Top 20 Artificial Intelligence (AI) Projects Ideas For Beginners

Artificial intelligence projects overview:

Artificial intelligence (AI) is a rapidly growing field that has the potential to transform a wide range of industries. As a result, it has become an increasingly popular area of study for engineering students and job aspirants. This blog will explore some of the most exciting and cutting-edge AI projects ideas for beginners that are currently being developed, and discuss the skills and knowledge needed to succeed in this field. From natural language processing and computer vision to deep learning and machine learning, we’ll delve into the latest technologies and techniques being used to create intelligent systems. 

Whether you’re an engineering student looking to gain an edge in the job market, or a professional looking to upskill, this blog will provide valuable insights and inspiration for your next AI graduation projects.

What skills did you gain out of developing these aI projects:?

By project ideas to start with ai for beginners, engineering students and job aspirants will gain a variety of skills that are highly valued in the job market. These skills include:

• Understanding of the basics of Artificial Intelligence, including machine learning, natural language processing, and computer vision

• Experience with popular AI programming languages such as Python, R, and C++

• Knowledge of popular AI libraries and frameworks such as TensorFlow, Keras, and PyTorch

• Understanding of the process of data preprocessing, model training, and validation

• Familiarity with various AI-based techniques such as supervised and unsupervised learning, deep learning, and reinforcement learning

• Experience with various types of AI applications such as image recognition, natural language understanding, and predictive modeling

• Knowledge of big data technologies and cloud computing platforms that are often used in AI projects.

• Hands-on experience in creating and deploying AI models in real-world scenarios which will help them to crack the interview in the job market.

Top ai projects list for beginners [updated – 2023]

1. Medical prescription recognition using a smart ML model to recommend a low-cost medicine.

“Revolutionizing Healthcare: AI-Powered Medical Prescription Recognition System for Affordable Medicine” – This project aims to utilize machine learning techniques to develop a smart model that can recognize medical prescriptions and recommend low-cost alternatives. By implementing this system, patients will have access to affordable medicine options while also reducing the burden on healthcare systems. The project will provide valuable hands-on experience in utilizing AI and machine learning to solve real-world problems, making it an ideal opportunity for engineering students and job aspirants looking to gain relevant skills in the field.

2. CNN-Based Object Recognition and Tracking System to Assist Visually Impaired People

The proposed system aims to assist visually impaired people in their daily activities by utilizing a CNN-based object recognition and tracking system. The system utilizes a camera, connected to a Raspberry Pi, to capture images and a pre-trained CNN model to recognize and identify objects in the scene. The Raspberry Pi then sends the object information to a speaker, allowing the visually impaired person to hear a description of the object. Additionally, the system includes object tracking capabilities, allowing the user to track and locate the object within their environment. The system is designed to be portable and can be used in various settings, such as public spaces or at home. This project aims to enhance the independence and quality of life for visually impaired individuals by providing real-time object recognition and tracking assistance.

3. Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model.

This project aims to develop a deep learning-based system for the automatic detection of citrus fruit and leaves diseases. A convolutional neural network (CNN) model will be trained on a dataset of images of citrus fruits and leaves with various disease symptoms. The model will be able to accurately classify the type of disease present in an image of a citrus fruit or leaf. The system will be implemented on a low-cost hardware platform such as Raspberry Pi, making it accessible to farmers and agricultural researchers. This project will provide a practical solution for the early detection and prevention of citrus diseases, potentially increasing crop yields and reducing the use of harmful pesticides.

4. CNN-Based Object Recognition and Tracking System to Assist Visually Impaired People

A smart and intelligent system is designed to assist visually impaired persons (VIPs) with mobility and safety using an automated voice for real-time navigation and a web-based application for location sharing and tracking. A deep Convolution Neural Network (CNN) model is used for object detection and recognition with an accuracy of 83.3%. Six pilot studies were conducted with satisfactory results. The proposed system fills a gap in existing literature and uses MobileNet architecture for low computational complexity on low-power devices.

5. CowXNet: An Automated Cow Estrus Detection System

CowXNet is an automatic estrus detection system for dairy farms that uses a camera and computer to analyze recorded videos of cows. It uses YOLOv4 for cow detection, a convolutional neural network for body part detection, and a classification algorithm to detect estrus behaviors. The system is designed to assist farmers in identifying estrus cows, reducing the need for electronic devices and continuous observation. Results from testing at Chokchai Farm, the largest dairy farm in Asia, showed an 83% success rate in correctly detecting estrus behavior intervals.

6. Recognition Of Objects In The Urban Environment using R-CNN and YOLO Deep Learning Algorithms.

This research focuses on the application and evaluation of two pre-trained deep learning algorithms, RCNN and YOLO, to recognize street objects in the urban environment. Using the GRAZ-02 dataset, which consists of 1476 raw images of cars, bikes and pedestrians, both algorithms were able to achieve an accuracy of more than 90% in object recognition. The fine-tuning and training of the algorithms was accomplished using the ImageNet and COCO databases, and the trained models were then applied to the test data. This study demonstrates the potential of deep learning algorithms to reliably detect objects in the urban environment.

7. TensorFlow-based Automatic Personality Recognition Used in Asynchronous Video Interviews

The use of AI in video interview analysis has enabled the development of an end-to-end system that utilizes asynchronous video interviews (AVIs) and a TensorFlow-based automatic personality recognition (APR) engine to identify individual personality traits. The APR model is based on convolutional neural networks (CNNs) that are capable of accurately detecting human nonverbal cues and assigning corresponding personality scores to them. This system has applications in personality computing, human-computer interaction, and psychological assessment, making it a valuable tool for identifying individual personality traits in video interviews.

8. What to play next? A Rnn-Based Music Recommendation System

Are you stuck on what music to listen to next? Our RNN-based music recommendation system is here to help! In recent years, the development of music recommendation systems has been pushed even further as digital music consumption increases and machine learning techniques become more advanced. Traditional approaches, such as collaborative filtering, have been very successful in helping music listeners find new music they may enjoy. However, they lack the ability to fully understand the content of a song and recommend based on a combination of lyrics and genre. This is where our improved algorithm based on deep neural networks comes in. With our end-to-end model, we can measure the similarity between different songs and make recommendations on a large scale while truly “understanding” the content of the songs.

9. Cnn Based Pneumonia Detection

Pneumonia is a serious lung infection that affects millions of people worldwide, and early detection is crucial for effective treatment. In this project, a Convolutional Neural Network (CNN) based model is proposed to detect pneumonia from chest X-rays. The model is trained on a large dataset of X-ray images, and the results show that it can accurately detect pneumonia with a high degree of accuracy. One of the main advantages of using a CNN based model for pneumonia detection is that it can automatically learn features from the images, eliminating the need for manual feature extraction. Additionally, the model can be integrated into existing healthcare systems for real-time detection and monitoring of patients. The use of CNN based model also reduces the human error, thus providing more accurate results. Overall, this project demonstrates the potential for using AI and CNNs in medical imaging for early detection and diagnosis of pneumonia, which can ultimately lead to better patient outcomes.

10. Computer-Aided Segmentation of Liver Lesions in CT Scans Using Cascade Convolutional Neural Networks and Genetically Optimized Classifier.

In this project, a computer-aided segmentation system for liver lesions in CT scans is proposed using cascade convolutional neural networks and a genetically optimized classifier. The system utilizes a cascaded architecture of CNNs for initial segmentation of liver and liver lesions, followed by a genetically optimized classifier for precise segmentation of lesions. The proposed method is evaluated on a dataset of CT scans with liver lesions and compared to traditional methods, showing improved performance in terms of accuracy and computation time. The advantages of this system include its ability to accurately and efficiently segment liver lesions in CT scans, which can aid in diagnosis and treatment planning for liver diseases.

11. Computer Vision for Attendance and Emotion Analysis in School Settings.

This paper presents facial detection and emotion analysis software developed with the goal of reducing the time teachers spend taking attendance while also collecting data that improve teaching practices. The inclusion of emotion recognition was motivated by the need to better monitor students’ emotional states over time, especially in light of current trends regarding school shootings. This project was designed to save teachers time, help teachers address students mental health needs, and motivate students and teachers to learn more computer science, computer vision, and machine learning as they use and modify the code in their own classrooms. Initial test results have revealed that increasing training images increases accuracy. With this project, teachers now have the opportunity to be proactive in monitoring student emotional states, providing them with the early warning notifications that can be critical in addressing mental health needs and preventing potential disasters.

12.Age and Gender Prediction using Deep Convolutional Neural Networks 

The ability to predict age and gender of an individual from a photo or video can have various applications, such as targeted marketing, security and surveillance, and human-computer interaction. In this project, a deep convolutional neural network (CNN) is trained to predict the age and gender of an individual from their image. The dataset used for training and testing the model consists of images of faces along with their corresponding age and gender labels. The CNN architecture is optimized using genetic algorithms for maximum accuracy. The final model is tested on a separate dataset for validation, and the results show a high level of accuracy in predicting age and gender. This project not only demonstrates the capabilities of CNNs in image analysis but also the potential of genetic algorithms in optimizing the performance of deep learning models.

13.Efficient Masked Face Recognition Method during the COVID-19 Pandemic

This project aims to address the challenges of face recognition in the current COVID-19 pandemic where masks have become a mandatory accessory for people in public spaces. The proposed method uses a deep convolutional neural network (CNN) to extract features from the unmasked portion of a person’s face and a genetically optimized classifier to match the features with a pre-existing database. The system is trained on a large dataset of masked and unmasked faces to improve its performance and accuracy. The proposed method offers a robust solution to the problem of face recognition during the pandemic and can be implemented in various applications such as security systems, attendance tracking, and access control. The advantages of this method include high accuracy, fast processing, and the ability to adapt to different mask types and occlusion levels.

14.Parkinson Disease Detection Using Deep Neural Networks. 

The proposed project aims to develop a deep neural network-based system for the early detection of Parkinson’s disease. The system utilizes various data inputs such as speech, gait, and hand tremors to train the model for accurate detection of the disease. The model will be trained on a large dataset of patients with Parkinson’s disease and healthy individuals. The system will be evaluated on a test dataset to assess its performance in detecting the disease in an early stage. The proposed method will be compared with traditional machine learning-based approaches to demonstrate its superiority in terms of accuracy and efficiency. The proposed system will be implemented on a low-cost device such as a Raspberry Pi to make it accessible to a wide range of patients. This project will provide a cost-effective and efficient solution for early detection of Parkinson’s disease which will aid in early treatment and improve the quality of life of patients.

15. AI Vision-Based Social Distancing Detection.

The rampant coronavirus disease 2019 (COVID-19) has sparked a global crisis, with its deadly spread to more than 180 countries, and about 3,519,901 confirmed cases along with 247,630 deaths globally as of May 4, 2020. With no active therapeutic agents available and the lack of immunity against COVID-19, the population is especially vulnerable. As there are still no vaccines in development, social distancing is the only feasible approach to combat this pandemic. In light of this, this article proposes a deep learning-based framework to automate the task of monitoring social distancing using surveillance video. The framework utilizes the YOLO object detection model to segregate humans from the background and to track the identified people with the help of bounding boxes. A violation index term is proposed to quantify a lack of social distancing protocol. Through experimental analysis, it is observed that the YOLO with the Deepsort tracking scheme delivered excellent results.

16.Plant Leaf Diseases Detection and Classification Using Image Processing and Deep Learning Techniques. 

This project aims to develop a system for detecting and classifying plant leaf diseases using a combination of image processing and deep learning techniques. The system will utilize a convolutional neural network (CNN) to analyze images of plant leaves and identify any signs of disease. The CNN will be trained on a dataset of images of diseased and healthy leaves, and will be able to accurately classify new images as healthy or diseased. The system will also use image processing techniques to enhance the images and extract features for improved classification performance. This project will have the potential to greatly assist farmers and agricultural professionals in early detection and management of plant diseases, resulting in improved crop yields and reduced costs. Additionally, the system can be integrated with IoT devices for remote monitoring and real-time alerts.

17..Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics

Diabetic retinopathy (DR) is a common complication of diabetes that can lead to blindness if left untreated. Early detection and treatment of DR is essential for preventing vision loss. In recent years, there has been an increasing interest in using image processing and deep learning techniques for automatic detection of DR. This review aims to provide an overview of the datasets, methods, and evaluation metrics used in the literature for DR detection. The datasets used in the literature include the EyePACS, DRISHTI-GRID, and MESSIDOR datasets. The methods used include traditional image processing techniques, such as feature extraction and classification, as well as deep learning techniques, such as convolutional neural networks (CNNs) and transfer learning. The evaluation metrics used include accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). This review highlights the challenges and limitations of current methods and the need for further research in this area.

18.Fire and Gun Violence based Anomaly Detection System Using Deep Neural Networks 

In summary, this research work presents a deep learning model based on the YOLOv3 algorithm for real-time detection of fire and handguns in surveillance footage. The model has been benchmarked on datasets such as IMFDB, UGR, and FireNet, achieving high accuracy rates of 89.3%, 82.6% and 86.5% respectively. The model is able to process video frames at a high rate of 45 frames per second, making it suitable for deployment in both indoor and outdoor settings. The proposed model can be used to improve surveillance methods and quickly alert authorities in case of emergencies, helping to prevent loss of life and property.

19. Human Activity Recognition Based on Graman Angular Field and Deep Convolutional Neural Network

This research work focuses on using the Internet of Things (IoT) and wearable devices for sensor-based human activity recognition (HAR). It aims to improve the accuracy and convenience of the traditional methods by using deep learning algorithms, specifically convolutional neural networks (CNNs). The paper proposes two improved methods, the Mdk-ResNet and Fusion-Mdk-ResNet, which use the multi-dilated kernel residual module to extract features among sampling points with different intervals and process and fuse data collected by different sensors. The methods have been tested on three public activity datasets (WISDM, UCI HAR, and OPPORTUNITY) and have shown optimal results in terms of accuracy, precision, recall, and F-measure, proving the effectiveness of the proposed methods.

20. Forgery image detection using neural network

The proposed system uses a CNN model trained on a dataset of real and tampered images to detect forgeries. The model is able to learn the features of an image and predict whether it is real or fake. The results show that the proposed system is able to accurately detect tampered images with a high degree of accuracy. This system can be used to enhance the security of image-based systems by providing a fast, user-friendly, and non-intrusive method of detecting forgeries. Overall, this paper demonstrates the effectiveness of using CNNs for image forensic tasks, making it a promising approach for detecting tampered images in various applications.

21.Detection of skin cancer using deep learning and image processing technique 

This investigation presents an audit of the current research on characterizing skin sores with Convolutional Neural Networks (CNNs). The review focuses on approaches which employ only a CNN for the classification of dermoscopic images. It is noted that, to date, there is no review of the existing work in this area. Additionally, the study discusses why the evaluation of the proposed methods is highly challenging and which issues need to be addressed in the future. A search of the Google Scholar, PubMed, Medline, Science Direct, and Web of Science databases was conducted to identify systematic reviews and original research articles published in English. Only papers that reported appropriate scientific methods were included. The findings demonstrate that CNNs could be a powerful tool for skin lesion classification and provide the potential for lifesaving and rapid decisions, even outside the clinician’s office, with the development of applications for mobile phones.

22.A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training 

This paper introduces a novel deep-learning architecture for sign language recognition. The technique combines convolutional neural networks (CNNs) and a recurrent neural network model called BiLSTM (Bidirectional Long Short-Term Memory) to improve accuracy. Previous methods relied on Hidden Markov Models (HMM) which were not always reliable. The proposed CNN+BiLSTM model can be used for feature extraction and training of deep learning models, followed by the sequence learning model for sign language recognition. This model learns iteratively with predicted, or recognised, sequences from the BiLSTM model.

In conclusion, investing in our project training platform is a valuable decision for engineering students and job aspirants who are looking to gain hands-on experience in the latest technologies and upskill themselves in the field of Artificial Intelligence. Our platform offers a wide range of projects that cover various domains such as healthcare, transportation, retail, and more, providing students with a comprehensive understanding of the potential and applications of AI in the industry.

By working on these projects, students will develop a range of skills including deep learning, computer vision, natural language processing, and more. These skills are in high demand in the job market and will give students a competitive edge in the job market.

Enrolling in our platform will not only help students to build a strong portfolio, but it will also give them the opportunity to work on cutting-edge technologies and contribute to the development of innovative solutions that can make a difference in the world. So, don’t wait, enroll now and take the first step toward a successful career in Artificial Intelligence (Ai) projects for final year students.

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