Membership Criterion Research Articles

ACSICS: Joint Distribution Mode Integrating Agricultural Industry Chain Logistics Under the Background of Artificial Intelligence

The purpose is to study the new application of artificial intelligence (AI) technology in the Agro logistics industry distribution modes. This work summarizes the problems in agricultural logistics by investigating the current situation of the logistics distribution modes (LDMs) of Agro products and establishes a joint LDM integrating logistics and agricultural industry chain (AIC). An intelligent virtual center is established for the joint LDM according to the specific situation of Xi’an. Experts are invited to evaluate the proposed LDM. The results show that the existing third-party logistics (3PL), agricultural supermarket docking LDM, and company+farmer shared LDM have respective advantages and disadvantages. The virtual center for the proposed joint LDM multiplies the weight matrix [Formula: see text] and fuzzy evaluation matrix A to obtain a comprehensive fuzzy evaluation result. It evaluates the result according to the maximum membership criterion. Most experts have a relatively good evaluation of the joint LDM. In the comprehensive fuzzy evaluation results, the comprehensive “excellent” score is 0.3755, and the comprehensive “good” score is 0.2678. According to the principle of maximum subordination, the AIC logistics integrating Agro products logistics has an excellent performance, and the overall satisfaction level of performance has exceeded 85%. In addition, Yonghui supermarket adopts the joint LDM, and the price of fruits and vegetables is lower than that of other LDMs. Therefore, the proposed AI-based joint LDM can optimize the distribution route, improve distribution efficiency, and save logistics costs to a great extent.

Impact of Farmer Producer Organizations on Price and Poverty Alleviation of Smallholder Dry Chillies Farmers in India

This study investigates the impact of Farmer Producer Organizations (FPOs) on smallholder dry chilli farmers in Guntur district, Andhra Pradesh, with a focus on price realization and poverty alleviation. Two specific FPOs, Red Chilli Farmer Producer Organisation and Spoorthi Chilli Producers Company Ltd., from the Guntur district of Andhra Pradesh were chosen for the study based on their substantial business turnover and comprehensive backward and forward linkages to their farmer-members. The smallholder farmers were stratified into two groups viz., treated (161) and untreated (n = 315) based on the FPO membership criterion. The Foster-Greer-Thorbecke model revealed that the poverty incidence among untreated farmers was recorded at 0.691, which was approximately 49 percent higher than the poverty incidence of treated farmers (0.352). The depth and severity of poverty were also greater among untreated farmers, with a poverty depth of 0.494 compared to the lower value of 0.126 observed among treated farmers. The results from Endogenous Switching Regression Model revealed a significant positive relationship between FPO membership and both price realization and poverty alleviation. Farmers with FPO membership experienced 2.11 percent higher prices and 39.14 percent higher annual agricultural income compared to untreated. Factors such as education, adherence to Good Agricultural Practices, farm experience, access to improved inputs, and credit significantly influenced FPO membership. The study concludes that FPO membership plays a crucial role in improving the standard of living for smallholder dry chilli farmers by increasing prices and income. So, this research sheds light on the significance of FPOs in enhancing the economic well-being of smallholder dry chilli farmers in Andhra Pradesh.

Application of explainable artificial intelligence in medical health: A systematic review of interpretability methods

This paper investigates the applications of explainable AI (XAI) in healthcare, which aims to provide transparency, fairness, accuracy, generality, and comprehensibility to the results obtained from AI and ML algorithms in decision-making systems. The black box nature of AI and ML systems has remained a challenge in healthcare, and interpretable AI and ML techniques can potentially address this issue. Here we critically review previous studies related to the interpretability of ML and AI methods in medical systems. Descriptions of various types of XAI methods such as layer-wise relevance propagation (LRP), Uniform Manifold Approximation and Projection (UMAP), Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), ANCHOR, contextual importance and utility (CIU), Training calibration-based explainers (TraCE), Gradient-weighted Class Activation Mapping (Grad-CAM), t-distributed Stochastic Neighbor Embedding (t-SNE), NeuroXAI, Explainable Cumulative Fuzzy Class Membership Criterion (X-CFCMC) along with the diseases which can be explained through these methods are provided throughout the paper. The paper also discusses how AI and ML technologies can transform healthcare services. The usability and reliability of the presented methods are summarized, including studies on the usability and reliability of XGBoost for mediastinal cysts and tumors, a 3D brain tumor segmentation network, and the TraCE method for medical image analysis. Overall, this paper aims to contribute to the growing field of XAI in healthcare and provide insights for researchers, practitioners, and decision-makers in the healthcare industry. Finally, we discuss the performance of XAI methods applied in medical health care systems. It is also needed to mention that a brief implemented method is provided in the methodology section.

Population coding in the cerebellum: a machine learning perspective.

The cere resembles a feedforward, three-layer network of neurons in which the "hidden layer" consists of Purkinje cells (P-cells) and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron is a prediction that is compared with the actual observation, resulting in an error signal that originates in the inferior olive. Efficient learning requires that the error signal reach the DCN neurons, as well as the P-cells that project onto them. However, this basic rule of learning is violated in the cerebellum: the olivary projections to the DCN are weak, particularly in adulthood. Instead, an extraordinarily strong signal is sent from the olive to the P-cells, producing complex spikes. Curiously, P-cells are grouped into small populations that converge onto single DCN neurons. Why are the P-cells organized in this way, and what is the membership criterion of each population? Here, I apply elementary mathematics from machine learning and consider the fact that P-cells that form a population exhibit a special property: they can synchronize their complex spikes, which in turn suppress activity of DCN neuron they project to. Thus complex spikes cannot only act as a teaching signal for a P-cell, but through complex spike synchrony, a P-cell population may act as a surrogate teacher for the DCN neuron that produced the erroneous output. It appears that grouping of P-cells into small populations that share a preference for error satisfies a critical requirement of efficient learning: providing error information to the output layer neuron (DCN) that was responsible for the error, as well as the hidden layer neurons (P-cells) that contributed to it. This population coding may account for several remarkable features of behavior during learning, including multiple timescales, protection from erasure, and spontaneous recovery of memory.

Explainable classifier for improving the accountability in decision-making for colorectal cancer diagnosis from histopathological images.

Pathologists are responsible for cancer type diagnoses from histopathological cancer tissues. However, it is known that microscopic examination is tedious and time-consuming. In recent years, a long list of machine learning approaches to image classification and whole-slide segmentation has been developed to support pathologists. Although many showed exceptional performances, the majority of them are not able to rationalize their decisions. In this study, we developed an explainable classifier to support decision making for medical diagnoses. The proposed model does not provide an explanation about the causality between the input and the decisions, but offers a human-friendly explanation about the plausibility of the decision. Cumulative Fuzzy Class Membership Criterion (CFCMC) explains its decisions in three ways: through a semantical explanation about the possibilities of misclassification, showing the training sample responsible for a certain prediction and showing training samples from conflicting classes. In this paper, we explain about the mathematical structure of the classifier, which is not designed to be used as a fully automated diagnosis tool but as a support system for medical experts. We also report on the accuracy of the classifier against real world histopathological data for colorectal cancer. We also tested the acceptability of the system through clinical trials by 14 pathologists. We show that the proposed classifier is comparable to state of the art neural networks in accuracy, but more importantly it is more acceptable to be used by human experts as a diagnosis tool in the medical domain.

Extended stellar systems in the solar neighborhood

Context.Nearby stellar streams carry unique information on the dynamical evolution and disruption of stellar systems in the Galaxy, the mass distribution in the disk, and they provide unique targets for planet formation and evolution studies. Recently, Meingast 1, a 120° stellar stream with a length of at least 400 pc, was dicovered.Aims.We aim to revisit the Meingast 1 stream to search for new members within its currently known 400 pc extent, usingGaiaDR2 data and an innovative machine learning approach.Methods.We used a bagging classifier of one-class support vector machines withGaiaDR2 data to perform a 5D search (positions and proper motions) for new stream members. The ensemble was created by randomly sampling 2.4 million hyper-parameter realizations admitting classifiers that fulfill a set of prior assumptions. We used the variable prediction frequency resulting from the multitude of classifiers to estimate a stream membership criterion, which we used to select high-fidelity sources. We used the HR diagram and the Cartesian velocity distribution as test and validation tools.Results.We find about 2000 stream members with high fidelity, or about an order of magnitude more than previously known, unveiling the stream’s population across the entire stellar mass spectrum, from B stars to M stars, including white dwarfs. We find that, apart from being slightly more metal poor, the HRD of the stream is indistinguishable from that of the Pleiades cluster. For the mass range at which we are mostly complete, ∼0.2M⊙ < M < ∼4 M⊙, we find a normal IMF, allowing us to estimate the total mass of stream to be about 2000M⊙, making this relatively young stream by far the most massive one known. In addition, we identify several white dwarfs as potential stream members.Conclusions.The nearby Meingast 1 stream, due to its richness, age, and distance, is a new fundamental laboratory for star and planet formation and evolution studies for the poorly studied and gravitationally unbound star formation mode. We also demonstrate that one-class support vector machines can be effectively used to unveil the full stellar populations of nearby stellar systems withGaiadata.

Proper-motion Membership Tests for Four Planetary Nebulae in Galactic Globular Clusters*

Abstract Four planetary nebulae (PNe) are considered to be probable or possible members of Galactic globular clusters (GCs). These are Ps 1 = K648 in M15, GJJC 1 = IRAS 18333−2357 in M22, JaFu 1 in Palomar 6, and JaFu 2 in NGC 6441. In addition to lying close to the host GCs on the sky, these PNe have radial velocities that are consistent, within the errors and stellar velocity dispersions, with cluster membership. The remaining membership criterion is whether the proper motions (PMs) of the central stars are in agreement with those of the host clusters. We have carried out the PM test for all four PNe. Two of the central stars—those of Ps 1 and GJJC 1—have PMs listed in the recent Gaia Data Release 2 (DR2). We updated the PM of the Ps 1 central star to a more precise value using archival Hubble Space Telescope (HST) frames. Both PMs are statistically consistent with cluster membership. For the other two PNe, we used archival HST images to derive the PMs of their nuclei. For JaFu 2, there are HST images at several epochs, and the measured PM of the nucleus is in excellent agreement with that of the host cluster. For JaFu 1 the available archival HST images are less optimal and the results are less conclusive; the measured PM for the central star is marginally consistent with cluster membership, but additional astrometric observations are desirable for a more robust membership test.

Representation and Reasoning of Fuzzy Knowledge Under Variable Fuzzy Criterion Using Extended Fuzzy Petri Nets

Fuzzy knowledge representation and reasoning is one of the key research subjects of artificial intelligence with uncertainty. Currently, fuzzy Petri nets (FPNs) cannot describe fuzzy nondeterministic knowledge based on the variable fuzzy criterion clearly. To solve this problem, we present an extended FPN model based on the qualitative mapping and the qualitative criterion transformation. First, the mechanism and the principles of the variable fuzzy criterion under qualitative mapping are introduced. The important theoretical basis of qualitative mapping is detection and integration of perceptual attribute features. Under transition degree function and the qualitative criterion transformation, the qualitative mapping better reflects the cases in which the fuzzy membership criterion is variable. Second, we use the characteristics of the qualitative mapping to propose a structure of attribute fuzzy information granule. Based on the logic operation of fuzzy information granule, we redescribe the four fuzzy production rules which are commonly used, and obtain four kinds of fuzzy qualitative judgment rules. They can explicitly express the occurrence process of transition nodes of FPNs, which strengthens the uncertain knowledge representation ability of FPNs. The new method makes it easy to construct a learning mechanism based on cognition in FPNs.

Semantically Explainable Fuzzy Classifier

In machine learning, there are many high-performance classifiers. However, because of lack of transparency, they are not able to explain the data in a human-friendly form. In this paper, Cumulative Fuzzy Class Membership Criterion (CFCMC), a recently proposed fuzzy modeling classifier, is modified and utilized for a novel approach of information extraction from the labeled data. This approach is able to explain the classifiability of the data in the form of semantics. Extracted semantics give information about the structure of the data and the similarities between classes. To get a relevant image of its classification performance, it is compared to three well-known and frequently used classifiers, which are considered as black boxes, namely, SVM, MLP, and kNN, and to a similar transparent approach, MF ARTMAP. To validate extracted semantics, they are compared to visualization of classified data and to confusion matrices generated during the evaluation of the created CFCMC models. The experimental result shows that CFCMC is not necessarily the best classifier, although, in most cases, it is not too far from the best performing methods. However, the semantical explanation potentially allows the classifier to be applied as a support for human decision processes in real-world problems.

Core Clustering as a Tool for Tackling Noise in Cluster Labels

Real-world data sets often contain mislabelled entities. This can be particularly problematic if the data set is being used by a supervised classification algorithm at its learning phase. In this case, the accuracy of this classification algorithm, when applied to unlabelled data, is likely to suffer considerably. In this paper, we introduce a clustering-based method capable of reducing the number of mislabelled entities in data sets. Our method can be summarised as follows: (i) cluster the data set; (ii) select the entities that have the most potential to be assigned to correct clusters; (iii) use the entities of the previous step to define the core clusters and map them to the labels using a confusion matrix; (iv) use the core clusters and our cluster membership criterion to correct the labels of the remaining entities. We perform numerous experiments to validate our method empirically using k-nearest neighbour classifiers as a benchmark. We experiment with both synthetic and real-world data sets with different proportions of mislabelled entities. Our experiments demonstrate that the proposed method produces promising results. Thus, it could be used as a preprocessing data correction step of a supervised machine learning algorithm.

Score-Group Framing Negatively Impacts Peer Evaluations

How does group membership framing affect the feedback students provide learners? This paper presents two between-subjects experiments investigating the effect of Ingroup/Outgroup membership on effort spent in peer evaluations, and whether the group membership criterion type affects quality and stringency of evaluation. Two peer-review assignments were implemented in two separate classes. In the first study, students were nominally grouped by location they sat in class and non-nominally grouped by current class score; each was asked to review an Ingroup and Outgroup peer assignment. A second study randomly assigned students to one of four group types (random, score, motivation, and location); student reviewed two Ingroup assignments. In both studies, score-grouped students graded their peers more stringently than students grouped by location. These studies illustrate for system designers the impacts of group framing - and the disclosure of that-in peer review tasks.

Selection Ideal Coal Suppliers of Thermal Power Plants Using the Matter-Element Extension Model with Integrated Empowerment Method for Sustainability

In order to reduce thermal power generation cost and improve its market competitiveness, considering fuel quality, cost, creditworthiness, and sustainable development capacity factors, this paper established the evaluation system for coal supplier selection of thermal power and put forward the coal supplier selection strategies for thermal power based on integrated empowering and ideal matter-element extension models. On the one hand, the integrated empowering model can overcome the limitations of subjective and objective methods to determine weights, better balance subjective, and objective information. On the other hand, since the evaluation results of the traditional element extension model may fall into the same class and only get part of the order results, in order to overcome this shortcoming, the idealistic matter-element extension model is constructed. It selects the ideal positive and negative matter-elements classical field and uses the closeness degree to replace traditional maximum degree of membership criterion and calculates the positive or negative distance between the matter-element to be evaluated and the ideal matter-element; then it can get the full order results of the evaluation schemes. Simulated and compared with the TOPSIS method, Romania selection method, and PROMETHEE method, numerical example results show that the method put forward by this paper is effective and reliable.

OXYGEN ABUNDANCES IN NEARBY FGK STARS AND THE GALACTIC CHEMICAL EVOLUTION OF THE LOCAL DISK AND HALO

Atmospheric parameters and oxygen abundances of 825 nearby FGK stars are derived using high-quality spectra and a non-LTE analysis of the 777 nm O I triplet lines. We assign a kinematic probability for the stars to be thin-disk (P1), thick-disk (P2), and halo (P3) members. We confirm previous findings of enhanced [O/Fe] in thick-disk (P2>0.5) relative to thin-disk (P1>0.5) stars with [Fe/H]<-0.2, as well as a "knee" that connects the mean [O/Fe]-[Fe/H] trend of thick-disk stars with that of thin-disk members at [Fe/H]>-0.2. Nevertheless, we find that the kinematic membership criterion fails at separating perfectly the stars in the [O/Fe]-[Fe/H] plane, even when a very restrictive kinematic separation is employed. Stars with "intermediate" kinematics (P1<0.7, P2<0.7) do not all populate the region of the [O/Fe]-[Fe/H] plane intermediate between the mean thin-disk and thick-disk trends, but their distribution is not necessarily bimodal. Halo stars (P3>0.5) show a large star-to-star scatter in [O/Fe]-[Fe/H], but most of it is due to stars with Galactocentric rotational velocity V<-200 km/s; halo stars with V>-200 km/s follow an [O/Fe]-[Fe/H] relation with almost no star-to-star scatter. Early mergers with satellite galaxies explain most of our observations, but the significant fraction of disk stars with "ambiguous" kinematics and abundances suggests that scattering by molecular clouds and radial migration have both played an important role in determining the kinematic and chemical properties of solar neighborhood stars.

A new solvability criterion for finite groups

In 1968, John Thompson proved that a finite group G is solvable if and only if every 2-generator subgroup of G is solvable. In this paper, we prove that solvability of a finite group G is guaranteed by a seemingly weaker condition: G is solvable if, for all conjugacy classes C and D of G consisting of elements of prime power order, there exist x in C and y in D with x and y generating a solvable group. We also prove the following property of finite nonabelian simple groups, which is the key tool for our proof of the solvability criterion: if G is a finite nonabelian simple group, then there exist two prime divisors a and b of |G| such that, for all elements x, y in G with |x|=a and |y|=b, the subgroup generated by x and y is not solvable. Further, using a recent result of Guralnick and Malle, we obtain a similar membership criterion for any family of finite groups closed under forming subgroups, quotients and extensions.

Redefining membership in animal groups

Groups of animals-flocks, herds, shoals, and swarms-are often dynamical entities. Relative positions of group members change, and most groups divide and re-form on multiple timescales. Few studies, however, have attempted to define when an animal is or is not within a group. Most authors adopt arbitrary distance thresholds, such as the elective group size (EGS), which assume that animals closer than some threshold distance are in the same group. In the present article, we define a group-membership criterion derived from dynamical statistical considerations and based on detailed trajectories of all members of a moving group. We demonstrate the use of our criterion to track the comings and goings in shoals of zebrafish (Danio rerio) and the gradual dissolution of the shoal across multiple exposures to a testing tank. We present a novel measure of group cohesion based on our group membership criterion and demonstrate that excursions away from the shoal explain some previous observations of the dynamics of shoaling. Finally, we show that excursions away from a shoal are accompanied by an increase in swimming speed. Applying similar criteria to data from other species may clarify some of the common features of animal collective motion.

Exploring the Dual Dichotomy within Urban Geography: An Application of Fuzzy Urban Sets

This study argues that the concept of fuzzy urban sets is particularly well suited to analyzing aspects of a dual dichotomy that characterizes much of the literature on urban geography. By treating individual urban areas ("cities," or in this case "Consolidated Metropolitan Statistical Areas") as subsets of one aggregate fuzzy urban set, the method can address the dichotomy pertaining to the structure of urban systems within cities versus those among cities. By defining the membership criterion by which individual elements ("districts," or in this case "census places") are included in the fuzzy urban set, one can determine what kind of fuzzy urban set one is dealing with. In our empirical example, we use service employment as the key criterion. We argue that the method employed is more broadly applicable than the specific example used here, and indeed is well suited generally to addressing the dual dichotomy within urban geography.

On the Type and the Minimal Presentation of Certain Numerical Semigroups

We study numerical semigroups generated by generalized arithmetic sequences. We present a membership criterion for such a numerical semigroup, and by this we are able to answer fundamental questions concerning a numerical semigroup such as computing the Frobenius number and the type of the numerical semigroup, and decide whether the numercial semigroup is symmetric. Also for this kind of numerical semigroups, we compute the cardinality of a minimal presentation and determine whether they are complete intersections.

Functional-logical simulation of quality of functioning integrated circuits under the effect of radiation and electromagnetic field

Methods of functional-logical simulation of the radiation behavior and effect of electromagnetic radiation on integrated circuits (ICs) based on the model of the Brauer digital fuzzy automation are considered. A principal distinction of this method from conventional ones is the possibility of taking into account the dependences of radiation resistance and pulsed electric strength on operation mode, functional state, and construction- topological and circuit parameters of ICs in the explicit form in functional-logical models of ICs. The procedures of construction of membership criterion functions of basic elements for ICs of various technologies are considered. Algorithms and recommendations for methods of predicting pulsed electric strength and transient and residual radiation effects in ICs are given. As an example, theoretical and experimental results of simulation of the quality of functioning of LSIC ROM of a series 1656 with respect to residual damages are presented.

A New Digital Image Watermarking Algorithm Resilient to Desynchronization Attacks

Synchronization is crucial to design a robust image watermarking scheme. In this paper, a novel feature-based image watermarking scheme against desynchronization attacks is proposed. The robust feature points, which can survive various signal-processing and affine transformation, are extracted by using the Harris-Laplace detector. A local characteristic region (LCR) construction method based on the scale-space representation of an image is considered for watermarking. At each LCR, the digital watermark is repeatedly embedded by modulating the magnitudes of discrete Fourier transform coefficients. In watermark detection, the digital watermark can be recovered by maximum membership criterion. Simulation results show that the proposed scheme is invisible and robust against common signal processing, such as median filtering, sharpening, noise adding, JPEG compression, etc., and desynchronization attacks, such as rotation, scaling, translation, row or column removal, cropping, and random bend attack, etc.

Wobbles, humps and sudden jumps: a case study of continuity, discontinuity and variability in early language development

AbstractCurrent individual‐based, process‐oriented approaches (dynamic systems theory and the microgenetic perspective) have led to an increase of variability‐centred studies in the literature. The aim of this article is to propose a technique that incorporates variability in the analysis of the shape of developmental change. This approach is illustrated by the analysis of time serial language data, in particular data on the development of preposition use, collected from four participants. Visual inspection suggests that the development of prepositions‐in‐contexts shows a characteristic pattern of two phases, corresponding with a discontinuity. Three criteria for testing such discontinuous phase‐wise change in individual data are presented and applied to the data. These are: (1) the sub‐pattern criterion, (2) the peak criterion and (3) the membership criterion. The analyses rely on bootstrap and resampling procedures based on various null hypotheses. The results show that there are some indications of discontinuity in all participants, although clear inter‐individual differences have been found, depending on the criteria used. In the discussion we will address several fundamental issues concerning (dis)continuity and variability in individual‐based, process‐oriented data sets. Copyright © 2007 John Wiley &amp; Sons, Ltd.