A chaotic hierarchical encryption/watermark embedding scheme for multi-medical images based on row-column confusion and closed-loop bi-directional diffusion

Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is designed,and a multiimage encryption(MIE)algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper.While ensuring secure communication of medical image information,people with different security levels have different levels of decryption keys,and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys.The highest security level can obtain decrypted images without watermarks,and at the same time,patient information and copyright attribution can be verified by obtaining watermark images.The experimental results show that the scheme is sufficiently secure as an MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.

Confusion of Life in The Catcher in the Rye:Viewed From Holden's Inner World

The Catcher in the Rye is a novel published in 1951.Since its publication,it has been listed as one of the world greatest masterpieces.The story in the novel deals with a rebellious teenage schoolboy Holden and his quixotic experiences in New York,taking place in December.Through the tone of a middle schoolboy,the author shows the deepest confusion of teenagers during 1950 s in their lives.It also exposes a clearer understanding of adolescents' particular psychology.This article gives a discussion of Holden's confusion of life viewed from his inner world.

Meige Syndrome: An Eternal Diagnostic Confusion

Meige syndrome is an idiopathic dystonia characterized by combination of blepharospasm and involuntary movements of the lower facial and/or masticatory (jaw) muscles. The condition is rare and has a variety of clinical presentations which often lead to its misdiagnosis. We report a case of Meige syndrome repeatedly misdiagnosed and treated unsuccessfully as conversion disorder.

Iatrogenic Horner's syndrome: A cause for diagnostic confusion in the emergency department

Dear editor,Horner's syndrome(HS)results from interruption of sympathetic nervous supply to the eye and manifests clinically with partial ptosis,miosis and enophthalmos,along with anhidrosis of face on the affected side.[1]HS is not an uncommon finding in patients visiting emergency department(ED),being reported in those with brainstem strokes,myelitis,malignancies of lung and thyroid,dissections of carotid and vertebral arteries,traumatic injury to neck and thorax and cervical or thoracic intervertebral disc herniation.[1]Some of the iatrogenic causes of

US STRATEGY TOWARD MIDDLE EAST:CONFUSION IN TRANSFORMATION

During the Cold War,the United States implemented an"offshore balance"policy in the Middle East,on the one hand,competing with the Soviet Union for a sphere of influence,and on the other hand,not directly engaging in the war.After the end of the Cold War,the US,as the sole super power,switched to the strategy of direct intervention in the Middle East.

A Brief Discussion on the Causes and Countermeasures of College Students'Psychological Confusion and Mental Health Problems

College period is one of the most critical periods of one person’s life for it is an important period of establishing world concept,outlook on life and values.Various challenges and pressures have had a great impact on the mental health of college students.In that case,college students’psychological confusion and mental health problems occur frequently and the overall condition of college students’psychological problems is worrying.The mental health of college students has also attracted the attention of colleges and the society.Besides,the mental health education courses have been adopted to guide college students.This article aims to analyze the causes of college students’psychological confusion and mental health problems and also the obstacles or difficulties in solving them from the perspective of college students'cultivation of positive psychological quality and mental health education.And corresponding solutions from both the outside and personal perspectives.

The Reflection on Spiritual Confusion and the Desire for Return to Humanity in T.S.Eliot's The Love Song of J.Alfred Prufrock

Although in the name of love,The Love Song is actually a middle-aged man's internal monologue of his courage and cowardice.This essay will analyze the stream of consciousness of Prufrock and the world in which he lives,the author's ruthless exposure to society's emptiness and ultimate concern for human development,and poetic techniques he utilizes including metaphor,repetition,and allusion,to express his reflection on spiritual confusion and desire for return to human nature.

Text retrieval algorithm that decreases confusion

To overcome the problem that the confusion between texts limits the precision in text retrieval,a new text retrieval algorithm that decrease confusion(DCTR)is proposed.The algorithm constructs the searching template to represent the user's searching intention through positive and negative training.By using the prior probabilities in the template,the supported probability and anti-supported probability of each text in the text library can be estimated for discrimination.The searching result can be ranked according to similarities between retrieved texts and the template.The complexity of DCTR is close to term frequency and mversed document frequency(TF-IDF).Its distinguishing ability to confusable texts could be advanced and the performance of the result would be improved with increasing of training times.

A method to predict rockburst using temporal trend test and its application

Rockbursts have become a significant hazard in underground mining,underscoring the need for a robust early warning model to ensure safety management.This study presents a novel approach for rockburst prediction,integrating the Mann-Kendall trend test(MKT)and multi-indices fusion to enable real-time and quantitative assessment of rockburst hazards.The methodology employed in this study involves the development of a comprehensive precursory index library for rockbursts.The MKT is then applied to analyze the real-time trend of each index,with adherence to rockburst characterization laws serving as the warning criterion.By employing a confusion matrix,the warning effectiveness of each index is assessed,enabling index preference determination.Ultimately,the integrated rockburst hazard index Q is derived through data fusion.The results demonstrate that the proposed model achieves a warning effectiveness of 0.563 for Q,surpassing the performance of any individual index.Moreover,the model’s adaptability and scalability are enhanced through periodic updates driven by actual field monitoring data,making it suitable for complex underground working environments.By providing an efficient and accurate basis for decision-making,the proposed model holds great potential for the prevention and control of rockbursts.It offers a valuable tool for enhancing safety measures in underground mining operations.

Incidence and Risk Factors of Sub-syndromal Delirium in Patients after Cardiac Surgery

[Objectives]This study aimed to investigate the incidence and risk factors associated with SSD in patients following cardiac surgery.[Methods]A total of 378 patients who underwent cardiac surgery in Taihe Hospital were recruited and screened.Diagnosis of delirium was made using evaluation methods and DSM-5 criteria.SSD was defined as the presence of one or more core features of delirium without meeting the full diagnostic criteria.Statistical analysis included independent samples t-test for group comparisons and binary logistic regression analysis to identify independent risk factors for SSD after cardiac surgery.[Results]Among the 378 subjects,112(29.63%)had SSD,28(7.41%)had delirium,and the remaining 238 patients(62.96%)did not present with delirium.Univariate analysis revealed that age,APACHE II score,duration of aortic clamping,length of ICU stay,duration of sedation use,and daily sleep time were significant risk factors for the occurrence of SSD(P<0.05).Logistic regression analysis identified age>70 years old,APACHE II score>20 points,length of ICU stay>5 d,and duration of sedation use>24 h as independent risk factors for SSD after cardiac surgery(P<0.05).A functional model was fitted based on the analysis results of the binary logistic regression model,yielding the equation logit P=1.472X_(1)+2.213X_(2)+3.028X_(3)+1.306X_(4).[Conclusions]Comprehensive clinical assessment is crucial for patients undergoing cardiac surgery,and appropriate preventive measures should be taken for patients with identified risk factors.Close monitoring of the patient s consciousness should be implemented postoperatively,and timely interventions should be conducted.Further research should focus on model validation and optimization.

Asymmetric pixel confusion algorithm for images based on RSA and Arnold transform

We propose a new asymmetric pixel confusion algorithm for images based on the Rivest-Shamir-Adleman(RSA)public-key cryptosystem and Arnold map.First,the RSA asymmetric algorithm is used to generate two groups of Arnold transform parameters to address the problem of symmetrical distribution of Arnold map parameters.Second,the image is divided into blocks,and the first group of parameters is used to perform Arnold confusion on each sub-block.Then,the second group of parameters is used to perform Arnold confusion on the entire image.The image correlation is thereby fully weakened,and the image confusion degree and effect are further enhanced.The experimental results show that the proposed image pixel confusion algorithm has better confusion effect than the classical Arnold map based confusion and the row-column exchange based confusion.Specifically,the values of gray difference are close to one.In addition,the security of the new confusion operation is dependent on RSA,and it can act as one part of a confusion-substitution structure in a cipher.

Transparency order versus confusion coefficient:a case study of NIST lightweight cryptography S‑Boxes

Side-channel resistance is nowadays widely accepted as a crucial factor in deciding the security assurance level of cryptographic implementations.In most cases,non-linear components(e.g.S-Boxes)of cryptographic algorithms will be chosen as primary targets of side-channel attacks(SCAs).In order to measure side-channel resistance of S-Boxes,three theoretical metrics are proposed and they are reVisited transparency order(VTO),confusion coefficients variance(CCV),and minimum confusion coefficient(MCC),respectively.However,the practical effectiveness of these metrics remains still unclear.Taking the 4-bit and 8-bit S-Boxes used in NIST Lightweight Cryptography candidates as concrete examples,this paper takes a comprehensive study of the applicability of these metrics.First of all,we empirically investigate the relations among three metrics for targeted S-boxes,and find that CCV is almost linearly correlated with VTO,while MCC is inconsistent with the other two.Furthermore,in order to verify which metric is more effective in which scenarios,we perform simulated and practical experiments on nine 4-bit S-Boxes under the nonprofiled attacks and profiled attacks,respectively.The experiments show that for quantifying side-channel resistance of S-Boxes under non-profiled attacks,VTO and CCV are more reliable while MCC fails.We also obtain an interesting observation that none of these three metrics is suitable for measuring the resistance of S-Boxes against profiled SCAs.Finally,we try to verify whether these metrics can be applied to compare the resistance of S-Boxes with different sizes.Unfortunately,all of them are invalid in this scenario.

Transparency order versus confusion coefficient:a case study of NIST lightweight cryptography S-Boxes

Side-channel resistance is nowadays widely accepted as a crucial factor in deciding the security assurance level of cryptographic implementations.In most cases,non-linear components(e.g.S-Boxes)of cryptographic algorithms will be chosen as primary targets of side-channel attacks(SCAs).In order to measure side-channel resistance of S-Boxes,three theoretical metrics are proposed and they are revisited transparency order(VTO),confusion coefficients variance(CCV),and minimum confusion coefficient(MCC),respectively.However,the practical effectiveness of these metrics remains still unclear.Taking the 4-bit and 8-bit S-Boxes used in NIST Lightweight Cryptography candidates as concrete examples,this paper takes a comprehensive study of the applicability of these metrics.First of all,we empirically investigate the relations among three metrics for targeted S-boxes,and find that CCV is almost linearly correlated with VTO,while MCC is inconsistent with the other two.Furthermore,in order to verify which metric is more effective in which scenarios,we perform simulated and practical experiments on nine 4-bit S-Boxes under the non-profiled attacks and profiled attacks,respectively.The experiments show that for quantifying side-channel resistance of S-Boxes under non-profiled attacks,VTO and CCV are more reliable while MCC fails.We also obtain an interesting observation that none of these three metrics is suitable for measuring the resistance of S-Boxes against profiled SCAs.Finally,we try to verify whether these metrics can be applied to compare the resistance of S-Boxes with different sizes.Unfortunately,all of them are invalid in this scenario.

A Novel Lightweight Image Encryption Scheme

Encryption algorithms are one of the methods to protect dataduring its transmission through an unsafe transmission medium. But encryptionmethods need a lot of time during encryption and decryption, so itis necessary to find encryption algorithms that consume little time whilepreserving the security of the data. In this paper, more than one algorithmwas combined to obtain high security with a short implementation time. Achaotic system, DNA computing, and Salsa20 were combined. A proposed5D chaos system was used to generate more robust keys in a Salsa algorithmand DNA computing. Also, the confusion is performed using a new SBox.The proposed chaos system achieves three positive Lyapunov values.So results demonstrate of the proposed scheme has a sufficient peak signalto-noise ratio, a low correlation, and a large key space. These factors makeit more efficient than its classical counterpart and can resist statistical anddifferential attacks. The number of changing pixel rates (NPCR) and theunified averaged changed intensity (UACI) values were 0.99710 and UACI33.68. The entropy oscillates from 7.9965 to 7.9982 for the tested encryptedimages. The suggested approach is resistant to heavy attacks and takes lesstime to execute than previously discussed methods, making it an efficient,lightweight image encryption scheme. The method provides lower correlationcoefficients than other methods, another indicator of an efficient imageencryption system. Even though the proposed scheme has useful applicationsin image transmission, it still requires profound improvement in implementingthe high-intelligence scheme and verifying its feasibility on devices with theInternet of Things (IoT) enabled.

Asymmetric image encryption algorithm based on a new three-dimensional improved logistic chaotic map

Based on some analyses of existing chaotic image encryption frameworks and a new designed three-dimensional improved logistic chaotic map(3D-ILM),an asymmetric image encryption algorithm using public-key Rivest–Shamir–Adleman(RSA)is presented in this paper.In the first stage,a new 3D-ILM is proposed to enhance the chaotic behavior considering analysis of time sequence,Lyapunov exponent,and Shannon entropy.In the second stage,combined with the public key RSA algorithm,a new key acquisition mathematical model(MKA)is constructed to obtain the initial keys for the 3D-ILM.Consequently,the key stream can be produced depending on the plain image for a higher security.Moreover,a novel process model(NPM)for the input of the 3D-ILM is built,which is built to improve the distribution uniformity of the chaotic sequence.In the third stage,to encrypt the plain image,a pre-process by exclusive OR(XOR)operation with a random matrix is applied.Then,the pre-processed image is performed by a permutation for rows,a downward modulo function for adjacent pixels,a permutation for columns,a forward direction XOR addition-modulo diffusion,and a backward direction XOR addition-modulo diffusion to achieve the final cipher image.Moreover,experiments show that the the proposed algorithm has a better performance.Especially,the number of pixels change rate(NPCR)is close to ideal case 99.6094%,with the unified average changing intensity(UACI)close to 33.4634%,and the information entropy(IE)close to 8.

Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

IIF(Indirect Immune Florescence)has gained much attention recently due to its importance in medical sciences.The primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune diseases.The use of IIF for detecting autoimmune diseases is widespread in different medical areas.Nearly 80 different types of autoimmune diseases have existed in various body parts.The IIF has been used for image classification in both ways,manually and by using the Computer-Aided Detection(CAD)system.The data scientists conducted various research works using an automatic CAD system with low accuracy.The diseases in the human body can be detected with the help of Transfer Learning(TL),an advanced Convolutional Neural Network(CNN)approach.The baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells(HEP)type II cells and the Sub Class Discriminant(SDA)analysis technique used to detect autoimmune diseases.The technique yielded an accuracy of up to 90.03%,which was not reliable for detecting autoimmune disease in the mitotic cells of the body.In the current research,the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of TL.Data augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL models.These models are named Inception V3,Dens Net 121,VGG-16,and Mobile Net,and their performance can be calculated through parameters of the confusion matrix(accuracy,precision,recall,and F1 measures).The results show that the accuracy value of VGG-16 is 78.00%,Inception V3 is 92.00%,Dense Net 121 is 95.00%,and Mobile Net shows 88.00%accuracy,respectively.Therefore,DenseNet-121 shows the highest performance with suitable analysis of autoimmune diseases.The overall performance highlighted that TL is a suitable and enhanced technique compared to its counterparts.Also,the proposed technique is used to detect autoimmune diseases with a minimal margin of errors and flaws.

Predictive-Analysis-based Machine Learning Model for Fraud Detection with Boosting Classifiers

Fraud detection for credit/debit card,loan defaulters and similar types is achievable with the assistance of Machine Learning(ML)algorithms as they are well capable of learning from previous fraud trends or historical data and spot them in current or future transactions.Fraudulent cases are scant in the comparison of non-fraudulent observations,almost in all the datasets.In such cases detecting fraudulent transaction are quite difficult.The most effective way to prevent loan default is to identify non-performing loans as soon as possible.Machine learning algorithms are coming into sight as adept at handling such data with enough computing influence.In this paper,the rendering of different machine learning algorithms such as Decision Tree,Random Forest,linear regression,and Gradient Boosting method are compared for detection and prediction of fraud cases using loan fraudulent manifestations.Further model accuracy metric have been performed with confusion matrix and calculation of accuracy,precision,recall and F-1 score along with Receiver Operating Characteristic(ROC)curves.

A New Method for Image Tamper Detection Based on an Improved U-Net

With the improvement of image editing technology,the threshold of image tampering technology decreases,which leads to a decrease in the authenticity of image content.This has also driven research on image forgery detection techniques.In this paper,a U-Net with multiple sensory field feature extraction(MSCU-Net)for image forgery detection is proposed.The proposed MSCU-Net is an end-to-end image essential attribute segmentation network that can perform image forgery detection without any pre-processing or post-processing.MSCU-Net replaces the single-scale convolution module in the original network with an improved multiple perceptual field convolution module so that the decoder can synthesize the features of different perceptual fields use residual propagation and residual feedback to recall the input feature information and consolidate the input feature information to make the difference in image attributes between the untampered and tampered regions more obvious,and introduce the channel coordinate confusion attention mechanism(CCCA)in skip-connection to further improve the segmentation accuracy of the network.In this paper,extensive experiments are conducted on various mainstream datasets,and the results verify the effectiveness of the proposed method,which outperforms the state-of-the-art image forgery detection methods.

Dual Image Cryptosystem Using Henon Map and Discrete Fourier Transform

This paper introduces an efficient image cryptography system.The pro-posed image cryptography system is based on employing the two-dimensional(2D)chaotic henon map(CHM)in the Discrete Fourier Transform(DFT).The proposed DFT-based CHM image cryptography has two procedures which are the encryption and decryption procedures.In the proposed DFT-based CHM image cryptography,the confusion is employed using the CHM while the diffu-sion is realized using the DFT.So,the proposed DFT-based CHM image crypto-graphy achieves both confusion and diffusion characteristics.The encryption procedure starts by applying the DFT on the image then the DFT transformed image is scrambled using the CHM and the inverse DFT is applied to get the final-ly encrypted image.The decryption procedure follows the inverse procedure of encryption.The proposed DFT-based CHM image cryptography system is exam-ined using a set of security tests like statistical tests,entropy tests,differential tests,and sensitivity tests.The obtained results confirm and ensure the superiority of the proposed DFT-based CHM image cryptography system.These outcomes encourage the employment of the proposed DFT-based CHM image cryptography system in real-time image and video applications.