Women Entrepreneurship Index Prediction Model with Automated Statistical Analysis

Recently,gender equality and women’s entrepreneurship have gained considerable attention in global economic development.Prior to the design of any policy interventions to increase women’s entrepreneurship,it is significant to comprehend the factors motivating women to become entrepreneurs.The non-understanding of the factors can result in the endurance of low living stan-dards and the design of expensive and ineffectual policies.But female involve-ment in entrepreneurship becomes higher in developing economies compared to developed economies.Women Entrepreneurship Index(WEI)plays a vital role in determining the factors that enable theflourishment of high potential female entrepreneurs which enhances economic welfare and contributes to the economic and social fabric of society.Therefore,it is needed to design an automated and accurate WEI prediction model to improve women’s entrepreneurship.In this view,this article develops an automated statistical analysis enabled WEI predic-tive(ASA-WEIP)model.The proposed ASA-WEIP technique aims to effectually determine the WEI.The proposed ASA-WEIP technique encompasses a series of sub-processes such as pre-processing,WEI prediction,and parameter optimiza-tion.For the prediction of WEI,the ASA-WEIP technique makes use of the Deep Belief Network(DBN)model,and the parameter optimization process takes place using Squirrel Search Algorithm(SSA).The performance validation of the ASA-WEIP technique was executed using the benchmark dataset from the Kaggle repo-sitory.The experimental outcomes stated the better outcomes of the ASA-WEIP technique over the other existing techniques.

A Hybrid Deep Learning Model for Real Time Hand Gestures Recognition

The performance of Hand Gesture Recognition(HGR)depends on the hand shape.Segmentation helps in the recognition of hand gestures for more accuracy and improves the overall performance compared to other existing deep neural networks.The crucial segmentation task is extremely complicated because of the background complexity,variation in illumination etc.The proposed mod-ified UNET and ensemble model of Convolutional Neural Networks(CNN)undergoes a two stage process and results in proper hand gesture recognition.Thefirst stage is segmenting the regions of the hand and the second stage is ges-ture identification.The modified UNET segmentation model is trained using resized images to generate a cost effective semantic segmentation model.The Central Processing Unit(CPU)utilization and training time taken by these models with respect to three public benchmark datasets are also analyzed.Recognition is carried out with the ensemble learning model consisting of EfficientNet B0,Effi-cientNet B4 and ResNet V2152.Experimentation on NUS hand posture dataset-II,OUHANDS and HGRI benchmark datasets show that our architecture achieves a maximum recognition rate of 99.07%through semantic segmentation and the Ensemble learning model.

An Efficient Framework for Utilizing Underloaded Servers in Compute Cloud

In cloud data centers,the consolidation of workload is one of the phases during which the available hosts are allocated tasks.This phenomenon ensures that the least possible number of hosts is used without compromise in meeting the Service Level Agreement(SLA).To consolidate the workloads,the hosts are segregated into three categories:normal hosts,under-loaded hosts,and over-loaded hosts based on their utilization.It is to be noted that the identification of an extensively used host or underloaded host is challenging to accomplish.Thresh-old values were proposed in the literature to detect this scenario.The current study aims to improve the existing methods that choose the underloaded hosts,get rid of Virtual Machines(VMs)from them,andfinally place them in some other hosts.The researcher proposes a Host Resource Utilization Aware(HRUAA)Algorithm to detect those underloaded and place its virtual machines on different hosts in a vibrant Cloud environment.The mechanism presented in this study is contrasted with existing mechanisms empirically.The results attained from the study estab-lish that numerous hosts can be shut down,while at the same time,the user's workload requirement can also be met.The proposed method is energy-efficient in workload consolidation,saves cost and time,and leverages active hosts.

Blockchain Enabled Metaheuristic Cluster Based Routing Model for Wireless Networks

With recent advancements made in wireless communication techniques,wireless sensors have become an essential component in both data collection as well as tracking applications.Wireless Sensor Network(WSN)is an integral part of Internet of Things(IoT)and it encounters different kinds of security issues.Blockchain is designed as a game changer for highly secure and effective digital society.So,the current research paper focuses on the design of Metaheuristic-based Clustering with Routing Protocol for Blockchain-enabled WSN abbreviated as MCRP-BWSN.The proposed MCRP-BWSN technique aims at deriving a shared memory scheme using blockchain technology and determine the optimal paths to reach the destination in clustered WSN.In MCRP-BWSN technique,Chimp Optimization Algorithm(COA)-based clustering technique is designed to elect a proper set of Cluster Heads(CHs)and organize the selected clusters.In addition,Horse Optimization Algorithm(HOA)-based routing technique is also presented to optimally select the routes based onfitness function.Besides,HOA-based routing technique utilizes blockchain technology to avail the shared mem-ory among nodes in the network.Sensor nodes are treated as coins whereas the ownership handles the sensor nodes and Base Station(BS).In order to validate the enhanced performance of the proposed MCRP-BWSN technique,a wide range of simulations was conducted and the results were examined under different measures.Based on the performance exhibited in simulation outcomes,the pro-posed MCRP-BWSN technique has been established as a promising candidate over other existing techniques.

Wireless Sensor Network-based Detection of Poisonous Gases Using Principal Component Analysis

This work utilizes a statistical approach of Principal Component Ana-lysis(PCA)towards the detection of Methane(CH_(4))-Carbon Monoxide(CO)Poi-soning occurring in coal mines,forestfires,drainage systems etc.where the CH_(4) and CO emissions are very high in closed buildings or confined spaces during oxi-dation processes.Both methane and carbon monoxide are highly toxic,colorless and odorless gases.Both of the gases have their own toxic levels to be detected.But during their combined presence,the toxicity of the either one goes unidentified may be due to their low levels which may lead to an explosion.By using PCA,the correlation of CO and CH_(4) data is carried out and by identifying the areas of high correlation(along the principal component axis)the explosion suppression action can be triggered earlier thus avoiding adverse effects of massive explosions.Wire-less Sensor Network is deployed and simulations are carried with heterogeneous sensors(Carbon Monoxide and Methane sensors)in NS-2 Mannasim framework.The rise in the value of CO even when CH_(4) is below the toxic level may become hazardous to the people around.Thus our proposed methodology will detect the combined presence of both the gases(CH_(4) and CO)and provide an early warning in order to avoid any human losses or toxic effects.

Adaptive Fuzzy Logic Despeckling in Non-Subsampled Contourlet Transformed Ultrasound Pictures

Signal to noise ratio in ultrasound medical images captured through the digital camera is poorer,resulting in an inaccurate diagnosis.As a result,it needs an efficient despeckling method for ultrasound images in clinical practice and tel-emedicine.This article proposes a novel adaptive fuzzyfilter based on the direc-tionality and translation invariant property of the Non-Sub sampled Contour-let Transform(NSCT).Since speckle-noise causes fuzziness in ultrasound images,fuzzy logic may be a straightforward technique to derive the output from the noisy images.Thisfiltering method comprises detection andfiltering stages.First,image regions classify at the detection stage by applying fuzzy inference to the directional difference obtained from the NSCT noisy image.Then,the system adaptively selects the better-suitedfilter for the specific image region,resulting in significant speckle noise suppression and retention of detailed features.The suggested approach uses a weighted averagefilter to distinguish between noise and edges at thefiltering stage.In addition,we apply a structural similarity mea-sure as a tuning parameter depending on the kind of noise in the ultrasound pic-tures.The proposed methodology shows that the proposed fuzzy adaptivefilter effectively suppresses speckle noise while preserving edges and image detailed structures compared to existing approaches.

Enabling Energy Efficient Sensory Data Collection Using Multiple Mobile Sink

Mobile sink is the challenging task for wireless sensor networks(WSNs).In this paper we propose to design an efficient routing protocol for single mobile sink and multiple mobile sink for data gathering in WSN.In this process,a biased random walk method is used to determine the next position of the sink.Then,a rendezvous point selection with splitting tree technique is used to find the optimal data transmission path.If the sink moves within the range of the rendezvous point,it receives the gathered data and if moved out,it selects a relay node from its neighbours to relay packets from rendezvous point to the sink.Proposed algorithm reduces the signal overhead and improves the triangular routing problem.Here the sink acts as a vehicle and collect the data from the sensor.The results show that the proposed model effectively supports sink mobility with low overhead and delay when compared with Intelligent Agent-based Routing protocol(IAR) and also increases the reliability and delivery ratio when the number of sources increases.

Interference Mitigation Techniques Using Receiver Processing and Resource Allocation

Heterogeneous network consists of the pico cells overlaid over the macro cell coverage area in a wireless cellular network. The pico cells are deployed to increase the capacity of the homogeneous network by reusing the spectrum further. However, more users will tend to be associated to the macro cell due to the fact that the transmit power of the pico cell is low. In order to increase the number of users associated to the pico cell, range extension techniques like biased association are used. This will cause severe interference to cell edge users of the pico cell from the macro cell causing degradation in throughput performance in the cell range extension area. In this paper, interference mitigation using receiver processing along with different scheduling techniques is proposed to improve the throughput, average delay, and the packet delivery ratio performance of the system. The performance comparison of the round robin, proportional fair and modified largest weighted delay first (MLWDF) algorithm for resource allocation using interference suppressing receiver is done, and analyzed. It is shown that the MLWDF algorithm achieves the highest throughput with minimum average delay of packets with the best delivery ratio.

Hybrid Teaching Learning Approach for Improving Network Lifetime in Wireless Sensor Networks

In a wireless sensor network(WSN),data gathering is more effectually done with the clustering process.Clustering is a critical strategy for improving energy efficiency and extending the longevity of a network.Hierarchical modeling-based clustering is proposed to enhance energy efficiency where nodes that hold higher residual energy may be clustered to collect data and broadcast it to the base station.Moreover,existing approaches may not consider data redundancy while collecting data from adjacent nodes or overlapping nodes.Here,an improved clustering approach is anticipated to attain energy efficiency by implementingMapReduction for regulatingmapping and reducing complexity in routing mechanisms for eliminating redundancy and overlapping.In order to optimize the network performance,this work considers intelligent behaviors’to adapt with network changes and to introduce computational intelligence ability.In the proposed research,improved teaching learning based optimization is used to evaluate the coordinates of target nodes and nodes upgradation for determining energy consumption.Node upgradation is performed by integratingMap reduction to attain modification in Hop size of nodes.This variation reduces communication complexities.Therefore,network lifetime is increased,and redundancy is reduced.While comparingwith existing approaches here,sleep and wake-up nodes are considered for data transmission.The proposed algorithm clearly demonstrates 50%,16%&12%improvement in nodes lifetime,residual energy and throughput respectively compared to other models.Also it shows progressive improvement in reducing average waiting time,average queuing time and average energy utilization as 30%,20%and 46%respectively.Simulation has been done in NS-2 environment for distributed heterogeneous networks.

Early Detection of Pancreatic Cancer Using Jaundiced Eye Images

Pancreatic cancer is one of the deadliest cancers,with less than 9%survival rates.Pancreatic Ductal Adeno Carcinoma(PDAC)is common with the general public affecting most people older than 45.Early detection of PDAC is often challenging because cancer symptoms will progress only at later stages(advanced stage).One of the earlier symptoms of PDAC is Jaundice.Patients with diabetes,obesity,and alcohol consumption are also at higher risk of having pancreatic cancer.A decision support system is developed to detect pancreatic cancer at an earlier stage to address this challenge.Features such as Mean Hue,Mean Saturation,Mean Value,and Mean Standard Deviation are computed after color space conversion from RGB to HSV.Fuzzy k-Nearest Neighbor(F-kNN)is designed for classification.The system proposed is trained and tested using features extracted from jaundiced eye images.The proposed system results indicate that this model can predict pancreatic cancer as earlier as possible,helping clinicians make better decisions for surgical planning.

Pattern Analysis and Regressive Linear Measure for Botnet Detection

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various researchers.However,certain limitations need to be addressed efficiently.The provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective constraints.The bots’patterns or features over the network have to be analyzed in both linear and non-linear manner.The linear and non-linear features are composed of high-level and low-level features.The collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier model.Here,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor model.Finally,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets detection.The simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so on.Here,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's reliability.The F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.

Performance analysis of thermal storage unit with possible nano enhanced phase change material in building cooling applications

The heat transfer performance of the phase change materials used in free cooling and air conditioning applications is low,due to the poor thermal conductivity of the materials.The recent phenomenal advancement in nano technology provides an opportunity for an appreciable enhancement in the thermal conductivity of the phase change materials.In order to explore the possibilities of using nano technology for various applications,a detailed parametric study is carried out,to analyse the heat transfer enhancement potential with the thermal conductivity of the conventional phase change materials and nano enhanced phase change materials under various flow conditions of the heat transfer fluid.Initially,the theoretical equation,used to determine the time for outward cylindrical solidification of the phase change material,is validated with the experimental results.It is inferred from the parametric studies,that for paraffinic phase change materials with air as the heat transfer fluid,the first step should be to increase the heat transfer coefficient to the maximum extent,before making any attempt to increase the thermal conductivity of the phase change materials,with the addition of nano particles.When water is used as the phase change material,the addition of nano particles is recommended to achieve better heat transfer,when a liquid is used as the heat transfer fluid.

High Frequency Charging Techniques—Grid Connected Power Generation Using Switched Reluctance Generator

Power generation becomes the need of developed, developing and under developed countries to meet their increasing power requirements. When affordability increases their requirement of power increases, this happens when increased per capita consumption. The existing power scenario states that highest power is produced using firing of coals called thermal energy. A high efficiency Switched Reluctance Generator (SRG) based high frequency switching scheme to enhance the output for grid connectivity is designed, fabricated and evaluated. This proposed method generates the output for the low wind speed. It provides output at low speed because of multi-level DC-DC converter and storage system. It is an efficient solution for low wind power generation. The real time readings and results are discussed.

An efficient botnet detection approach based on feature learning and classification

Bot detection is considered a crucial security issue that is extensively analysed in various existingapproaches. Machine Learning is an efficient way of botnet attack detection. Bot detectionis the major issue faced by the existing system. This research concentrates on adopting a graphbasedfeature learning process to reduce feature dimensionality. The incoming samples arecorrectly classified and optimised using an Adaboost classifier with an improved grey wolfoptimiser (g-AGWO). The proposed IGWO optimisation approach is adopted to fulfil the multiconstraintissues related to bot detection and provide better local and global solutions (to satisfyexploration and exploitation). The extensive results show that the proposed g-AGWO model outperformsexisting approaches to reduce feature dimensionality, under-fitting/over-fitting andexecution time. The error rate prediction shows the feasibility of the given model to work over thechallenging environment. This model also works efficiently towards the unseen data to achievebetter generalization.

Exploring the electromagnetic shielding behavior of lanthanum doped calcium nanoferrites

The present study focuses on the synthesis and characterization of lanthanum(La^(3+))-doped calcium nanoferrites(CaLa_(x)Fe_(2-x)O_(4):x=0.025,0.050,0.075 and 0.100)using the sonochemical method.Various techniques were employed to analyze the effect of La^(3+)infusion,Raman spectroscopy confirms the presence of active A_(1g),T_(2g)and E_g modes in the CaLa_(x)Fe_(2-x)O_(4)nanoferrite,indicating the formation of an active ferrite system.The introduction of La^(3+)doping results in a significant increase in the band gap energy,rendering the nanoferrites insulating(3.23-3,57 eV).At higher frequencies,the impedance studies reveal minimal losses and better AC conductivity,pointing to improved dielectric characteristics.At higher frequencies,the Q-factor of La-doped calcium nanoferrites shows lower electromagnetic losses.The M-H curve exhibits ferromagnetic behavior,with La^(3+)-doped calcium nano ferrites displaying a saturation magnetization ranging from 12.72 to 18.10 emu/g.The incorporation of La^(3+)also induces enhanced electrical polarization,leading to notable dielectric loss and increased absorption of electromagnetic waves.Consequently,these CaLa_(x)Fe_(2-x)O_(4)nanoferrites demonstrate potential as effective microwave absorbers across a wide frequency range,with significant shielding absorption observed at 8.8-9.1 GHz.