scholarly journals The Nooscope manifested: AI as instrument of knowledge extractivism

AI & Society ◽  
2020 ◽  
Author(s):  
Matteo Pasquinelli ◽  
Vladan Joler
Keyword(s):  
Machine Learning ◽  
Statistical Model ◽  
Assembly Line ◽  
Learning Algorithm ◽  
Machine Intelligence ◽  
Training Dataset ◽  
The Social ◽  
The World ◽  
History Of ◽  
Learning Data

AbstractSome enlightenment regarding the project to mechanise reason. The assembly line of machine learning: data, algorithm, model. The training dataset: the social origins of machine intelligence. The history of AI as the automation of perception. The learning algorithm: compressing the world into a statistical model. All models are wrong, but some are useful. World to vector: the society of classification and prediction bots. Faults of a statistical instrument: the undetection of the new. Adversarial intelligence vs. statistical intelligence: labour in the age of AI.

The problem of periodization of the history of religion

1997 ◽  
pp. 3-8
Author(s):  
Borys Lobovyk
Keyword(s):  
Religious Studies ◽  
Human Being ◽  
Religious Development ◽  
History Of Religion ◽  
The Social ◽  
The World ◽  
History Of ◽  
Role Of Religion

An important problem of religious studies, the history of religion as a branch of knowledge is the periodization process of the development of religious phenomenon. It is precisely here, as in focus, that the question of the essence and meaning of the religious development of the human being of the world, the origin of beliefs and cult, the reasons for the changes in them, the place and role of religion in the social and spiritual process, etc., are converging.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Myriad Aspects of Secular Thinking on Malayali Cuisine

GIS Business ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. 202-206
Author(s):  
SAJITHA M
Keyword(s):  
Social Status ◽  
Daily Life ◽  
Social Indicators ◽  
The Body ◽  
Human Being ◽  
Healthy Life ◽  
The Social ◽  
The World ◽  
History Of ◽  
Civilized Society

Food is one of the main requirements of human being. It is flattering for the preservation of wellbeing and nourishment of the body.  The food of a society exposes its custom, prosperity, status, habits as well as it help to develop a culture. Food is one of the most important social indicators of a society. History of food carries a dynamic character in the socio- economic, political, and cultural realm of a society. The food is one of the obligatory components in our daily life. It occupied an obvious atmosphere for the augmentation of healthy life and anticipation against the diseases.  The food also shows a significant character in establishing cultural distinctiveness, and it reflects who we are. Food also reflected as the symbol of individuality, generosity, social status and religious believes etc in a civilized society. Food is not a discriminating aspect. It is the part of a culture, habits, addiction, and identity of a civilization.Food plays a symbolic role in the social activities the world over. It’s a universal sign of hospitality.[1]

scholarly journals Deep Learning Classification of Canine Behavior Using a Single Collar-Mounted Accelerometer: Real-World Validation

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1549
Author(s):  
Robert D. Chambers ◽  
Nathanael C. Yoder ◽  
Aletha B. Carson ◽  
Christian Junge ◽  
David E. Allen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
Learning Algorithm ◽  
Drinking Behavior ◽  
True Positive Rate ◽  
Training Dataset ◽  
Activity Levels ◽  
Accelerometer Data ◽  
Activity Monitors

Collar-mounted canine activity monitors can use accelerometer data to estimate dog activity levels, step counts, and distance traveled. With recent advances in machine learning and embedded computing, much more nuanced and accurate behavior classification has become possible, giving these affordable consumer devices the potential to improve the efficiency and effectiveness of pet healthcare. Here, we describe a novel deep learning algorithm that classifies dog behavior at sub-second resolution using commercial pet activity monitors. We built machine learning training databases from more than 5000 videos of more than 2500 dogs and ran the algorithms in production on more than 11 million days of device data. We then surveyed project participants representing 10,550 dogs, which provided 163,110 event responses to validate real-world detection of eating and drinking behavior. The resultant algorithm displayed a sensitivity and specificity for detecting drinking behavior (0.949 and 0.999, respectively) and eating behavior (0.988, 0.983). We also demonstrated detection of licking (0.772, 0.990), petting (0.305, 0.991), rubbing (0.729, 0.996), scratching (0.870, 0.997), and sniffing (0.610, 0.968). We show that the devices’ position on the collar had no measurable impact on performance. In production, users reported a true positive rate of 95.3% for eating (among 1514 users), and of 94.9% for drinking (among 1491 users). The study demonstrates the accurate detection of important health-related canine behaviors using a collar-mounted accelerometer. We trained and validated our algorithms on a large and realistic training dataset, and we assessed and confirmed accuracy in production via user validation.

scholarly journals Study on Influence of Range of Data in Concrete Compressive Strength with Respect to the Accuracy of Machine Learning with Linear Regression

2021 ◽  
Vol 11 (9) ◽  
pp. 3866
Author(s):  
Jun-Ryeol Park ◽  
Hye-Jin Lee ◽  
Keun-Hyeok Yang ◽  
Jung-Keun Kook ◽  
Sanghee Kim
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Linear Regression ◽  
Coarse Aggregate ◽  
Learning Algorithm ◽  
Linear Regression Analysis ◽  
Aggregate Size ◽  
Training Dataset ◽  
Machine Learning Algorithm ◽  
Concrete Compressive Strength

This study aims to predict the compressive strength of concrete using a machine-learning algorithm with linear regression analysis and to evaluate its accuracy. The open-source software library TensorFlow was used to develop the machine-learning algorithm. In the machine-earning algorithm, a total of seven variables were set: water, cement, fly ash, blast furnace slag, sand, coarse aggregate, and coarse aggregate size. A total of 4297 concrete mixtures with measured compressive strengths were employed to train and testing the machine-learning algorithm. Of these, 70% were used for training, and 30% were utilized for verification. For verification, the research was conducted by classifying the mixtures into three cases: the case where the machine-learning algorithm was trained using all the data (Case-1), the case where the machine-learning algorithm was trained while maintaining the same number of training dataset for each strength range (Case-2), and the case where the machine-learning algorithm was trained after making the subcase of each strength range (Case-3). The results indicated that the error percentages of Case-1 and Case-2 did not differ significantly. The error percentage of Case-3 was far smaller than those of Case-1 and Case-2. Therefore, it was concluded that the range of training dataset of the concrete compressive strength is as important as the amount of training dataset for accurately predicting the concrete compressive strength using the machine-learning algorithm.

scholarly journals On the Unfounded Enthusiasm for Soft Selective Sweeps III: The Supervised Machine Learning Algorithm That Isn’t

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 527
Author(s):  
Eran Elhaik ◽  
Dan Graur
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
A Priori ◽  
Neutral Theory ◽  
Dominant Mode ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Selective Sweeps ◽  
Two Factors ◽  
Negative Controls

In the last 15 years or so, soft selective sweep mechanisms have been catapulted from a curiosity of little evolutionary importance to a ubiquitous mechanism claimed to explain most adaptive evolution and, in some cases, most evolution. This transformation was aided by a series of articles by Daniel Schrider and Andrew Kern. Within this series, a paper entitled “Soft sweeps are the dominant mode of adaptation in the human genome” (Schrider and Kern, Mol. Biol. Evolut. 2017, 34(8), 1863–1877) attracted a great deal of attention, in particular in conjunction with another paper (Kern and Hahn, Mol. Biol. Evolut. 2018, 35(6), 1366–1371), for purporting to discredit the Neutral Theory of Molecular Evolution (Kimura 1968). Here, we address an alleged novelty in Schrider and Kern’s paper, i.e., the claim that their study involved an artificial intelligence technique called supervised machine learning (SML). SML is predicated upon the existence of a training dataset in which the correspondence between the input and output is known empirically to be true. Curiously, Schrider and Kern did not possess a training dataset of genomic segments known a priori to have evolved either neutrally or through soft or hard selective sweeps. Thus, their claim of using SML is thoroughly and utterly misleading. In the absence of legitimate training datasets, Schrider and Kern used: (1) simulations that employ many manipulatable variables and (2) a system of data cherry-picking rivaling the worst excesses in the literature. These two factors, in addition to the lack of negative controls and the irreproducibility of their results due to incomplete methodological detail, lead us to conclude that all evolutionary inferences derived from so-called SML algorithms (e.g., S/HIC) should be taken with a huge shovel of salt.

Las Yslas de Esperar en Dios: The Jesuit Mission in Moro 1546–1571

1988 ◽  
Vol 22 (3) ◽  
pp. 593-606
Author(s):  
John Villiers
Keyword(s):  
Sixteenth Century ◽  
Social Economic ◽  
Narrative Form ◽  
Society Of Jesus ◽  
The Social ◽  
The World ◽  
History Of ◽  
Political Conditions ◽  
Jesuit Missionaries ◽  
Mission Field

The numerous and voluminous reports and letters which the Jesuits wrote on the Moro mission, as on all their missions in Asia, are perhaps of less interest to us now for what they reveal of the methods adopted by the Society of Jesus in this remote corner of their mission field or the details they contain about the successes and failures of individual missionaries, than for the wealth of information they provide on the islands where the Jesuits lived and the indigenous societies with which they came into contact through their work of evangelization. In other words, it is not theprimary purpose of this essay to analyse the Jesuit documents with a view to reconstructing the history of the Moro mission in narrative form but rather to glean from them some of the informationthey contain about the social and political conditions in Moro during the forty years or so in the sixteenth century when both the Jesuit missionaries and the Portuguese were active in the regio Because the Jesuits were often in close touch with local rulers and notables, whether or not they succeeded in converting them to Christianity, and because they lived among their subjects for long periods, depending upon them for the necessities of life and sharing their hardships, their letters and reports often show a deeper understanding of the social, economic and political conditions of the indigenous societies and, one suspects, give a more accurate and measured account of events and personalities than do the official chroniclers and historians of the time, most of whom never ventured further east than Malacca and who in any case were chiefly concerned to glorify the deeds of the Portuguese and justify their actions to the world.

Book Reviews: Studies in Sociology, Race Mixture, Hunger and Work in a Savage Tribe, Interpretations, 1931–1932, Faith, Hope and Charity in Primitive Religion, Genetic Principles in Medicine and Social Science, The Reorganisation of Education in China, Social Decay and Eugenical Reform, The Social and Political Ideas of Some Representative Thinkers of the Revolutionary Era, L. T. Hobhouse, His Life and Work, Corner of England, World Agriculture—An International Study, Small-Town Stuff, Methods of Social Study, Does History Repeat Itself? The New Morality, Culture and Progress, Language and Languages: An Introduction to Linguistics, The Theory of Wages, The Santa Clara Valley, California, Social Psychology, A History of Fire and Flame, Sin and New Psychology, Sociology and Education, Mental Subnormality and the Local Community: Am Outline or a Practical Program, Tyneside Council op Social Service, Reconstruction and Education in Rural India, The Contribution of the English Le Play School to Rural Sociology, Kagami Kenkyu Hokoku, President's, Pioneer Settlement: Co-Operative Studies, Birth Control and Public Health, Pioneer Settlement: Co-Operative Studies, Ourselves and the World: The Making of an American Citizen, The Emergence of the Social Sciences from Moral Philosophy, The Comparable Interests of the Old Moral Philosophy and the Modern Social Sciences, The World in Agony, Sheffield Social Survey Committee, Housing Problems in Liverpool, Council for the Preservation of Rural England, Forest Land Use in Wisconsin, The Growth Cycle of the Farm Family, The Farmer's Guide to Agricultural Research in 1931, A History of the Public Library Movement in Great Britain and Ireland, The Retirement of National Debts, Public and Private Operation of Railways in Brazil, The Indian Minorities Problem, The Meaning of the Manchurian Crisis, The Drama of the Kingdom, Social Psychology, Competition in the American Tobacco Industry, New York School Centers and Their Community Policy, Desertion of Alabama Troops from the Confederate Army, Plans for City Police Jails and Village Lockups

1933 ◽  
Vol a25 (1) ◽  
pp. 72-109
Author(s):  
R. R. Marbtt ◽  
E. E. Evans-Pritchard ◽  
E. O. Jambs ◽  
Florence Ayscough ◽  
C. H. Desch ◽  
...  
Keyword(s):  
Social Sciences ◽  
Social Psychology ◽  
Moral Philosophy ◽  
Local Community ◽  
Agricultural Research ◽  
Public Library ◽  
The Social ◽  
The World ◽  
History Of ◽  
Pioneer Settlement

The Social Life of Fighting Words

2017 ◽  
Vol 3 (1) ◽  
pp. 108-124
Author(s):  
Ronald S. Stade
Keyword(s):  
Social Life ◽  
Large Scale ◽  
Political Correctness ◽  
Conceptual History ◽  
Political Conflicts ◽  
Political Analysis ◽  
The Social ◽  
The World ◽  
History Of ◽  
Contemporary Context

Political correctness has become a fighting word used to dismiss and discredit political opponents. The article traces the conceptual history of this fighting word. In anthropological terms, it describes the social life of the concept of political correctness and its negation, political incorrectness. It does so by adopting a concept-in-motion methodology, which involves tracking the concept through various cultural and political regimes. It represents an attempt to synthesize well-established historiographic and anthropological approaches. A Swedish case is introduced that reveals the kind of large-scale historical movements and deep-seated political conflicts that provide the contemporary context for political correctness and its negation. Thereupon follows an account of the conceptual history of political correctness from the eighteenth century up to the present. Instead of a conventional conclusion, the article ends with a political analysis of the current rise of fascism around the world and how the denunciation of political correctness is both indicative of and instrumental in this process.

scholarly journals Comparison of Machine Learning algorithm for COVID-19 Death Risk Prediction

2021 ◽  
Author(s):  
Praveeen Anandhanathan ◽  
Priyanka Gopalan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Decision Tree Algorithm ◽  
The Past ◽  
Random Forest Method ◽  
Learning Techniques ◽  
The World

Abstract Coronavirus disease (COVID-19) is spreading across the world. Since at first it has appeared in Wuhan, China in December 2019, it has become a serious issue across the globe. There are no accurate resources to predict and find the disease. So, by knowing the past patients’ records, it could guide the clinicians to fight against the pandemic. Therefore, for the prediction of healthiness from symptoms Machine learning techniques can be implemented. From this we are going to analyse only the symptoms which occurs in every patient. These predictions can help clinicians in the easier manner to cure the patients. Already for prediction of many of the diseases, techniques like SVM (Support vector Machine), Fuzzy k-Means Clustering, Decision Tree algorithm, Random Forest Method, ANN (Artificial Neural Network), KNN (k-Nearest Neighbour), Naïve Bayes, Linear Regression model are used. As we haven’t faced this disease before, we can’t say which technique will give the maximum accuracy. So, we are going to provide an efficient result by comparing all the such algorithms in RStudio.

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