Spin Frequency of Neutron Star in the Bright Atoll Source 4U 1705-44 Using NICER Observation
We conducted a systematic study of two thermonuclear type I X-ray bursts (B1 and B2) and the spectral properties of the bright atoll type neutron star low-mass X-ray binary 4U 1705?44 using NICER observation. In our analysis, a burst oscillation at ?702 Hz was detected during the double-peaked profile of burst B1. This provides the first strong evidence for the spin frequency of the neutron star, which establishes 4U 1705?44 as a rapid rotator. The measured convexity parameters of the bursts indicate that both bursts likely ignited at off-equatorial latitudes. The ignition depth of burst B1 was nearly twice that of B2, indicating substantial fuel consumption between the two events. The recurrence time of B2 (?1 hr) categorizes it as a short waiting time burst. The complementary spectral analysis of persistent emission, modeled with tbabsnthcomp+ diskbb), showed a hard state with a photon index ? < 1.12. The hardnessintensity diagram was consistent with the source being in the island state. Notably, the accretion disk appeared to extend close to the neutron star surface. 2026. The Author(s). Published by the American Astronomical Society.
Giridharan, L.; Thomas, Neal Titus; Gudennavar, S.B.; Bubbly, S.G.
Astrophysical Journal;Volume;998;Issue;2;Article No.;338;
https://doi.org/10.3847/1538-4357/ae3e83 https://www.scopus.com/pages/publications/105034132038?origin=resultslist
Giridharan L., Department of Physics and Electronics, CHRIST University, Karnataka, Bangalore, 560029, India; Thomas N.T., Department of Physics and Electronics, CHRIST University, Karnataka, Bangalore, 560029, India; Gudennavar S.B., Department of Physics and Electronics, CHRIST University, Karnataka, Bangalore, 560029, India; Bubbly S.G., Department of Physics and Electronics, CHRIST University, Karnataka, Bangalore, 560029, India
Discovery of a 459 Hz Burst Oscillation in XTE J1810?189 with NICER
We present a detailed temporal study of a type I X-ray burst from the neutron star low-mass X-ray binary (NS-LMXB) XTE J1810?189, observed on 2023 April 27 using the Neutron Star Interior Composition Explorer. The burst exhibited a rapid rise time of 2.55 s, followed by an exponential decay lasting for 7.5 s, with a total duration of ?13 s. Type I X-ray bursts are driven by thermonuclear burning on the surface of a neutron star in an NS-LMXB. As these bursts originate from the stellar surface they can exhibit highly coherent signals known as burst oscillations, which serve as probes of the neutron stars spin frequency. We report the detection of a burst oscillation signal at ?459 Hz at the cooling tail of the burst. The oscillation showed a strong Leahy-normalized power of PL = 35.95 at 458.92 Hz, corresponding to a single-trial significance of 5.53? and a multiple-trial corrected significance of 3.14?. The folded pulse profile in the 0.212 keV band is well described by a constant plus sinusoid with a fractional rms amplitude of 14.63%. These results suggest that the burst oscillation frequency of XTE J1810-189 directly reflects on the neutron stars spin, measured here to be ?2.18 ms, placing it among the rapidly rotating NS-LMXBs. This burst oscillation signal at the cooling tail of the burst can be interpreted through surface mode model or the asymmetric cooling wake model. 2025. The Author(s). Published by the American Astronomical Society.
Varun, M.; Thomas, Neal Titus; Gudennavar, S.B.; Bubbly, S.G.
Astrophysical Journal;Volume;995;Issue;2;Article No.;153;
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.3847/1538-4357/ae21d3 https://www.scopus.com/pages/publications/105034005356?origin=resultslist
Varun M., Department of Physics and Electronics, CHRIST University, Karnataka, Bangalore, 560029, India; Thomas N.T., Department of Physics and Electronics, CHRIST University, Karnataka, Bangalore, 560029, India; Gudennavar S.B., Department of Physics and Electronics, CHRIST University, Karnataka, Bangalore, 560029, India; Bubbly S.G., Department of Physics and Electronics, CHRIST University, Karnataka, Bangalore, 560029, India
Probing the Origin of X-Ray Flares in the Low-hard State of GRS 1915+105 Using AstroSat and NuSTAR
We performed a detailed time-resolved spectral study of GRS 1915+105 during its low-flux rebrightening phase using the broadband capabilities of AstroSat and NuSTAR in 2019 MayJune. The AstroSat light curves revealed erratic X-ray flares with count rates rising by a factor of ?5. Flares with simultaneous LAXPC and SXT coverage were segmented and fitted using two degenerate but physically motivated spectral models: a reflection-dominated model (hereafter model A) and an absorption-dominated model (hereafter model B). In model A, the inner disk radius (Rin) shows a broken power-law dependence on flux, indicating rapid inward motion of the disk at higher flux levels. In contrast, model B shows variable column density in the range of 10231024 cm?2, displaying a strong anticorrelation with flux. Both models exhibit significant variation in the ionization parameter between low- and high-flux segments. The total unabsorbed luminosity in the 0.730 keV energy range ranged from 6.64 1036 to 6.33 1038 erg s?1Across both models, several spectral parameters exhibited step-function-like behavior around flux thresholds of 510 10?9 erg cm?2 s?1, indicating multiple spectral regimes. The disk flux contribution, more evident in model B, increased with total flux, supporting an intrinsic origin for the variability. These findings point to a complex interplay between intrinsic disk emission, structured winds, and variable local absorption in driving the flare activity. 2025. The Author(s).
Akhter, Shahzada; Boked, Sajad; Maqbool, Bari; Misra, Ranjeev; Jithesh, V.
Astrophysical Journal;Volume;994;Issue;2;Article No.;223;
https://doi.org/10.3847/1538-4357/ae0e23 https://www.scopus.com/pages/publications/105031925737?origin=resultslist
Akhter S., Islamic University of Science and Technology, Pulwama, Awantipora, 192122, India; Boked S., Department of Physics, University of Kashmir, Srinagar, 190006, India, Government Degree College, Anantnag, Bijbehara, 192124, India; Maqbool B., Islamic University of Science and Technology, Pulwama, Awantipora, 192122, India, Inter-University Centre for Astronomy and Astrophysics, Post Bag 4, Ganeshkhind, Pune, 411007, India; Misra R., Inter-University Centre for Astronomy and Astrophysics, Post Bag 4, Ganeshkhind, Pune, 411007, India; Jithesh V., Department of Physics and Electronics, Christ University, Hosur Main Road, Bengaluru, 560029, India
Spectral Evolution of GX 17+2 Using AstroSat and NICER Observations
We study the spectral evolution of the Z-track source GX 17+2 using AstroSat and NICER observations taken between 2016 and 2020. The AstroSat observations cover the period when the source is in the normal branch (NB) and the flaring branch (FB), while for the NICER ones the variability can be associated with the FB branch. The source spectra at different regions of the branches are well described by accretion disk emission, blackbody surface emission, and a thermal Comptonization component. In the NB, the total bolometric unabsorbed flux remains constant and the variation is due to changes in the Comptonization, disk fluxes. In particular, the inferred luminosity (LT) and accretion rate ( M ? ) remain constant, while there is significant variation in the inner disk radii and fraction of disk photons entering the corona, indicating changes in the geometry of the system. On the other hand, in the FB, there is significant variation in luminosity from ?4.0 to ?7.0 1038 erg s?1. Despite this significant variation in luminosity and in the inner disk radii, the accretion efficiency, defined as ? = L T / M ? c 2 , remains nearly constant at ?0.20 throughout the evolution of the source, as expected for a neutron star system. 2025. The Author(s). Published by the American Astronomical Society.
Bhattacherjee, Sree; Misra, Ranjeev; Sarkar, Biplob; Jithesh, V.; Roy, Jayashree; Bhulla, Yashpal
Astrophysical Journal;Volume;989;Issue;1;Article No.;23;
https://doi.org/10.3847/1538-4357/ade9ad https://www.scopus.com/pages/publications/105012271188?origin=resultslist
Bhattacherjee S., Department of Applied Sciences, Tezpur University, Assam, Napaam, 784028, India; Misra R., Inter-University Center for Astronomy and Astrophysics, Ganeshkhind, Pune, 411007, India; Sarkar B., Department of Applied Sciences, Tezpur University, Assam, Napaam, 784028, India; Jithesh V., Department of Physics and Electronics, Christ University, Hosur Main Road, Bengaluru, 560029, India; Roy J., Inter-University Center for Astronomy and Astrophysics, Ganeshkhind, Pune, 411007, India; Bhulla Y., Pacific Academy of Higher Education and Research University, Udaipur, 313003, India
First Polarimetric View of GX 349+2 with the Imaging X-Ray Polarimetry Explorer
We conducted a spectropolarimetric study of the bright Z source GX 349+2 using the Imaging X-ray Polarimetry Explorer (IXPE) observation. Our findings reveal a significant polarization degree (PD) of 1.1% 0.3% in the 2.0-8.0 keV energy range. Spectropolarimetric analysis was performed by modeling the source spectra with an absorbed multicolor disk component and a blackbody. This allowed us to constrain the polarization contributions from the disk and boundary/spreading layer. The results indicate that the observed polarization signal primarily originates from the disk and the spreading layer at the neutron stars surface, rather than the boundary layer. Additionally, we detect an excess polarization component, which we attribute to either an outflow or reflection processes within the system, indicating the presence of a third component, albeit not observed in the IXPE spectra. Furthermore, energy-resolved polarization analysis in the 2.0-4.0 and 4.0-8.0 keV energy ranges hinted at a marginal increase of PD with energy and rotation of polarization angle (PA). This also pointed to an energy-dependent dominance of emission and indicated that the variation in PA with energy (?17? in the 2.0-4.0 keV energy range and ?48? in the 4.0-8.0 keV energy range) is likely associated with the different nonorthogonal PAs of the disk and spreading layer components, which peak at different energies. 2025. The Author(s). Published by the American Astronomical Society.
Lavanya, S.; Giridharan, L.; Thomas, Neal Titus; Jirawala, Khushi; Varun, M.; Gudennavar, S.B.; Bubbly, S.G.
Astrophysical Journal;Volume;985;Issue;2;Article No.;229;
https://doi.org/10.3847/1538-4357/add330 https://www.scopus.com/pages/publications/105006917842?origin=resultslist
Lavanya S., Department of Physics and Electronics, CHRIST University, Bangalore, 560029, India; Giridharan L., Department of Physics and Electronics, CHRIST University, Bangalore, 560029, India; Thomas N.T., Department of Physics and Electronics, CHRIST University, Bangalore, 560029, India; Jirawala K., Department of Physics and Electronics, CHRIST University, Bangalore, 560029, India; Varun M., Department of Physics and Electronics, CHRIST University, Bangalore, 560029, India; Gudennavar S.B., Department of Physics and Electronics, CHRIST University, Bangalore, 560029, India; Bubbly S.G., Department of Physics and Electronics, CHRIST University, Bangalore, 560029, India
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.
Sharma, Tanvi; Chen, Wen Ping; Biller, Beth; Albert, Lo; Damian, Belinda; Jose, Jessy; Lalchand, Bhavana; Liu, Michael C.; Oasa, Yumiko
Astrophysical Journal;Volume;980;Issue;2;Article No.;205;
https://doi.org/10.3847/1538-4357/ada3c3 https://www.scopus.com/pages/publications/85217924727?origin=resultslist
Sharma T., Institute of Astronomy, National Central University, 300 Zhongda Road Zhongli, Taoyuan, 32001, Taiwan; Chen W.P., Institute of Astronomy, National Central University, 300 Zhongda Road Zhongli, Taoyuan, 32001, Taiwan, Department of Physics, National Central University, 300 Zhongda Road Zhongli, Taoyuan, 32001, Taiwan; Biller B., Institute for Astronomy, Royal Observatory, University of Edinburgh, Blackford Hill, Edinburgh, EH93HJ, United Kingdom; Albert L., Institut Trottier de recherche sur les exoplanes, Universitde Montrl, Montrl, H3C 3J7, QC, Canada, Dartement de physique, Universitde Montrl, Montrl, H3C 3J7, QC, Canada; Damian B., SUPA, School of Physics Astronomy, University of St Andrews, North Haugh, St Andrews, KY16 9SS, United Kingdom, Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Road, Bengaluru, 560029, India; Jose J., Department of Physics, Indian Institute of Science Education and Research Tirupati, Yerpedu Andhra Pradesh, Tirupati, 517619, India; Lalchand B., Institute of Astronomy, National Central University, 300 Zhongda Road Zhongli, Taoyuan, 32001, Taiwan; Liu M.C., Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, 96822, HI, United States; Oasa Y., Faculty of Education, Graduate School of Science and Engineering, Saitama University, 255, Shimo-Okubo, Sakura, Saitama, 338-8570, Japan
Tracing Early Enrichment Pathways: Chemical and Chemodynamical Analysis of Two CEMP-no Stars**Based on the data collected from HDS/SUBARU
Carbon stars (199); CEMP stars (2105); Chemical abundances (224); Population II stars (1284)
We present a detailed high-resolution spectroscopic and chemodynamic analysis of two carbon stars, HE 1148?0037 and HE 1246?1344, using SUBARU High Dispersion Spectrograph spectra (R ? 50,000). Our analysis confirms that both stars are extremely metal-poor and belong to the Carbon-Enhanced Metal-Poor (CEMP)-no class ([C/Fe] > 0.70 and [Ba/Fe] < ?1.0). Both of the stars are found to be group II CEMP-no stars in the A(C) versus [Fe/H] diagram. While both stars exhibit slightly enhanced ?-elements (Ca, Sc), their Mg, Fe-peak, and neutron-capture element abundances show distinct trends, indicating different progenitor pathways. From [O/Fe] and [Sr/Ba] ratios, we identify both metal-poor asymptotic giant branch stars and fast-rotating massive stars as potential progenitors. Further elemental abundance analysis suggests a multienrichment origin for these stars. Kinematic analysis reveals that HE 1148?0037 belongs to the halo, with a spatial velocity of 464.99 14 km s?1. Orbital calculations show that HE 1148?0037 follows a retrograde orbit and is associated with the high-energy retrograde halo, likely a member of the Iitoi substructure. In contrast, HE 1246?1344 follows a prograde orbit and is linked to Gaia-Sausage/Enceladus. 2026. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the https://creativecommons.org/licenses/by/4.0/. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Astronomical Journal;Volume;171;Issue;6;
https://doi.org/10.3847/1538-3881/ae5f93 https://www.scopus.com/pages/publications/105038453717?origin=resultslist
Purandardas M., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India
Neurofeedback Therapy Meets Transformers: Rewiring Sleep Disorders Through AI-Driven EEG Modulation
Artificial Intelligence; CBT; Neurofeedback; Polysomnography; Sleep Disorders; Transformers
Sleep disorders such as insomnia, sleep Apnea, and hypersomnia significantly impair neurophysiological functioning, yet conventional treatments like Cognitive Behavioral Therapy for Insomnia (CBT-I) remain resource-intensive and difficult to personalize. This study introduces a novel AI-powered neurofeedback simulation framework designed to detect dysregulated EEG frequency band activity across sleep stages and simulate targeted interventions. A Transformer-based model serves as the core component, offering a unique capability to model cross-epoch temporal dynamics and frequency-specific spectral patterns. Unlike traditional architectures that treat EEG epochs in isolation, the Transformer captures how EEG band activity evolves across the night, critical for identifying persistent dysregulation patterns and planning stage-specific interventions. Through its multi-head attention mechanism, the model can simultaneously monitor delta, theta, alpha, beta, and gamma fluctuations while preserving sleep architecture transitions using positional encoding. Dysregulated epochs are classified with 92% accuracy, and intervention simulations-such as beta suppression in N2 or delta enhancement in REM-led to measurable improvements: average WASO decreased by 23%, and Sleep Efficiency improved by 13%. This framework not only demonstrates the efficacy of Transformer-based temporal-spectral modelling in EEG but also lays the foundation for closed-loop, wearable-compatible, personalized neurofeedback systems for remote sleep therapy. 2026 A l A KA d V idh hi V
Journal of Computer Science;Volume;22;Issue;2;pp.708-723
All Open Access; Hybrid Gold Open Access
https://doi.org/10.3844/jcssp.2026.708.723 https://www.scopus.com/pages/publications/105038192317?origin=resultslist
Amala Ann K.A., Department of Statistics and Data Science, Christ (Deemed to be University), Bengaluru, India; Vaidhehi V., Department of Statistics and Data Science, Christ (Deemed to be University), Bengaluru, India
IoT-Integrated CNN Deep Learning for Automated Breast Cancer Detection and Diagnosis
Breast Cancer Detection; Convolutional Neural Network (CNN); Enhanced Cancer Classification; Health Automation; Internet of Things (IoT); Residual Neural Network (ResNet)
Breast cancer continues to be a primary cause of death in women, requiring prompt and accurate diagnosis to enhance treatment results. Traditional diagnostic techniques depend on manual assessment, which leads to possible misclassification, significant inter-observer variability, and delays in decision-making. Current deep learning models, including CNNs, frequently experience feature loss, gradient declining and restricted adaptability to real-time data. To overcome these restrictions, we present a hybrid framework combining CNN and ResNet that merges deep learning-based feature extraction with real-time data collecting from IoT devices. The proposed approach utilises CNNs for preliminary feature extraction, ResNet for hierarchical learning with residual connections, and IoT for real-time patient monitoring and automatic notifications. The dataset undergoes preprocessing through normalisation, augmentation, and histogram equalisation to improve image quality and learning efficacy. The model is trained with cross-entropy loss and the Adam optimiser, guaranteeing stability and excellent performance. The evaluation results indicate a substantial enhancement compared to baseline models, with an accuracy of 97, an F1-score of 95.3, and a recall rate of 96.4%, exceeding traditional deep learning (90 accuracy) and CNN-based models (80% accuracy). The suggested model similarly minimises mistakes, with RMSE and MSE values declining to 1.2 and 1.6, respectively, signifying reduced misclassification rates. The inclusion of IoT facilitates instantaneous data transmission with little latency, hence improving clinical decision-making and minimising diagnostic delays. This advanced system facilitates automated and precise breast cancer detection, providing an innovative method for early diagnosis, optimised treatment planning, and improved patient outcomes, while ensuring data privacy and security through encryption and commitment to healthcare regulations. 2026 Yamini Kalva, R. Ganesh Babu, Sindhu V, S. Gokul Pran, Garaga Srilakshmi, Kavitha C T, Sathish Kumar Shanmugam and V. Bhoopathy. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
Kalva, Yamini; Babu, R. Ganesh; Sindhu, V.; Pran, S. Gokul; Srilakshmi, Garaga; Kavitha, C.T.; Shanmugam, Sathish Kumar; Bhoopathy, V.
Journal of Computer Science;Volume;22;Issue;4;pp.1218-1230
https://doi.org/10.3844/jcssp.2026.1218.1230 https://www.scopus.com/pages/publications/105038163531?origin=resultslist
Kalva Y., Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India; Babu R.G., Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, Guntur, India; Sindhu V., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Pran S.G., Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. SagunthalaR&D Institute of Science and Technology, Avadi, Chennai, India; Srilakshmi G., Department of Electronics and Communication Engineering, Aditya University, Andhra Pradesh, India; Kavitha C.T., Department of ECE, St. Joseph's Institute of Technology, Chennai, India; Shanmugam S.K., Department of EEE, M.Kumarasamy College of Engineering, Tamilnadu, Karur, India; Bhoopathy V., Department of Computer Science and Engineering, Sree Rama Engineering College, Tirupathi, India
Optimized Feature Selection for Kidney Ultrasound Image Classification Using Binary Coati Weighted Mean Vector Algorithm
Coati Optimization Algorithm; Feature Selection; Kidney; Ultrasound; Weighted Mean Vector (INFO)
The analysis of medical images presents many challenges, especially when making precise diagnoses. In pediatric Chronic Kidney Disease (CKD), early identification is critical because of its gradual progression to significant kidney failure. This study proposes a diagnostic framework for pediatric ultrasound image classification that incorporated machine learning and advanced feature selection methods. This approach is divided into four stages: Preprocessing, feature extraction, feature selection, and classification. Initially, pediatric kidney ultrasound images are enhanced using gaussian median filter. Radiomics features were then extracted, including Gray Level Co-Occurrence Matrix (GLCM), Gray Level Size Zone Matrix (GLSZM), Gray Level Run Length Matrix (GLRLM), Neighboring Gray Tone Difference Matrix (NGTDM), Gray Level Dependence Matrix (GLDM), and first-order statistics. To optimize this feature space, we introduce the Binary Coati Weighted Mean Vector (BinCoWmv) optimization algorithm, which uses a customized fitness function. Herein, the selected features were evaluated using different classifiers: Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Nae Bayes (NB), K-Nearest Neighbor (KNN), and XG-Boost. Comparative evaluations with existing optimizers, such as the Coati Optimization Algorithm (COA), weighted average vector (INFO), Firefly Algorithm (FFA), and Harris Hawk Optimization (HHO), showed that BinCoWmv achieved a higher classification accuracy. Our framework improves diagnostic reliability and assists radiologist and nephrologist in the early detection of chronic kidney disease in children. 2025 Fizhan Kausar and Ramamurthy B.
Journal of Computer Science;Volume;21;Issue;12;pp.2986-3004
https://doi.org/10.3844/JCSSP.2025.2986.3004 https://www.scopus.com/pages/publications/105037648798?origin=resultslist
Kausar F., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Ramamurthy B., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
Exploring Applications, Datasets, Algorithms, and Technologies in Satellite Image Processing
Classification; Deep Learning; Remote Sensing; Satellite Data
Amidst an era filled with complex local and global problems, satellite data presents itself as a revolutionary tool with unmatched potential to tackle practical problems in a variety of fields. This article investigates how satellite imagery, which is available through open data programs and repositories, is a valuable tool for applications including wildlife conservation, urban planning, precision agriculture, and disaster management. It highlights the unique perspective that satellite data offers. Various sources for data acquisition, the applications that are suitable for a chosen satellite data and commonly used algorithms and techniques are discussed. Through case studies, the paper demonstrates how quick and reliable data provided by satellites can be used to solve complex real-world problems. The benefits of satellite data are emphasized, including its affordability, ability to monitor in real-time, and ability to support sustainable behaviours and policy-making. The study explores cutting-edge technologies, highlighting cloud computing and GIS integration as well as machine learning algorithms to build robust solutions using satellite data. The immense potential of satellite data is accompanied by challenges, including data integration, computational complexity, and ethical considerations. These challenges underscore the need for standardization and continuous efforts to fully realize the potential of satellite data in sustainable development and informed decision-making. 2025 Bijeesh TV, Bejoy BJ, Michael Moses Thiruthuvanthan and Raju G.
Bijeesh, T.V.; Bejoy, B.J.; Thiruthuvanthan, Michael Moses; Raju, G.
Journal of Computer Science;Volume;21;Issue;9;pp.2029-2048
https://doi.org/10.3844/jcssp.2025.2029.2048 https://www.scopus.com/pages/publications/105023118979?origin=resultslist
Bijeesh T.V., Department of Computer Science and Engineering, CHRIST University, India; Bejoy B.J., Department of Computer Science and Engineering, CHRIST University, India; Thiruthuvanthan M.M., Department of Artificial Intelligence, Machine Learning and Data Science, CHRIST University, India; Raju G., Department of Computer Science and Engineering, CHRIST University, India
Towards a Human-Like AGI Architecture: GeneralIntelligence Framework (GIF)
Artificial General Intelligence (AGI); General IntelligenceFramework (GIF); NeuromorphicComputing; Real-Time Learning (RTL); Spiking Neural Networks (SNNs)
Artificial Intelligence (AI) has achieved significant breakthroughsbut remains limited by its specialization and inability to generalize acrossdomains, unlike human cognition. Current models such as Large LanguageModels (LLMs) and Multimodal Large Language Models (MLLMs) excel atspecific tasks but struggle with real-time adaptability and cross-domaingeneralization. This paper introduces the General Intelligence Framework(GIF), an approach designed to bridge this gap by mimicking human-likecognitive processes. By integrating Deep Learning (DL), Spiking NeuralNetworks (SNNs), and neuromorphic hardware, the framework fosters Real-Time Learning (RTL) and adaptability. The proposed framework holdspotential for industries like robotics, healthcare, education, astronomy,defense, autonomous systems, etc., where flexible, adaptive AI is critical.We hypothesize that the framework will enable AI systems to handleunforeseen inputs and tasks without requiring extensive retraining,representing a step toward achieving Artificial General Intelligence (AGI). 2025 Jiran Kurian Puliyanmakkal and Rohini V. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
Journal of Computer Science;Volume;21;Issue;7;pp.1637-1650
https://doi.org/10.3844/jcssp.2025.1637.1650 https://www.scopus.com/pages/publications/105012831361?origin=resultslist
Puliyanmakkal J.K., Department of Computer Science, Christ (Deemed to be University), Bangalore, India; Rohini V., Department of Computer Science, Christ (Deemed to be University), Bangalore, India
A Machine Learning Approach to Consumer Behavior Analysis in Social Media-Influenced E-Book Markets
classification; Cognitive Learning; Education; Machine Learning; Non-cognitive Learning; student performance
Social media has emerged as a dominant marketing channel, significantly influencing consumer purchase decisions. Despite extensive global research, little is known about region-specific dynamics in emerging markets such as India. This study addresses this gap by applying Random Forest and Gradient Boosting models to survey data from 386 respondents in the Delhi-NCR region to analyze e-book purchasing behavior. Data were preprocessed through encoding, normalization, and stratified traintest splitting (80:20), with reproducibility ensured via a fixed random seed. Model evaluation employed R, RMSE, and MAE metrics, alongside a paired-sample t-test. Results showed that Gradient Boosting (R = 0.82) outperformed Random Forest (R = 0.78; p = 0.038). Feature importance analysis revealed that behavioral variablespurchase intention, brand awareness, and social media engagementwere the strongest predictors, whereas demographic features contributed minimally. These findings emphasize the primacy of behavioral traits in social mediadriven e-book markets and provide evidence for designing region-specific digital marketing strategies in emerging economies. 2025, Interdisciplinary Publishing Academia. All rights reserved.
Tripathi, Sachin; Tiwari, Akhilesh; Upreti, Kamal; Radhakrishnan, G.V.
Journal of Applied Science and Technology Trends;Volume;6;Issue;2;pp.242-250
https://doi.org/10.38094/jastt62318 https://www.scopus.com/pages/publications/105016567771?origin=resultslist
Tripathi S., Department of Business and Management, Christ University, Delhi NCR Campus, Ghaziabad, India; Tiwari A., Department of Business and Management, Christ University, Delhi NCR Campus, Ghaziabad, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Radhakrishnan G.V., Kalinga School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India
SALF: A Blockchain-Based Framework for Scalable Academic Credential Management and Institutional Governance
academic records; blockchain; encryption; proof of authority (poa); secure academic ledger framework (salf)
This study introduces SALF (Secure Academic Ledger Framework), a technically innovative blockchain-based system engineered to overcome persistent challenges in academic credential management, including latency bottlenecks, governance opacity, and integration inflexibility. SALF pioneers a hybrid on-chain/off-chain architecture optimized for low-latency operations while preserving blockchain immutability, and it employs a role-based smart contract suite tailored to institutional hierarchies. Unlike prior frameworks, SALF integrates a degree-based incentive mechanism that quantifies data quality metricslegibility, correctness, and non-redundancyto ensure equitable institutional participation and discourage centralization. Built upon a Proof of Authority (PoA) consensus model, SALF achieves high performance under load, maintaining a throughput of over 30 transactions per second (TPS) and P95 latency below 300 milliseconds. RESTful APIs ensure real-time interoperability with existing systems such as ERPs and academic dashboards. Compared to benchmark systems like EduCert-Chain and EduCopyRight-Chain, the proposed framework achieves a 41.3% reduction in latency and maintains stable throughput under high-load conditions, even as other systems exhibit significant degradation or integration constraints. These distinctive technical contributions position SALF as a scalable, governance-aware, and future-ready infrastructure for decentralized academic credentialing across heterogeneous institutions. 2025, Interdisciplinary Publishing Academia. All rights reserved.
Haque, Mustafizul; Hung, Bui Thanh; Upreti, Kamal; Sapra, Ritu; Jain, Rituraj; Radhakrishnan, G.V.; Kshirsagar, Pravin R.
Journal of Applied Science and Technology Trends;Volume;6;Issue;2;pp.203-218
https://doi.org/10.38094/jastt62295 https://www.scopus.com/pages/publications/105015460996?origin=resultslist
Haque M., Department of Management, Lalit Narayan Mishra Institute of Economic Development and Social Change, Bihar, Patna, India; Hung B.T., Data Science laboratory, Data Science Department, Faculty of Information Technology, Industrial University of Ho Chi Minh City, Viet Nam; Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India; Sapra R., University of Delhi, Delhi, India; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India; Radhakrishnan G.V., Kalinga School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Kshirsagar P.R., Department of Electronics & Telecommunication Engineering, J D College of Engineering & Management, Maharashtra, Nagpur, India
Identification of Brain Tumors Using CNN and ML with Diverse Feature Selection Techniques
brain tumor detection; CNN; feature selection; machine learning; medical imaging; MRI classification
Early diagnosis and treatment is very essential in monitoring Brain tumor using MRI images. Convolutional Neural Networks (CNN) and Machine Learning (ML) classifiers have been widely used but there is not much work on how feature selection techniques would affect the performance of the CNN. Secondly, there is a need for investigation concerning small dataset adaptability and ML-CNN comparisons. To improve the classification accuracy, we integrate Univariate, Recursive Feature Elimination (RFE), Recursive Feature Elimination with Cross Validation (RFECV) with CNN in this study. Preprocessing, feature extraction & selection was carried out on the dataset consisting of 253 MRI images and they are classified using CNN and ML models (Logistic Regression, Decision Tree, Random Forest, Nae Bayes). With the results 96%, CNN with Univariate Feature Selection performed better than ML classifiers, and other selection techniques. The results demonstrate that feature selection is necessary to get the best performance out of CNN models operating on small datasets. Future studies should be based on different deep learning architectures to improve classification and application i n other datasets. 2025, Interdisciplinary Publishing Academia. All rights reserved.
Singh, Suraj Pal; Upreti, Kamal; Jain, Rituraj; Arora, Rakesh Kumar; Tiwari, Akhilesh; Radhakrishnan, G.V.
Journal of Applied Science and Technology Trends;Volume;6;Issue;1;pp.74-86
https://doi.org/10.38094/jastt61240 https://www.scopus.com/pages/publications/105010274343?origin=resultslist
Singh S.P., Computer Science and Engineering, Chandigarh University, Mohali, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India; Arora R.K., Computer Science and Engineering, Dr. Akhilesh Das Gupta Institute of Professional Studies, Delhi, India; Tiwari A., Department of Business and Management, Christ University, Delhi NCR Campus, Ghaziabad, India; Radhakrishnan G.V., Kalinga School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India
AQUAPHISH: Leveraging Metaheuristics and Automated Machine Learning for Precision Phishing Detection
cybersecurity; optimization algorithm; Phishing attack; random forest algorithm; regression analysis; whale optimization algorithm
Phishing is an ongoing and dynamic threat in the field of cybersecurity, targeting user trust to capture sensitive data through fraudulent websites. Conventional detection systems tend to use binary classification and static features, which make them less flexible to new attack paradigms. This paper seeks to design a solid and comprehensible phishing detection system that alleviates the drawbacks of binary labeling by proposing a regression-based risk scoring model. The aim is to improve accuracy, feature interpretability, and deployment in real-time settings. The new method combines Whale Optimization Algorithm (WOA) for feature selection and H2O AutoML for model creation and assessment. A filtered dataset of 10,000 phishing and normal websites is operated upon using 48 features, which are then reduced to 36 using WOA. The last models are optimized with H2O AutoML, encompassing ensemble learners, and tested on various regression metrics. Interpretability is achieved with SHAP analysis. The best model had an R of 0.9534, RMSE of 0.1079, and MSE of 0.0116, better than traditional classification-based phishing detectors. The system, with only 36 features, had training time decreased by 23.6% and inference latency reduced by ~18%, without any sacrifice in detection accuracy (98.3%). Regression-based scoring also supported adaptive threat ranking in real time. By posing phishing detection as a regression problem and integrating metaheuristic feature selection with AutoML, this work introduces a scalable and explainable framework ready for real-world deployment. The low-latency yet high-accuracy model is best suited for integration into browser-level phishing filters and cloud-based threat intelligence platforms. 2025, Interdisciplinary Publishing Academia. All rights reserved.
Upreti, Kamal; Shankar, Uma; Upreti, Shitiz; Radhakrishnan, G.V.; Hundekari, Sheela; Date, Saroj
Journal of Applied Science and Technology Trends;Volume;6;Issue;1;pp.87-102
https://doi.org/10.38094/jastt61238 https://www.scopus.com/pages/publications/105012279379?origin=resultslist
Upreti K., Department Computer Applications, Christ University, Delhi NCR, Ghaziabad, India; Shankar U., Faculty of Management and Social Sciences, Qaiwan International University, Kurdistan Region, Sulaimanyah, Iraq; Upreti S., Lloyd Business School, Uttar Pradesh, Greater Noida, India; Radhakrishnan G.V., Department of Economics and Finance, KIIT School of Management (KSOM), KIIT University, Bhubaneswar, India; Hundekari S., Chinchwad University, School of Computer Applications, Pune, India; Date S., Department of Artificial Intelligence and Data Science, CSMSS Chh. Shahu College of Engineering, Chh. Sambhajinagar, Maharashtra, India
Designing Remote-Sensed Intelligent Visual Analytics Algorithms for Environmental Monitoring Systems
Deep Learning; Environmental Monitoring Systems; Intelligent Visual Analytics; Remote Sensing; Spatiotemporal Analysis; UAV and Satellite Imagery; XAI
Increasing climate variability and the rapid degradation of natural ecosystems have necessitated the development of intelligent systems that can track and assess environmental changes in real-time. By combining multi-modal remote sensing data with advanced machine learning and visual analytics techniques, this paper introduces a novel framework for Remote-Sensed Intelligent Visual Analytics (RS-IVA), which aims to improve environmental monitoring systems. To offer a comprehensive, scalable, and adaptable monitoring system, the proposed framework utilizes ground sensor inputs, UAV-based aerial photography, and high-resolution satellite imaging. To identify anomalies such as deforestation, urbanization, water pollution, and changes in air quality, a hybrid deep learning-based algorithm is employed. Explainable AI (XAI) elements make sure that the decision-making process is transparent and accessible. To assist stakeholders, investigate spatiotemporal patterns, forecast environmental hazards, and enhance evidence-based policy decisions, an interactive visual analytics dashboard is being developed. Experiments using benchmark datasets demonstrate that the system is highly accurate in identifying significant environmental changes and exhibits greater adaptability across a wide range of climatic and geographic regions. Intelligent analytics and remote sensing technologies collaborate to improve situational awareness and provide early warnings for sustainable resource planning and disaster management. This research advances the development of next-generation innovative environmental monitoring systems by integrating human-in-the-loop visualization, AI-driven analytics, and remote sensing for informed ecological governance. 2025, Interdisciplinary Publishing Academia. All rights reserved.
Priya, R.; Bhandari, Rajiv R.; Nitnaware, Vilas Namdeo; Ramesh, P.N.; Sivakumar, R.; Dhanapal, R.
Journal of Applied Science and Technology Trends;Volume;6;Issue;Special Issue;pp.1-14
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.38094/jastt605480 https://www.scopus.com/pages/publications/105025130265?origin=resultslist
Priya R., Department of Computer Science and Applications (MCA), Faculty of Science and Humanities, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, India; Bhandari R.R., Department of Computer Engineering, SNJB's Late Sau K B Jain College of Engineering, Nashik, Maharashtra, Chandwad, 423101, India; Nitnaware V.N., MAEERS MIT Thane, Near Green Valley Studio, Mira Road, Kashi gaon, Maharashtra, Mumbai, 401107, India; Ramesh P.N., Department of Computer Science and Engineering, Karpagam Institute of Technology, Tamil Nadu, Coimbatore, 641021, India; Sivakumar R., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Dhanapal R., Faculty of Engineering, Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Tamil Nadu, Coimbatore, India
Comparison of the Results of Steady Darcy-Be ?ard Convection Problems of the Classical and the Barletta Types
linear stability; Newtonian fluid; Nusselt number; Porous medium; weakly nonlinear stability
The linear stability analysis of the Barletta-Darcy-Bnard convection problem in a horizontal fluid-saturated porous layer is extended to a weakly nonlinear stability analysis considering local thermal equilibrium (LTE) between the fluid and solid phases. The minimal Fourier-Galerkin expansion is used for the case of a free upper surface (Neumann boundary condition on the stream function) along with isothermal boundary condition for which heat transport is quantified in terms of the Nusselt number. The present article aims to fill the literature gap between the linear and nonlinear stability analyses of classical Darcy-Bnard convection and of Barletta-Darcy-Bnard convection. Weakly non-linear stability analysis has not been performed in the case of the non-classical Darcy-Bnard convection problem. A comparison of results of the present problem with those of the classical Darcy-Bnard convection problem is made. It is found that the cell size is larger in the case of the former problem compared to the latter. The critical Darcy-Rayleigh number, however is smaller in the former one. The Nusselt number varies inversely as the Rayleigh number, R and hence the Nusselt number increases with decrease in R which implies that more heat is transported in Barletta-Darcy-Bnard convection compared to classical Darcy-Bnard convection. 2025, Semarak Ilmu Publishing. All rights reserved.
Soibam, Davita Devi; Siddheshwar, Pradeep G.; Idris, Ruwaidiah
CFD Letters;Volume;17;Issue;9;pp.163-177
https://doi.org/10.37934/cfdl.17.9.163177 https://www.scopus.com/pages/publications/105003423467?origin=resultslist
Soibam D.D., Centre for Mathematical Needs, Mathematics Department, CHRIST (Deemed to be University), Bengaluru, 560029, India; Siddheshwar P.G., Centre for Mathematical Needs, Mathematics Department, CHRIST (Deemed to be University), Bengaluru, 560029, India; Idris R., Special Interest Group for Modelling and Data Analytics, Faculty of Computer Science and Mathematics, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, 21030, Malaysia
Steady Finite Amplitude Convection in Type 2 Hybrid Nanofluids with Rough Boundaries and Robin Boundary Condition on Temperature
binary base fluid; general boundary condition; hybrid nanofluid; Mono nanofluid; rough boundaries; single-phase model
The study concerns linear and weakly non-linear analysis of a Rayleigh-Bard convection problem subjected to a most general boundary condition. This general boundary condition consists of rough boundaries on velocity and Robin boundary condition on temperature. With the help of specific non-dimensional parameters, i.e., the slip-Darcy number and the Biot number that arise at lower and horizontal boundaries, we have been able to integrate 16 Rayleigh-Bard convection problems into one. Both parameters display a stabilising effect on the onset of convection. Utilising a minimal Fourier series representation, a generalised Lorenz model is derived. The solution of this Lorenz model is used to obtain the Nusselt number expression. The study also involves the usage of mono nanofluid and hybrid nanofluid of the type where spherical-shaped nanoparticles (alumina/copper) are dispersed into a binary base fluid mixture (water-EG). The thermophysical properties of the binary base fluid mixture and the corresponding nanofluids are calculated using mixture theory. Also, the thermophysical properties of mono nanofluid are derived and calculated from the mixture theory defined for the hybrid nanofluid type, which accounts for the correctness of the mixture theory used (verified using phenomenological laws and mixture theory for mono nanofluid). The papers main aim is to throw light on the ease rendered by the usage of general boundary condition, along with presenting a theoretical base for choosing the most suitable nanofluid concerning convection problems. An increase of 96.2984% in critical Rayleigh number is observed in the case of water-EG-alumina nanofluid when Biot number is increased from 10?3 to 106. Likewise, an increase of 107.223% in critical Rayleigh number for water-EG-alumina nanofluid is observed when slip-Darcy number is increased from 10?3 to 106. Limiting cases of the Rayleigh-Bard problem for 16 boundary conditions including free/rigid isothermal/adiabatic combinations at lower and upper boundaries are obtained, thereby presenting a strong validation for the study. Plots of stream function for different boundary conditions are included for a better physical understanding of the problem. 2025, Penerbit Akademia Baru. All rights reserved.
Firdose, Heena; Siddheshwar, P.G.; Idris, Ruwaidiah
Journal of Advanced Research in Numerical Heat Transfer;Volume;36;Issue;1;pp.67-106
https://doi.org/10.37934/arnht.36.1.67106 https://www.scopus.com/pages/publications/105007470483?origin=resultslist
Firdose H., Centre for Mathematical Needs, Mathematics Department, Christ University, Karnataka, Bengaluru, 560029, India; Siddheshwar P.G., Centre for Mathematical Needs, Mathematics Department, Christ University, Karnataka, Bengaluru, 560029, India; Idris R., Special Interest Group for Modelling and Data Analytics, Faculty of Computer Science and Mathematics, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, 21030, Malaysia
Data-Driven Drug Discovery Optimization for Breast Cancer Using Interpretable Machine Learning Models
Breast cancer remains one of the most prevalent malignancies worldwide, posing significant therapeutic challenges due to tumor heterogeneity and drug resistance. This study presents a reproducible, data-driven machine learning protocol for predicting drug sensitivity in breast cancer cell lines, with the dual objective of identifying potent single agents and synergistic drug combinations. Using curated datasets from the Genomics of Drug Sensitivity in Cancer (GDSC), two predictive approaches were implemented: a standalone XGBoost regressor and a hybrid Autoencoder-XGBoost pipeline. Preprocessing included label encoding, one-hot encoding, Z-score standardization, missing value imputation, and dimensionality reduction via PCA. Model evaluation demonstrated that XGBoost achieved superior performance (MSE = 1.3789, R2 = 0.8145) compared to the hybrid model (MSE = 4.0322, R2 = 0.4577). Interpretability was addressed using SHapley Additive exPlanations (SHAP), which identified TARGET_PATHWAY, DRUG_ID, TARGET, and CELL_LINE_NAME as key predictive features, aligning with established pharmacological mechanisms. Predicted synergy scores, derived from combining model outputs with DrugComb and SynergyDB data, highlighted promising drug pairs such as Bortezomib + Romidepsin and Paclitaxel + Bortezomib. These findings were further supported by PCA-based pharmacological clustering, revealing biologically relevant groupings of drugs with similar mechanisms of action. The proposed protocol provides a transparent and adaptable framework for precision oncology research, enabling both predictive accuracy and biological interpretability. By integrating rigorous preprocessing, model validation, explainability, and drug synergy analysis, this workflow offers a scalable foundation for translational drug discovery and repurposing in breast cancer treatment. 2025 JoVE Journal of Visualized Experiments.
Banerjee, Dyuti; Krishnan, Sivaneasan Bala; Upreti, Kamal; Tharewal, Sumegh Shrikant; Shankar, Uma; Kshirsagar, Pravin; Kumar, Manoj
Journal of Visualized Experiments;Volume;2025;Issue;223;
https://doi.org/10.3791/68705 https://www.scopus.com/pages/publications/105018502113?origin=resultslist
Banerjee D., Koneru Lakshmaiah Education Foundation, India; Krishnan S.B., Singapore Institute of Technology, Singapore; Upreti K., Christ University, India; Tharewal S.S., DBS Global University, India; Shankar U., Qaiwan International University, Iraq; Kshirsagar P., J D College of Engineering & Management, India; Kumar M., Gurukula Kangri University, India