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A Particle Swarm Optimization-Backpropagation (PSO-BP) Model for the Prediction of Earthquake in Japan
Japan is a country that suffers a lot of earthquakes and disasters because it lies across four major tectonic plates. Subduction zones at the Japanese island curves are geologically complex and create various earthquakes from various sources. Earthquake prediction helps in evacuating areas, which are suspected and could save the lives of people. Artificial neural network is a computing model inspired by biological neurons, which learn from examples and can be able to do predictions. In this paper, we present an artificial neural network with PSO-BP model for the prediction of an earthquake in Japan. In PSO-BP model, particle swarm optimization method is used to optimize the input parameters of backpropagation neural network. Information regarding all major, minor and aftershock earthquake is taken into account for the input of backpropagation neural network. These parameters are taken from Japan seismic catalogue provided by USGS (United States Geological Survey) such as latitude, longitude, magnitude, depth, etc., of earthquake. 2019, Springer Nature Singapore Pte Ltd. -
A parallel approach for region-growing segmentation
Image Segmentations play a heavy role in areas such as computer vision and image processing due to its broad usage and immense applications. Because of the large importance of image segmentation a number of algorithms have been proposed and different approaches have been adopted. In this theme I tried to parallelize the image segmentation using a region growing algorithm. The primary goal behind this theme is to enhance performance or speed up the image segmentation on large volume image data sets, i.e. Very high resolution images (VHR). In parliamentary law to get the full advantage of GPU computing, equally spread the workload among the available threads. Threads assigned to individual pixels iteratively merge with adjacent segments and always ensuring the standards that the heterogeneity of image objects should be belittled. An experimental analysis upon different orbital sensor images has made out in order to assess the quality of results. 2015 IEEE. -
A Paradigmatic Shift: Telehealth Counselling's Expansion and Challenges in India
Background: This study provides a comprehensive analysis of the rapid expansion and transformative impact of telehealth counselling in India, a trend significantly propelled by the challenges posed by the COVID-19 pandemic. Methodology: This paper presents a perspective on the current telehealth landscape, synthesizing insights from an extensive literature review. The investigation integrates qualitative insights from health care practitioners and clients, allowing for a multifaceted understanding of the emerging obstacles linked to telehealth implementation. The synthesis is structured around several key concepts identified in the literature, including the efficacy of telehealth counselling services compared to traditional face-to-face interactions, the resilience of mental health services during crises, and the growing acceptance of digital modalities among patients. Additionally, it explores significant challenges such as disparities in technological access, the need for comprehensive regulatory frameworks, varying levels of patient receptivity, infrastructural limitations, and the readiness of health care professionals to adopt telehealth technologies. Results: By focusing on these areas, the paper elucidates the complex interplay of technical, regulatory, and cultural factors shaping the telehealth ecosystem in India. It advocates for urgent policy enhancements and the continuous integration of technology to effectively address these barriers. Discussion: This perspective underscores the potential for telehealth counselling to evolve into a permanent and essential component of India's mental health service delivery model, ultimately contributing to a more resilient and accessible health care system. Conclusion: The conclusions drawn emphasize the necessity for targeted policy interventions and the establishment of robust technological infrastructures to foster a more inclusive and effective telehealth environment, ensuring mental health services reach all segments of the population. 2025 John Wiley & Sons Ltd. -
A Paradigm shift in Family therapy in India : Exploration from Socioeconomic, Cultural and Spiritual Perspectives
International Journal of Physical and Social Sciences Vol. 3, Issue 3,pp. 153-166 , ISSN No. 2249-5894 -
A pair of kinematically related space curves
We investigate the relation between two types of space curves, the Mannheim curves and constant-pitch curves and primarily explicate a method of deriving Mannheim curves and constant-pitch curves from each other by means of a suitable deformation of a space curve. We define a "radius" function and a "pitch" function for any arbitrary regular space curve and use these to characterize the two classes of curves. A few non-trivial examples of both Mannheim and constant pitch curves are discussed. The geometric nature of Mannheim curves is established by using the notion of osculating helices. The Frenet-Serret motion of a rigid body in theoretical kinematics is studied for the special case of a Mannheim curve and the axodes in this case are deduced. In particular, we show that the fixed axode is developable if and only if the motion trajectory is a Mannheim curve. 2018 World Scientific Publishing Company. -
A Numerical Investigation on Thermal Gradients and Stresses in High Temperature PEM Fuel Cell During Start-up
The High Temperature Polymer Electrolyte Fuel Cell (HT-PEMFC) stacks using polybenzimidazole (PBI) based membranes have an inability to internally heat up at low temperatures to their nominal operating temperature (160C180C) during the start-up process. Several strategies, such as direct electrical heating, coolant/gas channel heating, catalytic hydrogen-oxygen combustion, etc., are proposed in the literature to assist the heating for quick start-up situations. However, little knowledge exists on the transient thermomechanical stresses induced during the start-up heating process due to non-uniformity in heat supply and disparity in thermal properties of the cell components. The objective of the present research is to analyze the thermal gradients and thermal stresses developed in the HT-PEMFC structure during the start-up with various heating methods discussed in the literature, as well as during the cell operation by exploiting the Fluid-Structure Interaction (FSI) approach. The use of polyalkylene glycol (Fragoltherm S-15-A) based Heat Transfer Fluid (HTF) in the coolant channel has substantially improved the start-up time due to the high Nusselt number. However, a significant gradient in temperature distribution is observed during the preheating process, which resulted in great inhomogeneous stresses in the membrane, particularly in the in-plane direction. Interestingly, the degree of uniformity in membrane current density distribution during cell operation is increased. A detailed heat analysis in the cell showed that the heat generated in the cell due to electrochemical reactions is sufficient to raise the cell temperature from 120C to operating temperature in a short time. Being subjected to a compressive stress of above 40 MPa, which is higher than the ultimate strength of a typical acid doped PBI membrane, the electrolyte is the most vulnerable component during the start-up. Hence, to inhibit the concomitant effect on cell performance and degradation, a novel start-up strategy should be implemented. 2021 Elsevier Ltd -
A numerical approach to the coupled atmospheric ocean model using a fractional operator
In the present framework, the coupled mathematical model of the atmosphere-ocean system called El Nino-Southern Oscillation (ENSO) is analyzed with the aid Adams-Bashforth numerical scheme. The fundamental aim of the present work is to demonstrate the chaotic behaviour of the coupled fractional-order system. The existence and uniqueness are demonstrated within the frame of the fixed-point hypothesis with the CaputoFabrizio fractional operator. Moreover, we captured the chaotic behaviour for the attained results with diverse order. The effect of the perturbation parameter and others associated with the model is captured. The obtained results elucidate that, the present study helps to understand the importance of fractional order and also initial conditions for the nonlinear models to analyze and capture the corresponding consequence of the fractional-order dynamical systems. 2021 by the authors. -
A novel wide slice kronecker forward fractional network for osteoporosis detection using knee X-ray image
Osteoporosis is an asymptomatic and progressive skeletal disorder that maximizes the risk of fractures in people aged 50 to 60. Early and accurate detection is critical, yet challenging, due to the fine structural changes in bone that are often difficult to identify in routine medical images. Knee X-rays are commonly used diagnostic tools, but interpreting them for osteoporosis detection remains complex because of variations in bone geometry and trabecular patterns. To solve these challenges, the novel Wide Slice Kronecker Forward Fractional Network (WKFF-Net) is developed to detect osteoporosis efficiently. Initially, the input image is taken from the database for detection. Here, the denoising process is done using the Non-Local Means (NLM) filter, and the Otsu thresholding method is considered for the segmentation process. Further, a template search method is used for analyzing the femur geometry. Next, features, like spatial, adaptive Local Binary Patterns (aLBP), Convolutional Neural Networks (CNN), and medical-level features, are extracted, and osteoporosis detection is accomplished by the hybrid WKFF-Net model that integrates Deep Kronecker Network (DKN), Wide Slice Residual Network (WISeR), and fractional calculus. The experimental results obtained by the WKFF-Net are 90.868% accuracy, 92.876% True Positive Rate (TPR), 87.766% True Negative Rate (TNR), 89.888% precision, and 91.357% F1-score, for 90% of the training samples. 2026 Elsevier B.V. -
A novel univariate feature selection with ANOVA F-test-based machine learning model for Intrusion Detection Framework of Robotics system
Robotic systems have become popular across various industries, ranging from manufacturing and healthcare to logistics and space exploration. However, increasing the integration of robotic systems into critical infrastructures exposes devices to cybersecurity threats. The intrusion detection system (IDS) plays a vital role in safeguarding the systems from malicious activities and unauthorized access. This paper presents a novel, robotics-aware IDS framework incorporating hybrid feature selection and tailored classification strategies for robotic system. To evaluate the efficacy of the presented framework, an algorithm is also designed and tested using multiple machine-learning techniques. The NSL-KDD dataset is utilized for training and evaluating machine learning models due to the inclusion of a wide range of attack scenarios and normal instances. The results demonstrate that the proposed IDS effectively classifies cyberattacks relevant to robotic systems. The presented framework is also evaluated against existing IDS approaches in robotic systems. The results demonstrate that the proposed approach exhibits better results in terms of accuracy, robustness, and adaptability to emerging cyber threats. 2025 The Author(s). Published with license by Taylor & Francis Group, LLC. -
A novel two-tier feature selection model for Alzheimers disease prediction
The interdisciplinary research studies of artificial intelligence in health sector is bringing drastic life saving changes in the healthcare domain. One such aspect is the early disease prediction using machine learning and regression algorithms. The purpose of this research is to improve the prediction accuracy of Alzheimers disease by analysing the correlation of unexplored Alzheimer causing diseases. The work proposes Chi square-lasso ridge linear (Chi-LRL) model, a new two-tier feature ranking model which recognizes the significance of including diabetes, blood pressure and body mass index as potential Alzhiemer predictive parameters. The newly added predictive parameters of Alzheimers disease were statistically verified along with the conventional prediction parameters using chi-square method (Chi) as Tier 1 and an embedded model of lasso, ridge and linear (LRL) Regression for feature ranking as Tier 2. The performance of the proposed Chi-LRL model with selected features were then analysed using machine learning algorithms for performance analysis. The result shows a noticeable performance by selecting eleven significant features and a 4.5% increase in the prediction accuracy of Alzheirmer disease. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
A Novel Two-Step Bayesian Hyperparameter Optimization Strategy for DoS Attack Detection in IoT
Variations of Hyperparameter in Machine Learning (ML) algorithm effectively strikes the model's performance in terms of accuracy, loss, F1 score and many others. In the current study a two-step hyperparameter optimization approach is represented to analyse selected ML models' performance in detecting specific Denial of Service attacks in IoT. These attacks are Synchronization Flooding Attack at Transport layer, DIS Flooding attack and Sinkhole attack at Network layer. The two-step approach is a combination of Manual Hyperparameter tuning followed by Bayesian Optimization technique. The first stage manually analyses the hyperparameters of ML algorithms by considering the nature of the attack datasets. This technique is quite rigorous as it demands thorough analysis of the dependencies of the nature of datasets with hyperparameter types. At the same time this process is time consuming. The output of the first stage is the ranges of independent hyperparameter values that give maximum accuracy (minimum error rate). In the next stage Bayesian Hyperparameter tuning is used to specifically derive the single set of all hyperparameters values that give optimized accuracy faster than the BO. The input to the second stage is the ranges of individual hyperparameters that gave maximum accuracy in the first stage. The efficiency of the approach is depicted by comparative analysis of training time between the proposed and existing BO. NetSim simulator is used for generating attack datasets and Python packages are used for executing the two-step approach. 2024 IEEE. -
A Novel Threshold based Method for Vessel Intensity Detection and Extraction from Retinal Images
Retinal vessel segmentation is an active research area in medical image processing. Several research outcomes on retinal vessel segmentation have emerged in recent years. Each method has its own pros and cons, either in the vessel detection stage or in its extraction. Based on a detailed empirical investigation, a novel retinal vessel extraction architecture is proposed, which makes use of a couple of existing algorithms. In the proposed algorithm, vessel detection is carried out using a cumulative distribution function-based thresholding scheme. The resultant vessel intensities are extracted based on the hysteresis thresholding scheme. Experiments are carried out with retinal images from DRIVE and STARE databases. The results in terms of Sensitivity, Specificity, and Accuracy are compared with five standard methods. The proposed method outperforms all methods in terms of Sensitivity and Accuracy for the DRIVE data set, whereas for STARE, the performance is comparable with the best method. 2021. All Rights Reserved. -
A Novel Technique for Magnetic Particle Separation Using Current-Carrying Slotted Plate
In this paper, a novel method for separating and trapping different magnetic particles is presented. Changes in the current-carrying structure yield disturbing the generated magnetic field. Here, slots were innovatively crafted on the current-carrying plate positioned beneath the microchannel, resulting in a non-uniform magnetic field distribution. This breakthrough enables the separation of different particle types using a constant and low electric current for the very first time, leading to a significant advancement in the field. More importantly, this proposed technique offers several advantages, including the generation of low levels of current and heat, ease of construction, and the ability to control the magnetic field produced by the electric current. In this study, the capability to effectively separate various particle types using a constant electric current was demonstrated with a remarkable separation efficiency of about 100%. By applying a 100[mA] electric current to the plate that carries electric current, the separation of two particle types M-450 and M-280 was achieved at a velocity of 2[?m/s]. 2024 IEEE. -
A novel technique for leaf disease classification using Legion Kernels with parallel support vector machine (LK-PSVM) and fuzzy C means image segmentation
Detection of plant disease and classificationare being investigated in many parts of the worldto save precious medical plants from becoming extinct.Major problem in this task, include the lack of advanced and technology driven solution. Manual identification is often time-consuming and prone to inaccuracies. Therefore, there is an urgent need for an automated and efficient method that can accurately identify and classify plant diseases. This article focuses on detecting the disease through classificationthrough a new technique using leaf images for automatic classification. This paper proposes a novel segmentation technique using Fuzzy C means and Particle Swarm Optimization for effective segmentation of leaf images and feature extraction that can help in classification of disease.The approach emphasizes on the integration of techniques such as image processing, segmentation and feature extraction and finally the classification, which offers a comprehensive solution for the disease detection. The work leverages on the advantages of Legion Kernels and Parallal support vector Machine (LK-PSVM) clubbed with fuzzy C means Image segmentation to offer a framework that can handle diverse leaf images and which can effectively differentiate the type of the disease.The proposed method LK-PSVM combined with Fuzzy C means presents a novel approach that is significantly deviated from the conventional methods of leaf disease classification.The proposed wok brings an integrated framework which can synergistically combine the Legion Kernels with the PSVM technique coupled with Fuzzy C Means Image segmentation which can handle the issue of overlapped data sets and support vector machines are used to handle the situation where the number of dimensions are more than the number of samples, which is more probable in the classification problem under consideration.By integrating these components, the proposed method achieves more accuracy and robustness when compared to the existing methods in the literature. The segmentation is carried out using PSO after pre-processing of images. The Gaussian functions are used to eliminate the background subtraction. Different features of the images are then computed. A total of 55,400 images were used for the experiment consisting of various plants leaves spreading across 38 labels. A classifier is then proposed using Machine learning methods for the detection of disease in apple fruit leaves. The experiments prove that the proposed method have high degree of classification accuracy when compared to existing methods. The proposed method not only cater to the need in terms of accuracy but also making it scalable for different types of leaves. 2024 The Authors -
A Novel Survey for Young Substellar Objects with the W-band Filter. VII. Water-bearing Objects in the Core of the ? Ophiuchi Cloud Complex
We present a study of very low mass stars and brown dwarfs in the rich star-forming core of the ? Ophiuchi cloud complex. The selection of the sample relies on detecting the inherent water absorption characteristic in young substellar objects. Of the 22 water-bearing candidates selected, 15 have a spectral type of M6 or later. Brown dwarf candidates too faint for membership determination by Gaia have their proper motions derived by deep-infrared images spanning 6 yr. Astrometric analysis confirms 21/22 sources as members, with one identified as a contaminant. Infrared colors and the spectral energy distribution of each water-bearing candidate are used to diagnose the mass, age, and possible existence of circumstellar dust. A total of 15 sources exhibit evidence of disks in their spectral energy distributions, as late as in M8-type objects. Spectroscopy for bright candidates has confirmed one as an M8 member and verified two sources (with disks) exhibiting signatures of magnetospheric accretion. 2025. The Author(s). Published by the American Astronomical Society. -
A Novel Survey for Young Substellar Objects with the W-band Filter. VI. Spectroscopic Census of Substellar Members and the IMF of the ? Orionis Cluster
Low-mass stars and substellar objects are essential in tracing the initial mass function (IMF). We study the nearby young ? Orionis cluster (d ? 408 pc, age ? 1.8 Myr) using deep near-infrared (NIR) photometric data in the J, W, and H bands from WIRCam on the Canada-France-Hawaii Telescope. We use the water absorption feature to select brown dwarfs photometrically and confirm their nature spectroscopically with IRTF-SpeX. Additionally we select candidate low-mass stars for spectroscopy and analyze their membership and those of literature sources using astrometry from Gaia DR3. We obtain NIR spectra for 28 very-low-mass stars and brown dwarfs and estimate their spectral type between M3 and M8.5 (masses ranging between 0.3 and 0.01 M ?). Apart from these, we also identify five new planetary-mass candidates which require further spectroscopic confirmation of youth. We compile a comprehensive catalog of 170 spectroscopically confirmed members in the central region of the cluster, for a wide mass range of ?19-0.004 M ?. We estimate the star-to-brown-dwarf ratio to be ?4, within the range reported for other nearby star-forming regions. With the updated catalog of members we trace the IMF down to 4 M Jup and we find that a two-segment power law fits the substellar IMF better than a log-normal distribution. 2023. The Author(s). Published by the American Astronomical Society. -
A Novel Survey for Young Substellar Objects with the W-band Filter. V. IC 348 and Barnard 5 in the Perseus Cloud
We report the discovery of substellar objects in the young star cluster IC 348 and the neighboring Barnard 5 dark cloud, both at the eastern end of the Perseus star-forming complex. The substellar candidates are selected using narrowband imaging, i.e., on and off photometric technique with a filter centered around the water absorption feature at 1.45 ?m, a technique proven to be efficient in detecting water-bearing substellar objects. Our spectroscopic observations confirm three brown dwarfs in IC 348. In addition, the source WBIS 03492858+3258064, reported in this work, is the first confirmed brown dwarf discovered toward Barnard 5. Together with the young stellar population selected via near- and mid-infrared colors using the Two Micron All Sky Survey and the Wide-field Infrared Survey Explorer, we diagnose the relation between stellar versus substellar objects with the associated molecular clouds. Analyzed by Gaia EDR3 parallaxes and kinematics of the cloud members across the Perseus region, we propose the star formation scenario of the complex under influence of the nearby OB association. 2022. The Author(s). Published by the American Astronomical Society. -
A novel survey for young substellar objects with the W-band filter IV: detection and characterization of low-mass brown dwarfs in Serpens Core
We present spectroscopic confirmation of nine M5 or later Serpens Core candidate members, identified using a combination of CFHT WIRCam photometry and IRTF SpeX spectroscopy. Through spectral fitting, we find that the latest of these nine candidate members is best fit by an L0 spectral standard (in the range of M8L2), implying a mass of ?0.010.035M?. If confirmed as a cluster member, this would be one of the lowest mass Serpens Core objects ever discovered. We present analysis of the physical properties of the sample, as well as the likely membership of the candidate Serpens Core members. 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
A novel survey for young substellar objects with the W-band filter III: Searching for very low-mass brown dwarfs in Serpens South and Serpens Core
We present CFHT photometry and IRTF spectroscopy of low-mass candidate members of Serpens South and Serpens Core (?430 pc, ?0.5 Myr), identified using a novel combination of photometric filters, known as the W-band method. We report SC182952+011618, SS182959-020335, and SS183032-021028 as young, low-mass Serpens candidate members, with spectral types in the range M7-M8, M5-L0, and M5-M6.5, respectively. Best-fitting effective temperatures and luminosities imply masses of < 0.12M? for all three candidate cluster members. We also present Hubble Space Telescope imaging data (F127M, F139M, and F850LP) for six targets in Serpens South. We report the discovery of the binary system SS183044-020918AB. The binary components are separated by ?45 AU, with spectral types of M7-M8 and M8-M9, and masses of 0.08-0.1 and 0.05-0.07 M. We discuss the effects of high dust attenuation on the reliability of our analysis, as well as the presence of reddened background stars in our photometric sample. 2021 The Author(s). -
A Novel Steganographic Approach for Image Encryption Using Watermarking
Steganography is a technique for obfuscating secret information by enclosing it in a regular, non-secret file or communication; the information is subsequently extracted at the intended location. Steganography can be used in addition to encryption to further conceal or safeguard data. Watermarking is one such technique practiced in the area of steganography. Watermarking can be practiced via multiple algorithmic techniques like Discrete Wavelength Transform (DWT), Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD), Discrete Fourier Transform (DFT). In this study, a combination of such approaches along with AES encrypted watermarked images has been implemented. Validation of these techniques has been achieved by evaluating the Peak Signal to Noise Ratio (PSNR). 2023 IEEE.