A New Double Layer Multi-Secret Sharing Scheme

Cryptography is deemed to be the optimum strategy to secure the data privacy in which the data is encoded ahead of time before sharing it.Visual Secret Sharing(VSS)is an encryption method in which the secret message is split into at least two trivial images called’shares’to cover it.However,such message are always targeted by hackers or dishonest members who attempt to decrypt the message.This can be avoided by not uncovering the secret message without the universal share when it is presented and is typically taken care of,by the trusted party.Hence,in this paper,an optimal and secure double-layered secret image sharing scheme is proposed.The proposed share creation process contains two layers such as threshold-based secret sharing in the first layer and universal share based secret sharing in the second layer.In first layer,Genetic Algorithm(GA)is applied to find the optimal threshold value based on the randomness of the created shares.Then,in the second layer,a novel design of universal share-based secret share creation method is proposed.Finally,Opposition Whale Optimization Algorithm(OWOA)-based optimal key was generated for rectange block cipher to secure each share.This helped in producing high quality reconstruction images.The researcher achieved average experimental outcomes in terms of PSNR and MSE values equal to 55.154225 and 0.79365625 respectively.The average PSNRwas less(49.134475)and average MSE was high(1)in case of existing methods.

An enhanced method for predicting and analysing forest fires using an attention-based CNN model

Prediction,prevention,and control of forest fires are crucial on at all scales.Developing effective fire detection systems can aid in their control.This study proposes a novel CNN(convolutional neural network)using an attention blocks module which combines an attention module with numerous input layers to enhance the performance of neural networks.The suggested model focuses on predicting the damage affected/burned areas due to possible wildfires and evaluating the multilateral interactions between the pertinent factors.The results show the impacts of CNN using attention blocks for feature extraction and to better understand how ecosystems are affected by meteorological factors.For selected meteorological data,RMSE 12.08 and MAE 7.45 values provide higher predictive power for selecting relevant and necessary features to provide optimal performance with less operational and computational costs.These findings show that the suggested strategy is reliable and effective for planning and managing fire-prone regions as well as for predicting forest fire damage.

ChatGPT in transforming communication in seismic engineering: Case studies, implications, key challenges and future directions

Seismic engineering,a critical field with significant societal implications,often presents communication challenges due to the complexity of its concepts.This paper explores the role of Artificial Intelligence(AI),specifically OpenAI’s ChatGPT,in bridging these communication gaps.The study delves into how AI can simplify intricate seismic engineering terminologies and concepts,fostering enhanced understanding among students,professionals,and policymakers.It also presents several intuitive case studies to demonstrate the practical application of ChatGPT in seismic engineering.Further,the study contemplates the potential implications of AI,highlighting its potential to transform decision-making processes,augment education,and increase public engagement.While acknowledging the promising future of AI in seismic engineering,the study also considers the inherent challenges and limitations,including data privacy and potential oversimplification of content.It advocates for the collaborative efforts of AI researchers and seismic experts in overcoming these obstacles and enhancing the utility of AI in the field.This exploration provides an insightful perspective on the future of seismic engineering,which could be closely intertwined with the evolution of AI.

A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.

A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet.Regrettably,this development has expanded the potential targets that hackers might exploit.Without adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or alteration.The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks.This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)units.The proposed model can identify various types of cyberattacks,including conventional and distinctive forms.Recurrent networks,a specific kind of feedforward neural networks,possess an intrinsic memory component.Recurrent Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended periods.Metrics such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual cyberattacks.RNNs are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection Model.This model utilises Recurrent Neural Networks,specifically exploiting LSTM techniques.The proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

Intelligent Energy Utilization Analysis Using IUA-SMD Model Based Optimization Technique for Smart Metering Data

Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.

Physical Layer Security of 6G Vehicular Networks with UAV Systems:First Order Secrecy Metrics,Optimization,and Bounds

The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research domains.However,their open communication makes UAVs susceptible toundesirablepassive attacks suchas eavesdroppingor jamming.Recently,the inefficiencyof traditional cryptography-based techniques has led to the addition of Physical Layer Security(PLS).This study focuses on the advanced PLS method for passive eavesdropping in UAV-aided vehicular environments,proposing a solution to complement the conventional cryptography approach.Initially,we present a performance analysis of first-order secrecy metrics in 6G-enabled UAV systems,namely hybrid outage probability(HOP)and secrecy outage probability(SOP)over 2×2 Nakagami-m channels.Later,we propose a novel technique for mitigating passive eavesdropping,which considers first-order secrecy metrics as an optimization problem and determines their lower and upper bounds.Finally,we conduct an analysis of bounded HOP and SOP using the interactive Nakagami-m channel,considering the multiple-input-multiple-output configuration of the UAV system.The findings indicate that 2×2 Nakagami-mis a suitable fadingmodel under constant velocity for trustworthy receivers and eavesdroppers.The results indicate that UAV mobility has some influence on an eavesdropper’s intrusion during line-of-sight-enabled communication and can play an important role in improving security against passive eavesdroppers.

Optimizing Spatial Pattern Analysis in Serial Remote Sensing Images through Empirical Mode Decomposition and Ant Colony Optimization

Serial remote sensing images offer a valuable means of tracking the evolutionary changes and growth of a specific geographical area over time.Although the original images may provide limited insights,they harbor considerable potential for identifying clusters and patterns.The aggregation of these serial remote sensing images(SRSI)becomes increasingly viable as distinct patterns emerge in diverse scenarios,such as suburbanization,the expansion of native flora,and agricultural activities.In a novel approach,we propose an innovative method for extracting sequential patterns by combining Ant Colony Optimization(ACD)and Empirical Mode Decomposition(EMD).This integration of the newly developed EMD and ACO techniques proves remarkably effective in identifying the most significant characteristic features within serial remote sensing images,guided by specific criteria.Our findings highlight a substantial improvement in the efficiency of sequential pattern mining through the application of this unique hybrid method,seamlessly integrating EMD and ACO for feature selection.This study exposes the potential of our innovative methodology,particularly in the realms of urbanization,native vegetation expansion,and agricultural activities.

An IoT-Cloud Based Intelligent Computer-Aided Diagnosis of Diabetic Retinopathy Stage Classification Using Deep Learning Approach

Diabetic retinopathy(DR)is a disease with an increasing prevalence and the major reason for blindness among working-age population.The possibility of severe vision loss can be extensively reduced by timely diagnosis and treatment.An automated screening for DR has been identified as an effective method for early DR detection,which can decrease the workload associated to manual grading as well as save diagnosis costs and time.Several studies have been carried out to develop automated detection and classification models for DR.This paper presents a new IoT and cloud-based deep learning for healthcare diagnosis of Diabetic Retinopathy(DR).The proposed model incorporates different processes namely data collection,preprocessing,segmentation,feature extraction and classification.At first,the IoT-based data collection process takes place where the patient wears a head mounted camera to capture the retinal fundus image and send to cloud server.Then,the contrast level of the input DR image gets increased in the preprocessing stage using Contrast Limited Adaptive Histogram Equalization(CLAHE)model.Next,the preprocessed image is segmented using Adaptive Spatial Kernel distance measure-based Fuzzy C-Means clustering(ASKFCM)model.Afterwards,deep Convolution Neural Network(CNN)based Inception v4 model is applied as a feature extractor and the resulting feature vectors undergo classification in line with the Gaussian Naive Bayes(GNB)model.The proposed model was tested using a benchmark DR MESSIDOR image dataset and the obtained results showcased superior performance of the proposed model over other such models compared in the study.

Enhanced Clustering Based OSN Privacy Preservation to Ensure k-Anonymity, t-Closeness, l-Diversity, and Balanced Privacy Utility

Online Social Networks (OSN) sites allow end-users to share agreat deal of information, which may also contain sensitive information,that may be subject to commercial or non-commercial privacy attacks. Asa result, guaranteeing various levels of privacy is critical while publishingdata by OSNs. The clustering-based solutions proved an effective mechanismto achieve the privacy notions in OSNs. But fixed clustering limits theperformance and scalability. Data utility degrades with increased privacy,so balancing the privacy utility trade-off is an open research issue. Theresearch has proposed a novel privacy preservation model using the enhancedclustering mechanism to overcome this issue. The proposed model includesphases like pre-processing, enhanced clustering, and ensuring privacy preservation.The enhanced clustering algorithm is the second phase where authorsmodified the existing fixed k-means clustering using the threshold approach.The threshold value is determined based on the supplied OSN data of edges,nodes, and user attributes. Clusters are k-anonymized with multiple graphproperties by a novel one-pass algorithm. After achieving the k-anonymityof clusters, optimization was performed to achieve all privacy models, suchas k-anonymity, t-closeness, and l-diversity. The proposed privacy frameworkachieves privacy of all three network components, i.e., link, node, and userattributes, with improved utility. The authors compare the proposed techniqueto underlying methods using OSN Yelp and Facebook datasets. The proposedapproach outperformed the underlying state of art methods for Degree ofAnonymization, computational efficiency, and information loss.

A Novel Machine Learning-Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

Machine learning is a technique for analyzing data that aids the construction of mathematical models.Because of the growth of the Internet of Things(IoT)and wearable sensor devices,gesture interfaces are becoming a more natural and expedient human-machine interaction method.This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns.The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition.Potential applications of hand gesture recognition research span from online gaming to surgical robotics.The location of the hands,the alignment of the fingers,and the hand-to-body posture are the fundamental components of hierarchical emotions in gestures.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.In this scenario,it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or trembling.When a user performs the same dynamic gesture,the hand shapes and speeds of each user,as well as those often generated by the same user,vary.A machine-learning-based Gesture Recognition Framework(ML-GRF)for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered generation.We have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions,and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture codes.The findings from the simulation support the accuracy,precision,gesture recognition,sensitivity,and efficiency rates.The Machine Learning-based Gesture Recognition Framework(ML-GRF)had an accuracy rate of 98.97%,a precision rate of 97.65%,a gesture recognition rate of 98.04%,a sensitivity rate of 96.99%,and an efficiency rate of 95.12%.

Community Detection Using Jaacard Similarity with SIM-Edge Detection Techniques

The structure and dynamic nature of real-world networks can be revealed by communities that help in promotion of recommendation systems.Social Media platforms were initially developed for effective communication,but now it is being used widely for extending and to obtain profit among business community.The numerous data generated through these platforms are utilized by many companies that make a huge profit out of it.A giant network of people in social media is grouped together based on their similar properties to form a community.Commu-nity detection is recent topic among the research community due to the increase usage of online social network.Community is one of a significant property of a net-work that may have many communities which have similarity among them.Community detection technique play a vital role to discover similarities among the nodes and keep them strongly connected.Similar nodes in a network are grouped together in a single community.Communities can be merged together to avoid lot of groups if there exist more edges between them.Machine Learning algorithms use community detection to identify groups with common properties and thus for recommen-dation systems,health care assistance systems and many more.Considering the above,this paper presents alternative method SimEdge-CD(Similarity and Edge between's based Community Detection)for community detection.The two stages of SimEdge-CD initiallyfind the similarity among nodes and group them into one community.During the second stage,it identifies the exact affiliations of boundary nodes using edge betweenness to create well defined communities.Evaluation of proposed method on synthetic and real datasets proved to achieve a better accuracy-efficiency trade-of compared to other existing methods.Our proposed SimEdge-CD achieves ideal value of 1 which is higher than existing sim closure like LPA,Attractor,Leiden and walktrap techniques.

Blockchain Merkle-Tree Ethereum Approach in Enterprise Multitenant Cloud Environment

This research paper puts emphasis on using cloud computing with Blockchain(BC)to improve the security and privacy in a cloud.The security of data is not guaranteed as there is always a risk of leakage of users’data.Blockchain can be used in a multi-tenant cloud environment(MTCE)to improve the security of data,as it is a decentralized approach.Data is saved in unaltered form.Also,Blockchain is not owned by a single organization.The encryption process can be done using a Homomorphic encryption(HE)algorithm along with hashing technique,hereby allowing computations on encrypted data without the need for decryption.This research paper is composed of four objectives:Analysis of cloud security using Blockchain technology;Exceptional scenario of Blockchain architecture in an enterprise-level MTCE;Implementation of cipher-text policy attribute-based encryption(CPABE)algorithm;Implementation of Merkle tree using Ethereum(MTuE)in a Multi-tenant system.Out of these four objectives,the main focus is on the implementation of CP-ABE algorithm.CP-ABE parameters are proposed for different levels of tenants.The levels include inner tenant,outer tenant,Inner-Outer-Tenant,Inner-Outer-External-Tenant,Outer-Inner-Tenant,External-Outer-Inner-Tenant and the parameters such as token,private key,public key,access tree,message,attribute set,node-level,cipher-text,salting which will help in providing better security using CP-ABE algorithm in a multitenant environment(MTE)where tenants can be provided with different levels of security and achieved 92 percentage of authenticity and access-control of the data.

Investigation of Single and Multiple Mutations Prediction Using Binary Classification Approach

The mutation is a critical element in determining the proteins’stability,becoming a core element in portraying the effects of a drug in the pharmaceutical industry.Doing wet laboratory tests to provide a better perspective on protein mutations is expensive and time-intensive since there are so many potential muta-tions,computational approaches that can reliably anticipate the consequences of amino acid mutations are critical.This work presents a robust methodology to analyze and identify the effects of mutation on a single protein structure.Initially,the context in a collection of words is determined using a knowledge graph for feature selection purposes.The proposed prediction is based on an easier and sim-pler logistic regression inferred binary classification technique.This approach can able to obtain a classification accuracy(AUC)Area Under the Curve of 87%when randomly validated against experimental energy changes.Moreover,for each cross-fold validation,the precision,recall,and F-Score are presented.These results support the validity of our strategy since it performs the vast majority of prior studies in this domain.

Copy Move Forgery Detection Using Novel Quadsort Moth Flame Light Gradient Boosting Machine

A severe problem in modern information systems is Digital media tampering along with fake information.Even though there is an enhancement in image development,image forgery,either by the photographer or via image manipulations,is also done in parallel.Numerous researches have been concentrated on how to identify such manipulated media or information manually along with automatically;thus conquering the complicated forgery methodologies with effortlessly obtainable technologically enhanced instruments.However,high complexity affects the developed methods.Presently,it is complicated to resolve the issue of the speed-accuracy trade-off.For tackling these challenges,this article put forward a quick and effective Copy-Move Forgery Detection(CMFD)system utilizing a novel Quad-sort Moth Flame(QMF)Light Gradient Boosting Machine(QMF-Light GBM).Utilizing Borel Transform(BT)-based Wiener Filter(BWF)and resizing,the input images are initially pre-processed by eliminating the noise in the proposed system.After that,by utilizing the Orientation Preserving Simple Linear Iterative Clustering(OPSLIC),the pre-processed images,partitioned into a number of grids,are segmented.Next,as of the segmented images,the significant features are extracted along with the feature’s distance is calculated and matched with the input images.Next,utilizing the Union Topological Measure of Pattern Diversity(UTMOPD)method,the false positive matches that took place throughout the matching process are eliminated.After that,utilizing the QMF-Light GBM visualization,the visualization of forged in conjunction with non-forged images is performed.The extensive experiments revealed that concerning detection accuracy,the proposed system could be extremely precise when contrasted to some top-notch approaches.

Web Page Recommendation Using Distributional Recurrent Neural Network

In the data retrieval process of the Data recommendation system,the matching prediction and similarity identification take place a major role in the ontology.In that,there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time.Since,in the data recommendation system,this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process.To improve the performance of data validation,this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing.In this paper advanced model of the Logarithmic Directionality Texture Pattern(LDTP)method with a Metaheuristic Pattern Searching(MPS)system was used to estimate the similarity between the query data in the entire database.The overall work was implemented for the application of the data recommendation process.These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period.Also,with the help of a neural network,the relevancies of feature attributes in the database are predicted,and the matching index was sorted to provide the recommended data for given query data.This was achieved by using the Distributional Recurrent Neural Network(DRNN).This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set.The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset.These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric.The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%,70%,and 80%.The parameters that are considered for performance analysis are Precision,Recall,F1-score and the accuracy of data retrieval,the query recommendation output,and comparison with other state-of-art methods.

Effective and Efficient Video Compression by the Deep Learning Techniques

Deep learning has reached many successes in Video Processing.Video has become a growing important part of our daily digital interactions.The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving,distributing,compressing and revealing highquality video content.In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask,which creatively combines the Deep Learning Techniques on Convolutional Neural Networks(CNN)and Generative Adversarial Networks(GAN).The video compression method involves the layers are divided into different groups for data processing,using CNN to remove the duplicate frames,repeating the single image instead of the duplicate images by recognizing and detecting minute changes using GAN and recorded with Long Short-Term Memory(LSTM).Instead of the complete image,the small changes generated using GAN are substituted,which helps with frame-level compression.Pixel wise comparison is performed using K-nearest Neighbours(KNN)over the frame,clustered with K-means and Singular Value Decomposition(SVD)is applied for every frame in the video for all three colour channels[Red,Green,Blue]to decrease the dimension of the utility matrix[R,G,B]by extracting its latent factors.Video frames are packed with parameters with the aid of a codec and converted to video format and the results are compared with the original video.Repeated experiments on several videos with different sizes,duration,Frames per second(FPS),and quality results demonstrated a significant resampling rate.On normal,the outcome delivered had around a 10%deviation in quality and over half in size when contrasted,and the original video.

Dynamic Analogical Association Algorithm Based on Manifold Matching for Few-Shot Learning

At present,deep learning has been well applied in many fields.However,due to the high complexity of hypothesis space,numerous training samples are usually required to ensure the reliability of minimizing experience risk.Therefore,training a classifier with a small number of training examples is a challenging task.From a biological point of view,based on the assumption that rich prior knowledge and analogical association should enable human beings to quickly distinguish novel things from a few or even one example,we proposed a dynamic analogical association algorithm to make the model use only a few labeled samples for classification.To be specific,the algorithm search for knowledge structures similar to existing tasks in prior knowledge based on manifold matching,and combine sampling distributions to generate offsets instead of two sample points,thereby ensuring high confidence and significant contribution to the classification.The comparative results on two common benchmark datasets substantiate the superiority of the proposed method compared to existing data generation approaches for few-shot learning,and the effectiveness of the algorithm has been proved through ablation experiments.

Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

Cloud data centers consume high volume of energy for processing and switching the servers among different modes.Virtual Machine(VM)migration enhances the performance of cloud servers in terms of energy efficiency,internal failures and availability.On the other end,energy utilization can be minimized by decreasing the number of active,underutilized sources which conversely reduces the dependability of the system.In VM migration process,the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations.In this view,the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization(IMFP-VMMO)model in cloud environment.The major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure prediction.To accomplish this,IMFPVMMO model employs Gradient Boosting Decision Tree(GBDT)classification model at initial stage for effectual prediction of VM failures.At the same time,VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm(QO-AFSA)which in turn reduces the energy consumption.The performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed model.The comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.

Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

Due to the enormous usage of the internet for transmission of data over a network,security and authenticity become major risks.Major challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database server.To describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical(Face,fingerprint,Ear etc.)and behavioural(Gesture,Voice,tying etc.)by means of matching and verification process.In this work,biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential data.The proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary templates.Here Face Attribute Convolutional Neural Network(FACNN)is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further processing.Implementing Artificial Intelligence(AI)into the proposed system,automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while processing.Binary codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and verification.Three main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify authenticity.This system enhances the system performance by 8%matching and verification and minimizes the False Acceptance Rate(FAR),False Rejection Rate(FRR)and Equal Error Rate(EER)by 6 times and increases the data privacy through the biometric cryptosystem by 98.2%while compared to other work.