Machine Learning-Based Approach for Parkinson's disease Detection B. Ramu, K. Naresh Babu, T.V. Chandra Sekhar, A. Naresh, P Chandra Sekhar Reddy 2nd Asian Conference on Intelligent Technologies Acoit 2025, 2025 Parkinson's disease (PD), a neurological condition that impairs motor skills, is brought on by the death of dopamine-secreting neurons in the brain. Timely detection and treatment of Parkinson's disease (PD) is essential for effective treatment and controlled disease. This study focuses on the detection of PD using machine learning (ML) algorithms by examining biomedical voice measurements alongside other available data. Several models including Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Neural Networks are tested for their accuracy on the classification task. It is shown that machine learning algorithms are efficacious in classifying patients suffering from PD and those not suffering from it, with SVM outperforming the rest. This work demonstrates the value of applying ML Physics based algorithms for the detection of PD, offering a simple and non-invasive method of diagnosing the disease.
Revolutionizing Facial Recognition: A Dolphin Glowworm Hybrid Approach for Masked and Unmasked Scenarios Naresh Babu KOSURI, Suneetha MANNE International Journal of Computational and Experimental Science and Engineering, 2024 Machine learning has several essential applications, including classification and recognition. Both people and objects may be identified using the Machine learning technique. It is particularly important in the verification process since it recognizes the characteristics of human eyes, fingerprints, and facial patterns. With the advanced technology developments, nowadays, Facial recognition is used as one of the authentication processes by utilizing machine learning and deep learning algorithms and it has been the subject of several academic studies. These algorithms performed well on faces without masks, but not well on faces with masks. since the masks obscured the preponderance of the facial features. As a result, an improved algorithm for facial identification with and without masks is required. After the Covid-19 breakout, deep learning algorithms were utilized in research to recognize faces wearing masks. Those algorithms, however, were trained on both mask- and mask-free faces. Hence, in this, the cropped region for the faces is only used for facial recognition. Here, the features were extracted using the texture features, and the best-optimized features from the glow worm optimization algorithm are used in this paper. With these features set, the hybrid Dolphin glow worm optimization is used for finding the optimal features and spread function value for the neural network. The regression neural network is trained with the optimized feature set and spread function for the face recognition task. The performance of the suggested method will be compared to that of known approaches such as CNN-GSO and CNN for face recognition with and without masks using accuracy, sensitivity, and specificity will next be examined.
An Automatic Student Attendance Monitoring System Using an Integrated HAAR Cascade with CNN for Face Recognition with Mask Kosuri Naresh Babu, Suneetha Manne Traitement Du Signal, 2023 In the olden day's many organizations including private and government finds it difficult to mark the attendance manually.A few decades back with the research on biometrics and image processing many smart applications like face recognizers and scanners came into existence but all these apps suffer from single face scanning problem but from the past 5 years many object detection algorithms help us to classify many objects or faces at a time based on multi facial points using boundary boxes to segment the regions.Many research works are carried out for the recognition of faces without masks.With the help of detection algorithms, the proposed algorithm tries to recognize the face of the students with or without masks to mark the attendance in this pandemic situation by designing HAAR integrated with LBP and CNN to find the multiple persons based on the facial points associated with the upper nose, eyes and other regions to extract the features.
RECENT SCHOLAR PUBLICATIONS
A Novel Approach for Accurate Identification in Masked and Unmasked Scenarios using Glowworm Swarm Optimization and Neural Networks KN Babu, S Manne Multimedia Tools and Applications 84 (22), 25385-25405 , 2025 2025 Citations: 1
An automatic student attendance monitoring system using an integrated HAAR cascade with CNN for face recognition with mask NB Kosuri, S Manne Traitement du Signal 40 (2), 743 , 2023 2023 Citations: 7
MOST CITED SCHOLAR PUBLICATIONS
An automatic student attendance monitoring system using an integrated HAAR cascade with CNN for face recognition with mask NB Kosuri, S Manne Traitement du Signal 40 (2), 743 , 2023 2023 Citations: 7
A Novel Approach for Accurate Identification in Masked and Unmasked Scenarios using Glowworm Swarm Optimization and Neural Networks KN Babu, S Manne Multimedia Tools and Applications 84 (22), 25385-25405 , 2025 2025 Citations: 1
