We discuss the role of perceptron (or threshold) connectives in the context of Description Logic, and in particular their possible use as a bridge between statistical learning of models from data and logical reasoning over knowledge bases. We prove that such connectives can be added to the language of most forms of Description Logic without increasing the complexity of the corresponding inference problem. We show, with a practical example over the Gene Ontology, how even simple instances of perceptron (...) connectives are expressive enough to represent learned, complex concepts derived from real use cases. This opens up the possibility to import concepts learnt from data into existing ontologies. (shrink)

We recently presented our Efimov K-theory of Diamonds, proposing a pro-diamond, a large stable (∞,1)-category of diamonds (D^{diamond}), and a localization sequence for diamond spectra. Commensurate with the localization sequence, we now detail four potential applications of the Efimov K-theory of D^{diamond}: emergent time as a pro-emergence (v-stack time) in a diamond holographic principle using Scholze’s six operations in the ’etale cohomology of diamonds; a pro-Generative Adversarial Network and v-stack perceptron; D^{diamond}cryptography; and diamond nonlocality in perfectoid quantum physics.

We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by means of two kinds of interpretable models. The first is perceptron (or threshold) connectives, which enrich knowledge representation languages such as Description Logics with linear operators that serve as a bridge between statistical learning and logical reasoning. The second is Trepan Reloaded, an ap- proach that builds post-hoc explanations of black-box classifiers in the form of decision trees enhanced by domain knowledge. Our aim is, firstly, to (...) target a model-agnostic distillation approach exemplified with these two frameworks, secondly, to study how these two frameworks interact on a theoretical level, and, thirdly, to investigate use-cases in ML and AI in a comparative manner. Specifically, we envision that user-studies will help determine human understandability of explanations generated using these two frameworks. (shrink)

In this paper an Artificial Neural Network (ANN) model, for predicting the category of a tumor was developed and tested. Taking patients’ tests, a number of information gained that influence the classification of the tumor. Such information as age, sex, histologic-type, degree-of-diffe, status of bone, bone-marrow, lung, pleura, peritoneum, liver, brain, skin, neck, supraclavicular, axillar, mediastinum, and abdominal. They were used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was established and trained (...) using data set which its title is “primary tumor” and was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia Test data evaluation shows that the ANN model is able to correctly predict the tumor category with 76.67 % accuracy. (shrink)

Abstract: Ensuring the proactive detection of transaction risks is paramount for financial institutions, particularly in the context of managing credit scores. In this study, we compare different machine learning algorithms to effectively and efficiently. The algorithms used in this study were: MLogisticRegressionCV, ExtraTreeClassifier,LGBMClassifier,AdaBoostClassifier, GradientBoostingClassifier,Perceptron,RandomForestClassifier,KNeighborsClassifier,BaggingClassifier, DecisionTreeClassifier, CalibratedClassifierCV, LabelPropagation, Deep Learning. The dataset was collected from Kaggle depository. It consists of 164 rows and 8 columns. The best classifier with unbalanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%,Recall100.0% and the (...) F1-score 100.0%. However, the best classifier with balanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%, Recall 100.0% and the F1-score 100.0%. (shrink)

The parallel research is contemporary to analyse processes and localisation in artificial intelligence (AI) associated with connexionism and learning algorithms. In machine learning, the perceptron (or McCulloch-Pitts neuron) is an algorithm for Boolean functions of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. With a pattern N we use the calculator in synthesis applying polynomial advanced systems.

Over 1.5 million living animal species have been described—of which around 1 million are insects—but it has been estimated there are over 7 million animal species in total. Animals range in length from 8.5 micrometres to 33.6 metres. In this paper an Artificial Neural Network (ANN) model, was developed and tested to predict animal species. There are a number of features that influence the classification of animal species. Such as the existence of hair/ feather, if the animal gives birth or (...) spawns, it is airborne, aquatic, predator, toothed, backboned, venomous, has –fins, has-tail, cat-sized, and domestic. A model based on the Multilayer Perceptron Topology was proposed and trained, using data set what was collected from UCI Machine Learning Repository. Evaluation of the proposed model shows that the ANN model is able to correctly predict the animal category with 100% accuracy. (shrink)

In this essay I propose that what design principles in systems biology and systems neuroscience do is to present abstract characterizations of mechanisms, and thereby facilitate mechanistic explanation. To show this, one design principle in systems neuroscience, i.e., the multilayer perceptron, is examined. However, Braillard (2010) contends that design principles provide a sort of non-mechanistic explanation due to two related reasons: they are very general and describe non-causal dependence relationships. In response to this, I argue that, on the one (...) hand, all mechanisms are more or less general (or abstract), and on the other, many (if not all) design principles are causal systems. (shrink)

This paper aims to offer a new view of the role of connectionist models in the study of human cognition through the conceptualization of the history of connectionism – from the simplest perceptrons to convolutional neural nets based on deep learning techniques, as well as through the interpretation of criticism coming from symbolic cognitive science. Namely, the connectionist approach in cognitive science was the target of sharp criticism from the symbolists, which on several occasions caused its marginalization and almost complete (...) abandonment of its assumptions in the study of cognition. Criticisms have mostly pointed to its explanatory inadequacy as a theory of cognition or to its biological implausibility as a theory of implementation, and critics often focused on specific shortcomings of some connectionist models and argued that they apply to connectionism in general. In this paper, we want to show that both types of critique are based on the assumption that the only valid explanations in cognitive science are instances of homuncular functionalism and that by removing this assumption and by adopting an alternative methodology – exploratory mechanistic strategy, we can reject most objections to connectionism as irrelevant, explain the progress of connectionist models despite their shortcomings and sketch the trajectory of their future development. By adopting mechanistic explanations and by criticizing functionalism, we will reject the objections of explanatory inadequacy, by characterizing connectionist models as generic rather than concrete mechanisms, we will reject the objections of biological implausibility, and by attributing the exploratory character to connectionist models we will show that practice of generalizing current to general failures of connectionism is unjustified. (shrink)

Objective. Study of the validity and reliability of the discourse approach for the psycholinguistic understanding of the nature, structure, and features of the linguistic consciousness functioning. -/- Materials & Methods. This paper analyzes artificial neural network models built on the corpus of texts, which were obtained in the process of experimental research of the coronavirus quarantine concept as a new category of linguistic consciousness. The methodology of feedforward artificial neural networks (multilayer perceptron) was used in order to assess the (...) possibility of predicting the leading texts semantics based on the discourses ranks and their place in the respective linear sequence. Same baseline parameters were used to predict respondents' self-assessments of changes in their psychological well-being and in daily life routine during the quarantine, as well as to predict their preferences of the quarantine strategies. The study relied on basic ideas about discourse as a meaning constituted by the dispersion of other meanings (Foucault). The same dispersion mechanism realizes itself in interdiscourse interaction, forming a discursive formation at a higher level. The method of T-units (Hunt) was used to identify and count discourses in the texts. The ranking of discourses was provided based on the criterion of their semantic-syntactic autonomy. -/- Results. The conducted neural network modeling revealed a high accuracy in predicting the work of the linguistic consciousness functions associated with retrospective self-assessment and anticipatory imagination of the respondents. Another result of this modeling is a partial confirmation of the assumption concerning existence a relationship between the structural parameters of the discursive field (the rank of the discourses and their place in the respective linear sequence) and the leading semantics of the text. -/- Conclusions. A discourse approach to the study of linguistic consciousness, understanding of its structure and functioning features seems to be reasonably appropriate. The implementation of the approach presupposes the need to form a base of linguistic corpora with the inclusion in each text markup of such parameters as: the presence of specific discourses, their ranks, positions in the linear sequence of discourses. (shrink)

Coronary Heart Disease (CHD) is one of the leading causes of death nowadays. Prediction of the disease at an early stage is crucial for many health care providers to protect their patients and save lives and costly hospitalization resources. The use of machine learning in the prediction of serious disease events using routine medical records has been successful in recent years. In this paper, a comparative analysis of different machine learning techniques that can accurately predict the occurrence of CHD events (...) from clinical data was performed. Four machine learning classifiers, namely Logistic Regression, Support Vector Machine (SVM), K- Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) Neural Networks were identified and applied to a dataset of 462 medical instances and 9 features as well as the class feature from the South African Heart Disease data retrieved from the KEEL repository. The dataset consists of 302 records of healthy patients and 160 records of patients who suffer from CHD. In order to handle the imbalanced classification problem, the K-means algorithm along with Synthetic Minority Oversampling TEchnique (SMOTE) was used in this study. The empirical results of applying the four machine learning classifiers on the oversampled dataset have been very promising. The results reported using different evaluation metrics showed that SVM has achieved the highest overall prediction performance. (shrink)

Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. There are no obvious incidental effects during the starting periods of CKD, patients routinely disregard to see the sickness. Early disclosure of CKD enables patients to seek helpful treatment to improve the development of this disease. AI models can effectively assist clinical with achieving this objective on account of their fast and exact affirmation execution. In this appraisal, proposed a Logistic (...) relapse framework for diagnosing CKD. Proposed calculation like NAIVE BAYES , DECISION TREE , KSTAR , LOGISITIC , AND SVM and look at these calculation and get the most noteworthy precision .AI store, which has an enormous number of missing qualities. Missing characteristics are for the most part found, taking everything into account, clinical conditions since patients might miss a couple of assessments for various reasons. By separating the misjudgements delivered by the set up models and proposed a fused model that unites determined backslide and sporadic woods by using perceptron. (shrink)

Abstract: In this research, the Artificial Neural Network (ANN) model was developed and tested to predict the rate of treatment expenditure on an individual or family in a country. A number of factors have been identified that may affect treatment expenses. Factors such as age, grade level such as primary, preparatory, secondary or college, sex, size of disability, social status, and annual medical expenses in fixed dollars excluding dental and outpatient clinics among others, as input variables for the ANN model. (...) A model based on the multi-layer Perceptron topology was developed and trained using data on 5574 cases. The evaluation of the test data shows that the ANN model is capable of predicting correctly Medical Expenses. (shrink)

Odată cu creșterea volumului de date pe Internet, în media socială, cloud computing, dispozitive mobile și date guvernamentale, Big Data devine în același timp o amenințare și o oportunitate în ceea ce privește gestionarea și utilizarea acestor date, menținând în același timp drepturile persoanelor implicate. În fiecare zi, folosim și generăm tone de date, alimentând bazele de date ale agențiilor guvernamentale, companiilor private și chiar cetățenilor privați. Beneficiem în multe feluri de existența și utilizarea Big Data, dar trebuie să ne (...) amintim de asemenea că „nu există nimic gratuit”. Există riscuri în utilizarea Big Data, un fel de parte întunecată. Schimbările de piață pot fi obținute prin utilizarea Big Data: în timp ce acestea pot fi pozitive pentru cineva, ele pot dăuna altcuiva. Riscurile potențiale provin și de la agențiile publice. Este clar că datele mari pot aduce beneficii cetățenilor și companiilor, dar trebuie să fie echilibrate de o conștientizare sporită a „părții întunecate” a Big Data în exploatarea noilor capacități și oportunități pe care le oferă tuturor . -/- CUPRINS -/- 1. Big Data - Definiție - Caracteristici - Clasificarea datelor mari - Arhitectura - Tehnologii - Oportunități - Provocări - Critici - - Criticile paradigmei Big Data - - Criticile modelului "V" - - Criticile de noutate - - Criticile execuției Big Data - 1.1 Viața datelor mari - - Nașterea - - Copilăria - - - Educație - - - Divertisment - - Adolescența - - - Servicii online - - - Comunicații mobile - - - Servicii de streaming - - Adulți tineri - - - Căutarea de locuri de muncă - - - Servicii bancare și asigurări - - - Utilități - - - Dating online și pornografie - - Vârsta medie - - - Servicii de sănătate - - - Aplicații pentru îngrijire și aplicații pentru sănătate și fitness - - - Căutarea de locuri de muncă - - - Dating online - - Bătrânețea - - - Îngrijirea sănătății - - Moartea - 1.2 Aplicații - - Guvern - - Dezvoltare internațională - - Fabrici - - Sănătate - - Educaţie - - Mass-media - - Asigurare - - Internetul obiectelor (IoT) - - Tehnologia informației - 1.3 Studii de caz - - Guvern - - - China - - - India - - - Israel - - - Regatul Unit - - - Statele Unite ale Americii - - Cu amănuntul - - Ştiinţă - - Sport - - Tehnologie - - Activități de cercetare - - - Prelevarea de date mari - 1.4 Agenda europeană privind societatea digitală - - Cadrul juridic - - Peisajul opiniei părților interesate - 1.5 GDPR - - Etapele procesării datelor personale - - Principiile procesării datelor - - Politica de confidențialitate și transparența - - Scopurile procesării datelor - - Confidențialitate prin design și implicită - - - Paradoxul (legal) al Big Data - - Domeniul teritorial al GDPR - - Definirea datelor mari, a datelor personale și a datelor anonime - - Etapele procesării datelor cu caracter personal - - Principii de prelucrare a datelor - - Politica de confidențialitate și Transparența - - Scopuri de prelucrare a datelor - - Confidențialitate prin design și confidențialitate în mod implicit - - Paradoxul (legal) al datelor mari 2. Analiza Big Data - 2.1 Analitica - - Analitica vs. analiză - - Aplicații ale analiticii - - - Optimizarea marketingului - - - Analiza oamenilor - - - Analiza portofoliului - - - Analitica riscurilor - - - Analitica digitală - - - Analitica securității - - - Analitica software-ului - - Provocări - - Riscuri - 2.2 Analitica predictivă - - Definiție - - Procesul de analitica predictivă - - Tipuri - - - Modele predictive - - - Modele descriptive - - - Modele de decizie - - Aplicații - - - Managementul relațiilor cu clienții analitic (CRM) - - - Protecția copilului - - - Sisteme de suport decizional clinic - - - Analiticile colecțiilor - - - Vânzări încrucișate - - - Retenția clienților - - - Marketing direct - - - Detectarea fraudelor - - - Prezicerea rezultatelor deciziilor legale - - - Previziuni privind portofoliul, produsele sau economia - - - Managementul riscului de proiect - - - Acceptanța (Underwriting) - - Tehnologia și influențele big data - - Tehnici analitice - - - Tehnici de regresie - - - - Model de regresie liniară - - - - Modele de alegere discretă - - - - Regresie logistică - - - - Regresie logistică multinomială - - - - Regresie probit - - - - Logit versus probit - - - - Modele pentru serii de timp - - - - Analiza supraviețuirii sau a duratei - - - - Arborele de clasificare și regresie (CART) - - - - Funcții spline adaptive de regresie multivariate - - - Tehnici de învățare automată - - - - Rețele neuronale - - - - Perceptron multistrat (MLP) - - - - Funcții radiale de bază - - - - Mașini cu vectori de suport - - - - Bayesiene naive - - - - Vecinul k-cel mai apropiat - - - - Modelarea predictivă geospațială - - Instrumente - - - PMML - - Critici - 2.3 Analitica prescriptivă - - Istorie - 2.4 Studiul critic al datelor - - Perspective critice - - Abordare: Pragmatismul și etica discursului 3. Etica Big Data - Datele personale ale copiilor - Rolul instituțiilor - - State naționale - - Bănci - Articole relevante de știri despre etica datelor - Legislație relevantă privind etica datelor - 3.1 Principii ale eticii Big Data - - Etica în cercetare - - Conștientizarea - - Consimţământul - - Controlul - - Transparența - - Încrederea - - Proprietatea - - Supravegherea și securitatea - - Identitatea digitală - - Realitatea ajustată - - De-anonimizarea - - Inegalitatea digitală - - Confidențialitatea - - Financiar - - - Cât de mult merită datele? - - Deschidere - 3.2 Probleme etice în cercetare - - Etica cercetării pentru cercetarea socială - - Principalele aspecte etice ale cercetării sociale - - - Intimitate - - - Consimțământ informat - - - De-identificarea - - - Inegalitate – Diviziune digitală - - Integritatea cercetării - - Probleme emergente - - Aviz legal - 3.3 Aspecte filosofice - 3.4 10 reguli de bază în lucrul cu Big Data - - Concluzie Referințe Despre autor - Nicolae Sfetcu - - De același autor - - Contact Editura - MultiMedia Publishing . 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