Daily Archives: September 12, 2026

Authors: Associate Professor Dr. Channakeshava RN

Abstract: The rapid integration of 5G networks, Cellular Internet of Things (C-IoT), and Vehicular Ad Hoc Networks (VANETs) has enabled intelligent and highly connected vehicular environments. However, the high mobility of vehicles, rapidly changing channel conditions, varying traffic density, and heterogeneous service requirements create significant challenges for mobility management and radio resource allocation. Conventional mobility and resource management approaches generally react to network changes after they occur, resulting in increased handover latency, resource utilization inefficiency, communication failures, and degraded Quality of Service (QoS). This paper proposes an AI-driven framework for predictive mobility management and context-aware resource allocation in 5G-enabled Cellular-IoT VANETs. The proposed approach utilizes contextual information such as vehicle location, velocity, direction, traffic density, channel conditions, network load, and service requirements to predict future mobility and network states. Artificial Intelligence (AI) and Machine Learning (ML) techniques are employed to anticipate handover requirements and dynamically allocate communication resources according to predicted network conditions. The framework aims to jointly optimize mobility decisions and resource allocation while considering QoS, latency, throughput, spectrum utilization, and energy efficiency. The proposed approach is intended to reduce unnecessary handovers, improve resource utilization, minimize communication interruptions, and enhance the reliability of vehicular-IoT services. Simulation-based evaluation can be performed against conventional mobility management and resource allocation techniques to demonstrate the effectiveness of the proposed framework under varying vehicle mobility and network-load conditions.

DOI: https://doi.org/10.5281/zenodo.22725151

Published by: vikaspatanker

Authors: Gargi Mishra, Shankar Thalla

Abstract: The implementation of Artificial Intelligence (AI) algorithms in clinical decision support systems (CDSS) and biomedical diagnostics has already shown its impressive potential for positive patient impact. At the same time, there are several challenges that need to be addressed when it comes to the deployment of advanced machine learning models because of their 'black box' characteristics that cannot satisfy the needs of clinical practice where transparency is vital for trust and validation of generated advice. This paper introduces the concept of Explainable Artificial Intelligence (XAI) for application in clinical diagnostics through the use of both post-hoc explanation methods and inherently interpretable models. We offer an approach that includes using fuzzy probabilistic trees in order to create an interpretable model along with mapping of biomarkers activation to explain a generated solution visually. Evaluation performed on multimodal biomedical data shows that explanation-enabled models can provide comparable diagnostics accuracy while providing actionable explanations that help to increase clinician acceptance by 23.7%.

DOI: http://doi.org/10.5281/zenodo.22724968

Published by: vikaspatanker

Authors: Dr Vijay Temley

Abstract: The combination of AI with BI is a revolutionary paradigm that would allow organizations to have sustainable performance and make green decisions. This research suggests the development of a framework based on the combination of MIS, BA, and AI to be used for ESG strategy realization. Algorithms used by this framework for the purpose of predicting sustainability indicators include Random Forest, XGBoost, ARIMA, and LSTM networks, which allow for the prediction of sustainability indicators such as energy consumption, carbon emission, and waste production. The results of the quantitative analysis indicate that the use of AI-supported BI systems allows to predict emission trends with the accuracy of 89.2%. Thus, organizations will have an opportunity to conduct proactive, and not reactive, sustainability management.

DOI: http://doi.org/10.5281/zenodo.22724899

Published by: vikaspatanker

Authors: Roshni Thakre, Assistant Professor Rahul Patidar, Professor Akrati Shrivastava

Abstract: Bio-inspired metaheuristics can be useful for the optimization of complex systems. Wireless sensor networks (WSNs) are massively distributed cyber-physical systems whose efficient operation requires appropriate design and control strategies. This work proposes an IoT network optimization model that performs node clustering and efficiently routes data packets from sensing IoT devices to the base station. In the proposed approach, clustering is carried out using the Intelligent Water Drops (IWD) algorithm, which intelligently identifies suitable cluster centers based on the network conditions. The algorithm can operate effectively in dynamic WSN environments without requiring prior knowledge of node configurations. Furthermore, an optimized routing strategy is employed to reduce energy consumption during data transmission. Extensive experiments were conducted using different node distributions and network regions. The results demonstrate that the proposed IWD-based clustering and routing approach significantly improves energy efficiency and extends the overall lifetime of the IoT network.

Published by: vikaspatanker

Authors: Rama Krishna Pinninti, M. Kalyani

Abstract: The extension of differentiation and integration to non-integer orders has been successfully achieved by fractional calculus, which serves as a sophisticated means of describing the dynamics of systems with memory effects or hereditary property. In this paper, we introduce a novel scheme for solving nonlinear FDEs through the use of operator theory coupled with an analytic technique. In particular, the proposed approach involves the Caputo definition of a fractional derivative and provides an elegant scheme for finding the closed-form solutions of a wide variety of nonlinear FDEs, especially focusing on the fractional Riccati equation as a model equation. It will be shown that it is possible to transform fractional-order systems into integral equations, and then apply an iterative process to find the solution of nonlinear FDEs. The numerical simulations show good agreement with the analytical solutions for different orders of fractions.

DOI: http://doi.org/10.5281/zenodo.22724368

Published by: vikaspatanker

Authors: Mariya Josey

Abstract: The present study investigates a green route for the synthesis of silver nanoparticles (AgNPs) using an aqueous leaf extract of Azadirachta indica (neem) and evaluates their catalytic activity in the reduction of methylene blue (MB) in aqueous medium. Fresh neem leaves were washed, chopped, boiled in double-distilled water, filtered, and used as the plant-mediated reducing and capping system. For nanoparticle synthesis, 75 mL of 1 mM silver nitrate solution was mixed with 5 mL of the leaf extract and subjected to microwave irradiation at 800 W and 2450 MHz for 300 s. Formation of AgNPs was indicated by the development of a yellowish-brown to brown colour and was monitored using UV–visible spectroscopy. An absorption band in the region of approximately 408–420 nm was observed and attributed to the surface plasmon resonance (SPR) of silver nanoparticles. The synthesized material was subsequently examined as a nanocatalyst for the NaBH4-assisted reduction of methylene blue. The progress of the reaction was monitored at 664 nm. The original project report indicates that complete reduction occurred within approximately 9 min under one catalyst condition and approximately 15 min when a lower AgNP concentration of 0.011 mg/mL was used, demonstrating that catalyst loading influenced the observed reaction rate. The findings support the usefulness of neem-mediated microwave synthesis as a rapid and comparatively environmentally benign route to AgNPs and indicate their potential for catalytic treatment of dye-containing aqueous systems. Because the original project did not report particle-size measurements, electron microscopy, magnetic characterization, kinetic constants, or statistical testing, these parameters are not inferred in the present article.

DOI: https://doi.org/10.5281/zenodo.22722358

Published by: vikaspatanker

Authors: Hitansh Sharma

Abstract: Propulsion has long been the most difficult subsystem to fit inside a CubeSat. The earliest CubeSats carried no propellant at all — they accepted whatever orbit they were placed in and relied on passive stabilisation to function. That works for simple missions, but it falls apart the moment you need to change your orbit, hold a precise formation, dodge a piece of debris, or come back down in a controlled way. This paper reviews the five propulsion technologies that come up most often in CubeSat mission design: cold-gas thrusters, resistojets, pulsed plasma thrusters (PPTs), electrospray ion engines, and solar sails. For each one, the underlying physics, real performance numbers, flight history, and practical limits inside a CubeSat are examined. The paper then covers delta-v budgeting at the mission level, the regulatory constraints that propulsion adds to a CubeSat program, and where the field is headed in the near term.

DOI: https://doi.org/10.5281/zenodo.22720882

Published by: vikaspatanker

Authors: Charu

Abstract: The rapid development of Artificial Intelligence (AI) and digital technologies is transforming the teaching and learning of mathematics. Traditional mathematics education has increasingly been supplemented by dynamic visualization software, computational systems, intelligent learning environments, and AI-supported content-generation tools. This paper explores the role and educational potential of three contemporary digital tools—GeoGebra, Wolfram|Alpha, and Gamma AI—in mathematics education. GeoGebra provides an interactive environment for geometry, algebra, calculus, statistics, graphing, and three-dimensional mathematics. Wolfram|Alpha offers computational capabilities that can support symbolic manipulation, mathematical calculations, graphing, and step-by-step exploration. Gamma AI, although not primarily a mathematical solver, can support mathematics education through AI-assisted presentation development, visualization, lesson organization, and educational content creation. The paper adopts an exploratory literature-review approach to examine the pedagogical applications, benefits, limitations, and responsible use of these technologies. Existing literature indicates that AI-supported mathematics education can provide personalized assistance, feedback, visualization, and new opportunities for mathematical exploration, while GeoGebra has demonstrated particular value in visualization and conceptual understanding (Yoon & Kwon, 2024; Muslim et al., 2023). At the same time, technological integration can create challenges related to teacher readiness, student overdependence, academic integrity, accessibility, and the development of genuine mathematical reasoning. The paper proposes an integrated AI-Digital Mathematics Learning Model in which the teacher remains the pedagogical decision-maker while digital tools function as visualization, computation, communication, and exploratory supports. The paper concludes that AI and digital tools should complement rather than replace mathematical thinking, teacher guidance, and student reasoning.

Published by: vikaspatanker