Author name: itdesigners.org

Maize Leaf Disease Detection Corn is one of the most important cereal crops globally, providing essential nutrients and calories to millions of people. However, corn plants are highly susceptible to various diseases, which can cause significant yield losses. Crop diseases are responsible for over 10% of global crop losses, and timely detection and management of these diseases are crucial to minimize these losses and ensure food security. Early detection and management of crop diseases require constant monitoring and identification of various pathogens, which can be labor-intensive and time-consuming. Advancements in machine learning and artificial intelligence have opened up new possibilities for the early detection of crop diseases, offering an alternative to manual inspection. Machine learning models can process large amounts of data and identify patterns that are not visible to the human eye, making them effective tools for crop disease detection. In this study, we propose a machine learning approach using a maize dataset to detect corn diseases. The maize dataset comprises images of corn leaves affected by various diseases such as gray leaf spot, common rust, and northern corn leaf blight. Our proposed approach uses convolutional neural networks (CNNs) to classify images into different disease categories, enabling accurate and timely detection of corn diseases. The CNNs are trained on a large dataset of corn leaf images, enabling them to identify patterns and features that are unique to different disease categories. Our study aims to provide a reliable and efficient approach to detecting corn diseases, which can assist farmers in making informed decisions regarding crop management. Early detection of diseases can lead to the timely implementation of management strategies, such as the use of fungicides or cultural practices, reducing the spread and severity of diseases. Furthermore, the proposed machine learning approach can reduce the dependency on manual inspection, which can be costly and often prone to errors. In conclusion, our study presents a novel approach to early detection of corn diseases using a maize dataset and convolutional neural networks. The proposed approach can assist in the development of sustainable agriculture practices by enabling timely and accurate disease detection, leading to improved crop yields and food security. We hope that this study will inspire further research in this field, ultimately leading to the development of more effective and efficient approaches to crop disease management. Background of the Study Corn is a staple food crop globally, and its cultivation and production are critical to food security. However, corn plants are susceptible to various diseases, such as gray leaf spot, common rust, and northern corn leaf blight, which can cause significant yield losses. Early detection and management of these diseases are crucial to minimize crop losses and ensure food security. Traditional methods of detecting and managing these diseases include visual inspection of crops, which can be time-consuming and prone to errors. Recent advancements in machine learning and computer vision techniques have opened up new opportunities for early detection of crop diseases. Machine learning models can process large amounts of data and identify patterns that are not visible to the human eye, making them effective tools for crop disease detection. These models have been applied to various crop diseases, including corn diseases, with promising results. In this study, we propose a machine learning approach to detect three common corn diseases, gray leaf spot, common rust, and northern corn leaf blight, using a maize dataset. The maize dataset comprises of images of corn leaves affected by the three diseases, enabling the development of a machine learning model that can identify and classify these diseases. Objectives of the Study The objective of this study is to develop a machine learning model that can detect and classify gray leaf spot, common rust, and northern corn leaf blight in corn leaves using a maize dataset. Developing a machine learning model using convolutional neural networks (CNNs) to identify and classify the three common corn diseases. Evaluating the performance of the developed model by measuring its accuracy, precision, and recall. Methodology The proposed methodology involves the following steps: Data Collection: We collected a maize dataset comprising of images of corn leaves affected by gray leaf spot, common rust, and northern corn leaf blight. The dataset will be curated to ensure that it is balanced, and each disease class has an adequate number of samples. Data Preprocessing: We preprocessed the data by resizing the images, removing noise, and augmenting the data to create a larger dataset for training the model. Model Development: We developed machine learning models Decision tree, Random Forest, Nave Baysien, Support Vector Machine, Support Vector Machine and CNN to identify and classify gray leaf spot, common rust, and northern corn leaf blight in corn leaves. The CNN model will be trained using the preprocessed dataset. Model Evaluation: We will evaluate the performance of the developed model by measuring its accuracy, precision, and recall. We will also compare the performance of the developed model with existing stateof-the-art methods for detecting corn diseases. Expected Outcome We expect that the developed machine learning model will achieve a high level of accuracy in detecting and classifying gray leaf spot, common rust, and northern corn leaf blight in corn leaves. The developed model can be used by farmers to make informed decisions regarding crop management, leading to improved crop yields and food security. Additionally, we expect to provide insights into the unique features and patterns associated with the three common corn diseases. This can assist in the development of more effective and efficient approaches to crop disease management, leading to sustainable agriculture practices. In conclusion, this study presents a novel approach to detecting common corn diseases using a maize dataset. Explanation of Dataset The maize dataset is a collection of images of corn leaves affected by three common corn diseases, gray leaf spot, common rust, and northern corn leaf blight. The dataset was collected from various farms and research institutions and curated to ensure that it is balanced, with each disease class having an adequate number of samples. The dataset comprises of

Titanic survival prediction using machine learning Technology’s impossible advancement has both facilitated and complicated our lives. One of the advantages of technology is that an extensive range of data can be retrieved quickly when needed. However, it can be challenging to obtain accurate information. Raw data that can be easily acquired from online sources does not make sense; it must be processed to serve as an information retrieval system. In this context, feature engineering techniques and machine learning algorithms are essential. This study aims to extract as many accurate findings as possible from raw and missing data using machine learning and feature engineering methods. Therefore, one of the most popular datasets in data science, Titanic, is used.  The science of machine learning has enabled analysts to gain insights from historical data and occurrences. The Titanic accident is one of the most famous shipwrecks in world history. The Titanic was a British cruise ship that sank in the North Atlantic Ocean a few hours after hitting an iceberg. While there are facts to back up the cause of the tragedy, there are numerous theories on how many passengers survived the Titanic disaster. Over the years, data on both survivors and dead passengers has been gathered. The dataset is publicly available on the website Kaggle.com.   The Kaggle Titanic dataset is one of the most widely used in machine learning. It is a dataset containing information about the passengers on the Titanic when it sank during its maiden voyage in 1912. The dataset is commonly used in predictive modeling and machine learning contests. The dataset has 891 rows, each representing a passenger, and 12 columns with information about each passenger, including their name, age, gender, cabin, and ticket number. The purpose of evaluating this dataset is to create a model that can correctly predict whether or not a passenger survived. Beginners and specialists commonly use the dataset for data cleaning, feature engineering, and model construction. It provides the ability to learn and use various machine-learning techniques, including logistic regression, decision trees, random forests, and neural networks, to mention a few.   The Kaggle Titanic dataset has become a benchmark dataset in the machine learning community, with numerous tutorials, blog posts, and courses developed around it to help beginners get started with machine learning. Using machine learning algorithms with a dataset of 891 rows in the train set and 418 rows in the test set, the article aims to discover the relationship between factors such as age, gender, fare, and the likelihood of passenger survival. These factors may have had an impact on the passengers’ survival rates. In this article work, multiple machine-learning techniques are used to predict passenger survivability. In particular, this article compares the algorithm based on the accuracy percentage on a test dataset. Background The R.M.S. Titanic is undoubtedly the most famous shipwreck in modern popular culture. Titanic was a British-registered ship in the White Star line controlled by a U.S. firm in which famous American financier John Pierpont “JP” Morgan held an important share. Harland & Wolff built the Titanic in Belfast, Northern Ireland, for the transatlantic passage from Southampton, England, to New York City. It was the largest and richest passenger ship of its time, and it was thought to be unsinkable. Titanic was launched on May 31, 1911, and set ship on its first trip from Southampton on April 10, 1912, carrying 2,240 passengers and crew. On April 15, 1912, after striking an iceberg, Titanic broke apart and sank to the bottom of the ocean, taking with it the lives of more than 1,500 passengers and crew. The sinking of the RMS Titanic in 1912 is one of history’s most horrific ocean tragedies, killing more than 1,500 people. The ship, which was on its first trip from Southampton to New York, collided with an iceberg in the North Atlantic and sank, prompting a worldwide flood of sadness and shock. The Titanic has remained a popular topic since its sinking, with countless books, films, and documentaries addressing the disaster and its aftereffects. The Titanic narrative became more than simply popular culture. The Titanic also caught the interest of the data science community, with the Kaggle Titanic dataset emerging as a classic example of machine learning. The Kaggle Titanic dataset was built to provide a real-world dataset for data scientists to practice their abilities on a relevant subject. The dataset includes information about the Titanic’s passengers, such as their age, gender, class, and whether they survived the catastrophe. The dataset has 891 passengers, which is enough for beginners. The dataset has become a standard for machine learning algorithms, creating a model that can reliably predict which passengers would most likely survive the disaster. The dataset is frequently used to teach data cleaning, feature engineering, and model-building approaches, making it an invaluable resource for those interested in data science and machine learning. Beyond its usage as a teaching tool, the Kaggle Titanic dataset has been the subject of numerous scholarly investigations. Researchers analyzed the dataset to look into the demographics of the Titanic’s passengers and the elements that may have influenced their chances of survival. According to studies, women and children are more likely to survive than men, and passengers in first class had a greater survival probability than those in third class. Overall, the Kaggle Titanic dataset provides fascinating insights into one of history’s most terrible tragedies and is an excellent resource for anyone interested in data science and machine learning. Explanation of dataset The Kaggle Titanic dataset is a popular dataset for machine learning and data analysis contests hosted on the Kaggle website. The collection includes information about the passengers on the RMS Titanic, which sank on its maiden journey in 1912. The dataset consists of 1309 rows, each representing a Titanic passenger, and 12 columns containing various pieces of passenger information. The dataset contains numerical and category information, with the goal variable representing whether or not the passenger survived the sinking. The columns in the dataset are: Passenger Id: A

Wine quality prediction using machine learning The quality of wine is crucial to both consumers and the wine business. The traditional (professional) method of determining wine quality is very complex. Nowadays, machine learning models are essential instruments for replacing human labor. In this scenario, various features can be used to predict wine quality, but not all will be significant for accurate prediction. As a result, our article focuses on what wine characteristics are critical for achieving a promising outcome. We employed three algorithms (SVM, NB, and ANN) to create a classification model and evaluate relevant features. This work examined two wine-quality datasets: red and white. We used the Pearson coefficient correlation and performance measurement matrices such as accuracy, recall, precision, and f1 score to compare the machine learning algorithms to determine feature importance. A grid search strategy was used to improve model accuracy.For this project, I used the Red Wine Quality dataset to create multiple classification models that predict whether a given red wine is “good quality” or not. Each wine in this dataset receives a “quality” score between 0 and 10. For this project, I changed the result to a binary output where each wine is either “good quality” (a score of 7 or more) or not (a score of less than 7). 11 input variables determine the quality of wine: Fixed acidity Volatile acidity Citric acid Residual sugar Chlorides Free sulfur dioxide Total sulfur dioxide Density pH Sulfates Alcohol Attributes Description fixed acidity Fixed acids, numeric from 3.8 to 15.9 volatile acidity Volatile acids, numeric from 0.1 to 1.6 citric acid Citric acids, numeric from 0.0 to 1.7 residual sugar residual sugar, numeric from 0.6 to 65.8 chlorides Chloride, numeric from 0.01 to 0.61 free sulfur dioxide Free sulfur dioxide, numeric: from 1 to 289 total sulfur dioxide Total sulfur dioxide, numeric: from 6 to 440 density Density, numeric: from 0.987 to 1.039 pH pH, numeric: from 2.7 to 4.0 sulfates Sulfates, numeric: from 0.2 to 2.0 alcohol Alcohol, numeric: from 8.0 to 14.9 quality Quality, numeric: from 0 to 10, the output target Background A variety of machine learning algorithms are available for the learning process. This section discusses classification algorithms used in wine quality prediction and related research. Classification algorithm Naive Bayesian The naive Bayesian is a simple supervised machine learning classification technique based on Bayes’ theorem. The algorithm assumes that the feature criteria are independent of the class. The naive Bayes algorithm contributes to developing fast machine-learning models capable of making quick predictions. The algorithm uses the likelihood probability to determine whether a specific section has a spot in a particular class. Support Vector Machine The most common machine learning algorithm is the support vector machine (SVM). It is a supervised learning model that performs classification and regression tasks. However, it is mainly employed to solve classification problems in machine learning. The SVM method seeks to find the best line or decision boundary to divide an n-dimensional space into classes. So we can quickly place the new data points in the appropriate groupings. The optimal choice boundary is known as a hyperplane. The support vector machine selects the extreme data points that contribute to the formation of the hyperplane. In the diagram above, two distinct groups are classified using the decision boundary or hyperplane. The SVM model applies to both nonlinear and linear data. It uses a nonlinear mapping to turn the primary preparation information into a larger measurement. The model searches for the linearly optimal splitting hyperplane in this new measurement. A hyperplane can divide the data into two classes using proper nonlinear mapping to achieve sufficiently high measurements, and this hyperplane SVM employs support vectors and edges to discover the solution. The SVM model represents the models as a point in space, with the distinct classes separated by a gap to be mapped to ensure that instances are as wide as possible. The model can do nonlinear classification. Artificial Neural Network An artificial neural network is a collection of neurons capable of processing information. It has been successfully applied to categorization tasks in various commercial, industrial, and scientific domains. The algorithm model is a connection between neurons linked to the input, hidden, and output layers. The neural network is constant because, even if one of its components fails, it can function in parallel without difficulty.The implementation of the artificial neural network consists of three layers: input, hidden, and output. The input layer’s function is mapped to the input attribute, which sends feedback to the hidden layer. Objectives The project’s objectives are as follows: Explaining data sets using Python code. To apply various machine learning techniques. Experiment with multiple ways to determine which produces the most accuracy. To establish which characteristics are most suggestive of high-quality wine. Wine quality prediction using machine learning with source code Step 1: Import Libraries Pyton import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from sklearn.datasets import load_wine from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier from sklearn.neural_network import MLPClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.preprocessing import StandardScaler, LabelEncoder from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix, classification_report,accuracy_score import warnings warnings.filterwarnings("ignore") import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from sklearn.datasets import load_wine from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier from sklearn.neural_network import MLPClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.preprocessing import StandardScaler, LabelEncoder from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix, classification_report,accuracy_score import warnings warnings.filterwarnings("ignore") Step 2: Reading Data Pyton import wine dataset wine = datasets.load_wine() # np.c_ is the numpy concatenate function wine_df = pd.DataFrame(data= np.c_[wine['data'], wine['target']], columns= wine['feature_names'] + ['target']) wine_df.head() import wine dataset wine = datasets.load_wine() # np.c_ is the numpy concatenate function wine_df = pd.DataFrame(data= np.c_[wine['data'], wine['target']], columns= wine['feature_names'] + ['target']) wine_df.head() There are 1599 rows and 12 columns. The data was clean in the first five rows, but I wanted to double-check that there were no missing values. Step 3:

10 exciting C++ projects with source code Are you looking for interesting C++ projects to work on? Look no further! This article will look at the top 10 C++ projects with source code. These projects are not only fun to work on, but they also offer excellent learning opportunities. Whether you’re a newbie or an experienced coder, there’s something here for you. Source Code is also available to help you in making these exciting projects. 1. Library management system The Library Management System aims to improve the library’s organization and retrieval of books. This C++ software lets librarians maintain book records, track borrowing and returning operations, and process user registrations. Users may use a user-friendly interface to browse for books, view availability, and check their borrowing history. Administrators can also generate reports, manage fines, and keep the library running smoothly. C++ #include <iostream> #include <vector> #include <string> using namespace std; // Class to represent a book class Book { public: string title; string author; int id; bool available; Book(string t, string a, int i) : title(t), author(a), id(i), available(true) {} }; // Class to represent a user class User { public: string name; int userId; User(string n, int id) : name(n), userId(id) {} }; // Class to manage the library class Library { private: vector<Book> books; vector<User> users; public: void addBook(string title, string author, int id) { Book book(title, author, id); books.push_back(book); } void addUser(string name, int userId) { User user(name, userId); users.push_back(user); } void displayBooks() { cout << "Library Books:" << endl; for (const Book& book : books) { cout << "ID: " << book.id << "tTitle: " << book.title << "tAuthor: " << book.author; if (book.available) { cout << "tStatus: Available" << endl; } else { cout << "tStatus: Checked Out" << endl; } } } void displayUsers() { cout << "Library Users:" << endl; for (const User& user : users) { cout << "ID: " << user.userId << "tName: " << user.name << endl; } } void borrowBook(int userId, int bookId) { for (Book& book : books) { if (book.id == bookId && book.available) { book.available = false; cout << "Book successfully borrowed by user ID " << userId << "." << endl; return; } } cout << "Book not available or invalid ID." << endl; } void returnBook(int bookId) { for (Book& book : books) { if (book.id == bookId && !book.available) { book.available = true; cout << "Book successfully returned." << endl; return; } } cout << "Invalid book ID or book already available." << endl; } }; int main() { Library library; // Adding some books and users for testing library.addBook("The Catcher in the Rye", "J.D. Salinger", 1); library.addBook("To Kill a Mockingbird", "Harper Lee", 2); library.addBook("1984", "George Orwell", 3); library.addUser("Alice", 101); library.addUser("Bob", 102); // Displaying the initial state of the library library.displayBooks(); library.displayUsers(); // Simulating book borrowing and returning library.borrowBook(101, 1); library.borrowBook(102, 2); library.returnBook(1); // Displaying the updated state of the library library.displayBooks(); library.displayUsers(); return 0; } #include <iostream> #include <vector> #include <string> using namespace std; // Class to represent a book class Book { public: string title; string author; int id; bool available; Book(string t, string a, int i) : title(t), author(a), id(i), available(true) {} }; // Class to represent a user class User { public: string name; int userId; User(string n, int id) : name(n), userId(id) {} }; // Class to manage the library class Library { private: vector<Book> books; vector<User> users; public: void addBook(string title, string author, int id) { Book book(title, author, id); books.push_back(book); } void addUser(string name, int userId) { User user(name, userId); users.push_back(user); } void displayBooks() { cout << "Library Books:" << endl; for (const Book& book : books) { cout << "ID: " << book.id << "tTitle: " << book.title << "tAuthor: " << book.author; if (book.available) { cout << "tStatus: Available" << endl; } else { cout << "tStatus: Checked Out" << endl; } } } void displayUsers() { cout << "Library Users:" << endl; for (const User& user : users) { cout << "ID: " << user.userId << "tName: " << user.name << endl; } } void borrowBook(int userId, int bookId) { for (Book& book : books) { if (book.id == bookId && book.available) { book.available = false; cout << "Book successfully borrowed by user ID " << userId << "." << endl; return; } } cout << "Book not available or invalid ID." << endl; } void returnBook(int bookId) { for (Book& book : books) { if (book.id == bookId && !book.available) { book.available = true; cout << "Book successfully returned." << endl; return; } } cout << "Invalid book ID or book already available." << endl; } }; int main() { Library library; // Adding some books and users for testing library.addBook("The Catcher in the Rye", "J.D. Salinger", 1); library.addBook("To Kill a Mockingbird", "Harper Lee", 2); library.addBook("1984", "George Orwell", 3); library.addUser("Alice", 101); library.addUser("Bob", 102); // Displaying the initial state of the library library.displayBooks(); library.displayUsers(); // Simulating book borrowing and returning library.borrowBook(101, 1); library.borrowBook(102, 2); library.returnBook(1); // Displaying the updated state of the library library.displayBooks(); library.displayUsers(); return 0; } 2. Online Exam System The Online Exam System provides a complete solution for administering exams digitally. Instead of traditional pen-and-paper tests, this system lets students take them on a computer or digital device. The goal is to make the test process more efficient for students and educators. C++ #include <iostream> #include <iomanip> #include <vector> #include <ctime> using namespace std; class Question { public: string question; vector<string> options; int correctOption; Question(string q, vector<string> opts, int correct) { question = q; options = opts; correctOption = correct; } }; class Exam { public: vector<Question> questions; int totalQuestions; Exam() { totalQuestions = 0; } void addQuestion(Question q) { questions.push_back(q); totalQuestions++; } void displayQuestion(int index) { cout << "Q" << index + 1 << ": " << questions[index].question << endl; for (size_t i = 0; i < questions[index].options.size(); i++) { cout << " " << char('A' + i) << ". " << questions[index].options[i] << endl; } } int

Top 13 IOT projects with source code The Internet of Things (IoT) is changing our lives with rapid technological advancements. It goes beyond just connecting devices; it transforms our daily lives. If you want to explore IoT, we have 13 project ideas with source code to help you learn and be creative. Get ready to code and join this exciting journey! 1. Wrong Posture Muscle Strain Detector A person’s posture is how they position their body to prevent excessive use their muscles when they move. Bad posture can lead to a number of health issues. Pains in the muscles might be caused by severe exhaustion, fractured bones, or any other type of damage. Two signs of bad posture that might occur and interfere with our daily activities are fatigue and back pain. The necessity for a device is growing since most individuals have back pain, injuries, neck pain, shoulder issues, etc., these days. Say goodbye to sitting over with an IoT-based wrong posture muscle strain detector. Integrate sensors that monitor body posture and provide real-time feedback to prevent muscle strain. This project is not only innovative but also promotes a healthier lifestyle. Source Code 2. Safety Monitoring System for Manual Wheelchairs Create a safety system for manual wheelchairs using sensors to detect obstacles and monitor speed. This project shows how IoT can improve people’s lives with diverse needs. Manual wheelchair users often encounter safety concerns, including accidents, falls, or difficulties navigating specific terrains. Traditional monitoring systems cannot provide timely assistance or alert caregivers in emergencies. The Safety Monitoring System aims to address these challenges by utilizing IoT technology to create a proactive and responsive solution for ensuring the safety of wheelchair users. Source Code 3. Remote Plant Monitor – IoT Home Automation Nowadays, it’s fashionable to decorate homes with lovely plants, and more and more people are buying indoor plants every day. Even though everyone has a hectic schedule these days, many people find that having indoor plants at home is a passion. We also know that having indoor plants at home is healthy, but taking care of the plants requires a lot of effort. Because indoor plants are difficult to care for and can die for unexpected reasons, growing them may often be quite challenging for people. So, It is a bright and innovative system designed to enhance plant care by integrating Internet of Things (IoT) technology into home gardening. The main purpose of this system is to address the challenges of monitoring and maintaining indoor plants, providing users with real-time insights and automated solutions for optimal plant health. Source Code 4. Tank Water Monitoring System A reliable source of water is essential to farm and agricultural productivity as well as our standard of living. In agriculture, keeping an eye on the water level in a source of water, like a borewell or water tank, is crucial. For instance, dry running of the pump motor may result in damage if the water level in a borewell falls below the level required for pumping. In this situation, keeping an eye on the water level and adjusting the water pump as needed become important responsibilities. Water level monitoring is a crucial responsibility in many other scenarios. It can be applied to research how much water a source uses or to preserve water. In response to worries about a lack of water, develop an IoT-based tank water monitoring system. Integrate sensors to track water levels and quality in tanks, providing valuable data for efficient water management. The Tank Water Monitoring System provides a modern alternative to traditional water level checks in tanks. This automated system provides real-time monitoring, which is more efficient and less prone to errors. Source Code 5. Crypto Alert System Using Bolt IoT Cryptocurrency markets operate 24/7, and sudden price fluctuations or significant events can significantly impact investment decisions. It becomes challenging for crypto enthusiasts and investors to stay updated on market changes continuously. The Crypto Alert System addresses this challenge by delivering timely alerts, ensuring that users are informed about critical market movements and can make informed decisions promptly. The system achieves real-time monitoring and alerting capabilities by integrating Bolt IoT, enhancing the overall user experience. This final year project idea is unique. If you select this definitely, you will definitely get good grades in the final year. Source Code 6. Mining Worker Safety Helmet- IoT-Based Project Mining is one of the riskiest occupations. In many countries, underground miners are not guaranteed social or safety, and in the event of an injury, they may be responsible for caring for themselves. Two of the negative societal outcomes include livelihood destruction and displacement. Among all industries combined, the mining sector has the highest rate of fatal workplace accidents. The Mining Worker Safety Helmet project showcases how IoT can be used to improve safety in mining. The project improves safety for mining workers by using innovative technology and real-time monitoring, reducing accidents and improving worker well-being. Mining operations involve inherent risks, with worker safety being a top priority. Accidents, such as falling objects or collisions, pose severe threats to the well-being of mining personnel. Source Code 7. IoT- Based Smoke Detector system Safeguard homes and businesses with an IoT-based smoke detector system. Create an intelligent system that detects smoke and triggers alarms or alerts. This project exemplifies how IoT can play a crucial role in emergencies, adding an extra layer of protection. Traditional smoke detectors only use sound alarms to alert people of possible fire risks. These alarms might not work well if people can’t hear them or are unable to move. The IoT-based smoke Detection system was developed to be a more robust and reliable smoke detection system. It can provide timely alerts and minimize false alarms. Source Code 8. IoT-Based Crop Monitoring System Since agriculture is such an important field, every technical improvement should be done in this domain. The demand for agriculture has grown significantly due to global population growth, and sadly, farmers are unable to meet this endless

Heart Disease Prediction Using Machine Learning Heart disease is a significant cause of death worldwide and requires creative solutions. Early heart disease detection and prediction are crucial for effective prevention and timely intervention. Technology and medicine can change how we predict heart disease in healthcare. With its ability to analyze large datasets and identify complex patterns, machine learning has emerged as a promising tool for predicting heart disease. In this article, we explore the application of machine learning in heart disease prediction, focusing on the best algorithms and discussing a sample project. This article explores heart disease prediction using machine learning, uncovering the reasons behind this exciting technological advance. We will also learn how to make heart disease predictions using machine learning. Source code is also given for your help. Understanding Heart Disease Prediction Heart disease prediction uses machine learning algorithms to analyze medical data and detect patterns that could suggest potential heart problems. This approach enables early detection and timely intervention, ultimately saving lives. Problem Statement Traditional methods to predict heart disease are unreliable because they require manual analysis and only consider a few pieces of information. This heart disease prediction project can cause delays in diagnosing and treating the disease. Also, these methods don’t provide real-time monitoring or personalized risk assessment, which is a big problem. Critical factors associated with heart disease Understanding and dealing with these factors through lifestyle changes, regular check-ups, and early treatment are vital to preventing and managing heart disease. Machine learning models can use these factors to predict a person’s risk and provide personalized precautions. Age: The risk of heart disease increases with age. Older individuals are more likely to develop cardiovascular conditions. Gender: Men tend to have a higher risk of heart disease than premenopausal women. However, after menopause, women’s risk increases and approaches that of men. Genetics and Family History: A family history of heart disease can significantly elevate an individual’s risk. Genetic factors can contribute to high blood pressure and high cholesterol. High Blood Pressure (Hypertension): High blood pressure strains the heart and blood vessels, increasing the risk of heart disease, stroke, and other cardiovascular conditions. High Cholesterol Levels: Increased levels of low-density lipoprotein (LDL or “bad” cholesterol) and low levels of high-density lipoprotein (HDL or “good” cholesterol) can contribute to the buildup of plaques in the arteries, leading to atherosclerosis. Smoking: Tobacco smoke contains chemicals that can damage blood vessels and heart tissue, leading to the development of atherosclerosis and other heart-related issues. Obesity and Overweight: Excess body weight, especially around the abdomen, is associated with an increased risk of heart disease. Obesity contributes to conditions such as diabetes and hypertension. Diabetes: Individuals with diabetes have a higher risk of heart disease. Diabetes can damage blood vessels and contribute to atherosclerosis. Physical Inactivity: A life of inactivity is a significant risk factor for heart disease. Regular physical activity helps maintain a healthy weight, lower blood pressure, and improve cardiovascular health. Unhealthy Diet: Diets high in saturated and trans fats, cholesterol, sodium, and added sugars contribute to elevated blood cholesterol levels, hypertension, and obesity, increasing the risk of heart disease. Excessive Alcohol Consumption: Heavy and chronic alcohol consumption can lead to high blood pressure, cardiomyopathy, and other heart-related issues. Stress: Chronic stress may contribute to heart disease through various mechanisms, including elevated blood pressure and unhealthy coping behaviors like overeating or smoking. Benefits of Machine Learning in Heart Disease Prediction Early Detection: Machine learning algorithms can find small patterns in health data to detect potential heart issues before symptoms appear. Personalized Risk Assessment: Customizing predictions based on a person’s health profile improves accuracy, enabling personalized preventive measures. Real-Time Monitoring: Continuous monitoring of health parameters in real time enables quick action in case of abnormalities, reducing response time and improving patient outcomes. Data analysis Perspectives: Machine learning analyzes large data sets to find patterns and trends, helping healthcare professionals make better decisions. Machine Learning Algorithms for Heart Disease Prediction Several machine learning algorithms have been successfully applied to predict heart disease. The choice of algorithm depends on the dataset characteristics and the specific goals of the prediction model. Some widely used algorithms include Logistic Regression: Logistic Regression is a commonly used algorithm for binary classification tasks, making it suitable for predicting whether an individual is at risk of heart disease. Decision Trees: Decision Trees are versatile and understandable, making them helpful in identifying patterns in heart disease risk factors. They can handle both numerical and categorical data. Random Forest: Random Forest is an ensemble learning technique that combines multiple decision trees to improve predictive accuracy and reduce overfitting. Support Vector Machines (SVM): SVM effectively separates data into classes and is particularly useful when dealing with complex datasets with non-linear relationships. Neural Networks: Deep learning models like Neural Networks can capture intricate patterns in large datasets, making them suitable for complex heart disease prediction tasks. Best Practices for Heart Disease Prediction Projects When doing a heart disease prediction project, it’s crucial to follow certain best practices: Data Preprocessing: Clean and preprocess the dataset to handle missing values, normalize features, and convert categorical variables into a suitable format for machine learning models. Feature Selection: Identify and select the most relevant features for the prediction model to improve accuracy and reduce computational complexity.  Model Evaluation: Employ appropriate evaluation metrics such as accuracy, precision, recall, and F1-score to assess the machine learning model’s performance.  Hyperparameter Tuning: Fine-tune the parameters of the chosen algorithm to optimize the model’s performance.  Validation and Testing: Split the dataset into training, validation, and testing sets to ensure the model generalizes well to new, unseen data. Challenges in Implementing Machine Learning for Heart Disease Prediction Data Quality: In healthcare, ensuring that the data used for training machine learning models is reliable and accurate is difficult. There are often issues with the quality and consistency of data sources. When health records are flawed or incomplete, it can introduce biases that make predictive models less effective. It is crucial to address these

Phishing website detection using Machine Learning What is Phishing? Phishing is a type of cyberattack in which hackers use fraudulent methods to deceive people to get sensitive information like passwords, credit card numbers, or personal details. This is often conducted through fake emails, websites, or other kinds of electronic communication that appear to originate from legitimate sources. Phishing aims to get personal or financial information that can then be utilized for identity theft, fraud, or other illegal activity. Phishing attacks usually involve the creation of fake websites or emails that seem like those of legitimate businesses, such as banks, social networking platforms, or online stores. These fraudulent websites or emails may include links or attachments that, when clicked or opened, push the victim to provide personal or financial information. Understanding Phishing Websites Before diving into the technical aspects of detecting phishing websites using machine learning, it’s essential to understand what phishing websites are and how they operate. Phishing websites are fraudulent websites that imitate legitimate ones, aiming to deceive users into disclosing sensitive information. These websites often have URLs that closely resemble those of reputable websites, making it challenging for users to distinguish between them. The Importance of Detecting Phishing Websites Detecting phishing websites is essential for several reasons. Most importantly, it helps customers avoid falling prey to phishing scams. Users can protect critical information from thieves by recognizing and blocking fake websites. Furthermore, detecting phishing websites helps businesses retain their reputation and integrity. If users link a brand with phishing attempts, they may lose trust in the brand, resulting in financial losses and reputational harm. Traditional Methods vs. Machine Learning Traditionally, phishing websites could be identified using rule-based systems that depended on established rules to identify phishing sites. While these procedures were beneficial in some cases, they had limits. For example, rule-based systems needed help to keep up with cybercriminals’ shifting strategies, causing them to be ineffective over time.  Machine learning, on the other hand, provides a more dynamic and flexible method for detecting phishing sites. ML algorithms can analyze vast volumes of data and uncover patterns that humans may miss. This enables ML models to detect phishing websites with greater accuracy and efficiency.  Recommended Reading Stock Price Prediction system using Machine Learning Real-Time Object Detection Using Machine Learning Ecommerce Sales Prediction using Machine Learning Key Features for Detecting Phishing Websites Many features can be used to detect phishing websites efficiently. Here are some key features which can be used to see phishing website   URL Analysis: Examining the URL of a website can reveal vital information about its validity. For example, phishing websites frequently utilize URLs similar to legal websites but have minor differences, such as misspellings or additional letters. Content Analysis: Analysing a website’s content can help detect phishing websites. Phishing sites, for example, frequently include generic or poorly written material since they are designed to deceive consumers quickly. SSL Certificate Analysis: Checking the SSL certificate of a website can help determine its legitimacy. Phishing websites often use self-signed or expired SSL certificates, which can be a red flag. Website Reputation: Analyzing a website’s reputation can also help detect phishing websites. For example, if a website has a history of hosting phishing attacks, it may be more likely to be a phishing website.   These are only few features there are many other feature which can be used to detect phishing website. Machine Learning Algorithms for Phishing Website Detection Several machine learning methods may be utilized to detect phishing websites efficiently. Some of the most widely used algorithms are: Random Forest: Random Forest is a group learning system that makes predictions based on several decision trees. It is ideal for detecting phishing websites since it can handle big datasets and is not prone to overfitting. Support vector machines (SVMs): SVM is a supervised learning technique that can be applied to classification tasks. It operates by determining which hyperplane best splits the data into multiple classes. SVM is good at detecting phishing websites because it can handle high-dimensional data and is resistant to noise. Logistic regression: Logistic regression is a statistical model used to perform binary classification tasks. It estimates the likelihood of a specific outcome based on the input features. Logistic regression helps detect phishing websites due to its simplicity and interpretability.But we will use random forest to create machine learning model for phishing detection Challenges and Limitations While machine learning is a promising way to detect phishing websites, it has drawbacks and limits. Some of the significant challenges are: Data Imbalance: Because phishing websites are uncommon compared to reputable websites, data imbalance concerns may arise. This can make it difficult for machine learning algorithms to learn from the data correctly. Feature Engineering: Identifying the appropriate elements for identifying phishing websites can be difficult. Phishing websites frequently employ advanced strategies to avoid detection, making it challenging to discover pertinent aspects. Model Interpretability: Certain machine learning algorithms, such as deep learning models, are challenging to interpret. This can make it difficult to comprehend why a specific website was flagged as phishing. Recommended Reading Hand Gesture Recognition Using Machine Learning 10 Advance Final Year Projects with source code Ecommerce Sales Prediction using Machine Learning Phishing website detection using Machine Learning Source Code Download Source Code Machine learning is a way to detect phishing websites accurately. Machine learning algorithms can find patterns in a website’s elements that humans may not see. However, it is critical to understand the problems and limitations of employing machine learning for phishing website identification. With additional study and development, machine learning has the potential to become a vital tool for countering phishing attempts. To Learn More: Phishing website detection using Machine Learning Research Paper Detecting phishing websites using machine learning Research Paper Detecting phishing websites Research Paper How do machine learning algorithms detect phishing websites? Machine learning algorithms detect phishing websites by investigating many aspects of the site, including its URL, content, SSL certificate, and reputation. Machine learning algorithms can detect phishing websites by recognizing trends in their properties. What are some

Top 10 ethical hacking projects Ethical hacking is a proactive defense strategy where authorized professionals test systems for vulnerabilities before malicious actors misuse them. In this article, we’ll explore ten intriguing ethical hacking projects designed to enhance your skills and contribute positively to the cybersecurity landscape. These projects cover a wide range of ethical hacking activities, from creating viruses for educational purposes to developing phishing website checkers. Let’s dip in and explore each ethical hacking project idea. 1. User Authentication System User authentication is like a lock that protects sensitive information. It guarantees that only authorized individuals have access to digital spaces. However, the growing complexity of cyber threats makes these systems vulnerable. The User Authentication System ethical hacking project aims to strengthen the security of sensitive information. Developers and security professionals can use ethical hacking principles to build robust authentication systems to withstand cyber threats. This project combines security and ethical practices to provide a safer digital experience for users everywhere. Pyton import sqlite3 import hashlib import os def hash_password(password): salt = os.urandom(32) key = hashlib.pbkdf2_hmac('sha256', password.encode('utf-8'), salt, 100000) return salt + key def verify_password(username, password): connection = sqlite3.connect('users.db') cursor = connection.cursor() cursor.execute('SELECT salt, key FROM users WHERE username = ?', (username,)) user = cursor.fetchone() connection.close() if user: salt = user[0] key = user[1] hashed_password = hashlib.pbkdf2_hmac('sha256', password.encode('utf-8'), salt, 100000) return hashed_password == key else: return False def register(username, password): try: connection = sqlite3.connect('users.db') cursor = connection.cursor() hashed_password = hash_password(password) cursor.execute('INSERT INTO users (username, salt, key) VALUES (?, ?, ?)', (username, hashed_password[:32], hashed_password[32:])) connection.commit() print(f"User {username} registered successfully!") except sqlite3.IntegrityError: print(f"User {username} already exists!") finally: connection.close() def login(username, password): connection = sqlite3.connect('users.db') cursor = connection.cursor() cursor.execute('SELECT * FROM users WHERE username = ?', (username,)) user = cursor.fetchone() connection.close() if user: if user[2] >= 3: print("Account locked. Too many failed login attempts.") else: salt = user[1] key = user[2] hashed_password = hashlib.pbkdf2_hmac('sha256', password.encode('utf-8'), salt, 100000) if hashed_password == key: print(f"Welcome back, {username}!") else: print("Invalid username or password.") connection = sqlite3.connect('users.db') cursor = connection.cursor() cursor.execute('UPDATE users SET attempts = attempts + 1 WHERE username = ?', (username,)) connection.commit() connection.close() else: print("Invalid username or password.") def change_password(username, old_password, new_password): if verify_password(username, old_password): connection = sqlite3.connect('users.db') cursor = connection.cursor() hashed_password = hash_password(new_password) cursor.execute('UPDATE users SET salt = ?, key = ? WHERE username = ?', (hashed_password[:32], hashed_password[32:], username)) connection.commit() connection.close() print(f"Password changed successfully for {username}!") else: print("Invalid username or password.") def reset_password(username, new_password): connection = sqlite3.connect('users.db') cursor = connection.cursor() hashed_password = hash_password(new_password) cursor.execute('UPDATE users SET salt = ?, key = ? WHERE username = ?', (hashed_password[:32], hashed_password[32:], username)) connection.commit() connection.close() print(f"Password reset successfully for {username}!") def main(): connection = sqlite3.connect('users.db') cursor = connection.cursor() # Create a table to store user credentials cursor.execute(''' CREATE TABLE IF NOT EXISTS users ( id INTEGER PRIMARY KEY AUTOINCREMENT, username TEXT UNIQUE NOT NULL, salt TEXT NOT NULL, key TEXT NOT NULL, attempts INTEGER DEFAULT 0 ); ''') connection.commit() connection.close() # Register a user register('alice', 'password123') # Login with the registered user login('alice', 'password123') # Try to register the same user again register('alice', 'password123') # Try to login with incorrect credentials login('alice', 'wrongpassword') # Change password change_password('alice', 'password123', 'newpassword456') # Login with the new password login('alice', 'newpassword456') # Reset password reset_password('alice', 'resetpassword789') # Login with the reset password login('alice', 'resetpassword789') if __name__ == "__main__": main() import sqlite3 import hashlib import os def hash_password(password): salt = os.urandom(32) key = hashlib.pbkdf2_hmac('sha256', password.encode('utf-8'), salt, 100000) return salt + key def verify_password(username, password): connection = sqlite3.connect('users.db') cursor = connection.cursor() cursor.execute('SELECT salt, key FROM users WHERE username = ?', (username,)) user = cursor.fetchone() connection.close() if user: salt = user[0] key = user[1] hashed_password = hashlib.pbkdf2_hmac('sha256', password.encode('utf-8'), salt, 100000) return hashed_password == key else: return False def register(username, password): try: connection = sqlite3.connect('users.db') cursor = connection.cursor() hashed_password = hash_password(password) cursor.execute('INSERT INTO users (username, salt, key) VALUES (?, ?, ?)', (username, hashed_password[:32], hashed_password[32:])) connection.commit() print(f"User {username} registered successfully!") except sqlite3.IntegrityError: print(f"User {username} already exists!") finally: connection.close() def login(username, password): connection = sqlite3.connect('users.db') cursor = connection.cursor() cursor.execute('SELECT * FROM users WHERE username = ?', (username,)) user = cursor.fetchone() connection.close() if user: if user[2] >= 3: print("Account locked. Too many failed login attempts.") else: salt = user[1] key = user[2] hashed_password = hashlib.pbkdf2_hmac('sha256', password.encode('utf-8'), salt, 100000) if hashed_password == key: print(f"Welcome back, {username}!") else: print("Invalid username or password.") connection = sqlite3.connect('users.db') cursor = connection.cursor() cursor.execute('UPDATE users SET attempts = attempts + 1 WHERE username = ?', (username,)) connection.commit() connection.close() else: print("Invalid username or password.") def change_password(username, old_password, new_password): if verify_password(username, old_password): connection = sqlite3.connect('users.db') cursor = connection.cursor() hashed_password = hash_password(new_password) cursor.execute('UPDATE users SET salt = ?, key = ? WHERE username = ?', (hashed_password[:32], hashed_password[32:], username)) connection.commit() connection.close() print(f"Password changed successfully for {username}!") else: print("Invalid username or password.") def reset_password(username, new_password): connection = sqlite3.connect('users.db') cursor = connection.cursor() hashed_password = hash_password(new_password) cursor.execute('UPDATE users SET salt = ?, key = ? WHERE username = ?', (hashed_password[:32], hashed_password[32:], username)) connection.commit() connection.close() print(f"Password reset successfully for {username}!") def main(): connection = sqlite3.connect('users.db') cursor = connection.cursor() # Create a table to store user credentials cursor.execute(''' CREATE TABLE IF NOT EXISTS users ( id INTEGER PRIMARY KEY AUTOINCREMENT, username TEXT UNIQUE NOT NULL, salt TEXT NOT NULL, key TEXT NOT NULL, attempts INTEGER DEFAULT 0 ); ''') connection.commit() connection.close() # Register a user register('alice', 'password123') # Login with the registered user login('alice', 'password123') # Try to register the same user again register('alice', 'password123') # Try to login with incorrect credentials login('alice', 'wrongpassword') # Change password change_password('alice', 'password123', 'newpassword456') # Login with the new password login('alice', 'newpassword456') # Reset password reset_password('alice', 'resetpassword789') # Login with the reset password login('alice', 'resetpassword789') if __name__ == "__main__": main() 2. Phishing Simulation Phishing simulation is a way to test and train people to recognize and stop phishing attacks. These attacks trick people into sharing sensitive information by pretending to be trustworthy sources. The project aims to help individuals and organizations understand these tactics and protect against them. The phishing simulation system creates fake phishing emails or messages to imitate real-world scenarios. It includes fake links, deceptive content,

Best 30 machine learning final year project This article provides 30 unique machine learning final-year projects, offering exceptional academic and professional growth opportunities. These projects provide valuable concepts essential for computer science students in their final year. The most significant benefit of machine learning is that it opens up new options and makes it possible to create incredible projects. 1. Fabric Defect Detection Introduction: Detecting fabric defects is crucial in the textile industry for high-quality production. It involves identifying imperfections like stains, holes, or misleading, which impact product quality and customer satisfaction. Machine learning enhances detection efficiency, automating the process for more accurate inspection. This fabric defect detection system uses a Convolutional Neural Network (CNN) algorithm. CNNs are deep learning algorithms specifically designed for image recognition tasks. The algorithm learns to identify standard and defective fabrics by studying labeled images with distinct visual characteristics of defects. Problem Statement: The textile industry needs help maintaining consistent product quality due to the limitations of the manual inspection process. Human Inspectors might miss minor defects, which could affect the quality of the product. Automating defect detection using machine learning provides a better and faster solution to identify defects in real time during manufacturing. Source Code 2. Plant Disease Detection Introduction: In agriculture, plant health is crucial for a good harvest. Diseases can harm plants and reduce food production. The system can rapidly and accurately identify plant diseases, allowing farmers to intervene promptly. Convolutional Neural Networks (CNNs) are algorithms that recognize image patterns, making them ideal for plant disease detection. Problem Statement: Traditional disease detection methods are often time-consuming and rely heavily on human expertise. The Plant Disease Detection system enables farmers to swiftly and precisely identify plant diseases. This system allows them to take targeted actions to prevent losses. Source Code 3. Credit Card Fraud Detection Introduction: Credit card fraud detection uses machine learning to protect financial transactions by identifying unusual activity. It works by analyzing patterns to spot any signs of fraudulent behavior. One key algorithm used in Credit Card Fraud Detection is the Random Forest algorithm. This intelligent system combines the predictions of multiple decision trees to enhance accuracy.  Problem Statement: Credit card fraud detection is crucial due to increasing online fraud. Traditional systems struggle to keep up, so advanced machine-learning algorithms are necessary.   Source Code 4. Phishing Website Detection Introduction: The “Phishing Website Detection” system is a vital tool that detects and stops deceptive online activities. Phishing websites deceive users into giving away sensitive information, a significant cybersecurity threat. This system uses machine learning to identify and reduce these websites’ risks effectively. Problem Statement: Phishing attacks are a significant problem in the digital age. Hackers create fake websites to trick people. This project aims to create a system that can quickly find and stop these fake websites, making the internet safer. Source Code 5. Heart Disease Detection Introduction: The “Heart Disease Detection” system uses machine learning to predict and identify the likelihood of heart diseases in individuals. Detecting cardiovascular diseases early is crucial for preventing deaths caused by them since they are a significant cause of death globally. The system assesses the risk of heart disease based on various health parameters. The algorithm used is a Support Vector Machine (SVM). SVM is good at classification tasks, like predicting if someone is at risk of heart disease. Problem Statement: To reduce heart disease, we must identify at-risk people. Traditional methods that rely on manual analysis may need to be more effective in efficiently managing the complexity of diverse health data.   Recommended Reading Hand Gesture Recognition Using Machine Learning 10 Advance Final Year Projects with source code Ecommerce Sales Prediction using Machine Learning Source Code 6. Breast Cancer Detection Introduction: The “Breast Cancer Detection” system uses machine learning to help diagnose breast cancer early. Breast cancer is a widespread and potentially fatal illness, and detecting it early greatly enhances treatment results. This system uses advanced machine learning to analyze medical data and identify meaningful patterns related to breast cancer. Problem Statement: The challenge in breast cancer diagnosis lies in accurately distinguishing between benign and malignant tumors, especially in the early stages when symptoms may not be apparent. Traditional diagnostic methods may have limitations, so this system solved these limitations.   Source Code 7. House Price Prediction Introduction: House Price Prediction is an intelligent application that uses machine learning to estimate the price of houses based on various factors. Whether you’re a home buyer, seller, or just curious about real estate, this system provides valuable insights into property values. We use data and algorithms to improve the accuracy and accessibility of predicting house prices. Problem Statement: The real estate market is complex, and determining the fair market value of a house involves considering numerous variables. Buyers and sellers often need help to accurately determine the value of a property, which can result in mispricing and financial dissatisfaction. House Price Estimation aims to tackle this problem using machine learning algorithms to offer accurate and data-driven predictions. Source Code 8. Big Mart Sales Prediction Machine Learning Project Introduction: This Project aims to revolutionize the retail industry by using predictive analytics to forecast sales. It’s like having a crystal ball that helps store owners prepare for how much of their products will be sold. This system allows for better inventory management and ensures customers find what they need when needed. Our project used a machine learning algorithm known as regression. In essence, regression helps us understand the relationship between various factors (like product visibility, store size, and promotional activities) and the sales of products. We train the algorithm using historical sales data, enabling it to predict future sales based on these learned patterns. Problem Statement: One of the significant challenges retailers face is the uncertainty surrounding product demand. This system often leads to overstocking or understocking, affecting profits and customer satisfaction. Source Code 9. Stock Prices Predictor using Time Series Introduction: Predicting stock prices is complex but essential in finance. Our project aims to forecast future stock prices by analyzing historical data using time series analysis and machine learning.

Fabric Defect Detection Fabric inspection is an essential step in every textile industry. Fabric detection is an important part of maintaining the fabric’s quality. The automatic fabric fault detection system is required to reduce the cost and waste of time. Quality inspection is a major aspect of the modern industrial manufacturing process. Due to a lack of consistency in quality inspection, defective fabric may be introduced to the market. This causes the industry’s name at stake, leading to heavy losses. With this concept moving forward, a new detection method, which has high detection accuracy and detection speed, is needed to replace the manual work currently used this problem can be resolved by using initial image processing techniques, and then a designed AI system can start working on finding the defect in the fabric. Our designed system is responsible for the quality of the fabric by capturing images from the rolling fabric with a camera. What is Fabric Defect Detection? Fabric defect detection is the technique of identifying and categorizing defects in textile materials like yarns, textiles, and garments. These defects can include missing threads, holes, stains, and gaps in the fabric or knitting. Detecting these defects is critical to keeping product quality and guaranteeing customer happiness. Traditional Methods vs. Deep Learning Traditionally, humans performed manual inspections to discover fabric defects. This method is difficult, not adaptable, and prone to errors. Deep learning systems, on the other hand, are capable of accurately identifying defects in fabric images through autonomous analysis. Objectives The major objectives of this project are:  Fabric inspection is done completely automatic, with no human involvement.  By using the latest trend Al (Machine Learning), the system self-learns by detecting the new defects on the fabric. The faulty fabric is automatically stamped by the robotic arm. Methodology Explanation Of Block Diagram As we can see from the block diagram, here we use the camera to capture the fabric, after capturing the image of the fabric the DIP(Digital Machine Learning) module does image processing through its algorithms & by this, the trained system performs Machine Learning(ML). After this the system decides whether the fabric is defective or non-defective if it’s defective then the robotic arm stamp at that particular area of the fabric, or if it is nondefective then the fabric passed through the roller and the process repeats Features The system is 24/7 capable for operation. The system works efficiently. The system is fully automatic, no human involvement.  AI based system with continuous self-learning. Challenges in Fabric Defect Detection Despite its benefits, fabric defect detection using deep learning poses some challenges:  Data Quality: Deep learning algorithms require substantial, high-quality labeled data to train efficiently. This can be difficult to get, especially in unusual or complex abnormalities.  Deep learning algorithms are complex and require specialized knowledge to create and deploy.  Interpretability: Deep learning algorithms might need help grasping how they make judgments.  Generalization: Deep learning algorithms may need help generalizing to new, previously unknown faults, particularly if they are underrepresented in training data. Recommended Reading Hand Gesture Recognition Using Machine Learning 10 Advance Final Year Projects with source code Ecommerce Sales Prediction using Machine Learning Fabric defect detection using deep learning To make a fabric defect detection system using deep learning, download a dataset from Kaggle. # Importing Libraries import os import cv2 import numpy as np import tensorflow as tf from sklearn.model_selection import train_test_split # Importing Libraries import os import cv2 import numpy as np import tensorflow as tf from sklearn.model_selection import train_test_split This block imports necessary libraries for file operations, image processing, numerical operations, deep learning, and dataset splitting. # Function to load and preprocess images def load_images(folder_path): images = [] for filename in os.listdir(folder_path): img = cv2.imread(os.path.join(folder_path, filename)) img = cv2.resize(img, (224, 224)) # Resize images if needed img = img / 255.0 # Normalize pixel values images.append(img) return np.array(images) # Function to load and preprocess images def load_images(folder_path): images = [] for filename in os.listdir(folder_path): img = cv2.imread(os.path.join(folder_path, filename)) img = cv2.resize(img, (224, 224)) # Resize images if needed img = img / 255.0 # Normalize pixel values images.append(img) return np.array(images) This block defines a function load_images that takes a folder path as input and returns a numpy array of preprocessed images. It reads each image using OpenCV (cv2), resizes it to 224×224 pixels, and normalizes pixel values to the range [0, 1].   # Load images from folders defect_images = load_images("Defect_images") non_defect_images = load_images("nondefect") # Load images from folders defect_images = load_images("Defect_images") non_defect_images = load_images("nondefect") This block loads images from two folders: “Defect_images” and “nondefect”. It uses the load_images function defined earlier to preprocess the images. labels = np.zeros((len(defect_images) + len(non_defect_images), 2)) labels[:len(defect_images), 0] = 1 labels[len(defect_images):, 1] = 1 labels = np.zeros((len(defect_images) + len(non_defect_images), 2)) labels[:len(defect_images), 0] = 1 labels[len(defect_images):, 1] = 1 This block creates labels for the images. It creates a numpy array of zeros with dimensions (len(defect_images) + len(non_defect_images), 2). It sets the first column to 1 for defect images and the second column to 1 for non-defect images. all_images = np.concatenate((defect_images, non_defect_images), axis=0) all_images = np.concatenate((defect_images, non_defect_images), axis=0) This block concatenates the defect and non-defect images into a single numpy array called all_images. # Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(all_images, labels, test_size=0.2, random_state=42) # Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(all_images, labels, test_size=0.2, random_state=42) This block splits the data into training and testing sets using the train_test_split function from sklearn.model_selection. It uses 80% of the data for training and 20% for testing.   # Define and train a simple CNN model model = tf.keras.Sequential([ tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Conv2D(64, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Conv2D(128, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Flatten(), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(2, activation='softmax') ]) # Define and train a simple CNN model model = tf.keras.Sequential([ tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Conv2D(64, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Conv2D(128, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Flatten(), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(2, activation='softmax') ]) This