Multiclass Stomach Diseases Classication Using Deep Learning Features Optimization

In the area of medical image processing,stomach cancer is one of the most important cancers which need to be diagnose at the early stage.In this paper,an optimized deep learning method is presented for multiple stomach disease classication.The proposed method work in few important steps—preprocessing using the fusion of ltering images along with Ant Colony Optimization(ACO),deep transfer learning-based features extraction,optimization of deep extracted features using nature-inspired algorithms,and nally fusion of optimal vectors and classication using Multi-Layered Perceptron Neural Network(MLNN).In the feature extraction step,pretrained Inception V3 is utilized and retrained on selected stomach infection classes using the deep transfer learning step.Later on,the activation function is applied to Global Average Pool(GAP)for feature extraction.However,the extracted features are optimized through two different nature-inspired algorithms—Particle Swarm Optimization(PSO)with dynamic tness function and Crow Search Algorithm(CSA).Hence,both methods’output is fused by a maximal value approach and classied the fused feature vector by MLNN.Two datasets are used to evaluate the proposed method—CUI WahStomach Diseases and Combined dataset and achieved an average accuracy of 99.5%.The comparison with existing techniques,it is shown that the proposed method shows signicant performance.

Cardiac Arrhythmia Disease Classication Using LSTM Deep Learning Approach

Many approaches have been tried for the classication of arrhythmia.Due to the dynamic nature of electrocardiogram(ECG)signals,it is challenging to use traditional handcrafted techniques,making a machine learning(ML)implementation attractive.Competent monitoring of cardiac arrhythmia patients can save lives.Cardiac arrhythmia prediction and classication has improved signicantly during the last few years.Arrhythmias are a group of conditions in which the electrical activity of the heart is abnormal,either faster or slower than normal.It is the most frequent cause of death for both men and women every year in the world.This paper presents a deep learning(DL)technique for the classication of arrhythmias.The proposed technique makes use of the University of California,Irvine(UCI)repository,which consists of a high-dimensional cardiac arrhythmia dataset of 279 attributes.In this research,our goal was to classify cardiac arrhythmia patients into 16 classes depending on the characteristics of the electrocardiography dataset.The DL approach in the form of long short-term memory(LSTM)is an efcient technique to deal with reduced accuracy due to vanishing and exploding gradients in traditional DL frameworks for big data analysis.The goal of this research was to categorize cardiac arrhythmia patients by developing an efcient intelligent system using the LSTM DL algorithm.This approach to arrhythmia classication includes classication algorithms along with noise removal techniques.Therefore,we utilized principal components analysis(PCA)for noise removal,and LSTM for classication.This hybrid comprehensive arrhythmia classication approach performs better than previous approaches to arrhythmia classication.We attained a highest classication accuracy of 93.5%with the DL based disease classication system,and outperformed the earlier approaches used for cardiac arrhythmia classication.

M-IDM: A Multi-Classication Based Intrusion Detection Model in Healthcare IoT

In recent years,the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected.Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset,such as the NSL-KDD dataset.However,such approaches do not reect the features that exist in real medical scenarios,leading to failure in potential threat detection.To address this problem,we proposed a novel intrusion classication architecture known as a Multi-class Classication based Intrusion Detection Model(M-IDM),which typically relies on data collected by real devices and the use of convolutional neural networks(i.e.,it exhibits better performance compared with conventional machine learning algorithms,such as naïve Bayes,support vector machine(SVM)).Unlike existing studies,the proposed architecture employs the actual healthcare IoT environment of National Cancer Center in South Korea and actual network data from real medical devices,such as a patient’s monitors(i.e.,electrocardiogram and thermometers).The proposed architecture classies the data into multiple classes:Critical,informal,major,and minor,for intrusion detection.Further,we experimentally evaluated and compared its performance with those of other conventional machine learning algorithms,including naïve Bayes,SVM,and logistic regression,using neural networks.

Classication of COVID-19 CT Scans via Extreme Learning Machin

Here,we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography(CT)scans.The scheme operates in four steps.Initially,we prepared a database containing COVID-19 pneumonia and normal CT scans.These images were retrieved from the Radiopaedia COVID-19 website.The images were divided into training and test sets in a ratio of 70:30.Then,multiple features were extracted from the training data.We used canonical correlation analysis to fuse the features into single vectors;this enhanced the predictive capacity.We next implemented a genetic algorithm(GA)in which an Extreme Learning Machine(ELM)served to assess GA tness.Based on the ELM losses,the most discriminatory features were selected and saved as an ELM Model.Test images were sent to the model,and the best-selected features compared to those of the trained model to allow nal predictions.Validation employed the collected chest CT scans.The best predictive accuracy of the ELM classier was 93.9%;the scheme was effective.

Inhibition of vertebrate complement system by hematophagous arthropods: inhibitory molecules, mechanisms, physiological roles, and applications

In arthropods,hematophagy has arisen several times throughout evolution.This specialized feeding behavior offered a highly nutritious diet obtained during blood feeds.On the other hand,blood-sucking arthropods must overcome problems brought on by blood intake and digestion.Host blood complement acts on the bite site and is still ac-tive after ingestion,so complement activation is a potential threat to the host's skin feed-ing environment and to the arthropod gut enterocytes.During evolution,blood-sucking arthropods have selected,either in their saliva or gut,anticomplement molecules that inac-tivate host blood complement.This review presents an overview of the complement sys-tem and discusses the arthropod's salivary and gut anticomplement molecules studied to date,exploring their mechanism of action and other aspects related to the arthropod-host-pathogen interface.The possible therapeutic applications of arthropod's anticomplement molecules arealsodiscussed.

Reference-frame-independent quantum key distribution with two-way classical communication

The data post-processing scheme based on two-way classical communication(TWCC)can improve the tolerable bit error rate and extend the maximal transmission distance when used in a quantum key distribution(QKD)system.In this study,we apply the TWCC method to improve the performance of reference-frame-independent quantum key distribution(RFI-QKD),and analyze the influence of the TWCC method on the performance of decoy-state RFI-QKD in both asymptotic and non-asymptotic cases.Our numerical simulation results show that the TWCC method is able to extend the maximal transmission distance from 175 km to 198 km and improve the tolerable bit error rate from 10.48%to 16.75%.At the same time,the performance of RFI-QKD in terms of the secret key rate and maximum transmission distance are still greatly improved when statistical fluctuations are considered.We conclude that RFI-QKD with the TWCC method is of practical interest.

Tailoring Classical Conditioning Behavior in TiO_(2) Nanowires:ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware

Neuromorphic hardware equipped with associative learn-ing capabilities presents fascinating applications in the next generation of artificial intelligence.However,research into synaptic devices exhibiting complex associative learning behaviors is still nascent.Here,an optoelec-tronic memristor based on Ag/TiO_(2) Nanowires:ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors.Effective implementation of synaptic behaviors,including long and short-term plasticity,and learning-forgetting-relearning behaviors,were achieved in the device through the application of light and electrical stimuli.Leveraging the optoelectronic co-modulated characteristics,a simulation of neuromorphic computing was conducted,resulting in a handwriting digit recognition accuracy of 88.9%.Furthermore,a 3×7 memristor array was constructed,confirming its application in artificial visual memory.Most importantly,complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli,respectively.After training through associative pairs,reflexes could be triggered solely using light stimuli.Comprehen-sively,under specific optoelectronic signal applications,the four features of classical conditioning,namely acquisition,extinction,recovery,and generalization,were elegantly emulated.This work provides an optoelectronic memristor with associative behavior capabilities,offering a pathway for advancing brain-machine interfaces,autonomous robots,and machine self-learning in the future.

Design of a novel hybrid quantum deep neural network in INEQR images classification

We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantum circuit, thereby propose a novel hybrid quantum deep neural network(HQDNN) used for image classification. After bilinear interpolation reduces the original image to a suitable size, an improved novel enhanced quantum representation(INEQR) is used to encode it into quantum states as the input of the HQDNN. Multi-layer parameterized quantum circuits are used as the main structure to implement feature extraction and classification. The output results of parameterized quantum circuits are converted into classical data through quantum measurements and then optimized on a classical computer. To verify the performance of the HQDNN, we conduct binary classification and three classification experiments on the MNIST(Modified National Institute of Standards and Technology) data set. In the first binary classification, the accuracy of 0 and 4 exceeds98%. Then we compare the performance of three classification with other algorithms, the results on two datasets show that the classification accuracy is higher than that of quantum deep neural network and general quantum convolutional neural network.

Structure-preserving algorithms for guiding center dynamics based on the slow manifold of classical Pauli particle

The classical Pauli particle(CPP) serves as a slow manifold, substituting the conventional guiding center dynamics. Based on the CPP, we utilize the averaged vector field(AVF) method in the computations of drift orbits. Demonstrating significantly higher efficiency, this advanced method is capable of accomplishing the simulation in less than one-third of the time of directly computing the guiding center motion. In contrast to the CPP-based Boris algorithm, this approach inherits the advantages of the AVF method, yielding stable trajectories even achieved with a tenfold time step and reducing the energy error by two orders of magnitude. By comparing these two CPP algorithms with the traditional RK4 method, the numerical results indicate a remarkable performance in terms of both the computational efficiency and error elimination. Moreover, we verify the properties of slow manifold integrators and successfully observe the bounce on both sides of the limiting slow manifold with deliberately chosen perturbed initial conditions. To evaluate the practical value of the methods, we conduct simulations in non-axisymmetric perturbation magnetic fields as part of the experiments,demonstrating that our CPP-based AVF method can handle simulations under complex magnetic field configurations with high accuracy, which the CPP-based Boris algorithm lacks. Through numerical experiments, we demonstrate that the CPP can replace guiding center dynamics in using energy-preserving algorithms for computations, providing a new, efficient, as well as stable approach for applying structure-preserving algorithms in plasma simulations.

Computational Analysis of Novel Extended Lindley Progressively Censored Data

A novel extended Lindley lifetime model that exhibits unimodal or decreasing density shapes as well as increasing,bathtub or unimodal-then-bathtub failure rates, named the Marshall-Olkin-Lindley (MOL) model is studied.In this research, using a progressive Type-II censored, various inferences of the MOL model parameters oflife are introduced. Utilizing the maximum likelihood method as a classical approach, the estimators of themodel parameters and various reliability measures are investigated. Against both symmetric and asymmetric lossfunctions, the Bayesian estimates are obtained using the Markov Chain Monte Carlo (MCMC) technique with theassumption of independent gamma priors. From the Fisher information data and the simulatedMarkovian chains,the approximate asymptotic interval and the highest posterior density interval, respectively, of each unknownparameter are calculated. Via an extensive simulated study, the usefulness of the various suggested strategies isassessedwith respect to some evaluationmetrics such as mean squared errors, mean relative absolute biases, averageconfidence lengths, and coverage percentages. Comparing the Bayesian estimations based on the asymmetric lossfunction to the traditional technique or the symmetric loss function-based Bayesian estimations, the analysisdemonstrates that asymmetric loss function-based Bayesian estimations are preferred. Finally, two data sets,representing vinyl chloride and repairable mechanical equipment items, have been investigated to support theapproaches proposed and show the superiority of the proposed model compared to the other fourteen lifetimemodels.

Evaluation of the famous classic formula Sanhua decoction based on network pharmacology and multi-component quantitative analysis

Background:Sanhua decoction has significant effects in the treatment of stroke.The study of the Sanhua decoction material benchmark was carried out to analyze the value transfer relationship between the Chinese herbal pieces and the substance benchmark.Methods:Network pharmacology was employed to investigate the potential active components and molecular mechanisms of Sanhua decoction in the treatment of stroke.15 batches of Sanhua decoction lyophilized powder were prepared using traditional formulas and subjected to high-performance liquid chromatography analysis to generate fingerprints of the Sanhua decoction substance benchmarks.Then,a multi-component quantitative analysis method was established,allowing for the simultaneous determination of ten components,to study the transfer of quantity values between pieces and substance benchmarks.Results:60 active ingredients were screened from Sanhua decoction by network pharmacology,of which gallic acid,magnolol honokiol,physcion,and aloe-emodin may have a greater effect than other active components.63 key targets and 134 pathways were predicted as the potential mechanism of Sanhua decoction in treating stroke.The fingerprint similarity of the Sanhua decoction substance benchmarks was found to be good among the 15 batches,confirming the 19 common peaks.The content of the 10 components was basically consistent.The components’transfer rates were within 30%of their respective means.Conclusions:This study provided a comprehensive and reliable strategy for the quality evaluation of Sanhua decoction substance benchmarks and held significant importance in improving its application value.

Dynamic and electrical responses of a curved sandwich beam with glass reinforced laminate layers and a pliable core in the presence of a piezoelectric layer under low-velocity impact

The dynamic responses and generated voltage in a curved sandwich beam with glass reinforced laminate(GRL)layers and a pliable core in the presence of a piezoelectric layer under low-velocity impact(LVI)are investigated.The current study aims to carry out a dynamic analysis on the sandwich beam when the impactor hits the top face sheet with an initial velocity.For the layer analysis,the high-order shear deformation theory(HSDT)and Frostig's second model for the displacement fields of the core layer are used.The classical non-adhesive elastic contact theory and Hunter's principle are used to calculate the dynamic responses in terms of time.In order to validate the analytical method,the outcomes of the current investigation are compared with those gained by the experimental tests carried out by other researchers for a rectangular composite plate subject to the LVI.Finite element(FE)simulations are conducted by means of the ABAQUS software.The effects of the parameters such as foam modulus,layer material,fiber angle,impactor mass,and its velocity on the generated voltage are reviewed.

Enhancing Critical Path Problem in Neutrosophic Environment Using Python

In the real world,one of the most common problems in project management is the unpredictability of resources and timelines.An efficient way to resolve uncertainty problems and overcome such obstacles is through an extended fuzzy approach,often known as neutrosophic logic.Our rigorous proposed model has led to the creation of an advanced technique for computing the triangular single-valued neutrosophic number.This innovative approach evaluates the inherent uncertainty in project durations of the planning phase,which enhances the potential significance of the decision-making process in the project.Our proposed method,for the first time in the neutrosophic set literature,not only solves existing problems but also introduces a new set of problems not yet explored in previous research.A comparative study using Python programming was conducted to examine the effectiveness of responsive and adaptive planning,as well as their differences from other existing models such as the classical critical path problem and the fuzzy critical path problem.The study highlights the use of neutrosophic logic in handling complex projects by illustrating an innovative dynamic programming framework that is robust and flexible,according to the derived results,and sets the stage for future discussions on its scalability and application across different industries.

The Hybrid Power Mean Involving the Character Sum of Polynomials and a Sum Analogous to Kloosterman Sum

The main purpose of this paper is using the properties of the classical Gauss sum and the analytic methods to study the computational problem of one kind of hybrid power mean involving the character sum of polynomials and a sum analogous to Kloosterman sum mod p,an odd prime,and give two sharp asymptotic formulae for them.

Clinical research report on traditional Chinese patent medicines and traditional Chinese classic famous prescriptions(2022)

Objective:The paper is to comprehensively summarize and analyze the basic situation and methodological quality of clinical randomized controlled trials(RCTs)of traditional Chinese patent medicines and traditional Chinese classic famous prescriptions published in 2022,to provide evidence and reasonable suggestions for the advancement of clinical research and the formulation of policies and guidelines.Methods:The Evidence Database System of clinical evidence-based evaluation of traditional Chinese medicine was searched,and data from China National Knowledge Infrastructure(CNKI),PubMed,and other databases were supplemented.The search duration was from January 1,2022,to December 31,2022.RCTs of traditional Chinese patent medicines and traditional Chinese classic famous prescriptions were included as the source of clinical evidence,and published information,sample size,intervention,control measures,treatment course,methodological quality,and key link report were analyzed and evaluated.Results:A total of 1,464 RCTs of traditional Chinese patent medicines were included,which comprised 667 types of traditional Chinese patent medicines;“traditional Chinese patent medicines+Western medicine vs.Western medicine”was the most widely used intervention and control setting,involving 417 RCTs(28.48%).A total of 245 RCTs of traditional Chinese classic famous prescriptions were included,comprising 55 types of traditional Chinese classic famous prescriptions.“Decoction+conventional treatment vs.conventional treatment”was the most widely used intervention and control setting,with 87 RCTs(35.51%).Published RCTs on traditional Chinese patent medicines and traditional Chinese classic famous prescriptions were limited by the study design and implementation.Most“allocation concealment”and“blinding of patients and personnel”were rated as medium to high risk.There are insufficient reports on key research links such as experimental registration and ethical approval.Conclusions:The number of RCTs on traditional Chinese patent medicines has decreased in 2022,but there has been a slight improvement in the research quality and impact.There are relatively few studies on traditional Chinese classic famous prescriptions.Measures must be taken to improve clinical trial design,implementation,and reporting.Methodological experts should be invited to provide professional technical guidance on the trial design.In the research implementation process,attention should be paid to quality control,particularly the standardization of the randomized execution.

Advances in Deep-Learning-based Precipitation Nowcasting Techniques

Precipitation nowcasting,as a crucial component of weather forecasting,focuses on predicting very short-range precipitation,typically within six hours.This approach relies heavily on real-time observations rather than numerical weather models.The core concept involves the spatio-temporal extrapolation of current precipitation fields derived from ground radar echoes and/or satellite images,which was generally actualized by employing computer image or vision techniques.Recently,with stirring breakthroughs in artificial intelligence(AI)techniques,deep learning(DL)methods have been used as the basis for developing novel approaches to precipitation nowcasting.Notable progress has been obtained in recent years,manifesting the strong potential of DL-based nowcasting models for their advantages in both prediction accuracy and computational cost.This paper provides an overview of these precipitation nowcasting approaches,from which two stages along the advancing in this field emerge.Classic models that were established on an elementary neural network dominated in the first stage,while large meteorological models that were based on complex network architectures prevailed in the second.In particular,the nowcasting accuracy of such data-driven models has been greatly increased by imposing suitable physical constraints.The integration of AI models and physical models seems to be a promising way to improve precipitation nowcasting techniques further.

Reflections on Studying Huang Di Nei Jing in the West

Huang Di Nei Jing(《黄帝内经》The Yellow Emperor’s Inner Classic) has been the source text of Chinese medicine knowledge and innovation for over two thousand years. Despite this key relevance, many of its ideas and practices have proven difficult to understand and implement fully into clinical practice. Cultural and language differences can be compounded with these challenges but may also present new opportunities for advancement and insight when studied by researchers outside of the originating culture. This article introduces the method of Classical-Text Archaeology and delves into the author’s two-decade journey of researching this text, with a discussion on cultural differences and issues of medical scholarship.

Guidance and Application of Chinese Classical Gardens in Modern Gardens

As a representative of Eastern gardens,Chinese classical gardens have always held an extremely important position in world gardens.They not only carry the profound cultural and ideological connotations of China,but also have great aesthetic achievements that are worth exploring.In the process of urbanization,the construction of urban gardens is also progressing rapidly with the development of the city,and modern gardens are reflected more.Gradually,people began to realize that the aesthetics of Chinese classical gardens can collide with the design concepts of modern gardens,in order to conform to the current development trend of the new era.This paper compares and analyzes the differences and connections between Chinese classical gardens and modern gardens from three aspects:gardening concepts,gardening elements,and gardening techniques.Combined with relevant cases,it studies the practical application of Chinese classical garden design techniques in modern gardens,and explores and promotes the art of Chinese classical garden design.

Chinese Culture,Classical Philosophy,and the Cultivation of Life

This paper explores the intricate relationship between Chinese culture and the nation’s rise,emphasizing the indispensable role of cultural education in fostering a robust national spirit and civilization.Drawing from classical Chinese texts and contemporary philosophical insights,the study examines how cultural virtues and educational practices can lead to societal harmony,national rejuvenation,and global contributions.The analysis highlights the enduring relevance of Confucian principles and their potential to address modern challenges,advocating for a renewed focus on cultural education to achieve a prosperous and peaceful world.Additionally,it underscores the necessity of integrating traditional values with modern educational practices to create a holistic approach that fosters ethical leadership and global cooperation.

Heuristic Estimation of the Vacuum Energy Density of the Universe: Part II-Analysis Based on Frequency Domain Electromagnetic Radiation

In Part I of this paper, an inequality satisfied by the vacuum energy density of the universe was derived using an indirect and heuristic procedure. The derivation is based on a proposed thought experiment, according to which an electron is accelerated to a constant and relativistic speed at a distance L from a perfectly conducting plane. The charge of the electron was represented by a spherical charge distribution located within the Compton wavelength of the electron. Subsequently, the electron is incident on the perfect conductor giving rise to transition radiation. The energy associated with the transition radiation depends on the parameter L. It was shown that an inequality satisfied by the vacuum energy density will emerge when the length L is pushed to cosmological dimensions and the product of the radiated energy, and the time duration of emission is constrained by Heisenberg’s uncertainty principle. In this paper, a similar analysis is conducted with a chain of electrons oscillating sinusoidally and located above a conducting plane. In the thought experiment presented in this paper, the behavior of the energy radiated by the chain of oscillating electrons is studied in the frequency domain as a function of the length L of the chain. It is shown that when the length L is pushed to cosmological dimensions and the energy radiated within a single burst of duration of half a period of oscillation is constrained by the fact that electromagnetic energy consists of photons, an inequality satisfied by the vacuum energy density emerges as a result. The derived inequality is given by where is the vacuum energy density. This result is consistent with the measured value of the vacuum energy density, which is 5.38 × 10-10 J/m. The result obtained here is in better agreement with experimental data than the one obtained in Part I of this paper with time domain radiation.