Philosophical, Logical and Scientific Perspectives in by Zekâi Şen

By Zekâi Şen

This booklet highlights and explains the importance of philosophical, logical, and medical ideas for engineering education/training and engineering works. In so doing, it goals to aid to rectify the overlook of philosophy and common sense in present schooling and coaching courses, which emphasize analytical and numerical tools on the cost of the leading edge useful and artistic talents so vital for engineering long ago. person chapters learn the relation of philosophy, good judgment, and technology to engineering, drawing realization to, for instance, the importance of ethics, the relevance of the philosophy of technological know-how, and the expanding value of software of fuzzy common sense to engineering. Modeling rules and philosophy in engineering are mentioned, and the effect of other schooling platforms, tested. Too usually engineers became reliant on on hand formulations and software program; this publication deals an antidote, selling the popularity of creative and humanitarian points and their integration in engineering works.

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Despite many valuable works, a detailed and systematic assessment of the field requires further attention, as it has been recognized from different parts [9]. A commonsense view considers the philosophy of engineering as an area of the philosophy of science, that part concerning in particular the applicative issues of science. This view lies on the idea that engineering is ‘just’ applied science. Accordingly, the philosophical problems of the philosophy of engineering would be ‘just’ the problems of analyzing the passage from theory to application, as if a clear-cut distinction between science and engineering should exist.

D. holder should know the basic philosophical structure about his/her research topics prior to any methodological applications, unfortunately in many research institutions all over the world, the students are trained in a rather dogmatic and mechanical manner. Hence, the end goal becomes in a dire “publish or perish” ideology without any slight, let along improvement or modification of the existing methodologies but only their mechanical uses in many applications through computers. For instance, Artificial Neural Networks (ANNs) are means of matching the given input data set to output data without knowing logically what is taking place between successions of layers.

If some of them are not, the hypothesis is disconfirmed; if all of them are, the hypothesis is confirmed and may eventually be inferred. Unfortunately, while this model does make room for vertical inferences, it remains, like the enumerative model, far too permissive, counting data as confirming a hypothesis which are in fact totally irrelevant to it. For example, since a hypothesis (H) entails the disjunction of itself and any prediction whatever (H or P), and the truth of the prediction establishes the truth of the disjunction (since P also entails H or P), any successful prediction will count as confirming any hypothesis, even if P is the prediction that the sun will rise tomorrow and H the hypothesis that all ravens are black [15].

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