PYTHON PROJECTS

Python – ML, AI, NN, IP, DL Based Project Title

 

Complete Software Project:

  • 15_Py_Machine Learning Techniques for Stress Prediction in Working Employees
  • 16_Py_Intraday Stock Price Forecasting using an Auto Regressive Time Series Model – ARIMA
  • 16_Py_Nifty Index Prediction Approach for Stock Market Volatility Based on Time Series – ARIMA
  • 17_Py_Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare
  • 18_Py_LOAN APPROVAL PREDICTION BASED ON MACHINE LEARNING APPROACH
  • 19_Py_Fruit Disease Classification and Identification using Image Processing
  • 20_Py_Clasification of Medicinal Plants by Visual Characteristics of Flowers
  • 21_Py_Krishi Sadana – Pests Classification and Detection Using Machine Learning
  • 22_Py_COVID-19 Future Forecasting of Death Rate Using ML
  • 25_Py_Suicidal Ideation Detection A Review of ML Methods
  • Crop Prediction and Efficient use of Fertilizers using Machine Learning
  • Machine Learning Analysis of Airbreathing Propulsion of Turojet Engine
  • Machine Learning Techniques for Stress Prediction in Working Employees
  • COVID-19 Future Forecasting of Death Rate Using Supervised Machine Leaning Algorithm
  • 1_Py_Machine Learning Approach for Air Quality Prediction and Analysis
  • 2_Py_Feature Extractor Analysis for Traffic Clearance in Emergency for Ambulance
  • 3_Py_Skin Disease Recognition CNN
  • 3_Py_Skin Disease Recognition Method Based on Image Color and Texture Features
  • 5_Py_Artifical Intelligence based Material Sorting for Industrial Production
  • 6_Py_CNN based Leaf Disease Identification and Remedy Recommendation System
  • 8_Py_Virtual Try ON System for Garments Outlets
  • 14_Py_Machine Learning based Brain Tumor Analysis using Convolutional Neural Network
  • 14_Py_Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems A Prospective Survey
  • Stress Prediction of Professional Students using Machine Learning
  • A Prediction Approach for Stock Market Volatility Based on Time Series Data
  • A Wavelet Based Deep Learning Method for Underwater Image Super Resolution Reconstruction
  • Deep Neural Network Architecture Application for Facial Expression Recognition
  • COVID-19 Social distancing detector in video
  • Machine Learning Methods for Disease Prediction with Claims Data
  • Time Series Prediction of Agricultural Products Price based on Time Alignment of RNN
  • An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis
  • Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum
  • CNN based Leaf Disease Identification and Remedy Recommendation System
  • Identification of Plant Disease using Image Processing Technique
  • Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
  • Comparison of Machine Learning Methods for Breast Cancer Diagnosis
  • Clusters of Features Using Complementary Information Applied to Gender Classification From Face Images
  • Air Learning Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality
  • Face Recognition and Age Estimation Implications of Changes in Facial Features
  • A Predictive Data Feature Exploration-Based Air Quality Prediction Approach
  • Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
  • Development of a Fully Cross-Validated Bayesian Network Approach for Local Control Prediction in Lung Cancer
  • Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood
  • Flight Ticket Price Predictor using Python

 

  • Automatic Salt Segmentation with UNET in Python using Deep

Learning

 

  • Transformer Conversational Chatbot in Python using TensorFlow 2.0

 

  • Lane-Line Detection System in Python using OpenCV

 

  • Online Sports Turf Playground Booking System

 

  • Price Comparison Website for Online Shopping Project

 

  • Online College Admission Management System Project

 

  • Web Based Blood Donation Management System Project

 

  • Online Property Management System Project

 

  • Online Employee Payroll Management System Project

 

  • Online Grocery Recommender System Using Collaborative Filtering

 

  • Online Shoes Shopping Website Project

 

  • Online Organic Health Food Store Project

 

  • Color Detection Using OpenCv Python Project

 

  • Logistics Management System Project in Python

 

  • Web Based Place Finder Using Django and GeoDjango

 

  • Online Transaction Fraud Detection using Python & Backlogging on

E-Commerce

 

Graphical Password Authentication System by Using Pass Point

  • Scheme

 

  • Ecommerce Food Products Sales Forecasting System

 

  • Predicting House Prices Using Linear Regression

 

  • Online Employee Recruitment System Project in Python

 

  • Decision Tree Based Tourism Recommendation System

 

  • Ecommerce Website Live Visitor Tracking System Project

 

  • Efficient Courier Tracking System Project

 

  • Online Crime Reporting System in Python Project

 

  • Image Steganography Project using Python

 

  • Web Based Pharmaceutical Store Sales Forecasting System

 

  • Online Healthcare Information Management System Project

 

  • Online Inventory Management System Project in Python

 

  • Wish list Products Price Comparison Website Project

 

  • Secure File Storage on Cloud Using Hybrid Cryptography in Python

 

  • Data Duplication Removal using File Checksum with Python

 

  • Efficient Courier Tracking System Project

 

  • GUI Based Stock Management & Control System Project

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

P.No  

IEEE2020-21 PROJECT TITLES

 

Domain Lang/Year
Py1*  

A Wavelet Based Deep Learning Method for Underwater Image Super Resolution Reconstruction

MACHINE LEARNING

 

PYTHON/IEEE2020

 

Py2  

BAT- Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset

MACHINE LEARNING PYTHON/IEEE2020
Py3*  

Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems A Prospective Survey

MACHINE LEARNING PYTHON/IEEE2020
Py4*  

Deep Neural Network Architecture Application for Facial Expression Recognition

 

MACHINE LEARNING PYTHON/IEEE2020
Py5*  

Drowsiness Detection of a Driver using Conventional Computer Vision Application

 

MACHINE LEARNING PYTHON/IEEE2020
Py6*  

End-to-End Speech Emotion Recognition With Gender Information

 

MACHINE LEARNING PYTHON/IEEE2020
Py7*  

Performance Comparison of Machine Learning Classifiers for Fake News Detection

 

MACHINE LEARNING PYTHON/IEEE2020

 

Py8*  

Prediction Of Heart Disease At Early Stages

 

MACHINE LEARNING PYTHON/IEEE2020
Py9  

Prediction of N-Gram Language Models Using Sentiment Analysis on E-Learning Reviews

 

Machine Learning PYTHON/IEEE2020
Py10  

RV-ML An effective Rumor Verification scheme based on Multi-task Learning Model

 

MACHINE LEARNING PYTHON/IEEE2020
Py11  

Supervised Machine Learning Algorithms for Credit Card Fraud Detection A Comparison

 

MACHINE LEARNING PYTHON/IEEE2020
Py12  

Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning

 

MACHINE LEARNING PYTHON/IEEE2020
Py13  

CovidGAN- Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection

 

MACHINE LEARNING PYTHON/IEEE2020
Py14*  

COVID-19 Social distancing detector in video

 

IMAGE PROCESSING PYTHON/IEEE2020
P.No  

IEEE2020-21 PROJECT TITLES

 

Domain Lang/Year
Py15*  

COVID-19 face mask detector using opencv

 

IMAGE PROCESSING PYTHON/IEEE2020
Py16  

An Evolutionary SVM Model for DDOS Attack Detection in Software Defined Networks

 

MACHINE LEARNING PYTHON/IEEE2020
Py17  

Multi-Task Learning Model Based on Multi-Sale CNN and LSTM for Sentiment Classification

 

MACHINE LEARNING PYTHON/IEEE2020
Py18  

Privacy Protection in Interactive Content Based Image Retrieval

 

MACHINE LEARNING PYTHON/IEEE2018
Py19  

COVID-19 Future Forecasting Using Supervised Machine Learning Models

 

MACHINE LEARNING PYTHON/IEEE2020
Py20  

Quantitative Determination of Optimal Harvest Time by Surface Color Analysis of Tree Fruits

 

MACHINE LEARNING PYTHON/IEEE2020
Py21  

Air Quality Prediction Using Improved PSO-BP Neural Network

 

MACHINE LEARNING PYTHON/IEEE2020
Py22  

Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare

 

MACHINE LEARNING PYTHON/IEEE2020
Py23*  

Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach

MACHINE LEARNING PYTHON/IEEE2020
Py24  

Study and analysis of unreliable news based on content acquired using ensemble learning

 

MACHINE LEARNING PYTHON/IEEE2020
Py25  

Dynamic knowledge graph based fake-review detection

 

MACHINE LEARNING PYTHON/IEEE2020
Py26  

Deep Learning Based Fusion Approach for Hate Speech Detection

 

MACHINE LEARNING PYTHON/IEEE2020
Py27  

An Empirical Evaluation of Machine Learning Techniques for Chronic Kidney Disease Prophecy

 

MACHINE LEARNING PYTHON/IEEE2020
Py28  

Length-of-Stay Prediction for Pediatric Patients with Respiratory Diseases Using Decision Tree Methods

MACHINE LEARNING PYTHON/IEEE2020
P.No  

IEEE2020-21 PROJECT TITLES

 

Domain Lang/Year
Py29  

Automated SMS Classification and Spam Analysis using Topic Modeling

 

MACHINE LEARNING PYTHON/IEEE2020
Py30  

Exploiting Deeply Supervised Inception Networks for Automatically Detecting Traffic  Congestion on Freeway in China Using Ultra-Low Frame Rate Videos

MACHINE LEARNING PYTHON/IEEE2020
Py31  

Particle Swarm Optimization-Based Feature Weighting for Improving Intelligent Phishing Website Detection

 

MACHINE LEARNING PYTHON/IEEE2020
Py32  

Bus Arrival Time Prediction: A Spatial Kalman Filter Approach

 

MACHINE LEARNING PYTHON/IEEE2019
Py33  

Automated detection of helmet on motorcyclists from traffic surveillance videos: a comparative analysis using hand-crafted features and CNN

 

MACHINE LEARNING PYTHON/IEEE2020
Py34  

Diagnostic Efficiency of the Breast Ultrasound Computer-Aided Prediction Model Based on Convolutional Neural Network in Breast Cancer

 

MACHINE LEARNING PYTHON/IEEE2020
Py35*  

Supervised Machine learning Approach for Crop Yield Prediction in Agriculture Sector

 

MACHINE LEARNING PYTHON/IEEE2020
Py36  

Intelligent SDN Traffic Classification Using Deep Learning: Deep-SDN

 

MACHINE LEARNING PYTHON/IEEE2020
Py37*  

Voice Based E-mail System for Visually Impaired

 

 

MACHINE LEARNING PYTHON/IEEE2020
Py38  

LDA–GA–SVM: improved heap-to-cellular carcinoma prediction through dimensionality reduction and genetically optimized support vector machine

 

MACHINE LEARNING PYTHON/IEEE2020
Py39  

Realizing a Stacking Generalization Model to Improve the Prediction Accuracy of Major Depressive Disorder in Adults

 

MACHINE LEARNING PYTHON/IEEE2020
Py40  

 

Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression

MACHINE LEARNING PYTHON/IEEE2020

 

P.No  

IEEE2020-21 PROJECT TITLES

 

Domain Lang/Year
Py41  

FPGA Simulation of Fingertip Digit Recognition Using CNN

 

MACHINE LEARNING PYTHON/IEEE2020
Py42*  

Sign Language Translation

 

MACHINE LEARNING PYTHON/IEEE2020
Py43*  

An Investigation on the Use of LBPH Algorithm for Face Recognition to Find Missing People

 

MACHINE LEARNING PYTHON/IEEE2020
Py44*  

Real time Eye Blink Password Authentication

 

MACHINE LEARNING PYTHON/IRJE2020
Py45*  

Forecasting method of stock market volatility in time series data based on mixed model of ARIMA and XGBoost

 

MACHINE LEARNING PYTHON/IEEE2020
Py46*  

Cricket Match Outcome Prediction Using Machine Learning

 

MACHINE LEARNING PYTHON/IJASRET2020
Py47*  

Implementing CCTV-Based Attendance Taking Support System Using Deep Face Recognition

 

MACHINE LEARNING PYTHON/SYMMETRY2020
Py48*  

Comparative Analysis of Machine Learning Techniques for Indian Liver Disease Patients

 

MACHINE LEARNING PYTHON/IEEE2020
Py49*  

An Effective Model-Based Trust Collaborative Filtering for Explainable Recommendations

 

MACHINE LEARNING PYTHON/IEEE2020
Py50  

T-PCCE: Twitter Personality based Communicative Communities Extraction System for Big Data

DATA MINING PYTHON/IEEE2020
Py51  

Citywide Bike Usage Prediction in a Bike-Sharing System

DATA MINING PYTHON/IEEE2020
Py52*  

Deep-CAPTCHA: A Deep Learning Based CAPTCHA Solver For Vulnerability Assessment

 

DEEP LEARNING PYTHON/ARIXV2020
Py53  

An Improved Enhancement Algorithm Based on CNN Applicable for Weak Contrast Images

 

DEEP LEARNING PYTHON/IEEE2020
Py54*  

PMs concentration forecasting using ARIMA algorithm

 

DEEP LEARNING PYTHON/IEEE2020
Py55 Rainfall Prediction using Machine Learning & Deep Learning Techniques

 

DEEP LEARNING PYTHON/IEEE2020
P.No  

IEEE2020-21 PROJECT TITLES

 

Domain Lang/Year
Py56*  

A Comprehensive Exploration on Spider with Fuzzy Decision Text-to-SQL Model

 

NLP PYTHON/IEEE2020
Py57*  

Learning Normal Patterns via Adversarial Attention-Based Autoencoder for Abnormal Event Detection in Videos

 

DEEP LEARNING PYTHON/IEEE2020
Py58*  

A New Reversible Database Watermarking Approach with Ant Colony Optimization Algorithm

 

DATAMINING PYTHON/IOPSCIENCE2020
Py59  

Handwritten character recognition using OpenCV

 

AI PYTHON/IRJE2020
Py60*  

Multiple face detection and attendance using Open CV

 

AI PYTHON/IEEE2020
Py61  

Content based Image retrieval using K-means algorithm

 

AI PYTHON/IJASRET2020
Py62  

Object detection, tracking and alert system for visually impaired persons

 

AI PYTHON/SYMMETRY2020
Py63  

Attention Based Fully Convolutional Network For Speech Emotion Recognition

 

AI PYTHON/IEEE2020
Py64  

Handwritten Hindi character recognition using Neural Networks

 

AI PYTHON/IEEE2020
Py65  

Skin lesion classification using hybrid deep neural networks

 

AI PYTHON/IEEE2020
Py66  

Traffic and accident prediction for Images and Video using Deep Learning techniques

 

AI PYTHON/IEEE2020
Py67  

Handwritten Tamil Character Recognition Using Deep Learning

 

AI PYTHON/ARIXV2020
Py68*  

Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder

 

AI PYTHON/IEEE2020
Py69  

Hindi Audio text extraction (Audio Indexing) and Retrieval

 

AI PYTHON/IEEE2020

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Image processing

  1. Bio medical image segmentation for Cell Nuclie Detection using Machine Learning
  2. Plant Disease Detection using CNN in Machine Learning.

Latest Topics:

  1. Air quality prediction for metro cities
  2. Digital signature verification for documents using ECC algorithm
  3. Lyrics Scrapper from website
  4. Phishing website detection
  5. Pneumonia detection using deep learning
  6. Customer Spending classification using K means clustering
  7. Titanic data clustering on survived data.
  8. Recipe Recommendation system using K means clustering
  9. Character detection from images using OCR
  10. Crude Oil Prediction using SVR & Linear Regression
  11. Face Recognition based Criminal Identification system
  12. Language Translator and converting voice to text
  13. Face detection based attendance system
  14. Automatic Land mark classification using Deep Learning
  15. Automatic Brand Logo detection using Deep learning
  16. Detecting Ripeness of fruits at different levels and detecting type of fruit category from image.
  17. Single Image Haze Removal Using Dark Channel Prior
  18. Gender and age detection from image and video
  19. Splitting video from images and detecting high quality images
  20. Detecting Persons who are wearing helmets
  21. Loan approval prediction system using machine learning
  22. Detecting bikes, cars, buses, pedestrians using machine learning
  23. Detecting type of vehicle (car, bus , truck) and counting vehicles on lanes
  24. Predicting tags for stack overflow questions using different machine learning classifiers.
  25. Live News Summerizer from top news channels
  26. Detecting stress of user from his Tweets
  27. Text semantic similarity
  28. Motion detection for security surveillance
  29. Automatic Grading System
  30. Securing Message with ECC digital signature
  31. Fake News Detection
  32. Real time Stock Price Prediction
  33. Credit Card Fraud detection using Machine Learning
  34. Air Quality Prediction using Machine Learning
  35. Text semantic similarity using Machine Learning
  36. Detecting Curved Lane Lines
  37. Voice Meeting using NLP and NLTK
  38. Wine Quality prediction using Machine Learning algorithms
  39. Detecting Depression from Tweets
  40. Utilization Aware Trip advisor
  41. Shift Allocation web application
  42. Detecting Cell Nuclie uisng Machine Learning
  43. Bitcoin Anomaly Detection
  44. Detecting cyber bullying in live chat application using Machine Learning
  45. Bug Reporting system machine learning
  46. Student performance prediction using machine learning
  47. Cab data incentive system web application
  48. Bitcoin crypto Currency Prediction

 

 

 

Python text editor

  • Number plate recognition using opencv
  • Emotion based music player
  • Detection of brand logos from given images
  • Color recognition using neural networks for determining the ripeness of a banana

Machine Learning

  • Vision Sentiment Analysis using googleapi cloud
  • Sentiment Analysis
  • Classification Of IRIS Flowers Using Scipy Library In Machine Learning
  • Visualize Machine Learning Data Using Pandas
  • A Framework for Analysis of Road Accidents
  • Wal-Mart Sales Prediction
  • Bigmart Sales Prediction
  • IIT-JEE paper analysis
  • Disease Prediction using machine learning
  • Heart Disease Prediction
  • Custom digit recognition
  • Rain fall prediction using svm, Artificial neural network, liner regression models.
  • Self driving car simulation using AI
  • Crop prediction using linear regression
  • Automatic question and answer generation using NLP
  • Vehicle counting for traffic management

 

Opencv:

  • Python Image processing using opencv.
  • Pedestrian detection
  • Custom Digit Recognization
  • Driver Drowsiness detection using opencv.

 

 

Web Applications

 

  1. Iris species predictor flask web app
  2. Medical data analysis using machine learning using flask webapp
  3. Youtube spam detection using flaskwebapp.
  4. Named Entity Recognition and sentiment analysis using flask webapp.
  5. Text summarizer and comparison using flaskwebapp.
  6. Gender classification based on name.
  7. Image encryption compression and decompression and decryption
  8. Data encryption using aes,des algorithms
  9. Tool gate management system
  10. Image stegnography using lsb algorithm
  11. Prediction house worth using machine learning
  12. Securing data using hybrid cryptography in cloud
  13. Evaluating Employee attrition
  14. Improving security for login using two factor( password and QR code) method.
  15. Heart Disease Diagnosis based on symptoms
  16. Automation of test evaluation for objective and subjective tests
  17. Phishing website detection
  18. License Detection Using QR Code
  19. E plastic
  20. Student help desk
  21. E waste
  22. Online Shopping
  23. E farming
  24. Visualizing Machine Learning Using Pandas
  25. Detecting Pneunomia using Machine Learning
  26. Two factor authentication using QR code APP for user login
  27. House Worth Prediction based on machine learning
  28. Water Marking Image

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Python – ML, AI, NN, IP Based Project Title

 

  • 2_PyEm_Human Stress Analysis using Sensors and Machine Learning Techniques
  • 7_PyEm_Feature Extractor Analysis for Traffic Clearance in Emergency for Ambulance and Fire Engines
  • 12_PyEm_Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation
  • Machine Learning Analysis of Emotion Detects, Ansiety and Depression in Adults
  • TerrorBot – Python Based Cascade Classifier to Detect Terrorist and Soldiers
  • Real Time Detection of Human Stress using Sensors and Machine Learning Techniques
  • 1_Py_Machine Learning Approach for Air Quality Prediction and Analysis
  • 3_Py_Skin Disease Recognition CNN
  • Skin Disease Recognition Method Based on Image Color and Texture Features
  • Machine Learning based Brain Tumor Analysis using Convolutional Neural Network
  • 5_Py_Artifical Intelligence based Material Sorting for Industrial Production
  • 6_Py_CNN based Leaf Disease Identification and Remedy Recommendation System
  • A Prediction Approach for Stock Market Volatility Based on Time Series Data
  • Feature Extraction Based Airport Baggage Conveyor Alert System
  • Automatic Control of Driver fatigue and drowsiness using landmark predictor.
  • Motorcycle Helmet Wear Analysis using SIFT Feature Extractor Image Processing Algorithm
  • Machine Learning based Finger Gesture Recognition from Hospital Assistant
  • Development of food tracking system using machine learning
  • Real-time Eye Tracking for Password Authentication using Gaze and Landmark Predictor

 

 

  • A Wavelet Based Deep Learning Method for Underwater Image Super Resolution Reconstruction
  • Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems A Prospective Survey
  • Deep Neural Network Architecture Application for Facial Expression Recognition
  • End-to-End Speech Emotion Recognition With Gender Information
  • COVID-19 Social distancing detector in video

 

 

 

 

 

PYTHON PROJECT TITLES

  • 18_Face Recognition and Age Estimation Implications of Changes in Facial Features
  • CVUCAMS: Computer Vision based Unobtrusive Classroom Attendance Management System
  • Machine Learning Methods for Disease Prediction with Claims Data
  • Time Series Prediction of Agricultural Products Price based on Time Alignment of RNN
  • 19_1_IEEE_IP-SUPER-PIXEL BASED FINGER EARTH MOVER’S DISTANCE FOR HAND GESTURE RECOGNITION
  • 19_3_IEEE_IP_Accelerometer-Based Human Fall Detection Using CNN
  • PY_An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis
  • Industrial Machine Shop Floor Operator Eye Closure and Yawning Analysis and Control using Landmark Predictor
  • Driver Drowsiness Detection Based on Eye
  • 19_6_IEEE_Driver’s Eyes Closure and Yawning
  • Sleepy behind studies – Student Drowsiness Control uisng Landmark Predictor
  • Student Eyes Closure and Yawning Detection for Drowsiness Analysis using Landmark Predictor
  • 19_10_EmoPlayer – Feature Extractor Approach for Emotion Based Music Player
  • 19_11_Modified Convolutional Neural Network Architecture Analysis for Facial Emotion Recognition
  • PY_Editorial of Special Issue on Human Behaviour Analysis In-the-Wild
  • 19_13_Elderly Assistant based on Face Emotion and Posture Analysis
  • 19_14_Face Feature Extractor for Emotion Analysis and Behavior Analysis of a Prisoner
  • 19_15_Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum
  • 19_16_Crop Yield Prediction and Efficient use of Fertilizers
  • 19_17_IEEE_CNN based Leaf Disease Identification and Remedy Recommendation System
  • 19_17_IEEE_Identification of Plant Disease using Image Processing Technique
  • 19_18_IEEE_Leaf Disease Detection Feature Extraction with
  • 19_19_Identification of Plant Disease using Image Processing Technique
  • 19_20_Soil Classification using Machine Learning Methods and Crop Suggestion Based on Soil Series
  • 19_21_Analyzing Beauty by Building Custom Profiles Using Machine Learning
  • 19_22_Clusters of Features Using Complementary Information Applied to Gender Classification
  • 19_25_Fingerprint Image Identification for Crime Detection
  • 19_26_A Hybrid Feature Extraction Method with Regularized Extreme Learning Machine for Brain Tumor
  • 19_27_IEEE_Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
  • 19_28_Comparison of Machine Learning Methods for Breast Cancer Diagnosis
  • 19_31_A Novel Cascade Classifier of Vehicle Unlocking System Based on Face Recognition
  • 19_32_Deep learning-based helmet wear analysis of a motorcycle rider for intelligent surveillance
  • 19_34_Motorcycle Helmet Wear Analysis using SIFT Feature Extractor Image Processing Algorithm
  • 19_36_Development of food tracking system using machine learning
  • 19_38_Image Cascade Classifier for Visitor, Seminar hall or Party Ambiance
  • 19_39_Restricted Zone SIFT Feature Extractor for ATM Security, Helmet Detection
  • Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks
  • A Novel Cascade Classifier of Vehicle Unlocking System Based on Face Recognition
  • Clusters of Features Using Complementary Information Applied to Gender Classification From Face Images
  • Deep learning-based helmet wear analysis of a motorcycle rider for intelligent surveillance system
  • 19_7_Student Eye Closure and Yawning Detection for Drowsiness Analysis & Control using Landmark Predictor
  • Industrial Machine Shop Floor Operator Eye Closure and Yawning Analysis and Control using Landmark Predictor
  • 18_Deep Air Learning Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality
  • 18_Face Recognition and Age Estimation Implications of Changes in Facial Features
  • Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum
  • Modified Convolutional Neural Network Architecture Analysis for Facial Emotion Recognition
  • Motorcycle Helmet Wear Analysis using SIFT Feature Extractor Image Processing Algorithm
  • A Predictive Data Feature Exploration-Based Air Quality Prediction Approach
  • Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
  • Multi-Classification of Brain Tumor Images Using Deep Neural Network
  • Fast Facial emotion recognition Using Convolutional Neural Networks and Gabor Filters
  • A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification
  • Development of a Fully Cross-Validated Bayesian Network Approach for Local Control Prediction in Lung Cancer
  • Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Python + Embedded Project List

  • Crop Prediction and Efficient use of Fertilizers using Machine Learning
  • ML using Sensors for Airbreathing Propulsion of Turojet Engine
  • ML using Sensors for Stress Prediction in Working Employees
  • Real Time Face Mask Detector for Covid-19 Safe Social Distancing
  • Artifical Intelligence based Material Sorting for Industrial Production
  • 1_PyEm_TerrorBot – Cascade Classifier to Detect Terrorist and Soldiers
  • 2_PyEm_Human Stress Analysis using Sensors and Machine Learning Techniques
  • 3_PyEm_Feature Extraction Based Airport Baggage Conveyor Alert System
  • 4_PyEm_Automatic Control of Driver fatigue and drowsiness Landmark Predictor
  • 5_Py_Artifical Intelligence based Material Sorting for Industrial Production
  • 6_Py_CNN based Leaf Disease Identification and Remedy Recommendation System
  • 7_PyEm_Feature Extractor Analysis for Traffic Clearance in Emergency for Ambulance and Fire Engines
  • 8_PyEm_Development of food tracking system using machine learning
  • 9_PyEm_Trainable Automatic Robot For Agriculture Plant Leaf Weeding
  • 10_PyEm_Machine Learning Approach for Air Quality Prediction and Analysis
  • 11_PyEm_ML Analysis of Emotion Detects, Anxiety and Depression in adult
  • 12_PyEm_Machine Learning Applied to Electrified Vehicle Battery SOC and SOH Estimation
  • 13_PyEm_Real-time Eye Tracking for Password – Gaze based Pin Authentication
  • 14_Py_Machine Learning based Brain Tumor Analysis using CNN with SMS Notification
  • 15_PyEm_Motorcycle Helmet Wear Analysis using SIFT Feature Extractor
  • 16_PyEm_Machine Learning based Finger Gesture Recognition from Hospital Assistant
  • 17_PyEm_Human Activity Analysis using Sensors and Machine Learning Techniques
  • 18_PyEm_Crop Prediction and Efficient use of Fertilizers using Machine Learning
  • 19_PyEm_COVID-19 FaceMask Detection with Temperature and Auto Sanitizer
  • 19_PyEm_Real Time Face Mask Detector for Covid-19 Safe Social Distancing
  • 21_PyEm_ML Approach for Air Quality Prediction and Analysis
  • 26_PyEm_Monitoring Social Distancing for Covid-19 Using OpenCV and Auto Sanitization
  • Face Recognition and Age Estimation Implications of Changes in Facial Features
  • CVUCAMS: Computer Vision based Unobtrusive Classroom Attendance Management System
  • IP-SUPER-PIXEL BASED FINGER EARTH MOVER’S DISTANCE FOR HAND GESTURE RECOGNITION
  • Accelerometer-Based Human Fall Detection Using CNN
  • Industrial Machine Shop Floor Operator Eye Closure and Yawning Analysis and Control using Landmark Predictor
  • Sleepy behind studies – Student Drowsiness Control uisng Landmark Predictor
  • Student Eyes Closure and Yawning Detection for Drowsiness Analysis using Landmark Predictor
  • EmoPlayer – Feature Extractor Approach for Emotion Based Music Player
  • Modified Convolutional Neural Network Architecture Analysis for Facial Emotion Recognition
  • Elderly Assistant based on Face Emotion and Posture Analysis
  • Face Feature Extractor for Emotion Analysis and Behavior Analysis of a Prisoner
  • Soil Classification using Machine Learning Methods and Crop Suggestion Based on Soil Series
  • Clusters of Features Using Complementary Information Applied to Gender Classification
  • Fingerprint Image Identification for Crime Detection
  • A Novel Cascade Classifier of Vehicle Unlocking System Based on Face Recognition
  • Deep learning-based helmet wear analysis of a motorcycle rider for intelligent surveillance
  • Restricted Zone SIFT Feature Extractor for ATM Security, Helmet Detection
  • Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks
  • A Novel Cascade Classifier of Vehicle Unlocking System Based on Face Recognition

 

 

 

 

 

 

 

 

 

 

 

 

 

MACHINE LEARNING AND AI PROJECT TITLES AND ABSTRACTS – 2021-22

 

 

 

PROJECT TITLE DIABETES PREDICTION AND MEDICAL ADVICE.
PROJECT ID SHML001
DOMAIN Machine learning
ABSTRACT Diabetes is a chronic disease with the potential to cause a worldwide health care crisis. According to International Diabetes Federation 382 million people are living with diabetes across the whole world. By 2035, this will be doubled as 592 million. A number of machine learning models have been applied to a prediction or classification task of diabetes. These models either tried to categories patients into insulin and non-insulin, or anticipate the patients’ blood surge rate. Most medical experts have realised that there is a great relationship between patient’s symptoms with some chronic diseases and the blood sugar rate. This paper proposes a diabetes-chronic disease prediction- description model in the form of two sub-modules to verify this relationship. The Prima Indian Diabetes Dataset has been used in this study, provided by the UCI Machine Learning Repository. The dataset has been originally collected from the National Institute of Diabetes and Digestive and Kidney Diseases. The dataset consists of some medical distinct variables, such as pregnancy record, BMI, insulin level, age, glucose concentration, diastolic blood pressure, triceps skin fold thickness, diabetes pedigree function, etc.

 

PROJECT TITLE HEART DESEASE PREDICTION USING MACHINE LEARNING
PROJECT ID SHML002
DOMAIN Machine learning
ABSTRACT Heart disease is the one of the most common disease. This disease is quite common now a days we used different attributes which can relate to this heart diseases well to find the better method to predict and we also used algorithms for prediction. Naive Bayes, algorithm is analyzed on dataset based on risk factors. We also used decision trees and combination of algorithms for the prediction of heart disease based on the above attributes. The results shown that when the dataset is small naive Bayes algorithm gives the accurate results and when the dataset is large decision trees gives the

accurate results.

 

 

PROJECT TITLE  

PREDICTING THE TOTAL CROP PRODUCTION IN AREA AND ADVISING BEST ALTERNATIVE CROP.

PROJECT ID SHML003
DOMAIN Machine learning
ABSTRACT ◗                Agriculture is the most important sector of Indian Economy.

◗                Indian agriculture sector accounts for 18 percent of India’s GDP and provides employment to 50% of the country’s workforce.

◗                But latest studies have shown a steady decline in the contribution made by agriculture to the Indian economy although it is demographically the broadest economic sector and plays a significant role in the overall socio-economic fabric of India.

◗                Smart agriculture is the way of conveying information from traditional farmers to the educated farmers.

 

 

 

 

PROJECT TITLE PREDICTING THE HIRING POSSIBILITY FOR CANDIDATE

 

PROJECT ID SHML004
DOMAIN Machine learning
ABSTRACT It is aimed to develop an automation in placement prediction at college level which predicts the chances of undergraduate students getting placed in an IT company and helps in profiling a candidate before the recruitment process starts. It involves the use of machine learning model of k-nearest neighbor algorithm as base model to classify students or users into appropriate clusters and the result would help them in improving their skills and other mindset. The results of the same is also compared with the results obtained from other models like logistic regression, random forest and SVM for optimal solution. With various data mining and machine learning techniques, this proposition would help both students as well as recruiters during placements and

other recruitment activities.

 

 

 

 

PROJECT TITLE

DETECTION OF BRAIN TUMOR FROM MRI IMAGES AND PREDICTING THE BEST
PROJECT ID SHML005
DOMAIN Machine learning
ABSTRACT A brain tumor is a growth of abnormal cells that has formed in the brain. Some brain tumors are cancerous (malignant), while others are not (non-malignant). Most Research in developed countries show that the number of people who have brain tumors were died due to the fact of inaccurate detection. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection & diagnosis of brain tumor. However this method of detection resists the accurate determination of stage & size of tumor.

 

 

 

 

 

 

PROJECT TITLE  

BONE TUMOUR   DETECTION   FROM   MRI   IMAGES   USING

 

IMAGE PROCESSING AND MACHINE LEARNING
PROJECT ID SHML006
Machine learning
ABSTRACT  

Early detection of the cancer-prone area in MRI scan is of great importance for the successful diagnosis and treatment of bone cancer. This paper proposes an approach to detect bone cancer in MR images using medical image processing techniques.

 

A proposed approach has some pre-processing techniques which use Gabor filter to smoothen the image and remove the noise from an image. The segmentation is carried out by using super pixel segmentation and multilevel segmentation. This methodology is used for identifying the bone cancer by various pre-processing techniques like filtering and gray conversion. After filtering, edge detection and morphological operations are applied. In the second

stage, super pixel segmentation is performed and some of the important features are extracted from the images. Then the extracted features are used to identify the bone cancer. In this project an approach of tumour detection using machine learning have been discussed and the data set for the performance analysis is MRI images.

 

 

PROJECT TITLE COVID 19 EPITOPE PREDICTION
PROJECT ID SHML007
DOMAIN Machine learning
ABSTRACT COVID-19 has created a havoc in the present time. The vaccine can control the spreading of the virus for which the study of the structure of B-cells is important. B-cells inducing antigen-specific immune responses in vivo produce large amounts of antigen-specific antibodies by recognizing the sub

regions (epitope regions) of antigen proteins. They can inhibit

 

their functioning by binding antibodies to antigen proteins. Predicting of epitope regions is beneficial for the design and development of vaccines aimed to induce antigen-specific antibody production. B-cells inducing antigen-specific immune responses in vivo produce large amounts of antigen-specific antibodies by recognizing the sub regions (epitope regions) of antigen proteins. They can inhibit their functioning by binding antibodies to antigen proteins. Predicting the epitope regions is beneficial for the design and development of vaccines aimed at

inducing antigen-specific antibody production.

 

 

 

 

PROJECT TITLE  

CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING ALGORITHMS

PROJECT ID SHML008
DOMAIN Machine learning
ABSTRACT  

Finance fraud is a growing problem with far consequences in the financial industry and while many techniques have been discovered. Data mining has been successfully applied to finance databases to automate analysis of huge volumes of complex data. Data mining has also played a salient role in the detection of credit card fraud in online transactions. Fraud detection in credit card is a data mining problem, It becomes challenging due to two major reasons–first, the profiles of normal and fraudulent behaviors change frequently and secondly due to reason that credit card fraud data sets are highly skewed. This paper investigates and checks the performance of Decision tree, Random Forest, SVM and logistic regression on highly skewed credit card fraud data. These techniques are applied on the raw and preprocessed data. The performance of the techniques is evaluated based on accuracy, sensitivity, specificity, precision. The results indicate about the optimal accuracy for logistic regression, decision tree, Random Forest and SVM classifiers are 97.7%, 95.5% and 98.6%, 97.5% respectively

 

 

 

 

PROJECT TITLE FAKE NEWS ANALYSIS USING MACHINE LEARNING
PROJECT ID SHML009
DOMAIN Machine learning
ABSTRACT  

This Project comes up with the applications of NLP (Natural Language Processing) techniques for detecting the ‘fake news’, that is, misleading news stories that comes from the non-reputable sources. Only by building a model based on a count vectorizer (using word tallies) or a (Term Frequency Inverse Document Frequency) tfidf matrix, (word tallies relative to how often they’re used in other articles in your dataset) can only get you so far. There is a Kaggle competition called as the “Fake News Challenge”

 

Social networks have become popular due to the ability to connect people around the world, Social media interaction especially the news spreading around the network is a great source of information nowadays. From one’s perspective, its negligible exertion, straightforward access, and quick dispersing of information that lead people to look out and eat up news from internet-based life. Twitter being a standout amongst the most well-known ongoing news sources additionally ends up a standout amongst the most dominant news radiating mediums. It is known to cause extensive harm by spreading bits of gossip previously.

 

 

 

 

 

 

PROJECT TITLE FETAL BIRTH WEIGHT ESTIMATION IN HIGH-RISK PREGNANCIES
PROJECT ID SHML010
DOMAIN Machine learning
ABSTRACT The low weight of fetus at birth is considered one         of the most critical problems in pregnancy care, affecting the newborn’s health and leading it

 

to death in more severe cases. This condition is responsible for the high infant mortality rates worldwide. In health, artificial intelligence techniques, especially those based on machine learning (ML), can early predict prob- lems related to the fetus’ health state during entire gestation, including at birth. Hence, this paper proposes an analysis of several ML techniques capable of predicting whether the fetus will born small for its gestational age. The results show   that   the hybrid model, named bagged tree, achieved excellent results concerning accuracy and area under the receiver operating characteristic curve, to know, 0.849 and 0.636, respectively. The importance of the early diagnosis of problems related to fetal development relies on the possibility of an increase in the gestation days through timely intervention. Such intervention would allow an improvement in fetal weight at birth, associated with a decrease in neonatal morbidity and mortality.

 

 

PROJECT TITLE FOREST FIRE PREDICTION USING MACHINE LEARNING
PROJECT ID SHML011
DOMAIN Machine learning and AI
ABSTRACT  

Forest fire prediction constitutes a significant component of forest fire management. It plays a major role in resource allocation, mitigation and recovery efforts. This paper presents a description and analysis of forest fire prediction methods based on artificial intelligence. A novel forest fire risk prediction algorithm, based on support vector machines, is presented. The algorithm depends on previous weather conditions in order to predict the fire hazard level of a day. The implementation of the algorithm using data from Lebanon demonstrated its ability to accurately predict the hazard of fire occurrence.

 

 

 

 

PROJECT TITLE IPL Match Prediction using Machine Learning

 

PROJECT ID SHML012
DOMAIN Machine learning
ABSTRACT  

Cricket, the mainstream and widely played sport across India which has the most noteworthy fan base. Indian Premier League follows 20-20 format which is very unpredictable. IPL match predictor is a ML based prediction approach where the data sets and previous stats are trained in all dimensions covering all important factors such as: Toss, Home Ground, Captains, Favourite Players, Opposition Battle, Previous Stats etc, with each factor having different strength with the help of intelligence of Naive Bayes

 

 

 

 

PROJECT TITLE MOVIE SUCCESS PREDICTION USING MACHINE LEARNING
PROJECT ID SHML013
DOMAIN Machine learning
ABSTRACT  

The number of movies produced in the world is growing at an exponential rate and success rate of movie is of utmost importance since billions of dollars are invested in then making of each of these movies. In such a scenario, prior knowledge about the success or failure of a particular movie and what factor affect the movie success will benefit the production houses since these predictions will give them a fair idea of how to go about with the advertising and campaigning which itself is an expensive affair altogether. So, the prediction of the success of a movie is very essential to the film industry.

 

PROJECT TITLE  

PARKINSON’S      DISEASE      DIAGNOSIS      USING      MACHINE LEARNING AND VOICE

PROJECT ID SHML014
DOMAIN Machine learning
ABSTRACT  

Parkinson’s disease (PD) is a neuropathological disorder which deteriorates the motor functions of the human body, Parkinson’s disease (PD) is one of the most common chronic neurological diseases and one of the significant causes of disability for middle-aged and elderly people, because of their underlying cognitive and neuromuscular function. PD is a progressive neurodegenerative disorder that affects about one million people in the the United States, with approximately sixty thousand new clinical diagnoses made each year[1]. Historically, PD has been difficult to quantify and doctors have tended to focus on some symptoms while ignoring others, relying primarily on subjective rating scales [2]. Due to the decrease in motor control that is the hallmark of the disease, voice can be used as a means to detect and diagnose PD. With advancements in technology and the prevalence of audio collecting devices in daily lives, reliable models that can translate this audio data into a diagnostic tool for healthcare professionals would potentially provide diagnoses that are cheaper and more accurate. We provide evidence to validate this concept here using a voice dataset collected from people with and without PD. This paper explores the effectiveness of using supervised classification algorithms, such as deep neural networks, to accurately diagnose individuals with the disease. Our peak accuracy of 85% provided by the machine learning models exceed the average clinical diagnosis accuracy of non-experts (73.8%) and average accuracy of movement disorder specialists (79.6% without follow-up, 83.9% after follow-up) with pathological post-mortem examination as

ground truth

 

 

 

 

PROJECT TITLE EMPLOYEE SALARY PREDICTION
PROJECT ID SHML015
DOMAIN Machine learning
ABSTRACT Accurate recruitment of employees is a key element in the business

 

strategy of every company due to its impact on companies’ productivity and competitiveness. At present, recruitment processes have evolved into complex tasks involving rigorous evaluations and interviews of candidates, with the goal of hiring the best suited professionals for each company’s needs. With the advent of Internet and the web, eRecruitment has become an essential element of all hiring strategies, after the selection salary will be predicted based on test score, interview score, experience

 

 

PROJECT TITLE SENTIMANTAL ANALYSIS FOR TWITTER DATA
PROJECT ID SHML016
DOMAIN Machine learning
ABSTRACT  

This project addresses the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in them: positive, negative. Twitter is an online micro-blogging and social- networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users [24] – out of which 100 million are active users and half of them log on twitter on a daily basis – generating nearly 250 million tweets per day [20]. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange. The aim of this project is to develop a functional classifier for accurate and automatic sentiment classification of an unknown tweet stream.

 

 

PROJECT TITLE SPAM DETECTION USING MACHINE LEARNING
PROJECT ID SHML017
DOMAIN Machine learning

 

ABSTRACT Nowadays, a big part of people rely on available content in social media in their decisions (e.g., reviews and feedback on a topic or product). The possibility that anybody can leave a review provides a golden opportunity for spammers to write spam reviews about products and services for different interests. Identifying these spammers and the spam content is a hot topic of research, and although a considerable number of studies have been done recently toward   this end, but so far the methodologies put forth still barely detect spam reviews, and none of them show the importance of each extracted feature type. In this paper, we propose a novel framework, named NetSpam, which utilizes spam features for modeling review data sets as heterogeneous information networks to map spam detection procedure into a classification problem in such networks. The results show that NetSpam outperforms the existing methods and among four categories of features, including review-behavioral, user- behavioral, review- linguistic, and user-linguistic, the first type of features performs better than the other categories.

 

 

PROJECT TITLE HATE SPEECH DETECTION ON TWITTER
PROJECT ID SHML018
DOMAIN Machine learning
ABSTRACT Hate speech in the form of racist and sexist remarks are a common occurrence on social media. For that reason, many social media services address the problem of identifying hate speech, but the definition of hate speech varies markedly and is largely a manual effort (BBC, 2015; Lomas, 2015). We provide a list of criteria founded in critical race theory, and use them to annotate a publicly available corpus of more than 16k tweets. We analyze the impact of

various extra-linguistic  features  in  conjunction  with  character n-grams  for

 

hatespeech detection. We also present a dictionary based the most indicative

words in our data.

 

 

PROJECT TITLE HAND DRAWN OPTICAL CIRCUIT RECOGNITION
PROJECT ID SHML019
DOMAIN Machine learning
ABSTRACT Electrical diagram is foundation of studies in electrical science. A circuit diagram convey many information about the system. Behind any device there are plenty of electrical ingredients which perform their specific tasks, today all the electrical software tools failed to effectively convert the information automatically from a circuit image diagram to digital form. Hence electrical engineers should manually enter all information into computers, and this process takes time and bring errors with high probability. Moreover, when the diagram is hand drawn, the problem is more complicated for any electrical analysis. Thus, in this paper we propose a new method using Artificial Neural Network (ANN) to make a machine that can directly read the electrical symbols from a hand drawn circuit image. The recognition process involves two steps: first step is feature extraction using shape based features, and the second one

is a classification procedure using ANN through a back propagation algorithm

 

 

 

 

 

 

PROJECT TITLE SPEECH EMOTION RECOGNIZER
PROJECT ID SHML020
DOMAIN Machine learning
ABSTRACT Speech is the most natural way of expressing ourselves as humans. It is only natural then to extend this communication medium to computer applications. We  define  speech  emotion  recognition  (SER)  systems  as  a  collection  of

methodologies  that  process  and  classify  speech  signals  to  detect  the

 

embedded emotions. SER is not a new field, it has been around for over two decades, and has regained attention thanks to the recent advancements. These novel studies make use of the advances in all fields of computing and technology, making it necessary to have an update on the current methodologies and techniques that make SER possible. We have identified and discussed distinct areas of SER, provided a detailed survey of current literature

of each, and also listed the current challenges.

 

 

PROJECT TITLE ANALYZING THE WATER QUALITY
PROJECT ID SHML021
DOMAIN Machine learning
ABSTRACT Water is a critical resource for sustainable economic and social development of a country. To maintain health & hygiene, energy & agricultural products, and the environment management water plays a key role. Water demand prediction is essential to analyze the requirement that indicate emergency state for water management decisions. This project explores the water usage data for dairy plants to understand the spatial and temporal patterns for future water requirements, to optimize the water demand estimation. It uses concept of Machine learning algorithms to compare and achieve an effective and

reliable system for water prediction.

 

 

 

 

 

 

PROJECT TITLE GROUND WATERP REDICTION USING MACHINE LEARNING
PROJECT ID SHML022
DOMAIN Machine learning
ABSTRACT To address challenges associated with climate resilience and sustainability principles, the importance of urban groundwater must be integrated into urban planning and design. Groundwater systems are dynamic and adjust continually to short-term and long-term changes in

climate,    groundwater    withdrawal,    and    land    use.    Water    level

 

measurements from observation wells are the principal source of information about the hydrologic stresses acting on aquifers and how these stresses affect ground-water recharge, storage, and discharge. In this research we focus on Ljubljana polje aquifer. Traditionally groundwater levels are modeled with process-based models, which rely on the profound knowledge of the observed system dynamics. They require many additional spatial data on geological and hydrological properties of the aquifer. On the other hand, in data-driven modeling with machine-learning techniques our model is based solely on the data and some domain-specific knowledge is incorporated in to the system via appropriate data transformation (within engineering of new attributes). The goal in such a scenario would be to predict groundwater levels based on temporal data inputs (historic groundwater and surface water level data, weather data and forecasts, land-use, groundwater withdrawal and other anthropogenic data) and outputs (groundwater level).

 

 

 

 

PROJECT TITLE DIAMOND PRICE PREDICTION USING MACHINE LEARNING
PROJECT ID SHML023
DOMAIN Machine learning
ABSTRACT  

Precious stones like diamond are in high demand in the investment market due to their monetary rewards. Thus, it is of utmost importance to the diamond dealers to predict the accurate price. However, the prediction process is difficult due to the wide variation in the diamond stones sizes and characteristics. In this paper, several machine learning algorithms were used to help in predicting diamond price, among them Liner regression, Random forest regression, polynomial regression, Gradient descent and Neural network. After training several models, testing their accuracy and analyzing their results, it turns out that the best of them is the random forest regression.

 

PROJECT TITLE PREDICTING THE PRICE OF HOUSE
PROJECT ID SHML024
DOMAIN Machine learning
ABSTRACT  

 

Machine learning plays a major role from past years in image detection, spam reorganization, normal speech command, product recommendation and medical diagnosis. Present machine learning algorithm helps us in enhancing security alerts, ensuring public safety and improve medical enhancements. Machine learning system also provides better customer service and safer automobile systems. In the present paper we discuss about the prediction of future housing prices that is generated by machine learning algorithm. For the selection of prediction methods we compare and explore various prediction methods. We utilize lasso regression as our model because of its adaptable and probabilistic methodology on model selection. Our result exhibit that our approach of the issue need to be successful, and has the ability to process predictions that would be comparative with other house cost prediction models. More over on other hand housing value indices, the advancement of a housing cost prediction that tend to the advancement of real estate policies schemes. This study utilizes machine learning algorithms as a research method that develops housing price prediction models. We create a housing cost prediction model In view of machine   learning   algorithm   models   for   example,   XGBoost,   lasso

regression and neural system on look at their order precision execution.

 

 

We in that point recommend a housing cost prediction model to support a house vender or a real estate agent for better information based on the valuation of house. Those examinations exhibit that lasso regression algorithm, in view of accuracy, reliably outperforms alternate models in the execution of housing cost prediction.

 

Problem Statement

 

The goal of this statistical analysis is to help us understand the relationship between house features and how these variables are used to predict house price.

 

Objective

 

·         Predict the house price

 

·         Using two different models in terms of minimizing the difference between predicted and actual rating

 

 

PROJECT TITLE CAR PRICE PREDICTION
PROJECT ID SHML025
DOMAIN Machine learning
ABSTRACT A car price prediction has been a high-interest research area, as it requires noticeable effort and knowledge of the field expert. Considerable number of distinct attributes are examined for the reliable and accurate prediction. To build a model for predicting the price of used cars in Bosnia and Herzegovina, we applied three machine learning techniques (Artificial

Neural Network, Support Vector Machine and Random Forest). However,

 

the mentioned techniques were applied to work as an ensemble. The data used for the prediction was collected from the web portal autopijaca.ba using web scraper that was written in PHP programming language. Respective performances of different algorithms were then compared to find one that best suits the available data set. The final prediction model was integrated into Java application. Furthermore, the model was evaluated using test data and the accuracy of 87.38% was obtained.

 

 

PROJECT TITLE MOBILE     PHONE     COST     PREDICTION     USING     MACHINE LEARNING.
PROJECT ID SHML026
DOMAIN Machine learning
ABSTRACT To predict “If the mobile with given features will be Economical or Expensive” is the main motive of this research work. Real Dataset is collected from website https://www.kaggle.com Different feature selection algorithms are used to identify and remove less important and redundant features and have minimum computational complexity. Different classifiers are used to achieve as higher accuracy as possible. Results are compared in terms of highest accuracy achieved and minimum features selected. Conclusion is made on the base of best feature selection algorithm and best classifier for the given dataset. This work can be used in any type of marketing and business to find optimal product (with minimum cost and maximum features). To predict the accuracy of the mobile price range.

 

 

PROJECT TITLE RAINFALL PREDICTION USING MACHINE LEARNING
PROJECT ID SHML027
DOMAIN Machine learning
ABSTRACT Rainfall forecasting is very important because heavy and irregular rainfall can have many impacts like destruction of crops and farms, damage of property so a better forecasting model is essential for an early warning that can minimize risks to life and property and also managing the agricultural farms in better way. This prediction mainly

helps farmers and also water resources can be utilized efficiently.

 

Rainfall prediction is a challenging task and the results should be accurate. There are many hardware devices for predicting rainfall by using the weather conditions like temperature, humidity, pressure. These traditional methods cannot work in an efficient way so by using machine learning techniques we can produce accurate results. We can just do it by having the historical data analysis of rainfall and can predict the rainfall for future seasons. We can apply many techniques like classification, regression according to the requirements and also we can calculate the error between the actual and prediction and also the accuracy. Different techniques produce different accuracies so it is important to choose the right algorithm and model it according to the

requirements.

 

 

PROJECT TITLE FUEL     PRICE    PREDICTION    USING     MACHINE     LEARNING

ALGORITHMS

PROJECT ID SHML028
DOMAIN Machine learning
ABSTRACT Crude oil is world’s most leading fuel. Some machine learning models fits the dataset efficiently depending upon the type of datapoints provided. The main aim of this project is to find the different models that efficiently fit the datapoints and predict the price of fuel with the help of machine learning model[5]. This project aims to compare the different supervised learning models and bring a conclusion based on the efficiency. We have used 5 supervised learning models SVR(linear,RBF,polynomial),RandomForestRegressio-n,Linear

Regression, to know which gives best in terms of accuracy and performance we have tried these algorithms which are mostly adaptive to many environments. Now-a-days the oil price has been increasing in leaps and bounds due to certain reason like inflation throughout the world. This has become a major problem in India where prices of LPG (Liquified Petroleum Gas), Petroleum, Diesel have been increasing. Hence these are derived or extracted from crude oil; India gets its source of crude oil from neighbouring countries like Dubai and Saudi- Arabia. To predict the values of the petroleum and Diesel in the mere future, we have decided to use the Machine Learning algorithms and after choosing set of algorithms we have chosen the Linear Regression algorithm, which have given the most accurate results

 

 

PROJECT TITLE STOCK MARKET VALUE PREDICTION OF COMPANY

 

PROJECT ID SHML029
DOMAIN Machine learning
ABSTRACT An intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literatur. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company’s stock value based on its stock share value history. The experimental results show that the application of MLP neural network is more promising in predicting stock value changes rather than Elman recurrent network and linear regression method. However, based on the standard measures that will be presented in the paper we find that the Elman recurrent network and linear regression can predict the direction of the changes of the stock value better than the MLP.

 

 

PROJECT TITLE FLIGHT TICKET AND DEMAND PREDICTION FOR AIRLINE SECTOR
PROJECT ID SHML030
DOMAIN Machine learning
ABSTRACT Nowadays, airline ticket prices can vary dynamically and significantly for the same flight, even for nearby seats within the same cabin. Customers are seeking to get the lowest price while airlines are trying to keep their overall revenue as high as possible and maximize their profit. Airlines use various kinds of computational techniques to increase their revenue such as demand prediction and price discrimination. From the customer side, two kinds of models are proposed by different researchers to save money for customers: models that predict the optimal time to buy a ticket and models that predict the minimum ticket price. In this paper, we present a review of customer side and airlines side prediction models. Our review analysis shows that models on both sides rely on limited set of features such as historical ticket price data, ticket purchase

date and departure date. Features extracted from external factors such

 

as social media data and search engine query are not considered. Therefore, we introduce and discuss the concept of using social media data for ticket/demand prediction

 

 

PROJECT TITLE VEGETABLE AND FRUITS PRICE PREDICTION USING MACHINE

LEARNING

PROJECT ID SHML031
DOMAIN Machine learning
ABSTRACT The theory and construction methods of four models are presented for predicting the vegetable market price, which are BP neural network model, the neural network model based on genetic algorithm, RBF neural network model and an integrated prediction model based on the three models above. The four models are used to predict the Lentinus edodes price for Beijing Xinfadi wholesale market. A total of 84 records collected between 2003 and 2009 were fed into the four models for training and testing. In summary, the predicting ability of BP neural network model is the worst. The neural network model based on genetic algorithm was generally more accurate than RBF neural network model. The integrated prediction model has the best results.

 

 

 

 

 

 

PROJECT TITLE TUBERCULOSIS     DETECTION      USING     ML     AND     IMAGE

PROCESSING

PROJECT ID SHML032
DOMAIN Machine learning & AI
ABSTRACT •      Tuberculosis is amongst the top reasons of death around the world. It is caused by a bacterium called Mycobacterium tuberculosis and it affects the lungs.

•      Predicting it on time and properly diagnosing tuberculosis is a prominent problem in medical field. The treatment process also varies from one patient to another, as in some cases the patient develops resistance to drugs.

With the help of machine learning algorithm assistance can be provided to physician to diagnose and provide suitable treatment and to make faster and better decision

 

 

 

PROJECT TITLE UBER DATA ANALYSIS USING MACHINE LEARNING
PROJECT ID SHML033
DOMAIN Machine learning
ABSTRACT Urban liveability is a key concept in the New Urban Agenda (NUA) adopted by the United Nations (UN) in 2016. The UN has recognized that effective benchmarks and monitoring mechanisms are essential for the successful implementation of the NUA. However, the timely and cost effective collection of objective international quality of life urban data remains a significant challenge. Urban liveability indexes are often complex, resource intensive and time consuming to collect, and as a result costly. At the same time, competing methodologies and agendas may result in subjective or non-comparable data. Historically, transit has been a central organizing factor around which communities have been built. This paper explores the use of Uber data as a simple real-time indicator of urban liveability. Using data from the Uber Ride Request (URR) API for the Brazilian city of Natal, our preliminary findings suggest that Uber Estimated Time to Arrive (ETA) data is strongly correlated with selected quality of life indicators at a neighborhood and region level. Furthermore, unlike other urban liveability indicators, our findings suggest that Uber ETA data is context-sensitive reflecting daily and seasonal factors thereby providing more granular insights. This preliminary study finds strong evidence that Uber data can provide a simple, comparable, low cost, international urban liveability indicator at both city and neighborhood level for urban policy setting and planning

 

PROJECT TITLE WEED DETECTION AND CLASSIFICATION
PROJECT ID SHML034
DOMAIN Machine learning& Image processing
ABSTRACT The identification and classification of weeds are of major technical and economical importance in the agricultural industry. To automate these activities, like in shape, color and texture, weed control system is feasible. The goal of this paper is to build a real-time, machine vision weed control system that can detect weed locations. The algorithm is developed to classify images into broad and narrow class for real-time selective herbicide application. The developed algorithm based on Edge Link Detector has been tested on weeds at various locations, which have shown that the algorithm to be very effectiveness in weed identification. Further the results show a very reliable performance on weeds under varying field conditions. The analysis of the results shows over 93 % classification accuracy over 240 sample images (broad, narrow and no or little weeds) with 100 samples from broad weeds, 100 samples from narrow weeds and the remaining 40 from no or little weeds.

 

 

PROJECT TITLE PHISHING ATTACK ON WEBSITE DETECTION
PROJECT ID SHML035
DOMAIN Machine learning & Networking
ABSTRACT In the last few years a large number of internet users are increasing additionally different companies, banks and service providers are providing services online. So various sensitive and financial data are becomes online now in these days. This aspect of internet users are an evolution for us but the dark side of this advantage is too hard to accept, because of hackers and intruders are working between end clients and service providers. Phishing Websites are duplicate Web pages created to mimic real Websites in-order to deceive people to get their personal information. Because of the adaptability of their tactics with little cost Detecting and identifying Phishing Websites is really a complex and dynamic problem. In this project, a novel approach is proposed for detecting phishing Websites. In the proposed model three layers of criteria are used. In the first layer Google page rank and IP address in URL are used. In the second layer domain name characteristics are used. In the third layer quality of the web page content are used. Fuzzy logic is used to classify the web pages according to their rank.

 

 

PROJECT TITLE PREDICTING DOCTOR CONSULTANT FEES
PROJECT ID SHML036
DOMAIN Machine learning
ABSTRACT We have all been in situation where we go to a doctor in emergency and find that the consultation fees are too high. As a data scientist we all should do better. What if you have data that records important details about a doctor and you get to build a model to predict the doctor’s consulting fee.? This is the hackathon that lets you do that.

 

Size of training set: 5961 records Size of test set: 1987 records

 

Features

 

·         Qualification: Qualification and degrees held by the doctor

·         Experience: Experience of the doctor in number of years

·         Rating: Rating given by patients

·         Profile: Type of the doctor

·         Miscellaeous_Info: Extra information about the doctor

·         Fees: Fees charged by the doctor

·         Place: Area and the city where the doctor is located.

 

 

PROJECT TITLE CRIME PREDICTION AND ANALYSIS USING MACHINE LEARNING
PROJECT ID SHML037
DOMAIN Machine learning
ABSTRACT Crime is one of the biggest and dominating problem in our society and its prevention is an important. task. Daily there are huge numbers of crimes committed frequently. This require keeping track of all the crimes and maintaining a database for same which may be used for future reference. The current problem faced are maintaining of proper dataset of crime and analyzing this data to help in predicting and solving crimes in future. The objective of this project is to analyze dataset which consist

of numerous crimes and predicting the type of crime which may happen

 

in future depending upon various conditions. In this project, we will be using the technique of machine learning and data science for crime prediction of Chicago crime data set. The crime data is extracted from the official portal of Chicago police. It consists of crime information like location description, type of crime, date, time, latitude, longitude. Before training of the model data preprocessing will be done following this feature selection and scaling will be done so that accuracy obtain will be high. The K-Nearest Neighbor (KNN) classification and various other algorithms will be tested for crime prediction and one with better accuracy will be used for training. Visualization of dataset will be done in terms of graphical representation of many cases for example at which time the criminal rates are high or at which month the criminal activities are high. The soul purpose of this project is to give a jest idea of how machine learning can be used by the law enforcement agencies to detect, predict and solve crimes at a much faster rate and thus reduces the crime rate. It not restricted to Chicago, this can be used in other

states or countries depending upon the availability of the dataset

 

 

PROJECT TITLE AGRICULTURE CROP PREDICTION
PROJECT ID SHML038
DOMAIN Machine learning
ABSTRACT India being an agricultural country, its economy predominantly depends on agriculture yield growth and allied agro industry products. In India, agriculture is largely influenced by rainwater which is highly unpredictable. Agriculture growth also depends on diverse soil parameters, namely Nitrogen, Phosphorus, Potassium, Crop rotation, Soil moisture, Surface temperature and also on weather aspects which include temperature, rainfall, etc. India now is rapidly progressing towards technical development. Thus, technology will prove to be beneficial to agriculture which will increase crop productivity resulting in better yields to the farmer. The proposed project provides a solution for Smart Agriculture by monitoring the agricultural field which can assist the farmers in increasing productivity to a great extent. Weather forecast data obtained from IMD (Indian Metrological Department) such as temperature and rainfall and soil parameters repository gives insight into which crops are suitable to be cultivated in a particular area. This

work presents a system, in form of an android based application, which

 

uses data analytics techniques in order to predict the most profitable crop in the current weather and soil conditions. The proposed system will integrate the data obtained from repository, weather department and by applying machine learning algorithm: Multiple Linear Regression, a prediction of most suitable crops according to current environmental conditions is made. This provides a farmer with variety of options of crops that can be cultivated. Thus, the project develops a system by integrating data from various sources, data analytics, prediction analysis which can improve crop yield productivity and increase the profit margins of farmer helping them over a longer run.

 

 

PROJECT TITLE LICENSE PLATE RECOGNITION
PROJECT ID SHML039
DOMAIN Machine learning & IP
ABSTRACT License Plate Recognition was a computer system that recognizes any digital image automatically on the number plate. This system includes various operations such as taking pictures, localizing the number pad, truncating characters and OCR from alphanumeric characters. The main idea of this system is to design and develop effective image processing techniques and algorithms to localize the license plate in the captured image, to divide the characters from that number plate and to identify each character of the segment by using the Open Computer Vision Library. This has been implemented in K- NN algorithm and python programming language. Many applications can be implemented by using this system, such as security, highway speed detection, violation of light, identification of handwritten text, discovery of stolen cars, automatic fee collection systems.

 

PROJECT TITLE ALZAMEIR DISEASE DETECTION
PROJECT ID SHML040
DOMAIN Machine learning & IP
ABSTRACT Alzheimer’s Disease is a progressive and irreversible neurological disease and is the most common cause of Dementia in people of the age 65 years and above. Detection of Alzheimer’s disease at prodromal stage is very important as it can prevent serious damage to the patient’s brain. In this paper, a method to detect Alzheimer’s Disease from MRI using Machine Learning approach is proposed. The proposed approach extracts texture and shape features of the Hippocampus region from the MRI scans and a Neural Network is used as Multi-Class Classifier for detection of various stages of Alzheimer’s Disease. The proposed approach is under implementation and is expected to give better accuracy as compared to conventional approaches.

 

 

PROJECT TITLE ANALYSIS OF WOMENS SAFETY IN MACHINE LEARNING
PROJECT ID SHML041
DOMAIN Machine learning
ABSTRACT Women and girls have been experiencing a lot of violence and harassment in public places in various cities starting from stalking and leading to sexual harassment or sexual assault. This research paper basically focuses on the role of social media in promoting the safety of women in Indian cities with special reference to the role of social media websites and applications including Twitter platform Facebook and Instagram. This paper also focuses on how a sense of responsibility on part of Indian society can be developed the common Indian people so that we should focus on the safety of women surrounding them. Tweets on Twitter which usually contains images and text and also written messages and quotes which focus on the safety of women in Indian cities can be used to read a message amongst the Indian Youth Culture and educate people to take strict action and punish those who harass the women. Twitter and other Twitter handles which include hash tag messages that are widely spread across the whole globe sir as a platform

for women to express their views about how they feel while we go out

 

for work or travel in a public transport and what is the state of their mind when they are surrounded by unknown men and whether these

women feel safe or not?

 

 

PROJECT TITLE BRAIN TUMOUR DETECTION
PROJECT ID SHML042
DOMAIN Machine learning & Image processing
ABSTRACT A brain tumor is a growth of abnormal cells that has formed in the brain. Some brain tumors are cancerous (malignant), while others are not (non-malignant). Most Research in developed countries show that the number of people who have brain tumors were died due to the fact of inaccurate detection. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection & diagnosis of brain tumor. However this method of detection resists the accurate determination of stage & size of tumor.

In recent times, the introduction of information technology and e-health care system in the medical field helps clinical experts to provide better health care to the patient. This study addresses the problems of segmentation of abnormal brain tissues and normal tissues such as gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) from magnetic resonance (MR) images. Firstly, brain tumor is detected using the MRI scan further biomarkers are used for further treatment.

 

 

PROJECT TITLE BREAST CANCER DETECTION
PROJECT ID SHML043
DOMAIN Machine learning & IP
ABSTRACT The signs of detection are Masses and micro calcification clusters which are important in early detection of breast cancer.

 

Micro calcification are nothing but tiny mineral deposits within the breast tissue. They look similar to small white colored spots. They may or may not be caused by cancer.

 

Masses can be many things, including cysts (fluid-filled sacs) and non- cancerous solid tumors, but they could also be cancerous.

 

The difficulty in cancer detection is that the abnormalities from normal breast tissues are hard to read because of their subtle appearance and ambiguous margins.Automated tools which can help radiologist in early detection of breast cancer.

 

Further we have classified the cancer into three categories after its detection- Normal,Malignant,Benign.

 

 

PROJECT TITLE ROAD ACCIDENT DETECTION USING MACHINE LEARNING
PROJECT ID SHML044
DOMAIN Machine learning
ABSTRACT In this paper, a novel approach for automatic road accident detection is proposed. The approach is based on detecting damaged vehicles from footage received from surveillance cameras installed in roads and highways which would indicate the occurrence of a road accident. Detection of damaged cars falls under the category of object detection in the field of machine vision and has not been achieved so far. In this paper, a new supervised learning method comprising of three different stages which are combined into a single framework in a serial manner which successfully detects damaged cars from static images is proposed. The three stages use five support vector machines trained with Histogram of gradients (HOG) and Gray level co-occurrence matrix (GLCM) features. Since damaged car detection has not been attempted, two datasets of damaged cars – Damaged Cars Dataset-1 (DCD-1) and Damaged Cars Dataset-2 (DCD-2) – was compiled for public release. Experiments were conducted on DCD-1 and DCD-2 which differ based on the distance at which the image is captured and the quality of the images. The accuracy of the system is 81.83% for DCD-1 captured at

approximately 2 meters   with good quality   and 64.37% for   DCD-2

 

 

captured at approximately 20 meters with poor quality.

 

 

PROJECT TITLE DROWSINESS DETECTION
PROJECT ID SHML045
DOMAIN Machine learning & AI
ABSTRACT According to analysis reports on road accidents of recent years, it’s renowned that the main cause of road accidents resulting in deaths, severe injuries and monetary losses, is due to a drowsy or a sleepy driver. Drowsy state may be caused by lack of sleep, medication, drugs or driving continuously for long time period. An increase rate of roadside accidents caused due to drowsiness during driving indicates a need of a system that detects such state of a driver and alerts him prior to the occurrence of any accident. During the recent years, many researchers have shown interest in drowsiness detection. Their approaches basically monitor either physiological or behavioral characteristics related to the driver or the measures related to the vehicle being used. A literature survey summarizing some of the recent techniques proposed in this area is provided. To deal with this problem we propose an eye blink monitoring algorithm that uses eye feature points to determine the open or closed state of the eye and activate an alarm if the driver is drowsy. Detailed experimental findings are also presented to highlight the strengths and weaknesses of our technique. An accuracy of 94% has been recorded for the proposed methodology.

 

 

PROJECT TITLE Eye controlled virtual mouse
PROJECT ID SHML046
DOMAIN Machine learning & AI
ABSTRACT In this paper, an individual human computer interface system using eye motion is introduced. Traditionally, human computer interface uses mouse, keyboard as an input device. This paper presents hands free interface between computer and human. This technology is intended to replace the conventional computer screen pointing devices for the use

of disabled. The paper presents a novel idea to control computer mouse

 

cursor movement with human eyes It controls mouse-moving by automatically affecting the position where eyesight focuses on, and simulates mouse-click by affecting blinking action. However, the proposed vision-based virtual interface controls system work on various eye movements such as eye blinking.

 

 

PROJECT TITLE HANDGESTURE TEXT RECOGNITION
PROJECT ID SHML047
DOMAIN Machine learning
ABSTRACT Optical Character Recognition(OCR) market size is expected to be USD

13.38 billion by 2025 with a year on year growth of 13.7 %. This growth is driven by rapid digitization of business processes using OCR to reduce their labor costs and to save precious man hours. Although OCR has been considered a solved problem there is one key component of it, Handwriting Recognition or Handwritten Text Recognition(HTR) which is still considered a challenging problem statement. The high variance in handwriting styles across people and poor quality of the handwritten text compared to printed text pose significant hurdles in converting it to machine readable text. Nevertheless it’s a crucial problem to solve for multiple industries like healthcare, insurance and banking.

 

 

PROJECT TITLE NETWORK INTRUSION DETECTION USING MACHINE LEARNING
PROJECT ID SHML048
DOMAIN Machine learning
ABSTRACT Internet use has become an essential part of our daily activities as a result of rapidly growing technology. Due to this rapid growth of technology and intensive use of digital systems, data security of these

systems   has   gained   great   importance.   The   primary   objective   of

 

 

maintaining security in information technologies is to ensure that necessary precautions are taken against threats and dangers likely to be faced by users during the use of these technologies.

Network intrusion is defined as imitating reliable websites in order to obtain the proprietary information entered into websites every day for various purposes, such as usernames, passwords and citizenship numbers. Network intrusion detecttion websites contain various hints among their contents and web browser-based information . Individual(s) committing the fraud sends the fake website or e-mail information to the target address as if it comes from an organization, bank or any other reliable source that performs reliable transactions. Contents of the website or the e-mail include requests aiming to lure the individuals to enter or update their personal information or to change their passwords as well as links to websites that look like exact copies of the websites of the organizations concerne

 

 

PROJECT TITLE WINE QUALITY DETECTION THROUGH MACHINE LEARNING

ALGORITHMS

PROJECT ID SHML049
DOMAIN Machine learning
ABSTRACT Machine learning is one of the emerging areas of research. Many algorithms of data mining have already been used on wine quality dataset to analyze the wine attributes such as quality or class. The quality of wine is not only based on the quantity of alcohol but it also depends on various attributes, these attributes changes with time and so the quality of wine also refines. In this report, machine learning techniques are utilized to analyze those attributes. Firstly data pre- processing takes place i.e. making data appropriate for the models that are built for prediction. Defining independent and dependent variables, missing data handling, feature scaling and data splitting is done to improve the data standard. Then, Logistic regression and Random forest classifier are performed individually on data to predict the test data values. Random forest (RF) classifier outperforms logistic regression (LR) with accuracy 84% while LR has 76% accuracy rate.

 

 

 

PROJECT TITLE UNIVERSITY    ADMISSION    PREDICTION    USING    MACHINE LEARNING
PROJECT ID SHML050
DOMAIN Machine learning
ABSTRACT Students are often worried about their chances of admission in graduate school. The aim of this blog is to help students in shortlisting universities with their profiles. The predicted output gives them a fair idea about their admission chances in a particular university. This analysis should also help students who are currently preparing or will be preparing to get a better idea.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  • Read Me My Book App

 

  • Customer Targeted E-Commerce

 

  • Android General Knowledge Chatbot

 

  • Customer Focused Ecommerce Site With AI Bot

 

  • Your Personal Nutritionist Using Fat Secret API

 

  • Price Negotiator Ecommerce Chatbot System

 

  • Personality Prediction System Through CV Analysis

 

  • TV Show Popularity Analysis Using Data Mining

 

  • Twitter Trend Analysis Using Latent Dirichlet Allocation

 

  • Online Book Recommendation Using Collaborative Filtering

 

  • Movie Success Prediction Using Data Mining Php

 

  • Fake Product Review Monitoring & Removal For Genuine Ratings

Php

 

  • A Commodity Search System For Online Shopping Using Web

Mining

 

  • College Enquiry Chat Bot

 

  • Stream Analysis For Career Choice Aptitude Tests

 

  • Product Review Analysis For Genuine Rating

 

  • Android Smart City Traveller

 

  • Artificial Intelligence Dietician

 

  • Heart Disease Prediction Project

 

  • Smart Health Consulting Project

 

  • Banking Bot Project

 

  • Sentiment Based Movie Rating System

 

  • Online AI Shopping With M-Wallet System

 

  • Question paper generator system

 

  • Student Information Chatbot Project

 

  • Website Evaluation Using Opinion Mining

 

  • Android Attendance System

 

  • Intelligent Tourist System Project

 

  • AI Desktop Partner

 

  • Intelligent Chat Bot

 

  • Stock Market Analysis and Prediction

 

  • Automatic Answer Checker

 

  • Voice based Intelligent Virtual Assistance for Windows

 

  • Online Logistic Chatbot System

 

  • Transformer Conversational Chatbot in Python using TensorFlow 2.0

 

  • Lane-Line Detection System in Python using OpenCV

 

  • Facial Emotion Recognition and Detection in Python using Deep

Learning

 

  • Artificial Intelligence HealthCare Chatbot System

 

  • Online Assignment Plagiarism Checker Project using Data Mining

 

  • Teachers Automatic Time-Table Software Generation System using

PHP

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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