Journal of Scientific & Industrial Research (JSIR) https://or.niscpr.res.in/index.php/JSIR <p style="text-align: justify;">This oldest journal of NISCAIR (started in1942) carries comprehensive reviews in different fields of science &amp; technology (S&amp;T), including industry, original articles, short communications and case studies, on various facets of industrial development, industrial research, technology management, technology forecasting, instrumentation and analytical techniques, specially of direct relevance to industrial entrepreneurs, debates on key industrial issues, editorials/technical commentaries, reports on S&amp;T conferences, extensive book reviews and various industry related announcements.It covers all facets of industrial development.<strong> Impact Factor of JSIR is 0.7 (JCR 2023).</strong></p> <p style="text-align: justify;"><strong><a href="https://nopr.niscpr.res.in/jinfo/jsir/JSIR%2082(05)%20Instruction%20to%20Contributers.pdf" target="_blank" rel="noopener">Instructions to Author Guidelines</a></strong></p> en-US jsir@niscpr.res.in (Dr Narendra Kumar Sahoo) or@niscpr.res.in (Digital Information Resources Division) Sat, 18 Jan 2025 22:30:45 +0530 OJS 3.3.0.13 http://blogs.law.harvard.edu/tech/rss 60 An Ensemble Stacked Bi-LSTM with ResNet50 Method for Glaucoma Classification in IoT Framework https://or.niscpr.res.in/index.php/JSIR/article/view/5976 <p>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.</p> Sudeshna Pattanaik, Subhasikta Behera, Santosh Kumar Majhi, Rosy Pradhan, Pratyusa Dwibedy Copyright (c) 2024 Journal of Scientific & Industrial Research (JSIR) https://or.niscpr.res.in/index.php/JSIR/article/view/5976 Sat, 18 Jan 2025 00:00:00 +0530 Ensemble Learning based EEG Classification – Investigating the Effects of Combined Yoga and Rajyog Meditation https://or.niscpr.res.in/index.php/JSIR/article/view/10829 <p>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.</p> Shobhika Madhu, Prashant Kumar , Sushil Chandra Copyright (c) 2024 Journal of Scientific & Industrial Research (JSIR) https://or.niscpr.res.in/index.php/JSIR/article/view/10829 Sat, 18 Jan 2025 00:00:00 +0530 A Genetic Algorithm based Feature Selection and CNN based Ensemble Model for Intrusion Detection in IoT Smart Environments https://or.niscpr.res.in/index.php/JSIR/article/view/11616 <p>The growing number of Internet of Things (IoT) devices has resulted in a significant surge in network attacks, frequently causing harmful and catastrophic consequences. Malicious actors may utilize these devices to infiltrate the network infrastructure by taking advantage of hardware and software weaknesses through uninterrupted internet access. Despite significant advancements in the field of network IDS (Intrusion Detection System), there is still a lack of employing intrusion detectors in IoT environments. Hence, to address this issue, a neural network model-based intrusion detection system is introduced, which can effectively detect and classify various types of attacks on IoT devices used in intelligent applications. A feature reduction technique and a hyperparameter optimization strategy to reduce both the computing time and overhead were utilized. Important features chosen via a genetic algorithm-based feature selection model are transformed into colour images for use as input to several Convolutional Neural Network (CNN) architectures, including Xception, VGG16, and VGG19 models. The suggested ensemble model, which combines Xception, VGG16, and VGG19 classifiers using a genetic algorithm to select the most relevant features, is 98.7% accurate, which is 5% better than individual classifiers. This novel approach significantly reduces false positives while cutting computational latency when compared to existing models. By optimizing both detection speed and accuracy, the proposed system enables real-time intrusion detection, offering a scalable and efficient solution for securing IoT devices in smart environments. These advancements underscore the system’s potential to set a new standard in IoT security.</p> Hidangmayum Satyajeet Sharma, Khundrakpam Johnson Singh Copyright (c) 2024 Journal of Scientific & Industrial Research (JSIR) https://or.niscpr.res.in/index.php/JSIR/article/view/11616 Sat, 18 Jan 2025 00:00:00 +0530 Hybrid Quantum Graph Neural Network for Brain Tumor MR Image Classification https://or.niscpr.res.in/index.php/JSIR/article/view/14112 <p>Brain tumor rank as the tenth leading cause of mortality among both adults and children. Early detection and treatment significantly enhance survival rates. Recent advancements in deep learning have demonstrated promise in identifying and classifying brain tumors using Magnetic Resonance Imaging (MRI) scans. This paper presents a novel approach that integrates classical and hybrid quantum-inspired graph neural networks for tumor classification. Classical Graph Convolutional Neural Networks (GCNN) analyse complex relationships within medical imaging data, while hybrid Quantum Graph Neural Networks (QGNN) improves performance by leveraging principles of quantum computing. Previous studies highlight the challenges posed by the diverse nature of brain imaging data. This study compares various classification approaches, emphasizing architectures, training techniques, and performance metrics. The objective is to train and evaluate models for identifying brain tumors from MRI scans. The hybrid QGNN demonstrates accuracy and loss metrics comparable to advanced classical GCNN, with accuracy improving from 0.41(41%) to 0.64(64%) during training and from 0.31(31%) to 0.52(52%) in validation datasets, thereby showcasing its effectiveness in distinguishing between normal and tumor images.</p> S P Rajamohana, Vani Yelamali, Pallavi Soni, Manjunath Vanahalli, Prabu Prasad Copyright (c) 2024 Journal of Scientific & Industrial Research (JSIR) https://or.niscpr.res.in/index.php/JSIR/article/view/14112 Sat, 18 Jan 2025 00:00:00 +0530 Heavy-ion Radiation Strikes on LDD Implanted RingFET using 3D Numerical Device Simulations https://or.niscpr.res.in/index.php/JSIR/article/view/7272 <p>A ringFET is formed on vertically revolving bulk MOSFET with concentrical cylinders acting as the source, gate, and drain areas. By integrating lightly doped regions into conventional ringFET structures, three distinct types of LDD implanted ringFETs can be designed, with the implantation location defining each type. If LDD is implanted merely on the source side, it creates an SLDD ringFET, and if LDD is implanted only on the drain side, it results in a DLDD ringFET. Lastly, upon implanting LDD on both the drain and source sides, it forms an LDD ringFET structure. The effects of heavy ion radiation on three different types of LDD ringFET structures are assessed using 3D TCAD simulations and compared to the effects on a conventional ringFET structure under normal incidence. The ion strike's position, angle of incidence, and the resulting transient current and charge collected all affect the device's sensitivity and can be used to identify its vulnerable area. It has been found that the ringFET structure with LDD implanted on both the source and drain sides is more resilient to radiation-induced damage, as it exhibits a lower collected charge of 98.271 fC compared to conventional ringFET (106.768 fC), SLDD ringFET (101.549 fC), and DLDD ringFET (100.468 fC) for a LET value of 100 MeV/(mg/cm²). Additionally, the LDD-implanted ringFET exhibits a superior I<sub>ON</sub>/I<sub>OFF</sub> ratio compared to the other two LDD structures and conventional ringFET structures.</p> Ramya M, Nagarajan K K Copyright (c) 2024 Journal of Scientific & Industrial Research (JSIR) https://or.niscpr.res.in/index.php/JSIR/article/view/7272 Sat, 18 Jan 2025 00:00:00 +0530 Effects of Cutting Parameters on Delamination in Machining for S 2 Glass Fiber Reinforced Polymer Composites https://or.niscpr.res.in/index.php/JSIR/article/view/7744 <p>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.</p> Murat Koyunbakan, Zafer Kaya Copyright (c) 2024 Journal of Scientific & Industrial Research (JSIR) https://or.niscpr.res.in/index.php/JSIR/article/view/7744 Sat, 18 Jan 2025 00:00:00 +0530 Design of a New Washing Machine to Clean the Needle Bed of an Electronic Flat Knitting Machine https://or.niscpr.res.in/index.php/JSIR/article/view/8727 <p>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&amp;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.</p> Nevin Celik, Irfan Yolcular, Songul Kasap, Mehmet B Golen, Ali Taskiran Copyright (c) 2024 Journal of Scientific & Industrial Research (JSIR) https://or.niscpr.res.in/index.php/JSIR/article/view/8727 Sat, 18 Jan 2025 00:00:00 +0530 New Approach to Design Optimal Robust Controller for a 2-D Discrete System https://or.niscpr.res.in/index.php/JSIR/article/view/8044 <p>This paper investigates the problem of ensuring the stability of an uncertain system using the unsymmetric Lyapunov function for the two-dimensional discrete system as represented by the Roesser model using the LMI approach. By employing a two-dimensional unsymmetric Lyapunov function, novel LMIs have been developed to ensure stability. The key finding of the present investigation is employing an unsymmetrical Lyapunov matrix for ensuring the stability of a two-dimensional discrete Roesser model, which is a more generalized approach to guarantee the stability of any system. This address the issues of norm-bounded parameter uncertainties, calculate the cost function using an unsymmetric Lyapunov function, and finally design the guaranteed cost controller via a static state feedback technique that not only ascertains the stability of the system but also guarantees an adequate level of performance. The advantages of this newly proposed technique are that it is LMI solvable and numerically tractable. The stability criteria have been checked and ensured based on newly developed stability conditions by considering several examples demonstrating the results effectiveness and supremacy over the previously reported techniques.</p> Govind Prasad Pandiya, Abhay Vidyarthi, Amrita Pahadia Copyright (c) 2024 Journal of Scientific & Industrial Research (JSIR) https://or.niscpr.res.in/index.php/JSIR/article/view/8044 Sat, 18 Jan 2025 00:00:00 +0530 Cinematic Technology Augmenting Narrative Pace and Progress: The Role of Audiovisual Breathers in Communicating Scientific Concepts https://or.niscpr.res.in/index.php/JSIR/article/view/11717 <p>In this research paper a framework for analysis of narrative progress in the popular Indian science television serial titled <em>Bharat Ki Chhāp </em>(The Identity of India) was attempted on the basis of 643 minutes (10.717 hours) of film data by locating Audiovisual (AV) ‘spaces’ that did not have dialogues, voice-over or a narrative spoken by an offscreen commentator and instead relied either on music and ambient sound or a blend of both. These spaces which we would prefer calling Audiovisual Breathers, show the visuals of the preceding story sequence with visuals of the sequence to follow. This technique of editing such shots together create a transition and link the narrative of the next story. These Audiovisual Breathers were analysed for their role in driving the narrative progress by looking at their total count, temporal occurrence, as well as the percentage and screen-time share in the total duration of each episode of the TV serial <em>Bharat Ki Chhāp</em>. The cumulative duration and cumulative percentage of AV Breathers were also analysed to show how they maintained the flow of the various stories in each episode to sustain audiences’ interest. Having classified the AV Breathers on the basis of their duration and terming them as Short AV Breathers (SABs), Long AV Breathers (LABs) and Song Interludes (SIs), our study revealed that a combination of all the three categories of breathers occupied a screen-time of 73 minutes (1:33) in a 643 minutes(10.717) long television serial, with the SIs taking the largest share of 45 minutes and 12 seconds, followed by LABs (25’ 24”) and SABs (2’ 20”) providing a clear pointer of what constitutes the communication of core scientific concepts in the popular Indian science TV serial <em>Bharat Ki Chhāp</em>, in terms of a single metric of AV Breathers.</p> Matiur Rahman, N K Prasanna Copyright (c) 2024 Journal of Scientific & Industrial Research (JSIR) https://or.niscpr.res.in/index.php/JSIR/article/view/11717 Sat, 18 Jan 2025 00:00:00 +0530 Building Better Defence: Overcoming Challenges in Product Development through Supplier Integration in India https://or.niscpr.res.in/index.php/JSIR/article/view/11901 <p>This study investigates supplier integration challenges and strategies in Indian defence product development, employing stakeholder theory, resource-based view, and relational view as theoretical lenses. Through qualitative analysis of semi-structured interviews with key stakeholders, we identify critical barriers including information security concerns, regulatory complexities, and inadequate domestic technological capabilities. Conversely, collaborative platforms, streamlined procurement, and R&amp;D incentives emerge as potential enablers. The findings reveal a complex interplay between national security imperatives and the need for innovation, highlighting the unique context of the defence sector. We propose a novel framework that integrates stakeholder management with innovation and supply chain theories, offering a more nuanced understanding of supplier integration in high-stakes, technology-intensive industries. The study's implications extend beyond theoretical contributions, providing actionable insights for policymakers and industry leaders. We advocate for a paradigm shift in defence industrial policy, emphasizing long-term supplier partnerships, bilateral technology transfer agreements, and robust IP management strategies. Furthermore, we explore how emerging technologies like blockchain and AI can address persistent information security challenges. By synthesizing theoretical perspectives with practical recommendations, this research not only advances the scholarly discourse on supplier integration but also offers a roadmap for fostering a self-reliant, innovative defence ecosystem. These findings have broad implications for other emerging defence industries grappling with similar challenges in an increasingly complex global landscape.</p> Ashish Singh, Neeraj Kaushik, Prashant Kumar Pandey Copyright (c) 2024 Journal of Scientific & Industrial Research (JSIR) https://or.niscpr.res.in/index.php/JSIR/article/view/11901 Sat, 18 Jan 2025 00:00:00 +0530