Artificial Neural Fuzzy Inference in Task-Based Learning Support System for Distance Education

Distance education learning systems have become one of the major investigation areas nowadays because the various categories of graduate learning are studied exclusively through the distance learning system. It provides the desired knowledge in various applications under the same domain or category in a well-organized manner. But the distance education learning system also has some major issues. In order to solve the problem of the distance education learning system, in this paper we present a novel sentiment analysis-based learning algorithm to learn the result of each learner in earlier classes and the level of each learner. The proposed sentiments analysis-based Fuzzy Neural Network learning methods analyze the results of previous classes’ positive and negative comments specified by the learner and the task result of the learner. Initially, to convey the message or information about the individual learner, the system is connected to the videoconferencing, and then the camera is connected to avoid delay problems during the conversation. To increase the teacher closeness and social occurrence, it proposes a learning method to review the comments of the previous classes and perform some of the tasks, such as taking tests on 10 min from previous classes and make a review on that the task based on the sentiment analysis mining methods to develop the learning participation, training efficiency, and value of communication in the distance education learning system. After the learning results are found from each one of the students in the class, they are sent to the teacher. The instructors and learners are exactly identified based on the face and speech recognition performed using the automation recognition system

Machine Unlearning: A Survey

Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision, speech recognition, medical diagnosis, and more. Yet a special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. At the same time, this ambitious problem has led to numerous research efforts aimed at confronting its challenges. To the best of our knowledge, no study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. Accordingly, with this survey, we aim to capture the key concepts of unlearning techniques. The existing solutions are classified and summarized based on their characteristics within an up-to-date and comprehensive review of each category's advantages and limitations. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities

Access to recorded interviews: A research agenda

Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems