Visually impaired people face numerous difficulties in their daily life, and technological interventions may assist them to meet these challenges. This paper proposes an artificial intelligence-based fully automatic assistive technology to recognize different objects, and auditory inputs are provided to the user in real time, which gives better understanding to the visually impaired person about their surroundings. A deep-learning model is trained with multiple images of objects that are highly relevant to the visually impaired person. Training images are augmented and manually annotated to bring more robustness to the trained model. In addition to computer vision-based techniques for object recognition, a distance-measuring sensor is integrated to make the device more comprehensive by recognizing obstacles while navigating from one place to another. The auditory information that is conveyed to the user after scene segmentation and obstacle identification is optimized to obtain more information in less time for faster processing of video frames. The average accuracy of this proposed method is 95.19% and 99.69% for object detection and recognition, respectively. The time complexity is low, allowing a user to perceive the surrounding scene in real time.

It is easy for a normal human being to perceive and recognize any banknote effortlessly but it is really much difficult for any visually impaired or blind person to perform the same task. As money has an important role in daily lives for any business transaction, real-time detection and recognition of banknotes become a necessity for a person especially who is blind or visually impaired. For that purpose, the YOLO-v3 CNN model based banknote detection and recognition system is proposed which is fast and accurate. Images of different denominations and in different conditions were are collected initially and then, these images are augmented with different geometric and image transformations on images, to make the system robust. These augmented images are then annotated manually, from which training sets and validation image sets are prepared. Later, the performance of the trained model has evaluated on a real-time scene as well as a test dataset. The test result shows that the proposed YOLO-v3 model based method has detection and recognition accuracy of 95.71% and 100%, respectively. The whole system is standalone and works in real-time.

The prediction analysis is the approach which predicts future possibilities from the previous data. The student performance prediction technique has the three phases which are pre-processing, feature extraction and classification. In the previous technique approach of SVM classifier is applied for the student performance prediction. The approach of naive bayes is applied in the proposed work for the student performance prediction analysis. The proposed and existing algorithm is implemented in python. The results of proposed model are compared with existing model in terms of accuracy and execution time.

An assistive device is proposed for visually impaired people to provide them automatic navigation and guidance. The device can impart navigation free of obstacles and perform real-time image processing to acknowledge the obstacle. The method is intended to perform all tasks in real-time to get all the information about obstacles in the path with optimised information flow. The proposed navigation stick is low cost obstacle detector, obstacle identifier and object detector. The whole setup for rehabilitation for visually impaired people consists of the heterogeneous set of sensors and computation components such as - ultrasonic sensors, camera, single-board DSP processor, a wet floor sensor, and a battery. The machine learning model is used for object recognition to make the user familiar with the environments. To provide the best navigation guidance, proposed device can be used to detect obstacles, up-stairs, down-stairs, edges, potholes, speed breaker, narrowing passages, pothole, wet floors, etc. The output is provided in the form of the audio prompt instead of the vibration, to make user comfortable and user-friendly.

Currently, a lot of methods have been implemented for prediction analysis. In the proposed study, clustering and classification of the input information for heart disease forecasting is executed with the help of k-means clustering algorithm and SVM (support vector machine) classification model on the basis of prediction analysis methods. The back propagation algorithm along with k-means clustering algorithm is applied for the clustering of information. These algorithms support to enhance the precision of prediction analysis. A heart disease data suite obtained from the UCI repository is used for judging the performance of proposed algorithm. This data suite comprises total 76 features. But, whole tests require a subset of 14 features. Particularly, Cleveland database particularly is utilized by the machine learning researchers throughout the tests. A comparison between proposed study and earlier method (using arithmetic mean) is performed in terms of certain parameters such as accuracy, error recognition rate and execution time.