Supervised Approach to Identify Autism Spectrum Neurological Disorder via Label Distribution Learning

Autism Spectrum Disorder (ASD) is a complicated collection of neurodevelopmental illnesses characterized by a variety of developmental defects. It is a binary classification system that cannot cope with reality. Furthermore, ASD, data label noise, high dimension, and data distribution imbalance have all hampered the existing classification algorithms. As a result, a new ASD was proposed. This strategy employs label distribution learning (LDL) to deal with label noise and uses support vector regression (SVR) to deal with sample imbalance. The experimental results show that the proposed method balances the effects of majority and minority classes on outcomes. It can effectively deal with imbalanced data in ASD diagnosis, and it can help with ASD diagnosis. This study presents a cost-sensitive approach to correct sample imbalance and uses a support vector regression (SVR)-based method to remove label noise. The label distribution learning approach overcomes high-dimensional feature classification issues by mapping samples to the feature space and then diagnosing multiclass ASD. This technique outperforms previous methods in terms of classification performance and accuracy, as well as resolving the issue of unbalanced data in ASD diagnosis.

Load Prediction Model of Athletes' Physical Training Competition Based on Nonlinear Algorithm Combined with Ultrasound

In order to provide theoretical support and ideas for the "dose" of high-stakes physical activity in athletics, the author has developed models for athletic competition based on nonlinear techniques together with ultrasound. Based on test data, average mean estimation method, and nonlinear regression model estimates, 52 points (46 test points, 6 point estimates) is enrolled in the highest voltage and maximum voltage measurement based on the BP neural network model. The estimation method was developed and the accuracy of the estimation of our estimation method was compared and evaluated using the estimation data. Experimental results show that the average relative error of the average estimate compared to the accuracy of the bench press was 25%, the standard estimate which is not linear regression is 31%, and BP neural network model estimation is 9%. Compared with the accuracy of the assumption of half squatting, the average relative error of the estimated velocity is 13%, the standard nonlinear regression estimate is 20%, and BP neural network model estimated method is 9%. The BP neural network predicts the method with the best performance and intelligence, but its actual functioning and application are complex. The average speed estimate is the most appropriate for use, but the equipment must be high. The process of estimating a linear regression model requires minimal equipment, but its prediction error is high.

Neutrosophic QUALIFLEX based on neutrosophic hesitancy index for selecting a potential antivirus mask supplier over COVID-19 pandemic

In order to handle simultaneously the cardinal and ordinal information in decision-making process, QUALIFLEX (QUALItative FLEXible multiple criteria method) is a very well-known decision-making approach. In this work, we extend the classical QUALIFLEX method to neutrosophic environment and develop a neutrosophic QUALIFLEX (N-QUALIFLEX) method that uses the newly defined distance-based comparison approach. It is highly effective in solving multi-criteria decision problems in which both ratings of alternatives on criteria and weights of criteria are single-valued neutrosophic numbers (SVNNs), and their aggregated values are single-valued neutrosophic hesitant fuzzy numbers (SVNHFNs). A neutrosophic hesitancy index (NHI) of a SVNHN is introduced based on degrees of the truth-membership, indeterminacy-membership and falsity-membership, which is used to measure the degree of hesitancy of SVNHN. Considering the NHIS of SVNHFNs, we propose a distance-based comparison approach to determine the magnitude of the SVNHFNs. Then, we apply the comparison approach to define the concordance/discordance index, the weighted concordance/discordance index and the comprehensive concordance/discordance index that are steps of the developed N-QUALIFLEX. By taking all possible permutations of alternatives with respect to the level of concordance/discordance into account, we determine the order of alternatives in final decision. Finally, a practical example on antivirus mask selection over the COVID-19 pandemic is provided to present the effectiveness and applicability of the proposed method, and a comparative study is conducted to show the advantages of the proposed method over other existing methods.

Optimization Design of Street Public Space Layout on Account of Internet of Things and Deep Learning

With the gradual improvement of material living standards, people have higher and higher requirements for the livability of modern cities. As an important component of urban construction, the optimal layout of street public space has gradually received more and more attention. In the development stage of the new era, it is very important to improve the image of the city by transforming the street construction, optimizing the urban public space, and building a place full of vitality. Implementing the people-oriented connotation and improving the green travel components in the city, such as encouraging walking and increasing bicycles, are of great significance for optimizing the street public space. This article studies the relevant content of the optimization design of street public space layout based on the Internet of Things and deep learning and expounds the solutions for the optimization design of street public space layout based on the Internet of Things and deep learning. Design research provides cutting-edge scientific theories and evidence. This paper uses data to prove that based on the Internet of Things and deep learning technology, the optimized design of street public space layout has increased the latter’s recognition among residents by an average of 21.7%. The designed model has both space utilization and environmental protection. Very good results have been obtained.

Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia

Coronavirus disease (COVID-19) has created an unprecedented devastation and the loss of millions of lives globally. Contagious nature and fatalities invariably pose challenges to physicians and healthcare support systems. Clinical diagnostic evaluation using reverse transcription-polymerase chain reaction and other approaches are currently in use. The Chest X-ray (CXR) and CT images were effectively utilized in screening purposes that could provide relevant data on localized regions affected by the infection. A step towards automated screening and diagnosis using CXR and CT could be of considerable importance in these turbulent times. The main objective is to probe a simple threshold-based segmentation approach to identify possible infection regions in CXR images and investigate intensity-based, wavelet transform (WT)-based, and Laws based texture features with statistical measures. Further feature selection strategy using Random Forest (RF) then selected features used to create Machine Learning (ML) representation with Support Vector Machine (SVM) and a Random Forest (RF) to make different COVID-19 from viral pneumonia (VP). The results obtained clearly indicate that the intensity and WT-based features vary in the two pathologies that are better differentiated with the combined features trained using SVM and RF classifiers. Classifier performance measures like an Area Under the Curve (AUC) of 0.97 and by and large classification accuracy of 0.9 using the RF model clearly indicate that the methodology implemented is useful in characterizing COVID-19 and Viral Pneumonia.

A proposed artificial intelligence-based real-time speech-to-text to sign language translator for South African official languages for the COVID-19 era and beyond: In pursuit of solutions for the hearing impaired

Background

The emergence of the coronavirus disease 2019 (COVID-19) pandemic has resulted in communication being heightened as one of the critical aspects in the implementation of interventions. Delays in the relaying of vital information by policymakers have the potential to be detrimental, especially for the hearing impaired.

Objectives

This study aims to conduct a scoping review on the application of artificial intelligence (AI) for real-time speech-to-text to sign language translation and consequently propose an AI-based real-time translation solution for South African languages from speech-to-text to sign language.

Methods

Electronic bibliographic databases including ScienceDirect, PubMed, Scopus, MEDLINE and ProQuest were searched to identify peer-reviewed publications published in English between 2019 and 2021 that provided evidence on AI-based real-time speech-to-text to sign language translation as a solution for the hearing impaired. This review was done as a precursor to the proposed real-time South African translator.

Results

The review revealed a dearth of evidence on the adoption and/or maximisation of AI and machine learning (ML) as possible solutions for the hearing impaired. There is a clear lag in clinical utilisation and investigation of these technological advances, particularly in the African continent.

Conclusion

Assistive technology that caters specifically for the South African community is essential to ensuring a two-way communication between individuals who can hear clearly and individuals with hearing impairments, thus the proposed solution presented in this article.

Analysis of Machine Translation and Post-Translation Editing Ability Using Semantic Information Entropy Technology

Large-scale corpus application has presented MT with new opportunities as well as challenges in recent years. This study investigates MT and post-translation editing capability using AI technology. The grammar rules of the target language are first examined. Then, a significant amount of data on semantic information entropy are projected, and the semantic Gaussian marginal rectangular window function is obtained. The semantic correlation factors of words are added to the text information entropy and information gain, and the nonlinear spectral properties of adaptive matching semantics are obtained. In this way, it corrects the significant flaw in the way semantic features are extracted using conventional techniques. In order to speed up MT and enhance translation quality, this study proposes automatic post-translation editing to filter those common MT errors that occur frequently and regularly. According to the experimental findings, word translation and segmentation accuracy can both reach 95.27 and 93.12 percent, respectively. In terms of language translation, this approach is accurate and trustworthy. I hope it will serve as a useful source for subsequent research.

CIE-LSCP: color image encryption scheme based on the lifting scheme and cross-component permutation

Encryption of meaningful images into unidentifiable noise-like images can improve the security of images during storage and transmission. In this paper, a novel color image encryption method based on the lifting scheme and cross-component permutation (CIE-LSCP) is introduced. First, original image is divided into red, green and blue components, and then the three components are processed by a preprocessing strategy based on the lifting scheme (PSLS) to change the statistical distribution of the pixels. Second, a block-based cross-component permutation based on index vectors (BCPIV) is presented to divide three preprocessed components into blocks and perform the cross-component permutation operation on them, and three random matrices are utilized to determine the target component, target block and target pixel position of the current pixel to be moved, respectively. Subsequently, a multi stochastic diffusion based on random sequences (MSDRS) is developed to alter the pixel values of the permutated components, and finally a ciphertext image is gotten by merging the three components. Moreover, the SHA256 hash values of the plaintext image are used to obtain the initial parameters of the chaotic system, and the obtained chaotic sequences are applied in the image encryption process. Wherein the generated random sequences are highly reliant on the plaintext image, making the encryption scheme resistant to both known-plaintext attacks and chosen-plaintext attacks. Experimental results demonstrate that the proposed scheme has good security and effectiveness and can be applied for secure transmission of digital images over the Internet.

Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model

Due to the COVID-19 pandemic, computerized COVID-19 diagnosis studies are proliferating. The diversity of COVID-19 models raises the questions of which COVID-19 diagnostic model should be selected and which decision-makers of healthcare organizations should consider performance criteria. Because of this, a selection scheme is necessary to address all the above issues. This study proposes an integrated method for selecting the optimal deep learning model based on a novel crow swarm optimization algorithm for COVID-19 diagnosis. The crow swarm optimization is employed to find an optimal set of coefficients using a designed fitness function for evaluating the performance of the deep learning models. The crow swarm optimization is modified to obtain a good selected coefficient distribution by considering the best average fitness. We have utilized two datasets: the first dataset includes 746 computed tomography images, 349 of them are of confirmed COVID-19 cases and the other 397 are of healthy individuals, and the second dataset are composed of unimproved computed tomography images of the lung for 632 positive cases of COVID-19 with 15 trained and pretrained deep learning models with nine evaluation metrics are used to evaluate the developed methodology. Among the pretrained CNN and deep models using the first dataset, ResNet50 has an accuracy of 91.46% and a F1-score of 90.49%. For the first dataset, the ResNet50 algorithm is the optimal deep learning model selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5715.988 for COVID-19 computed tomography lung images case considered differential advancement. In contrast, the VGG16 algorithm is the optimal deep learning model is selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5758.791 for the second dataset. Overall, InceptionV3 had the lowest performance for both datasets. The proposed evaluation methodology is a helpful tool to assist healthcare managers in selecting and evaluating the optimal COVID-19 diagnosis models based on deep learning.

Neutrosophic F-Test for Two Counts of Data from the Poisson Distribution with Application in Climatology

This paper addresses the modification of the F-test for count data following the Poisson distribution. The F-test when the count data are expressed in intervals is considered in this paper. The proposed F-test is evaluated using real data from climatology. The comparative study showed the efficiency of the F-test for count data under neutrosophic statistics over the F-test for count data under classical statistics.

The Reform of University Education Teaching Based on Cloud Computing and Big Data Background

In the era of big data and cloud computing, traditional college teaching model needs to be revolutionized in order to adapt to the needs of the present generation. The traditional college teaching model is currently facing unprecedented severe challenges which could be optimistically considered as a huge scope of development opportunity. In order to promote the gradual transformation of college teaching toward digitization, intelligence, and modernization, this paper comprehensively analyzes the impact of science and technology on college teaching. It further encourages the omnidirectional and multifaceted amalgamation of education with big data and cloud computing technology with an objective to improve the overall teaching level of colleges and universities. In order to realize the accurate evaluation of university teaching reform and improve teaching quality, the study presents an evaluation method of university teaching reform based on in-depth research network. Then, it further analyzes the main contents of university teaching reform, establishes the evaluation department of university teaching reform, and then establishes the evaluation model of university education reform. This is achieved by analyzing the relationship between university education reform and indicators using in-depth learning network followed by the development of simulation experiments pertinent to evaluation of university education reform. The results show that this method is helpful in improving the teaching quality.

Systematic review for lung cancer detection and lung nodule classification: Taxonomy, challenges, and recommendation future works

Nowadays, lung cancer is one of the most dangerous diseases that require early diagnosis. Artificial intelligence has played an essential role in the medical field in general and in analyzing medical images and diagnosing diseases in particular, as it can reduce human errors that can occur with the medical expert when analyzing medical image. In this research study, we have done a systematic survey of the research published during the last 5 years in the diagnosis of lung cancer classification of lung nodules in 4 reliable databases (Science Direct, Scopus, web of science, and IEEE), and we selected 50 research paper using systematic literature review. The goal of this review work is to provide a concise overview of recent advancements in lung cancer diagnosis issues by machine learning and deep learning algorithms. This article summarizes the present state of knowledge on the subject. Addressing the findings offered in recent research publications gives the researchers a better grasp of the topic. We checked all the characteristics, such as challenges, recommendations for future work were analyzed in detail, and the published datasets and their source were presented to facilitate the researchers’ access to them and use it to develop the results achieved previously.

Analysis and Research on Influencing Factors of College Students' Satisfaction with Physical Education

Physical education is an important part of a university, and the satisfaction of college students for physical education directly determines the teaching effect of physical education. Therefore, it is of great significance to understand college students' satisfaction with physical education and its influencing factors for improving the level of physical education. In this paper, by means of multistage sampling, probability sampling according to scale and random equidistant sampling, 7 main campuses, including 36 subcampuses, are selected for data entry, cleaning, and calculation by using the college physical education teaching system. Through the investigation of 1752 students, the results show that there are significant differences in grade, gender, cognition, credit, sense of responsibility, and teaching content (P > 0.05), which are all factors affecting college students' satisfaction. Cognition, grades, credits, and make-up test rate are the main influencing factors, with the influence degree ranging from 1 to 3, and there are significant differences in OR value and P value. Therefore, in the process of physical education, we should pay attention to the above-mentioned influencing factors, effectively reduce the occurrence rate of make-up examination and reexamination, adjust unreasonable teaching content, and improve students' satisfaction with physical education.

Some metaheuristic algorithms for solving multiple cross-functional team selection problems

We can find solutions to the team selection problem in many different areas. The problem solver needs to scan across a large array of available solutions during their search. This problem belongs to a class of combinatorial and NP-Hard problems that requires an efficient search algorithm to maintain the quality of solutions and a reasonable execution time. The team selection problem has become more complicated in order to achieve multiple goals in its decision-making process. This study introduces a multiple cross-functional team (CFT) selection model with different skill requirements for candidates who meet the maximum required skills in both deep and wide aspects. We introduced a method that combines a compromise programming (CP) approach and metaheuristic algorithms, including the genetic algorithm (GA) and ant colony optimization (ACO), to solve the proposed optimization problem. We compared the developed algorithms with the MIQP-CPLEX solver on 500 programming contestants with 37 skills and several randomized distribution datasets. Our experimental results show that the proposed algorithms outperformed CPLEX across several assessment aspects, including solution quality and execution time. The developed method also demonstrated the effectiveness of the multi-criteria decision-making process when compared with the multi-objective evolutionary algorithm (MOEA).

Application of Isotope Tracer in Cross-Well Nanometre Tracer Testing

In order to understand the dynamic monitoring characteristics and heterogeneity characteristics of injection production wells in the monitoring area and judge the reservoir connectivity, this study puts forward the application method of isotope nanometre tracer in interwell nanometre tracer testing. The trace material nanometre tracer interwell monitoring technology was applied to well block J in a basin. Through the analysis of the morphological characteristics of the nanometre tracer production curve and the quantitative interpretation of trace material tracer, combined with the analysis of the advancing speed of the water line at the front edge of the nanometre tracer and the distribution of injected water wells, the nanometre tracer connectivity, and advancing speed, the characteristics of seepage channels and heterogeneity of the well groups in the study area were defined. The research results show that 8 oil wells in the monitoring area are controlled by water injection well J1, the injection water inrush direction is generally north, the main channel of injection water is mainly high-permeability strip, and the heterogeneity contradiction between layers is strong. It is recommended to adopt mild water injection. Conclusion. This study found that the dynamic connectivity between oil and water wells in the monitoring area is good.

Adolescent Identity Search Algorithm Based on Fast Search and Balance Optimization for Numerical and Engineering Design Problems

This paper proposed a fast convergence and balanced adolescent identity search algorithm (FCBAISA) for numerical and engineering design problems. The main contributions are as follows. Firstly, a hierarchical optimization strategy is proposed to balance the exploration and exploitation better. Secondly, a fast search strategy is proposed to avoid the local optimization and improve the accuracy of the algorithm; that is, the current optimal solution combines with the random disturbance of Brownian motion to guide other adolescents. Thirdly, the Chebyshev functional-link network (CFLN) is improved by recursive least squares estimation (RSLE), so as to find the optimal solution more effectively. Fourthly, the terminal bounce strategy is designed to avoid the algorithm falling into local optimization in the later stage of iteration. Fifthly, FCBAISA and comparison algorithms are tested by CEC2017 and CEC2022 benchmark functions, and the practical engineering problems are solved by algorithms above. The results show that FCBAISA is superior to other algorithms in all aspects and has high precision, fast convergence speed, and excellent performance.

Gait Recognition Based on the Plantar Pressure Data of Ice and Snow Athletes

The study of plantar pressure has become a research consensus in the field of biomechanics. The purpose of this paper is to study some lower limb movements in the daily activities of ice and snow athletes to obtain relevant data so as to carry out gait recognition analysis research. This paper selects the average foot pressure, forefoot foot pressure, front and rear foot pressure, foot pressure, toe pressure, 2–5 toe pressure, standing with eyes closed, x- and y-axes speed, foot length, foot width, and other actions of ice and snow athletes. Therefore, correlation analysis, work analysis, and curve fitting analysis were carried out on the joint motion in a single gait cycle. The collection and application of foot pressure and foot posture information are also analyzed. According to the plantar structure, the sole is divided into four parts. The maximum pressure point and coordinates of each part, the pressure center point, the ratio of the width and height of the sole of the foot, and so on are extracted as the haptic features of the gait. The experimental data shows that it can be seen that if the plantar area is divided in advance and the weight of each area is marked, whether standing, walking, or standing with one leg closed eyes can achieve better recognition results, and the accuracy rate is all more than 90 percent. The average recognition accuracy rate using the method of dividing four regions is only about 80%, and the accuracy rate of recognition using the method of dividing eight regions is 82%. It can be seen that the features extracted by the FCM model proposed in this paper contain more information of the plantar pressure image, and the accuracy rate is higher in the classification and recognition.

Advances in Vision-Based Gait Recognition: From Handcrafted to Deep Learning

Identifying people’s identity by using behavioral biometrics has attracted many researchers’ attention in the biometrics industry. Gait is a behavioral trait, whereby an individual is identified based on their walking style. Over the years, gait recognition has been performed by using handcrafted approaches. However, due to several covariates’ effects, the competence of the approach has been compromised. Deep learning is an emerging algorithm in the biometrics field, which has the capability to tackle the covariates and produce highly accurate results. In this paper, a comprehensive overview of the existing deep learning-based gait recognition approach is presented. In addition, a summary of the performance of the approach on different gait datasets is provided.

Healthy and sustainable development of sports economy based on artificial intelligence and mental model

In recent years, sports have achieved rapid development worldwide, and the global economy has been significantly improved and improved. With the in-depth development of the two, the connection between sports and the economy has also become closer. Sports economy is a new type of economic form bred by specialization of sports organization, participation in consumerization, and profit-oriented operation under the condition of market economy. And the development of sports economy cannot be developed at once; it needs healthy and sustainable development. In order to find a better way to study the healthy and sustainable development of sports economy, this paper uses deep learning network algorithm and supports vector machine learning algorithm to build a mental model. It then uses the model to analyze various indicators of the sports industry in a province in China. This article is looking for information and summarizes the province’s sports data from 2017 to 2021. The sports indicators of this experiment include regional GDP, total output of sports industry, sports practitioners, local financial sports expenditures, the number of policies, the number of people participating in physical exercise, and fitness venues and facilities. The realization results show that these variables develop at a relatively small rate under normal conditions, and then predict the data in the next few years under the healthy and sustainable development of the next few years through the mental model. The growth rates of various indicators of the sports economy have increased significantly, and they have been optimized by about 20% compared with the normal development.

Robust Zero-Watermarking of Color Medical Images Using Multi-Channel Gaussian-Hermite Moments and 1D Chebyshev Chaotic Map

Copyright protection of medical images is a vital goal in the era of smart healthcare systems. In recent telemedicine applications, medical images are sensed using medical imaging devices and transmitted to remote places for screening by physicians and specialists. During their transmission, the medical images could be tampered with by intruders. Traditional watermarking methods embed the information in the host images to protect the copyright of medical images. The embedding destroys the original image and cannot be applied efficiently to images used in medicine that require high integrity. Robust zero-watermarking methods are preferable over other watermarking algorithms in medical image security due to their outstanding performance. Most existing methods are presented based on moments and moment invariants, which have become a prominent method for zero-watermarking due to their favorable image description capabilities and geometric invariance. Although moment-based zero-watermarking can be an effective approach to image copyright protection, several present approaches cannot effectively resist geometric attacks, and others have a low resistance to large-scale attacks. Besides these issues, most of these algorithms rely on traditional moment computation, which suffers from numerical error accumulation, leading to numerical instabilities, and time consumption and affecting the performance of these moment-based zero-watermarking techniques. In this paper, we derived multi-channel Gaussian-Hermite moments of fractional-order (MFrGHMs) to solve the problems. Then we used a kernel-based method for the highly accurate computation of MFrGHMs to solve the computation issue. Then, we constructed image features that are accurate and robust. Finally, we presented a new zero-watermarking scheme for color medical images using accurate MFrGHMs and 1D Chebyshev chaotic features to achieve lossless copyright protection of the color medical images. We performed experiments where their outcomes ensure the robustness of the proposed zero-watermarking algorithms against various attacks. The proposed zero-watermarking algorithm achieves a good balance between robustness and imperceptibility. Compared with similar existing algorithms, the proposed algorithm has superior robustness, security, and time computation.

Learning-Based Slip Detection for Robotic Fruit Grasping and Manipulation under Leaf Interference

Robotic harvesting research has seen significant achievements in the past decade, with breakthroughs being made in machine vision, robot manipulation, autonomous navigation and mapping. However, the missing capability of obstacle handling during the grasping process has severely reduced harvest success rate and limited the overall performance of robotic harvesting. This work focuses on leaf interference caused slip detection and handling, where solutions to robotic grasping in an unstructured environment are proposed. Through analysis of the motion and force of fruit grasping under leaf interference, the connection between object slip caused by leaf interference and inadequate harvest performance is identified for the first time in the literature. A learning-based perception and manipulation method is proposed to detect slip that causes problematic grasps of objects, allowing the robot to implement timely reaction. Our results indicate that the proposed algorithm detects grasp slip with an accuracy of 94%. The proposed sensing-based manipulation demonstrated great potential in robotic fruit harvesting, and could be extended to other pick-place applications.

A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation

Brain tissue segmentation is an important component of the clinical diagnosis of brain diseases using multi-modal magnetic resonance imaging (MR). Brain tissue segmentation has been developed by many unsupervised methods in the literature. The most commonly used unsupervised methods are K-Means, Expectation-Maximization, and Fuzzy Clustering. Fuzzy clustering methods offer considerable benefits compared with the aforementioned methods as they are capable of handling brain images that are complex, largely uncertain, and imprecise. However, this approach suffers from the intrinsic noise and intensity inhomogeneity (IIH) in the data resulting from the acquisition process. To resolve these issues, we propose a fuzzy consensus clustering algorithm that defines a membership function resulting from a voting schema to cluster the pixels. In particular, we first pre-process the MRI data and employ several segmentation techniques based on traditional fuzzy sets and intuitionistic sets. Then, we adopted a voting schema to fuse the results of the applied clustering methods. Finally, to evaluate the proposed method, we used the well-known performance measures (boundary measure, overlap measure, and volume measure) on two publicly available datasets (OASIS and IBSR18). The experimental results show the superior performance of the proposed method in comparison with the recent state of the art. The performance of the proposed method is also presented using a real-world Autism Spectrum Disorder Detection problem with better accuracy compared to other existing methods.

Big data clustering using fractional sail fish-sparse fuzzy C-means and particle whale optimization based MapReduce framework

The process of retrieving essential information from the dataset is a significant data mining approach, which is specifically termed as data clustering. However, nature-inspired optimizations are designed in recent decades to solve optimization problems, particularly for data clustering complexities. However, the existing methods are not feasible to process with a large amount of data, as the execution time taken by the traditional approaches is larger. Hence, an efficient and optimal data clustering scheme is designed using the devised Fractional Sail Fish-Sparse Fuzzy C-Means + Particle Whale optimization (FSF-Sparse FCM + PWO) based MapReduce Framework (MRF) to process high dimensional data. Theproposed FSF-Sparse FCM is designed by the integration of Sail Fish Optimization (SFO) with fractional concept and Sparse FCM. The proposed MRF poses two functions, such as the mapper function and reducer function to perform the process of data clustering. Moreover, the proposed FSF-Sparse FCM is employed in the mapper phase to compute the cluster centroids, and thereby the intermediate data is generated. The intermediate data is tuned in the reducer phase using Particle Whale Optimization (PWO), which is the integration of Particle Swarm Optimization (PSO) and Whale optimization algorithm (WOA). Accordingly, the optimal cluster centroid is computed at the reducer phase using the objective function based on DB-Index. The proposed FSF-Sparse FM + PWO obtained the highest accuracy of 0.903 and lowest DB-Index of 39.07.

Semantic Construal Mechanism of the Linguistic Environment Parameter Theory in Business English Translation

The theory of linguistic environment parameters has an important influence on English translation. Based on this parameter, it can provide guidance for the semantic generation of English translation. In the actual translation process, people's native language thinking amount accounts for 56.2% and English thinking amount accounts for 43.8%. Translation is mostly used in texts, accounting for 40.6%, and thinking based on the context parameter theory accounts for only 3.2%. To a certain extent, it can be seen that people's thinking about business English translation will be affected by different amounts of thinking. By mapping context parameters to corresponding factors, this paper analyzes the interpretation mechanism of translation semantics, to a certain extent, more intuitively expounds the semantic generation of business English translation, puts forward higher requirements for people's translation ability, and further obtains new knowledge, so as to provide practical guidance for English translation.

A room-oriented artificial bee colony algorithm for optimizing the patient admission scheduling problem

Patient admission scheduling (PAS) is a tasking combinatorial optimization problem where a set of patients is assigned to limited facilities such as rooms, timeslots, and beds subject to satisfying a set of predefined constraints. The investigations into the performance of population-based algorithms that utilized to tackle the PAS problem considered in this paper reveal their weaknesses in obtaining quality solutions that create a space to investigate the performance of another population-based method. Thus, in this paper, an Artificial Bee Colony Algorithm (ABC) is proposed to tackle the formulation of the PAS problem under consideration. It is a class of swarm intelligence metaheuristic algorithms based on the intelligent foraging behaviour of honey bees developed to solve continuous and complex optimization problems. Due to the discretization of the PAS, the continuous nature of the ABC algorithm is changed to cope with the rugged solution space of the PAS. The initial feasible solution to the PAS problem is obtained using the room-oriented approach. Then the ABC algorithm optimizes the feasible solutions with the aid of three neighbourhood structures embedded within the employed bee and the onlooker bee operators of the algorithm. The performance of the proposed ABC algorithm based on three different parameters, the solution number (SN), limit value (LV), and the maximum cycle number (MCN) is evaluated on six standard benchmark datasets of the PAS. Two of these main parameters (i.e. SN and LV) are fine-tuned to obtain the best solutions on instances like Test-data 1 = 679.80, Test-data 2 = 1180.40, Test-data 3 = 787.40, Test-data 4 = 1198.60, Test-data 5 = 636.80, and Test-data 6 = 818.60. The best solutions obtained by the proposed method are evaluated against the results of the 19 comparative algorithms comprising five population-based methods, eleven heuristic, and hyperheuristic-based methods, and three integer programming-based methods. The proposed method shows its supremacy in the performance by achieving the best results in all the instances of the dataset when compared with five population-based methods (DFPA, HSA, MBBO-GBS, BBO-GBS, and BBO-RBS) and producing the best results in five instances when compared with eleven heuristic and hyperheuristic-based methods (LAHC, DHS-GD, HTS, DHS-SA, ADAPTIVE GD, GD, HH-GD, DHS-IO, HH-SA, HH-IE, TA) and Finally, it had a competitive performance with the other three Integer programming methods (MIP warm start, MIP-Heuristic, CG) that worked on the same formulations of the PAS. In a nutshell, the proposed ABC algorithm could be adopted as a new template algorithm for the PAS community.

An Improved Air Quality Index Machine Learning-Based Forecasting with Multivariate Data Imputation Approach

Accurate, timely air quality index (AQI) forecasting helps industries in selecting the most suitable air pollution control measures and the public in reducing harmful exposure to pollution. This article proposes a comprehensive method to forecast AQIs. Initially, the work focused on predicting hourly ambient concentrations of PM2.5 and PM10 using artificial neural networks. Once the method was developed, the work was extended to the prediction of other criteria pollutants, i.e., O3, SO2, NO2, and CO, which fed into the process of estimating AQI. The prediction of the AQI not only requires the selection of a robust forecasting model, it also heavily relies on a sequence of pre-processing steps to select predictors and handle different issues in data, including gaps. The presented method dealt with this by imputing missing entries using missForest, a machine learning-based imputation technique which employed the random forest (RF) algorithm. Unlike the usual practice of using RF at the final forecasting stage, we utilized RF at the data pre-processing stage, i.e., missing data imputation and feature selection, and we obtained promising results. The effectiveness of this imputation method was examined against a linear imputation method for the six criteria pollutants and the AQI. The proposed approach was validated against ambient air quality observations for Al-Jahra, a major city in Kuwait. Results obtained showed that models trained using missForest-imputed data could generalize AQI forecasting and with a prediction accuracy of 92.41% when tested on new unseen data, which is better than earlier findings.

The Optimization Strategy of College Students' Ideological and Political Management under the Internet+ Environment

In order to better deal with the challenges brought by the changes of the external environment to the ideological and political work of college students and better solve the problems of dogma, lack of operability, and inability to adapt to the changes of the new situation and environment, this topic proposes a student management information system based on the artificial neural network. This method strengthens the construction of the student management system, deeply interprets the connotation and practical needs of ideological and political work in colleges and universities in the new environment under the background of the "Internet+" era, and is committed to further optimizing the strategy of student ideological and political work on the basis of following the principles of innovation, human nature, openness, and ecology of ideological and political work. The results show that the construction of a more scientific student teaching management information system can provide intelligent and digital technical support for the improvement of ideological and political work efficiency and teaching quality and promote the innovative development of ideological and political work in colleges and universities. Therefore, the ideological and political work in colleges and universities should gradually overcome preaching, build new methods to measure students' achievements and ideological and political arms, actively occupy the dominant power of Internet discourse, and avoid the negative impact of the network by spreading positive energy.

Blockchain-Based Optimization Model for Evaluating Psychological Mental Disease and Mental Fitness

The current work describes a blockchain-based optimization approach that mimics the psychological mental illness evaluation procedure and evaluates mental fitness. Combining lightweight models with blockchains can give a variety of benefits in the healthcare business. This study aims to offer an improved review and learning optimization technique (SPLBO) based on the social psychology theory to overcome the biogeography-based optimization (BBO) algorithm’s shortcomings of low optimization accuracy and instability. It also creates high-accuracy solutions in recognized domains quickly. To retain student individuality, students can be divided into two groups: Human psychological variables are incorporated in the algorithm’s improvement: in the “teaching” step of the original BBO algorithm; the “expectation effect” theory of social psychology is combined: “field-independent” and “field-dependent” cognitive styles. As a consequence, low-weight deep neural networks have been designed in such a manner that they require fewer resources for optimal design while also improving quality. A responsive student update component is also introduced to duplicate the effect of the environment on students’ learning efficiency, increase the method’s global search capabilities, and avoid the problem of falling into a local optimum in the first repetition.

Soft Attention Based DenseNet Model for Parkinson’s Disease Classification Using SPECT Images

Objective

Deep learning algorithms have long been involved in the diagnosis of severe neurological disorders that interfere with patients’ everyday tasks, such as Parkinson’s disease (PD). The most effective imaging modality for detecting the condition is DaTscan, a variety of single-photon emission computerized tomography (SPECT) imaging method. The goal is to create a convolutional neural network that can specifically identify the region of interest following feature extraction.

Methods

The study comprised a total of 1,390 DaTscan imaging groups with PD and normal classes. The architecture of DenseNet-121 is leveraged with a soft-attention block added before the final classification layer. For visually analyzing the region of interest (ROI) from the images after classification, Soft Attention Maps and feature map representation are used.

Outcomes

The model obtains an overall accuracy of 99.2% and AUC-ROC score 99%. A sensitivity of 99.2%, specificity of 99.4% and f1-score of 99.1% is achieved that surpasses all prior research findings. Soft-attention map and feature map representation aid in highlighting the ROI, with a specific attention on the putamen and caudate regions.

Conclusion

With the deep learning framework adopted, DaTscan images reveal the putamen and caudate areas of the brain, which aid in the distinguishing of normal and PD cohorts with high accuracy and sensitivity.

Network Pharmacology-Based Strategy to Reveal the Mechanism of Cassiae Semen against Cataracts

Cassiae semen (CS) is one of the most well-known herbs used in the treatment of cataracts in China. However, the potential mechanisms of its anticataract effects have not been fully explored. In this study, network pharmacology was used to investigate the potential mechanism underlying the actions of CS against cataracts, and molecular docking was performed to analyze the binding activity of proteins and compounds. qPCR was performed to detect the mRNA level of genes, and the cell apoptotic rate was measured using flow cytometry. We identified 13 active compounds from CS and 105 targets, as well as 238 cataract-related targets. PPI networks were constructed, and fifty key targets were obtained. These key targets were enriched in the regulation of transcription, apoptotic process, and signal transduction pathways. Molecular docking demonstrated that the compounds of CS exhibited good affinity to some critical targets. Furthermore, CS prevented the apoptosis of human lens epithelial cells induced by UVB lights by decreasing the gene expression of CASP3, ESR1, and TP53 and increasing the CRYAB gene expression. The present study attempted to explain the mechanisms for the effects of CS in the prevention and treatment of cataracts and provided an effective strategy to investigate active ingredients from natural medicines. Further studies are required to verify these findings via in vivo and in vitro experiments.

Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease

In recent years, agricultural image processing research has been a key emphasis. Image processing techniques are used by computers to analyze images. New advancements in image capture and data processing have simplified the resolution of a wide range of agricultural concerns. Crop disease classification and identification are crucial for the agricultural industry’s technical and commercial well-being. In agriculture, image processing begins with a digital color picture of a diseased leaf. Plant health and disease detection must be monitored on a regular basis in property agriculture. Plant diseases have had a tremendous impact on civilization and the Earth as a whole. Extensions of detection strategies and classification methods try to identify and categorize each ailment that affects the plant rather than focusing on a single disease among several illnesses and symptoms. This article describes a new support vector machine and image processing-enabled approach for detecting and classifying grape leaf disease. The given architecture includes steps for image capture, denoising, enhancement, segmentation, feature extraction, classification, and detection. Image denoising is conducted using the mean function, image enhancement is performed using the CLAHE method, pictures are segmented using the fuzzy C Means algorithm, features are retrieved using PCA, and images are eventually classed using the PSO SVM, BPNN, and random forest algorithms. The accuracy of PSO SVM is higher in performing classification and detection of grape leaf diseases.

An Efficient Heap Based Optimizer Algorithm for Feature Selection

The heap-based optimizer (HBO) is an innovative meta-heuristic inspired by human social behavior. In this research, binary adaptations of the heap-based optimizer B_HBO are presented and used to determine the optimal features for classifications in wrapping form. In addition, HBO balances exploration and exploitation by employing self-adaptive parameters that can adaptively search the solution domain for the optimal solution. In the feature selection domain, the presented algorithms for the binary Heap-based optimizer B_HBO are used to find feature subsets that maximize classification performance while lowering the number of selected features. The textitk-nearest neighbor (textitk-NN) classifier ensures that the selected features are significant. The new binary methods are compared to eight common optimization methods recently employed in this field, including Ant Lion Optimization (ALO), Archimedes Optimization Algorithm (AOA), Backtracking Search Algorithm (BSA), Crow Search Algorithm (CSA), Levy flight distribution (LFD), Particle Swarm Optimization (PSO), Slime Mold Algorithm (SMA), and Tree Seed Algorithm (TSA) in terms of fitness, accuracy, precision, sensitivity, F-score, the number of selected features, and statistical tests. Twenty datasets from the UCI repository are evaluated and compared using a set of evaluation indicators. The non-parametric Wilcoxon rank-sum test was used to determine whether the proposed algorithms’ results varied statistically significantly from those of the other compared methods. The comparison analysis demonstrates that B_HBO is superior or equivalent to the other algorithms used in the literature.

Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure

Mesothelioma is a dangerous, violent cancer, which forms a protecting layer around inner tissues such as the lungs, stomach, and heart. We investigate numerous AI methodologies and consider the exact DM conclusion outcomes in this study, which focuses on DM determination. K-nearest neighborhood, linear-discriminant analysis, Naive Bayes, decision-tree, random forest, support vector machine, and logistic regression analyses have been used in clinical decision support systems in the detection of mesothelioma. To test the accuracy of the evaluated categorizers, the researchers used a dataset of 350 instances with 35 highlights and six execution measures. LDA, NB, KNN, SVM, DT, LogR, and RF have precisions of 65%, 70%, 92%, 100%, 100%, 100%, and 100%, correspondingly. In count, the calculated complication of individual approaches has been evaluated. Every process is chosen on the basis of its characterization, exactness, and calculated complications. SVM, DT, LogR, and RF outclass the others and, unexpectedly, earlier research.

Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification

Breast cancer (BC) is the second leading cause of death in developed and developing nations, accounting for 8% of deaths after lung cancer. Gene mutation, constant pain, size fluctuations, colour (roughness), and breast skin texture are all characteristics of BC. The University of Wisconsin Hospital donated the WDBC dataset, which was created via fine-needle aspiration (biopsies) of the breast. We have implemented multilayer perceptron (MLP), K-nearest neighbor (KNN), genetic programming (GP), and random forest (RF) on the WBCD dataset to classify the benign and malignant patients. The results show that RF has a classification accuracy of 96.24%, which outperforms all the other classifiers.

The application of an artificial neural network for 2D coordinate transformation

Clark1880, WGS1984, and ITRF08 are the reference systems used in Iraq. The ITRF08 and WGS84 represent the global reference frames. In the majority of instances, the transformation from one coordinate system to another is required. The ability of the artificial neural network (ANN) to identify the connection between two coordinate systems without the need for a mathematical model is one of its most significant benefits. In this study, an ANN was employed for two-dimensional coordinate transformation from local Clark1880 to the global reference system ITRF08. To accomplish so, 68 stations with known coordinates in both systems were utilized in this research and were split into two groups: the first set of data (38 stations) was used as the training data and the second set of data (38 stations) was used as the validation data. A root-mean-square error (RMSE) was used to examine the performance of each transformation. The results showed that the RMSE using the ANN was 0.08 m in the east and 0.17 m in the north. The results indicated that the ANN can be used for 2D coordinate transformation with the results that are better than those of the authorized techniques such as 2D conformal transformation and 2D conformal least square.

Image Segmentation of Retinal Blood Vessels Based on Dual-Attention Multiscale Feature Fusion

Aiming at the problem of insufficient details of retinal blood vessel segmentation in current research methods, this paper proposes a multiscale feature fusion residual network based on dual attention. Specifically, a feature fusion residual module with adaptive calibration weight features is designed, which avoids gradient dispersion and network degradation while effectively extracting image details. The SA module and ECA module are used many times in the backbone feature extraction network to adaptively select the focus position to generate more discriminative feature representations; at the same time, the information of different levels of the network is fused, and long-range and short-range features are used. This method aggregates low-level and high-level feature information, which effectively improves the segmentation performance. The experimental results show that the method in this paper achieves the classification accuracy of 0.9795 and 0.9785 on the STARE and DRIVE datasets, respectively, and the classification performance is better than the current mainstream methods.

Topological Structure of Single-Valued Neutrosophic Hesitant Fuzzy Sets and Data Analysis for Uncertain Supply Chains

From production to retail, the food supply chain (FSC) encompasses all stages of food production. Food is now transmitted across continents over long ranges. People depend on supply chains for basic necessities such as food, water, drinks, etc. Any disruption in these shipment pipelines poses a serious threat to human life. Supplier selection (SS) has been identified as a crucial component of FSC, which has been contemplated as a multi-criteria decision-making (MCDM) problem in many studies. The failure of some specific MCDM problems is due to failure in contemplating the relationships between alternatives under uncertain circumstances. To address such challenges, we present a contemporary method for designating green suppliers based on single-valued neutrosophic hesitant fuzzy (SVNHF) information, in which the input assessment is taken into account using single-valued neutrosophic hesitant fuzzy numbers (SVNHFNs). The foremost purpose of this analysis is to construct a topological structure on single-valued neutrosophic hesitant fuzzy sets (SVNHFSs) as well as to validate several key properties with examples. We discuss certain properties of SVNHF topology such as the SVNHF closure, SVNHF interior, SVNHF exterior, and SVNHF frontier. We also examine the conceptualization of the SVNHF dense set and SVNHF base in SVNHF topology using comprehensive examples. Eventually, to demonstrate and validate the superiority and inferiority ranking (SIR) method and choice value (CV) method in terms of their rationality and scientific basis, a real-world example of supplier selection in a food supply chain is provided. A comparative analysis is also performed to discuss the symmetry, validity and advantage of the proposed techniques.