Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

EnerMorph-net: energy state morphing hyper network for self-optimizing IoT-driven electric vehicle systems

Abstract Aim An adaptive deep learning architecture, termed EnerMorph-Net, is proposed to overcome the limitations of conventional models in Internet of Things (IoT)-enabled electric vehicle transportation systems. The framework captures dynamic energy state variations influenced...

Solving Bi-objective Interval Assignment Problem using Trisectional Fuzzy Trapezoidal Approach

Background The conventional Assignment Problem (AP) may no longer be sufficient to handle real-world problems as they get more multifaceted and incorporate numerous, frequently conflicting objectives. In response, the Bi-Objective Assignment Problem (BOAP) provides an extended op...

Fuzzy-logic enhanced internet of things model for uncertainty-adaptive medical image segmentation

Deep learning (DL) models for brain tumour segmentation (BTS) typically produce a fixed decision boundary and do not allow the uncertainty they estimate to impact the segmentation decision (SD). This work presents a FUZIONet-Med: A Fuzzy Uncertainty-Adaptive IoT Edge Intelligence...

Integrated Hydrochemical and Statistical Assessment of Groundwater Quality and Irrigation Suitability in the Semi‐Arid Hard‐Rock Aquifer of Madurai North Taluk, Tamil Nadu, India

ABSTRACT Groundwater is the principal irrigation source in the semiarid hard‐rock aquifer of Madurai North Taluk, Tamil Nadu, India, where increasing agricultural demand and climatic variability have intensified pressure on groundwater resources. This study presents an integrated...

Comprehensive review of substrate selection process conditions risk and industrial feasibility for sustainable lactic acid production

One important framework chemical is lactic acid (2-hydroxypropionic acid) for food, medicine, and bioplastics. To meet the increasing demand for lactic acid, its procedures must be optimized for higher yields, improved efficiency, and sustainable development. Traditional fermenta...

Deep reinforcement learning-optimized adaptive authenticated encryption and dynamic transmission control for secure LoRaWAN IoT communication

The growth of Internet of Things (IoT) devices in the industrial, environmental and urban sectors has raised the need for secure, energy efficient, and adaptive communication solutions. The LoRa (Long Range) technology combined with the LoRaWAN protocol stack is very promising du...

A Graph-Theoretical Framework for Identifying Critical Data Center Using Status Unequal Domination

Critical nodes are essential components of a graph network as they drive information flow and significantly influence the system’s ability and vulnerability. This work introduces status unequal domination ([Formula: see text]-domination), a new framework for identifying critical...

Advancing Soil Assessment Quality for Crop Yield Prediction Using an Optimized Category Integrated Dual Task Graph Neural Network Approach

ABSTRACT Soil quality assessment plays a role in improving agricultural productivity and sustainability, as it is essential for making informed decisions in precision farming. This study proposes a new model for soil assessment quality (SAQ) and crop yield prediction (CYP) based...

Sparse attention FT-transformer for early breast cancer prediction using structured clinical data

Early detection of breast cancer is essential for improving clinical outcomes and reducing mortality. This study proposes a Sparse Attention–based FT-Transformer framework for early prediction using structured clinical diagnostic data, designed to capture high-order feature inter...

Data-driven robust design for industrial cylindrical plunge grinding of piston rings: Requirements for reliable digital-twin development under production noise

Cylindrical plunge grinding of piston rings is a precision finishing operation, and its dimensional accuracy governs engine sealing and emission compliance. It is widely assumed that such accuracy can be controlled through fixed machine settings; this study tests this assumption...

Effect of hybrid layer dominance on mechanical performance, moisture diffusion, and biodegradation of kenaf/pineapple leaf fiber-reinforced PLA laminated biocomposites

Abstract Hybrid natural fiber-reinforced biocomposites can be sustainable materials for lightweight engineering, but the impact of layer dominance on the multifunctional performance of such biocomposites is still poorly known. This study focused on kenaf fiber (KF) and pineapple...

Investigation of Si1-xGeₓ nanosheet TFET performance and single-event transients through germanium mole fractions

This paper presents a novel vertically stacked SiGe nanosheet Tunnel Field-Effect Transistor (SiGe NS-TFET) structure in which the channel region is engineered with a Si 1-x Geₓ compound material to improve the device electrical performance. The electrical performance of the devi...

Digital readiness, trust, and collaboration: pathways to AI adoption in informal supply chain workers

Purpose The study looks at the ways that digital readiness affects the adoption of artificial intelligence (AI) and, in turn, the improvement of livelihoods for informal supply-chain workers. It focuses on the mediating functions of collaborative strategies and trust in AI tools....

Closed-loop predictive resonance control through adaptive stiffness–damping co-regulation for continuous piezoelectric energy harvesting

Piezoelectric energy harvesting offers a sustainable solution for powering autonomous electronic devices, but conventional harvesters suffer from resonance mismatch under varying vibration frequencies. In this research, a Closed-Loop Predictive Resonance Control with Adaptive Sti...

An energy-aware federated intelligence framework for sustainable WSN-IoT ecosystems

With the rapid development of Internet of Things (IoT), the deployment of Wireless Sensor Networks (WSNs) as vital infrastructure for environmental monitoring, industrial automation, precision agriculture, and smart city applications has increased significantly. Despite this, WSN...

Adaptive timestamp based concurrency control for efficient transaction processing in permissioned blockchains

Efficient concurrency control is key on permissioned blockchain platforms like Corda, where transaction volumes are high and contention is rife. In some hotspot situations, conventional concurrency control mechanisms such as Two-Phase Locking, Optimistic Concurrency Control, Mult...

ConTMSLE: A Deep Learning Approach Using a Taylor-Based MSLE Loss Function to Detect Blackhole and Sybil Attacks in WSNs

Blackhole and Sybil attacks represent major security threats to Wireless Sensor Networks (WSNs). Detecting these threats is essential to preserving the integrity of network operations. However, their detection is complicated by the various ways they undermine the system, causing...

Predator–prey dynamics in the interaction of HIV with CD4+ T cells, incorporating cytotoxic immune response

This study presents a mathematical model describing the interaction between human immunodeficiency virus (HIV) and CD4+ T cells within a predator–prey framework, where HIV acts as the predator and CD4+ T cells serve as the prey. Unlike previous simplified models that treat infect...

Efficient Resource Management in 6G for Sustainable Spectrum Allocation Using Dual‐Channel Temporal Convolutional Frequency‐Separated Attention Network with Generation Rights Trading Blockchain

ABSTRACT The need for effective spectrum allocation to guarantee highly reliable, minimal‐latency, and sustainable connections has increased due to the exponential expansion of wireless communications and the introduction of 6G technologies. In order to cope with these challenges...

Adaptive multimodal large language model with hierarchical cross modal grounding for real time industrial visual anomaly detection on edge devices

The problem of industrial surface anomaly detection is still a disheartening one because there are light defect data, computationally intensive, and interpretable only to a limited extent when applied in real-world systems. In this paper, we present the HCG-MLLMAD - Hierarchical...