For many employees, ‘work’ is no longer something performed while sitting at a computer in an office. Employees in a growing number of industries are expected to carry mobile devices and be available for work-related interactions even when beyond the workplace and outside of normal business hours. In this article it is argued that a future step will increasingly be to move work-related information and communication technology (ICT) inside the human body through the use of neuroprosthetics, to create employees who (...) are always ‘online’ and connected to their workplace’s digital ecosystems. At present, neural implants are used primarily to restore abilities lost through injury or illness, however their use for augmentative purposes is expected to grow, resulting in populations of human beings who possess technologically altered capacities for perception, memory, imagination, and the manipulation of physical environments and virtual cyberspace. Such workers may exchange thoughts and share knowledge within posthuman cybernetic networks that are inaccessible to unaugmented human beings. Scholars note that despite their potential benefits, such neuroprosthetic devices may create numerous problems for their users, including a sense of alienation, the threat of computer viruses and hacking, financial burdens, and legal questions surrounding ownership of intellectual property produced while using such implants. Moreover, different populations of human beings may eventually come to occupy irreconcilable digital ecosystems as some persons embrace neuroprosthetic technology, others feel coerced into augmenting their brains to compete within the economy, others might reject such technology, and still others will simply be unable to afford it. In this text we propose a model for analyzing how particular neuroprosthetic devices will either facilitate human beings’ participation in new forms of socioeconomic interaction and digital workplace ecosystems – or undermine their mental and physical health, privacy, autonomy, and authenticity. We then show how such a model can be used to create device ontologies and typologies that help us classify and understand different kinds of advanced neuroprosthetic devices according to the impact that they will have on individual human beings. (shrink)

Recent developments in neuroscience create new opportunities for understanding the human brain. The power to do good, however, is also the power to harm, so scientific advances inevitably foster as many dystopian fears as utopian hopes. For instance, neuroscience lends itself to the fear that people will be forced to reveal thoughts and feelings which they would not have chosen to reveal, and of which they may be unaware. It also lends itself to the worry that people will be encouraged (...) to submit to medication or surgery which, even if otherwise beneficial, alters their brain in ways that undermine their identity and agency. As Kenneth Foster notes, neural implants can have surprising and unintended adverse effects, even when they help to mitigate the loss of bodily control associated with Parkinson’s disease, or help to provide hearing for children who would otherwise be profoundly deaf. While the risk of adverse outcomes are scarcely specific to neuroscience, he thinks that ‘These issues are perhaps more acute’ with the latter than with other medical interventions, ‘because they are intimately and fundamentally related to a person’s communication with the outside world’. [ 2006 196] -/- Neuroscience, like genomic science, then, is likely to create new ways of harming people. Many of these will involve violations of privacy. However, these are unlikely fundamentally to challenge the reasons to value privacy, or our ability to protect it in the foreseeable future. Rather, I would suggest, the major threat to privacy comes from the difficulty of determining its nature and value and when, if ever, efforts to protect it are justified. So I will start by examining some threats to privacy, and their implications for neuroscience, before turning to philosophical problems in understanding the nature and value of privacy, and the practical consequences of those philosophical difficulties. (shrink)

This chapter examines how advances in nanotechnology might impact criminal sentencing. While many scholars have considered the ethical implications of emerging technologies, such as nanotechnology, few have considered their potential impact on crucial institutions such as our criminal justice system. Specifically, I will discuss the implications of two types of technological advances for criminal sentencing: advanced tracking devices enabled by nanotechnology, and nano-neuroscience, including neural implants. The key justifications for criminal punishment- including incapacitation, deterrence, rehabilitation, and retribution – (...) apply very differently to criminal sentences using these emerging technologies than they do to imprisonment. Further, use of these technologies would represent a shift away from retribution as the primary justification for criminal punishment. In addition, the possibility of nano-neural implants entails a new model of rehabilitation: namely, involuntary rehabilitation aimed at changing an offender’s character, rather than his environment. (shrink)

Deep Brain Stimulation is currently being investigated as an experimental treatment for patients suffering from treatment-refractory AN, with an increasing number of case reports and small-scale trials published. Although still at an exploratory and experimental stage, initial results have been promising. Despite the risks associated with an invasive neurosurgical procedure and the long-term implantation of a foreign body, DBS has a number of advantageous features for patients with SE-AN. Stimulation can be fine-tuned to the specific needs of the particular patient, (...) is relatively reversible, and the technique also allows for the crucial issue of investigating and comparing the effects of different neural targets. However, at a time when DBS is emerging as a promising investigational treatment modality for AN, lesioning procedures in psychiatry are having a renaissance. Of concern it has been argued that the two kinds of interventions should instead be understood as rivaling, yet “mutually enriching paradigms” despite the fact that lesioning the brain is irreversible and there is no evidence base for an effective target in AN. We argue that lesioning procedures in AN are unethical at this stage of knowledge and seriously problematic for this patient group, for whom self-control is particularly central to wellbeing. They pose a greater risk of major harms that cannot justify ethical equipoise, despite the apparent superiority in reduced short term surgical harms and lower cost. (shrink)

This paper addresses concerns raised recently by Datteri (Biol Philos 24:301–324, 2009) and Craver (Philos Sci 77(5):840–851, 2010) about the use of brain-extending prosthetics in experimental neuroscience. Since the operation of the implant induces plastic changes in neural circuits, it is reasonable to worry that operational knowledge of the hybrid system will not be an accurate basis for generalisation when modelling the unextended brain. I argue, however, that Datteri’s no-plasticity constraint unwittingly rules out numerous experimental paradigms in behavioural and (...) systems neuroscience which also elicit neural plasticity. Furthermore, I propose that Datteri and Craver’s arguments concerning the limitations of prosthetic modelling in basic neuroscience, as opposed to neuroengineering, rests on too narrow a view of the ways models in neuroscience should be evaluated, and that a more pluralist approach is needed. I distinguish organisational validity of models from mechanistic validity. I argue that while prosthetic models may be deficient in the latter of these explanatory virtues because of neuroplasticity, they excel in the former since organisational validity tracks the extent to which a model captures coding principles that are invariant with plasticity. Changing the brain, I conclude, is one viable route towards explaining the brain. (shrink)

Cyborgs are ongoing becomings of a doubly “in-between” temporality of humans and machines. Materially made from components of both sorts of beings, cyborgs gain increasing function through an interweaving in which each alters the other, from the level of “neural plasticity” to software updates to emotional breakthroughs of which both are a part. One sort of temporal in-between is of the progressive unfolding of a deepening becoming as “not-one-not-two” and the other is a “doubling back” of time into itself (...) in which moments that were once disparate are conjoined or enjambed. Tracing the experience of Michael Chorost during a four year period of coming to terms with his cochlear implant, related in Rebuilt: How Becoming Part Computer Made Me More Human, the essay pinpoints shifts in awareness, perceptual belief, and being-with others that unfold within the in-between of person and machine. (shrink)

In this chapter, we discuss a selection of current views of the neural correlates of consciousness (NCC). We focus on the different predictions they make, in particular with respect to the role of prefrontal cortex (PFC) during visual experiences, which is an area of critical interest and some source of contention. Our discussion of these views focuses on the level of functional anatomy, rather than at the neuronal circuitry level. We take this approach because we currently understand more about (...) experimental evidence at this coarse level and because these results are appropriate for arbitrating between current theoretical frameworks. We discuss the Two-Visual-Systems Hypothesis (Milner & Goodale 1995; 2006), Local Recurrency (Lamme 2010; Lamme 2006), Higher Order (Lau 2008; Lau & Rosenthal 2011) and Global Workspace theories (Baars 1997; Baars 2005; Dehaene & Naccache 2001; Dehaene 2014). Despite the apparent stark differences between conscious and unconscious perceptual processing, available evidence suggests that their neural substrates must be largely shared. This indicates that the difference in neural activity between conscious and unconscious perceptual processing is likely to be subtle and highly specialized. We argue that current experimental evidence about the involvement of specific activity in prefrontal cortex supports the higher order neural theory of consciousness. In consequence, imaging techniques that focus only on marked differences between conscious and unconscious level of activity are likely to be insensitive to the relevant neural activity patterns that underlie conscious experiences. Finally, it follows from the evidence we discuss that the functional advantages of conscious over unconscious perceptual processing may be more limited than commonly thought. (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)

To find the neural substrates of consciousness, researchers compare subjects’ neural activity when they are aware of stimuli against neural activity when they are not aware. Ideally, to guarantee that the neural substrates of consciousness—and nothing but the neural substrates of consciousness—are isolated, the only difference between these two contrast conditions should be conscious awareness. Nevertheless, in practice, it is quite challenging to eliminate confounds and irrelevant differences between conscious and unconscious conditions. In particular, there (...) is an often-neglected confound that is crucial to eliminate from neuroimaging studies: task performance. Unless subjects’ task performance is matched (and hence perceptual signal processing is matched), researchers risk finding the neural correlates of perception, rather than conscious perception. Here, we discuss the theoretical motivations for the performance matching framework and review empirical demonstrations of, and theoretical inferences derived from, obtaining differences in consciousness while controlling for task performance. We summarize signal detection theoretic modeling frameworks that explain how it is that we can derive performance-matched differences in consciousness without the effect being trivially driven by differences in criterion setting, and also provide principles for designing experimental paradigms that yield performance-matched differences in awareness. Finally, we address potential technical and theoretical issues that stem from matching performance across conditions of awareness, and we introduce the notion of “triangulation” for designing comprehensive experimental sets that can better reveal the neural substrates of consciousness. (shrink)

This paper uses the fictional case of the ‘Babel fish’ to explore and illustrate the issues involved in the controversy about the use of cochlear implants in prelinguistically deaf children. Analysis of this controversy suggests that the development of genetic tests for deafness poses a serious threat to the continued flourishing of Deaf culture. I argue that the relationships between Deaf and hearing cultures that are revealed and constructed in debates about genetic testing are themselves deserving of ethical evaluation. (...) Making good policy about genetic testing for deafness will require addressing questions in political philosophy and anthropology about the value of culture and also thinking hard about what sorts of experiences and achievements make a human life worthwhile. (shrink)

I introduce the implantation argument, a new argument for the existence of God. Spatiotemporal extensions believed to exist outside of the mind, composing an external physical reality, cannot be composed of either atomlessness, or of Democritean atoms, and therefore the inner experience of an external reality containing spatiotemporal extensions believed to exist outside of the mind does not represent the external reality, the mind is a mere cinematic-like mindscreen, implanted into the mind by a creator-God. It will be shown that (...) only a creator-God can be the implanting creator of the mindscreen simulation, and other simulation theories, such as Bostrom’s famous account, that do not involve a creator-God as the mindscreen simulation creator, involve a reification fallacy. (shrink)

Human neural organoid research is advancing rapidly. As Greely notes in the target article, this progress presents an “onrushing ethical dilemma.” We can’t rule out the possibility that suff...

Many human decisions and behaviors in daily life entail a cost-benefit analysis. From selecting what to eat for dinner to determining the career to pursue, we more or less assess the cost and benefit of each choice. Given the frequent occurrences of cost-benefit thinking in our minds, some intriguing questions arise: how do the cost-benefit thinking processes emerge? How does the brain function to generate such thoughts? Although these inquiries have yet to be thoroughly answered, scientists are adding new clues (...) to our knowledge, such as the neural representation of our subjective cost-benefit judgments. (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)

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)

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)

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)

One of the most important recent developments in the study of concepts has been the resurgence of interest in nativist accounts of the human conceptual system. However, many theorists suppose that a key feature of neural organization—the brain’s plasticity—undermines the nativist approach to concept acquisition. We argue that, on the contrary, not only does the brain’s plasticity fail to undermine concept nativism, but a detailed examination of the neurological evidence actually provides powerful support for concept nativism.

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)

The development of predictive brain implant (PBI) technology that is able to forecast specific neuronal events and advise and/or automatically administer appropriate therapy for diseases of the brain raises a number of ethical issues. Provided that this technology satisfies basic safety and functionality conditions, one of the most pressing questions to address is its relation to the autonomy of patients. As Frederic Gilbert in his article asks, if autonomy implies a certain idea of freedom, or self-government, how can an individual (...) be considered to decide freely if the implanted device stands at the inception of the causal chain producing his decisions? He claims that PBIs threaten persons’ autonomy by diminishing their post-operative experience of self-control. In this commentary, I wish to discuss this claim. Contrary to Gilbert, I will suggest that PBIs do not pose a significant threat to patient’s autonomy, as self-control, but rather to his/her sense of authenticity. My claim is that the language of authenticity, already introduced in the recent bioethical literature, may offer a better way to voice some of the concerns with PBIs that Gilbert recognized. (shrink)

It appears that consciousness science is progressing soundly, in particular in its search for the neural correlates of consciousness. There are two main approaches to this search, one is content-based (focusing on the contrast between conscious perception of, e.g., faces vs. houses), the other is state-based (focusing on overall conscious states, e.g., the contrast between dreamless sleep vs. the awake state). Methodological and conceptual considerations of a number of concrete studies show that both approaches are problematic: the content-based approach (...) seems to set aside crucial aspects of consciousness; and the state-based approach seems over-inclusive in a way that is hard to rectify without losing sight of the crucial conscious-unconscious contrast. Consequently, the search for the neural correlates of consciousness is in need of new experimental paradigms. (shrink)

Despite the ubiquity of knowledge attribution in human social cognition, its associated neural and cognitive mechanisms are poorly documented. A wealth of converging evidence in cognitive neuroscience has identified independent perspective-taking and inhibitory processes for belief attribution, but the extent to which these processes are shared by knowledge attribution isn't presently understood. Here, we present the findings of an EEG study designed to directly address this shortcoming. These findings suggest that belief attribution is not a component process in knowledge (...) attribution, contra a standard attitude taken by philosophers. Instead, observed differences in P3b amplitude indicate that knowledge attribution doesn't recruit the strong self-perspective inhibition characteristic of belief attribution. However, both belief and knowledge attribution were observed to display a late slow wave widely associated with mental state attribution, indicating that knowledge attribution also shares in more general processing of others' mental states. These results provide a new perspective both on how we think about knowledge attribution, as well as Theory of Mind processes generally. (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)

The nuclear engineer emerged as a new form of recognised technical professional between 1940 and the early 1960s as nuclear fission, the chain reaction and their applications were explored. The institutionalization of nuclear engineering—channelled into new national laboratories and corporate design offices during the decade after the war, and hurried into academic venues thereafter—proved unusually dependent on government definition and support. This paper contrasts the distinct histories of the new discipline in the USA and UK (and, more briefly, Canada). In (...) the segregated and influential environments of institutional laboratories and factories, historical actors such as physicist Walter Zinn in the USA and industrial chemist Christopher Hinton in the UK proved influential in shaping the roles and perceptions of nuclear specialists. More broadly, I argue that the State-managed implantation of the new subject within further and higher education curricula was shaped strongly by distinct political and economic contexts in which secrecy, postwar prestige and differing industrial cultures were decisive factors. (shrink)

Accurate assessment and comprehension of smoking behavior are pivotal for elucidating associated health risks and formulating effective public health strategies. In this study, we introduce an innovative approach to predict and analyze smoking prevalence using an artificial neural network (ANN) model. Leveraging a comprehensive dataset spanning multiple years and geographic regions, our model incorporates various features, including demographic data, economic indicators, and tobacco control policies. This research investigates smoking trends with a specific focus on gender-based analyses. These findings are (...) pivotal for enhancing public health initiatives aimed at mitigating tobacco use, assessing associated health risks, and mitigating smoking-related diseases. (shrink)

In liberal moral theory, interfering with someone’s deliberate engagement in a self-harming practice in order to promote their own good is often considered wrongfully paternalistic. But what if self-harming decisions are the product of an oppressive social context that imposes harmful norms on certain individuals, such as, arguably, in the case of cosmetic breast surgery? Clare Chambers suggests that such scenarios can mandate state interference in the form of prohibition. I argue that, unlike conventional measures, Chambers’ proposal recognises that harmful, (...) discriminatory norms entail a twofold collective moral obligation: to eliminate the harmful norm in the long run, but also to address unjust harm that is inflicted in the meantime. I show that these two obligations tend to pull in opposite directions, thus generating a serious tension in Chambers’ proposal which eventually leads to an undue compromising of the second obligation in favour of the first. Based on this discussion, I develop an alternative proposal which, instead of prohibiting breast implant surgery, offers compensation for the disadvantages suffered by individuals who decide not to have surgery. (shrink)

Contemplative scholarship has recently reoriented attention towards the neuroscientific study of the soteriological ambition of Buddhist practice, 'awakening'. This article evaluates the project of seeking neural correlates for awakening. Key definitional and operational issues are identified demonstrating that: the nature of awakening is highly contested both within and across Buddhist traditions; the meaning of awakening is both context- and concept-dependent; and awakening may be non-conceptual and ineffable. It is demonstrated that operationalized secular conceptions of awakening, divorced from soteriological and (...) cultural factors, have little relationship to traditional Buddhist construct(s) of awakening. This article identifies methodological issues for secular conceptions of awakening concerning introspection and neuroimaging yet demonstrates also the value of recent advancements in empirical first-person phenomenology for attenuating introspective bias. Overall, it is contended that significant problems arise when decontextualizing awakening and placing it within a scientific naturalistic framework. Careful attention to the definitional, operational, and methodological neuroscientific obstacles identified herein is required in the responsible approach to the investigation of awakening states. (shrink)

The centerpiece of the scientific study of consciousness is the search for the neural correlates of consciousness. Yet science is typically interested not only in discovering correlations, but also – and more deeply – in explaining them. When faced with a correlation between two phenomena in nature, we typically want to know why they correlate. The purpose of this chapter is twofold. The first half attempts to lay out the various possible explanations of the correlation between consciousness and its (...) neural correlate – to provide a sort of “menu” of options from which we probably would ultimately have to choose. The second half raises considerations suggesting that, under certain reasonable assumptions, the choice among these various options may be in principle underdetermined by the relevant scientific evidence. (shrink)

Atualmente, biotecnologias são empregadas na forma de protocolos a fim de sincronizar o crescimento folicular e a ovulação, bem como permitir que vacas atuem como receptoras de embriões visando melhorar o potencial genético do rebanho. O objetivo desse estudo foi avaliar o efeito da utilização de implantes intravaginais de progesterona de 1º, 2º e 3º uso no protocolo de transferência de embriões em tempo fixo (TETF) sobre a taxa de prenhez em receptoras girolandas. O experimento ocorreu em uma fazenda localizada (...) no município de São Sebastião do Maranhão (MG). Foram utilizadas 74 vacas submetidas aos seguintes protocolos: No dia 0 (D0) administrou-se 2 ml de 17β-Estradiol e progesterona e foi colocado o implante intravaginal de progesterona. Estes implantes foram colocados de forma aleatória, sendo de 1° uso (grupo 1; n=15), 2° (grupo 2; n=24) ou 3° (grupo 3; n=35).No D8, os implantes foram retirados e administrou-se 2 ml PGF2α; 1,5 ml de eCG e 1 ml de 17β-Estradiol e progesterona. A inovulação dos embriões in vitroo correu no D (16) para todas as vacas que apresentaram um corpo lúteo. As variáveis qualitativas (prenhe ou não prenhe) foram comparadas em tabelas de contingência e analisadas pelo teste de qui-quadrado a 5% de probabilidade. Neste estudo não houve diferença estatística entre os grupos protocolados em relação ao aproveitamento como receptoras: 1º uso (53,3%), 2º (79,2%) e 3º (68,65). Ademais, observou-se uma taxa de prenhez entre os grupos de 37,5% para o 1º uso, 42,1% para o 2º e 33,3% para o 3º (P>0,05). Dentre as vantagens ao reaproveitamento dos implantes multiusos destaca-se a redução dos custos dos protocolos hormonais, já que a progesterona é o hormônio mais oneroso, desta forma, a maximização do uso reduz o valor final do protocolo sem prejuízos a taxa de prenhez. (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)

We explore the contribution made by oscillatory, synchronous neural activity to representation in the brain. We closely examine six prominent examples of brain function in which neural oscillations play a central role, and identify two levels of involvement that these oscillations take in the emergence of representations: enabling (when oscillations help to establish a communication channel between sender and receiver, or are causally involved in triggering a representation) and properly representational (when oscillations are a constitutive part of the (...) representation). We show that even an idealized informational sender–receiver account of representation makes the representational status of oscillations a non-trivial matter, which depends on rather minute empirical details. (shrink)

The purpose of this paper is to address a well-known dilemma for physicalism. If mental properties are type identical to physical properties, then their causal efficacy is secure, but at the cost of ruling out mentality in creatures very different to ourselves. On the other hand, if mental properties are multiply realizable, then all kinds of creatures can instantiate them, but then they seem to be causally redundant. The causal exclusion problem depends on the widely held principle that realized properties (...) inherit their causal powers from their realizers. While this principle holds for functional realization, it fails on a broader notion of realization that permits the realization of complex qualitative properties such as spatial and temporal patterns. Such properties are best seen as dependent powerful qualities, which have their causal roles in virtue of being the qualities they are, and do not inherit powers from their realizers. Recent studies have identified one such property—neural synchrony—as a correlate of consciousness. If synchrony is also partially constitutive of consciousness, then phenomenal properties are both multiply realizable and causally novel. I outline a version of representationalism about consciousness on which this constitution claim holds. (shrink)

The higher-order thought (HOT) theory of consciousness is a reductive representational theory of consciousness which says that what makes a mental state conscious is that there is a suitable HOT directed at that mental state. Although it seems that any neural realization of the theory must be somewhat widely distributed in the brain, it remains unclear just how widely distributed it needs to be. In section I, I provide some background and define some key terms. In section II, I (...) argue against the view that HOT theory should treat first-order (i.e. world-directed) conscious states as requiring prefrontal cortical activity though it is reasonable to suppose that conscious states are realized in the brain. In section III, I then explore some of the key background metaphysical issues involved in understanding the nature of consciousness, such as the debate between realism and idealism as well as the prospects for solving the so-called “hard problem” of consciousness. Some of the differences in question often mirror the traditional differences between Western and Eastern perspectives on the nature of consciousness. Overall, I argue that some form of realism and physicalism is more plausible than the opposing views. I also argue that materialists (and especially HOT theorists) can offer plausible replies to the hard problem. (shrink)

In modern days, person-computer communication systems have gradually penetrated our lives. One of the crucial technologies in person-computer communication systems, Speech Emotion Recognition (SER) technology, permits machines to correctly recognize emotions and greater understand users' intent and human-computer interlinkage. The main objective of the SER is to improve the human-machine interface. It is also used to observe a person's psychological condition by lie detectors. Automatic Speech Emotion Recognition(SER) is vital in the person-computer interface, but SER has challenges for accurate recognition. (...) In this work to resolve the above problem, automatic Speech enhancement shows that deep learning techniques effectively eliminate background noise. Using Deep leaning models for four states were created: happy, sad, angry, and intoxicated. Recurrent Neural Network (RNN) algorithm used to reduce the possibility of over fitting by randomly omitting neurons in the hidden layers. The proposed RNN method could be implemented in personal assistant systems to give better and more appropriate state-based interactions between humans. In the simulation results shows Improving accuracy, Time complexity, Error rate is also reduced to using the proposed method. -/- . (shrink)

Susceptibility to the rubber hand illusion (RHI) varies. To date, however, there is no consensus explanation of this variability. Previous studies, focused on the role of multisensory integration, have searched for neural correlates of the illusion. But those studies have failed to identify a sufficient set of functionally specific neural correlates. Because some evidence suggests that frontal α power is one means of tracking neural instantiations of self, we hypothesized that the higher the frontal α power during (...) the eyes-closed resting state, the more stable the self. As a corollary, we infer that the more stable the self, the less susceptible are participants to a blurring of boundaries—to feeling that the rubber hand belongs to them. Indeed, we found that frontal α amplitude oscillations negatively correlate with susceptibility. Moreover, since lower frequencies often modulate higher frequencies, we explored the possibility that this might be the case for the RHI. Indeed, some evidence suggests that high frontal α power observed in low-RHI participants is modulated by δ frequency oscillations. We conclude that while neural correlates of multisensory integration might be necessary for the RHI, sufficient explanation involves variable intrinsic neural activity that modulates how the brain responds to incompatible sensory stimuli. (shrink)

It seems intuitively obvious that metameric matching of color samples entails a loss of information, for spectrophotometrically diverse materials appear the same. This intuition implicitly relies on a conception of the function of color vision and on a related conception of how color samples should be individuated. It assumes that the function of color vision is to distinguish among spectral energy distributions, and that color samples should be individuated by their physical properties. I challenge these assumptions by articulating a different (...) conception of the function of color vision, according to which color vision serves to partition object surfaces into discrimination classes. (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)

Researchers in the cognitive sciences often seek neural correlates of psychological constructs. In this paper, I argue that even when these correlates are discovered, they do not always lead to reductive outcomes. To this end, I examine the psychological construct of a critical period and briefly describe research identifying its neural correlates. Although the critical period is correlated with certain neural mechanisms, this does not imply that there is a reductionist relationship between this psychological construct and its (...) neural correlates. Instead, this case study suggests that there may be many-to-many psychological-neural mappings, not just one-to-one or even one-to-many relations between psychological kinds and types of neural mechanisms. (shrink)

Neural structural representations are cerebral map- or model-like structures that structurally resemble what they represent. These representations are absolutely central to the “cognitive neuroscience revolution”, as they are the only type of representation compatible with the revolutionaries’ mechanistic commitments. Crucially, however, these very same commitments entail that structural representations can be observed in the swirl of neuronal activity. Here, I argue that no structural representations have been observed being present in our neuronal activity, no matter the spatiotemporal scale of (...) observation. My argument begins by introducing the “cognitive neuroscience revolution” (Sect. 1 ) and sketching a prominent, widely adopted account of structural representations (Sect. 2 ). Then, I will consult various reports that describe our neuronal activity at various spatiotemporal scales, arguing that none of them reports the presence of structural representations (Sect. 3 ). After having deflected certain intuitive objections to my analysis (Sect. 4 ), I will conclude that, in the absence of neural structural representations, representationalism and mechanism can’t go together, and so the “cognitive neuroscience revolution” is forced to abandon one of its commitments (Sect. 5 ). (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)

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)

One of the most widely recognised intuitions about knowledge is that knowing precludes believing truly as a matter of luck. On Pritchard’s highly influential modal account of epistemic luck, luckily true beliefs are, roughly, those for which there are many close possible worlds in which the same belief formed in the same way is false. My aim is to introduce a new challenge to this account. Starting from the observation—as documented by a number of recent EEG studies—that our capacity to (...) detect visual stimuli fluctuates with the phase of our neural oscillations, I argue that there can be very close possible worlds in which an actual-world detectable stimulus is undetectable. However, this doesn’t diminish our willingness to attribute knowledge in the case that the stimulus is detectable, even when undetectability would result in the same belief formed in the same way being false. As I will argue at length, the modal account appears unable to accommodate this result. (shrink)

The so-called Baldwin Effect generally says how learning, as a form of ontogenetic adaptation, can influence the process of phylogenetic adaptation, or evolution. This idea has also been taken into computation in which evolution and learning are used as computational metaphors, including evolving neural networks. This paper presents a technique called evolving self-taught neural networks – neural networks that can teach themselves without external supervision or reward. The self-taught neural network is intrinsically motivated. Moreover, the self-taught (...) neural network is the product of the interplay between evolution and learning. We simulate a multi-agent system in which neural networks are used to control autonomous agents. These agents have to forage for resources and compete for their own survival. Experimental results show that the interaction between evolution and the ability to teach oneself in self-taught neural networks outperform evolution and self-teaching alone. More specifically, the emergence of an intelligent foraging strategy is also demonstrated through that interaction. Indications for future work on evolving neural networks are also presented. (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)

This study compares the neural substrate of moral decision making processes between Korean and American participants. By comparison with Americans, Korean participants showed increased activity in the right putamen associated with socio-intuitive processes and right superior frontal gyrus associated with cognitive control processes under a moral-personal condition, and in the right postcentral sulcus associated with mental calculation in familiar contexts under a moral-impersonal condition. On the other hand, American participants showed a significantly higher degree of activity in the bilateral (...) anterior cingulate cortex (ACC) associated with conflict resolution under the moral-personal condition, and in the right medial frontal gyrus (MFG) associated with simple cognitive branching in non-familiar contexts under the moral-impersonal condition when a more lenient threshold was applied, than Korean participants. These findings support the ideas of the interactions between the cultural background, education, and brain development, proposed in the field of cultural psychology and educational psychology. The study introduces educational implications relevant to moral psychologists and educators. (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)

We describe and try to motivate our project to build systems using both a knowledge based and a neural network approach. These two approaches are used at different stages in the solution of a problem, instead of using knowledge bases exclusively on some problems, and neural nets exclusively on others. The knowledge base (KB) is defined first in a declarative, symbolic language that is easy to use. It is then compiled into an efficient neural network (NN) representation, (...) run, and the results from run time and (eventually) from learning are decompiled to a symbolic description of the knowledge contained in the network. After inspecting this recovered knowledge, a designer would be able to modify the KB and go through the whole cycle of compiling, running, and decompiling again. The central question with which this project is concerned is, therefore, How do we go from a KB to an NN, and back again? We are investigating this question by building tools consisting of a repertoire of language/translation/network types, and trying them on problems in a variety of domains. (shrink)

Interventions that modify a person’s motivations through chemically or physically influencing the brain seem morally objectionable, at least when they are performed nonconsensually. This chapter raises a puzzle for attempts to explain their objectionability. It first seeks to show that the objectionability of such interventions must be explained at least in part by reference to the sort of mental interference that they involve. It then argues that it is difficult to furnish an explanation of this sort. The difficulty is that (...) these interventions seem no more objectionable, in terms of the kind of mental interference that they involve, than certain forms of environmental influence that many would regard as morally innocuous. The argument proceeds by comparing a particular neurointervention with a comparable environmental intervention. The author argues, first, that the two dominant explanations for the objectionability of the neurointervention apply equally to the environmental intervention, and second, that the descriptive difference between the environmental intervention and the neurointervention that most plausibly grounds the putative moral difference in fact fails to do so. The author concludes by presenting a trilemma that falls out of the argument. (shrink)

From our perspective, we believe that it is necessary to abandon the idea that all those who suffer from SI or SA are mentally ill, which causes them to be stigmatized and subjected to psychiatric treatment, when what they need is a different vision of life as well as adaptive strategies to employ a new philosophy. Public policies should consider implementing programs that reinforce cognitive flexibility in addition to character strengths.

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)