Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map

With the rapid evolution of Internet technology,fog computing has taken a major role in managing large amounts of data.The major concerns in this domain are security and privacy.Therefore,attaining a reliable level of confidentiality in the fog computing environment is a pivotal task.Among different types of data stored in the fog,the 3D point and mesh fog data are increasingly popular in recent days,due to the growth of 3D modelling and 3D printing technologies.Hence,in this research,we propose a novel scheme for preserving the privacy of 3D point and mesh fog data.Chaotic Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness,deterministic nature,sensitivity to initial conditions,ergodicity,etc.To boost encryption efficiency significantly,in this work,we propose a novel Chaotic Cat map.The sequence generated by this map is used to transform the coordinates of the fog data.The improved range of the proposed map is depicted using bifurcation analysis.The quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate entropy.We also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical attack.The experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.

An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

In today’s digital world,millions of individuals are linked to one another via the Internet and social media.This opens up new avenues for information exchange with others.Sentiment analysis(SA)has gotten a lot of attention during the last decade.We analyse the challenges of Sentiment Analysis(SA)in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which wefirst produced an annotated dataset composed of Marathi text acquired from microblogging websites such as Twitter.We also choose domain experts to manually annotate Marathi microblogging posts with positive,negative,and neutral polarity.In addition,to show the efficient use of the annotated dataset,an ensemble-based model for sentiment analysis was created.In contrast to others machine learning classifier,we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation(cv),outcomes as 97.77%,f-score is 97.89%.

Comparative Analysis of Execution of CNN-Based Sanguine Data Transmission with LSB-SS and PVD-SS

The intact data transmission to the authentic user is becoming crucial at every moment in the current era.Steganography;is a technique for concealing the hidden message in any cover media such as image,video;and audio to increase the protection of data.The resilience and imperceptibility are improved by choosing an appropriate embedding position.This paper gives a novel system to immerse the secret information in different videos with different methods.An audio and video steganography with novel amalgamations are implemented to immerse the confidential auditory information and the authentic user’s face image.A hidden message is first included in the audio from the multimedia file;using LSB Technique.The Stego-video is created in the second stage by merging the authorized user’s face into the frame of the video;by using PVD technology.Stego-audio is linked again with the stego-video in the third stage.The incorporated perspective techniques(LSB-SS and PVD-SS algorithms)with more significant data immersing capacity,good robustness and imperceptibility are proposed in this research work.The spread spectrum approach is used to increase the complexity of secret data recognition.Two different video files are tested with different voice files with the results such as PSNR,SSIM,RMSE and MSE as 52.3,0.9963,0.0024 and 0.0000059,respectively.

Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

Fuzzy sets have undergone several expansions and generalisations in the literature,including Atanasov’s intuitionistic fuzzy sets,type 2 fuzzy sets,and fuzzy multisets,to name a few.They can be regarded as fuzzy multisets from a formal standpoint;nevertheless,their interpretation differs from the two other approaches to fuzzy multisets that are currently available.Hesitating fuzzy sets(HFS)are very useful if consultants have hesitation in dealing with group decision-making problems between several possible memberships.However,these possible memberships can be not only crisp values in[0,1],but also interval values during a practical evaluation process.Hesitant bipolar valued fuzzy set(HBVFS)is a generalization of HFS.This paper aims to introduce a general framework of multi-attribute group decision-making using social network.We propose two types of decision-making processes:Type-1 decision-making process and Type-2 decision-making process.In the Type-1 decision-making process,the experts’original opinion is proces for thefinal ranking of alternatives.In Type-2 decision making processs,there are two major aspects we consider.First,consistency tests and checking of consensus models are given for detecting that the judgments are logically rational.Otherwise,the framework demands(partial)decision-makers to review their assessments.Second,the coherence and consensus of several HBVFSs are established forfinal ranking of alternatives.The proposed framework is clarified by an example of software packages selection of a university.

Energy and Bandwidth Based Link Stability Routing Algorithm for IoT

Internet of Things(IoT)is becoming popular nowadays for collecting and sharing the data from the nodes and among the nodes using internet links.Particularly,some of the nodes in IoT are mobile and dynamic in nature.Hence maintaining the link among the nodes,efficient bandwidth of the links among the mobile nodes with increased life time is a big challenge in IoT as it integrates mobile nodes with static nodes for data processing.In such networks,many routing-problems arise due to difficulties in energy and bandwidth based quality of service.Due to the mobility and finite nature of the nodes,transmission links between intermediary nodes may fail frequently,thus affecting the routing-performance of the network and the accessibility of the nodes.The existing protocols do not focus on the transmission links and energy,bandwidth and link stability of the nodes,but node links are significant factors for enhancing the quality of the routing.Link stability helps us to define whether the node is within or out of a coverage range.This paper proposed an Optimal Energy and bandwidth based Link Stability Routing(OEBLS)algorithm,to improve the link stable route with minimized error rate and throughput.In this paper,the optimal route from the source to the sink is determined based on the energy and bandwidth,link stability value.Among the existing routes,the sink node will choose the optimal route which is having less link stability value.Highly stable link is determined by evaluating link stability value using distance and velocity.Residual-energy of the node is estimated using the current energy and the consumed energy.Consumed energy is estimated using transmitted power and the received power.Available bandwidth in the link is estimated using the idle time and channel capacity with the consideration of probability of collision.

Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

One of the fast-growing disease affecting women’s health seriously is breast cancer.It is highly essential to identify and detect breast cancer in the earlier stage.This paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer accurately.Deep learning algorithms are fully automatic in learning,extracting,and classifying the features and are highly suitable for any image,from natural to medical images.Existing methods focused on using various conventional and machine learning methods for processing natural and medical images.It is inadequate for the image where the coarse structure matters most.Most of the input images are downscaled,where it is impossible to fetch all the hidden details to reach accuracy in classification.Whereas deep learning algorithms are high efficiency,fully automatic,have more learning capability using more hidden layers,fetch as much as possible hidden information from the input images,and provide an accurate prediction.Hence this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram images.The performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

In this research work,we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma(SS)is the cell structure for cancer.Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform.Subsequently,the structure features(SFs)such as PrincipalComponentsAnalysis(PCA),Independent ComponentsAnalysis(ICA)and Linear Discriminant Analysis(LDA)were extracted from this subband image representation with the distribution of wavelet coefficients.These SFs are used as inputs of the Support Vector Machine(SVM)classifier.Also,classification of PCA+SVM,ICA+SVM,and LDA+SVM with Radial Basis Function(RBF)kernel the efficiency of the process is differentiated and compared with the best classification results.Furthermore,data collected on the internet from various histopathological centres via the Internet of Things(IoT)are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices.Due to this,the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration.Consequently,these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell(SSC)histopathological imaging databases.The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics(ROC)curve,and significant differences in classification performance between the techniques are analyzed.The combination of LDA+SVM technique has been proven to be essential for intelligent SS cancer detection in the future,and it offers excellent classification accuracy,sensitivity,specificity.

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Data is always a crucial issue of concern especially during its prediction and computation in digital revolution.This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication.It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data.The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means.The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data.The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables.This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach.It also demonstrates the generative function forKalman-filer based prediction model and its observations.This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration(CPE)for Python.