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A novel fibrinolytic enzyme producer from mangrove soil sediments: Screening, isolation, strain improvement, and fermentation
Fibrinolytic enzymes are used for addressing many cardiovascular disease conditions. However, current fibrinolytic enzymes are highly expensive with many side effects which necessitate the development of alternative cost-effective processes for low-cost production of these lifesaving enzymes. Since the availability of a suitable strain is considered the basic requirement for any process development effort, we started our work in this direction with screening and isolation of fibrinolytic enzyme producers. The goal of this study was to screen and isolate fibrinolytic enzyme producers from a relatively unexplored environment, i.e., the mangroves. Mangroves are wetlands and are one of the unique and less studied habitats for the production of therapeutic molecules. Microbiota that produces fibrinolytic enzymes from Coringa mangroves located in Kakinada District, Andhra Pradesh, India, is lacking. Therefore, in this study, fibrinolytic enzyme-producing bacterium was screened from samples obtained from Coringa mangroves. Out of 200, protease enzyme-producing isolates obtained from screening 30 mangrove soil samples, 7 isolates exhibiting fibrinolytic activity were selected. Out of these 7, the highest fibrinolytic enzyme-producing bacterial strain (AIBL_AMSB2) was characterized by biochemical and genomic methods, which was finally identified as Bacillus subtilis subsp. Inaquosorum by 16S rRNA analysis. The strain was also found to be an amylase producer. AIBL_AMSB2 was subjected to strain improvement using random mutagenesis techniques (i) Ultraviolent radiations (UV) and (ii) Ethyl methyl Sulphonate (EMS), which resulted in an improved mutant strain AIBL_AMSB2_M7E32 exhibiting 54.70% improvement in fibrinolytic activity. Batch fermentation in controlled experimental conditions using the obtained mutant strain resulted in a 133.54% increase in growth OD and a 391.11% increase in enzyme activity. Thus, the study reports an increased fibrinolytic enzyme activity producing mangrove isolate and its production by submerged fermentation. Further studies to prove the potential of the enzyme on blood clots are necessary to utilize for industrial application. 2024 Bhavana Sompalli and Alok Malaviya. -
A Novel Framework for Harnessing AI for Evidence-Based Policymaking in E-Governance Using Smart Contracts
Harnessing AI for evidence-based policymaking in e-governance has the potential to revolutionize the way governments formulate and implement policies. By leveraging AI technologies, governments can analyze vast amounts of data, extract valuable insights, and make informed decisions based on evidence. This chapter explores the various ways in which AI can be employed in e-governance to facilitate evidence-based policymaking. It discusses the use of AI algorithms for data analysis and prediction, enabling governments to identify patterns, trends, and emerging issues from diverse data sources. Moreover, AI-powered tools can enhance citizen engagement and participation, by facilitating data-driven decision-making processes and providing personalized services. Additionally, AI can assist in policy evaluation and impact assessment, by automating the collection and analysis of data, thus enabling governments to measure the effectiveness of their policies in real-time. Furthermore, AI can contribute to enhancing transparency and accountability in e-governance, by automating processes such as fraud detection and risk assessment. Despite the immense potential, the adoption of AI in e-governance must address challenges such as data privacy, algorithmic bias, and ethical considerations. This chapter concludes by emphasizing the importance of building trust, ensuring fairness, and promoting responsible AI practices to maximize the benefits of AI in evidence-based policymaking for e-governance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
A novel free space communication system using nonlinear InGaAsP microsystem resonators for enabling power-control toward smart cities
Nowadays, the smart grid has demonstrated a great ability to make life easier and more comfortable given recent advances. This paper studies the above issue from the perspective of two important and very useful smart grid applications, i.e., the advanced metering infrastructure and demand response using the instrumentality of a set of well-known scheduling algorithms, e.g., best-channel quality indicator, log rule, round robin, and exponentialproportional fairness to validate the performance. To increase the data transmission bandwidth, a new concept of optical wireless communication known as free-space optical communication (FSO) system based on microring resonator (MRR) with the ability to deliver up to gigabit (line of sight) transmission per second is proposed for the two studied smart grid applications. The range between 374.7 and 374.79THz frequency band was chosen for the generation of 10 successive-carriers with a free spectral range of 8.87GHz. The ten multi-carriers were produced through drop port of the MRR. The results show up to 10 times bandwidth improvement over the radius as large as 600m and maintain receive power higher than the minimum threshold (? 20dBm) at the controller/users, so the overall system is still able to detect the FSO signal and extract the original data without detection. 2019, Springer Science+Business Media, LLC, part of Springer Nature. -
A Novel Fuzzy-Based Thresholding Approach for Blood Vessel Segmentation from Fundus Image
Retinal vessel segmentation is a vital part of pathological analysis in Fundus imaging. The automatic detection of blood vessels resolves several issues in the manual segmentation process. Most unsupervised segmentation methods depend on conventional thresholding techniques for final vessel extraction. It may lead to the loss of some vessel pixels, leading to inaccurate analysis of retinal diseases. In this work, we incorporate fuzzy concepts into two threshold-based vessel detection methods, namely mean-c thresholding and Iso-Data thresholding, which results in a mask consisting of membership values rather than binary values. The two fuzzy-based thresholding algorithms are applied independently on each image, and the resultant membership image (mask) is fused to get a single membership mask. The fusion is performed using fuzzy union operation. Experiments are carried out with Fundus images from DRIVE, STARE and CHASE_DB1 databases.ses. The proposed fusion framework gives a 3%, 6%, and 5% increase in sensitivity compared to traditional thresholding methods when applied to the DRIVE, STARE, and CHASE_DB1 databases, respectively. The accuracy obtained for the datasets is 96.02%, 94.57%, and 94.34%, respectively. 2023 by the authors. -
A Novel Generative Adversarial Network-Based Approach for Automated Brain Tumour Segmentation
Background: Medical image segmentation is more complicated and demanding than ordinary image segmentation due to the density of medical pictures. A brain tumour is the most common cause of high mortality. Objectives: Extraction of tumorous cells is particularly difficult due to the differences between tumorous and non-tumorous cells. In ordinary convolutional neural networks, local background information is restricted. As a result, previous deep learning algorithms in medical imaging have struggled to detect anomalies in diverse cells. Methods: As a solution to this challenge, a deep convolutional generative adversarial network for tumour segmentation from brain Magnetic resonance Imaging (MRI) images is proposed. A generator and a discriminator are the two networks that make up the proposed model. This network focuses on tumour localisation, noise-related issues, and social class disparities. Results: Dice Score Coefficient (DSC), Peak Signal to Noise Ratio (PSNR), and Structural Index Similarity (SSIM) are all generally 0.894, 62.084 dB, and 0.88912, respectively. The models accuracy has improved to 97 percent, and its loss has reduced to 0.012. Conclusions: Experiments reveal that the proposed approach may successfully segment tumorous and benign tissues. As a result, a novel brain tumour segmentation approach has been created. 2023 by the authors. -
A Novel Georouting Potency based Optimum Spider Monkey Approach for Avoiding Congestion in Energy Efficient Mobile Ad-hoc Network
Mobile Ad-hoc Network (MANET) is one of the recent fields in wireless communication that involves a large number of wireless nodes, which could be changed arbitrarily with the ability to link or exit the system anytime. Nevertheless, network congestion and energy management is a major problem in MANET. Consequently, the infrastructure of a network changes frequently which results in data loss and communication overheads. Therefore, in this paper, a novel Georouting Potency based Optimum Spider Monkey algorithm has been proposed for energy management and network congestion. The proposed technique in MANET is implemented using Network Simulator2 platform and the proposed outcomes show that the node energy, overload, and delay are minimized by increasing the quantity of packets transmitted through the network. Moreover, the delay in routing overhead and congestion is decreased by the proposed protocol. Consequently, the energy management is enhanced based on constraints of delay, energy consumption, and routing overhead of the nodes. Thus the effectiveness of the proposed protocol is enhanced by selecting the optimal path within the network, decreasing the consumption of energy, and congestion avoidance. Sequentially, the performance of the proposed routing algorithm is compared to existing protocols in terms of end-to-end delay, throughput, Packet Delivery Ratio, energy consumption, etc. Thus the result shows that the lifetime of the nodes have been enhanced by a high 98% of throughput ratio, less 0.01% of energy consumption, and congestion avoidance using the proposed network. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
A Novel Hybrid Model for Time Series Forecasting Using Artificial Neural Network and Autoregressive Integrated Moving Average Models
Enhancing forecast accuracy while using time series is a potential area of research. Evidences exist in the literature to show that hybrid models can significantly improve the forecasting performance, as they combine the exclusive strengths of different models. This paper presents a novel hybrid model by combining forecasts from Autoregressive Integrated Moving Average (ARIMA) and artificial neural network (ANN) models with suitable weights, thereby improving the forecast accuracy. The methodology employs appropriate error metrics to construct the weights. The paper further demonstrates the efficiency of the proposed methodology through an empirical study, based on two real-world time series data sets. Thus, the new methodology can be used for enhancing the forecast accuracy in a number of fields of research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A novel image compression method using wavelet coefficients and Huffman coding
Compressing medical images to reduce their size while maintaining their clinical and diagnostic information is crucial. Because medical images can be large and demand a lot of storage and transmission capacity, effective compression methods aid medical institutions in better storing and transmitting medical images, reducing costs, speeding up data transfer, and simplifying managing image databases. However, it is essential to note that image compression in medical imaging can also introduce drawbacks, such as loss of information and poor output image quality. Therefore, a suitable compression algorithm and parameter must be chosen to balance file size and visual fidelity. This paper suggests an effective image compression method employing the Discrete Wavelet Transform (DWT), followed by a reduction operation and Huffman coding to produce a mere lossless encoding to transmit the images over a channel. The extracted DWT coefficients are mapped to the nearest integral value. All four sub-bands of DWT are joined, and then a window of 3 3 is selected for reduction operation by choosing the origin as the pivot element. The Huffman coding algorithm is used to compress the processed image. The pivot origin element is used in the reversible reduction while uncompressing the image. When sending compressed data across an unreliable route, the window size and pivot element selection keep the compressed data secure. Standard measures such as bits per pixel (BPP) and compression ratio (CR) are used to assess the suggested approach. The efficiency of the suggested course of action is supported by the research's findings, which use a peak signal-to-noise ratio (PSNR) of 54.66 dB. 2023 The Authors -
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-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 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 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 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 route for isomerization of ?-pinene oxide at room temperature under irradiation of light-emitting diodes
Present investigation demonstrates the potential use of HY-zeolite for photochemical applications in the selective isomerization of ?-pinene oxide to carveol. In this study, ultraviolet lamp and LED (390 nm) light sources were employed under atmospheric conditions. The results revealed that light penetration through protonated zeolite cavity promotes the hydrogen radical formation, facilitating the isomerization reaction in the presence of dimethylacetamide solvent to achieve up to 60% and 40% conversion of ?-pinene oxide to selective carveol (71%) under light irradiation. Here, using in situ spectroscopic studies (EPR and fluorescence), to confirm the hydrogen radical generation after light irradiation on the reaction mixture. Besides, the mechanistic pathway is proposed based on the experimental evidence of the formation of radicals, which is validated by the Density Functional Theory (DFT). By comparing electrical energy consumption for the same reaction using different reaction setups, it is understood that the energy requirement is nearly the same in the case of a reaction performed using a thermal reactor. The power consumption in reactions conducted using thermal, UV lamp and LED-based reactors was 1.6 kW/h, 1.5 kW/h, and 0.00144 kW/h, respectively. It is clear that the energy consumption in thermal and UV lamp-based reactors is higher than that of LED-based reactors, which was 1111 and 1041 times more than LED reactors respectively. Notably, the catalyst was found to be recyclable at least five consecutive runs, and the successful protocol was demonstrated up to 50 g scale. 2023 Elsevier Ltd -
A novel scheme for energy enhancement in wireless sensor networks
Wireless sensor networks consists of a large amount of miniaturized battery-powered wireless networked sensors which are intended to function for years without any human intervention. Because of the large number of sensors and the restrictions on the environment of their deployment, replacing the components cannot be thought of. So the only viable way out is to efficiently use the available resources. Energy efficiency is a major matter of concern in such networks even though energy harvesting techniques exists. Recent times have shown a growing interest on understanding and developing new strategies of wireless sensor network routing especially focussing on the optimal use of the limited and constrained resources like energy, memory and processing capabilities. Routing have to be given due importance as it consumes major part of the energy compared to that of sensing and processing. Adopting the natures self organising system intelligence for the emerging technologies is quite interesting and has proved to be efficient. This article sheds some light on the existing bio inspired routing protocols and explains a new procedure with mobile sinks for energy efficient routing in wireless sensor networks. 2015 IEEE.