Stay in touch with our team we share best Detection Project Using Machine Learning ideas and topics. Get your research work done from hands of phdtoic.com team to shine in your academics. To a wide range of applications, we utilize the term “detection” in machine learning. Step by step guidance  along with brief explanation are shared. Here we give some detection- based project plans by utilizing the machine learning that span different fields:

1. Face Detection:

• Objective: In images, we detect human faces.
• Data Source: For face detection, we use the data source like Haar Cascades, LFW (Labeled Faces in the Wild) dataset.
• Tools: some of the tools we utilize for face detection are Open CV, Dlib and MTCNN.

2. Anomaly Detection:

• Objective: We detect uncommon patterns which do not conform to expected activities.
• Data Source: Credit card transactions and log files are the data source we use for anomaly detection.
• Tools: In our work, we use tools like Scikit-learn (One-Class SVM, Isolation Forest), Autoencoders.

3. Object Detection:

• Objective: Our work identifies and categorizes objects in images.
• Data Source: COCO dataset and Pascal VOC are the datasets assisted by us.
• Tools: By detecting objects, we use the tools like TensorFlow Object Detection API, YOLO, SSD and Faster R-CNN.

4. Change Detection in Satellite Imagery:

• Objective: At various times, we identify modifications in satellite images.
• Data Source: We implement the data sources like Google Earth engine and Sentinel Hub.
• Tools: Image differencing and CNN are the tools used by us.

5. Textual Entailment Detection:

• Objective: In our work, we define if one text indicates another.
• Data Source: For textual entailment identification we use the data sources such as SNLI (Stanford Natural Language Inference) datasets.
• Tools: BERT and LSTM are the tools we implemented.

6. Spam Detection:

• Objective: Our work categorizes the emails or messages as spam or not spam.
• Data Source: SMS spam collection dataset and Email datasets are the data sources we utilize for spam detection.
• Tools: Naive Bayes and LSTM are the tools utilized by us.

7. Intrusion Detection:

• Objective: In computer networks, our work identifies malicious activities.
• Data Source: We use the data sources for intrusion detection as the KDD Cup 1999 dataset and CICIDS 2017.
• Tools: Decision Trees, Random Forests and Deep Learning are the tools we used in our work.

8. Fall Detection for Elderly:

• Objective: By utilizing wearable sensors, we identify falls in the elderly.
• Data Source: Some of the data sources we use for fall detection for elderly are data from accelerometers or gyroscopes.
• Tools: Our work uses various tools like threshold-based methods, LSTM, SVM.

9. Fake News Detection:

• Objective: In our work, we categorize the news article as true or fake.
• Data Source: From the online accessible datasets, we use true and fake datasets.
• Tools: TF-IDF, BERT and LSTM are the tools we used in our work.

10. Defect Detection in Manufacturing:

• Objective: By utilizing images, we identify defects in products during the developing process.
• Data Source: Our work uses images from developing lines.
• Tools: We implement some of the tools like CNN and Autoencoders.

11. Pedestrian Detection for Autonomous Driving:

• Objective: In video streams from car cameras, we identify pedestrians.
• Data Source: In our work, we use data sources such as pedestrian datasets like Caltech, INRIA.
• Tools: HOG+SVM, YOLO and SSD are the tools we implement in our work.

12. Voice Activity Detection:

• Objective: For a given audio clip, we identify speech activity.
• Data Source: Some of the data sources we use for voice activity detection are audio datasets like Google speech commands.
• Tools: We utilize tools like FFT, Spectrograms and RNN.

For each project, we have some common steps that involve:

• Data Collection & Preprocessing: In data collection & preprocessing, we first clean the data and transform it into a relevant format.
• Feature Engineering: For a machine learning model, we extract the meaningful features.
• Model Selection & Training: Our work selects relevant methods and trains it on data.
• Evaluation: By using appropriate metrics our work estimates the framework’s achievements.
• Deployment: For real-time or batch processing use-cases we deploy the framework.

Recall initiating with a clear issue statement and concentrating on interpreting the data and the field before we split complicated frameworks.

Detection Project Using Machine Learning Thesis Ideas

Some of the best Detection Project Using Machine Learning thesis ideas that we have recently worked are shared below.

1. Detection of DDOS Attack using Machine Learning Models

Keywords

Cyber Attack, Attack Detection Mechanisms, DDOS Attacks, Machine Learning, Random Forest Algorithm, XGBoost Algorithm

A distributed denial-of-service (DDoS) attack detection framework is recommended in our paper by employing ML techniques. We trained and evaluated this assault detection framework by utilizing XGBoost Classifier and Random Forest method. As a consequence, from the analysis of research findings, XGBoost technique offers better end results.

2. Cardiac Anomaly Detection Using Machine Learning

Keywords

Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Supervised learning, Accuracy, Precision, Unsupervised learning, Recall, F1 Score, Semi-supervised learning

A cardiac anomaly identification model is recommended in our paper by employing several ML methods including Support Vector Machine, K-Nearest Neighbour, Logistic regression, Random Forest, and Artificial Neural Network. We considered attributes such as Age, Blood Pressure rate, Cholesterol, Glucose, Smoke, and alcohol for forecasting the outcomes. As a result, KNN method achieved greater efficiency than other existing methods.

3. Mental Health Disorder Detection Using Machine Learning and Deep Learning Techniques

Keywords

Logistic Regression, Decision Tree, Boosting Algorithm

A major aim of our article is to solve the various mental health problems by utilizing ML and DL approaches. We employed various ML methods such as Logistic Regression, K-Neighbors Classifier, Decision Tree Classifier and bagging to deal with the problem of mental health. To detect whether the person is depressed or not, we utilized deep learning based CNN method.

4. Threat Detection and Classification Using ML Techniques

Keywords

Threats, Cyber security, Classification, Artificial Intelligence (AI)

An intrusion that might be in the form of spams, ads, fraudulent sites, etc are dangerous to the users is identified in our approach. We utilized several ML approaches including random forest and SVM and these are evaluated to find out which method offers better results with greater precision and lower time consumption. Results show that, our suggested model provides greater efficiency in prediction process in a real time environment.

5. Early Detection of lung Cancer Using Machine Learning Technique

Keyword

Framework

We recommended a neural network framework and AI in our paper for the detection of cancer cells in CT images. Our framework is also utilized to identify the problems in therapeutic imaging applications. We categorize the stage of lung cancer by employing CNN method. We performed image collection, pre preparation, pixel improvement, image segmentation and extraction of features in our suggested framework.

6. Intrusion Detection and Prevention System to Analyse and Prevent Malware using Machine Learning

Keywords

Intrusion Detection and Prevention System, Network based Intrusion Detection System, Principal Component Analysis, Docker jail system

A main concentration of our project is to develop and execute an intrusion detection and prevention system by utilizing NIDS and Docker Jail system. We performed dimension minimization process by employing PCA. Some supervised techniques such as SVM and KNN methods are used as categorization methods. The Docker jail system is associated with Artillery for the purpose of assault prevention.

7. Spam Email Detection Using Machine Learning Integrated In Cloud

Keywords

Emails, Spam, Naive Bayes

We designed an integrated approach through the use of ML techniques for the detection of spam on email. A suggested integrated method includes Bagging and boosting of ML oriented multinomial DT, NB, KNN, RF, and the SVM techniques. To enhance the categorization efficiency, bagging approach utilized the integration of weak classifiers. Here, we performed various steps like data preprocessing. Feature extraction, selection and data categorization.

8. Phishing Detection System through Hybrid Machine Learning Based on URL

Keywords

Voting classifier, ensemble classifier, uniform resource locator (URL), protocol, cyber security, social networks

A phishing identification model based on URL is suggested in our research. After we preprocessed the data, we obstruct phishing URL and offers security to the users by the development of ML model. Methods like DT, LR, RF, NB, GBM, KNN, SVC and an integrated LSD model are utilized to prevent phishing assaults. We employed the canopy feature selection method with cross fold validation and Grid Search Hyperparameter Optimization approach.

9. Pneumonia Detection and Classification using Hybrid Convolution Neural Network and Machine Learning Classifiers

Keywords

Hybrid Convolutional Neural Network, Chest X-rays

A combined CNN method is utilized with ML techniques for the identification of Pneumonia disease. We utilized CNN in the construction of DL oriented CAD model and we performed image segmentation and classification process. We conclude that, our combined approach including Radial Basis Function (RBF), classifiers utilizing support vector machines (SVM) and logistic regression (LR) provides better outcomes.

10. Automatic anomaly detection in engineering diagrams using machine learning

Keywords

Engineering Diagram, Objective Detection, Graph Pattern Mining, Piping and Instrumentation Diagram

An autonomous anomaly identification system is proposed in our work. After analyzing, the patterns are retrieved from the graphs. Our work includes three phases. Initially, we created graphs by using symbol and line recognition, after that, we extracted the subgraphs by employing frequent subgraph mining method and we categorized the graph by considering the frequency of the retrieved subgraphs. It is trained using a SVM method if the frequency is high.