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.

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)

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)

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)

Visual imagery is a form of sensory imagination, involving subjective experiences typically described as similar to perception, but which occur in the absence of corresponding external stimuli. We used the Activation Likelihood Estimation algorithm (ALE) to identify regions consistently activated by visual imagery across 40 neuroimaging studies, the first such meta-analysis. We also employed a recently developed multi-modal parcellation of the human brain to attribute stereotactic co-ordinates to one of 180 anatomical regions, the first time this approach has been combined (...) with the ALE algorithm. We identified a total 634 foci, based on measurements from 464 participants. Our overall comparison identified activation in the superior parietal lobule, particularly in the left hemisphere, consistent with the proposed ‘top-down’ role for this brain region in imagery. Inferior premotor areas and the inferior frontal sulcus were reliably activated, a finding consistent with the prominent semantic demands made by many visual imagery tasks. We observed bilateral activation in several areas associated with the integration of eye movements and visual information, including the supplementary and cingulate eye fields (SCEFs) and the frontal eye fields (FEFs), suggesting that enactive processes are important in visual imagery. V1 was typically activated during visual imagery, even when participants have their eyes closed, consistent with influential depictive theories of visual imagery. Temporal lobe activation was restricted to area PH and regions of the fusiform gyrus, adjacent to the fusiform face complex (FFC). These results provide a secure foundation for future work to characterise in greater detail the functional contributions of specific areas to visual imagery. (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)

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)

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)

In this paper I discuss one of the key issuesin the philosophy of neuroscience:neurosemantics. The project of neurosemanticsinvolves explaining what it means for states ofneurons and neural systems to haverepresentational contents. Neurosemantics thusinvolves issues of common concern between thephilosophy of neuroscience and philosophy ofmind. I discuss a problem that arises foraccounts of representational content that Icall ``the economy problem'': the problem ofshowing that a candidate theory of mentalrepresentation can bear the work requiredwithin in the causal economy of a mind (...) and anorganism. My approach in the current paper isto explore this and other key themes inneurosemantics through the use of computermodels of neural networks embodied and evolvedin virtual organisms. The models allow for thelaying bare of the causal economies of entireyet simple artificial organisms so that therelations between the neural bases of, forinstance, representation in perception andmemory can be regarded in the context of anentire organism. On the basis of thesesimulations, I argue for an account ofneurosemantics adequate for the solution of theeconomy problem. (shrink)

Magnetic levitation system is operated primarily based at the principle of magnetic attraction and repulsion to levitate the passengers and the train. However, magnetic levitation trains are rather nonlinear and open loop unstable which makes it hard to govern. In this paper, investigation, design and control of a nonlinear Maglev train based on NARMA-L2, model reference and predictive controllers. The response of the Maglev train with the proposed controllers for the precise role of a Magnetic levitation machine have been as (...) compared for a step input signal. The simulation consequences prove that the Maglev teach system with NARMA-L2 controller suggests the quality performance in adjusting the precise function of the system and the device improves the experience consolation and street managing criteria. (shrink)

In this paper a NARMA L2, model reference and neural network predictive controller is utilized in order to control the output flow rate of the steam in furnace by controlling the steam flow valve. The steam flow control system is basically a feedback control system which is mostly used in cement production industries. The design of the system with the proposed controllers is done with Matlab/Simulink toolbox. The system is designed for the actual steam flow output to track the (...) desired steam that is given to the system as input for two desired steam input signals (step and sine wave). In order to analyze the performance of the system, comparison of the proposed controllers is done by simulating the system for the two reference signals for the system with and without sensor noise disturbance. Finally the comparison results prove the effectiveness of the presented process control system with model reference controller. (shrink)

This study employs a neural network to analyze the connection between smoking, drinking, and various health-related factors using a dataset of 5148 samples. Achieving an impressive 99.94% accuracy and an average training error of 0.0016, the model identifies influential factors such as serum aminotransferases, serum creatinine, sex, weight, and triglyceride levels. These findings enhance our understanding of lifestyle choices and their impact on health. This research underscores the potential of machine learning in studying complex health phenomena.

The goal of philosophy of information is to understand what information is, how it operates, and how to put it to work. But unlike ‘information’ in the technical sense of information theory, what we are interested in is meaningful information. To understand the nature and dynamics of information in this sense we have to understand meaning. What we offer here are simple computational models that show emergence of meaning and information transfer in randomized arrays of neural nets. These we (...) take to be formal instantiations of a tradition of theories of meaning as use. What they offer, we propose, is a glimpse into the origin and dynamics of at least simple forms of meaning and information transfer as properties inherent in behavioral coordination across a community. (shrink)

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

In this paper we describe a technical system for DC motor speed control. The speed of DC motor is controlled using Neural Network Based Model Reference and Predictive controllers with the use of Matlab/Simulink. The analysis of the DC motor is done with and without input side Torque disturbance input and the simulation results obtained by comparing the desired and actual speed of the DC motor using random reference and sinusoidal speed inputs for the DC motor with Model Reference (...) and Predictive controllers. The DC motor with Model Reference controller shows almost the actual speed is the same as the desired speed with a good performance than the DC motor with Predictive controller for the system with and without input side disturbance. Finally the comparative simulation result prove the effectiveness of the DC motor with Model Reference controller. (shrink)

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

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)

A network of gene regulation organized in a hierarchical and combinatorial manner is crucially involved in the development of the neural network, and has to be considered one of the main substrates of genetic change in its evolution. Though qualitative features may emerge by way of the accumulation of rather unspecific quantitative changes, it is reasonable to assume that at least in some cases specific combinations of regulatory parts of the genome initiated new directions of evolution, leading to novel (...) capabilities of the brain. These notions are applied, in this paper, to the evolution of the capability of cognition-based human empa­thy. It is suggested that it has evolved as a secondary effect of the evolution of strategic thought. Development of strategies depends on abstract representations of one’s own pos­sible future states in one’s own brain to allow assessment of their emotional desirability, but also on the representation and emotional evaluation of possible states of others, allowing anticipation of their behaviour. This is best achieved if representations of others are con­nected to one’s own emotional centres in a manner similar to self-representations. For this reason, the evolution of the human brain is assumed to have established representations with such linkages. No group selection is involved, because the quality of strategic thought affects the fitness of the individual. A secondary effect of this linkage is that both the actual states and the future perspectives of others elicit vicarious emotions, which may contribute to the motivations of altruistic behaviour. (shrink)

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

This paper adopts two modeling tools, namely, multiple linear regression (MLR) and artificial neural networks (ANNs), to predict the concentrations of heavy metals (zinc, boron, and manganese) in surface waters of the Oued Inaouen watershed flowing towards Inaouen, using a set of physical-chemical parameters. XLStat was employed to perform multiple linear and nonlinear regressions, and Statista 10 was chosen to construct neural networks for modeling and prediction. The effectiveness of the ANN- and MLR-based stochastic models was assessed by (...) the determination coefficient (R²), the sum squared error (SSE) and a review of fit graphs. The results demonstrate the value of ANNs for prediction modeling. Drawing on supervised learning and back propagation, the ANN-based prediction models adopt an architecture of [18-15-1] for zinc, [18-11-1] for manganese, and [18-8-1] for boron, and perform effectively with a single cached layer. It was found that the MLR-based prediction models are substantially less accurate than those based on the ANNs. In addition, the physical-chemical parameters being investigated are nonlinearly correlated with the levels of heavy metals in the surface waters of the Oued Inaouen watershed flowing towards Inaouen. (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)

Metacontrast is a visual illusion in which the visibility of a target stimulus is virtually lost when immediately followed by a nonoverlapping mask stimulus. For a colored target, metacontrast is color-selective, with target visibility markedly reduced when the mask and target are the same color, but only slightly reduced when the colors differ. This study investigated neural correlates of color-selective metacontrast for cone-opponent red and green stimuli in the human V1, V2, and V3 using functional magnetic resonance imaging. (...) class='Hi'>Neural activity was suppressed when the target was rendered less visible by the same-colored mask, and the suppression was localized in the cortical region retinotopically representing the target, correlating with the perceptual topography of visibility/invisibility rather than the physical topography of the stimulus. Retinotopy-based group analysis found that activity suppression was statistically significant for V2 and V3 and that its localization to the target region was statistically significant for V2. These results suggest that retinotopic color representations in early visual areas, especially in V2, are closely linked to the visibility of color. (shrink)

Abstract: The aim behind analyzing Google Stock Prices dataset is to get a fair idea about the relationships between the multiple attributes a day might have, such as: the opening price for each day, the volume of trading for each day. With over a hundred thousand days of trading data, there are some patterns that can help in predicting the future prices. We proposed an Artificial Neural Network (ANN) model for predicting the closing prices for future days. The prediction (...) is based on these features (date, opening price, highest price, lowest price, volume), which were used as input variables and (closing price) as output variable for our ANN model. Our model was created, trained, and validated using data set in JNN environment, which its title is “Google-Stock-Prices”. The model evaluation showed that the ANN model is able to predict correctly 99.48% of the validation samples. (shrink)

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

Western science claims to provide unique, objective information about the world. This is supported by the observation that peoples across cultures will agree upon a common description of the physical world. Further, the use of scientific instruments and mathematics is claimed to enable the objectification of science. In this work, carried out by reviewing the scientific literature, the above claims are disputed systematically by evaluating the definition of physical reality and the scientific method, showing that empiricism relies ultimately upon the (...) human senses for the evaluation of scientific theories and that measuring instruments cannot replace the human sensory system. Nativist and constructivist theories of human sensory development are reviewed, and it is shown that nativist claims of core conceptual knowledge cannot be supported by the findings in the literature, which shows that perception does not simply arise from a process of maturation. Instead, sensory function requires a long process of learning through interactions with the environment. To more rigorously define physical reality and systematically evaluate the stability of perception, and thus the basis of empiricism, the development of the method of dimension analysis is reviewed. It is shown that this methodology, relied upon for the mathematical analysis of physical quantities, is itself based upon empiricism, and that all of physical reality can be described in terms of the three fundamental dimensions of mass, length and time. Hereafter the sensory modalities that inform us about these three dimensions are systematically evaluated. The following careful analysis of neuronal plasticity in these modalities shows that all the relevant senses acquire from the environment the capacity to apprehend physical reality. It is concluded that physical reality is acquired rather than given innately, and leads to the position that science cannot provide unique results. Rather, those it can provide are sufficient for a particular environmental setting. (shrink)

Abstract: Pistachio nuts are a valuable source of nutrition and are widely cultivated for commercial purposes. The accurate classification of different pistachio varieties is important for quality control and market analysis. In this study, we propose a new model for the classification of different pistachio varieties using Convolutional Neural Networks (CNNs). We collected a dataset of pistachio images form Kaggle depository with two varieties (Kirmizi and Siirt). The images were then preprocessed and used to train a CNN model based (...) on VGG16. We evaluated the performance of the VGG16 on a held-out test set. Our results show that the CNN-based model VGG16 performed very well in terms of accuracy and efficiency. The VGG16 model was able to accurately classify different pistachio varieties with an accuracy of over 99.91%. Additionally, the VGG16 was able to generalize well to new and unseen data, demonstrating its ability to handle variability in the pistachio images. This study highlights the potential of CNNs for the classification of different pistachio varieties, and has important implications for the pistachio industry. The results suggest that this approach can be used to improve the quality control process and enhance market analysis. (shrink)

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing (...) to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain. (shrink)

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

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

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

Abstract: The aim behind analyzing the Goodreads dataset is to get a fair idea about the relationships between the multiple attributes a book might have, such as: the aggregate rating of each book, the trend of the authors over the years and books with numerous languages. With over a hundred thousand ratings, there are books which just tend to become popular as each day seems to pass. We proposed an Artificial Neural Network (ANN) model for predicting the overall rating (...) of books. The prediction is based on these features (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which were used as input variables and (average_rating) as output variable for our ANN model. Our model were created, trained, and validated using data set in JNN environment, which its title is “Goodreads-books”. Model evaluation showed that the ANN model is able to predict correctly 99.78% of the validation samples. (shrink)

Current research on the various forms of autoscopic phenomena addresses the clinical and neurological correlates of out-of-body experiences, autoscopic hallucinations,and heautoscopy. Yet most of this research is based on functional magnetic resonance imaging results and focuses predominantly on abnormal cortical activity. Previously we proposed that visual consciousness resulted from the dynamic retinogeniculo-cortical oscillations, such that the photoreceptors dynamically integrated with visual and other vision-associated cortices, and was theorized to be mapped out by photoreceptor discs and rich retinal networks which synchronized (...) with the retinotopic mapping and the associated cortex. The feedback from neural input that is received from the thalamus and cortex via retinogeniculo-cortical oscillations and sent to the retina is multifold higher than feed-forward input to the cortex. This can effectively translate into out-of-body experiences projected onto the screen formed by the retina as it is perceived via feedback and feed-forward oscillations from the reticular thalamic nucleus, or “internal searchlight”. This article explores the role of the reticular thalamic nucleus and the retinogeniculo-cortical oscillations as pivotal internal components in vision and various autoscopic phenomena. (shrink)

Recently, active suspension system will become important to the vehicle industries because of its advantages in improving road managing and ride comfort. This paper offers the development of mathematical modelling and design of a neural network control approach. The paper will begin with a mathematical model designing primarily based at the parameters of the active suspension system. A nonlinear three by four-way valve-piston hydraulic actuator became advanced which will make the suspension system under the active condition. Then, the model (...) can be analyzed thru MATLAB/Simulink software program. Finally, the NARMA-L2, model reference and predictive controllers are designed for the active suspension system. The results are acquired after designing the simulation of the quarter-car nonlinear active suspension system. From the simulation end result using MATLAB/Simulink, the response of the system might be as compared between the nonlinear active suspension system with NARMA-L2, model reference and predictive controllers. Besides that, the evaluation has been made between the proposed controllers thru the characteristics of the manage objectives suspension deflection, body acceleration and body travel of the active suspension system. . As a conclusion, designing a nonlinear active suspension system with a nonlinear hydraulic actuator for quarter car model has improved the car performance by using a NARMA-L2 controller. The improvements in performance will improve road handling and ride comfort performance of the active suspension system. (shrink)

Electromagnetic Suspension System (EMS) is mostly used in the field of high-speed vehicle. In this study, a space exploring vehicle quarter electromagnetic suspension system is modelled, designed and simulated using Neural network-based control problem. NARMA-L2, Model reference and predictive controllers are designed to improve the body travel of the vehicle using bump road profile. Comparison between the proposed controllers is done and a promising simulation result have been analyzed.

[This is an edited and improved version of "You Are Not Your Brain: Against 'Teaching to the Brain'" previously published in *Review of Higher Education and Self-Learning* 5(15), Summer 2012.] Since educators are always looking for ways to improve their practice, and since empirical science is now accepted in our worldview as the final arbiter of truth, it is no surprise they have been lured toward cognitive neuroscience in hopes that discovering how the brain learns will provide a nutshell explanation (...) for student learning in general. I argue that identifying the person with the brain is scientism (not science), that the brain is not the person, and that it is the person who learns. In fact the brain only responds to the learning of embodied experience within the extra-neural network of intersubjective communications. Learning is a dynamic, cultural activity, not a neural program. Brain-based learning is unnecessary for educators and may be dangerous in that a culturally narrow ontology is taken for granted, thus restricting our creativity and imagination, and narrowing the human community. (shrink)

In his rich and provocative paper, Peter Carruthers announces two related theses: (a) a positive thesis that “central cognition is sensory based, depending on the activation and deployment of sensory images of various sorts” (Carruthers 2013) and (b) a negative thesis that the “central mind does not contain any workspace within which goals, decisions, intentions, or non-sensory judgments can be active” (Carruthers 2013). These are striking claims suggesting that a natural view about cognition, namely that explicit theoretical reasoning involves direct (...) operations over beliefs, is wrong. Our beliefs, on this natural view, interact with each other to yield new beliefs. If Carruthers is right, beliefs have only an indirect or mediated influence in cognition with sensory states playing the direct role. I think it an important feature of Carruthers’ discussion that he draws support from work on the neural circuits and mechanisms that subserve these processes. In what follows, I want to regis. (shrink)

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

Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function (e.g., Piccinini & Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience (MBCN) epitomizes the integrative mechanistic perspective and (...) concentrates the most revolutionary elements of the cognitive neuroscience revolution (Boone & Piccinini, 2016). I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration. (shrink)

We propose that there are four fundamental kinds of metaphor that are uniquely mapped onto specific brain ‘‘networks’’ and present preliterate (i.e., evolutionary, including before the appearance of written language in the historical record), prelinguistic (i.e., developmental, before the appearance of speech in human development), and extralinguistic (i.e., neuropsychological, cognitive) evidence supportive of this view. We contend that these basic metaphors are largely nonconceptual and entail (a) perceptual–perceptual, (b) cross-modal, (c) movement–movement, and (d) perceptual-affective mappings that, at least, in the (...) initial stages of processing may operate largely outside of conscious awareness. In opposition to our basic metaphor theory (BmT), the standard theory (SmT) maintains that metaphor is a conceptual mapping from some base domain to some target domain and/or represents class-inclusion (categorical) assertions. The SmT captures aspects of secondary or conceptual metaphoric relations but not primary or basic metaphoric relations in our view. We believe our theory (BmT) explains more about how people actually recognize or create metaphoric associations across disparate domains of experience partly because they are ‘‘pre-wired’’ to make these links. (shrink)

Magnetic levitation system is operated primarily based at the principle of magnetic attraction and repulsion to levitate the passengers and the train. However, magnetic levitation trains are rather nonlinear and open loop unstable which makes it hard to govern. In this paper, investigation, design and control of a nonlinear Maglev train based on NARMA-L2, model reference and predictive controllers. The response of the Maglev train with the proposed controllers for the precise role of a Magnetic levitation machine have been as (...) compared for a step input signal. The simulation consequences prove that the Maglev teach system with NARMA-L2 controller suggests the quality performance in adjusting the precise function of the system and the device improves the experience consolation and street managing criteria. (shrink)

Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. (...) To this end, we exploit a neural network metric in the output of a biologically inspired Self- Organizing Map Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, a longer choice response time (RT) for “yes” responses relative to symmetry. These data are consistently mirrored by the variations in the SOM-QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metric’s proven selectivity to local contrast and color variations in large and highly complex image data. (shrink)

How tight is the conceptual connection between imagination and perception? A number of philosophers, from the early moderns to present-day predictive processing theorists, tie the knot as tightly as they can, claiming that states of the imagination, i.e. mental imagery, are a proper subset of perceptual experience. This paper labels such a view ‘perceptualism’ about the imagination and supplies new arguments against it. The arguments are based on high-level perceptual content and, distinctly, cognitive penetration. The paper also defuses a recent, (...) influential argument for perceptualism based on the ‘discovery’ that visual perception and mental imagery share a significant neural substrate: circuitry in V1, the brain’s primary visual cortex. Current neuropsychology is shown to be equivocal at best on this matter. While experiments conducted on healthy, neurotypical subjects indicate substantial neural overlap, there is extensive clinical evidence of dissociations between imagery and perception in the brain, most notably in the case of aphantasia. (shrink)

This study presents a neural network-based model for predicting smoke potential in a specific area using a Kaggle-derived dataset with 15 environmental features and 62,631 samples. Our five-layer neural network achieved an accuracy of 89.14% and an average error of 0.000715, demonstrating its effectiveness. Key influential features, including temperature, humidity, crude ethanol, pressure, NC1.0, NC2.5, SCNT, and PM2.5, were identified, providing insights into smoke occurrence. This research aids in proactive smoke mitigation and public health protection. The model's accuracy (...) and feature analysis empower decision-makers, with potential applications in real-time smoke event monitoring and preparedness strategies. This work contributes to the field of air quality forecasting and environmental stewardship, offering a data-driven approach to address smoke-related challenges and enhance community well-being. (shrink)

: One of the most active study fields in natural language processing, web mining, and text mining is sentiment analysis. Big data is an important research component in education that is used to advance the value of education by watching students' performance and understanding their learning habits. Real-time student feedback will enable teachers and students to understand teaching and learning challenges in the most user-friendly manner for students. By linking learning analytics to grounded theory, the proposed Deep Convolutional Neural (...) Network (DCNN) analyses students' sentiments and emotions through feedback using a Lexicon-based emotional analysis approach. The sentiment analysis approach is a computer procedure that identifies and classifies subjective information from the source material as good, negative, or neutral. (shrink)

Introduction: This article discusses the brain hierarchical organization/evolution as a consequence of the information-induced brain development, from the perspective of the Informational Model of Consciousness. Analysis: In the frame of the Informational Model of Consciousness, a detailed info-neural analysis ispresented, concerning the specific properties/functions of the informational system of the human body composed by the Center of Acquisition and Storing of Information, Center of Decision and Command, Info-Emotional Center, Maintenance Informational System, Genetic Transmission System, Info Genetic Generator and Info- (...) Connection center, in relation with the neuro-connected brain areas, with a special attention to the Info-Connection and its specific properties. Besides a meticulous analysis of the info-connections/neuro-functions of these centers, a special attention was paid to limbic/cingulate cortex activities. Defined as a trust/confidence center, additional features are highlighted in correlation with the activity of the anterior cingulate cortex, consisting in the intervention/moderation of amygdala emotional signals, conflicting opposite YES/NO data and error elimination in the favor of the organism adaptation/survival, the intervention in the certainty/uncertainty balance to select a suitable pro-life information (antientropic effect), in moderation of pain and in the stimulation of the empathic inter-human relations/communication. Representing the correspondence between the informational subsystems and the brain area map, itis shown that the up/down integration of information by epigenetic mechanisms and the down/ up evolution are correlated. Results: The analysis of the functions of the anterior cingulate opens new gates of investigations concerning the involved intimate mechanisms at the level of cell microstructure, specifically on the compatibility with quantum assisted processes admitted by the Informational Model of Consciousness and the quantum-based models The discussion on the information integration/codification by epigenetic mechanisms shows that this process starts from the superior levels of brain conscious info-processing areas and progressively advances to the automatic/autonomic inferior levels ofthe informational system, under insistent/repetitive cues/stress conditions, pointing out an hierarchical functional/anatomical structure of the brain organization. Additional arguments are discussed, indicating thatthe down/up progressive scale representation is a suggestive illustration of the brain evolution, induced/assisted/determined by information, accelerated at humans by the antientropic functions of the Info-Connection center. Conclusions: The hierarchical organization of the brain is a consequence of the integration process of information, defining its development accordingly to the adaptation requirements for survival during successive evolution stages of the organism, information playing a determinant/key role. (shrink)

Neural machine translation (NMT) is one of the success stories of deep learning and artificial intelligence. Revolutionary innovations in the computational architectures made in 2015–2017 have led to dramatic improvements in the quality of machine translation (MT) and changed the field forever. Some professional translators welcome these changes with enthusiasm, others less so. But everyone has to deal with them. Historically, the relationship between human translation and MT has been uneasy and complicated, but an increasing number of players in (...) both fields are now coming to view it as synergistic. -/- OPUS-CAT is a collection of machine translation software based on a Windows implementation of Marian NMT—a stable production-ready neural machine translation framework used in many industrial and research applications. It can be installed on your PC, fine-tuned to specialized translation domains, and integrated into CAT tools. (shrink)

A model of consciousness and conscious experience is introduced. Starting with a non-Lipschitz Chaotic dynamics of neural activity, we propose that the synaptic transmission between adjacent as well as distant neurons should be regulated in brain dynamics through quantum tunneling. Further, based on various studies of different previous authors, we consider the emergence of very large quantum mechanical system representable by an abstract quantum net entirely based on quantum-like entities having in particular the important feature of expressing self-reference similar (...) to what occurs in consciousness. The properties of such quantum-like mind entities are discussed in detail. A quantum-like model of conscious experience is also discussed. It is shown that such quantum mechanical entities are able to arrange themselves alternatively on the basis of the subject story, memory, and pain-pleasure in response to an external stimulus, thus giving the subject the possibility to response to the stimulus on the basis of his emotion as well as cognitive state. Finally, we discuss the possible connections between the quantum-like model introduced in this paper and the chaotic behaviors often identified experimentally in studies on brain dynamics. Part I of this article contains: Introduction; 1. Non Lipschitz Terminal Dynamics of Single Neuron Activity; and References; 2. Quantum Mechanical Properties of Neuron Dynamics; and 3. A Quantum Model of Consciousness I. (shrink)

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

Functional decomposition is an important goal in the life sciences, and is central to mechanistic explanation and explanatory reduction. A growing literature in philosophy of science, however, has challenged decomposition-based notions of explanation. ‘Holists’ posit that complex systems exhibit context-sensitivity, dynamic interaction, and network dependence, and that these properties undermine decomposition. They then infer from the failure of decomposition to the failure of mechanistic explanation and reduction. I argue that complexity, so construed, is only incompatible with one notion of decomposition, (...) which I call ‘atomism’, and not with decomposition writ large. Atomism posits that function ascriptions must be made to parts with minimal reference to the surrounding system. Complexity does indeed falsify atomism, but I contend that there is a weaker, ‘contextualist’ notion of decomposition that is fully compatible with the properties that holists cite. Contextualism suggests that the function of parts can shift with external context, and that interactions with other parts might help determine their context-appropriate functions. This still admits of functional decomposition within a given context. I will give examples based on the notion of oscillatory multiplexing in systems neuroscience. If contextualism is feasible, then holist inferences are faulty—one cannot infer from the presence of complexity to the failure of decomposition, mechanism, and reductionism. (shrink)

June 2022 A Revolutionary New Metaphysics, Based on Consciousness, and a Call to All Philosophers We are in a unique moment of our history unlike any previous moment ever. Virtually all human economies are based on the destruction of the Earth, and we are now at a place in our history where we can foresee if we continue on as we are, our own extinction. As I write, the planet is in deep trouble, heat, fires, great storms, and record flooding, (...) weather patterns drastic everywhere. Capitalism has been so successful in supplying us with an endless array of consumer items, that we have never noticed there is a cost. But there is a cost, the cost is the very life of the Earth herself, and now the day of reckoning is here. Days of reckoning are most often days of rath. But I am hoping that this is a day of major Awakening over the entire planet. We have caused a crisis so great for the Earth we can hardly get our minds around it. But I am proposing that deeply embedded in the crisis are ideas, about who and what the Earth is, Ourselves, and what we are here for. I am proposing that this is a call to all philosophy and philosophers to rethink ourselves from the ground up. The human race has never gotten the Earth right and now we must, for the Earth is the true foundation for civilization. We are in deep trouble with the Earth, and it owes to the fact that the great male thinkers never got the Earth right, they could never make the connection between the life of mind and the life of the Earth. I am proposing that deeply embedded in the crisis we are in are ideas. And that in fact the planet is in the grip of a false metaphysics, and scientific image of nature that really comes most recently from Descartes. His conviction that matter, and with it the Earth, are simply the weighable, the measurable the extended in space and time, utterly devoid of any inner life of mind thoughts or Consciousness. This view of matter and the Earth goes back to the very dawn of thought, Descartes is just its most recent and comprehensive version of things. This view of matter is really a false metaphysics that dominates most of the modern world and underpins the whole scientific image of nature itself. I am proposing this is a false metaphysics and offering a new version of things based on Consciousness. If there is a single idea fueling and driving the destruction of the planet, it is the conviction that the Earth is something without Consciousness. When you intend to ravage and savage the planet, turn it into consumer items to be converted into money in the bank, first pretend that all of the bright beings in the natural world are devoid of Consciousness, just “things” you do not have to have any conscious about destroying them. I am proposing that this term Consciousness, that has appeared as term and a problem for modern science, is the key to getting ourselves right with the Earth. I propose this new theory for science will ramify into every other disciplines as well, and the ways we think just about everything. This old paradigm based on a false theory of matter, has run its course, it has brought us to the brink of our own extinction. We need new ideas, and we need them now. You will find my recent work in the Scientific God Journal under the title: Consciousness as the Organizing Principle, and a very complete version of my arguments for this term Consciousness, along with radical reinterpretation of modern science itself, on the Galileo Commission site. It is a radical moment for all of the thinkers among us, take this as my small contribution to rethinking ourselves in new terms, based on my understanding that the true basis for the whole of Being, the Universe, the Cosmos, and with them Ourselves, is this term Consciousness. And unpack it. And I predict that with this term Consciousness every intractable human problem in both science and the modern world will go down like a line of dominos before it. And so, here are a few new ideas I would offer you. These are the times of the Reappearance of the Feminine, a great Awakening as of Springtime after a long dark and lonely Winter. We are leaving behind us an old civilization based on male dominance, fear lies and control, now in decay, disease, and death disintegrating all around us. We are moving into a whole new civilization based on Love and Truth. A new civilization requires new basic terms values and first principles. And so, I return to Descartes. 1. Just over 300 years ago a young man travelling with the army found himself stranded in a foreign town in a bare room lit by a candle with nothing to do but think. And in a couple of nights Descartes laid the entire thought foundations for modern science and most of the modern world. It isn’t clear that modern philosophers with their funding sources of grants, their graduate students, their reputation and all the perks and privileges of academia can do any better. Descartes claimed there are just two principles in the Universe, Matter and Mind. God was pure mind; human beings were mind and matter. And we are the only ones on the planet with mind. Everything else all of the bright array of beings who share the planet with us are simply matter. And matter? The weighable the measurable the extended in space and time utterly devoid of any inner life of thoughts, feelings, or Consciousness This classification was a boon for developing science. Scientists did not have to worry about any inner life in nature, ideas or Consciousness, they could just set about weighing and measuring, as they have until the present day. But it was a disaster for the Earth. Earth was once worshiped as a Goddess. With Descartes she became almost overnight “things.” Simply commodities and resources, an endless of supply of consumer items, to be turned into money in the bank. And now Descartes concept of matter is up for us. If there is a single idea that is enabling and driving the destruction of the Earth, it is the conviction that Earth is something without Consciousness. This whole scientific image of nature as something devoid of Consciousness is driving the destruction of the Earth, and driving us all insane, and it needs to change. Ever since Descartes formulated Dualism, scientists and philosophers have been trying to get rid of it. They have done this by attempting to make matter the fundamental term of reality, and to explain mind or Consciousness in terms of matter. And now, Consciousness has emerged as term and a problem for modern science. 2. The prevailing Universe picture has been mainly based on the belief that Consciousness appears only at the end of evolution in ourselves and some of the higher animals when matter attains “a certain state of complexity” and it is produced by neurons in the brain. But Consciousness is nothing real in itself, the neurons are doing the work, Consciousness is simply a byproduct of neural activity. As William James summed up this position, not his: Consciousness is to the brain as a shadow to the runner, it runs beside him but never influences his stride. And so, the great problem of neuroscience: How do neurons produce Consciousness? So far, no one has been able to say how. All we have is parallelism. Two processes are running in parallel a train of neural events and a train of Consciousness events. And so, which one is primary? The face of neuroscience and much of the modern world is that the neural events are what is really real, Consciousness is secondary. The conviction that Consciousness is something produced by neurons is so deeply held and has been so for so long, that it seems like an obvious fact. It is in fact an assumption and a theory, and it could all well be the other way around. The scientific image of nature as something devoid of Consciousness is now up for us. It has enabled the destruction of the Earth, and we are now at a unique place in our own evolution where we can foresee if we continue on as we are, our own extinction. And so, there are the best reasons for rethinking Descartes concepts of matter. Scientists and philosophers have been trying to escape dualism by making matter the fundamental term. And now, this has not worked. In a nutshell: The mind body problem, the “hard problem of Consciousness” ---how do neurons in the brain, atoms and molecules give rise to ideas thoughts and feelings and who what and where is the “I” who sees them? And then, the near-death experience. I remind you of the truth of the near-death experience. The body in the bed is brain dead eeg flat, but the patient, pure Consciousness is hovering at the ceiling watching the doctors work over the body below, able to read dials he could not have seen from the bed, sometimes able to events in the corridor beyond the room, all subsequently confirmed by the doctors. Evidence as good as it gets that the patient really was out of his body. And so, the patient is able to see without eyes, hear without ears and to think and remember without a brain. There is not a single scientific principle that can account for this. And I am proposing that the near-death experience is a threshold and a key for a much deeper understanding of the Universe. These three problems are so hard they cannot be solved by present scientific principles and therefore call all present principles and ways of thought into question. When your axioms cannot account for the data, it is time to drop them and find better fundamental terms. We have not been able to solve the problems of Consciousness with the assumption that matter is the most basic and fundamental term. And so, if Cartesian dualism is to be overcome at all, we need to try it the other way around. 3. We are in the position of Copernicus. Remember Copernicus? He was making his calculations on the assumption that the Earth was the center of the Universe until they became so unwieldy that he decided to try it all the other way around. To make the sun the center, the Earth revolving around it. And lo, all of his data fell neatly into place. I am proposing a whole new Universe picture. Evolution makes us continuous with every other being, Consciousness would not be in us if it were not in them also, and then, in the atoms and molecules and cells that make up living beings. I propose to make Consciousness the most basic term and see what follows. I am in fact, proposing a new Copernican Revolution, that Consciousness is the true basis for the Universe and the right fundamental term, for science itself all other disciplines as well, and the whole of the modern world. And I predict that every intractable problem in science or the modern world, will go down like a line of dominos before it. In a nutshell: Consciousness and not matter is “first and fundamental” in the Universe, it is there from the very “Beginning” everything has it and all of the true causalities, the explanatory principles belong to it. Not to matter. What then is matter? All matter is an Appearance of something much more real, Consciousness. All matter is an expression of Consciousness, even the least little bit of matter contains Consciousness, and is an expression of Consciousness. And so, we revise the scientific image of nature. Science at present is two termed, matter/energy. It needs to become three termed: Matter/energy/Consciousness, with the recognition that all of the basic principles, the true causalities of events, and of the Universe belong to Consciousness. And now physics. To accommodate this new Universe picture physics needs to alter just one term, energy. All energy contains Consciousness, that idea alone will change our world forever. The animals the trees the plants are just as conscious as we are, as is the Earth herself, a continuum of many forms of Consciousness of which our own is one. The Earth consists of interacting and intersecting forms of Consciousness, Consciousness within Consciousness within Consciousness within Consciousness. A remarkable work has just appeared entitled “Beyond Words What Animals Feel and Think.” Describing in detail how like ourselves all of the animals are. And the more we can see how like us everything else really is, the harder it will be for us to do them all in, in the interest of turning them into consumer items. And so, here is a new principle of understanding, that entails completely new forms of explanation, and a whole new Universe picture to be spelled out in terms of Consciousness and its various properties. This is a Universe that is aware awake and Enspirited throughout. And so, there is only one principle, not two. And so, how does Consciousness work? There is “no mind/body problem.” My Consciousness is aligning the Consciousness of neurons in the brain, that aligns the Consciousness of all other cells in the body, that aligns the Consciousness of molecules and atoms themselves. Mainly from the top down, but also interactive, and not reduction to the purely physical forces. 4. And so, here is a radical new Universe picture, and with it a new way to think about the Universe itself, the Earth, Ourselves, and Spirit. It requires a complete revision of both of the terms of modern science itself. The problem of the modern world owes in deep measure to Descartes concept of matter as something devoid of interior life. But he is only the most powerful and simple expression of a whole tradition of thought that predates him, that goes back to the very dawn of thought itself. It owes to the conviction of both Plato and Aristotle that the Earth was something devoid of reason, or mind, and therefore something to be held in complete contempt. We are emerging from a civilization based on contempt for the Earth, and down the road, it will become contempt for the body, sex, women, the Native people, and people of color everywhere. And so, this entire tradition is now up for us, it has brought us to the brink of our own extinction, and we need to get beyond it. I am proposing that this term Consciousness is the way out. The missing piece of the puzzle and the idea whose time has come. And it will enable us to move into a completely new civilization based on respect for the Earth, women, sex, people of color, that honors everyone and has a place for all. Revolutions in science occur when new data show up that cannot be accounted for by existing paradigms (Thomas Kuhns: The Structure of Scientific Revolutions). Consciousness, and all the properties and problems of Consciousness are just such data, and they compel a new scientific revolution. But we are a scientific age. A new Copernican revolution in science is such as revolution everywhere. And Consciousness is the key to getting every other reality at all levels and depths right as well. It will enable us finally to get the Earth right, get ourselves right with the Earth, and with one another. Archimedes: Give me a place to stand, and I will move the world. I am proposing a new synthesis among our fundamental terms in which this term Consciousness is the new lynchpin. And so, I return once more to Descartes. Descartes in the Mediations was a dualist, they were two realities interacting in the pineal gland mind and body. But Descartes age was high on “Cartesianisms,” on Descartes concept of matter. Animals were simply cleverly wired machines (Hobbes called them automata). Rumor had it that Descartes followers went about kicking dogs reasoning that their howls were not due to any feelings of pain, but simply the wiring of the machine. We have been doing this to the Earth ever since, and now the Earth is not going to stand for it any longer. It is time we all woke up. There is no matter in Descartes sense of it, or in Plato and Aristotle’s sense of it. This old concept of matter is embedded in all of the crisis and catastrophes---fires, drought, pandemics, over population, the disintegrating economies, our ways of life, capitalism and consumerism themselves. All matter is Enspirited, contains Consciousness, and is an expression of Consciousness. Now I tell you something about Descartes you may not know. In the Mediations was a dualist, there are minds and bodies. But by the end of his life, he knew the truth. Descartes guided his whole life by these vivid dreams, and toward the end of his life he had this dream: He saw the Universe as a giant machine and in the heart of the machine propelling the whole, was Consciousness. And so, out of the shades and shadows and into the light of day. Here is a renewed and a new science, a new cosmology. And with it, a revolutionary new metaphysics. The whole of the modern world is in the grip of a false metaphysics, based on Descartes theory of matter. Here is a new, and finally true metaphysics based on the single term, Consciousness. 5. And in all my works, I spell out this new metaphysics based on Consciousness, and what it means for how we think about virtually everything: The Universe, the Earth, Ourselves, and Spirit. Of who, what and where Spirit is, that we may connect with Spirit everywhere. There is only one term for the Universe, and not two, this single term Consciousness, that manifests itself in two modes: Consciousness in Form, Consciousness Formless. And so, here is a sketch of what this new term Consciousness really means. For four great aspects of the real, four new cornerstones for a whole new civilization on Earth, based on Love and Truth. A. For the Universe: New causalities, all matter is an expression of Consciousness, awake and aware, Enspirited throughout, which has a place for the causalities of love ideas intent plan and purpose, what Aristotle called final causality, that for the sake of which. And it has a place for subjectivity, the greatest mystery in the whole of the Universe, knowable only in ourselves, but there in everything else as well, bedrock in the chain of causalities. The buck stops here. Here are two very different Universe pictures. The Cartesian Universe consists of things, pushed, and pulled around by the physical forces that function blind. The true Universe consists of selves and not things. Selves at least in ourselves, are organized and focused about an I, an I am, or a sense of self in everything, ever other being dear to itself. Just one aspect of subjectivity. And so, it is Consciousness all the way down, and subjectivity all the way down. Science proceeds on the basis of the negligible of what it can leave out as unimportant. Subjectivity is the ultimate animating principle of the Universe. What Descartes really did was to strip from nature what is most essential about it, its animating principle Consciousness. And so, we come out of a long history of “clunk.” The great male thinkers have killed creation. First in concepts and now in the lived life. And so, after centuries of fruitless search, not all of it in vain, finally, and just in time the true basic principle for the Universe has shown up in virtually all disciplines, this term Consciousness. B. And so, the Earth: What does this term Consciousness really mean for the Earth? It means that all of the bright array of beings around us have Consciousness. And the more we can realize how like us they are, the harder it will be to do them all in. Is this what it really means to be “made in the image of Spirit?” To destroy ever other living being? The god of Genesis is a creator god who has created billions and billions of living forms. Man, taking himself to be made in the image of Spirit has destroyed virtually all of it. The Earth is not things, not commodities and resources, not an endless array of consumer goods. The Earth consists of conscious living spiritual beings like ourselves, who need to be honored and respected, nurtured and cared for, to be Loved. And if the human race can finally pull it off and bring Love here and start helping the Earth rather than destroying her, even our vast numbers now at 8 billion, may not matter. Deeply embedded in the modern world is a false cosmology based on the hitherto useful but ultimately false metaphysics of Descartes. In particular it owes to Descartes concepts of matter, and of the Earth, as something devoid of Consciousness. And now this concept is up for us. If there is a single idea enabling and driving the destruction of the planet, it is the conviction that the Earth is something devoid of Consciousness. And we all have a vested interest in keeping this false concept in place. When you intend to ravage and savage the Earth, to turn it into consumer items, and money in the bank, first pretend she has no Consciousness that way we need not have any conscious about it. We are in deep trouble with the planet. Virtually all human economies are based on the destruction of the Earth, and we are now at a place in our history where we can foresee if we continue on as we are, our own extinction. And so, here are a few new guiding principles for the planet, based on Consciousness. Consciousness has field properties. The Earth is Consciousness throughout, connected and interconnected throughout, intersecting and interacting fields of Consciousness of which our own is one. To harm one is to harm all. Only this understanding of the Earth can set our destructive technologies, and capitalism, their goals, and their limits. The real problem with capitalism, and with civilization itself is it has no true understanding of the Earth, all the exquisite networks and webworks that connect all beings with one another. Nor it is about to admit that the Earth is our “life support system,” reason gone insane in the modern world. We are destroying our life support system, capitalism is the full expression of the human death wish in the modern world, and it needs to change. And now, let the barriers between ourselves and all other beings go down. C. And so, Ourselves. This new Universe picture based on Consciousness opens into and has a place for new concepts of human identity. In particular, it has a place for reincarnation, a process whereby one Consciousness dons’ bodies again and again and lays them down. This is a process wholly impossible on materialistic theories. This new view of things puts a higher perspective on Freudian determinism, which is a partial knowing in a greater account. We are something more than the victims of genes drives and childhood. We are immortally eternal evolving spiritual beings who take on a cycle of earthly lives in order to achieve certain goals and abilities, and then, our earthly cycle complete, we depart the Earth plane for other areas of the Universe physical or purely spiritual, to continue our evolution elsewhere. The scientific evidence for reincarnation is increasing on all sides, see for example the work of Michael Newton, a mainstream scientist forced to confront the fact of reincarnation. A completely new reincarnational picture is now emerging everywhere. We choose our parents for abilities we could acquire through them. We all come here with reasons for being here, and we all have free will, an original property of Consciousness, and we work out our issues accordingly. We all come here with the same reason for being here. It is to experience the Earth plane and human society at all levels and depths, in every social role, every climate, both sexes male and female and in all races. We are all here to acquire a deep understanding of the Earth plane with all its challenges and all of its present problems. We are not here to devour and consume the Earth, but to honor her for the learning experience she makes available for us. And so, reincarnation is the great equalizer, the fundamental basis for any bill of rights. Many of the problems of the modern world owe to the fact that reincarnation has not been taught in the West. Reincarnation was the center piece of Jesus teachings. It was banned by a fourth century council that decided the populous could best be controlled through fear. They edited all mention of reincarnation from the scriptures, and inserted fear terms instead, a judgmental god, and so on. If reincarnation had been taught, there would have been no suppression of women, but the gift of both sexes would have been honored and developed. And there would have been no reason for contempt for people of color because who they are, we have been or will be. There is no reason to look down on anyone when we know that our own identity includes membership in theirs. The truth of reincarnation needs to be the foundation for getting ourselves right with the Earth, and with one another. D. And so, Spirit. This new Universe picture, based on Consciousness, has a place for Spirit as simply Infinite Consciousness. All universes all worlds are formed out of this Consciousness, divine and sacred throughout. And Spirit is everywhere in the Earth and can be connected everywhere. And the Native North American people had things right. Everything has its own Spirit, and without contradiction is also an expression of Spirit. And what I am really doing in terms of concepts of modern science is presenting the Native North American version of the Real. And this new Universe picture has a place for the essential insight of all religions almost smothered by church tradition: Love. Not an emotion but a great Oneness, that can be found everywhere, even in a busy city street. But especially in the Earth, that is levels and depths of Oneness everywhere. And so, for the first time in their long and bitter antagonistic history, science and religion now share a common base with this term Spirit, the lynchpin in a new conceptual framework. And I offer an interpretation of where we are in our history, this present moment of historical grace, and these tumultuous times in which we live, the return of the Goddess and the Divine Feminine, the Reappearance of the Feminine in history, the Advent of Women, this great Awakening, as of Springtime after a long dark Winter, the Awakening, the Second Coming. And so, stay tuned! (shrink)