Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

Exploring the interplay of T cell receptor-V gene copy numbers and major histocompatibility complex selection pressure in avian species:Insights into immune system evolution and reproductive investment

Birds,a fascinating and diverse group occupying various habitats worldwide,exhibit a wide range of life-history traits,reproductive methods,and migratory behaviors,all of which influence their immune systems.The association between major histocompatibility complex(MHC)genes and certain ecological factors in response to pathogen selection has been extensively studied;however,the role of the co-working molecule T cell receptor(TCR)remains poorly understood.This study aimed to analyze the copy numbers of TCR-V genes,the selection pressure(ωvalue)on MHC genes using available genomic data,and their potential ecological correlates across 93 species from 13 orders.The study was conducted using the publicly available genome data of birds.Our findings suggested that phylogeny influences the variability in TCR-V gene copy numbers and MHC selection pressure.The phylogenetic generalized least squares regression model revealed that TCR-Vαδcopy number and MHC-I selection pressure were positively associated with body mass.Clutch size was correlated with MHC selection pressure,and Migration was correlated with TCR-Vβcopy number.Further analyses revealed that the TCR-Vβcopy number was positively correlated with MHC-IIB selection pressure,while the TCR-Vγcopy number was negatively correlated with MHC-I peptide-binding region selection pressure.Our findings suggest that TCR-V diversity is significant in adaptive evolution and is related to species’life-history strategies and immunological defenses and provide valuable insights into the mechanisms underlying TCR-V gene duplication and MHC selection in avian species.

Optimization-based mode selection scheme for OAM millimeter wave system in the off-axis misalignment case

Millimeter-wave transmission combined with Orbital Angular Momentum(OAM)has the advantage of reducing the loss of beam power and increasing the system capacity.However,to fulfill this advantage,the antennas at the transmitter and receiver must be parallel and coaxial;otherwise,the accuracy of mode detection at the receiver can be seriously influenced.In this paper,we design an OAM millimeter-wave communication system for overcoming the above limitation.Specifically,the first contribution is that the power distribution between different OAM modes and the capacity of the system with different mode sets are analytically derived for performance analysis.The second contribution lies in that a novel mode selection scheme is proposed to reduce the total interference between different modes.Numerical results show that system performance is less affected by the offset when the mode set with smaller modes or larger intervals is selected.

Pancreatic stent improves the success rate of needle-knife papillotomy in patients with difficult biliary cannulation

BACKGROUND Needle-knife precut papillotomy(NKP)is typically performed freehand.However,it remains unclear whether pancreatic stent(PS)placement can improve the outcomes of NKP.AIM To explore whether PS placement improves the success rate of NKP in patients with difficult biliary cannulation.METHODS This single-center retrospective study included 190 patients who underwent NKP between January 2017 and December 2021 after failed conventional biliary cannulation.In cases with incidental pancreatic duct cannulation during conventional biliary cannulation,the decision for pre-NKP PS placement was made at the endoscopist's discretion.The primary outcome was the difference in the NKP success rate between patients with and without PS placement;the secondary outcome was the adverse event rate.RESULTS Among the 190 participants,82 received pre-NKP PS(PS-NKP group)whereas 108 did not[freehand or freehand NKP(FH-NKP)group].Post-NKP selective biliary cannulation was successful in 167(87.9%)patients,and the PS-NKP had a significantly higher success rate than the FH-NKP group(93.9%vs 83.3%,P=0.027).The overall adverse event rates were 7.3%and 11.1%in the PS-NKP and FH-NKP groups,respectively(P=0.493).A periampullary diverticulum(PAD)and significant intraoperative bleeding during NKP were independently associated with NKP failure;however,a pre-NKP PS was the only predictor of NKP success.Among the 44 participants with PADs,the PS-NKP group had a non-significantly higher NKP success rate than the FH-NKP group(87.5%and 65%,respectively;P=0.076).CONCLUSION PS significantly improved the success rate of NKP in patients with difficult biliary cannulation.

The sexually dimorphic expression of glutamate transporters and their implication in pain after spinal cord injury

In addition to the loss of motor function,~60% of patients develop pain after spinal cord injury.The cellular-molecular mechanisms are not well understood,but the data suggests that plasticity within the rostral,epicenter,and caudal penumbra of the injury site initiates a cellularmolecular interplay that acts as a rewiring mechanism leading to central neuropathic pain.Sprouting can lead to the formation of new connections triggering abnormal sensory transmission.The excitatory glutamate transporters are responsible for the reuptake of extracellular glutamate which makes them a critical target to prevent neuronal hyperexcitability and excitotoxicity.Our previous studies showed a sexually dimorphic therapeutic window for spinal cord injury after treatment with the selective estrogen receptor modulator tamoxifen.In this study,we investigated the anti-allodynic effects of tamoxifen in male and female rats with spinal cord injury.We hypothesized that tamoxifen exerts anti-allodynic effects by increasing the expression of glutamate transporters,leading to reduced hyperexcitability of the secondary neuron or by decreasing aberrant sprouting.Male and female rats received a moderate contusion to the thoracic spinal cord followed by subcutaneous slow-release treatment of tamoxifen or matrix pellets as a control(placebo).We used von Frey monofilaments and the“up-down method”to evaluate mechanical allodynia.Tamoxifen treatment decreased allodynia only in female rats with spinal cord injury revealing a sexdependent effect.The expression profile of glutamatergic transporters(excitatory amino acid transporter 1/glutamate aspartate transporter and excitatory amino acid transporter 2/glutamate transporter-1)revealed a sexual dimorphism in the rostral,epicenter,and caudal areas of the spinal cord with a pattern of expression primarily on astrocytes.Female rodents showed a significantly higher level of excitatory amino acid transporter-1 expression while male rodents showed increased excitatory amino acid transporter-2 expression compared with female rodents.Analyses of peptidergic(calcitonin gene-related peptide-α)and non-peptidergic(isolectin B4)fibers outgrowth in the dorsal horn after spinal cord injury showed an increased calcitonin gene-related peptide-α/isolectin B4 ratio in comparison with sham,suggesting increased receptive fields in the dorsal horn.Although the behavioral assay shows decreased allodynia in tamoxifen-treated female rats,this was not associated with overexpression of glutamate transporters or alterations in the dorsal horn laminae fibers at 28 days post-injury.Our findings provide new evidence of the sexually dimorphic expression of glutamate transporters in the spinal cord.The dimorphic expression revealed in this study provides a therapeutic opportunity for treating chronic pain,an area with a critical need for treatment.

EXPERT SYSTEM AND ITS APPLICATION IN THE SELECTION OF PIT RETAINING STRUCTURES

This paper describes the development of an expert system(ES) on earth retaining structures for the selection and design.The ES retaining is an interactive menudriven system and consists of two main parts—the selection part,selectwall and the design part.Selectwall is developed using the knowledge base and it makes a choice of the most appropriate retaining structure.The design part is developed by three independent subprograms which perform detailed design including strength,deformation,stability of the retaining structure.The calculation results are illustrated by plotting the diagram.Using this program,the design procedure of the retaining structure can be performed automatically.

Evaluating the performance of genomic selection on purebred population by incorporating crossbred data in pigs

Genomic selection(GS)has been widely used in livestock,which greatly accelerated the genetic progress of complex traits.The population size was one of the significant factors affecting the prediction accuracy,while it was limited by the purebred population.Compared to directly combining two uncorrelated purebred populations to extend the reference population size,it might be more meaningful to incorporate the correlated crossbreds into reference population for genomic prediction.In this study,we simulated purebred offspring(PAS and PBS)and crossbred offspring(CAB)base on real genotype data of two base purebred populations(PA and PB),to evaluate the performance of genomic selection on purebred while incorporating crossbred information.The results showed that selecting key crossbred individuals via maximizing the expected genetic relationship(REL)was better than the other methods(individuals closet or farthest to the purebred population,CP/FP)in term of the prediction accuracy.Furthermore,the prediction accuracy of reference populations combining PA and CAB was significantly better only based on PA,which was similar to combine PA and PAS.Moreover,the rank correlation between the multiple of the increased relationship(MIR)and reliability improvement was 0.60-0.70.But for individuals with low correlation(Cor(Pi,PA or B),the reliability improvement was significantly lower than other individuals.Our findings suggested that incorporating crossbred into purebred population could improve the performance of genetic prediction compared with using the purebred population only.The genetic relationship between purebred and crossbred population is a key factor determining the increased reliability while incorporating crossbred population in the genomic prediction on pure bred individuals.

Contribution Tracking Feature Selection (CTFS) Based on the Fusion of Sparse Autoencoder and Mutual Information

For data mining tasks on large-scale data,feature selection is a pivotal stage that plays an important role in removing redundant or irrelevant features while improving classifier performance.Traditional wrapper feature selection methodologies typically require extensive model training and evaluation,which cannot deliver desired outcomes within a reasonable computing time.In this paper,an innovative wrapper approach termed Contribution Tracking Feature Selection(CTFS)is proposed for feature selection of large-scale data,which can locate informative features without population-level evolution.In other words,fewer evaluations are needed for CTFS compared to other evolutionary methods.We initially introduce a refined sparse autoencoder to assess the prominence of each feature in the subsequent wrapper method.Subsequently,we utilize an enhanced wrapper feature selection technique that merges Mutual Information(MI)with individual feature contributions.Finally,a fine-tuning contribution tracking mechanism discerns informative features within the optimal feature subset,operating via a dominance accumulation mechanism.Experimental results for multiple classification performance metrics demonstrate that the proposed method effectively yields smaller feature subsets without degrading classification performance in an acceptable runtime compared to state-of-the-art algorithms across most large-scale benchmark datasets.

Sufficient variable selection of high dimensional nonparametric nonlinear systems based on Fourier spectrum of density-weighted derivative

The variable selection of high dimensional nonparametric nonlinear systems aims to select the contributing variables or to eliminate the redundant variables.For a high dimensional nonparametric nonlinear system,however,identifying whether a variable contributes or not is not easy.Therefore,based on the Fourier spectrum of densityweighted derivative,one novel variable selection approach is developed,which does not suffer from the dimensionality curse and improves the identification accuracy.Furthermore,a necessary and sufficient condition for testing a variable whether it contributes or not is provided.The proposed approach does not require strong assumptions on the distribution,such as elliptical distribution.The simulation study verifies the effectiveness of the novel variable selection algorithm.

The environmental analysis and site selection of mussel and large yellow croaker aquaculture areas based on high resolution remote sensing

Mussel aquaculture and large yellow croaker aquaculture areas and their environmental characteristics in Zhoushan were analyzed using satellite data and in-situ surveys.A new two-step remote sensing method was proposed and applied to determine the basic environmental characteristics of the best mussel and large yellow croaker aquaculture areas.This methodology includes the first step of extraction of the location distribution and the second step of the extraction of internal environmental factors.The fishery ranching index(FRI1,FRI2)was established to extract the mussel and the large yellow croaker aquaculture area in Zhoushan,using Gaofen-1(GF-1)and Gaofen-6(GF-6)satellite data with a special resolution of 2 m.In the second step,the environmental factors such as sea surface temperature(SST),chlorophyll a(Chl-a)concentration,current and tide,suspended sediment concentration(SSC)in mussel aquaculture area and large yellow croaker aquaculture area were extracted and analyzed in detail.The results show the following three points.(1)For the extraction of the mussel aquaculture area,FRI1 and FRI2 are complementary,and the combination of FRI1 and FRI2 is suitable to extract the mussel aquaculture area.As for the large yellow croaker aquaculture area extraction,FRI2 is suitable.(2)Mussel aquaculture and the large yellow croaker aquaculture area in Zhoushan are mainly located on the side near the islands that are away from the eastern open waters.The water environment factor template suitable for mussel and large yellow croaker aquaculture was determined.(3)This two-step remote sensing method can be used for the preliminary screening of potential site selection for the mussels and large yellow croaker aquaculture area in the future.the fishery ranching index(FRI1,FRI2)in this paper can be applied to extract the mussel and large yellow croaker aquaculture areas in coastal waters around the world.

CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

Analyzing the Impact of Feature Selection on Crop Yield Prediction

In agriculture sector, machine learning has been widely used by researchers for crop yield prediction. However, it is quite difficult to identify the most critical features from a dataset. Feature selection techniques allow us to remove the extraneous and noisy features from the original feature set. The feature selection techniques help the model to focus only on the important features of the data, thus reducing execution time and improving efficiency of the model. The aim of this study is to determine relevant subset features for achieving high predictive performance by using different feature selection techniques like Filter methods, Wrapper methods and embedded methods. In this work, different feature selection techniques like Rank-based feature selection technique, weighted feature selection technique and Hybrid Feature Selection Technique have been applied to the agricultural data. The optimal feature set returned by different feature selection techniques is used for yield prediction using Linear regression, Random Forest, and Decision Tree Regressor. The accuracy of prediction obtained using the above three methods has been analyzed by using different evaluation parameters. This study helps in increasing predictive accuracy with the minimum number of features.

Exploration of the “Four-Three-Four” Internship and Training Mode in Higher Vocational Colleges Under the Modern Vocational Education System

This article mainly introduces the innovative internship and training model of vocational colleges,including five aspects such as“four-party collaboration,three-stage progression,four-way integration,value-added evaluation,and double selection and promotion.”The model aims to improve students’practical skills and professional quality to better adapt to market demand and social development.The article also presents the prospects of the future internship and training model,including strengthening cooperation and communication with industry enterprises,focusing on students’personalized development and practical skill cultivation,and establishing a scientific and objective evaluation and feedback mechanism.

ML and DL-based Phishing Website Detection:The Effects of Varied Size Datasets and Informative Feature Selection Techniques

Onemust interact with a specific webpage or website in order to use the Internet for communication,teamwork,and other productive activities.However,because phishing websites look benign and not all website visitors have the same knowledge and skills to inspect the trustworthiness of visited websites,they are tricked into disclosing sensitive information and making them vulnerable to malicious software attacks like ransomware.It is impossible to stop attackers fromcreating phishingwebsites,which is one of the core challenges in combating them.However,this threat can be alleviated by detecting a specific website as phishing and alerting online users to take the necessary precautions before handing over sensitive information.In this study,five machine learning(ML)and DL algorithms—cat-boost(CATB),gradient boost(GB),random forest(RF),multilayer perceptron(MLP),and deep neural network(DNN)—were tested with three different reputable datasets and two useful feature selection techniques,to assess the scalability and consistency of each classifier’s performance on varied dataset sizes.The experimental findings reveal that the CATB classifier achieved the best accuracy across all datasets(DS-1,DS-2,and DS-3)with respective values of 97.9%,95.73%,and 98.83%.The GB classifier achieved the second-best accuracy across all datasets(DS-1,DS-2,and DS-3)with respective values of 97.16%,95.18%,and 98.58%.MLP achieved the best computational time across all datasets(DS-1,DS-2,and DS-3)with respective values of 2,7,and 3 seconds despite scoring the lowest accuracy across all datasets.

Improving the phenotypic expression of rice genotypes:Rethinking “intensification” for production systems and selection practices for rice breeding

Intensification in rice crop production is generally understood as requiring increased use of material inputs: water, inorganic fertilizers, and agrochemicals. However, this is not the only kind of intensification available. More productive crop phenotypes, with traits such as more resistance to biotic and abiotic stresses and shorter crop cycles, are possible through modifications in the management of rice plants, soil, water, and nutrients, reducing rather than increasing material inputs. Greater factor productivity can be achieved through the application of new knowledge and more skill, and(initially) more labor, as seen from the System of Rice Intensification(SRI), whose practices are used in various combinations by as many as 10 million farmers on about 4 million hectares in over 50 countries. The highest yields achieved with these management methods have come from hybrids and improved rice varieties, confirming the importance of making genetic improvements. However,unimproved varieties are also responsive to these changes, which induce better growth and functioning of rice root systems and more abundance, diversity, and activity of beneficial soil organisms. Some of these organisms as symbiotic endophytes can affect and enhance the expression of rice plants' genetic potential as well as their phenotypic resilience to multiple stresses, including those of climate change. SRI experience and data suggest that decades of plant breeding have been selecting for the best crop genetic endowments under suboptimal growing conditions, with crowding of plants that impedes their photosynthesis and growth, flooding of rice paddies that causes roots to degenerate and forgoes benefits derived from aerobic soil organisms, and overuse of agrochemicals that adversely affect these organisms as well as soil and human health. This review paper reports evidence from research in India and Indonesia that changes in crop and water management can improve the expression of rice plants' genetic potential, thereby creating more productive and robustphenotypes from given rice genotypes. Data indicate that increased plant density does not necessarily enhance crop yield potential, as classical breeding methods suggest. Developing cultivars that can achieve their higher productivity under a wide range of plant densities—breeding for density-neutral cultivars using alternative selection strategies—will enable more effective exploitation of available crop growth resources. Density-neutral cultivars that achieve high productivity under ample environmental growth resources can also achieve optimal productivity under limited resources, where lower densities can avert crop failure due to overcrowding. This will become more important to the extent that climatic and other factors become more adverse to crop production. Focusing more on which management practices can evoke the most productive and robust phenotypes from given genotypes is important for rice breeding and improvement programs since it is phenotypes that feed our human populations.

Evaluation of the effect and profitability of gene-assisted selection in pig breeding system

Objective:To evaluate the effect and profitability of using the quantitative trait loci (QTL)-linked direct marker (DR marker) in gene-assisted selection (GAS). Methods: Three populations (100, 200, or 300 sows plus 10 boars within each group) with segregating QTL were simulated stochastically. Five economic traits were investigated, including number of born alive (NBA), average daily gain to 100 kg body weight (ADG), feed conversion ratio (FCR), back fat at 100 kg body weight (BF) and intramuscular fat (IMF). Selection was based on the estimated breeding value (EBV) of each trait. The starting frequencies of the QTL's favorable allele were 0.1, 0.3 and 0.5, respectively. The economic return was calculated by gene flow method. Results: The selection efficiency was higher than 100% when DR markers were used in GAS for 5 traits. The selection efficiency for NBA was the highest, and the lowest was for ADG whose QTL had the lowest variance. The mixed model applied DR markers and obtained higher extra genetic gain and extra economic returns. We also found that the lower the frequency of the favorable allele of the QTL, the higher the extra return obtained. Conclusion: GAS is an effective selection scheme to increase the genetic gain and the eco- nomic returns in pig breeding.

Traditional Selection Criteria of Ogaden Cattle in Pastoral and Agro Pastoral Production Systems and Its Implication to Resilience of the Breed in the Face of Climate Change in the Future

Selecting for adapted livestock breeds that will produce at optimum in the anticipated climate change scenario is one way of sustaining the production system. Africa has accumulated indigenous knowledge to coping up with climate variability in the past. It is common practice to shift to livestock agriculture as climate becomes less predicted in Africa. Cattle contribute more than 80% of meat and milk in Ethiopia. The aim of this paper was to identify traditional selection criteria of Ogaden cattle breed in pastoral and agro pastoral production systems and suggest possible intervention strategies. Questionnaire survey was conducted in Wabeshebele Zone of Ethiopia, in three districts based on cattle population and accessibility on 126 households. The results of the study revealed that farmers preferred to own diversified livestock species to cope up with unpredicted climate. Milk production and body conformation were ranked first and second criteria in selecting female which were followed by adaptive characteristics such as coat color, fertility and longevity, respectively, while in male, adaptive characters such as hump size, body conformation, coat color and fertility were given priority over milk production. Selection criteria of cattle practiced in the society strongly favored adaptation and ability to perform in feed constraint environment in addition to meat, milk and fertility. However, the simplest reproductive biotechnology, AI, was rarely practiced in the study area. It was recommended that indigenous knowledge of the society on selection criteria of the cattle breed need to be integrated with utilization of assisted reproductive biotechnologies and on farm research innovations on the breed so far acquired in any improvement program of the breed in the future.

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

The necessity of having an effective computer-aided decision support system in the housing construction industry is rapidly growing alongside the demand for green buildings and green building products. Identifying and defining financially viable low-cost green building materials and components, just like selecting them, is a crucial exercise in subjectivity. With so many variables to consider, the task of evaluating such products can be complex and discouraging. Moreover, the existing mode for selecting and managing, often very large information associated with their impacts constrains decision-makers to perform a trade-off analysis that does not necessarily guarantee the most environmentally preferable material. This paper introduces the development of a multi-criteria decision support system (DSS) aimed at improving the understanding of the principles of best practices associated with the impacts of low-cost green building materials and components. The DSS presented in this paper is to provide designers with useful and explicit information that will aid informed decision-making in their choice of materials for low-cost green residential housing projects. The prototype MSDSS is developed using macro-in-excel, which is a fairly recent database management technique used for integrating data from multiple, often very large databases and other information sources. This model consists of a database to store different types of low-cost green materials with their corresponding attributes and performance characteristics. The DSS design is illustrated with particular emphasis on the development of the material selection data schema, and application of the Analytical Hierarchy Process (AHP) concept to a material selection problem. Details of the MSDSS model are also discussed including workflow of the data evaluation process. The prototype model has been developed with inputs elicited from domain experts and extensive literature review, and refined with feedback obtained from selected expert builder and developer companies. This paper further demonstrates the application of the prototype MSDSS for selecting the most appropriate low-cost green building material from among a list of several available options, and finally concludes the study with the associated potential benefits of the model to research and practice.

Research and Development on the Visualization System for Official and Scientific Management in Enterprise

A visualization system for the official and scientific management has been designed on C++. By this system, the information of database has been shown to users intuitively, which makes it possible for users to be free from the data sea. The system can realize the information mining and the integrated display, at the same time, improve the information’s constructive level and integration degree.

Type selection and maintenance of the control system of large-scale air separation units

Based on the practice of Baosteel＇ s 60000 m3/h air separation unit （ASU） ,which is the first domestically- integrated unit of such a scale, this paper studies the principles of type selection of the distribution control system （DCS）. It discusses the design of the unit＇s control system,which involves a compressor system,a purification system （molecular sieving）, a turbo expansion system and an air separation system. The final part of the paper discusses the maintenance and future development of the ASU control system at Baosteel.