IEEE Python Projects 2022 – 2023 Python Project Titles

IEEE Python Projects 2022 2023 Machine Learning Projects, Deep Learning Projects, Artificial Intelligence Titles, Data Science Project Ideas for Final Year 2022 2023

Python Projects 2022 Machine Learning Projects, Artificial Intelligence, Deep Learning Projects title, Data Science project ideas for Final Year IEEE Projects 2022 – 2023 Ieee machine learning Projects Ieee machine learning Projects 

IXP2301 Blockchain A Decentralized Voting system using Blockchain ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2302 Machine Learning UPI Fraud Detection Using Machine Learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2303 Blockchain Secure Banking Transactions Using Blockchain Technology ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2304 Machine Learning Fake Job Post Detection using Machine Learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2305 Deep Learning  AI Car with Real-time detection of Damaged Road and Lane Detection ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2306 Blockchain A Blockchain-Based Credible and Secure Education Data Management ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2307 Deep Learning Heart Attack Risk Prediction using Retinal Eye Images ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2308 Machine Learning Realtime Face Speech Emotion Recognition in Worker Stress Analysis ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2309 Machine Learning Mental Health Detection Machine Learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2310 Machine Learning Vitamin Deficiency Detection using Image Processing ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2311 Deep Learning Intelligent Traffic System for Urban Conditions using Real-time Vehicle Tracking ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2312 Machine Learning Medicine Overdose Prediction Using Machine Learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2313 Machine Learning Election Results Prediction Using Machine Learning and Twitter ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2314 Machine Learning Student Teacher Performance Prediction Using Machine Learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2315 Machine Learning Predicting Poverty Level from Satellite Imagery ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2316 Machine Learning Subjective Answer Evaluation using Machine Learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2317 Machine Learning PHISHSIM:  Phishing Website Detection
With a Feature-Free Tool
IXP2318 Machine Learning Train Delay Prediction using ML ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2319 Deep Learning Blood Grouping Detection Using Image Processing ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2320 Deep Learning Human Authentication using Walking Style ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2321 Machine Learning Food Recognition and Calorie Measurement using Artificial Intelligence ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2322 Machine Learning Parkinson’s Disease Detection Using Spiral Images (Hand Drawings) ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2322 Deep Learning WildTrackAI – Applies Computer Vision to Animal Footprint Classification ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2323 Blockchain Secure Land Registry System using Blockchain ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2324 Machine Learning Medicinal Plant Classification using Machine Learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2325 Machine Learning Drug Recommendation System based on Sentiment Analysis of Drug Reviews using ML ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2326 Machine Learning Stress Detection In IT Professionals by Image Processing and Artificial Intelligence ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2327 Machine Learning Employee Attrition Prediction Using RNN ABSTRACT BASEPAPER Rs.4000 VIDEO

Importance of Artificial intelligence 

Accordingly, In predictive analytics, companies, businesses, and healthcare professionals apply artificial intelligence, machine learning, and data mining to look at quiet records so as to decide conceivable patient results, for example, the chance of an exacerbating or Specifically improving well-being condition, or odds of acquiring an ailment in a person’s and group of the patient. In summary Artificial Intelligence much a useful solution to Improve overall computational processes. Similarly to Artificial Intelligence Data Science is Also an Emerging topic in recent Days.

IXP2214 Deep Learning Diabetic Retinopathy Detection Using Machine Learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2215 Machine Learning Diabetes Prediction Using Machine Learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2216 Machine Learning Stock Market Price Prediction using Machine Learning and Twitter Sentimental Approach ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2217 Machine Learning Car Price Prediction using Machine Learning ABSTRACT BASE PAPER Rs.4000 VIDEO
IXP2109 Machine Learning Potato Disease Detection using Machine Learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2110 Deep Learning Vehicle Speed Detection Using Deep Learning Approach ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2111 Machine Learning Credit card Fraud Detection using Machine learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2112 Machine Learning Stress Detection In IT Professional by Image Processing and Machine Learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2113 Machine Learning Chronic Kidney Disease prediction using Machine Learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2114 Deep Learning A Deep Learning-Based Framework for Robust Traffic Sign Detection Under Challenging Weather Condition ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2115 Deep Learning Brain Tumor Classifications using Deep learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2116 Deep Learning Diagnosis of COVID-19 from Chest X-Ray Images Using Wavelets-Based Depthwise Convolution Network ABSTRACT BASEPAPER Rs.4000 VIDEO

Importance of Machine Learning

In short Machine learning is an emerging field in computer science as it has given a new dimension to the concept of learning. Particularly By learning from past data, it emulates human intelligence in the system. In this case, Due to its self-adaptive capability and mathematical basis, machine learning has huge applications in healthcare, sentiment analysis, recommendation systems, natural language processing, information retrieval, gameplay, market analysis, text recognition, and computer vision. For this reason, Machine learning research was the most demanding research domain Accordingly.

IXP2118 Machine Learning Cyberbullying Detection on Social Networks Using Machine Learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2119 Machine Learning A Road Accident Prediction Model Using Data Mining Techniques ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2120 Machine Learning A Systematic Review of Predicting Elections Based on Social Media Data Research Challenges and Futures ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2121 Machine Learning A Neural Based Approach for Detecting the Situational Information From Twitter During Disaster ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2122 Machine Learning WELFake Word Embedding Over Linguistic Features for Fake News Detection ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2123 Machine Learning Detecting Fake Reviews Using Multidimensional Representations With Fine Grained Aspects Plan ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2124 Deep Learning Data Fusion based Two-stage Cascade Framework for Multi-Modality Face Anti-Spoofing ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2125 Machine Learning Fake Profile Detection for Matrimonial Sites ABSTRACT BASEPAPER Rs.3000 VIDEO
IXP2126 Machine Learning Heart Disease Prediction System using CNN ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2127 Machine Learning Multiplex Regulation System With Personalised Recommendation Using ML ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2128 Deep Learning Multi Crop Convolutional Neural Networks for Fast Lung Nodule Segmentation ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2129 Deep Learning Chest Xray Pulmonary Tuberculosis Detection using Deep learning ABSTRACT BASEPAPER Rs.4000 VIDEO

Importance of Deep Learning

To begin with, Deep Learning (DL) a type of ML solves the problems that were unsolvable with machine learning. In short Deep learning uses neural networks to increase computational work and provide accurate results. Some of the incredible applications of deep learning are NLP, speech recognition, and face recognition. For this reason, DL in collaboration with IoT, is witnessing revolutionary innovations.

IXP2130 Machine Learning Cyclone Prediction and Rainfall Prediction using Machine Learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2131 Machine Learning Intrusion Detection System Using Machine Learning ABSTRACT BASE PAPER Rs.4000 VIDEO
IXP2132 Machine Learning Fraud App Detection Using Machine Learning ABSTRACT BASE PAPER Rs.4000 VIDEO
IXP2133 Machine Learning Lung Cancer Detection using Machine Learning ABSTRACT BASE PAPER Rs.4000 VIDEO
IXP2134 Blockchain EHRChain: A Blockchain Based EHR System Using Attribute-Based and Homomorphic Cryptosystem ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2135 Machine Learning House Price Prediction using Machine Learning ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2136 Deep Learning Blockchain based solution to improve the Supply Chain Management in Indian agriculture ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2137 Deep Learning Drowsiness detection system based on Eye State Analysis ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2138 Deep Learning DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing ABSTRACT BASEPAPER Rs.4000 VIDEO
IXP2139 Machine Learning Agricultural Crop Recommendations based on Productivity and Season ABSTRACT BASEPAPER Rs.4000 VIDEO
More Titles will be updated Regularly



Machine learning projects for final year 2022 2023 Data Science projects for final year 2022 2023

Importance of Data Science

As a matter of fact, Data Science (DS) has matured as a field of basic and applied research in computer science in general and e-commerce in particular. Although,  some of the recent approaches and architectures where data mining has been applied in the fields of e-commerce and e-business. Comparatively the dominant goal of DM  is the generation of non-obvious yet useful information for decision-makers from very large databases. The various mechanisms of this generation include abstractions, aggregations, summarizations, and characterizations of data as proposed by the Authors. Correspondingly these forms, in turn, are the result of applying modeling techniques from the diverse fields of statistics, artificial intelligence, database management, and computer graphics.

IEEE machine learning Projects 2022 2023 ieee python projects

IEEE python projects machine learning 2021 2022 Final Year  Python projects 2022 2023 IEEE machine learning Projects 

Python Projects source code 2022 2023

IEEE papers on Python Machine Learning final year projects IEEE Machine learning projects for final year with source code in Python IEEE projects for CSE 2022 2023


What is Machine Learning?

Basically, An ML model is defined as a computer-intensive mechanism and applies resampling and iterative methodologies for classification approaches. Especially  ML approaches are considered with optimal subset selection and eliminate the issues of classical classifiers like over-fitting as well as distributional demands of parameters. Following ML technologies that have emerged in computer science with logic and basic mathematics, statistics, as ML approaches do not estimate the group, features rather it is initialized with an arbitrary group separator and tunes frequently till satisfying the classification groups. Significantly ML examines the tuning variables and individual ML functions that became unstable, Balanced against which makes a suitable process. Furthermore, As the non-statistical nature is embedded, overall these approaches can apply the data in various formats like nominal data that generates maximum classification accuracies.

Project on Machine Learning 

In this Section Specifically, we have discussed about some of the projects on Machine Learning with current trends.

Predicting Poverty Level from Satellite Imagery

Determining the poverty levels of various regions throughout the world is still crucial in identifying interventions for poverty reduction initiatives and directing resources fairly. Generally, reliable data on global economic livelihoods is hard to come by, especially for areas in the developing world, hampering efforts to both deploy services and monitor/evaluate progress. However, the current challenge in this domain is that agencies across the world that predict income levels take a huge amount of time to do the same.

Instead of a Manual Human Taking process, This project proposes to use satellite images to detect economic activity and, as a result, estimate poverty in a location. lastly, A Recurrent neural network is trained to learn various developmental parameters like rooftop type, source of lighting, proximity to water sources, Agriculture Areas, Road Structure, and Industrial Areas. Likewise, it will estimate the poverty level of each Area using Satellite Images. particularly it will cost you less amount as well comparatively less time as well.

Phishing Website Monitoring Using ML

In this project, we propose a feature-free method for detecting phishing websites correspondingly based on a similarity measure that computes the similarity of two websites by compressing them, thus eliminating the need to perform any feature extraction. Additionally, It also removes any dependence on a specific set of website features. Altogether this method examines the HTML of webpages and computes their similarity with known phishing websites during testing phases, in order to classify them.

Furthermore, We use the Furthest Point First algorithm to perform phishing prototype extractions, in order to select instances that are representative of a cluster of phishing web pages. Identically We also introduce the use of an incremental learning algorithm as a framework for continuous and adaptive detection without extracting new features when concept drift occurs. Lastly, On a large dataset, our proposed method significantly outperforms previous methods in detecting phishing websites, with an AUC score of 98.68%, a high true positive rate (TPR) of around 90%, while maintaining a low false positive rate (FPR) of 0.58%. Particularly Our approach uses prototypes, eliminating the need to retain long-term data in the future, and simultaneously is feasible to deploy in real systems with a processing time of roughly 0.3 seconds.

Food Recognition and Calorie Measurement using Machine Learning

Presently This is one of the projects particularly to discuss the relationship between nutritional ingredients identification in food and inspecting Calories through Machine Learning models to perform the data analysis, Significantly the experiments on real-life datasets show that our method improves the performance with efficient accuracy . Specifically, Our system will recommend food for some Different Age groups. Subsequently, Our work is able to identify the Nutrition that we may get affected by lacking certain nutritional ingredients in our body and recommends the food that can benefit the rehabilitation of those Age Groups. Thereafter To achieve high accuracy and low time complexity, the proposed system was implemented using CNN Machine Learning models. Lastly, The model, when trained convolutionally, generates the natural image samples which give a better broad statistical structure of the natural images as compared with comparatively existing parametric generative methods.

Train Delay Prediction using Machine Learning

Eventually Delay prediction is a process of estimating delay probability based on formerly known data at a given checkpoint and is typically measured via arrival (departure) delay. Furthermore the key to making delay predictions based on actual operational data involves establishing the relationship between train delays and various characteristics of a railway system. Henceforth this provides a basis for the operator’s scheduling decision Train delay is a significant problem that negatively impacts the railway industry and costs billions of dollars each year. Simultaneously In this project we have used Train delay dataset from IRTC to predict Train delays. Specifically  We have used Faster RCNN algorithm to predict flight departure delay and thereupon our model can identify which features were more important when predicting Train delays.

Blood Grouping Detection Using Image Processing

Basically, there is worldwide demand for an affordable Blood Group measurement solution, although this is a particularly urgent need in developing countries. Altogether Image Processing, which is the most penetrated device in both rich and resource-constrained areas, would be a suitable choice to build this solution. Comparatively This Project proposes a noninvasive Blood Group measurement process.  Also, it compared the variation in data collection sites, biosignal processing techniques, theoretical foundations, photoplethysmogram (PPG) signal and features extraction process, Image Processing algorithms, and Detection models to calculate Blood Groups. Especially This analysis was then used to recommend realistic approaches to build an Image Processing-based point-of-care tool for Blood Group measurement in a non-invasive manner. Certainly, This project proposes approaches for blood Group measurement with the aim of recommending data collection techniques, signal extraction processes, feature calculation processes, and Image Processing algorithms for developing a noninvasive Blood Group estimation using an Image.

Parkinson’s Disease Detection Using Spiral Images

Correspondingly Parkinson’s Disease (PD) is a progressive neurodegenerative disorder emphatically, which is characterized by  Various symptoms. Furthermore, the progressive neurodegenerative disorder affects the nervous system in the elderly, which is characterized by motor symptoms such as tremors, rigidity, slowness of movement, and problems with gait. Obviously, In this work, an attempt has been made to classify the spiral images of healthy control and Parkinson’s disease subjects using deep-learning neural networks. Particularly The Vision-based Convolutional Neural Network architecture is used to refine the diagnosis of neurodegenerative disorder disease.

Nevertheless, This project proposes a Vision-Based novel deep learning architecture for neuro-generative disorder screening. obviously, this project, analysis of Spiral images for discrimination of healthy control and NDD (Neurodegenerative disorder) subjects is attempted using the CNN model. The proposed FAST-RCNN exploits Feature Extraction to tackle multi-view data from the Spiral Image data. significantly training, the proposed model employs a data enhancement technology called SCI-KIT’s Image Data Generator API on multi-view data.

Drug Recommendation System based on Sentiment Analysis of Drug Reviews using Machine Learning

Since the coronavirus has shown up, the inaccessibility of legitimate clinical resources is at its peak, previously like the shortage of specialists and healthcare workers, secondly lack of proper equipment and medicines, etc. Specifically, The entire medical fraternity is in distress, which results in numerous individuals’ demise. unlike Due to unavailability, individuals started taking medication independently without appropriate consultation, making their health condition worse than usual. Identically As of late, machine learning has been valuable in numerous applications, and there is an increase in innovative work for automation.

Meanwhile, This project intends to present a drug recommender system that can drastically reduce specialists’ heap. Regardless In this research, we build a medicine recommendation system that uses patient reviews to predict the sentiment using various vectorization processes like Bow, TF-IDF, Word2Vec, and Manual Feature Analysis, which can help recommend the top drug for a given disease by different classification algorithms. specifically, The predicted sentiments were evaluated by precision, recall, F1 score, accuracy, and AUC score. whereas The results show that classifier DNN using TF-IDF vectorization outperforms all other models with 98% accuracy.

Stress Detection In IT Professionals by Image Processing and Machine Learning

Significantly The main motive of our project is to detect stress in IT professionals using vivid Machine learning and Image processing techniques. obviously, Our system is an upgraded version of the old stress detection systems which excluded live detection and personal counseling but this system comprises of live detection and periodic analysis of employees and detects physical as well as mental stress levels in his/her by providing them with proper remedies for managing stress by providing survey form periodically. Moreover, Our system mainly focuses on managing stress and making the working environment healthy and spontaneous for the employees and furthermore it getting the best out of them during working hours.

Henceforth The proposed System Machine Learning algorithms like KNN classifiers are applied to classify stress. Following Image Processing is used at the initial stage for detection, the employee‟s image is given by the browser which serves as input. further, In order to get an enhanced image or to extract some useful information from it image processing is used by converting image into digital form and performing some operations on it. Generally By taking input as an image and output may be image or characteristics associated with that images. The emotion are displayed on the rounder box. The stress level indicated by Angry, Disgusted, Fearful, Sad.

Human Authentication using Gait Recognition (Walking Style)

Evidently, Biometric identification like fingerprints, retina, palm, and voice recognition needs the subject’s permission and physical attention. Correspondingly Human Gait recognition works on the gait of walking subjects to identify people without them knowing or without their permission. Basically, The purpose of this Project is to detect humans based on their Waling styles. We first extract the gait features from image sequences using the Feature Module. Features are then trained based on the frequencies of these feature trajectories, from which recognition is performed.

Gait recognition is the process where the features of human motion are automatically obtained/extracted and later these features enable us to authenticate the identity of the person in motion. Besides As with other pattern recognition techniques, the gait recognition technique also involves 2 stages: Firstly Information is derived from human locomotion in the first stage i.e. feature extraction stage. Formerly In the next stage, i.e. the recognition stage, a standard similarity computation technique (Incremental Component Analysis) is used to obtain results for being a match or a mismatch.

Decentralized Voting system using Blockchain

Certainly, Electronic voting or e-voting has been used in varying forms since the 1970s with fundamental benefits over paper-based systems such as increased efficiency and reduced errors. However, there remain challenges to achieving widespread adoption of such systems, especially with respect to improving their resilience against potential faults. Generally, Blockchain is a disruptive technology of the current era and promises to improve the overall resilience of e-voting systems. Likewise, This project presents an effort to leverage the benefits of blockchain such as cryptographic foundations and transparency to achieve an effective scheme for e-voting.

Moreover, The proposed scheme conforms to the fundamental requirements for e-voting schemes and achieves end-to-end verifiability. The paper presents details of the proposed e-voting scheme along with its implementation using the Multichain platform. Obviously, The project presents an in-depth evaluation of the scheme which successfully demonstrates its effectiveness to achieve particularly an end-to-end verifiable e-voting scheme. Ieee machine learning Projects

Diabetic Retinopathy Detection Using Machine Learning

Diabetic Retinopathy is the most common cause of vision loss among people particularly diabetes and the leading cause of vision impairment and blindness among working-age adults. Secondly, By using a certain algorithm the retinal image from the user is fed into the system. significantly The blood vessels are extracted from the image then it is pre-processed by filtering and segmentation process. Rather It is followed by fractional edge reduction which is used for the feature extraction and by using a Faster retinal convolutional neural network algorithm to automate the diagnosis process. It improves the resultant accuracy and by this classification technique,overall we can achieve high accuracy. Ieee machine learning Projects





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