Ensuring Information Security in Electronic Health Record System Using Cryptography and Cuckoo Search Algorithm

In the contemporary era,the abundant availability of health information through internet and mobile technology raises concerns.Safeguarding and maintaining the confidentiality of patients’medical data becomes paramount when sharing such information with authorized healthcare providers.Although electronic patient records and the internet have facilitated the exchange of medical information among healthcare providers,concerns persist regarding the security of the data.The security of Electronic Health Record Systems(EHRS)can be improved by employing the Cuckoo Search Algorithm(CS),the SHA-256 algorithm,and the Elliptic Curve Cryptography(ECC),as proposed in this study.The suggested approach involves usingCS to generate the ECCprivate key,thereby enhancing the security of data storage in EHR.The study evaluates the proposed design by comparing encoding and decoding times with alternative techniques like ECC-GA-SHA-256.The research findings indicate that the proposed design achieves faster encoding and decoding times,completing 125 and 175 iterations,respectively.Furthermore,the proposed design surpasses other encoding techniques by exhibiting encoding and decoding times that are more than 15.17%faster.These results imply that the proposed design can significantly enhance the security and performance of EHRs.Through the utilization of CS,SHA-256,and ECC,this study presents promising methods for addressing the security challenges associated with EHRs.

A Novel Steganography Scheme Combining Coverless Information Hiding and Steganography

At present,the coverless information hiding has been developed.However,due to the limited mapping relationship between secret information and feature selection,it is challenging to further enhance the hiding capacity of coverless information hiding.At the same time,the steganography algorithm based on object detection only hides secret information in foreground objects,which contribute to the steganography capacity is reduced.Since object recognition contains multiple objects and location,secret information can be mapped to object categories,the relationship of location and so on.Therefore,this paper proposes a new steganography algorithm based on object detection and relationship mapping,which integrates coverless information hiding and steganography.In this method,the coverless information hiding is realized by mapping the object type,color and secret information in object detection method.At the same time,the object detection method is used to find the safe area to hide secret messages.The proposed algorithm can not only improve the steganographic capacity of the two information hiding methods but also make the coverless information hiding more secure and robust.

Deep Learning for Distinguishing Computer Generated Images and Natural Images:A Survey

With the development of computer graphics,realistic computer graphics(CG)have become more and more common in our field of vision.This rendered image is invisible to the naked eye.How to effectively identify CG and natural images(NI)has been become a new issue in the field of digital forensics.In recent years,a series of deep learning network frameworks have shown great advantages in the field of images,which provides a good choice for us to solve this problem.This paper aims to track the latest developments and applications of deep learning in the field of CG and NI forensics in a timely manner.Firstly,it introduces the background of deep learning and the knowledge of convolutional neural networks.The purpose is to understand the basic model structure of deep learning applications in the image field,and then outlines the mainstream framework;secondly,it briefly introduces the application of deep learning in CG and NI forensics,and finally points out the problems of deep learning in this field and the prospects for the future.

Splicing Image and Its Localization:A Survey

With the rapid development of information technology,digital images have become an important medium for information transmission.However,manipulating images is becoming a common task with the powerful image editing tools and software,and people can tamper the images content without leaving any visible traces of splicing in order to gain personal goal.Images are easily spliced and distributed,and the situation will be a great threat to social security.The survey covers splicing image and its localization.The present status of splicing image localization approaches is discussed along with a recommendation for future research.

Design and Implementation of Log Data Analysis Management System Based on Hadoop

With the rapid development of the Internet,many enterprises have launched their network platforms.When users browse,search,and click the products of these platforms,most platforms will keep records of these network behaviors,these records are often heterogeneous,and it is called log data.To effectively to analyze and manage these heterogeneous log data,so that enterprises can grasp the behavior characteristics of their platform users in time,to realize targeted recommendation of users,increase the sales volume of enterprises’products,and accelerate the development of enterprises.Firstly,we follow the process of big data collection,storage,analysis,and visualization to design the system,then,we adopt HDFS storage technology,Yarn resource management technology,and gink load balancing technology to build a Hadoop cluster to process the log data,and adopt MapReduce processing technology and data warehouse hive technology analyze the log data to obtain the results.Finally,the obtained results are displayed visually,and a log data analysis system is successfully constructed.It has been proved by practice that the system effectively realizes the collection,analysis and visualization of log data,and can accurately realize the recommendation of products by enterprises.The system is stable and effective.

Image Retrieval Based on Deep Feature Extraction and Reduction with Improved CNN and PCA

With the rapid development of information technology,the speed and efficiency of image retrieval are increasingly required in many fields,and a compelling image retrieval method is critical for the development of information.Feature extraction based on deep learning has become dominant in image retrieval due to their discrimination more complete,information more complementary and higher precision.However,the high-dimension deep features extracted by CNNs(convolutional neural networks)limits the retrieval efficiency and makes it difficult to satisfy the requirements of existing image retrieval.To solving this problem,the high-dimension feature reduction technology is proposed with improved CNN and PCA quadratic dimensionality reduction.Firstly,in the last layer of the classical networks,this study makes a well-designed DR-Module(dimensionality reduction module)to compress the number of channels of the feature map as much as possible,and ensures the amount of information.Secondly,the deep features are compressed again with PCA(Principal Components Analysis),and the compression ratios of the two dimensionality reductions are reduced,respectively.Therefore,the retrieval efficiency is dramatically improved.Finally,it is proved on the Cifar100 and Caltech101 datasets that the novel method not only improves the retrieval accuracy but also enhances the retrieval efficiency.Experimental results strongly demonstrate that the proposed method performs well in small and medium-sized datasets.

A Location Prediction Method Based on GA-LSTM Networks and Associated Movement Behavior Information

Due to the lack of consideration of movement behavior information other than time and location perception in current location prediction methods,the movement characteristics of trajectory data cannot be well expressed,which in turn affects the accuracy of the prediction results.First,a new trajectory data expression method by associating the movement behavior information is given.The pre-association method is used to model the movement behavior information according to the individual movement behavior features and the group movement behavior features extracted from the trajectory sequence and the region.The movement behavior features based on pre-association may not always be the best for the prediction model.Therefore,through association analysis and importance analysis,the final association feature is selected from the pre-association features.The trajectory data is input into the LSTM networks after associated features and genetic algorithm(GA)is used to optimize the combination of the length of time window and the number of hidden layer nodes.The experimental results show that compared with the original trajectory data,the trajectory data associated with the movement behavior information helps to improve the accuracy of location prediction.

A Multi-Scale Network with the Encoder-Decoder Structure for CMR Segmentation

Cardiomyopathy is one of the most serious public health threats.The precise structural and functional cardiac measurement is an essential step for clinical diagnosis and follow-up treatment planning.Cardiologists are often required to draw endocardial and epicardial contours of the left ventricle(LV)manually in routine clinical diagnosis or treatment planning period.This task is time-consuming and error-prone.Therefore,it is necessary to develop a fully automated end-to-end semantic segmentation method on cardiac magnetic resonance(CMR)imaging datasets.However,due to the low image quality and the deformation caused by heartbeat,there is no effective tool for fully automated end-to-end cardiac segmentation task.In this work,we propose a multi-scale segmentation network(MSSN)for left ventricle segmentation.It can effectively learn myocardium and blood pool structure representations from 2D short-axis CMR image slices in a multi-scale way.Specifically,our method employs both parallel and serial of dilated convolution layers with different dilation rates to capture multi-scale semantic features.Moreover,we design graduated up-sampling layers with subpixel layers as the decoder to reconstruct lost spatial information and produce accurate segmentation masks.We validated our method using 164 T1 Mapping CMR images and showed that it outperforms the advanced convolutional neural network(CNN)models.In validation metrics,we archived the Dice Similarity Coefficient(DSC)metric of 78.96%.

Research on the Association of Mobile Social Network Users Privacy Information Based on Big Data Analysis

The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications.The existing researches on user privacy protection in mobile social network mainly focus on privacy preserving data publishing and access control.There is little research on the association of user privacy information,so it is not easy to design personalized privacy protection strategy,but also increase the complexity of user privacy settings.Therefore,this paper concentrates on the association of user privacy information taking big data analysis tools,so as to provide data support for personalized privacy protection strategy design.

Deep Learning Trackers Review and Challenge

Recently,deep learning has achieved great success in visual tracking.The goal of this paper is to review the state-of-the-art tracking methods based on deep learning.First,we categorize the existing deep learning based trackers into three classes according to network structure,network function and network training.For each categorize,we analyze papers in different categories.Then,we conduct extensive experiments to compare the representative methods on the popular OTB-100,TC-128 and VOT2015 benchmarks.Based on our observations.We conclude that:(1)The usage of the convolutional neural network(CNN)model could significantly improve the tracking performance.(2)The trackers with deep features perform much better than those with low-level hand-crafted features.(3)Deep features from different convolutional layers have different characteristics and the effective combination of them usually results in a more robust tracker.(4)The deep visual trackers using end-to-end networks usually perform better than the trackers merely using feature extraction networks.(5)For visual tracking,the most suitable network training method is to per-train networks with video information and online fine-tune them with subsequent observations.Finally,we summarize our manuscript and highlight our insights,and point out the further trends for deep visual tracking.

A Survey on Face Anti-Spoofing Algorithms

The development of artificial intelligence makes the application of face recognition more and more extensive,which also leads to the security of face recognition technology increasingly prominent.How to design a face anti-spoofing method with high accuracy,strong generalization ability and meeting practical needs is the focus of current research.This paper introduces the research progress of face anti-spoofing algorithm,and divides the existing face anti-spoofing methods into two categories:methods based on manual feature expression and methods based on deep learning.Then,the typical algorithms included in them are classified twice,and the basic ideas,advantages and disadvantages of these algorithms are analyzed.Finally,the methods of face anti-spoofing are summarized,and the existing problems and future prospects are expounded.

Feature-Enhanced RefineDet: Fast Detection of Small Objects

Object detection has been studied for many years.The convolutional neural network has made great progress in the accuracy and speed of object detection.However,due to the low resolution of small objects and the representation of fuzzy features,one of the challenges now is how to effectively detect small objects in images.Existing target detectors for small objects:one is to use high-resolution images as input,the other is to increase the depth of the CNN network,but these two methods will undoubtedly increase the cost of calculation and time-consuming.In this paper,based on the RefineDet network framework,we propose our network structure RF2Det by introducing Receptive Field Block to solve the problem of small object detection,so as to achieve the balance of speed and accuracy.At the same time,we propose a Medium-level Feature Pyramid Networks,which combines appropriate high-level context features with low-level features,so that the network can use the features of both the low-level and the high-level for multi-scale target detection,and the accuracy of the small target detection task based on the low-level features is improved.Extensive experiments on the MS COCO dataset demonstrate that compared to other most advanced methods,our proposed method shows significant performance improvement in the detection of small objects.

Joint Self-Attention Based Neural Networks for Semantic Relation Extraction

Relation extraction is an important task in NLP community.However,some models often fail in capturing Long-distance dependence on semantics,and the interaction between semantics of two entities is ignored.In this paper,we propose a novel neural network model for semantic relation classification called joint self-attention bi-LSTM(SA-Bi-LSTM)to model the internal structure of the sentence to obtain the importance of each word of the sentence without relying on additional information,and capture Long-distance dependence on semantics.We conduct experiments using the SemEval-2010 Task 8 dataset.Extensive experiments and the results demonstrated that the proposed method is effective against relation classification,which can obtain state-ofthe-art classification accuracy just with minimal feature engineering.

An Efficient Energy Routing Protocol Based on Gradient Descent Method in WSNs

In a wireless sensor network[1],the operation of a node depends on the battery power it carries.Because of the environmental reasons,the node cannot replace the battery.In order to improve the life cycle of the network,energy becomes one of the key problems in the design of the wireless sensor network(WSN)routing protocol[2].This paper proposes a routing protocol ERGD based on the method of gradient descent that can minimizes the consumption of energy.Within the communication radius of the current node,the distance between the current node and the next hop node is assumed that can generate a projected energy at the distance from the current node to the base station(BS),this projected energy and the remaining energy of the next hop node is the key factor in finding the next hop node.The simulation results show that the proposed protocol effectively extends the life cycle of the network and improves the reliability and fault tolerance of the system.

Random Forests Algorithm Based Duplicate Detection in On-Site Programming Big Data Environment

On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time,complexity and high-difficulty for processing.Therefore,data cleaning is essential for on-site programming big data.Duplicate data detection is an important step in data cleaning,which can save storage resources and enhance data consistency.Due to the insufficiency in traditional Sorted Neighborhood Method(SNM)and the difficulty of high-dimensional data detection,an optimized algorithm based on random forests with the dynamic and adaptive window size is proposed.The efficiency of the algorithm can be elevated by improving the method of the key-selection,reducing dimension of data set and using an adaptive variable size sliding window.Experimental results show that the improved SNM algorithm exhibits better performance and achieve higher accuracy.

Research on Prevention of Citrus Anthracnose Based on Image Retrieval Technology

Citrus anthracnose is a common fungal disease in citrus-growing areas in China,which causes very serious damage.At present,the manual management method is time-consuming and labor-consuming,which reduces the control effect of citrus anthracnose.Therefore,by designing and running the image retrieval system of citrus anthracnose,the automatic recognition and analysis of citrus anthracnose control were realized,and the control effect of citrus anthracnose was improved.In this paper,based on the self-collected and collated citrus anthracnose image database,we use three image features to realize an image retrieval system based on citrus anthracnose through SMV,AP clustering optimization.The results show that:1)In the accuracy of image feature retrieval,Gist feature extraction>cumulative color histogram>Gabor texture feature;2)In the maximum divergence diversity retrieval,semi-supervised AP clustering retrieval>AP clustering retrieval>SVM relevance feedback results>initial retrieval.3)Practice shows that this technology can reduce the workload of monitoring and management in the control process of citrus planting area,and promote the intelligent and efficient control of citrus anthracnose,which has high practical application value.

A Reversible Data Hiding Algorithm Based on Secret Sharing

In traditional secret sharing schemes,all shared images containing secret segments are needed to recover secret information.In this paper,a reversible data hiding scheme based on Shamir secret sharing is used.Secret information can be recovered even if only part of the encrypted sharing is owned.This method can reduce the vulnerability of traditional encryption sharing schemes to attack.Before uploading the secret information to the cloud server,embed the encrypted n segments of secret information into n different pictures.The receiver downloads t images from the cloud server(t<n),extracts the encrypted information using the watermark extraction algorithm,and obtains the original secret information after decryption through Shamir secret sharing.

A Survey of GAN-Generated Fake Faces Detection Method Based on Deep Learning

In recent years,with the rapid growth of generative adversarial networks(GANs),a photo-realistic face can be easily generated from a random vector.Moreover,the faces generated by advanced GANs are very realistic.It is reasonable to acknowledge that even a well-trained viewer has difficulties to distinguish artificial from real faces.Therefore,detecting the face generated by GANs is a necessary work.This paper mainly introduces some methods to detect GAN-generated fake faces,and analyzes the advantages and disadvantages of these models based on the network structure and evaluation indexes,and the results obtained in the respective data sets.On this basis,the challenges faced in this field and future research directions are discussed.

A Survey on Cryptographic Security and Information Hiding Technology for Cloud or Fog-Based IoT System

Internet of Things(IoT)is an emerging paradigm involving intelligent sensor networks that incorporates embedded technology for collecting data,communicating with external environments.Recently,cloud computing together with fog computing has become an important research area of the Internet of Things because of big data processing capabilities.It is a promising technology that utilizes cloud or fog computing/architecture to improve sensor computing,storage,and communication capabilities.However,recently it has been shown that this computing/architecture may be vulnerable to various attacks because of the openness nature of the wireless network.Therefore,it becomes more and more important to ensure the security and privacy in these scenes.Encryption security and information hiding technology can provide authentication,confidentiality,integrity,anti-eavesdropping,availability and so on for these computing models or architectures.The purpose of this review is to look for original articles with novel ideas and solutions to address encryption security and information hiding technologies in cloud or fog-based Internet of Things systems.We hope this review will provide an opportunity for scientists,researchers and industry engineers to study original manuscripts and know developments in all aspects of security,privacy,trust,and covert communication issues in cloud or fog computing/architecture Internet of Things systems.

A Survey on Digital Image Steganography

Internet brings us not only the convenience of communication but also some security risks,such as intercepting information and stealing information.Therefore,some important information needs to be hidden during communication.Steganography is the most common information hiding technology.This paper provides a literature review on digital image steganography.The existing steganography algorithms are classified into traditional algorithms and deep learning-based algorithms.Moreover,their advantages and weaknesses are pointed out.Finally,further research directions are discussed.