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A novel laccase-based biocatalyst for selective electro-oxidation of 2-thiophene methanol
An effective biocatalyst was fabricated for TEMPO-mediated electrooxidation of 2-thiophene methanol. Laccase obtained from Trametes versicolor was covalently immobilized onto electrochemically polymerized ortho-amino benzoic acid (PABA) layer on carbon fiber paper (CFP) electrode. The composite material was characterized by Fourier transformed infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), Optical profilometry (OP), and scanning electron microscopy (SEM). Electrochemical parameters were studied using cyclic voltammetry (CV). Moreover, the developed biocatalyst (Lac-PABA/CFP) was used for selective conversion of 2-thiophene methanol to 2-thiophene carboxaldehyde using 2,2,6,6-Tetramethyl-1-piperidinyloxy, free radical (TEMPO) as a mediator. The formation of the product was confirmed via FTIR, GCMS, 1HNMR and 13CNMR. The enzyme activity of free and immobilized laccase was studied using 2, 2?-Azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) substrate at optimal conditions. Computational In silico analysis also suggested the presence of active sites (T2/T3 trimeric sites-copper ions) in laccase (PDB id: 1KYA's) interacting amino acid residues with the TEMPO and 2-thiophene methanol. Additionally, molecular dynamics simulations revealed that 2-thiophene methanol as compared to TEMPO is more stable (better RMSD, RMSF) in interacting with laccase specifically having strong interaction residues at Asp206, Glu242, Gly262, Gln293, and Glu302. Furthermore, the proposed strategy was confirmed by assessing the various interactions using computational tools. This work would be highly beneficial to develop an electrocatalyst for effective synthesis of 2-thiophene carboxaldehyde, a common intermediate in pharmaceutical, agrochemical, dye, fertilizer and chemical industries. 2021 -
A novel launch power determination strategy for physical layer impairment-aware (PLI-A) lightpath provisioning in mixed-line-rate (MLR) optical networks
In mixed-line-rate (MLR) networks, various data rates, on varied wavelengths, exist on a fiber. In MLR networks, end-to-end lightpaths can be established with the desired line rate; requiring advanced modulation formats for higher data rates. However, along the route, the signals experience different physical layer impairments (PLIs), and their quality also worsens. The transmission signal quality is affected by the launch power, which must be high for lesser noise at the receiver, and must also be low, such that the PLIs do not start to distort the signal. Further, higher launch power also disrupts the existing lightpath and its neighbours. We propose a weighted strategy for provisioning PLI-aware (PLI-A) lightpaths in MLR networks. Through the simulations, we compare and demonstrate that the proposed strategy demonstrates better performances than our previously proposed algorithm (i.e. PLI-Average (PLI-A)), and existing approaches. 2016 IEEE. -
A Novel Machine Learning Approach for Tuberculosis Detection using Volatile Organic Compounds
For world health, better TB diagnosis is still absolutely necessary. Using VOC Atlas, this study assesses a few machine learning techniques for categorizing breath samples depending on volatile organic compound (VOC) profiles. We created a machine learning pipeline and tried out four different models: Random Forest, XGBoost, Multi-Layer Perceptron (MLP), and a 1D-Convolutional Neural Network (1D-CNN). There were 1,500 patient profiles in the dataset spanning three groups: healthy people, drug-sensitive TB cases, and multidrug-resistant TB cases. Using VOC biomarker patterns found in VOC Atlas and prior TB research, these profiles were developed. While XGBoost stood out by reaching 100% accuracy, our studies revealed that most models performed rather well. This implies that gradient boosting-based ensemble models can adequately grasp the complex patterns found in breath data. One major caveat is that we have not tested these models on real clinical breath samples to validate them. Testing these models with actual patient samples in clinical settings would be the next reasonable step. All told, this research provides a strong basis for creating non-invasive ways to detect illnesses. 2025 IEEE. -
A Novel Machine Learning Ensemble Approach for Corrupt Data Packet Identification
In contemporary network infrastructures, ensuring the fidelity of data transmission is paramount for robust communication and security. The intrusion of corrupted data packets can severely degrade network efficiency, resulting in critical data loss, exploitable security gaps, and suboptimal resource allocation. This paper indicates the significantly increase detection accuracy and system resilience by synergistically using the predictive capability of many machine learning paradigms especially. This paper employs sophisticated feature engineering to extract discriminative attributes from network packet headers and payloads, followed by a refined ensemble learning strategy that leverages both stacking and boosting techniques for optimal classification performance. Compared to conventional single-model techniques, evaluated on real-world network traffic datasets our model shows a significant increase in key performance measures. Here a pioneering hybrid machine learning ensemble framework designed for the precise identification and mitigation of corrupted data packets. Notably, the ensemble framework excels in minimizing false positives, enabling real-time packet analysis and bolstering network security. This study contributes to the evolution of intelligent, adaptive network defense mechanisms, providing a scalable and high-performance solution for safeguarding data integrity and mitigating the deleterious effects of corrupted data packets in modern, high-throughput communication environments. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing
Congenital heart disease (CHD) represents a multifaceted medical condition that requires early detection and diagnosis for effective management, given its diverse presentations and subtle symptoms that manifest from birth. This research article introduces a groundbreaking healthcare application, the Machine Learning-based Congenital Heart Disease Prediction Method (ML-CHDPM), tailored to address these challenges and expedite the timely identification and classification of CHD in pregnant women. The ML-CHDPM model leverages state-of-the-art machine learning techniques to categorize CHD cases, taking into account pertinent clinical and demographic factors. Trained on a comprehensive dataset, the model captures intricate patterns and relationships, resulting in precise predictions and classifications. The evaluation of the models performance encompasses sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Remarkably, the findings underscore the ML-CHDPMs superiority across six pivotal metrics: accuracy, precision, recall, specificity, false positive rate (FPR), and false negative rate (FNR). The method achieves an average accuracy rate of 94.28%, precision of 87.54%, recall rate of 96.25%, specificity rate of 91.74%, FPR of 8.26%, and FNR of 3.75%. These outcomes distinctly demonstrate the ML-CHDPMs effectiveness in reliably predicting and classifying CHD cases. This research marks a significant stride toward early detection and diagnosis, harnessing advanced machine learning techniques within the realm of ECG signal processing, specifically tailored to pregnant women. 2024 by the authors. -
A novel map matching algorithm for real-time location using low frequency floating trajectory data
The continuous enhancement of technologies and modern well-equipped infrastructures are necessary for easy life. Road accident and missing vehicle ratio are very challenging in preventing misshapenness because these are continually increasing due to traffic hazards. The single way to protect human life from such type of conditions that is more reliable navigation services such as correct location tracking of vehicles on the road network. The real-time location tracking methods fully depends on the map matching algorithms, which also compute a reliable path on the road network. A smart vehicle can provide more reliable tracking services during or before any misshaping using proposed map matching algorithm. This work contributes to ensure correct location for necessary action during misshaping, alert accident zone and communicate messages without wasting valuable time. The proposed approach is validated on the real tracking data and is compared against poor GPS service. Copyright 2023 Inderscience Enterprises Ltd. -
A novel mathematical investigation of carbon emissions, economic growth, carbon taxation and renewable energy dynamics: stability analysis and forecasting
The main cause of global warming is carbon dioxide (CO2) emissions, acting as a significant greenhouse gas. These emissions stem from various sources and significantly contribute to climate change. Fortunately, we have countermeasures like carbon taxes to curb CO2 output. Carbon taxes incentivise a reduction in CO2 production and a shift towards cleaner energy sources by placing a cost on emissions. This paper investigates the interplay between carbon tax policy, carbon emissions, economic output (GDP) and renewable energy consumption. A system of differential equations is constructed to model these relationships based on a comprehensive literature review. Parameter estimation based on real-world data yielded successful fits for the variables. However, the fit for the carbon tax equation is less conclusive, suggesting a more complex relationship with carbon emissions. Stability analysis and the boundedness of the system are carried out. Auto-regressive integrated moving average (ARIMA) forecasting is employed to predict future trends. The results suggest a projected increase in GDP and renewable energy consumption over the next ten years, indicating a potential for a cleaner energy transition. Furthermore, the forecasts anticipate a rise in carbon tax implementation. This analysis emphasises how important carbon taxes are for cutting emissions and advancing renewable energy. Results indicate that carbon taxes can promote decarbonisation and economic growth, despite the complicated link between them and CO2 emissions. Both GDP growth and the use of renewable energy are anticipated to increase. However, policies must be improved to combat climate change effectively. Future studies should improve parameters and investigate other relevant elements to promote a low-carbon future. Indian Academy of Sciences 2025. -
A novel mobile sink placement in wireless sensor network using deep maxout network based energy prediction with adjacent cell score
The majority of Wireless Sensor Networks (WSNs) are made up of energy- and cost-efficient detecting nodes. Traditional wireless sensor networks encounter serious problems, including latency, network failure, and congestion, since they rely on individual base stations (BSs) to gather data from the whole network. Sensor nodes adjacent to the base station will use more energy because of excessive energy consumption and energy-hole constraints, affecting the network's life. Understanding the best place for mobile sink nodes can help alleviate this issue by lowering energy usage and extending the network's lifespan. In this paper, utilizing a deep learning-based energy prediction and neighbour cell score model, we build and construct an efficient method to locate mobile receivers using distance, expected energy, and fairness variables. Furthermore, a Deep Maximum Output Network (DMN) calculates the desired power. However, the minimum length, maximum residual energy, complete normalized right, maximum network lifespan, and maximum normalized throughput for our suggested neighbor-based cell scoring with Deep Maxout Network are 137.364, 30.903, 64.426, and 60.613, respectively. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
A novel model for speech to text conversion /
International Refered Journal of Engineering And Science, Vol-3 (1), pp. 39-41,ISSN-2319-183X. -
A novel moems sensor design simulation and analysis with MEEP /
International Journal Of Engineering Technology Science And Research, Vol.2, Issue 8, pp.319-325, ISSN No: 2394-3386. -
A novel multi functional multi parameter concealed cluster based data aggregation scheme for wireless sensor networks (NMFMP-CDA)
Data aggregation is a promising solution for minimizing the communication overhead by merging redundant data thereby prolonging the lifetime of energy starving Wireless Sensor Network (WSN). Deployment of heterogeneous sensors for measuring different kinds of physical parameter requires the aggregator to combine diverse data in a smooth and secure manner. Supporting multi functional data aggregation can reduce the transmission cost wherein the base station can compute multiple statistical operations in one query. In this paper, we propose a novel secure energy efficient scheme for aggregating data of diverse parameters by representing sensed data as number of occurrences of different range value using binary encoded form thereby enabling the base station to compute multiple statistical functions over the obtained aggregate of each single parameter in one query. This also facilitates aggregation at every hop with less communication overhead and allows the network size to grow dynamically which in turn meets the need of large scale WSN. To support the recovery of parameter wise elaborated view from the multi parameter aggregate a novelty is employed in additive aggregation. End to end confidentiality of the data is secured by adopting elliptic curve based homomorphic encryption scheme. In addition, signature is attached with the cipher text to preserve the data integrity and authenticity of the node both at the base station and the aggregator which filters out false data at the earliest there by saving bandwidth. The efficiency of the proposed scheme is analyzed in terms of computation and communication overhead with respect to various schemes for various network sizes. This scheme is also validated against various attacks and proved to be efficient for aggregating more number of parameters. To the best of our understanding, our proposed scheme is the first to meet all of the above stated quality measures with a good performance. 2020, Springer Science+Business Media, LLC, part of Springer Nature. -
A novel multigrade classification in FL using brain MRI images based on FHAT_EfficientNet
This paper establishes the fractional harmony artificial tree (FHAT)_EfficientNet for multi-grade classification in federated learning (FL). Here, the established FHAT is attained by the integration of the fractional calculus (FC) and harmony search-based feedback artificial tree (HSFAT) algorithm, and the HSFAT is developed by the combination of harmony search (HS) and feedback artificial tree (FAT). Initially, the input MRI image is taken from a particular dataset and subjected to pre-processing. Thereafter, tumour segmentation is accomplished based on fuzzy local information c-means (FLICM). Later, image augmentation and feature extraction are performed. Finally, the multi-grade classification is carried out using EfficientNet fine-tuned based on the proposed FHAT. Moreover, the established FHAT_EfficientNet attained better accuracy, specificity, sensitivity, mean squared error (MSE), root mean square error (RMSE), and loss function of 0.917, 0.936, 0.966, 0.058, 0.241, and 0.083. Copyright 2025 Inderscience Enterprises Ltd. -
A Novel Nature-Inspired Coconut Tree Optimization Technique forEngineering Applications
Coconut tree optimization technique is a novel optimization algorithm that is motivated by the physical structure of the coconut tree. The search for optima in a feasible region is chosen between a random root and any point in a leaf. Coconut tree optimization is a pseudo meta-heuristic algorithm wherein search of solution is carried out using random as well as gradient movement with a memory stack that contains local optima. Nonlinear optimization problems consisting of equality and inequality constraints were solved using the proposed algorithm. The algorithm is validated for linear and nonlinear optimization problems. The comparative study and analysis were detailed for existing algorithms used in domain-specific physical problems. The algorithm is compared with the genetic algorithm and particle swarm optimization by considering standard test functions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Novel Network-Based Digital Payment Fraud Detection using OP-ELM Network
Internet and Industry 4.0 have helped banks and other financial organizations enhance procedures and decrease fraud. Digital payment techniques have helped internet buying skyrocket. Industry 4.0 promotes process optimization, ecosystem collaboration, and growth by integrating digital systems with physical and IoT devices. Unfortunately, digital payment cybercrime has grown rapidly, causing large annual financial losses. Because of this, fraud detection systems must be constantly improved. The suggested TLELM approach includes preprocessing, feature selection, and model training. Preprocessing involves standardizing data, eliminating outliers, and handling null or missing values. The CSO technique selects relevant features by optimizing selection. A new approach combining TL and ELM improves DPFD procedures. The new metaheuristic TL excels at combinatorial optimization. TLELM efficacy was examined using multiple datasets. The recommended method was compared to top-tier algorithms for binary and multiclass data categorization. Experimental data shows that TLELM outperforms other models with 99.37% accuracy. This study found that TLELM can detect online payment fraud. The method optimizes fraud detection and classification accuracy using TL and ELM. Add more real-world datasets to strengthen robustness and make additional improvements to handle future fraud methods. 2025 IEEE. -
A novel optimised method for speckle reduction in medical ultrasound images
The advancement of medical imaging techniques evolving from X-ray to PET images and the medical image analysis helped medical experts to detect, diagnose and offer treatments for complex disorders and deadly diseases in the human body. Among the various modalities used, Ultrasound imaging is the most widely accepted modality because of its affordability, non-invasive nature and various other features. But the presence of speckle noise in ultrasound image lowers the image quality and reduces diagnostic value. This article states an improved hybrid speckle noise reduction method, a combined application of Kuan and non-local means filters. In this method, Kuan filter is used to sharpen the edges and thereafter the speckle noise elimination is done by using the non-local means. In addition, the performance of the proposed hybrid filter and its design parameters are optimised by using a meta-heuristic called grey wolf optimiser. The performance of hybrid method is evaluated by analysing a chosen set of well-known post filtering methods used for speckle reduction with given ultrasound B-mode images. The comparison of test results using remarkable performance metrics and computation time demonstrate that the hybrid method can be used as the efficient speckle reduction method for image analysis. Copyright 2022 Inderscience Enterprises Ltd. -
A Novel Paradigm for IoT Security: ResNet-GRU Model Revolutionizes Botnet Attack Detection
The rapid proliferation of the Internet of Things (IoT) has engendered substantial security apprehensions, chiefly due to the emergence of botnet attacks. This research study delves into the realm of Intrusion Detection Systems (IDS) by leveraging the IoT23 dataset, with a specific emphasis on the intricate domain of IoT at the network's edge. The evolution of edge computing underscores the exigency for tailored security solutions. An array of statistical methodologies, encompassing ANOVA, Kruskal-Wallis, and Friedman tests, is systematically employed to illuminate the evolving trends across multiple facets of the study. Given the intricacies entailed in feature selection within edge environments, Chi-square analyses, Recursive Feature Elimination (RFE), and Lasso-based techniques are strategically harnessed to unearth meaningful feature subsets. A meticulous evaluation encompassing 19 classifiers, meticulously selected from both machine learning (ML) and deep learning (DL) paradigms, is rigorously conducted. Initial findings underscore the potential of the Gated Recurrent Unit (GRU) model, especially when coupled with intrinsic lasso-based feature selection. This promising outcome catalyzes the formulation of an ensemble approach that harnesses multiple LassoCV models, aimed at amplifying feature selection proficiency. Furthermore, an optimized ResNet-GRU model emerges from the fusion of the GRU and ResNet architectures, with the objective of augmenting classification performance. In response to mounting concerns regarding data privacy at the edge, a resilient federated learning ecosystem is meticulously crafted. The seamless integration of the optimized ResNet-GRU model into this framework facilitates the employment of FedAvg, a widely acclaimed federated learning methodology, to adeptly navigate the intricacies associated with data sharing challenges. A comprehensive performance evaluation is undertaken, wherein the ResNet-GRU model is benchmarked against FedAvg and a diverse array of other federated learning algorithms, including FedProx and Fed+. This extensive comparative analysis encompasses a spectrum of performance metrics and processing time benchmarks, shedding comprehensive light on the capabilities of the model. (2023), (Science and Information Organization). All Rights Reserved. -
A Novel Preprocessing Model for Multi Modal Brain MRI image Classification for Stroke Prognosis
Magnetic Resonance Imaging (MRI) is an imaging technique used for the diagnosis and observing the progression in various neurological disorders. Stroke is one of the prominent neurological disorders that creates significant impacts in the patients. It occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissues from getting oxygen and nutrients. Multimodal data from various modalities help clinicians in proper prognosis of stroke. Ischemic Stroke Lesion Segmentation Challenge (ISLES22) provides data of stroke data for various stroke patients, the dataset consists of three modalities of data Fluid Attenuated Inversion Recovery (FLAIR), Apparent Diffusion Coefficient (ADC) and Diffusion-Weighted Imaging (DWI). Multimodal data gives a comprehensive understanding of the brain and the stroke lesions. Complex algorithms and processing steps are required to ensure that the data is prepared for further processing. The objective of this experimental research is to create a novel multimodal preprocessing model that can be used for the preprocessing of the multimodal data from various MRI modalities (FLAIR, DWI and ADC). The proposed model supports the automatic removal of artefacts from the multimodal data, by identifying and applying the best preprocessing techniques for Image Registration (Affine or non-rigid transformations), Normalization (Z Score or min-max normalizations), Denoising Techniques (Gaussian, Median, Non-Local Means, or Anisotropic Diffusion filters) and Bias Field correction. The best technique is identified using the evaluation techniques of Dice Coefficient, Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Root Mean Squared Error (RMSE). Preprocessing is critical process to improve the outcome of the subsequent analysis including segmentation. Here, we propose an Enhanced Image Registration and Artefact Correction (EIRAC) model with Best Image Registration Technique (BIRT) and Multiple Orientation Normalization Denoising and Bias field correction Parallelly (MONDBP) algorithms for the preprocessing of multimodal MRI images to provides better results for the segmentation of stroke lesions through Machine Learning models. 2025, Binghamton University Libraries. All rights reserved. -
A Novel Preprocessing Technique to Aid the Detection of Infected Areas of CT Images in COVID-19 Patients Artificial Intelligence (AI) for Communication Systems
An innovative preprocessing method for discerning infected areas in CT images of COVID-19 is described in this abstract. The methodology being suggested exploits the capabilities of artificial intelligence (AI) to improve disease detection communication systems. By employing sophisticated AI algorithms to preprocess CT images, the method seeks to increase the precision and effectiveness of COVID-19-associated area detection. The incorporation of artificial intelligence (AI) into communication systems facilitates enhanced image analysis, resulting in improved diagnostic capabilities and treatment strategizing. The study's findings demonstrate the potential of preprocessing techniques powered by artificial intelligence in augmenting communication systems with the aim of enhancing healthcare outcomes. 2024 IEEE. -
A Novel Real-Time Posture Monitoring System Using Signal Processing and Computer Vision Techniques
This paper presents a novel real-time posture monitoring system using signal processing and computer vision techniques to provide accurate feedback on body posture. By measuring key angles between the head-shoulder and shoulder-hip regions, the system identifies deviations from ideal posture. A Butterworth low-pass filter is employed to smooth the posture data, significantly reducing noise and misclassification of sudden movements as poor posture. The proposed systems novelty lies in the integration of signal processing to enhance data interpretation, ensuring that momentary shifts are filtered out, resulting in more reliable classification and feedback. The system was tested in real-world scenarios, demonstrating its ability to offer immediate, high-accuracy posture feedback. Unlike conventional systems that rely solely on raw data, our approach uses smoothed, noise-free data to provide a clearer understanding of posture, making it suitable for deployment in workplaces, home offices, and rehabilitation centers. Future work will focus on multi-joint analysis, duration-based feedback mechanisms for sustained posture deviations, and the impact of camera angle on measurement accuracy. Overall, the system provides a cost-effective and efficient solution for continuous posture monitoring, aiming to improve health and ergonomics across various settings. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A Novel Ridge Estimator for the Liu-Type Logistic Regression Model and Its Application to Demographic Data from Urban Slums in Karnataka
This study introduces new ridge estimators for the Liu-type logistic regression model which helps to improve the model performance if multicollinearity is present in the independent variables. Logistic regression is the regression that helps to model binary outcomes but it provides inaccurate and unstable regression coefficients in the presence of multicollinearity. As a result of this, the variance might increase and the predictive accuracy of the model gets reduced. To overcome this issue, the Liu-type logistic regression is used which uses ridge and Liu parameters to provide stable and accurate regression coefficients. Several ridge estimators are proposed in this study based on the Liu-type logistic model which can handle multicollinearity and give better predictive performance of the model. The proposed estimators have been tested on the demographic dataset from Urban Slums in Karnataka and through the empirical analysis it is observed that one among the new ridge estimators give the lowest Mean Square Error (MSE) when compared to the existing ridge estimators. The results show the usefulness of the new estimators to improve the performance of the model and also contribute to the betterment of the logistic regression techniques. This work highlights the critical need to handle multicollinearity in regression analysis and sets the path for researchers to further improve the estimators in the future. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.