CVDs continue to be the number one cause of death, therefore early detection and treatment is crucial. Currently, the application of Artificial Intelligence (AI) in the cardiac image analysis has become popular due to increased accuracy, productivity, and modelling. In this paper, the use of the AI system to study the echocardiogram, CT, MRI, and other images of the heart and blood vessels to view the risk factors for cardiovascular diseases is discussed. We focus on the current (...) methods of artificial intelligence, how it is implemented in clinical practice, problems, and prospects for AI development. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging marks a transformative advancement in healthcare, significantly enhancing diagnostic accuracy, efficiency, and patient outcomes. This paper delves into the application of AI technologies in medical image analysis, with a particular focus on techniques such as convolutional neural networks (CNNs) and deep learning models. We examine how these technologies are employed across various imaging modalities, including X-rays, MRIs, and CT scans, to improve disease detection, image segmentation, and diagnostic (...) support. Furthermore, the paper discusses the challenges associated with AI-driven medical imaging, such as data quality, model interpretability, and ethical considerations. By analyzing recent advancements and real-world case studies, this paper offers insights into the current landscape of AI in medical imaging and explores its potential future directions. The findings underscore the ongoing evolution of AI technologies and their pivotal role in advancing medical diagnostics and treatment strategies. (shrink)

Additionally, privacy and security concerns surrounding medical data are addressed by ensuring compliance with stringent data protection standards such as HIPAA and GDPR, thereby ensuring users that their medical information is handled securely and confidentially. The solution aims to improve healthcare accessibility, reduce delays in diagnosis, and provide users with greater control over their health management. This report outlines the chatbot’s design, the integration of image processing techniques, the use of machine learning models for disease identification, and the importance (...) of data privacy in healthcare applications. (shrink)

The ongoing COVID-19 pandemic has caused a global health crisis, and accurate diagnosis and early detection are essential for successful management of the outbreak. Convolutional neural networks and pre-processed chest X-ray pictures are the two main components of the unique proposed method for the identification of COVID-19, which is presented in this paper (CNNs). Image enhancement and segmentation are performed during the pre-processing stage. These operations increase the overall quality and contrast of the pictures, which in turn makes it (...) simpler for the CNN to recognise significant aspects of the images. The CNN model was trained using a large dataset of pre- processed X-ray pictures that included both COVID-19 positive and negative instances. The dataset was used to train the model. In comparison to more conventional diagnostic approaches, and this strategy was successful in achieving high levels of accuracy, sensitivity, and specificity in the detection of COVID-19. Moreover, this model designed an automated reporting system that saves time and costs by providing healthcare providers with diagnostic reports that are both prompt and accurate. This research demonstrates the viability of using CNNs and pre-processed X-ray images for the purpose of early identification of COVID-19 and offers an important resource for the efficient management of this worldwide health concern. (shrink)

Most research on image schema examined the meaning configuration of words connotation. However, previous studies of adjectives are meaningful in cognitive linguistics because they provide insight into how those adjectives are involved with psychological movement. In this sense, from the perspective of cognitive linguistics, one’s conceptualization and cognition are closely associated with their bodily experience and surroundings; adjectives are no exception. The varieties of transformations of image schemas lay the foundation for the conception and perception. Accordingly, this study (...) is an investigation into the perception of adjectives and psychological activities associated with image schema. Data in this study come from adjectives in the novel The Great Gatsby, which were tagged and calculated with the help of software Python and the NLTK package. The results demonstrate that a significant amount of adjectives were used in the novel. Moreover, Nick’s psychological activities changed significantly from the SOURCE to the DESTINATION schematic stage, which is uncovered by his expression of adjectives. Finally, the results further suggest that transformations of image schema as BALANCE, CONTAINER, and PATH work together to motivate one’s linguistic expression of adjectives. The present findings have several implications for research on lexicons and literacy interpretation from cognitive linguistics. (shrink)

In this paper I argue that radiological attempts to elucidate the properties of self -- an endeavor currently popular in the social neurosciences -- are fraught with conceptual difficulties. I first discuss several philosophical criteria that increase the chances we are posing the “right” questions to nature. I then discuss whether these criteria are met when empirical efforts are directed at one of the central constructs in the social sciences – the human self. In particular, I consider whether recent attempts (...) to map the neural correlates of self and its assumed properties using brain scanning technology satisfy the conceptual conditions minimally required to ask well-formed, theoretically satisfying questions of nature. I conclude that much theoretical work remains to be done. (shrink)

The increase in claim sophistication in both the insurance and legal domains is a result of an increase in stokes and heterogeneity of data needed to assess the claim validity. Originally, this task was performed by some sort of subjectivity assessments and graphical rule sets, which is very slow and may be inherently erroneous due to its purely manual nature. Hence, with progressivity in multimodal learning, specifically in AI, there is now a unique chance of solving these challenges through the (...) use of text data, which may include policies, reports and images, which may include accident images, evidence images, videos such as surveillance, cam videos among others. However, existing AI-based solutions usually address only one of the modalities, which makes it difficult to evaluate an integrated situation. This has led to the need for systems that will integrate information from all these modalities and come up with an accurate, efficient, and transparent processing system. Indeed, this paper seeks to discuss the use of Multimodal generative AI to address this need as one of the most recent approaches that rely on high-performing models that can process and integrate text, image, and video data. The proposed system combines these modalities to ensure that the system captures relevant data from each data type and combines all in a way that provides more comprehensive and enriched decision support. An initial system was designed and empirically tested against current claim adjudication techniques and was found to yield substantial enhancements in all utilization rates, throughput and main rationale for the claim decisions. The findings shown in the study stress the capability of multimodal generative AI for revolutionizing the present approaches of claims analysis and developing more efficient, accurate, and capable responses to various real-life conditions. This integration of technologies is an unprecedented advance towards advancing functional processes in the insurance and legal industries. (shrink)

Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology is a controlled structured vocabulary consisting of general terms (such as “cell” or “image” or “tissue” or “microscope”) that form the basis for such tagging. These terms are designed to represent the types of entities in the domain of reality that the ontology has been devised to capture; the terms are provided with logical defi nitions thereby also supporting reasoning over the tagged data. Aim: (...) This paper provides a survey of the biomedical imaging ontologies that have been developed thus far. It outlines the challenges, particularly faced by ontologies in the fields of histopathological imaging and image analysis, and suggests a strategy for addressing these challenges in the example domain of quantitative histopathology imaging. The ultimate goal is to support the multiscale understanding of disease that comes from using interoperable ontologies to integrate imaging data with clinical and genomics data. (shrink)

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

Students do not necessarily use the definitions presented to them when determining examples or non-examples of given mathematical ideas. Instead, they utilize the concept image they carry with them as a result of experiences with such examples and nonexamples. Hence, teachers should try exploring students‟ images of various mathematical concepts in order to improve communication between students and teachers. This suggestion can be addressed through error analysis. This study therefore is a descriptive-qualitative type that looked into the errors (...) committed by senior high school students in dealing with mathematical functions, explored the underlying reasons for such errors, and provided recommendations on how students could learn to manage their errors and how teachers could realize how to manage students‟ errors. Initial findings showed that the students generally demonstrated mathematical, logical and strategic errors. Mathematical error was generally exhibited in their inability to use properties and operations properly. Logical error was exhibited in their false arguments, rearranging concepts, improper classifications, arguing cyclically, and using equivalent transforms. Strategic error was manifested in their not being able to distinguish patterns, lack of integral concept, and not being able to transform a word problem into symbols. Errors prevailed despite the students‟ positive view and confidence in mathematics not only because of poor image concepts but because of lack of exposure to certain important mathematical tasks. Further investigation on the other sources of these errors is therefore necessary. (shrink)

Nowadays, many false images are spreading in digital media. The detection of these false images is inevitable for the unveiling of image-based cybercrimes. Forging images and identifying such images are promising research areas in this digital era. Altered images are detected using a neural network that also recognizes the regions of the image that have been manipulated and reveals the segments of the image. This original can be implemented on the Android platform and therefore made available to (...) ordinary users. The compression ratio of foreign content in a false image is different from that of the original image and is detected using an error level analysis. The another function used with compression ratio is image metadata. Although it is possible to modify the metadata content, which makes it unreliable, here it is used as a support parameter for an analysis of the decision error level. (shrink)

Computer vision, or image recognition, analyses and interprets visual data in real-world scenarios like images and videos. AI and ML research focusses on object, scene, action, and feature identification because of its usefulness in image processing. Neural networks and deep learning have improved image recognition systems significantly in recent years. Early image recognition used template matching to identify objects. A photo is compared to a stored template using similarity measures like correlation to get the best match. (...) There are several constraints, especially with distorted, scaled, or noisy images. Template matching is computationally expensive. CNNs are the most common deep learning architecture for computer vision. CNNs automatically learn visual input hierarchies. They include pooling, convolutional, and fully connected layers. Regional patterns like shapes, textures, and edges in the input image are filtered by convolutional layers. Pooling layers reduce feature map spatial dimensions, making feature extraction consistent and robust. Thick layers, or fully linked layers, classify or regress using learning features. CNN training requires a big labelled dataset. This research article introduces a deep learning model for the classification and recognition of images. The input data for this model is the Images Data Set. The GenNet Algorithm is employed to derive critical features. The accuracy of classification results is enhanced through the use of preprocessing and feature extraction. The classification model is generated using Convolution Neural Network, AlexNet, ResNet, and VGG. (shrink)

The Czech-Brazilian philosopher Vilém Flusser (1920–1991) has been recognized as a decisive past master in the emergence of contemporary media theory and media archeology. His work engages and also rethinks several mythologies of modernity, devising new methodologies, experimental literary practices, and expanded hermeneutics that trouble traditional practices of literary/literate knowledge, shared experience, reception, and communication. -/- Working within an expanded concept of modernism, Flusser presciently noted the power inherent in algorithmic information apparatuses to reshape our fundamental conceptions of culture and (...) history. In an increasingly technological world, Flusser's form of experimental theory-fiction pits philosophy against cybernetics as it forces the category of “the human” to confront the inhuman world of animals and machines. -/- The contributors to Understanding Flusser, Understanding Modernism engage with the multiplicity of Flusser's thought as they provide a general analysis of his work, engage in comparative readings with other philosophers, and offer expanded conceptualizations of modernism. The final section of the volume includes an extended glossary clarifying the playful terminology used by Flusser, which will be a valuable resource for experts and students alike. -/- . (shrink)

In the contemporary world, there is lot of research going on in creating novel nano materials that are essential for many industries including electronic chips and storage devices in cloud to mention few. At the same time, there is emergence of usage of machine learning (ML) for solving problems in different industries such as manufacturing, physics and chemical engineering. ML has potential to solve many real world problems with its ability to learn in either supervised or unsupervised means. It is (...) inferred from the state of the art that that it is essential to use ML methods for analysing imagery of nano materials so as to ascertain facts further towards characterization and analysis of microstructure and spectral data of materials. Towards this end, in this paper, we proposed a ML based methodology for STEM image analysis and spectral data analysis from STEM image of a nano material. We proposed an algorithm named Machine Learning for STEM Image Analysis (ML-SIA) for analysing STEM image of a nano material. We proposed another algorithm named Machine Learning for STEM Image Spectral Data Analysis (ML-SISDA) for analysing spectral data of STEM image of a nano material. We developed a prototype ML application to implement the algorithms and evaluate the proposed methodology. Experimental results revealed that the ML based approaches are useful for characterization of nano materials. Thus this research helps in taking this forward by triggering further work in the area of material analysis with artificial intelligence. (shrink)

Sut Jhally begins his essay “Advertising at the Edge of the Apocalypse” with the following provocative claim: “Advertising is the most powerful and sustained system of propaganda in human history and its cumulative effects, unless quickly checked, will be responsible for destroying the world as we know it.” Jhally argues that the advertising industry, in fostering an association between human aspiration and desire for consumable goods, creates an artificial demand for such goods that is, at once, far in excess of (...) what is needed for human flourishing and catastrophic to the global environment. Jhally sees capitalism as the ultimate culprit. In a system in which wealth is defined as a “vast collection of commodities” (Marx, Capital), those who produce such commodities have no other choice but to promote excess consumption. Hence the advertising industry emerges as the propaganda arm of capitalism. My paper argues that, though Jhally’s analysis of the problem is both compelling and alarming, his diagnosis fails to reach its heart. Indeed, it is not Marx but Plato who provides us with the terms needed both to understand and respond to the problem Jhally cites. My paper provides a Platonic analysis of these issues and concludes with some suggestions as to how to address them. (shrink)

Generative AI is reshaping sectors like banking and healthcare by enabling innovative applications such as personalized service offerings, predictive analytics, and automated content generation. In banking, generative AI drives customer engagement through tailored financial advice, fraud detection, and streamlined customer service. Meanwhile, in healthcare, it enhances medical imaging analysis, drug discovery, and patient diagnostics, significantly impacting patient care and operational efficiency. This paper presents an experimental study examining the implementation and effectiveness of generative AI in these sectors.

Optical Coherence Tomography (OCT) proves essential as an imaging modality for detecting early diseases especially by helping patients who age and face increased susceptibility to retinal and systemic conditions. The development of artificial intelligence technology now boosts OCT diagnostic features to identify conditions like diabetic retinopathy in addition to age-related macular degeneration and cardiovascular diseases at an early stage. This paper examines two main advancements in artificial intelligence for OCT imaging monitoring such as Google Health's Retinal Disease Predictor and (...) AI systems used to evaluate cardiovascular risks. This research develops HealthSight AI which combines deep learning algorithms with real-time predictive analytics to detect multiple diseases in healthcare. Medical studies demonstrate how AI-enhanced OCT technology can transform preventive healthcare delivery through its clinical implementations. The integration of AI in OCT imaging holds vast prospective advantages yet operational hurdles stem from ethical matters and system adherence needs together with healthcare structure implementation barriers. The findings emphasize the necessity to develop additional research together with collaboration so AI-powered OCT imaging can reach broad clinical implementation. (shrink)

Formal systems are standardly envisaged in terms of a grammar specifying well-formed formulae together with a set of axioms and rules. Derivations are ordered lists of formulae each of which is either an axiom or is generated from earlier items on the list by means of the rules of the system; the theorems of a formal system are simply those formulae for which there are derivations. Here we outline a set of alternative and explicitly visual ways of envisaging and analyzing (...) at least simple formal systems using fractal patterns of infinite depth. Progressively deeper dimensions of such a fractal can be used to map increasingly complex wffs or increasingly complex 'value spaces', with tautologies, contradictions, and various forms of contingency coded in terms of color. This and related approaches, it turns out, offer not only visually immediate and geometrically intriguing representations of formal systems as a whole but also promising formal links (1) between standard systems and classical patterns in fractal geometry, (2) between quite different kinds of value spaces in classical and infinite-valued logics, and (3) between cellular automata and logic. It is hoped that pattern analysis of this kind may open possibilities for a geometrical approach to further questions within logic and metalogic. (shrink)

In order to maintain the existence of the business, it always creates and implements various marketingstrategies to maintain and maintain business operations in the midst of fierce business competition. Allbusinesses have competition so this makes the company must have a strategy to be able to excel incompetition. Companies must be able to know how to retain their customers so as not to turn to competitors.Especially in the field of coffee drinks business which is currently being rife in Surabaya. This research (...) usesa Quantitative Approach is a research approach that uses data in the form of figures from survey answersdistributed to research samples and analyzed using statistical analysis techniques. The partial regressionresults (t-test) showed the results of the brand image proved to have a significant effect on the decision topurchase coffee drinks at Janji Jiwa Royal Plaza Surabaya. Value is proven to have a significant effect oncoffee beverage purchase decisions at Janji Jiwa Royal Surabaya. Simultaneous regression analysis (F-test)proved to have a significant effect on brand image and value on coffee beverage purchase decisions at JanjiJiwa Royal Surabaya. (shrink)

Will brain imaging technology soon enable neuroscientists to read minds? We cannot answer this question without some understanding of the state of the art in neuroimaging. But neither can we answer this question without some understanding of the concept invoked by the term "mind reading." This article is an attempt to develop such understanding. Our analysis proceeds in two stages. In the first stage, we provide a categorical explication of mind reading. The categorical explication articulates empirical conditions that must (...) be satisfied if mind reading is to be achieved. In the second stage, we develop a metric for judging the proficiency of mind reading experiments. The conception of mind reading that emerges helps to reconcile folk psychological judgments about what mind reading must involve with the constraints imposed by empirical strategies for achieving it. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging represents a transformative shift in healthcare, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. This paper explores the application of AI technologies in the analysis of medical images, focusing on techniques such as convolutional neural networks (CNNs) and deep learning models. We discuss how these technologies are applied to various imaging modalities, including X-rays, MRIs, and CT scans, to enhance disease detection, image segmentation, and diagnostic support. (...) Additionally, the paper addresses the challenges faced in AI-driven medical imaging, including data quality, model interpretability, and ethical considerations. By examining recent advancements and real-world case studies, this paper provides insights into the current state of AI in medical imaging and its potential future directions. The findings highlight the ongoing evolution of AI technologies and their crucial role in advancing medical diagnostics and treatment strategies. (shrink)

‘The Image of Thought’ could be considered to be the most important piece of writing in the entire Deleuzian corpus. This is the chapter of Difference and Repetition that several decades later, Deleuze claims is the ‘most necessary and the most concrete’ (Deleuze 1994: xvii) section of the book, and the one that provides a basis for his later work with Guattari. Here, Deleuze engages with two basic issues. First, he separates out his conception of thinking, and with it, (...) philosophy, from prior philosophical approaches, explaining why the difference of his philosophy from prior systems itself differs from the traditional relationship between philosophical positions. Second, he raises the question of how one should begin to philosophise. As we will see, philosophy often operates by the recognition of implicit presuppositions in prior thinkers, and their refutation. Descartes, for instance, criticises Aristotle for presupposing the transparency of categories such as rational and animal. Kant, in turn, criticises Descartes for presupposing the determinability of his own foundational moment, the cogito. If we see the development of philosophy as the unmasking and critique of presuppositions of prior systems, then the endpoint of philosophy will be a system entirely without presuppositions. This is the goal Hegel aims at with his system of absolute idealism. Deleuze’s claim is that such a model of the progress of philosophy itself operates within one overarching assumption: the good will of thinking. In this essay, I want to relate these questions of thinking and beginnings to the work of one of Deleuze’s predecessors. Deleuze writes of Ludwig Feuerbach that ‘Feuerbach is among those who have pursued farthest the problem of where to begin.’ (Deleuze 1994: 319) As we shall see, prefiguring Deleuze, Feuerbach accuses Hegel of operating within ‘an image of Reason’. While Feuerbach is famous as a precursor of Marx, and for his critique of traditional accounts of religion, I want to here focus on an early piece by Feuerbach, his Critique of Hegelian Philosophy. Here Feuerbach sets out the limitations of traditional philosophical accounts, and provides the groundwork for his own later materialism. Deleuze discovered Feuerbach through his friend Althusser’s translation of his work, published in 1960. While many of the intuitions behind Deleuze’s critique of the image of thought can be found within a broader pantheon of thinkers, including Nietzsche, Bergson, and Foucault, the specific mechanics of Deleuze’s criticism bear a striking resemblance to the formulation of the critique of philosophy in Feuerbach’s earlier thought. I want to explore this connection, first by looking at the dialectical interrelation of two thinkers, Descartes and Hegel, to get a sense for how criticism in philosophy typically operates. I then want to move on to look at Feuerbach’s critique of Hegel and philosophy more generally, drawing out several aspects that will be taken up by Deleuze himself. I will then look at some of the ways in which Deleuze goes beyond Feuerbach by recognising some of the limitations that persist in Feuerbach’s own analysis. I will conclude by showing how Deleuze’s desire to think the two common acceptations of aesthetics together (the artistic and the sensible) bring Feuerbach into relation with an unexpected figure: the playwright and poet, Antonin Artaud. (shrink)

Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster organized, cross-disciplinary, information-driven collaborations in the pathological imaging field, we propose to develop an ontology to represent imaging data and methods used in pathological imaging and analysis, and call it Quantitative Histopathological Imaging Ontology – QHIO. We apply QHIO to breast cancer hot-spot (...) detection with the goal of enhancing reliability of detection by promoting the sharing of data between image analysts. (shrink)

The purpose of this paper is to show that images have an ontological support by which they obtain an independent existence from the mind. In accordance with the new theories of aesthetics, we will see that the object of art is taken as an object of thought. Image has an important role in the existence of the work of art; therefore the image becomes an object of thought. To show how the image is independent from the mind (...) or to show how it is not a mind-dependent object, the analysis has to start from the pre-ontological level of the work of art in order to understand the existence of images in correlation with the work of art. At the beginning there is only the object. The object is the work of art before its concretization. It is not nature; it is the natural support of art. From this point of view, the role of the image can be explored in all ontological stages of the work of art. Firstly, it can be argued that the intuition of the artist is the image of the idea underlying the work of art. The intentional image is a subjective image. Secondly, it seems that people can see or have in mind different images (representations) referring to a work of art. This is possible in time and space and implies cultural differences, historical events and so on. A question arises: what supports the intentional image? One answer put forward is that the mind is the support of the image, but it seems that the original intentional image has something that can make other intentional images possible, an infinite number of representations, in other words an infinite number of images. The aim of this paper is to show that we can talk about an original intentional image (the first idea), intentional images (the representations of the work of art in time and space), and the transcendental image which can be defined as the ontological condition for the existence of the intentional image. At the end I will argue that the transcendental image is the support or the entity which offers the ontological conditions necessary for all intentional images. -/- Tags: intentional image, transcendental image, ontology, object and subjectivity. (shrink)

After preprocessing the input image, the model classifies the food and estimates the calorie count by leveraging its learned features. The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, (...) ensuring reliable calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier eating habits. (shrink)

This article is an inferential analysis of the Bloody Night and Open Truth: a Nollywood movie series that reflects the rot in Nigerian Police. The objective of this paper is to identify the image problems of the Nigerian Police as reflected by the Nollywood movie series. The study adopts a content analysis approach. Acts of bribery, false allegations, extra-judicial killings, torture and attempts to suppress justice are all exposed as perpetrated by the senior and junior officers of (...) the Nigerian police. Apart from the majority of corrupt officers; there are some non-corrupt Nigerian police officers who are emblems of justice and truth as shown in the behaviour of some characters in the movie. Their desire for truth represents a new and positive image of the Nigerian police. The study concludes that there is hope for a committed and rebranded Nigerian Police if the Nigerian Government should do the needful. The study recommends an improvement in wages, sustained retraining and punishment of police officers found wanting. (shrink)

Researchers in the health sciences regularly discover information of potential health importance unrelated to their object of study in the course of their research. However, there appears to be little guidance available on what researchers should do with this information, known in the scientific literature as incidental findings. The study described here was designed to determine the extent of guidance available to researchers from public sources. This empirical study was part of a larger two-year project funded by the National Human (...) Genome Research Institute to generate guidance on how incidental findings should be managed in human subjects research, especially genetics, genomics, and imaging research. We generated empirical analysis of publicly available guidance and consent forms to help guide a multidisciplinary Working Group of experts in their formulation of normative recommendations reported in this symposium. (shrink)

This article examines Machiavelli’s image of humanity. It argues against the prevailing views that characterize it either as pessimistic or optimistic and defends the thesis that the Florentine has a realist image of humanity. Machiavelli is a psychological egoist who conceives of man as a being whose actions are motivated by his drives, appetites, and passions, which lead him often to immoral behavior. Man’s main drives are “ambition” (ambizione) and “avarice” (avarizia). This article also investigates Machiavelli’s concept of (...) nature and shows that, for him, the constancy of human nature is the central premise that makes the scientific analysis of politics possible. Despite the fact that human drives and capabilities are the same at all times, good laws, military training, and religion allow man to be changed and educated toward “virtue” (virtù). To make such changes in man, however, presupposes a good legal and political order. Machiavelli justifies the state because of its capacity to reshape human nature and to improve man. The state is not only a coercive power but a moral institution. This leads to the conclusion that Machiavelli does not separate politics from morality as most scholars claim. (shrink)

Identification and mapping of natural vegetation are major issues for biodiversity management and conservation. Remote sensing monitoring has two important sub fields viz, classification and change detection. Change detection from remotely sensed images is a process that utilizes the images acquired over the same geographical area at different times to identify the changes that may have occurred between the considered acquisition dates. Remotely sensed data with very high spatial resolution are currently used to study vegetation, but most satellite sensors are (...) limited to four spectral bands, which is insufficient to identify some natural vegetation formations. To address this issue, this work intends to use multispectral and high spatial resolution data (Sentinel-2) to do the task of natural vegetation analysis using a popular CNN architecture. This proposed project concept is completely based on Deep Learning architecture using Remote Sensing technology for vegetation of lands in Hassan city of Karnataka India. The results are discussed within the scope of recent studies involving machine learning and Sentinel-2 data and key knowledge gaps identified. The changes of various vegetations are compared and their changes difference is displayed. (shrink)

Claims such as ‘there are no tables and chairs’ have become increasingly common in the philosophical context, and eliminativism is now a fairly well-established position in contemporary debates in analytic metaphysics. This outbreak of eliminativism has prompted a number of responses aimed at saving the manifest image of reality. Prominent amongst the attempts to save the manifest image is a view, powerfully articulated by Frank Jackson in From Metaphysics to Ethics , according to which the manifest properties of (...) objects, properties such as solidity or fragility, can be spared from the eliminativist’s guillotine if it can be shown that they are entailed (through the relation of supervenience) by scientific properties. Jackson’s strategy for saving the manifest image rests on a modest conception of the role of conceptual analysis in metaphysics. On this view, the role of conceptual analysis is modest because it is not the task of philosophy to establish a priori what there is, but rather to determine which features of the manifest image can be located within the scientific image: the manifest properties which cannot be so located are shown to be rogue concepts that have no place in serious metaphysics. This chapter argues against the attempt to save the manifest image by invoking the relation of entailment (whether that implied by the notion of supervenience or by the more traditional notion of analytic entailment) on the grounds that the manifest image is sui generis. The defence of the manifest image that I propose as an alternative here rests on the idealist assumption that knowledge makes a difference to what is known, and that since the manifest and the scientific image are the correlative of two different ways of knowing they do not compete with one another. I call the idealist view that knowledge makes a difference to what is known the Reciprocity Thesis. Rather than seeking to determine what kind of manifest image one is entitled to have in order to comply with the scientific image, the Reciprocity Thesis limits the claims of science to its own explanandum and therefore sees no need to legitimise the manifest image by invoking the relation of entailment. The need to legitimise the manifest image in the light of the scientific image arises because the relation between the scientific and the manifest image has not been properly conceptualised. Once the scientific and the manifest image are understood as the correlative of different ways of knowing, the problem which the location strategy seeks to solve is shown to rest on a misconception of the relationship holding between different kinds of knowledge. (shrink)

Sunohara Yuuri and Akita Masami’s series of six seppuku films (1990) are solely constituted by images of fictionalized death, revolving around the prolonged self-torture of a lone figure committing harakiri. I contend that the protagonist’s auto-immolation mirrors a formal death, each frame ‘killing’ the moment it represents. My analysis aims to explore how the solipsistic nature of selfhood is appositely symbolized by the isolation of the on-screen figures and the insistence with which the six films repeat the same scenario (...) of protracted agony across the cycle. The centralization of suffering, I argue, parallels the distance between viewer and image with the isolating nature of embodied existence. Thus, this article seeks to probe the relationship between form and content, asking what the image of death reveals about the death of the image. -/- . (shrink)

The article explores the striking coincidences in Heidegger's and Blanchot's account of the image as death mask. The analysis of the respective theories of the image brings forth two radically divergent conceptions of thinking as "laying patent" (Heidegger) and of thinking as "laying bare" (Blanchot).

The aim of this paper is to demonstrate the applicability of multivariate mapping techniques to the exploration of the rhetorical semiotic brand image structure of ad films. By drawing on correspondence analysis and multidimensional scaling, two techniques that are amply used in corpus linguistics and in marketing research, but also on the data reduction technique of factor analysis, it will be displayed how a set of nuclear semes and classemes or an intended semic structure that underlies ad (...) filmic discursive structures may be projected alongside rhetorical figures by its internal stakeholders (i.e., a brand management team, an account planning team or a marketing research team) with view to attaining differential brand associations. The illustration of the exploratory analytical methods takes place by recourse to a corpus of 62 ad filmic texts from 13 sub-brands of the 3 major brands in the UK cereals market and 321 ad filmic segments that resulted from the segmentation procedure. This paper seeks to contribute to advancements in the brand image and advertising rhetoric research streams by addressing distinctive modes of advertising rhetorical configuration at the level of the ad filmic text (as against print ads where the bulk of research on advertising rhetoric has concentrated), and moreover on a segment-by-segment level, rather than treating the ad film as a standalone unit of analysis, by adopting a multimodal outlook to advertising configurations that takes into account not only the verbal or the visual mode, but also interactions among modes, by adopting a product category-specific approach to advertising/brand textuality that draws on rhetorical semiotics and by employing exploratory statistical methods against the background of content analytic output. (shrink)

The model is trained on a large dataset containing images of various food items along with their nutritional information. After preprocessing the input image, the model classifies the food and estimates the calorie count by leveraging its learned features. The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users (...) to track their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier eating habits. (shrink)

This project aims to provide an automated system for accurately estimating the calorie content of food and beverages using advanced deep learning algorithms. With the increasing demand for health-conscious individuals, there is a need for a reliable, efficient, and easy-to-use tool that can help users make informed dietary choices. The project utilizes image processing techniques and deep learning models, such as Convolutional Neural Networks (CNN), to analyze food images and predict the corresponding calorie content.

The fields of gender and social movements have traditionally consisted of separate literatures. Recently, however, a number of scholars have begun a fruitful exploration of the ways in which gender shapes political protest. This study adds three things to this ongoing discussion. First, the authors offer a systematic typology of the various ways in which movements are gendered and apply that typology to a wide variety of movements, including those that do not center on gender issues in any obvious way. (...) Second, the authors discuss the process by which movements become gendered. In doing so, they go beyond current scholarship by bringing “others” squarely into the gendered analysis. The article concludes by speculating about the outcomes of these processes and suggests that movements that draw on feminine stereotypes face a double bind that hampers their success. Illustrations come from movements in the United States, Europe, and Latin America. (shrink)

Electrocardiogram (ECG) signal analysis is a critical task in healthcare for diagnosing cardiovascular conditions such as arrhythmias, heart attacks, and other heart-related diseases. With the growth of Internet of Things (IoT) networks, real-time ECG monitoring has become possible through wearable devices and sensors, providing continuous patient health monitoring. However, real-time ECG signal analysis in IoT environments poses several challenges, including data latency, limited computational power of IoT devices, and energy constraints. This paper proposes a framework for Optimized (...) Machine Learning Algorithms designed to analyze ECG signals in real time within IoT networks. The proposed system leverages lightweight machine learning models, including support vector machines (SVM) and convolutional neural networks (CNNs), optimized to run efficiently on low-power IoT devices while maintaining high accuracy. The system addresses the computational limitations of IoT devices by employing edge computing techniques that distribute the processing load between IoT devices and edge servers. Additionally, data compression and feature extraction techniques are applied to reduce the size of the data transmitted over the network, thereby minimizing latency and bandwidth usage. This paper reviews the current advancements in real-time ECG analysis, explores the challenges posed by IoT environments, and presents the optimized machine learning algorithms that enhance real-time monitoring of heart health. The system is evaluated for its performance in terms of accuracy, energy efficiency, and data transmission speed, showing promising results in improving real-time ECG signal analysis in resource-constrained IoT networks. (shrink)

The aim of the study is to identify the effect of Service Quality and Corporate Image on Customer’s Loyalty .Questionnaires were distributed to collect responses from restaurant users in the Greater Accra region of Ghana. Descriptive statistics and correlation analysis were used to analyze the data and draw the conclusions. It was revealed that service quality and corporate image have strong positive association with customer’s loyalty .The study suggested that managers of restaurants provide needed logistics to aid (...) frontline staff to avoid delays in meeting customer demands. This will lead to high level of customer retention, loyalty, high market share and profitability. (shrink)

After preprocessing the input image, the model classifies the food and estimates the calorie count by leveraging its learned features. The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, (...) ensuring reliable calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier eating habits. (shrink)

This narrative study explores the late-career issues among 15 senior teacher educators from Israeli Academic Colleges of Education (ACEs), in light of the growing conversation about pre-pension maintenance of senior faculty members employed in teacher education institutions. The data analysis of semi-structured interviews highlighted dedication to daily tasks (research activity, administration, teacher education and leading Professional Learning Communities), and a sense of mission during career experiences (leaving legacy to student teachers and colleagues). It was reflected in two parallel work (...) patterns: the ‘work pattern of sharing’, and the ‘work pattern of expertise’. The discussion raises similarities between the literature and the first work pattern, in line with the unique culture of teacher education systems in the world. However, the existence of the ‘work pattern of expertise’ in the literature remains vague and requires more in-depth thinking and research. (shrink)

The model is trained on a large dataset containing images of various food items along with their nutritional information. After preprocessing the input image, the model classifies the food and estimates the calorie count by leveraging its learned features. The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users (...) to track their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable calorie estimation across different food items. (shrink)

Christopher Gauker argues that no concept can be extracted from perceptual experience and that imagistic thought cannot draw boundaries between one kind and another. Here, it is argued, on the contrary, that images have extension and are consequently Fregean concepts. Hume’s theory of abstraction as indifference is offered as an account of extra-sensory concepts. Finally, it is argued that modern theories of sensory data processing run parallel to Kant’s idea of synthesis as a pre-condition for perception.

Consumers often buy products not because of their attributes per se but rather because of the ultimate benefits that these attributes provide, in turn leading to the satisfaction of ultimate values. And Apple is a type of company which understand their customers & future aspects of innovation in a way that no other companies do. That’s the reason that apple is at No. 1 place in Top 50 Most innovative companies. This article endeavors to recognize the consumer perception towards the (...) Apple products and what is Apple’s ideology. In this article the researcher has listed down 12 variables which affect the consumer behavior most while purchasing Apple products. The researcher took 50 sample sizes to make this paper and this study is being done in Tiruchirappalli District. To prepare the factors, I have used Factor analysis test. (shrink)

The fundamental purpose of software testing is to develop new test case sets that demonstrate the software product's deficiencies. Upon preparation of the test cases, the Test Oracle delineates the expected program behavior for each scenario. The application's correct functioning and its properties will be assessed by prioritizing test cases and running its components, which delineate inputs, actions, and outputs. The prioritization methods include initial ordering, random ordering, and reverse ranking based on fault detection capabilities. software application development often used (...) a test suite, which was less well recognized as a suite for validating software correctness. Each test case set in the suite had distinct instructions and goals based on the system and its configuration that were evaluated. This article presents a Generative AI artificial neural network model for automated software testing based on particle swarm optimization. Generative AI is the method by which computers use existing data, including text, audio/video files, images, and code, to produce new material. An artificial neural network (ANN) is a complex adaptive system capable of altering its internal structure in response to the information it processes. Precisely manipulate the connection and its weight to achieve optimal accuracy. The PSO is a heuristic, population-based global optimization method. (shrink)

Neither the art historians Panofsky and Warburg nor the philosopher Cassirer had any interest with their cultural-historical research in fact-based, historical questions. An approach that had become common in the 19th century due to the loss of validity of the speculative aesthetics. On the contrary, instead of this substantial understanding as the documentary concept represents, these researchers focused on a functional understanding of art historical sources. Nevertheless, in contrast to this starting point, Panofsky invented a methodological procedure, the so-called iconological (...) method, which in turn led back to a documentary-focused historical analysis of artistic artefacts that is still recognized today. The goal is to rediscover the original background of Panofsky´s method. This should make it clear that the original idea of the image concept pursued by Warburg and Cassirer, which had been lost or obscured by the aftermath of National Socialism in Germany, can be made fruitful in a new light today. This path may open up iconology to “image science”/Bildwissenschaft and thus transform the former from a purely historical approach to an understanding relevant to cultural processes and thus to human action. (shrink)

Everyday media consumption leaves no doubt that the perception of moving images from various media is characterized by experience and understanding. Corresponding research in this field has shown that the stimulus patterns flooding in on us are not only processed mentally, but also bodily. Building on this, the following study argues that incoming stimuli are processed not only visually, but multimodally, with all senses, and moreover affectively. The classical binding of a sensory organ to a medium, on whose delimitation the (...) common understanding of multimodality is based, is thus abolished. Can arguments for this assumption be found? Building on the research of cultural anthropologist Ernst Cassirer and his successors, the two American philosophers Susanne K. Langer and John M. Krois, and supported by research from the life sciences in which, in parallel, the arts also play an important role in understanding image formation, as in the work of Heinz Werner and, following on from this, in the more recent research of the child psychologist Daniel N. Stern in collaboration with mirror neuron research by the group of neuroscientists around Giacomo Rizzolatti in Parma, justifications for this will be provided. What the multimodal processing of film images is concretely based on will finally be made clear by means of an exemplary analysis of a video film by the Swiss artist Pipilotti Rist. It aims to prove, that the prerequisites for the thesis can only lie in the fact, that both the modes of perception (visual, tactile, auditory, olfactory, gustatory) as well as the medially conditioned modes of image formation are based on non-discursive, formal-abstract structures that are also processed affectively. At the same time, the analysis thus shows how the selection and composition of the vital experienced forms can provide information about the socio-culturally shaped self-image of the producer and the viewer. (shrink)

In this article I analyse current efforts in cognitive neuroscience to explore the organic and cognitive processes involved in problem-solving. This analysis highlights a problem with assuming that cognitive processes can be wholly explained once one has explained organic processes. Reflection on scientific performance suggests how this problem can be evaded. This reflection on performance can also provide a paradigm for future neuroscientific research leading to a more detailed account of how brain locales and activities can be correlated with (...) conscious cognitive acts. (shrink)

Recent advances in neuroimaging technologies have rendered multimodal analysis of operators’ cognitive processes in complex task settings and environments increasingly more practical. In this exploratory study, we utilized optical brain imaging and mobile eye tracking technologies to investigate the behavioral and neurophysiological differences among expert and novice operators while they operated a human-machine interface in normal and adverse conditions. In congruence with related work, we observed that experts tended to have lower prefrontal oxygenation and exhibit gaze patterns that are (...) better aligned with the optimal task sequence with shorter fixation durations as compared to novices. These trends reached statistical significance only in the adverse condition where the operators were prompted with an unexpected error message. Comparisons between hemodynamic and gaze measures before and after the error message indicated that experts’ neurophysiological response to the error involved a systematic increase in bilateral dorsolateral prefrontal cortex (dlPFC) activity accompanied with an increase in fixation durations, which suggests a shift in their attentional state, possibly from routine process execution to problem detection and resolution. The novices’ response was not as strong as that of experts, including a slight increase only in the left dlPFC with a decreasing trend in fixation durations, which is indicative of visual search behavior for possible cues to make sense of the unanticipated situation. A linear discriminant analysis model capitalizing on the covariance structure among hemodynamic and eye movement measures could distinguish experts from novices with 91% accuracy. Despite the small sample size, the performance of the linear discriminant analysis combining eye fixation and dorsolateral oxygenation measures before and after an unexpected event suggests that multimodal approaches may be fruitful for distinguishing novice and expert performance in similar neuroergonomic applications in the field. (shrink)

Recent advances in neuroimaging technologies have rendered multimodal analysis of operators’ cognitive processes in complex task settings and environments increasingly more practical. In this exploratory study, we utilized optical brain imaging and mobile eye tracking technologies to investigate the behavioral and neurophysiological differences among expert and novice operators while they operated a human-machine interface in normal and adverse conditions. In congruence with related work, we observed that experts tended to have lower prefrontal oxygenation and exhibit gaze patterns that are (...) better aligned with the optimal task sequence with shorter fixation durations as compared to novices. These trends reached statistical significance only in the adverse condition where the operators were prompted with an unexpected error message. Comparisons between hemodynamic and gaze measures before and after the error message indicated that experts’ neurophysiological response to the error involved a systematic increase in bilateral dorsolateral prefrontal cortex (dlPFC) activity accompanied with an increase in fixation durations, which suggests a shift in their attentional state, possibly from routine process execution to problem detection and resolution. The novices’ response was not as strong as that of experts, including a slight increase only in the left dlPFC with a decreasing trend in fixation durations, which is indicative of visual search behavior for possible cues to make sense of the unanticipated situation. A linear discriminant analysis model capitalizing on the covariance structure among hemodynamic and eye movement measures could distinguish experts from novices with 91% accuracy. Despite the small sample size, the performance of the linear discriminant analysis combining eye fixation and dorsolateral oxygenation measures before and after an unexpected event suggests that multimodal approaches may be fruitful for distinguishing novice and expert performance in similar neuroergonomic applications in the field. (shrink)

In August 2022, only a few months before ChatGPT was released, Barry Smith, well-known contemporary philosopher, together with Jobst Landgrebe, artificial intelligence entrepreneur, published a book entitled Why Machines will Never Rule the World: Artificial Intelligence without Fear (Landgrebe and Smith 2022). In this important, dense and far-reaching work, Landgrebe and Smith argue from the mathematical theory of complex systems, and a sophisticated analysis of the capabilities of human intelligence, that AGI— at the level of human intelligence—will never be (...) possible. In broad outline, Landgrebe and Smith claim that intelligent human behaviour is a facility that develops from a complex world and for a complex world; that the complexities of the real world and our behaviour in it can never be fully specified or simulated mathematically; that digital systems can only execute what can be simulated mathematically; and that therefore human-level intelligence will never be manifested by a digital system. The present collection of articles is in response to and inspired by Landgrebe and Smith. It provides reflections from a variety of perspectives and disciplines on the possibility of AGI, as well as on the transformations that AI technology is already bringing even in its current incarnation. (shrink)