https://dspace.iiti.ac.in:8080/jspui/handle/123456789/3645 2026-05-22T04:50:56Z 2026-05-22T04:50:56Z Singh, Sanjeev https://dspace.iiti.ac.in:8080/jspui/handle/123456789/18399 2026-05-18T09:56:11Z 2026-01-01T00:00:00Z

Title: Analytical and Numerical Study of Fractional Logistic Equation With Variable Kernel in the Caputo Sense Authors: Singh, Sanjeev Abstract: We consider a fractional logistic equation involving a Caputo-type fractional derivative of order (Formula presented.) with a variable kernel (Formula presented.), a formulation introduced for its versatility in modeling complex real-world phenomena through an appropriate selection of fractional derivatives. The equilibrium points are identified, and their stability is rigorously analyzed using the (Formula presented.) Laplace transform technique. The existence and uniqueness of the solution are established via the fixed-point theorem. Furthermore, we express the analytic solution as an infinite series by introducing the fractional (Formula presented.) series expansion, which has a positive radius of convergence. By truncating this series, we demonstrate its practical applicability for various kernel functions and different values of (Formula presented.). Additionally, we present an innovative adaptive predictor–corrector method for solving initial value problems (IVPs) that involve a Caputo-type fractional derivative with a variable kernel, taking graded meshes into account. We conducted extensive numerical simulations across various fractional orders and kernels, demonstrating that the obtained results closely align with exact solutions in the integer case, as well as with the truncated (Formula presented.) -series expansion when a large number of nodes are used. Moreover, our approach exhibits satisfactory numerical stability in fractional scenarios. © 2026 John Wiley & Sons Ltd.

2026-01-01T00:00:00Z Tanveer, M. https://dspace.iiti.ac.in:8080/jspui/handle/123456789/18389 2026-05-18T09:56:11Z 2026-01-01T00:00:00Z

Title: Depression and anxiety characterization and detection with multimodal deep learning Authors: Tanveer, M. Abstract: Depression and anxiety are among the most prevalent mental disorders, necessitating accurate characterization for effective diagnosis and treatment. Multimodal deep learning has emerged as an effective approach to enhance diagnostic precision by integrating diverse data sources, including electronic health records, physiological signals and neuroimaging. This Review provides an overview of the recent advancements in multimodal deep learning for depression and anxiety estimation. Key neural network architectures—such as convolutional neural networks for image analysis, recurrent and transformer models for sequential and textual data, and graph neural networks for capturing complex neuroimaging connectivity patterns—are examined. Challenges in data fusion, feature extraction and model interpretability are discussed, alongside strategies to improve generalizability through transfer learning. Future challenges and opportunities are discussed: large-scale datasets, standardized evaluation protocols and interdisciplinary collaboration to bridge the gap between multimodal deep learning and clinical relevance. By summarizing current practices and identifying critical challenges, this Review highlights the transformative potential of multimodal deep learning in advancing the characterization and detection of depression and anxiety. © Springer Nature America, Inc. 2026.

2026-01-01T00:00:00Z Akhtar, Mushir Quadir, A. Tanveer, M. Arshad, Mohd. https://dspace.iiti.ac.in:8080/jspui/handle/123456789/18292 2026-05-14T12:28:22Z 2026-01-01T00:00:00Z

Title: Dual-center RAPID-LSSVM: Radius-adaptive, probability and imbalance driven weighting for Alzheimer’s diagnosis Authors: Akhtar, Mushir; Quadir, A.; Tanveer, M.; Arshad, Mohd. Abstract: Alzheimer’s disease (AD) is a leading neurodegenerative disorder and the primary cause of dementia, where early, reliable diagnosis remains challenging. Although many machine learning methods have been developed for early AD detection, their performance often degrades under label noise, outliers, and class imbalance. To counter these issues, we propose RAPID, a Radius-Adaptive, Probability and Imbalance Driven flexible weighting mechanism, and integrate it into least-squares SVM to obtain two models: RAPID-LSSVM-I (mean-center) and RAPID-LSSVM-II (median-center). RAPID combines three complementary components. First, a radius-adaptive proximity weight that plateaus for samples near the class center and decays smoothly beyond a threshold, preserving the influence of boundary samples while improving robustness to central noise. Second, a local class-probability term that down-weights potentially mislabeled or ambiguous instances, and third, an imbalance-ratio term that compensates for class prior skew. The dual-center design enables either conventional mean centering or a median-based center that is resilient to outliers and asymmetric distributions. To validate the effectiveness of the proposed RAPID-LSSVM models, experiments are conducted on benchmark KEEL and UCI datasets under both clean and label-noise settings. Additionally, we tested the models on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset for AD diagnosis. Empirical findings demonstrate the superiority of the RAPID-LSSVM models over baseline models, highlighting their potential in improving AD diagnosis and handling noisy data. © 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

2026-01-01T00:00:00Z Zachariah, Swaroop Georgy Arshad, Mohd. Sarkar, Mojammel Haque https://dspace.iiti.ac.in:8080/jspui/handle/123456789/18288 2026-05-14T12:28:22Z 2026-01-01T00:00:00Z

Title: Nonparametric estimation of a bivariate mean inactivity time function with application in pink eye disease data Authors: Zachariah, Swaroop Georgy; Arshad, Mohd.; Sarkar, Mojammel Haque Abstract: We propose a novel nonparametric estimator for the bivariate mean inactivity time function (BMITF), study its asymptotic properties, and validate performance via simulations. Its practical relevance is demonstrated using pink eye disease data, estimating infection duration in both eyes. © 2026 Elsevier B.V.

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