In this work, I discuss the role of Husserl’s phenomenology in Paolo Parrini’s positive philosophy. In the first section, I highlight the presence of both empiricist and constructivist elements in Parrini’s anti-foundationalist and anti-absolutist conception of knowledge. In the second section, I stress Parrini’s acknowledgement of the crucial role of phenomenology in investigating the empirical basis of knowledge, thanks to its analysis of the relationship between form and matter of cognition. In the third section, I point out some lines of (...) development of the phenomenological form of empirical realism as revealed in Parrini’s reflection, through a comparison of Husserl’s genetic phenomenology, Mary Hesse’s network model and the tradition of neutral monism. (shrink)

In this work, I discuss the role of Husserl’s phenomenology in Paolo Parrini’s philosophical view. In the first section, I highlight the presence of both empiricist and constructivist elements in Parrini’s anti-foundationalist and anti-absolutist conception of knowledge. In the second section, I stress Parrini’s acknowledgement of the crucial role of phenomenology in investigating the empirical basis of knowledge, thanks to its analysis of the relationship between form and matter of cognition. In the third section, I point at some lines of (...) development of phenomenology that are revealed by Parrini’s reflection, through the comparison between Husserl’s genetic phenomenology, Mary Hesse’s network model, and the tradition of neutral monism. (shrink)

The discovery of the role of models in science by Granger parallels the analogous discovery made by Mary Hesse and Marx Wartofsky. The role models are granted highlights the linguistic dimension of science, resulting in a 'softening' of Bachelard's rationalistic epistemology without lapsing into relativism. A 'linguistic' theory of metaphor, as contrasted with Bachelard's 'psychological' theory, is basic to Granger's account of models. A final paragraph discusses to what extent Granger's 'mature' theory of models would imply a revision of (...) his early economic methodology. (shrink)

Public health care interventions—regarding vaccination, obesity, and HIV, for example—standardly take the form of information dissemination across a community. But information networks can vary importantly between different ethnic communities, as can levels of trust in information from different sources. We use data from the Greater Pittsburgh Random Household Health Survey to construct models of information networks for White and Black communities--models which reflect the degree of information contact between individuals, with degrees of trust in information from various sources correlated with (...) positions in that social network. With simple assumptions regarding belief change and social reinforcement, we use those modeled networks to build dynamic agent-based models of how information can be expected to flow and how beliefs can be expected to change across each community. With contrasting information from governmental and religious sources, the results show importantly different dynamic patterns of belief polarization within the two communities. (shrink)

I analyse three most interesting and extensive approaches to theoretical models: classical ones—proposed by Peter Achinstein and Michael Redhead, and the rela-tively rareanalysed approach of Ryszard Wójcicki, belonging to a later phase of his research where he gave up applyingthe conceptual apparatus of logical semantics. I take into consideration the approaches to theoretical models in which they are qualified as models representing the reality. That is why I omit Max Black’s and Mary Hesse’s concepts of such models, as those (...) two concepts belong to the analogue model group if we consider the main function of the model of a given class as its classification criterion. My main focus is on theoretical models with representative functions as these very models and, in a broader context, the question of representation. (shrink)

I start with a reconstruction of the discussion on the nature of scientific models and on their relationship to metaphors that has taken place in the Anglo-Saxon philosophy of Science starting from the Fifties; the discovery started with Stephen Pepper and Kenneth Burke, reaching Thomas Kuhn, Marx Wartofsky, and George Lakoff via Max Black's and Mary Hesse's interaction view. I argue that Hesse's view has a number of weak points: uncritically accepting Black's idea of "interaction", for keeping (...) too much of a logical empiricist view of observation language, and for keeping too much of a Scholastic distinction between formal and material analogy. I argue that a few insights from Kuhn and Wartofsky seem to go in the same direction as Granger's view of metaphor as opposed to metonymy, thus severing the link between metaphor and the "qualitative". I argue that the Cartesian legacy and its implicit ideal of a "mirroring" relationship between the world and the mind is the main obstacle to be eventually overcome if we are to work out a satisfactory account of the language of science and that suggestions from pragmatism as well as from French epistemology may be used in order to work out a post-Cartesian view of science. -/- . (shrink)

The story of Miss Mary Mitchell Slessor is not a story of a clairvoyant legend who existed in an abstract world but a historical reality that worked around the then Old Calabar estuary and died on the 15th of January, 1915 at Ikot Oku Use, near Ikot Obong in the present day Akwa Ibom State and was buried at “Udi Mbakara” (Whiteman’s grave) in Calabar, Cross River State. Mary was one of those early missionaries that went to villages (...) in the then Old Calabar where few missionaries dared to go in order to bring hope and light to the people that were in darkness. Through her evangelistic efforts, schools and hospitals were erected on her initiative, babies and twins saved from death, barbaric rites and customs stopped because of her undaunted love and passion for God and the people. After a centenary of death, one can easily conclude that what immortalizes a person is not what he does for himself but what he does for others. Mary Slessor’s name, work and care for twins can never be forgotten even in another century to come. The tripartite purpose of this paper is to first examine the stepping out of Mary Slessor from her comfort zone to Calabar (her initial struggle), her passion for the people of Old Calabar and her relational method of evangelism that endeared her to the heart of the people. (shrink)

Deidealization as a topic in its own right has attracted remarkably little philosophical interest despite the extensive literature on idealization. One reason for this is the often implicit assumption that idealization and deidealization are, potentially at least, reversible processes. We question this assumption by analyzing the challenges of deidealization within a menu of four broad categories: deidealizing as recomposing, deidealizing as reformulating, deidealizing as concretizing, and deidealizing as situating. On closer inspection, models turn out much more inflexible than the reversal (...) thesis would have us believe, and deidealization emerges as a creative part of modeling. (shrink)

This short paper grew out of an observation—made in the course of a larger research project—of a surprising convergence between, on the one hand, certain themes in the work of Mary Hesse and Nelson Goodman in the 1950/60s and, on the other hand, recent work on the representational resources of science, in particular regarding model-based representation. The convergence between these more recent accounts of representation in science and the earlier proposals by Hesse and Goodman consists in the recognition (...) that, in order to secure successful representation in science, collective representational resources must be available. Such resources may take the form of (amongst others) mathematical formalisms, diagrammatic methods, notational rules, or—in the case of material models—conventions regarding the use and manipulation of the constituent parts. More often than not, an abstract characterization of such resources tells only half the story, as they are constituted equally by the pattern of (practical and theoretical) activities—such as instances of manipulation or inference—of the researchers who deploy them. In other words, representational resources need to be sustained by a social practice; this is what renders them collective representational resources in the first place. (shrink)

In this paper I argue that it is finally time to move beyond the Nagelian framework and to break new ground in thinking about epistemic reduction in biology. I will do so, not by simply repeating all the old objections that have been raised against Ernest Nagel’s classical model of theory reduction. Rather, I grant that a proponent of Nagel’s approach can handle several of these problems but that, nevertheless, Nagel’s general way of thinking about epistemic reduction in terms (...) of theories and their logical relations is entirely inadequate with respect to what is going on in actual biological research practice. (shrink)

Brain-related diseases are among the most difficult diseases due to their sensitivity, the difficulty of performing operations, and their high costs. In contrast, the operation is not necessary to succeed, as the results of the operation may be unsuccessful. One of the most common diseases that affect the brain is Alzheimer’s disease, which affects adults, a disease that leads to memory loss and forgetting information in varying degrees. According to the condition of each patient. For these reasons, it is important (...) to classify memory loss and to know the patient at what level and his assessment of Alzheimer's disease through CT scans of the brain. In this thesis, we review ways and techniques to use deep learning classification to classifying the Alzheimer's Disease The proposed method used to improve patient care, reduce costs, and allow fast and reliable analysis in large studies. The model will be designed using Python language for implementing the system, which is very useful for doctors, classifying the Alzheimer's Disease, was used. The model used 70% from image for training and 30% from image for validation, our trained model achieved an accuracy of 100% on a held-out test set. (shrink)

Recent work by Ian Aitken and others has sought to re-establish a "Realist approach" to the documentary film in reaction to the postmodernist, pragmatist approach popular in the 1970s and 80s. The Saussurian/Lacanian orientation o f the semiotics that played a large role in the older film theory is rejected and replaced by an analytic theory of representation based on the work of Mary Hesse, Hilary Putnam and W.V.O. Quine. Although this may seem a setback vis-a-vis semiotics, it actually (...) opens up Realist Film Theory to an application o f the doctrine of signs more closely aligned to traditional realism, that of Pierce and Poinsot. This presentation outlines how Realist Film Theory can be enriched and developed by such an application. In particular, Aitken's model for the processing of the truth-value communicated through a documentary film can be strengthened in this manner. We will look at a short filmic example to illustrate the resulting development of the theory, manifesting how the documentary film is anchored in both reliablyrepresenting reality and creatively organizing and construing it. (shrink)

In Wollstonecraft’s early writings, she articulates the foundational theological and philosophical principles that would underpin her work throughout her career. One difference between her early and later work lies in the way that the values to which she refers are combined. Whereas Wollstonecraft at first appeals to the separate ideals of independence, equality, and virtue, from the 1790s onwards she integrates these into a characteristic republican framework that was in common use amongst dissenting theorists at the time. The set of (...) values on which Wollstonecraft draws, and the version of the republican framework that she develops, closely resemble those of Richard Price with whose work she was familiar. Examining the structural similarities between their respective models, I argue, gives us an insight into the systematic manner of aspects of Wollstonecraft’s own thought. It also allows us to see how Wollstonecraft goes beyond the republicanism of Price and others as she develops her own feminist solutions to the problems of structural domination. (shrink)

Why interdisciplinary research in AI is so important, according to Jurassic Park. -/- “Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.” -/- I think this quote resonates with us now more than ever, especially in the world of technological development. The writers of Jurassic Park were years ahead of their time with this powerful quote. -/- As we build new technology, and we push on to see what can actually (...) be achieved there is an undertone of sales to whatever we build. The end product must be sold somewhere and to someone. This can derail any good intentions, just as building a resort full of dinosaurs was sold as a fun attraction, we see later in the film that it became a resort of terror. -/- In the field of AI we are certainly late to the party with ethics and regulation. Indeed, even existing modelling protocols have been, in many cases, circumvented or potentially ignored. This has led to a widening of the gap in the interdisciplinary field of AI. This is compounded by pop culture representations of AI and the Ethicist’s potential lack of knowledge surrounding technical progression in the field of AI. There are now seemingly two separate branches that ought to be in sync. The first branch is a group of Philosophers led by the Author of Superintelligence, Nick Bostrom, who believe that there is a singularity where AI takes over the world and starts to kill off humans. The second branch is the Technical Cohort who are led by companies such as Google and Deepmind. The remit of these developers is to see what can be developed and produced. Ultimately a separate sales team will determine what products can be sold. -/- We have seen multiple failures of AI due to lack of interdisciplinary discourse. Some cases include misallocated or cut off financial support and healthcare. This indicates the prevalence of issues that can grow and become ever more complex. -/- So, we have to ask ourselves two questions: One, do the Philosophers have a point? Two, does that point refer to humans programming AI in such a way that the destruction of the human race is its optimal aim in order to create a better future world? -/- Is the development of AI that different to an island of genetically engineered Dinosaurs? -/- The only solution, in my view, is interdisciplinary discourse. (shrink)

The relationship between topological explanation and mechanistic explanation is unclear. Most philosophers agree that at least some topological explanations are mechanistic explanations. The crucial question is how to make sense of this claim. Zednik (Philos Psychol 32(1):23–51, 2019) argues that topological explanations are mechanistic if they (i) describe mechanism sketches that (ii) pick out organizational properties of mechanisms. While we agree with Zednik’s conclusion, we critically discuss Zednik’s account and show that it fails as a general account of how and (...) when topological explanations are mechanistic. First, if topological explanations were just mechanism sketches, this implies that they could be enriched by replacing topological terms with mechanistic detail. This, however, conflicts how topological explanations are used in scientific practice. Second, Zednik’s account fails to show how topological properties can be organizational properties of mechanisms that have a place in mechanistic explanation. The core issue is that Zednik’s account ignores that topological properties often are _global_ properties while mechanistic explanantia refer to _local_ properties. We demonstrate how these problems can be solved by a recent account of mechanistic completeness (Craver and Kaplan in Br J Philos Sci 71(1):287–319, 2020; Kohár and Krickel in Calzavarini and Viola (eds) Neural mechanisms—new challenges in the philosophy of neuroscience, Springer, New York, 2021) and use a multilayer network model of Alzheimer’s Disease to illustrate this. (shrink)

An increasingly popular solution to the anti-scientific climate rising on social media platforms has been the appeal to more critical thinking from the user's side. In this paper, we zoom in on the ideal of critical thinking and unpack it in order to see, specifically, whether it can provide enough epistemic agency so that users endowed with it can break free from enclosed communities on social media (so called epistemic bubbles). We criticise some assumptions embedded in the ideal of critical (...) thinking online and, instead, we propose that a better way to understand the virtuous behaviour at hand is as critical engagement, namely a mutual cultivation of critical skills among the members of an epistemic bubble. This mutual cultivation allows members within an epistemic bubble (in contrast, as we will show, with the authority-based models of epistemic echo chambers) to become more autonomous critical thinkers by cultivating self-trust. We use the model of relational autonomy as well as resources from work on epistemic self-trust and epistemic interdependence to develop an explanatory framework, which in turn may ground rules for identifying and creating virtuous epistemic bubbles within the environments of social media platforms. (shrink)

In recent years, the prediction of air quality has become a critical task due to its significant impact on human health and the environment. With urbanization and industrial growth, the need for accurate air quality forecasting has become more urgent. Traditional methods for air quality prediction are often based on statistical models or physical simulations, which, while valuable, can struggle to capture the complexity of air pollution dynamics. This study explores the use of deep learning techniques to predict air quality, (...) providing a comparative analysis of different neural network models and their performance. (shrink)

This article presents a simple derivation of optimization models for reaction networks leading to a generalized form of the mass-action law, and compares the formal structure of Minimum Information Divergence, Quadratic Programming and Kirchhoff type network models. These optimization models are used in related articles to develop and illustrate the operation of ontology alignment algorithms and to discuss closely connected issues concerning the epistemological and statistical significance of sharp or precise hypotheses in empirical science.

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)

Water quality assessment is critical for environmental sustainability and public health. This research employs neural networks to predict water quality, utilizing a dataset of 21 diverse features, including metals, chemicals, and biological indicators. With 8000 samples, our neural network model, consisting of four layers, achieved an impressive 94.22% accuracy with an average error of 0.031. Feature importance analysis revealed arsenic, perchlorate, cadmium, and others as pivotal factors in water quality prediction. This study offers a valuable contribution to enhancing (...) water quality monitoring and decision-making for stakeholders and policymakers. (shrink)

Abstract Alzheimer's disease has spread insanely throughout the world. Early detection and intervention are essential to improve the chances of a positive outcome. This study presents a new method to predict a person's likelihood of developing Alzheimer's using a neural network model. The dataset includes 373 samples with 10 features, such as Group,M/F,Age,EDUC, SES,MMSE,CDR ,eTIV,nWBV,Oldpeak,ASF.. A four-layer neural network model (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 98.10% (...) and an average error of 0.01. This study provides a valuable tool for early detection and intervention of Alzheimer's, thus contributing to the field of health and medicine. (shrink)

Create a novel network model for mobile ad hoc network (MANET) nodes and actors in wireless sensor networks to collaborate on event processing. There are two stages in the development of distributed algorithms: setup and negotiation. The first uses weighted proportional max-min fairness to initially allocate MANET nodes across event zones, whereas the latter uses a market-based method to re-distribute the number of MANET nodes based on existing and new events. A detection technique for malicious packet dropping (...) attacks in MANETs. The mechanism of the suggested approach is Collaborative Convolutional Neural Network (CCNN), which is based on the reputation value computed for that node by its neighbours. A node's reputation value is determined by its network packet forwarding behaviour. The reputation information is collected, saved, and transferred between nodes before being calculated under various scenarios. A network simulator was used to test the proposed protocol. The simulation results demonstrate the effectiveness of its performance. Even in the presence of cryptographic procedures, our method incurs negligible network bandwidth and latency costs. Moreover, we demonstrate that the protection is still effective in the presence of misbehaving nodes and routing changes caused by mobility. While further research is needed to thoroughly evaluate our method, we feel that the concept of collaborative security in MANETs is a potential future area. (shrink)

This article explores André-Marie Ampère's autobiography in order to analyse the dynamics of science in early 19th century French institutions. According to recent works that have emphasised the value of biographies in the history of science, this study examines Ampère's public self-representation to show the cultural transformations of a life dedicated to science in post-revolutionary French society. With this aim, I have interpreted this manuscript as an outstanding example of the scientific rhetoric flourishing in early 19th century French Romanticism, which (...) celebrated the life and works of men of science by means of biographies. Following this approach, Ampère's account has been analysed in relation to certain commonplaces shared with other autobiographies of that time, such as his traumatic experience linked to the French Revolution. Finally, this article discusses Ampère's autobiography as revealing an emerging model of scientific personae, i.e. a new collective way of thinking, feeling and perceiving, which announced the category of the modern scientist. (shrink)

The role and importance of entrepreneur’s network for International New Ventures (INVs) are highlighted in much research. However, there is a lack of more profound studies on how different perspectives of network influence INVs. Therefore, this thesis aims to develop a deeper understanding of the multiple aspects of entrepreneurs’ networks involvement in INVs with regard to opportunity development process. Theoretical framework constitutes of three aspects of entrepreneur’s networks: type, strength and functions of relationships, put in the context of (...) entrepreneurial opportunity’s exploration and exploitation. The theoretical framework is empirically examined through two cases study of two prominent business models in Vietnam economy: State-owned company and Private-owned company. Lastly, empirical part validates and contributes to a better understanding of the framework, hence, answers the research question. It is found that entrepreneurs utilize social network as the most efficient means of rapid internationalization. While entrepreneurs’ weak relationships positively impact business opportunities exploration, strong relationships greatly affect business opportunities exploitation. This study also uncovers more clearly the functions of networks in various critical activities related to opportunity exploration and exploitation. (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)

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to (...) predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3%. (shrink)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary classification, (...) identifying the presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

The Eleusinian Mysteries are religious rituals that include rites of initiation, purification, and revelation. The high point of these Mysteries is the moment when a priest reveals the secret of the Mysteries to the newly initiated. Plato frequently uses language and motifs from the Mysteries in his dialogues, yet Plato scholars have not paid much attention to this usage, and those who have done so have not found much philosophical significance in it. I argue that in explaining his epistemology in (...) three middle and late period dialogues Plato consciously and systematically uses Eleusinian Mystery motifs to convey the idea of a unique kind of knowledge. This immediate, direct, and incorrigible knowledge bursts upon Plato's initiates after they undergo preparatory processes such as purification through elenchus. I examine the Eleusinian Mystery motifs that Plato employs in the Ladder of Eros at Symposium 209e--212a, in the middle books of the Republic, including the Myth of the Cave at Republic 509a--518d, and in the Myth of the Soul at Phaedrus 246a--253c, and I argue that Plato finds these Mystery elements useful for two reasons. First, in many cases before an individual can come to know a form he must go through certain conditioning and transformatory processes to prepare him for it, and motifs from the Mysteries help Plato to describe these processes. Second, knowledge of a form is different from other kinds of knowledge, and the motif of the visual revelation of the epopteia helps him to express the direct, unmediated contact that constitutes knowledge of a form. ;The structure of the dissertation is as follows. In chapter one I present background information concerning the stages and events of the Mysteries. I consider the purpose and effects of initiation into the Eleusinian Mysteries, paying special attention to the preparatory rituals. In chapter two I present a general discussion of certain aspects of Plato's epistemology and show how Plato employs the five Mystery motifs of progressing through a sequence of stages, purification, being led by a mystagogos, experiencing the epopteia, and achieving eudaimonia in order to explicate these aspects. In chapter three I consider the specifics of Plato's use of Mystery terminology in the Symposium. And finally, in an Appendix, I argue that Plato has a model of knowledge by acquaintance, since I assert in chapters two and three that Plato uses the Mystery theme of an epopteia to express features of this model. (shrink)

We discuss two models of virtue cultivation that are present throughout the Republic: the self-mastery model and the harmony model. Schultz (2013) discusses them at length in her recent book, Plato’s Socrates as Narrator: A Philosophical Muse. We bring this Socratic distinction into conversation with two modes of intentional regulation strategies articulated by James J. Gross. These strategies are expressive suppression and cognitive reappraisal. We argue that that the Socratic distinction helps us see the value in cognitive reappraisal (...) and that the contemporary neurological research supports the wide range of attitudes toward the value of emotional experience that mirror those found in the Republic. (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)

Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as Genetic Algorithms (...) (GA) or Particle Swarm Optimization (PSO)—to enhance the accuracy and response time of the detection process. The integration of optimization methods ensures that the model adapts to various driving conditions and individual differences, providing a more reliable and robust detection mechanism. Data from multiple sources, including camera feeds and wearable sensors, are used to train and validate the models, ensuring a comprehensive understanding of drowsiness indicators. (shrink)

Kidney stones pose a significant health concern, and early detection can lead to timely intervention and improved patient outcomes. This research endeavours to predict the presence of kidney stones based on urine analysis, utilizing a neural network model. A dataset of 552 urine specimens, comprising six essential physical characteristics (specific gravity, pH, osmolarity, conductivity, urea concentration, and calcium concentration), was collected and prepared. Our proposed neural network architecture, featuring three layers (input, hidden, output), was trained and validated, (...) achieving an impressive accuracy of 98.67% and an average error of 0.012. In addition to model performance, feature importance analysis was conducted to determine the most influential factors in predicting kidney stone presence. The findings underscore the significance of urea concentration, specific gravity, calcium concentration, conductivity, osmolarity, and pH as key indicators. This research contributes to the early diagnosis of kidney stones and demonstrates the potential of neural networks in medical diagnostics. The clinical implications of these findings are discussed, emphasizing the importance of timely intervention in managing kidney stone-related health issues. (shrink)

heart attack analysis & prediction dataset is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 304 samples with 11 features, such as age, sex, chest pain type, Trtbps, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, (...) and HeartDisease. A neural network model with four layers (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 88% and an average error of 0078296. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

The Complex Emergent Model of Language Acquisition (CEMLA) offers a new perspective on how humans acquire language, drawing on principles from complexity theory to explain this dynamic, adaptive process. Moving beyond linear and reductionist models, CEMLA views language acquisition as a system of interconnected nodes, feedback loops, and emergent patterns, operating at the edge of chaos. This framework captures the fluidity and adaptivity of language learning, highlighting how understanding and fluency arise through self-organisation, phase transitions, and interaction with diverse (...) linguistic input. By embracing the complexity of language acquisition, CEMLA provides a comprehensive model that reflects the true nature of human communication. (shrink)

Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, History_score, score, and Risk. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model (...) was trained and validated, achieving an impressive accuracy of 100% and an average error of 0.000015, indicating its robust predictive capability. Moreover, we conducted feature importance analysis to identify the most influential features for predicting audit risk. The key features found to be critical for classifying fraudulent activities in audit risk prediction are Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, and History_score. This research contributes to the field of audit risk prediction by demonstrating the effectiveness of a neural network-based approach and highlighting the importance of specific features in detecting fraudulent activities. The findings have significant implications for auditors and organizations seeking to enhance their audit risk assessment processes, ultimately leading to improved financial transparency and fraud detection. (shrink)

Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, consisting of (...) three layers (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (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)

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)

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)

Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies that provide (...) personalized feedback and interventions based on detected emotional states. Our approach surpasses traditional machine learning methods, demonstrating superior performance in real-time applications. We also explore the ethical implications and challenges associated with deploying such systems, particularly regarding privacy concerns and the potential for misuse. Through extensive experiments, our model achieved an average accuracy rate of 92%, highlighting its robustness across different environments and user demographics. This research not only contributes to the growing field of affective computing but also lays the groundwork for future developments in emotionally intelligent systems. (shrink)

Papaya is a tropical fruit with a green cover, yellow pulp, and a taste between mango and cantaloupe, having commercial importance because of its high nutritive and medicinal value. The process of sorting papaya fruit based on maturely is one of the processes that greatly determine the mature of papaya fruit that will be sold to consumers. The manual grading of papaya fruit based on human visual perception is time-consuming and destructive. The objective of this paper is to the status (...) classification of papaya fruits if it's mature or partially matured or unmatured. A deep learning technique that was extensively applied to image recognition was used. The trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. Classification model of VGG16 achieved a 100% accuracy and 112 seconds of training time. (shrink)

In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases (...) in hospitals. The evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the birth weight with 100% accuracy. (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)

Web page phishing is a type of phishing attack that targets websites. In a web page phishing attack, the attacker creates a fake website that looks like a legitimate website, such as a bank or credit card company website. The attacker then sends a fraudulent message to the victim, which contains a link to the fake website. When the victim clicks on the link, they are taken to the fake website and tricked into entering their personal information.Web page phishing attacks (...) are a serious threat to online security. They can be very effective, as they often look very convincing. To protect yourself from web page phishing attacks, you should be suspicious of any emails or messages that ask for your personal information. Do not click on links in emails or messages from unknown senders. If you are unsure whether a website is legitimate, do not enter your personal information. Therefore, in this study, we present a novel approach to detect whether a web page is phishing or legitimate using a neural network model. (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: Pistachio nuts are a valuable source of nutrition and are widely cultivated for commercial purposes. The accurate classification of different pistachio varieties is important for quality control and market analysis. In this study, we propose a new model for the classification of different pistachio varieties using Convolutional Neural Networks (CNNs). We collected a dataset of pistachio images form Kaggle depository with two varieties (Kirmizi and Siirt). The images were then preprocessed and used to train a CNN model (...) based on VGG16. We evaluated the performance of the VGG16 on a held-out test set. Our results show that the CNN-based model VGG16 performed very well in terms of accuracy and efficiency. The VGG16 model was able to accurately classify different pistachio varieties with an accuracy of over 99.91%. Additionally, the VGG16 was able to generalize well to new and unseen data, demonstrating its ability to handle variability in the pistachio images. This study highlights the potential of CNNs for the classification of different pistachio varieties, and has important implications for the pistachio industry. The results suggest that this approach can be used to improve the quality control process and enhance market analysis. (shrink)

Abstract: This research delves into the utilization of Artificial Neural Networks (ANNs) as a powerful tool for predicting the overall ratings of books by leveraging a diverse set of attributes. To achieve this, we employ a comprehensive dataset sourced from Goodreads, enabling us to thoroughly examine the intricate connections between the different attributes of books and the ratings they receive from readers. In our investigation, we meticulously scrutinize how attributes such as genre, author, page count, publication year, and reader reviews (...) influence a book's overall rating. Through rigorous analysis and experimentation, we construct an advanced ANN model tailored for predictive analysis in the realm of book ratings. The outcomes of our study reveal the remarkable potential of ANNs in this domain. The ANN model exhibits an impressive level of accuracy when it comes to forecasting book ratings, underlining the efficacy and promise of artificial neural networks in enhancing our understanding and prediction of book evaluations. This research opens up new avenues for leveraging machine learning techniques to gain deeper insights into the dynamics of book ratings and reader preferences. (shrink)

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)

Heart failure is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 918 samples with 11 features, such as age, sex, chest pain type, resting blood pressure, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and HeartDisease. (...) A neural network model with four layers (1 input and 1 output) was trained on the dataset and achieved an accuracy of 90% and an average error of 0.009. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)