vehicles and the travelers.By analyzing the severity of the road accidents that happened in the past, and the factors that caused it, it is possible to take precautionary measures to reduce the road accidents rate significantly in the future.This project includes developing a machine learning model that can categorize accident severity depending on the circumstances that affected the accident. A prediction model was created using a variety of machine learning classifiers, including Gradient Boosting Classifiers (GBC), K-Nearest Neighbour (KNN), Random Forest (RF), and Decision Tree (DT). The severity of a road accident can be detected 90% accurately, according to the results of a gradient boosting algorithm.The study makes use of publicly accessible European data.The approach presented in the research is broad enough to be used with various data sets from other nations.Additionally, the web portal\u27s model was used to create an intelligent system for predicting the severity of accidents
Intelligent Intrusion Detection Categorization using Support Vector and Fuzzy Logic
Get PDFIntelligent intrusion detection is a crucial component in ensuring the security of computer networks and systems. Traditional intrusion detection systems (IDS) often struggle to handle the complexities and uncertainties associated with modern network environments. To address these challenges, a novel approach is proposed in this paper that combines fuzzy logic and support vector classification.
The main objective of this research is to enhance the accuracy and efficiency of intrusion detection by leveraging the complementary strengths of both fuzzy logic and support vector classification. Fuzzy logic allows for the representation of uncertainty and imprecision in data, making it well-suited for handling the vagueness often present in network traffic and system logs. On the other hand, support vector classification is a powerful machine learning technique known for its ability to handle high-dimensional data and effectively classify complex patterns
Implementation of Decision Tree Algorithm for Prediction of Rheumatoid Arthritis Disease
Get PDFA very challenging and difficult part of finding a cure for Rheumatoid Arthritis ( RA) disease is the same as finding a cure for an autoimmune disease. In the case of autoimmune diseases, it is very difficult to find the cause. RA is also one of the autoimmune diseases that causes inflammation in joints. It does not only limit to joints but also can spread or occur in various parts of the body like skin, lungs, and many others. Many features like age, sex, and previous history of certain diseases affect the occurrence of RA. The proposed ML model uses a decision tree algorithm to analyze a comprehensive set of clinical and laboratory variables collected from RA patients. These variables include age, ID, time, treatment, gender, and other baseline features. The ML model employs feature selection techniques to identify the most relevant predictors that contribute to disease progression. By training the decision tree algorithm on a large dataset of RA patients, the model generates an accurate predictive model for assessing disease severity and progression. The model is able to detect RA disease with almost 80% accuracy with the given training dataset. RA can have many other symptoms and characteristics. This data was trained on a basic dataset available from the Kaggle website. The dataset is in CSV format and has 6 features. The model was trained using a decision tree algorithm, which categorizes the data into different categories and then checks for specific data when needed in Jupyter Notebook. The study explored detecting basic symptoms and characteristics of RA disease. The Decision tree model has given accuracy of 86.5% and 74.4% on training and testing respectively. So Accuracy of the model is pretty good for the dataset used
Tuberculosis Prediction using KNN Algorithm
Get PDFIn this paper, a machine learning model is used to develop a model that is used for tuberculosis prediction. Tuberculosis is known to be one of the top reasons for death from an infectious agent that affects the lungs and continues to threaten the human population on a wider basis. According to WHO, tuberculosis is a serious threat to the human population after HIV/AIDS. It is estimated by the World Health Organization (WHO) that 1/3rd of the global population is infected with TB and that seven to eight million new cases of TB occur each year across the globe Because the disease is difficult to differentiate between the common cold, it takes a long time to decide the patient is affected by the disease. So we use the detection of tuberculosis by utilizing the K-NN algorithm method for classification and HOG as feature extraction. K-NN abbreviated as K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on the Supervised Learning technique.
The data provided K-NN model should be labeled one. Then these datasets are given to a training model where the training process of the model is being undergone. Once the training is completed, the next step is to predict the output. For this process, we have to provide new data that may or may not belong to the dataset, so that the model can predict the output of it. If the prediction is wrong, again the training is done until we get the actual output matching with the desired output given by the designer for verification purposes. This is the basic working process under the K-NN algorithm. The data that is used for this separation is a Tuberculosis dataset that contains various information about the different symptoms that are helpful in detecting tuberculosis effectively. Here it is used in the early detection of tuberculosis which helps save millions of people which might otherwise lead to death because of lack of detection. ML model helps to improve the efficiency in detecting by considering various symptoms. ML models are more accurate at differentiating even the slightest difference that deviates from the data that was used to train the model. Unlike the manpower we fail to detect the slightest as we notice the symptoms only after they become more severe. The accuracy of this model was found to be 98%. The following model uses a dataset consisting of data that contrasts between males and females and the various symptoms are shown in them. It also contrasts the severity of these two
Online Candidate Selection System for Elections
Get PDFThis research proposes an innovative online platform for political parties in Sri Lanka to enhance the candidate selection process. The platform incorporates features such as sentiment analysis, background checks, aptitude tests, ranking system, and analysis of candidates\u27 promises and activities. Developed using advanced technologies, it aims to ensure transparency, efficiency, and accessibility for all eligible candidates, ultimately contributing to a more democratic election
A Framework for Successful CRM Implementation in Indian Banking Sector
Get PDFBanking sector is a pivot of economy & also plays vital role for boosting GDP rate in Indian economy. It is highly required that customer should delight after perceiving services in banking sector. As well as financial inclusion is spreading, the need of maintain good relationship with customers is also increasing after implementation of financial inclusion scheme. In India, most of people have covered by financial inclusion but still one thing remains, how can bank maintain good relationship with existing customer & prospective customer? Good relationship with customers is main key for boosting growth rate of country. If banks get success for creating good relationship with customers, in fact it will be great achievement for country. CRM is always circumspect for good relationship. It provides healthy financial track for running banking sector in economy, but successfully implementation for CRM is more important term to boost growth rate of country. Indian banking sector is backbone of economy. We can\u27t ignore it for the development of economy but now banking sector is facing hyper competition in present scenario. Indian banking sector is passing through critical stage & facing a lot of changes. With help of successfully CRM implementation in Indian banking sector, we can solve this problem with easy way and also generate sound banking in country. According to RBI, “CRM is a continuous process – it is a journey, not a destination. To be successful in this arena, banks need to embrace CRM as a philosophy and adopt a strategy for managing customer relationships that effectively address three key areas: people, processes and technology and even in the context of financial stability, CRM is important.” CRM model in banking sector must integrate of technological adaptation, organise training & skill development programme, collection of customer data, analysis & interpretation, timely integration and evaluation for achieving the goal
Brief Overview on Wireless Sensor Network Technologies in the Internet of Things (IoT)
Get PDFWireless systems based on IoT-based has evolved rapidly in various sectors. The IoT (Internet of Things) is the network in which physical devices, equipment, sensors and other objects can communicate among themselves without human involvement. The WSN (Wireless Sensor Network) is a central component of the IoT, which has expanded across several different applications in real-time. Various approaches and algorithms for energy-efficient data aggregation in IoT-WSN systems were presented. Wireless Sensor Network technologies play a vital role in enabling the IoT. In this analysis, we delve deep into the essential elements, functionalities, and applications of WSN technologies within the context of IoT. We explore the diverse sensor nodes, communication protocols, and network architectures that constitute WSNs, highlighting their crucial function in gathering, transmitting, and processing data from the physical environment.
The Internet of Things (IoT), which enables seamless connectivity among a variety of physical objects, has completely changed how we interact with the world around us. Wireless Sensor Network (WSN) technologies, which is the central nervous system of this interconnected ecosystem are a fundamental IoT enabler. This paper overview the essential elements, functionalities, and applications of WSN technologies in context of Internet of Things (IoT). We cover the various sensor nodes, communication protocols, and network architectures that make up WSNs in this thorough analysis, highlighting their functions in gathering, transmitting, and processing data from the physical environment. We explore the particular difficulties.
WSN technologies find applications across various domains within the IoT landscape. From smart cities and precision agriculture to healthcare and industrial automation, WSNs play a crucial role in transforming these sectors. We showcase real-world case studies that illustrate the practical implementation and impact of WSNs in these domains, providing concrete examples of their effectiveness. Our goal is to provide you with engaging and content that adds value to your understanding of the Internet of Things and Wireless Sensor Networks
Entrepreneurial Inclination: Reviewing Literature for Entrepreneurship among Tribal Women
Get PDFPurpose- As stated earlier, the goal of this research is to give a complete overview of the existing literature on entrepreneurial inclination. The paper examines and reviews the different definitions and terminologies used by previous scholars towards defining entrepreneurial inclination which includes definition of entrepreneur, women entrepreneur, entrepreneurship, entrepreneurial intention, entrepreneurial inclination, entrepreneurship education, entrepreneurial culture and their views. The review found that the term ‘entrepreneurial inclination’ is of psychological phenomenon which influences by many other factors for a woman in deciding entrepreneurial behaviour. The study identified and addressed the causes of inadequate supply of entrepreneurship among tribal women. The study provide suggestions putting forward the policy measures for inclusive entrepreneurship.
Methodology- Uses a desk-based approach.
Findings- If one wants to understand the motivations of an entrepreneur, one must first grasp the individual\u27s psychological characteristics. Based on what we\u27ve learned from the study, we can begin a discussion about how we can help indigenous women develop their entrepreneurial skills and abilities so that they may pursue their dreams of starting up a business.
Research Limitations/Implications- The research is summarised in this paper. The lack of information on one\u27s aptitude for entrepreneurship lays the groundwork for more study.
Originality- Tribal women\u27s entrepreneurial inclination is summarised in this article, and the likely policy measures needed to understand and foster entrepreneurship among tribal people are addressed.
Paper Type- Literature Review