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A novel congestion-aware approach for ECC based secured WSN multicasting
--Multicasting in Wireless Sensor Networks greatly reduces the communication complexity between The Base station and set of sensor nodes deployed in a given region. It reduces the number of packets to be sent thus minimizing the chance of congestion. Still the existence of congestion appears due to improper channel utilization resulting in low throughput. In this paper, we have addressed the issue of congestion with reference to WSN multicasting. The Simulation results have shown that our approach is better in terms of throughput and delay compared with existing approaches. 2018, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
A novel data mining and machine learning-based framework for cyber security intrusion detection /
Patent Number: 202231027007, Applicant: Lingaraj Sethi.
This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics supporting intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of emerging practice, papers representing each method were identified, read, and summarised. Because data are so crucial in ML/DM approaches, some well- known cyber data sets used in ML/DM are described. -
A Novel Deep Learning Approach for Identifying Interstitial Lung Diseases from HRCT Images
Interstitial lung diseases (ILDs) are defined as a group of lung diseases that affect the interstitium and cause death among humans worldwide. It is more serious in underdeveloped countries as it is hard to diagnose due to the absence of specialists. Detecting and classifying ILD is a challenging task and many research activities are still ongoing. High-resolution computed tomography (HRCT) images have essentially been utilized in the diagnosis of this disease. Examining HRCT images is a difficult task, even for an experienced doctor. Information Technology, especially Artificial Intelligence, has started contributing to the accurate diagnosis of ILD from HRCT images. Similar patterns of different categories of ILD confuse doctors in making quick decisions. Recent studies have shown that corona patients with ILD also go on to sudden death. Therefore, the diagnosis of ILD is more critical today. Different deep learning approaches have positively impacted various image classification problems recently. The main objective of this proposed research work was to develop a deep learning model to classify the ILD categories from HRCT images. This proposed work aims to perform binary and multi-label classification of ILD using HRCT images on a customized VGG architecture. The proposed model achieved a high test accuracy of 95.18% on untrained data. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
A Novel Deep Learning Approach for Retinopathy Prediction Using Multimodal Data Fusion
In contemporary research on mild cognitive disorders (MCI) and Alzheimer's disease (AD), the predominant approach involves the utilization of double data modalities for making predictions related to AD stages. However, there is a growing recognition of the potential benefits that could be derived from the fusion of multiple data modalities to obtain a more comprehensive perspective in the analysis of AD staging. To address this, we have employed deep learning techniques to holistically assess data from various sources, including, genetic (single nucleotide polymorphisms (SNPs)), imaging (magnetic resonance imaging (MRI)), and clinical tests, with the objective of categorizing patients into distinct groups: AD, MCI, and controls (CN). For the analysis of imaging data, convolutional neural networks have been employed. Moreover, we have introduced a novel approach for data interpretation, enabling the identification of the most influential features learned by these deep models. This interpretation process incorporates clustering and perturbation analysis, shedding light on the crucial aspects of the data contributing to our classification results. Our experimentation, conducted on the dataset (i.e., ADNI), has yielded compelling results. Furthermore, our findings have underscored the significant advantage of integrating multi-modality data over solely relying on double modality models, as it has led to improvements in terms of accuracy, precision, recall, and mean F1 scores. 2024, Ismail Saritas. All rights reserved. -
A novel discrete slash family of distributions with application to epidemiology informatics data
This study puts forward a new class of discrete distribution that can be used by the epidemiologists and medical scientists to model data relating to epidemiology informatics. The proposed distribution is superior to traditional discrete modeling alternatives, viz., discrete Weibull and geometric distributions in terms of its model fit and flexibility to handle heavy-tailed dataset. It is a flexible three-parameter discrete distribution, grounded in the slash family and can be considered as a refined extension to the geometric distribution. We explored the diverse properties of this novel distribution thoroughly by evaluating the mathematical properties. The models parameters are estimated using the maximum likelihood estimation method, where the methodology validity is confirmed through an extensive simulation study. Furthermore, the practical utility of the distribution to model epidemiology informatics was examined with the help of eight different datasets representing three different dimensions of the epidemiology informatics, viz., mortality, infection and medication statistics. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
A Novel Dynamic Physical Layer Impairment-Aware Routing and Wavelength Assignment (PLI-RWA) Algorithm for Mixed Line Rate (MLR) Wavelength Division Multiplexed (WDM) Optical Networks
The ever-increasing global Internet traffic will inevitably lead to a serious upgrade of the current optical networks' capacity. The legacy infrastructure can be enhanced not only by increasing the capacity but also by adopting advance modulation formats, having increased spectral efficiency at higher data rate. In a transparent mixed-line-rate (MLR) optical network, different line rates, on different wavelengths, can coexist on the same fiber. Migration to data rates higher than 10 Gbps requires the implementation of phase modulation schemes. However, the co-existing on-off keying (OOK) channels cause critical physical layer impairments (PLIs) to the phase modulated channels, mainly due to cross-phase modulation (XPM), which in turn limits the network's performance. In order to mitigate this effect, a more sophisticated PLI-Routing and Wavelength Assignment (PLI-RWA) scheme needs to be adopted. In this paper, we investigate the critical impairment for each data rate and the way it affects the quality of transmission (QoT). In view of the aforementioned, we present a novel dynamic PLI-RWA algorithm for MLR optical networks. The proposed algorithm is compared through simulations with the shortest path and minimum hop routing schemes. The simulation results show that performance of the proposed algorithm is better than the existing schemes. 2016 by De Gruyter. -
A novel dynamic Physical Layer Impairment-Aware Routing and Wavelength Assignment (PLI-RWA) algorithm for Mixed Line Rate (MLR) Wavelength Division Multiplexed (WDM) optical networks /
Journal of Optical Communications, Vol.37, Issue 4, pp.349-356, ISSN: 2191-6322 (Online) 0173-4911 (Print). -
A Novel Edge-Based Trust Management System for the Smart City Environment Using Eigenvector Analysis
The proposed Edge-based Trust Management System (E-TMS) uses an Eigenvector-based approach for eliminating the security threats present in the Internet of Things (IoT) enabled smart city environment. In most existing trust management systems, the trust aggregation process completely depends on the direct trust ratings obtained from both legitimate and malicious neighboring IoT devices. E-TMS possesses an edge-assisted two-level trust computation approach for ensuring the malicious free trust evaluation of IoT devices. The E-TMS aims at removing the false contribution on aggregated trust data. It utilizes the properties of the Eigenvector for identifying compromised IoT devices. The Eigenvector Analysis also helps to avoid false detection. The analysis involves a comparison of all the contributed trust data about every single connected device. A spectral matrix will be generated corresponding to the contributions and the received trust will be scaled based on the obtained spectral values. The absolute sum of obtained values will contain only true contributions. The accurate identification of false data will remove the effect of malicious contributions from the final trust value of a connected IoT device. Since the final trust value calculated by the edge node contains only the trustworthy data, the prediction about the malicious nodes will be accurate. Eventually, the performance of E-TMS has been validated. Throughput and network resilience are higher than the existing system. 2022 G. Nagarajan et al. -
A Novel Energy-Efficient Hybrid Optimization Algorithm for Load Balancing in Cloud Computing
In the field of Cloud Computing (CC), load balancing is a method applied to distribute workloads and computing resources appropriately. It enables organizations to effectively manage the needs of their applications or workloads by spreading resources across numerous PCs, networks, or servers. This research paper offers a unique load balancing method named FFBSO, which combines Firefly algorithm (FF) which reduces the search space and Bird Swarm Optimization (BSO). BSO takes inspiration from the collective behavior of birds, exhibiting tasks as birds and VMs as destination food patches. In the cloud environment, tasks are regarded as autonomous and non-preemptive. On the other hand, the BSO algorithm maps tasks onto suitable VMs by identifying the possible best positions. Simulation findings reveal that the FFBSO algorithm beat other approaches, obtaining the lowest average reaction time of 13ms, maximum resource usage of 99%, all while attaining a makespan of 35s. 2023 IEEE. -
A Novel Ensemble based Model for Intrusion Detection System
In the present interconnected world, the increasing reliance on computer networks has made them susceptible to multiple security threats and intrusions. Intrusion Detection Systems (IDS) is essential for shielding these networks by detecting and mitigating potential threats in real-time. This research paper presents an in-depth study of employing the Random Forest algorithm for building an effective intrusion detection System. The proposed IDS uses the power of the Random Forest algorithm, a popular ensemble learning technique, to detect various types of intrusions in network traffic effectively. The algorithm integrates more than one decision trees to produce a robust and accurate classifier, capable of handling large-scale and complex datasets typical of network traffic. The proposed system can be used in various industries and sectors to protect critical assets, ensuring the uninterrupted operation of computer networks. Evolving cyber threats have encouraged further research into ensemble analytics methods to increase the resilience of Intrusion Detection Systems in an ever-changing threat landscape. 2024 IEEE. -
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 cloud-based analytic of massive and multi-structured healthcare images for real-time insights
The dependency of healthcare industry on the information and communication technology newline(ICT) domain is consistently on the rise in order to conceptualize and provide1537608 newlinesophisticated services to various newlinestakeholders including patients, newlinecaregivers, support service providers, medical practitioners, and experts. There are a variety of decisive advancements in the diagnosis, medication and surgical processes, medical electronics, instruments and equipment, healthcare-centric robots, a bevy of cloud-based healthcare software solutions, medical data hubs, etc. One direct offshoot of all these developments is that the amount of multi-structured data is exponentially growing. There is a litany of support and expert systems in order to lessen the doctors workloads. However the brewing challenges and new-generation requirements include the real-time processing of medical data to extract real-time insights and decision-enablement, the substantial enhancements in appropriate and accurate processing and understanding of various and overlapped symptoms towards correct and strategically sound decisions, the real-time analytics of medical data, the empowerment of medical devices to assist surgeons and specialists in performing their tasks in an assured manner, etc. newlineThe Problem Description - Medical imaging is one of the fundamental and most important areas of the healthcare system. This needs accuracy in processing and producing best results for further diagnosis and action. There are various factors impelling medical imaging like patient preparation, different scanning modalities, the scanner used to capture the image and various algorithms adopted for processing the captured images. -
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