Interdisciplinary

what is Interdisciplinarity ؟The growing specialization of science in recent centuries under the pretext of taking advantage of minute knowledge has led to alienation and even enmity and competition between the various sciences. This competition has diverted knowledge from the main goal, which is to understand and explain phenomena, and put it in a deviant direction, contrary to the enlightening task defined for it. One of the detrimental consequences of this specialization was the loss of opportunities for creativity and innovation in researchers who had confined themselves to inflexible disciplines. The solution to this dilemma, of course, was to become more specialized and to take a new approach, which has been introduced to scientific circles since the late 1950s in the form of the title “Intermediate”. Intermediate in the general sense is “the cooperation and accompaniment of different Interdisciplinarity perspectives to reach common ground on understanding phenomena and access to perspectives other than Maloff’s field perspective.” Interdisciplinary research revealed two obvious advantages. It first presents a critical and new understanding by challenging previously accepted identities and ideas. Second, it provides an opportunity for scholars to gain a better understanding of each other’s scientific activities. Dealing with mediocrity, of course, has its own complexities and is methodologically different from Interdisciplinarity approaches. Based on this, it should be said that mere coexistence and cooperation of several fields together do not have this feature. This claim can be clearly deduced from the various terms used in the texts on this subject; Vocabulary such as: interdisciplinary, multidisciplinary, transdisciplinary, pluridisciplinary, crossdiscplinary, metadisciplinary. Dealing with mediocrity, of course, has its own complexities and is methodologically different from Interdisciplinarity approaches. Based on this, it should be said that mere coexistence and cooperation of several fields together do not have this feature. This claim can be clearly deduced from the various terms used in the texts on this subject; Vocabulary such as: interdisciplinary, multidisciplinary, transdisciplinary, pluridisciplinary, crossdiscplinary, metadisciplinary. Dealing with mediocrity, of course, has its own complexities and is methodologically different from disciplinary approaches. Based on this, it should be said that mere coexistence and cooperation of several fields together do not have this feature. This claim can be clearly deduced from the various terms used in the texts on this subject; Vocabulary such as: interdisciplinary, multidisciplinary, transdisciplinary, pluridisciplinary, crossdiscplinary, metadisciplinary.

what is  Interdisciplinarity ?

Interdisciplinary terms, interdisciplinary education, and interdisciplinary programs are increasingly used in higher education. These terms are often used with great care and are often used or replaced with similar words, including multidisciplinary and supra-disciplinary. In previous decades, the interdisciplinary approach to the curriculum lost its relevance, and for two decades, the interdisciplinary approach was considered important, but the idea of ​​integration and the interdisciplinary curriculum was not a new idea and the integration of the curriculum. It is rooted in the thoughts and ideas of earlier thinkers who have been concerned with the unity of knowledge, the formation of coherent science, general and all-encompassing knowledge, and the combination and integration of knowledge

Interdisciplinarity

About KSRA

The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.

KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.

Our main services Based on Education for all spectrums people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.

The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.

IoT, big data and artificial intelligence in agriculture and food industry

IoT, big data and artificial intelligence in agriculture and food industry

Abstract Internet of things (IoT) results in a massive amount of streaming data, often referred to as “big data”, which brings new opportunities to monitor agricultural and food processes. Besides […]

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Advanced Visualisation of Big Data for Agriculture as Part of Databio Development

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Abstract There is an increasing tension in agriculture between the requirements to assure full safety on the one hand and keep costs under control on the other hand, both with

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De-identification and Privacy Issues on Bigdata Transformation

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A Two-Directional BigData Sorting Architecture on FPGAs

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Abstract Sorting is pivotal data analytics and becomes challenging with intensive computation on drastically growing data volume. Sorting on FPGA has shown superior throughput, but the limited in-system memory causes

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gravitational search algorithm

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Mining conditional functional dependency rules on big data

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Abstract Current Conditional Functional Dependency (CFD) discovery algorithms always need a well-prepared training dataset. This condition makes them difficult to apply on large and low-quality datasets. To handle the volume

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The big data analysis and mining of people's livelihood appeal based on time series modeling and algorithm

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Abstract In order to analyze the big data of people’s livelihood appeal, this paper proposes a time series modeling and algorithm to decompose the time series {x(t)} of data into

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M3ER: Multiplicative Multimodal Emotion Recognition Using Facial, Textual, and Speech Cues

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Abstract We present M3ER, a learning-based method for emotion recognition from multiple input modalities. Our approach combines cues from multiple co-occurring modalities (such as face, text, and speech) and also

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The AICO Multimodal Corpus – Data Collection and Preliminary Analyses

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Contributions of modern network science to the cognitive sciences: revisiting research spirals of representation and process

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Abstract Modeling the structure of cognitive systems is a central goal of the cognitive sciences—a goal that has greatly benefitted from the application of network science approaches. This paper provides

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