http://nopr.niscpr.res.in/handle/123456789/65125 2026-10-09T19:44:31Z http://nopr.niscpr.res.in/handle/123456789/65143 Title: Effects of Cutting Parameters on Delamination in Machining for S 2 Glass Fiber Reinforced Polymer Composites Authors: Koyunbakan, Murat; Kaya, Zafer Abstract: Fiber-reinforced polymer composite materials are new engineering materials that are preferred in engineering applications due to their superior properties. Today, S 2 glass fibers are used as reinforcement elements in composite applications requiring high strength. Polymer composite materials are usually produced close to their final shape. In order to perform mechanical joining operations on these materials, additional machining operations are required. Drilling and milling operations are the most preferred machining processes for polymer composites. The holes and grooves opened for the bolts and rivets used in the joining processes are required to be of high quality. In this study, the machinability properties of S 2 glass fiber-reinforced polymer composite materials with an average thickness of 1.8 mm are investigated by drilling and grooving operations. Machinability experiments are carried out in a dry environment using different cutting parameters in a CNC milling machine (Drilling-VMC850B branded CNC, Grooving-Skilled 2040 CNC). The deformation on the surfaces has been visualized and examined using an optical microscope. As a result of machining operations, it has been determined that the drill bit angle is the most important parameter for the drilling process, and there is less deformation in the channels opened in the 45° direction for the grooving process. The roughnesses formed on the hole and groove surfaces were measured and the most effective parameters were found. The effective parameters for drilling tests were the tip angle; for grooving tests, the speed and the number of revolutions. Page(s): 5-16 2024-12-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/65141 Title: Design of a New Washing Machine to Clean the Needle Bed of an Electronic Flat Knitting Machine Authors: Celik, Nevin; Yolcular, Irfan; Kasap, Songul; Golen, Mehmet B; Taskiran, Ali Abstract: An electronic flat knitting machine is traditionally used for knitting pullovers and other outerwear garments. When a typical electronic flat knitting machine runs for a certain time, the needle bed is filled with dust and lubrication. In order to solve this problem, a new needle bed cleaning machine is designed and manufactured by R&D department of “NIT ORME” Co. located in Turkey. In this article, the introduction of the washing machine, of which the prototype is produced and patented, and some analyzes, such as; structural, fluid flow and vibration, performed during the design of the machine is presented. SoloidWorks, ANSYS-Structural and ANSYS-CFX are the commercial softwares used for the analyses. As a result of the prototype production, the needle bed of knitting machines is automatically washed and dried faster than similar products in a practical, easier and functional way. Additionally, the cleaning costs are reduced by 70% with the washing machine. Page(s): 17-23 2024-12-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/65139 Title: An Ensemble Stacked Bi-LSTM with ResNet50 Method for Glaucoma Classification in IoT Framework Authors: Pattanaik, Sudeshna; Behera, Subhasikta; Majhi, Santosh Kumar; Pradhan, Rosy; Dwibedy, Pratyusa Abstract: Rural areas in India face significant healthcare challenges, particularly in managing diabetic complications such as glaucoma due to the lack of timely medical facilities. This study proposes an IoT-based healthcare framework designed to connect rural populations with distant healthcare units, enabling medical professionals to provide necessary interventions promptly. The framework employs an ensemble learning-based Bidirectional Long Short-Term Memory (Bi-LSTM) architecture integrated with ResNet50 for glaucoma classification and detection. The methodology involves pre-processing input images, extracting features, and balancing the dataset using the Synthetic Minority Oversampling Techniques (SMOTE). The balanced dataset is then fed into the model, and the results are classified using a sigmoid function. The framework was validated on four datasets such as ACRIMA, Fundus, ORIGA, and Retinal image datasets. Key findings demonstrate that the proposed model achieves superior performance compared to other models for datasets considered, as evidenced by metrics for ACRIMA datasets such as precision (97%), specificity (99%), accuracy (99%), AUC (97%), recall (97%), and F1-score (97%). For fundus dataset, it obtains accuracy of 99%, precision of 92%, recall of 96%, specificity of 94%, F1-score of 95% and AUC of 90%; accuracy of 99%, precision of 95%, recall of 97%, specificity of 93%, F1-score of 94% and AUC of 88% for ORIGA dataset. For retinal datasets, it yields 97% of accuracy, 93% of precision, 93% of recall, 98% of specificity, 93% of F1-score and AUC of 93%. The study's uniqueness lies in its practical utility for addressing healthcare disparities in rural areas through IoT and machine learning, offering promising solutions for real-world applications. Page(s): 24-35 2024-12-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/65137 Title: Ensemble Learning based EEG Classification – Investigating the Effects of Combined Yoga and Rajyog Meditation Authors: Madhu, Shobhika; Kumar, Prashant; Chandra, Sushil Abstract: The ability to detect and prevent mental health deterioration has been one of the major achievements of digital psychiatry using artificial intelligence and machine learning. The aim of this paper is to address the issue of preventing the mental health disorders of young generation by developing a system to predict the changes in an individual's states of psychological health. Pre-and post-yoga and Rajyoga meditation states were analyzed for classification of data. Also, the paper investigates if bidirectional long-short-term memory BiLSTM-based ensemble models outperform the CNN-based models in prediction modeling. The EEG data was collected from 69 students for pre- and post-intervention. To determine an objective marker for yoga and meditation, collected data were analyzed using spectrum analysis, and classification. The post meditation group exhibited highest band powers and wavelet coefficients, indicating the differences in meditation and control conditions. Additionally, in this study, an ensemble model classifier has been developed utilizing EEG data that was more accurate (82%) than other models at differentiating between meditation and control situations. To the best of the knowledge of the authors, this is the first research to apply ensemble model-based classifiers to distinguish between states of meditation and non-meditation. The performance of BiLSTM-DT was the highest among all other models in terms of precision, recall, f-measure, and accuracy. Therefore, the BiLSTM-DT ensemble model is a viable objective marker for psychological health states. Page(s): 36-47 2024-12-01T00:00:00Z