Blockchain-Based Decentralized Knowledge Marketplace Using Active Inference

A knowledge market can be described as a type of market where there is a consistent supply of data to satisfy the demand for information and is responsible for the mapping of potential problem solvers with the entities which need these solutions. It is possible to define them as value-exchange systems in which the dynamic features of the creation and exchange of intellectual assets serve as the fundamental drivers of the frequency, nature, and outcomes of interactions among various stakeholders. Furthermore, the provision of financial backing for research is an essential component in the process of developing a knowledge market that is capable of enduring over time, and it is also an essential driver of the progression of scientific investigation. This paper underlines flaws associated with the conventional knowledge-based market, including but not limited to excessive financing concentration, ineffective information exchange, a lack of security, mapping of entities, etc. The authors present a decentralized framework for the knowledge marketplace incorporating technologies such as blockchain, active inference, zero-knowledge proof, etc. The proposed decentralized framework provides not only an efficient mapping mechanism to map entities in the marketplace but also a more secure and controlled way to share knowledge and services among various stakeholders.

Human Emotion Classification based on EEG Signals Using Recurrent Neural Network And KNN

In human contact, emotion is very crucial. Attributes like words, voice intonation, facial expressions, and kinesics can all be used to portray one's feelings. However, brain-computer interface (BCI) devices have not yet reached the level required for emotion interpretation. With the rapid development of machine learning algorithms, dry electrode techniques, and different real-world applications of the brain-computer interface for normal individuals, emotion categorization from EEG data has recently gotten a lot of attention. Electroencephalogram (EEG) signals are a critical resource for these systems. The primary benefit of employing EEG signals is that they reflect true emotion and are easily resolved by computer systems. In this work, EEG signals associated with good, neutral, and negative emotions were identified using channel selection preprocessing. However, researchers had a limited grasp of the specifics of the link between various emotional states until now. To identify EEG signals, we used discrete wavelet transform and machine learning techniques such as recurrent neural network (RNN) and k-nearest neighbor (kNN) algorithm. Initially, the classifier methods were utilized for channel selection. As a result, final feature vectors were created by integrating the features of EEG segments from these channels. Using the RNN and kNN algorithms, the final feature vectors with connected positive, neutral, and negative emotions were categorized independently. The classification performance of both techniques is computed and compared. Using RNN and kNN, the average overall accuracies were 94.844 % and 93.438 %, respectively.