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)

Throughout this research, imposing the training of an Artificial Neural Network (ANN) to play tic-tac-toe bored game, by training the ANN to play the tic-tac-toe logic using the set of mathematical combination of the sequences that could be played by the system and using both the Gradient Descent Algorithm explicitly and the Elimination theory rules implicitly. And so on the system should be able to produce imunate amalgamations to solve every state within the game course to make better of results (...) of winnings or getting draw. (shrink)

In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is “survey lung (...) cancer”. Model evaluation showed that the ANN model is able to detect the absence or presence of lung cancer with 96.67 % accuracy. (shrink)

Abstract: This research employs a Random Forest classification model to predict and assess obesity and cardiovascular disease (CVD) risk based on a comprehensive dataset collected from individuals in Mexico, Peru, and Colombia. The dataset comprises 17 attributes, including information on eating habits, physical condition, gender, age, height, and weight. The study focuses on classifying individuals into different health risk categories using machine learning algorithms. Our Random Forest model achieved remarkable performance with an accuracy, F1-score, recall, and precision all reaching 97.23%. (...) The model's success is attributed to its ability to effectively leverage attributes related to eating habits, physical condition, and demographic factors. Feature importance analysis reveals key factors contributing to accurate predictions. This paper contributes to the field of health analytics by demonstrating the effectiveness of Random Forest in predicting and categorizing health risks. The insights gained from this research have the potential to inform personalized health interventions and contribute to the advancement of precision health strategies. Furthermore, the study highlights the importance of leveraging machine learning techniques for health risk assessment in diverse populations. (shrink)

Abstract: This research paper explores the transformative impact of artificial intelligence (AI) on digital media, examining both the opportunities it presents and the challenges it poses. The integration of AI into digital media has revolutionized content creation, distribution, and analytics, offering unprecedented levels of personalization, efficiency, and insight. Automated journalism, AI- driven recommendation systems, and advanced audience analytics are among the key areas where AI is making significant contributions. However, the adoption of AI also brings ethical considerations, including concerns about (...) bias, privacy, and transparency. This paper analyzes case studies of successful and unsuccessful AI implementations in digital media, highlighting lessons learned and best practices. Finally, it looks ahead to future trends and developments, providing a comprehensive overview of the current state and potential future of AI in digital media. Through this exploration, the paper aims to provide valuable insights for academics, industry professionals, and policymakers on harnessing the power of AI while addressing its challenges. (shrink)

Abstract Heart Attack is the Cardiovascular Disease (CVD) which causes the most deaths among CVDs. We collected a dataset from Kaggle website. In this paper, we propose an ANN model for the predicting whether a patient has a heart attack or not that. The dataset set consists of 9 features with 1000 samples. We split the dataset into training, validation, and testing. After training and validating the proposed model, we tested it with testing dataset. The proposed model reached an accuracy (...) of 98.01% on the Heart Disease Dataset. (shrink)

In this paper, a predictive artificial neural network (ANN) model was developed and validated for the purpose of prediction whether a watermelon is good or bad, the model was developed using JUSTNN software environment. Prediction is done based on some watermelon attributes that are chosen to be input data to the ANN. Attributes like color, density, sugar rate, and some others. The model went through multiple learning-validation cycles until the error is zero, so the model is 100% percent accurate for (...) the purpose of prediction whether good or bad the watermelon is. (shrink)

Abstract: As the integration of Artificial Intelligence (AI) continues to permeate various sectors, the legal domain stands on the cusp of a transformative era. This research paper delves into the multifaceted relationship between AI and the law, scrutinizing the profound implications and innovative applications that emerge at the intersection of these two realms. The study commences with an examination of the current landscape, assessing the challenges and opportunities that AI presents within legal frameworks. With an emphasis on efficiency, accuracy, and (...) accessibility, AI technologies are reshaping traditional legal processes, ranging from document analysis and contract review to predictive legal analytics. Furthermore, the paper scrutinizes the ethical considerations and potential biases inherent in AI algorithms, exploring the delicate balance between technological advancements and the preservation of legal principles such as fairness, accountability, and transparency. The research also delves into the evolving role of legal professionals in navigating and overseeing AI applications, emphasizing the importance of responsible AI deployment. Drawing on case studies and real-world examples, this paper showcases instances where AI has already demonstrated its efficacy in legal contexts, highlighting successful implementations and identifying areas for improvement. The discussion extends to the evolving regulatory landscape, as legal systems grapple with the need to adapt and establish frameworks that ensure the responsible and ethical use of AI technologies. In conclusion, this research contributes to the growing discourse on the dynamic interplay between AI and the legal domain. By illuminating the potential benefits, ethical considerations, and regulatory challenges, it provides a comprehensive overview for legal practitioners, policymakers, and technologists alike, fostering a nuanced understanding of the evolving landscape where artificial intelligence intersects with the law. (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

The scholarly search engine Google Scholar (G.S.) has problems that make it not a 100% trusted search engine. In this research, we discussed a few drawbacks that we noticed in Google Scholar, one of them is related to how does it perform (add articles) option for adding new articles that are related to the registered researchers. Our suggestion is an attempt for making G.S. more efficient by improving the searching method that it uses and finally having trusted statistical results.

The computer science (CS) graduated students suffered from documenting their projects and specially from coordinating it. In addition, students’ supervisors faced difficulties with guiding their students to an efficient process of documenting. In this paper, we will offer a suggestion as a solution to the mentioned problems; that is an application to make the process of documenting computer science (CS) student graduation project easy and time-cost efficient. This solution will decrease the possibility of human mistakes and reduce the effort of (...) documenting process. (shrink)

Abstract: This research explores the application of artificial neural networks (ANNs) in predicting cancer using a synthetically generated dataset designed for research purposes. The dataset comprises 10,000 pseudo-patient records, each characterized by gender, age, smoking history, fatigue, and allergy status, along with a binary indicator for the presence or absence of cancer. The 'Gender,' 'Smoking,' 'Fatigue,' and 'Allergy' attributes are binary, while 'Age' spans a range from 18 to 100 years. The study employs a three-layer ANN architecture to develop a (...) predictive model. The achieved accuracy of 97.24% and a low loss value of 0.02 indicate promising performance. Further analysis involves a comprehensive examination of the confusion matrix, precision, recall, and F1 score metrics, along with the ROC curve and AUC score, to provide a detailed understanding of the model's strengths and weaknesses. Additionally, considerations for cross-validation, hyperparameter tuning, and interpretability are discussed. It is emphasized that while the synthetic nature of the dataset allows for controlled experimentation, results should be cautiously interpreted and validated on authentic clinical data before extrapolation to real-world medical scenarios. This research serves as a valuable contribution to the exploration of predictive models for cancer detection, encouraging continued investigation and development in the field. (shrink)

Abstract: Online book review platforms generate vast user data, making accurate rating prediction crucial for personalized recommendations. This research explores neural networks as simple models for predicting book ratings without complex algorithms. Our novel approach uses neural networks to predict ratings solely from user-book interactions, eliminating manual feature engineering. The model processes data, learns patterns, and predicts ratings. We discuss data preprocessing, neural network design, and training techniques. Real-world data experiments show the model's effectiveness, surpassing traditional methods. This research can (...) enhance user experience, book catalog organization, and aid publishers, simplifying recommendation processes and providing tailored suggestions based on user preferences. (shrink)

Online education has become an essential component of education. Thus, several factors, such as the student’s learning strategy and motivation, generally contribute to their academic success. This study investigates the relationship between learning strategies, motivation, and online learning environment among 150 first-year college students. Employing correlational design, the statistical findings of the study reveal that the r coefficient of 0.59 indicates a moderate positive correlation between the variables. The p-value of 0.00, which is less than 0.05, leads to the decision (...) to reject the null hypothesis. Hence, a significant relationship exists between learning strategies, motivation, and the online learning environment. (shrink)

This paper offers an interpretation and discussion of the later Foucault’s multifaceted concept of ‘critique’. It argues that critique for Foucault is composed of three main elements: the ‘spirit’ (though not all of the substance) of Kant’s understanding of the Enlightenment; the practice of parrhesia that emerged in Ancient Greece and became central to Christian subjectivity; and the transfigurative aesthetic experience of modernity that was most richly depicted by Baudelaire. In the second section, there is a discussion of Foucault’s view (...) of an event that continues to perplex Western observers, the 1979 Iranian Revolution, juxtaposed with a Marxist understanding of the upheaval. Rejecting both historical materialist and liberal historiography, Foucault offers a unique perspective on the Iranian Revolution, deeming it to be a practical manifestation of critique in an ‘irreducibly’ religious context and based on a reformation of the self situated within a wider ‘political spirituality’. However, the trajectory of politics in Iran since 1979 bears resemblance to those of other, resolutely secular post-revolutionary societies, and thus raises the questions of whether Foucault ignores the universal in privileging the particular and in refusing synthesis between the West and the Orient, adopts an Orientalist epistemology. (shrink)

Rules are meant to apply equally to all within their jurisdiction. However, some rules are frequently broken without consequence for most. These rules are only occasionally enforced, often at the discretion of a third-party observer. We propose that these rules—whose violations are frequent, and enforcement is rare—constitute a unique subclass of explicitly codified rules, which we call ‘phantom rules’ (e.g., proscribing jaywalking). Their apparent punishability is ambiguous and particularly susceptible to third-party motives. Across six experiments, (N = 1440) we validated (...) the existence of phantom rules and found evidence for their motivated enforcement. First, people played a modified Dictator Game with a novel frequently broken and rarely enforced rule (i.e., a phantom rule). People enforced this rule more often when the “dictator” was selfish (vs. fair) even though the rule only proscribed fractional offers (not selfishness). Then we turned to third person judgments of the U.S. legal system. We found these violations are recognizable to participants as both illegal and commonplace (Experiment 2), differentiable from violations of prototypical laws (Experiments 3) and enforced in a motivated way (Experiments 4a and 4b). Phantom rule violations (but not prototypical legal violations) are seen as more justifiably punished when the rule violator has also violated a social norm (vs. rule violation alone)—unless the motivation to punish has been satiated (Experiment 5). Phantom rules are frequently broken, codified rules. Consequently, their apparent punishability is ambiguous, and their enforcement is particularly susceptible to third party motives. (shrink)

In this paper, we investigated multiple machine learning classifiers which are, Multinomial Naive Bayes, Support Vector Machine, Decision Tree, K Nearest Neighbors, and Random Forest in a text classification problem. The data we used contains real and fake job posts. We cleaned and pre-processed our data, then we applied TF-IDF for feature extraction. After we implemented the classifiers, we trained and evaluated them. Evaluation metrics used are precision, recall, f-measure, and accuracy. For each classifier, results were summarized and compared with (...) others. (shrink)

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the proposed model, (...) it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers. (shrink)

In this paper, an artificial neural network (ANN) model was developed and validated to predict whether a couple is going to get divorced or not. Prediction is done based on some questions that the couple answered, answers of those questions were used as the input to the ANN. The model went through multiple learning-validation cycles until it got 100% accuracy.

Abstract: The emergence of the novel coronavirus (COVID-19) in 2019 has presented the world with an unprecedented global health crisis. The rapid and widespread transmission of the virus has strained healthcare systems, disrupted economies, and challenged societies. In response to this monumental challenge, the intersection of technology and healthcare has become a focal point for innovation. This research endeavors to leverage the capabilities of Artificial Neural Networks (ANNs) to develop an advanced predictive model for forecasting the spread of COVID-19. It (...) involves the collection, analysis, and integration of diverse datasets encompassing epidemiological, clinical, and social factors that influence the virus's dissemination. (shrink)

In 2020, the European Union announced the award of the contract for the biometric part of the new database for border control, the Entry Exit System, to two companies: IDEMIA and Sopra Steria. Both companies had been previously involved in the development of databases for border and migration management. While there has been a growing amount of publicly available documents that show what kind of technologies are being implemented, for how much money, and by whom, there has been limited engagement (...) with digital methods in this field. Moreover, critical border and security scholarship has largely focused on qualitative and ethnographic methods. Building on a data feminist approach, we propose a transdisciplinary methodology that goes beyond binaries of qualitative/quantitative and opacity/transparency, examines power asymmetries and makes the labour of coding visible. Empirically, we build and analyse a dataset of the contracts awarded by two European Union agencies key to its border management policies – the European Agency for Large-Scale Information Systems (eu-LISA) and the European Border and Coast Guard Agency (Frontex). We supplement the digital analysis and visualisation of networks of companies with close reading of tender documents. In so doing, we show how a transdisciplinary methodology can be a device for making datafication ‘intelligible’ at the European Union borders. (shrink)

It is crucial to actively detect the risks of transactions in a financial company to improve customer experience and minimize financial loss. In this study, we compare different machine learning algorithms to effectively and efficiently predict the legitimacy of financial transactions. The algorithms used in this study were: MLP Repressor, Random Forest Classifier, Complement NB, MLP Classifier, Gaussian NB, Bernoulli NB, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Bagging Classifier, Decision Tree Classifier and Deep Learning. The dataset (...) was collected from Kaggle depository. It consists of 6362620 rows and 10 columns. The best classifier with unbalanced dataset was the Random Forest Classifier. The Accuracy 99.97%, precession 99.96%, Recall 99.97% and the F1-score 99.96%. However, the best classifier with balanced dataset was the Bagging Classifier. The Accuracy 99.96%, precession 99.95%, Recall 99.98% and the F1-score 99.96%. (shrink)

As one tries to grasp love and its images within José Leonilson's production, a multiplicity of aspects and meanings are seen that also relate to Louise Bourgeois's oeuvre in regard to the interest in human relations. Through a comparative approach to both artists' poetics, an understanding is created that love is not a simplistic action and all the words read in or applied to their visual discourse must be considered within a wide range of love in visual and literary images. (...) Keywords: literature and visual arts / love / creativity / Bourgeois, Louise / Leonilson, José / word and image. (shrink)

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a (...) dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%. (shrink)

This study aimed to determine the effectiveness of using Team Assisted Individualization (TAI) as cooperative learning teaching approach to improve students’ mathematical cognition. A quantitative research design via experimental design using one group pretest and post-test was employed. This only concentrated on specific students’ processing speed and working memory and included 67 grade 9 students from a public school in the Philippines. The findings showed that the components of TAI were moderately effective under the placement test, effective under the teams, (...) teaching group, student creative, team study, fact test and team score than whole class and highly effective under team recognition, Furthermore, there is a significant difference between the pre-test and post-test scores in each completed stage, and TAI approach is an effective strategy in improving the mathematical cognition of students. This study argues that TAI approach may be utilized in mathematics classrooms to improve the mathematical cognition of students. The gravity of the lessons or topics may be put into consideration to balance the learning and performances of students. (shrink)

This article explores the implicit theories of morality, or the conceptions regarding the patterns of stability, continuity and change in moral dispositions, both in lay and academic discourses. The controversies surrounding these conceptions and the fragmentation of the models and perspectives in metaethics and moral psychology endangers the pursuit of adequate operationalizations of morally relevant constructs. The current debate between situationists, who deny that character is an useful concept for understanding human behavior, which is better explained by contextual factors (Doris (...) 1998; Harman 1998) and dispositionists, who advocate the cross-situational stability of traits, is also present in the lay discourse, through the existence of competing commonsense ontological assumptions regarding the mutability or alterability of moral features, namely the implicit theories perspective (Chiu, Dweck, Tong, & Fu 1997). These personal theories are primary suspects in the affective and cognitive reactions to transgressions: the type of attended information in formulating evaluative judgments, the calibration of moral responsibility and blameworthiness, the assignment of retribution or reparatory recommendations to transgressors. In the second part of the study we attempt to advance toward a more fine-grained inspection of these lay beliefs, arguing that the construct of implicit theories of morality, as it is currently treated and measured, tends to be restrictive and oversimplifying. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City with 97.50 (...) % accuracy. The factor of DriveTrain has the most influence on MPG_City evaluation. Similar studies can be carried out for the evaluation of other characteristics of cars. (shrink)

As a type of evidence glass can be very useful contact trace material in a wide range of offences including burglaries and robberies, hit-and-run accidents, murders, assaults, ram-raids, criminal damage and thefts of and from motor vehicles. All of that offer the potential for glass fragments to be transferred from anything made of glass which breaks, to whoever or whatever was responsible. Variation in manufacture of glass allows considerable discrimination even with tiny fragments. In this study, we worked glass classification (...) and testing of artificial neural network model created by the JustNN. The aim of the study is help investigator in identifying the type of glass found in arena of the crime. The Neural Network model was trained and validated using the type of glass dataset. The accuracy of model in predicting the type of glass reached 96.7%. Thus neural network is suitable for predicating type of glasses. (shrink)

Estudo comparado sobre o tratamento dado à reprodução humana assistida no direito do Brasil, Estados Unidos, Portugal, Espanha e Itália.

The purpose of this paper is to contextualize the study of metaphors within constructivist-informed research, in the hope that this process will orient cognitive scientists to the usefulness of implementing qualitative research methodologies, especially to using the person of the researcher as the primary research instrument. First, I explore some of the differences between Johnson and Lakoff’s Contemporary Metaphor Theory (CMT) and approaches evolving from it on one hand, and the clinical approach to metaphor based on a constructivist therapy model, (...) on the other. CMT has been one of the most significant forces that helped shift cognitive science toward an embodied approach to cognition. While it has succeeded to place physical experience back where it belongs in reason and meaning, CMT has, however, also fallen into some positivist traps which lead to problems such as a dualism, a split between the knower and the known, and with that, to a distrust of introspective, first-person accounts. In the process of finding conceptual metaphors — generalizations that govern metaphorical expressions — CMT often deletes the idiosyncratic characteristics and presuppositions implicit in linguistic metaphors; it divorces them from people’s sensory experiences, the “here and now” and the intent of their communications. The constructivist approach to metaphor that I present here accepts as a priori assumptions much of what workers of CMT are out to prove. In particular, it takes the correlation of conceptual metaphors and physical experience, as well as the unity of language and thought as pragmatic givens. Emulating the constructivist therapist’s approach to metaphors, I show how it is possible to deconstruct conceptual metaphors into minute sensory distinctions, using one’s own person as the main tool, for the purpose of helping people change their experiences in desired ways, at will. I illustrate this process by numerous examples from a wide field of applications, including mathematics education and psychotherapy. (shrink)

El presente artículo analiza la asimilación en la obra de Tomás de Aquino de los principios fundamentales del necesitarismo físico aristotélico así como la introducción, desde el punto de vista de la cosmología cristiana, dos tipos de fenómenos ajenos a la filosofía de la naturaleza de Aristóteles: las operaciones ocultas de la naturaleza y los milagros. Se estudia la postura del Aquinate en torno al magnetismo, las mareas, las propiedades terapéuticas de los compuestos y el origen de los poderes de (...) las figuras nigrománticas y reliquias. -/- . (shrink)

Breast cancer remains a significant health concern worldwide, necessitating the development of effective diagnostic tools. In this study, we employ a neural network-based approach to analyze the Wisconsin Breast Cancer dataset, sourced from Kaggle, comprising 570 samples and 30 features. Our proposed model features six layers (1 input, 1 hidden, 1 output), and through rigorous training and validation, we achieve a remarkable accuracy rate of 99.57% and an average error of 0.000170 as shown in the image below. Furthermore, our investigation (...) identifies the most influential features in breast cancer diagnosis, shedding light on the key determinants of malignancy. Notably, we find that factors such as fractal dimension_se, symmetry worst, compactness_worst, symmetry_se, and smoothness_se play pivotal roles in distinguishing between benign and malignant cases. This research contributes to the ongoing efforts to enhance breast cancer diagnosis, providing valuable insights into feature importance and showcasing the potential of neural networks in medical applications. Our findings have implications for improving early detection and treatment strategies, ultimately contributing to improved patient outcomes. (shrink)

As ciências médicas, biomédicas, humanas e sociais têm avançado de forma frenética nos últimos anos. Com isso, se faz cada vez mais necessário o debate ético que visa contemplar o respeito à dignidade humana, animal e ao meio ambiente. A bioética se dedica a esse debate e se propõe a estendê-lo a setores não acadêmicos, como sociedades de proteção dos animais, dos direitos humanos e até grupos religiosos. Essa diversidade de opiniões não só é interessante, mas necessária, uma vez que (...) os valores praticados em uma sociedade provêm de uma construção social cujos blocos fundamentais têm origem na família e nos diversos grupos sociais nos quais os indivíduos estão inseridos desde o seu nascimento. A proposta deste livro é fomentar esse debate, fornecendo conceitos e visões sobre temas selecionados da pesquisa e da prática médica, biotecnológica e de áreas correlatas. Em um contexto mundial de intolerância política, social, étnica e de gênero, oportunidades de debate também possuem um papel social de auxiliar no desenvolvimento consciente da cidadania e do respeito ao próximo. Sobretudo, o debate nos faz enxergar o outro como parelho, ainda que cada um possua as suas especificidades, opiniões e histórias de vida. (shrink)

The structuralist reconstruction of the metabolic biochemistry here presented is a more complete and revised version than the one presented in Donolo, Federico & Lorenzano (2006). This version, as the previous one, continues with the reconstructive task initiated by César Lorenzano (2002), but advances further on those elements which remained pendent of reconstruction: applications subsequent to the paradigmatic one, for being these “too diversified and numerous” (p. 210).In line with which is said before, the objective of this new reconstruction is (...) to make the theoretical network of the biochemistry wider, in order to be able to capture the many successful applications (paradigmatic examples or exemplars) which appear in modern university textbooks. In order to accomplish this, major conceptual precisions are being introduced which will have repercussions in a modification and increased complexity of the fundamental law implicit in the text books, but still conserving the previous basic idea. Because of all this we can say that the present article goes further into the reconstruction task of the metabolic biochemistry theory. (shrink)

Nesta comunicação pretende-se desenvolver a relação entre violência e política enquadrada no pensamento de Hannah Arendt e a partir de duas obras fundamentais, On Revolution (1963) e On Violence (1970). Investigando-se sobre o que constitui cada experiência em particular, a da violência (ainda que sob a forma da guerra ou da revolução) e a da política, esta relação permitirá equacionar criticamente as possibilidades e os limites das sociedades democráticas actuais como o resultado da tradição política e das revoluções da modernidade.

This article analyzes the evolution of aesthetic role of the nature in the Middle Ages from the point of view of the philosophical systems influence on the interpretation of the corporal as a legitim way to the knowledge of the truth. it studies the intimate approach of neo-platonism, the shaping of its premises in the rejection of physical beauty and the change that occured after the assimilation of Aristotelianism toward a naturalistic outsourcing of the intellectual and artistic interests.

This article analyzes the treatment of natural philosophy in the work of Thomas Aquinas from the point of view of assimilation of the Aristotelian physical corpus. It focuses primarily on the Aquinas’s defense of the conception of the fallibility of the natural reason, the provisional and revisable character of all physical theories, the necessity of intercultural dialogue to discover the truths about nature, and Aquinas’s role in the development of the skeptical attitude in scientific research of the mobile’s world.

Giere’s analysis of the epistemic role of fiction in science and literature is the representative of antifictionists. Our research finds the three inconsistencies in his main paper regarding the comparison of fiction in scientific models and literary works. We analyze his argument and offer our solution to the issue favoring the perspective of fictionalism. Further, we support a typological differentiation of false representation in science into fictional and fictitious. The value of this differentiation we demonstrate by giving the example of (...) digital organisms in system biology. The paper aims to help better understanding of fiction in science and to avoid the oversimplification of literary fiction. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method is (...) suitable for the evaluation of car acceptability forecasting, can also be extended to all other areas. (shrink)

http://www.bibliotecadigital.unicamp.br/document/?code=vtls000363614.

En esta modernización y cambios permanentes, los museos necesitan aplicar calidad en su gestión incorporando criterios de control y evaluación para obtener los mejores resultados. El desarrollo del presente trabajo estará enfocado en el análisis de la gestión de calidad en las instituciones museísticas. Considerando que los modelos de calidad son un referente de la gestión, la investigación estará dirigida a la determinación de si es aplicable el modelo iberoamericano de gestión en los museos. Este modelo de excelencia en gestión (...) fue desarrollado a partir de la iniciativa de la Fundación Iberoamericana para la Gestión de la Calidad (Fundibeq) y reúne la opinión de países de América y Europa. Constituye una guía que posibilita a las organizaciones medir el grado de avance respecto a las variables que integran el modelo y, en ese marco, definir acciones que permitan cubrir la brecha entre el modelo y la realidad organizacional. La investigación se enfoca en analizar su aplicabilidad a los museos como unidades complejas, con misiones específicas, en proceso de importante transformación cualitativa y la medida que contribuye a enriquecer su gestión. Para ello se realizó en una primera instancia una revisión bibliográfica respecto de la concepción de museo, calidad y gestión organizacional. Posteriormente, se efectuó la investigación sobre la base de entrevistas a los directores de los museos seleccionados. Desde el punto de vista metodológico formal –para la elección de la muestra y siguiendo a Dillon – se usó un tipo de muestreo no probabilístico denominado muestra de criterio que consiste en la selección de determinados encuestados para que participen en el estudio. La selección busca satisfacer las necesidades concretas de la investigación para lo que se incluyeron en la muestra los principales museos montevideanos públicos y privados que representan 27% del total. El trabajo se divide en cuatro apartados. El primero muestra una revisión de la bibliografía al respecto de la gestión y su aplicabilidad en los museos y, en el segundo, se presentan distintos modelos de gestión de calidad haciendo hincapié en el modelo iberoamericano de gestión. En la tercera parte del trabajo se desarrolla la investigación empírica y en la cuarta se exponen las conclusiones de la investigación. (shrink)

Abstract: This research paper presents the development and evaluation of a neural network-based model for predicting fire alarms in smoke detection systems. Using a dataset from Kaggle containing 15 features and 3487 samples, we trained and validated a neural network with a three-layer architecture. The model achieved an accuracy of 100% and an average error of 0.0000003. Additionally, we identified the most influential features in predicting fire alarms.

In 1988, J. Ivlev proposed some (non-normal) modal systems which are semantically characterized by four-valued non-deterministic matrices in the sense of A. Avron and I. Lev. Swap structures are multialgebras (a.k.a. hyperalgebras) of a special kind, which were introduced in 2016 by W. Carnielli and M. Coniglio in order to give a non-deterministic semantical account for several paraconsistent logics known as logics of formal inconsistency, which are not algebraizable by means of the standard techniques. Each swap structure induces naturally a (...) non-deterministic matrix. The aim of this paper is to obtain a swap structures semantics for some Ivlev-like modal systems proposed in 2015 by M. Coniglio, L. Fariñas del Cerro and N. Peron. Completeness results will be stated by means of the notion of Lindenbaum–Tarski swap structures, which constitute a natural generalization to multialgebras of the concept of Lindenbaum–Tarski algebras. (shrink)

A continuing challenge for researchers and practitioners alike is the lack of data on the effectiveness of corporate–community investment programmes. The focus of this article is on the minerals industry, where companies currently face the challenge of matching corporate drivers for strategic partnership with community needs for programmes that contribute to local and regional sustainability. While many global mining companies advocate a strategic approach to partnerships, there is no evidence currently available that suggests companies are monitoring these partnerships to see (...) if they do, in fact, represent ‘strategic’ investments. This article argues that applying the management concept of ‘investment performance’ to corporate–community partnerships requires questioning traditional evaluation methods that focus on the results of programmes or activities. We adopt a case study approach to introduce an evaluation framework that considers performance from both corporate and community perspectives and that conceptualises partnership performance as comprising four aspects: (1) the contribution of the partnership to the overall portfolio of a company’s community investment programmes, (2) the appropriateness of the partnership model, (3) the effectiveness of the partnering relationship and (4) the ability of the partners to achieve programme goals. The application of this evaluation framework to an established corporate–community partnership programme provided some useful insights as to how partnership performance can be improved. (shrink)