Numerous agricultural wastes are impractically discarded every day, and one of these is rice bran. This study investigated the production of a biodegradable straw made of cassava starch and powdered rice bran. It aimed to determine the effectiveness of the different treatments of Cassava Starch-Rice Bran in terms of water resistance, tensile strength, and biodegradability. An experimental design was used in conducting the study. There were three treatments made in making CSRB straws: the first, with more rice bran; the second, (...) with the same cassava starch and rice bran ratio; and the third, with more cassava starch. These treatments produced three replicates each. The straws were placed in a dehydrating machine for 24 hours at a temperature of 60° Celsius. The straws were then coated with beeswax after being removed from their respective molders. The CSRB straws were found to be water resistant due to the beeswax coating applied, although it had a weak tensile strength even with the presence of beeswax. The rate of biodegradability of the CSRB straw was fast due to it being made up of organic materials. The results revealed that straws made from Cassava Starch and Powdered Rice Bran are effective in terms of water resistance and biodegradability. (shrink)

The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the (...) development of the community-based Coronavirus Infectious Disease Ontology in early 2020. -/- As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. -/- CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications. (shrink)

This article surveys different philosophical theories about the nature of truth. We give much importance to truth; some demand to know it, some fear it, and others would even die for it. But what exactly is truth? What is its nature? Does it even have a nature in the first place? When do we say that some truth-bearers are true? Philosophers offer varying answers to these questions. In this article, some of these answers are explored and some of the problems (...) raised against them are presented. (shrink)

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

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

3rd ed, 2021. A circumscription of the classical theory of computation building up from the Chomsky hierarchy. With the usual topics in formal language and automata theory.

Dictionary entry discussing the main moral and meta-ethical doctrines found in the works of James Griffin.

Delicate Tissue Tumors (STT) are a type of sarcoma found in tissues that interface, backing, and encompass body structures. Due to their shallow recurrence in the body and their extraordinary variety, they seem, by all accounts, to be heterogeneous when seen through Magnetic Resonance Imaging (MRI). They are effortlessly mistaken for different infections, for example, fibro adenoma mammae, lymphadenopathy, and struma nodosa, and these indicative blunders have an extensive unfavorable impact on the clinical treatment cycle of patients. Analysts have proposed (...) a few AI models to characterize cancers, however none have sufficiently tended to this misdiagnosis issue. Likewise, comparative investigations that have proposed models for assessment of such cancers generally don't think about the heterogeneity and the size of the information. Thusly, we propose an AI based approach which joins another strategy of pre handling the information for highlights change, resampling methods to dispense with the predisposition and the deviation of precariousness and performing classifier tests in light of the and Deep learning Algorithm as Artificial brain organization. -/- Tumors (STT). (shrink)

This article assesses how autonomy and machine learning impact the existential risk of nuclear war. It situates the problem of cyber security, which proceeds by stealth, within the larger context of nuclear deterrence, which is effective when it functions with transparency and credibility. Cyber vulnerabilities poses new weaknesses to the strategic stability provided by nuclear deterrence. This article offers best practices for the use of computer and information technologies integrated into nuclear weapons systems. Focusing on nuclear command and control, avoiding (...) autonomy and machine learning is recommended as one means to reduce the existential risk of unintended nuclear conflict. (shrink)

Abstract: Ensuring the proactive detection of transaction risks is paramount for financial institutions, particularly in the context of managing credit scores. In this study, we compare different machine learning algorithms to effectively and efficiently. The algorithms used in this study were: MLogisticRegressionCV, ExtraTreeClassifier,LGBMClassifier,AdaBoostClassifier, GradientBoostingClassifier,Perceptron,RandomForestClassifier,KNeighborsClassifier,BaggingClassifier, DecisionTreeClassifier, CalibratedClassifierCV, LabelPropagation, Deep Learning. The dataset was collected from Kaggle depository. It consists of 164 rows and 8 columns. The best classifier with unbalanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%,Recall100.0% and the F1-score (...) 100.0%. However, the best classifier with balanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%, Recall 100.0% and the F1-score 100.0%. (shrink)

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

Fichte assigns ‘intellectual intuition’ a new meaning after Kant. But in 1799, his doctrine of intellectual intuition is publicly deemed indefensible by Kant and nihilistic by Jacobi. I propose to defend Fichte’s doctrine against these charges, leaving aside whether it captures what he calls the ‘spirit’ of transcendental idealism. I do so by articulating three problems that motivate Fichte’s redirection of intellectual intuition from being to acting: (1) the regress problem, which states that reflecting on empirical facts of consciousness leads (...) only to further facts and so cannot yield a first principle; (2) the rhapsody problem, which states that the categories form a haphazard set and so lack necessity unless they derive from a first principle; and (3) the nihilism problem, which states that a first principle cannot lie outside our cognition of it, lest it be the cause of our cognition and, being first, the cause of all our actions, reducing us to machines. Crucially, Fichte’s three motivating problems are in fact aspects of a single problem. Leaving any aspect unsolved spoils putative solutions to the other two. Consequently, Fichte requires a single unified solution to all three, which his doctrine of intellectual intuition provides. (shrink)

In the field of machine consciousness, it has been argued that in order to build human-like conscious machines, we must first have a computational model of qualia. To this end, some have proposed a framework that supports qualia in machines by implementing a model with three computational areas (i.e., the subconceptual, conceptual, and linguistic areas). These abstract mechanisms purportedly enable the assessment of artificial qualia. However, several critics of the machine consciousness project dispute this possibility. For instance, Searle, in his (...) Chinese room objection, argues that however sophisticated a computational system is, it can never exhibit intentionality; thus, would also fail to exhibit consciousness or any of its varieties. This paper argues that the proposed architecture mentioned above answers the problem posed by Searle, at least in part. Specifically, it argues that we could reformulate Searle’s worries in the Chinese room in terms of the three-stage artificial qualia model. And by doing so, we could see that the person doing all the translations in the room could realize the three areas in the proposed framework. Consequently, this demonstrates the actualization of self-consciousness in machines. (shrink)

Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.

Sara Suleri has written recently, in Meatless Days, of being treated as an "otherness machine"-and of being heartily sick of it.20 Perhaps the predicament of the postcolonial intellectual is simply that as intellectuals-a category instituted in black Africa by colonialism-we are, indeed, always at the risk of becoming otherness machines, with the manufacture of alterity as our principal role. Our only distinction in the world of texts to which we are latecomers is that we can mediate it to our fellows. (...) This is especially true when postcolonial meets postmodern; for what the postmodern reader seems to demand of Africa is all too close to what modernism-in the form of the postimpressionists-demanded of it. The role that Africa, like the rest of the Third World, plays for Euro-American postmodernism-like its better-documented significance for modernist art-must be distinguished from the role postmodernism might play in the Third World; what that might be it is, I think, too early to tell. What happens will happen not because we pronounce on the matter in theory, but will happen out of the changing everyday practices of African cultural life.For all the while, in Africa's cultures, there are those who will not see themselves as Other. Despite the overwhelming reality of economic decline; despite unimaginable poverty; despite wars, malnutrition, disease, and political instability, African cultural productivity grows apace: popular literatures, oral narrative and poetry, dance, drama, music, and visual art all thrive. The contemporary cultural production of many African societies, and the many traditions whose evidences so vigorously remain, is an antidote to the dark vision of the postcolonial novelist. 20. Sara Suleri, Meatless Days , p. 105. Kwame Anthony Appiah, professor of philosophy and literature at Duke University, is the author of a number of books, including For Truth in Semantics , Necessary Questions , and In My Father's House , a collection of essays on African cultural politics. His first novel, Avenging Angel, was published in 1990. (shrink)

This paper focuses on the research field of machine ethics and how it relates to a technological singularity—a hypothesized, futuristic event where artificial machines will have greater-than-human-level intelligence. One problem related to the singularity centers on the issue of whether human values and norms would survive such an event. To somehow ensure this, a number of artificial intelligence researchers have opted to focus on the development of artificial moral agents, which refers to machines capable of moral reasoning, judgment, and decision-making. (...) To date, different frameworks on how to arrive at these agents have been put forward. However, there seems to be no hard consensus as to which framework would likely yield a positive result. With the body of work that they have contributed in the study of moral agency, philosophers may contribute to the growing literature on artificial moral agency. While doing so, they could also think about how the said concept could affect other important philosophical concepts. (shrink)

The questions that have been looming at the thoughts of many African theologians for years have been: How can the Christian faith be made to be at home in Africa, in such a way that it harmo nizes with African beliefs and practices, thus becoming the religion of the African people? What is the relationship between African traditional religion and culture with the Christian faith? Does being a Christian mean that I should distance myself from m y traditional religious and (...) cultural heritages? It is from this background that Igwebuike theology emerges as an African theology , posting that effort towards evangelization must begin from the understanding of the African worldview and the incorporation of such in the processes of the communication and learning of the Christian faith. This work presented the theology of African typo logy ( u di, meaning type, kind or nature) within the context of Igwebuike theology , with a view to creating a balance in the midst of the conflicts between Christianity and African religion and culture by connecting the events of the New Testament to events and persons in African traditional religion and culture. This connection is such that the events, persons, realities, etc., in African traditional religion and culture find meaning and fulfillment in the New Testament Scriptures. The Igwebuike theology of u di has created an aperture for further dialogue of culture with faith. It provided a deeper meaning and wider capacity for the application of the concept , ‘typology’. It has contributed to the continuous search for better ways of making the Christian fai th feel at home in Africa, and also making it possible for the redemption of the African culture through its interaction with faith. For the purpose of this study, the Igwebuike theoretical framework , which emphasizes beginning from and valuing what is kno wn, and using what is already known as a stepping stone to get to the unknown , was adopted. (shrink)

The questions that have been looming at the thoughts of many African theologians for years have been: How can the Christian faith be made to be at home in Africa, in such a way that it harmo nizes with African beliefs and practices, thus becoming the religion of the African people? What is the relationship between African traditional religion and culture with the Christian faith? Does being a Christian mean that I should distance myself from m y traditional religious and (...) cultural heritages? It is from this background that Igwebuike theology emerges as an African theology , posting that effort towards evangelization must begin from the understanding of the African worldview and the incorporation of such in the processes of the communication and learning of the Christian faith. This work presented the theology of African typo logy ( u di, meaning type, kind or nature) within the context of Igwebuike theology , with a view to creating a balance in the midst of the conflicts between Christianity and African religion and culture by connecting the events of the New Testament to events and persons in African traditional religion and culture. This connection is such that the events, persons, realities, etc., in African traditional religion and culture find meaning and fulfillment in the New Testament Scriptures. The Igwebuike theology of u di has created an aperture for further dialogue of culture with faith. It provided a deeper meaning and wider capacity for the application of the concept , ‘typology’. It has contributed to the continuous search for better ways of making the Christian fai th feel at home in Africa, and also making it possible for the redemption of the African culture through its interaction with faith. For the purpose of this study, the Igwebuike theoretical framework , which emphasizes beginning from and valuing what is kno wn, and using what is already known as a stepping stone to get to the unknown , was adopted. (shrink)

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

This is a Turing Machine which computes the exponential function f(x,y) = xˆy. Instructions format and operation of this machine are intended to best reflect the basic conditions outlined by Alan Turing in his On Computable Numbers, with an Application to the Entscheidungsproblem (1936), using the simplest single-tape and single-symbol version, in essence due to Kleene (1952) and Carnielli & Epstein (2008). This machine is composed by four basic task machines: one which checks if exponent y is zero, a second (...) which checks if base x is zero, a third that is able to copy the base, and a fourth able to multiply multiple factors (in this case, factors will be all equal). They were conveniently separated in order to ease the reader's task to understand each step of its operation. We adopt the convention that a number n is represented by a string of n+1 symbols "1". Thus, an entry (x, y) will be represented by two respective strings of x+1 and y+1 symbols "1", separated by a single "0" (or a blank), and as an output, this machine will generate a string of (xˆy)+1 symbols "1". Some of the instructions are followed by a brief description of what's going on. (shrink)

Man is a seeker by nature. He searches for truth. An ordinary man cannot be indifferent to truth because of the deep quest within him for truth. Gandhi lived his whole life in the perpetual quest for truth. He lived and moved in pursuit of this goal. This pursuit of seeking truth under the banner of philosophical education makes educational philosophizing moral. One can perfect these ideologies of different schools and make philosophizing in education better by placing truth in the (...) center. Thus, an idealist can evolve a realization of truth, a naturalist can bring about the true nature and potentialities of the individual, and pragmatists can rejuvenate the minds with a truth-oriented strategy and existential thinkers can invite truth into them so that they can avoid fear and dread. The Gandhian vision of Satya offers us the key to integral education. Following the footprints of Satya not only satisfies the Gandhian aims for education but it also helps the human person to continue his role on the earth as the seeker of truth. The aim of my research is to explore whether the concept of the truth of Mahatma Gandhi could be used as a platform for philosophical education? Can we take inspiration from Gandhian thoughts so that philosophizing may not engage itself in hermeneutical violence to bring out the truth (of our making) but a hermeneutical ahimsa that lets the truth show itself? Truth for Gandhi was equal to one of his lungs. He spoke the truth, wrote on truth, and exhorted people to be truthful. Gandhi made the concept of truth central to metaphysics, epistemology, morality, and the philosophy of religion. Truth was the guiding principle for Gandhi in all these disciplines. When we bring in truth as the guiding principle in the philosophy of education we envisage an educational community of all truth seekers traveling together in freedom to come to an ever-increasing approximation of truth. Should philosophy stop the pursuit of truth (satya) as Rorty claims? Is communication of truth irrelevant as no truth is ascertainable as skeptics claim? Is the will to communicate truth senseless since truth is an illusion? Do these approaches satisfy the philosophical minds that are after truth? The methodology I pursue in this thesis is an exploratory, analytical, and critical approach. a) Analytical: The basic method to be used is to analyze the existing works and apprehend their significance in the area of education, in particular philosophical education. b) Critical: a critical and comprehensive analysis of the concept of truth is followed in the research to represent the significance of truth in philosophical education. Man cannot be indifferent to truth since there is a longing for truth in the heart of every human being. Hence the need to search for it with all one’s might through success does not come everyone’s way in the same measure. The reason for this is that there are certain factors that impede our search and they even distort our understanding. They are natural limitations of reason, inconsistency of the heart, worldly concerns, and fear of the demand that truth makes. Hence it is necessary to overcome these in the search for truth. In his dealings with others, the human person wishes that the other may communicate his truth and only truth. Gandhian invitation is to be an experimenter with truth. Truth is that all seek and want above everything. No one wishes to compromise on that. When we bring in truth as the guiding principle in the philosophy of education we envisage an educational community of all truth seekers traveling together in freedom to come to an ever-increasing approximation of truth. They are compelled to study and defend truth in a better way when they are confronted. They will be forced to discern the core of truth from its formulations. That is the role of the experimenter with the truth. -/- In this research paper, I attempt to present M. K. Gandhi as an educationist giving emphasis only on his concept of truth. It is a limited approach to Gandhian educational philosophy because when I attend only to the concept of truth I fail to include very many other aspects of his educational philosophy like craft-centered education, teaching methods, education for different sections of society, etc. It also limits me to criticizing Gandhian dislike for the higher education system appropriately. However, the notion of truth is a common platform that could be shared by three important branches of philosophical education, namely the naturalistic, the idealistic, and the pragmatic. (shrink)

{June 2018 UPDATE: This work has been greatly surpassed by "Utilizing Future-Viewing Instruments" which will appear in the July 2018 issue of Progress in Physics. It can now be downloaded in PDF form from their website.} This is the full text of the paper that was published as a Kindle book on April 6th, 2014. In most respects, it has since been surpassed by "Potentials of Future-Viewing Machines," which is available here in this repository. Nevertheless, this earlier work addresses minutia (...) and interesting considerations that do not appear elsewhere, and it has a large bibliography, so it remains very useful for research purposes. (shrink)

{June 2018 UPDATE: This work has been greatly surpassed by "Utilizing Future-Viewing Instruments" which will appear in the July 2018 issue of Progress in Physics. It can now be downloaded in PDF form from their website.} -/- The introduction of new scientific instruments has always played a vital role in the advancement of science and society. All scientific instruments to date have only been able to gain information pertaining to events of our immediate and distant past. Concerning our attempts to (...) gain information about future events, thus far our sciences have been limited to prediction. However, what if we could develop instruments that would enable us, in a wide range of circumstances, to achieve direct empirical access to future states of our world? It is clear that any kind of future-viewing machine would have enormous impacts upon science and society, but what kinds of impacts? Questions as to whether such machines are physically possible and might ever be feasible from an engineering perspective will not be addressed; this work is devoted to exploring the logic of such machines and deducing some of the possibilities of their utilization. (shrink)

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

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and react like humans. Some of the activities computers with artificial intelligence are designed for include, Speech, recognition, Learning, Planning and Problem solving. Deep learning is a collection of algorithms used in machine learning, It is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is a technique used (...) to produce Pneumonia detection and classification models using x-ray imaging for rapid and easy detection and identification of pneumonia. In this thesis, we review ways and mechanisms to use deep learning techniques to produce a model for Pneumonia detection. The goal is find a good and effective way to detect pneumonia based on X-rays to help the chest doctor in decision-making easily and accuracy and speed. The model will be designed and implemented, including both Dataset of image and Pneumonia detection through the use of Deep learning algorithms based on neural networks. The test and evaluation will be applied to a range of chest x-ray images and the results will be presented in detail and discussed. This thesis uses deep learning to detect pneumonia and its classification. (shrink)

Automatic detection of diseases and anatomical landmarks in medical images by the use of computers is important and considered a challenging process that could help medical diagnosis and reduce the cost and time of investigational procedures and refine health care systems all over the world. Recently, gastrointestinal (GI) tract disease diagnosis through endoscopic image classification is an active research area in the biomedical field. Several GI tract disease classification methods based on image processing and machine learning techniques have been proposed (...) by diverse research groups in the recent past. However, yet effective and comprehensive deep ensemble neural network-based classification model with high accuracy classification results is not available in the literature. In this thesis, we review ways and mechanisms to use deep learning techniques to research on multi-disease computer-aided detection about gastrointestinal and identify these images. We re-trained five state-of-the-art neural network architectures, VGG16, ResNet, MobileNet, Inception-v3, and Xception on the Kvasir dataset to classify eight categories that include an anatomical landmark (pylorus, z-line, cecum), a diseased state (esophagitis, ulcerative colitis, polyps), or a medical procedure (dyed lifted polyps, dyed resection margins) in the Gastrointestinal Tract. Our models have showed results with a promising accuracy which is a remarkable performance with respect to the state-of-the-art approaches. The resulting accuracies achieved using VGG, ResNet, MobileNet, Inception-v3, and Xception were 98.3%, 92.3%, 97.6%, 90% and 98.2%, respectively. As it appears, the most accurate result has been achieved when retraining VGG16 and Xception neural networks with accuracy reache to 98% due to its high performance on training on ImageNet dataset and internal structure that support classification problems. (shrink)

As inductive decision-making procedures, the inferences made by machine learning programs are subject to underdetermination by evidence and bear inductive risk. One strategy for overcoming these challenges is guided by a presumption in philosophy of science that inductive inferences can and should be value-free. Applied to machine learning programs, the strategy assumes that the influence of values is restricted to data and decision outcomes, thereby omitting internal value-laden design choice points. In this paper, I apply arguments from feminist philosophy of (...) science to machine learning programs to make the case that the resources required to respond to these inductive challenges render critical aspects of their design constitutively value-laden. I demonstrate these points specifically in the case of recidivism algorithms, arguing that contemporary debates concerning fairness in criminal justice risk-assessment programs are best understood as iterations of traditional arguments from inductive risk and demarcation, and thereby establish the value-laden nature of automated decision-making programs. Finally, in light of these points, I address opportunities for relocating the value-free ideal in machine learning and the limitations that accompany them. (shrink)

The proposed IDS model is aimed at detecting network intrusions by classifying all the packet traffic in the network as benign or malicious classes. The Canadian Institute for Cyber security Intrusion Detection System (CICIDS2017) dataset has been used to train and validate the proposed model. The model has been evaluated in terms of the overall accuracy, attack detection rate, false alarm rate, and training overhead. DDOS attacks based on Canadian Institute for Cyber security Intrusion Detection System (KDD Cup 99) dataset (...) has been used to train and validate. For validation, comparison for 2 dataset (CICIDS2017 and KDD Cup 99) is done. Then, to implement the Deep learning algorithms is proposed. Method Classification using SVM algorithm Model predict is done. (shrink)

Are living organisms--as Descartes argued--just machines? Or is the nature of life such that it can never be fully explained by mechanistic models? In this thought-provoking and controversial book, eminent geophysicist Walter M. Elsasser argues that the behavior of living organisms cannot be reduced to physico-chemical causality. Suggesting that molecular biology today is at the same point as Newtonian physics on the eve of the quantum revolution, Elsasser lays the foundation for a theoretical biology that points the way toward a (...) natural philosophy of organic life. Explicitly repudiating "vitalism" (the notion that the laws of nature need to be modified when applied to living organisms), Elsasser argues instead that the structural complexity of even a single living cell is "transcomputational"--that is, beyond the power of any imaginable system to compute. Beginning from this insight, Elsasser leads the reader through a step-by-step process that ultimately arrives at the conclusion that living and non-living matter are separated by "a no-man's land of irrationality." Trained in Germany as a physicist, Elsasser first pondered the implications of quantum mechanics for biology as early as 1951. The more closely he studied the inherent complexity of life, the more skeptical he became of the reductionist view of organisms as tiny machines. "An organism," he concluded, "is a source of causal chains which cannot be traced beyond a terminal point because they are lost in the unfathomable complexity of the organism." Like the physicist who works within the bounds of an unfathomable universe, Elsasser argues, the biologist must seek answers within a system that is no less unfathomable. (shrink)

This article focuses on a particular issue under machine ethics—that is, the nature of Artificial Moral Agents. Machine ethics is a branch of artificial intelligence that looks into the moral status of artificial agents. Artificial moral agents, on the other hand, are artificial autonomous agents that possess moral value, as well as certain rights and responsibilities. This paper demonstrates that attempts to fully develop a theory that could possibly account for the nature of Artificial Moral Agents may consider certain philosophical (...) ideas, like the standard characterizations of agency, rational agency, moral agency, and artificial agency. At the very least, the said philosophical concepts may be treated as signposts for further research on how to truly account for the nature of Artificial Moral Agents. (shrink)

Abstract: Forests are areas with a high density of trees, and they play a vital role in the health of the planet. They provide a habitat for a wide variety of plant and animal species, and they help to regulate the climate by absorbing carbon dioxide from the atmosphere. While in 2010, the world had 3.92Gha of forest cover, covering 30% of its land area, in 2019, there was a loss of forest cover of 24.2Mha according to the Global Forest (...) Watch institute. Discovery and classification depend on human experience and effort, so the error in the results of this process can lead to forest fires and disasters. Therefore, deep learning algorithms from artificial intelligence and machine learning sciences have been applied to help specialists avoid false or inaccurate diagnoses when detecting Forest fires in images using a pre-trained convolutional neural network called VGG16. The model was customized to fit the Forest fires classification and then applied to a dataset consisting of (14,000) of the Forests collected from the Kaggle depository. We trained, validated, and tested the modified VGG16 model. The proposed VGG16 model obtained Precision (99.96%), Recall (99.96%), and F1-Score (99.96%). (shrink)

The concept of bias is pervasive in both popular discourse and empirical theorizing within philosophy, cognitive science, and artificial intelligence. This widespread application threatens to render the concept too heterogeneous and unwieldy for systematic investigation. This article explores recent philosophical literature attempting to identify a single theoretical category—termed ‘bias’—that could be unified across different contexts. To achieve this aim, the article provides a comprehensive review of theories of bias that are significant in the fields of philosophy of mind, cognitive science, (...) machine learning, and epistemology. It focuses on key examples such as perceptual bias, implicit bias, explicit bias, and algorithmic bias, scrutinizing their similarities and differences. Although these explorations may not conclusively establish the existence of a natural theoretical kind, pursuing the possibility offers valuable insights into how bias is conceptualized and deployed across diverse domains, thus deepening our understanding of its complexities across a wide range of cognitive and computational processes. (shrink)

When comparing alternative courses of action, modern military decision makers often must consider both the military effectiveness and the ethical consequences of the available alternatives. The basis, design, calibration, and performance of a principles-based computational model of ethical considerations in military decision making are reported in this article. The relative ethical violation (REV) model comparatively evaluates alternative military actions based upon the degree to which they violate contextually relevant ethical principles. It is based on a set of specific ethical principles (...) deemed by philosophers and ethicists to be relevant to military courses of action. A survey of expert and non-expert human decision makers regarding the relative ethical violation of alternative actions for a set of specially designed calibration scenarios was conducted to collect data that was used to calibrate the REV model. Perhaps unsurprisingly, the survey showed that people, even experts, disagreed greatly amongst themselves regarding the scenarios’ ethical considerations. Despite this disagreement, two significant results emerged. First, after calibration the REV model performed very well in terms of replicating the ethical assessments of human experts for the calibration scenarios. The REV model outperformed an earlier model that was based on tangible consequences rather than ethical principles, that earlier model performed comparably to human experts, the experts outperformed human non-experts, and the non-experts outperformed random selection of actions. All of these performance comparisons were measured quantitatively and confirmed with suitable statistical tests. Second, although humans tended to value some principles over others, none of the ethical principles involved—even the principle of not harming civilians—completely overshadowed all of the other principles. (shrink)

Compulsive smartphone users’ psyches, today, are increasingly directed away from their bodies and onto their devices. This phenomenon has now entered our global vocabulary as “smartphone zombies,” or what I will call “iZombies.” Given the importance of mind to virtually all conceptions of human identity, these compulsive users could thus be productively understood as a kind of human-machine hybrid entity, the cyborg. Assuming for the sake of argument that this hybridization is at worst axiologically neutral, I will construct a kind (...) of phenomenological psychological profile of the type of cyborg which engages in these patterns of behavior. I follow Judith Butler in seeing this identity as the result of performance practices, which as such can be modified or replaced using other performances. Pursing one such alternative, I compose a dancing critique that “reverse engineers” the choreographies implied by these cyborgs’ survival practices. The upshot of this critique is that their movement patterns do indeed align closely to those of horror cinema’s zombies. I therefore conclude by suggesting a few possible choreographic imperatives to facilitate more enabling ways of being for iZombie cyborgs today. (shrink)

Growing awareness of the ethical implications of neuroscience in the early years of the 21st century led to the emergence of the new academic field of “neuroethics,” which studies the ethical implications of developments in the neurosciences. However, despite the acceleration and evolution of neuroscience research on nonhuman animals, the unique ethical issues connected with neuroscience research involving nonhuman animals remain underdiscussed. This is a significant oversight given the central place of animal models in neuroscience. To respond to these concerns, (...) the Center for Neuroscience and Society and the Center for the Interaction of Animals and Society at the University of Pennsylvania hosted a workshop on the “Neuroethics of Animal Research” in Philadelphia, Pennsylvania. At the workshop, expert speakers and attendees discussed ethical issues arising from neuroscience research involving nonhuman animals, including the use of animal models in the study of pain and psychiatric conditions, animal brain-machine interfaces, animal–animal chimeras, cerebral organoids, and the relevance of neuroscience to debates about personhood. This paper highlights important emerging ethical issues based on the discussions at the workshop. This paper includes recommendations for research in the United States from the authors based on the discussions at the workshop, loosely following the format of the 2 Gray Matters reports on neuroethics published by the Presidential Commission for the Study of Bioethical Issues. (shrink)

Cloud computing is the provision of IT resources (IaaS) on-demand using a pay as you go model over the internet. It is a broad and deep platform that helps customers builds sophisticated, scalable applications. To get the full benefits, research on a wide range of topics is needed. While resource over provisioning can cost users more than necessary, resource under provisioning hurts the application performance. The cost effectiveness of cloud computing highly depends on how well the customer can optimize the (...) cost of renting resources (VMs) from cloud providers. The issue of resource provisioning optimization from cloud-consumer potential is a complicated optimization issue, which includes much uncertainty parameters. There is a much research avenue available for solving this problem as it is in the real-world. Here, in this paper we provide details about various optimization techniques for resource provisioning. (shrink)

Francis Bacon (15611626) wrote that good scientists are not like ants (mindlessly gathering data) or spiders (spinning empty theories). Instead, they are like bees, transforming nature into a nourishing product. This essay examines Bacon's "middle way" by elucidating the means he proposes to turn experience and insight into understanding. The human intellect relies on "machines" to extend perceptual limits, check impulsive imaginations, and reveal nature's latent causal structure, or "forms." This constructivist interpretation is not intended to supplant inductivist or experimentalist (...) interpretations, but is designed to explicate Bacon's account of science as a collaborative project with several interdependent methodological goals. (shrink)

Due to his significant role in the development of computer technology and the discipline of artificial intelligence, Alan Turing has supposedly subscribed to the theory of mind that has been greatly inspired by the power of the said technology which has eventually become the dominant framework for current researches in artificial intelligence and cognitive science, namely, computationalism or the computational theory of mind. In this essay, I challenge this supposition. In particular, I will try to show that there is no (...) evidence in Turing’s two seminal works that supports such a supposition. His 1936 paper is all about the notion of computation or computability as it applies to mathematical functions and not to the nature or workings of intelligence. On the other hand, while his 1950 work is about intelligence, it is, however, particularly concerned with the problem of whether intelligence can be attributed to computing machines and not of whether computationality can be attributed to human intelligence or to intelligence in general. (shrink)

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. Reports indicate that wearing facemasks while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. A hybrid model using (...) deep and classical machine learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images, we are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. Our goal is to identify whether the person on image/video stream is wearing a face mask or not with the help of computer vision and deep learning. (shrink)

This is the 3rd edition. Although a number of new technological applications require classical deductive computation with non-classical logics, many key technologies still do well—or exclusively, for that matter—with classical logic. In this first volume, we elaborate on classical deductive computing with classical logic. The objective of the main text is to provide the reader with a thorough elaboration on both classical computing – a.k.a. formal languages and automata theory – and classical deduction with the classical first-order predicate calculus with (...) a view to computational implementations, namely in automated theorem proving and logic programming. The present third edition improves on the previous ones by providing an altogether more algorithmic approach: There is now a wholly new section on algorithms and there are in total fourteen clearly isolated algorithms designed in pseudo-code. Other improvements are, for instance, an emphasis on functions in Chapter 1 and more exercises with Turing machines. (shrink)

With the current improvement of development towards pharmaceutical, distinctive ultrasound methodologies are open to find the fetal prosperity. It is analyzed with diverse clinical parameters with 2-D imaging and other test. In any case, prosperity desire of fetal heart still remains an open issue due to unconstrained works out of the hatchling, the minor heart appraise and inadequate of data in fetal echocardiography. The machine learning strategies can find out the classes of fetal heart rate which can beutilized for earlier (...) evaluating. With this background, we have utilized Cardiotocographic Fetal heart rate dataset removed from UCI Machine Learning Store for predicting the fetal heart rate health classes. The Prediction of fetal health rate are achieved in six ways. Firstly, the data set is preprocessed with Feature Scaling and missing values. Secondly, exploratory data investigation is done and the dispersion of target feature is visualized. Thirdly, the raw data set is fitted to all the classifiers and the performance is analysed before and after feature scaling. Fourth, the raw data set is subjected to undersampling methods like ClusterCentroids, RepeatedENN, AllKNN, CondensedNearestNeighbour, EditedNearestNeighbours, InstanceHardnessThreshold and NearMiss. Fifth, the undersampled dataset by above mentioned methods are fitted to all the classifiers and the performance is analyzed before and after feature scaling. Sixth, performance analysis is done using metrics like Precision, Recall, F-score, Accuracy and running time. The execution is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that the Decision Tree classifier tends to retain 98% before and after feature scaling for the underrsampling with EditedNearestNeighbours, RepeatedENN and AllKNN methods. (shrink)

What were the reasons of the Copernican Revolution ? How did modern science (created by a bunch of ambitious intellectuals) manage to force out the old one created by Aristotle and Ptolemy, rooted in millennial traditions and strongly supported by the Church? What deep internal causes and strong social movements took part in the genesis, development and victory of modern science? The author comes to a new picture of Copernican Revolution on the basis of the elaborated model of scientific revolutions (...) that takes into account some recent advances in philosophy, sociology and history of science. The model was initially invented to describe Einstein’s Revolution of the XX century beginning. The model considers the growth of knowledge as interaction, interpenetration and unification of the research programmes, springing out of different cultural traditions. Thus, Copernican Revolution appears as a result of revealation and (partial) resolution of the dualism , of the gap between Ptolemy’s mathematical astronomy and Aristotelian qualitative physics. The works of Copernicus, Galileo, Kepler and Newton were all the stages of mathematics descendance from skies to earth and reciprocal extrapolation of earth physics on skies. The model elaborated enables to reassess the role of some social factors crucial for the scientific revolution. It is argued that initially modern science was a result of the development of Christian Weltanschaugung . Later the main support came from the absolute monarchies. In the long run the creators of modern science appeared to be the “apparatchics” of the “regime of truth” built-in state machine. Natural science became a part of ideological state apparatus providing not only scientific education but the internalization of values crucial for the functioning of state. -/- . (shrink)

This book is an edited collection of original research papers on the digital revolution of the public and governance. It covers cyber governance in Finland, and the securitization of cyber security in Finland. It investigates the cases of Brexit, the 2016 US presidential election of Donald Trump, the 2017 presidential election of Volodymyr Zelensky, and Brexit. It examines the environmental concerns of climate change and greenwashing, and the impact of digital communication giving rise to the #MeToo and Incel movements. It (...) considers how digitilization can serve to emancipate women through ride-sharing, and how it leads to the question of robot rights. It considers fake news and algorithmic governance with respect to case studies of the Chinese social credit system, the US FICO credit score, along with Facebook, Twitter, Cambridge Analytica and the European effort to regulate and protect data usage. (shrink)

Autonomy has been central to moral and political philosophy for millenia, and has been positioned as a critical aspect of both justice and wellbeing. Research in psychology supports this position, providing empirical evidence that autonomy is critical to motivation, personal growth and psychological wellness. Responsible AI will require an understanding of, and ability to effectively design for, human autonomy (rather than just machine autonomy) if it is to genuinely benefit humanity. Yet the effects on human autonomy of digital experiences are (...) neither straightforward nor consistent, and are complicated by commercial interests and tensions around compulsive overuse. This multi-layered reality requires an analysis that is itself multidimensional and that takes into account human experience at various levels of resolution. We borrow from HCI and psychological research to apply a model (“METUX”) that identifies six distinct spheres of technology experience. We demonstrate the value of the model for understanding human autonomy in a technology ethics context at multiple levels by applying it to the real-world case study of an AI-enhanced video recommender system. In the process we argue for the following three claims: 1) There are autonomy-related consequences to algorithms representing the interests of third parties, and they are not impartial and rational extensions of the self, as is often perceived; 2) Designing for autonomy is an ethical imperative critical to the future design of responsible AI; and 3) Autonomy-support must be analysed from at least six spheres of experience in order to approriately capture contradictory and downstream effects. (shrink)

This book constitutes refereed proceedings of the COST 2102 International Training School on Cognitive Behavioural Systems held in Dresden, Germany, in February 2011. The 39 revised full papers presented were carefully reviewed and selected from various submissions. The volume presents new and original research results in the field of human-machine interaction inspired by cognitive behavioural human-human interaction features. The themes covered are on cognitive and computational social information processing, emotional and social believable Human-Computer Interaction (HCI) systems, behavioural and contextual analysis (...) of interaction, embodiment, perception, linguistics, semantics and sentiment analysis in dialogues and interactions, algorithmic and computational issues for the automatic recognition and synthesis of emotional states. (shrink)

Concurring to information from the Centresfor Illness Control and Anticipation, instruction and religion are both capable indicators of lasting or dissolving unions. The chance of a marriage finishing in separate was lower for individuals with more knowledge, with over half of relational unions of those who did not complete high school having finished in separate compared with roughly 30 percent of relational unions of college graduates. With this overview, the divorce rate dataset from UCI dataset repository is used for predicting (...) the divorce class target with the following contributions. Firstly, the Divorce rate dataset is subjected with the data cleaning and exploratory data analysis. Secondly, the data set is settled with different classifiers to look at the classification before and after feature scaling. Thirdly, the dataset is processed with various cross validation of training and testing dataset i.e 80:20, 30:70, 40:60, 50:50 to improve the accuracy of all the classifiers. Fourth, the dataset is processed with 15, 20 and 30 components of principal component analysis and then applied with all classifier algorithm to analyze the accuracy of divorce rate prediction. Fifth, the performance analysis is done with precision, recall, accuracy, fscore and running time to infer the classification before and after feature scaling. Experimental results show that the Random Forest classifier is found to have the accuracy of 98% for all PCA reduced dataset with 15, 20 and 30 components. The result showsthat Random Forest classifier is found to have the accuracy of 98% for 40:60, 50:50 of training and testing dataset. (shrink)

A pineapple is a tropical plant with eatable leafy foods most monetarily critical plant in the family Bromeliaceous. The pineapple is native to South America, where it has been developed for a long time. The acquaintance of the pineapple with Europe in the seventeenth century made it a critical social symbol of extravagance. Since the 1820s, pineapple has been industrially filled in nurseries and numerous tropical manors. Further, it is the third most significant tropical natural product in world creation. In (...) the twentieth century, Hawaii was a prevailing maker of pineapples, particularly for the US, be that as it may, by 2016, Costa Rica, Brazil, and the Philippines represented almost 33% of the world's creation of pineapples. In this paper, machine learning based approach is presented for identifying type pineapple with a dataset that contains 1,311images use 946 images for training, 197 images for validation and 168 images for testing. A deep learning technique that extensively applied to image recognition was used. use 70% from image for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a heldout test set. (shrink)

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create (...) new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease. (shrink)