Students’ Entire Deep Learning Personality Model and Perceived Teachers’ Emotional Support

In recent years, deep learning as the requirement of higher education for students has attracted the attention of many scholars, and previous studies focused on defining deep learning as the deep processing of knowledge of the brain, however, in the process of knowledge processing, the brain not only involves the deep processing of information but also participates in learning consciously and emotionally. Therefore, this research proposed a four-factor model hypothesis for deep learning that includes deep learning investment, deep cognitive-emotional experience, deep information processing, and deep learning meta-cognitive. In addition, the research proposed teachers’ emotional support perceived by students has an effect on the four factors of deep learning. Through SPSS 26 and AMOS 24, this research has verified the four-factor model of deep learning applying exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) and verified that the perceived teacher emotional support has an impact on the four factors of students’ deep learning using the SEM.

Real-Time Nanoscopic Rider Safety System for Smart and Green Mobility Based upon Varied Infrastructure Parameters

To create a safe bicycle infrastructure system, this article develops an intelligent embedded learning system using a combination of deep neural networks. The learning system is used as a case study in the Northumbria region in England’s northeast. It is made up of three components: (a) input data unit, (b) knowledge processing unit, and (c) output unit. It is demonstrated that various infrastructure characteristics influence bikers’ safe interactions, which is used to estimate the riskiest age and gender rider groups. Two accurate prediction models are built, with a male accuracy of 88 per cent and a female accuracy of 95 per cent. The findings concluded that different infrastructures pose varying levels of risk to users of different ages and genders. Certain aspects of the infrastructure are hazardous to all bikers. However, the cyclist’s characteristics determine the level of risk that any infrastructure feature presents. Following validation, the built learning system is interoperable under various scenarios, including current heterogeneous and future semi-autonomous and autonomous transportation systems. The results contribute towards understanding the risk variation of various infrastructure types. The study’s findings will help to improve safety and lead to the construction of a sustainable integrated cycling transportation system.

Road ahead of Indian banking through universal banking and regulatory environment

The challenges staring at banking today need not result in a feeling of inadequacy. The IT framework in India had strong ground and the industry has been showing significant growth rates in the previous years, with the financial sector having benefited immensely from the offerings of IT. Banks have commenced to grow laterally and at an exponential rate. As India develops into a formidable force, it is Information Technology, which holds the key to the process of transformation. The key strengths lie in knowledge processing and as bankers performing this function quite well both for customers as well as for the growth of organizations. This underlying strength will ensure the key to success.

Comprehensive Opportunity Assessment Using Commercial and Moral Intensities

We propose a partial theory explaining the processing of opportunities by individuals in organizations, specifically for opportunities with both commercial and moral significance (measured as intensities). The goal of such theorizing is to identify and analyze the range of interactions that the ethical and economic impacts of an opportunity can have so that managers can make better decisions on their exploitation and modification. We explain why and how there is variance in the processing of the ideas behind such opportunities as caused by their moral and commercial intensities. We explain the likely interactions between those two intensities, and when they occur and what can result. Doing so complements work in social entrepreneurship and corporate social responsibility by filling the gaps of the possible combinations of economic and ethical interactions. We provide these explanations by leveraging a precedent model that had adapted a standard knowledge-processing method to ethical decision-making issues. The explanations resonate because our model leverages the traditional textbook entrepreneurship opportunity evaluation criteria to provide a holistic view of an underlying idea’s commercial intensity, a view that aligns with the driving assumption that the focal decision-makers are boundedly rational.

Impact of Internet of Things (IoT) in Smart Agriculture

In today’s world, technology is constantly evolving; various instruments and techniques are available in the agricultural field. And within the agrarian division, the IoT preferences are Knowledge processing. With the help of introduced sensors, all information can be gathered. The reduction of risks, the mechanization of industry, the enhancement of production, the inspection of livestock, the monitoring of environment conditions, the roboticization of greenhouses, and crop monitoring Nearly every sector, like smart agriculture, has been modified by Internet-of-Things (IoT)-based technology, which has shifted the industry from factual to quantitative approaches. The ideas help to link real devices that are equipped with sensors, actuators, and computing power, allowing them to collaborate on a task while staying connected to the Internet, dubbed the “Internet of Things” (IoT). According to the World Telecommunication Union’s Worldwide Guidelines Operation, the Internet of Things (IoT) is a set of sensors, computers, software, and other devices that are connected to the Internet. The paper is highly susceptible to the consequences of its smart agriculture breakthrough.

Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis

Sentiment classification, which uses deep learning algorithms, has achieved good results when tested with popular datasets. However, it will be challenging to build a corpus on new topics to train machine learning algorithms in sentiment classification with high confidence. This study proposes a method that processes embedding knowledge in the ontology of opinion datasets called knowledge processing and representation based on ontology (KPRO) to represent the significant features of the dataset into the word embedding layer of deep learning algorithms in sentiment classification. Unlike the methods that lexical encode or add information to the corpus, this method adds presentation of raw data based on the expert’s knowledge in the ontology. Once the data has a rich knowledge of the topic, the efficiency of the machine learning algorithms is significantly enhanced. Thus, this method is appliable to embed knowledge in datasets in other languages. The test results show that deep learning methods achieved considerably higher accuracy when trained with the KPRO method’s dataset than when trained with datasets not processed by this method. Therefore, this method is a novel approach to improve the accuracy of deep learning algorithms and increase the reliability of new datasets, thus making them ready for mining.

The Organic Turn: Coping With Pandemic and Non-pandemic Challenges by Integrating Evidence-, Theory-, Experience-, and Context-Based Knowledge in Advising Health Policy

The COVID-19 pandemic has posed an extraordinary challenge for public health and health policy. Questions have arisen concerning the main strategies to cope with this situation and the lessons to be learned from the pandemic. This conceptual paper aims to clarify these questions via sociological concepts. Regarding coping strategies used during the pandemic, there is a strong tendency for health policymakers to rely on expert knowledge rather than on evidence-based knowledge. This has caused the evidence-based healthcare community to respond to urgent demands for advice by rapidly processing new knowledge. Nonetheless, health policymakers still mainly rely on experts in making policy decisions. Our sociological analysis of this situation identified three lessons for coping with pandemic and non-pandemic health challenges: (1) the phenomenon of accelerating knowledge processing could be interpreted from the organizational innovation perspective as a shift from traditional mechanistic knowledge processing to more organic forms of knowledge processing. This can be described as an “organic turn.” (2) The return of experts is part of this organic turn and shows that experts provide both evidence-based knowledge as well as theoretical, experiential, and contextual knowledge. (3) Experts can use theory to expeditiously provide advice at times when there is limited evidence available and to provide complexity-reducing orientation for decisionmakers at times where knowledge production leads to an overload of knowledge; thus, evidence-based knowledge should be complemented by theory-based knowledge in a structured two-way interaction to obtain the most comprehensive and valid recommendations for health policy.

A Conversation History-Based Q&A Cache Mechanism for Multi-Layered Chatbot Services

Chatbot technologies have made our lives easier. To create a chatbot with high intelligence, a significant amount of knowledge processing is required. However, this can slow down the reaction time; hence, a mechanism to enable a quick response is needed. This paper proposes a cache mechanism to improve the response time of the chatbot service; while the cache in CPU utilizes the locality of references within binary code executions, our cache mechanism for chatbots uses the frequency and relevance information which potentially exists within the set of Q&A pairs. The proposed idea is to enable the broker in a multi-layered structure to analyze and store the keyword-wise relevance of the set of Q&A pairs from chatbots. In addition, the cache mechanism accumulates the frequency of the input questions by monitoring the conversation history. When a cache miss occurs, the broker selects a chatbot according to the frequency and relevance, and then delivers the query to the selected chatbot to obtain a response for answer. This mechanism showed a significant increase in the cache hit ratio as well as an improvement in the average response time.