Papers by lakshmi haritha
Machine Learning Techniques for Predicting Pregnancy Complications
Advances in computational intelligence and robotics book series, Sep 24, 2023
Addressing Challenges in Data Analytics
Advances in computer and electrical engineering book series, May 17, 2024
Predictive Analytics for High-Tech Agriculture
Advances in environmental engineering and green technologies book series, Jun 30, 2023
Crop Prediction using Machine Learning Techniques and IoT
2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN)
An Integrated Optimal Resource Management Scheduling for Dual Target in Remote MIMO Systems
2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)
2023 2nd International Conference on Edge Computing and Applications (ICECAA)
An aspect of computer vision called object recognition searches for significant items in images a... more An aspect of computer vision called object recognition searches for significant items in images and movies (by creating a bounding box). According to WHO, there are atleast 2.2 billion visually impaired persons in the globe. Their mobility is limited in strange environments due to their vision impairment. This research study intends to convert the image to text and then text to voice. Here, an intelligent system is proposed to recognize the common objects and generates verbal feedback to inform the user about the object's position. The best YOLO (You Only Look Once) version is selected after being compared. Following the process of object detection, the proposed model determines its spatial location before generating the speech output.
Deep Learning and Edge Computing Solutions for High Performance Computing, 2021
International Journal of Engineering and Advanced Technology, 2019
The advent of internet has lead to colossal development of e-learning frameworks. The efficiency ... more The advent of internet has lead to colossal development of e-learning frameworks. The efficiency of such systems however relies on the effectiveness and fast content based retrieval approaches. This paper presents a methodology for efficient search and retrieval of lecture videos based on Machine Learning (ML) text classification algorithm. The text transcript is generated exclusively from the audio content extracted from the video lectures. This content is utilized for the summary and keyword extraction which is used for training the ML text classification model. An optimized search is achieved based on the trained ML model. The performance of the system is compared by training the system using Naive Bayes, Support Vector Machine and Logistic Regression algorithms. Performance evaluation was done by precision, recall, F-score and accuracy of the search for each of the classifiers. It is observed that the system trained on Naive Bayes classification algorithm achieved better perform...
International Journal of Advanced Computer Science and Applications, 2021
Now-a-days, the video recording technologies have turned out to be more and more forceful and eas... more Now-a-days, the video recording technologies have turned out to be more and more forceful and easier to utilize. Therefore, numerous universities are recording and publishing their lectures online in order to make them reachable for learners or students. These lecture videos encapsulate the handwritten text written either on a paper or blackboard or on a tablet using a stylus. On the other hand, this mechanism of recording the lecture videos consumes huge quantity of multimedia data in a faster manner. Thus, handwritten text recognition on the lecture video portals has turned out to be an incredibly significant and demanding task. Thus, this paper intends to develop a novel handwritten text detection and recognition approach on the video lecture dataset by following four major phases, viz. (a) Text Localization, (b) Segmentation (c) Pre-processing and (d) Recognition. The text localization in the lecture video frames is the initial phase and here the arbitrarily oriented text on video frames is localized using the Modified Region Growing (MRG) algorithm. Then, the localized words are subjected to segmentation via the K-means clustering, in which the words from the detected text regions are segmented out. Subsequently, the segmented words are pre-processed to avoid the blurriness artifacts as well. Finally, the pre-processed words are recognized using the Deep Convolutional Neural Network (DCNN). The performance of the proposed model is analyzed in terms of the performance measures like accuracy, precision, sensitivity and specificity to exhibit the supremacy of the text detection and recognition in lecture video. Experimental results reveal that at Learning Percentage of 70, the presented work has the highest accuracy of 89.3% for 500 count of frames.
Survey on Semantic Indexing of High dimensional Data with Deep Learning Techniques
i-manager’s Journal on Software Engineering
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Papers by lakshmi haritha