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Your search keyword '"Hassan, Syed Imtiyaz"' showing total 2 results
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2 results on '"Hassan, Syed Imtiyaz"'

1. Early spotting of Parkinson's illness using machine learning techniques.

Author

Kaifi, Khushter, Rahman, Nafisur, Nafis, Md. Tabrez, and Hassan, Syed Imtiyaz

Subjects
*PARKINSON'S disease, *SUPPORT vector machines, *K-nearest neighbor classification, *SPEECH disorders, *RANDOM forest algorithms
Abstract

Early Parkinson's disease (PD) diagnosis is essential for preventing the illness's development and providing patients with the opportunity to take disease-specific therapy. Speech signal processing is widely used and has gained attention recently. In this project, we carry out a comparative analysis for effective dysphonia (a speech disorder) and Parkinson's disease spotting using Machine Learning methods. To distinguish between PD patients and normal individuals. We used Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LREG), and K-Nearest Neighbor (KNN) algorithms to demonstrate a robust detection process. According to experimental findings, the KNN outperformed other ML algorithms. The results of the trial conducted at UCI contain data from 31 patients, 23 of whom were diagnosed with PD. After implementing these algorithms, we found that our accuracy rates for these algorithms are NB (86.95%), RF (85.77%), SVM (79.18%), LREG (91.56%), DT (87.16%), and KNN (97.67%). [ABSTRACT FROM AUTHOR]

Published
2024
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2. A Novel Memory and Time-Efficient ALPR System Based on YOLOv5.

Author

Batra, Piyush, Hussain, Imran, Ahad, Mohd Abdul, Casalino, Gabriella, Alam, Mohammad Afshar, Khalique, Aqeel, and Hassan, Syed Imtiyaz

Subjects
*OPTICAL character recognition, *DEEP learning, *MEMORY, *COMPUTER performance, *PARK management, *PARKING lots
Abstract

With the rapid development of deep learning techniques, new innovative license plate recognition systems have gained considerable attention from researchers all over the world. These systems have numerous applications, such as law enforcement, parking lot management, toll terminals, traffic regulation, etc. At present, most of these systems rely heavily on high-end computing resources. This paper proposes a novel memory and time-efficient automatic license plate recognition (ALPR) system developed using YOLOv5. This approach is ideal for IoT devices that usually have less memory and processing power. Our approach incorporates two stages, i.e., using a custom transfer learned model for license plate detection and an LSTM-based OCR engine for recognition. The dataset that we used for this research was our dataset consisting of images from the Google open images dataset and the Indian License plate dataset. Along with training YOLOv5 models, we also trained YOLOv4 models on the same dataset to illustrate the size and performance-wise comparison. Our proposed ALPR system results in a 14 megabytes model with a mean average precision of 87.2% and 4.8 ms testing time on still images using Nvidia T4 GPU. The complete system with detection and recognition on the other hand takes about 85 milliseconds. [ABSTRACT FROM AUTHOR]

Published
2022
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