Deep Psychological Learning of Japanese Anime Graphics in India Using Cognitive Computation

This paper analyzes the Japanese Animation (Anime), their art style, how it is created, about popular Anime series and movies, their growth, and adaptation in India and mainly about the growing Anime fans. Majority of Anime series and movies still use two-dimensional (2D) animation style even though there are constant technological advancements in the field of media and entertainment. Though there are setbacks in Anime, we can observe that the growth in Anime viewership is constantly rising. The animation pipeline system that is involved in the making of an Anime will also be explained in detail for a better understanding of the animation process. A research is conducted through a questionnaire form to collect the required information for the study. The data collected is examined methodically and reported. The respondents are the Anime fans, Anime viewers, Anime influencers of all age groups. The survey is mainly to understand why they prefer watching Anime, how often they watch anime? what do they like about it? how were thy influenced to watch Anime? and if Anime fans influenced other individuals to watch Anime and how many people have, they influenced to watch Anime.

Distributed Visual Category Processing Across Medial Superior Temporal and Lateral Intraparietal Cortices

Categorization is an essential cognitive and perceptual process for recognition and decision making. The posterior parietal cortex (PPC), particularly the lateral intraparietal (LIP) area has been suggested to transform visual feature encoding into cognitive or abstract category representations. By contrast, areas closer to sensory input, such as the middle temporal (MT) area, encode stimulus features but not more abstract categorical information during categorization tasks. Here, we compare the contributions of PPC subregions in category computation by recording neuronal activity in the medial superior temporal (MST) and LIP areas during a categorization task. MST is a core motion processing area interconnected with MT, and often considered an intermediate processing stage between MT and LIP. Here we show that MST shows robust decision-correlated category encoding and working memory encoding similar to LIP, suggesting that MST plays a substantial role in cognitive computation, extending beyond its widely recognized role in visual motion processing.

An Intelligent Prediction System of Students Academic Performance based on Deep Learning and FPSOPCNN

This paper proposes a new method based on text extraction techniques for predicting student outcomes using cognitive computation. Predicting student academic achievement is most helpful in helping educators and learners improve their teaching and learning processes. This shows that these students have different experiences that influence their level of information capture in the classroom as they have the potential to use different lenses for training. This document provides a predictive examination of student academic performance in Tamil Nadu College in India during the academic year 2018 and 2019. First, this work applies statistical examination to gain insights from the data. Then, two datasets were obtained. The first dataset contains variables obtained before the beginning of the school year and the second includes study variables collected two months after the beginning of the semester. Convolution Neural Network and Fuzzy Particle Swarm Optimization Pulse Coupled Neural Network (FPSOPCNN) are designed to predict the end-of-year student performance for each dataset. .

Cognitive Computation

Probabilistic programming allows artificial systems to better operate with uncertainty, and stochastic arithmetic provides a way to carry out approximate computations with few resources. As such, both are plausible models for natural cognition. The authors' work on the automatic design of probabilistic machines computing soft inferences, with an arithmetic based on stochastic bitstreams, allowed to develop the following compilation toolchain: given a high-level description of some general problem, formalized as a Bayesian Program, the toolchain automatically builds a low-level description of an electronic circuit computing the corresponding probabilistic inference. This circuit can then be implemented and tested on reconfigurable logic. This paper describes two circuits as validating examples. The first one implements a Bayesian filter solving the problem of Pseudo Noise sequence acquisition in telecommunications. The second one implements decision making in a sensorimotor system: it allows a simple robot to avoid obstacles using Bayesian sensor fusion.

Cognitive computation on consumer’s decision making of internet financial products based on neural activity data

Internet finance has become a popular business in today?s society. However, different from the physical objects or services sold online, Internet financial products are actually contracts defined by financial terms which make customers bear the possibility of capital losses and liquidity restrictions, but they can obtain profits in the future with some uncertainties. This paper takes consumer?s cognition in the decision making of Internet financial products as research circumstances, studies the above issue by conducting an EEG-fNIRS experiment, and proposes an effective cognitive computation method based on neural activity data through BP-GA algorithm. On this basis, a new recommendation approach of Internet financial products is explored according to consumer?s typical shared mental model. The computing and testing results indicate that researches of this paper provide promising new ideas and novel methods for the cognitive computation of artificial intelligence and the recommendation of Internet financial products.

Creating a Computable Cognitive Model of Visual Aesthetics for Automatic Aesthetics Evaluation of Robotic Dance Poses

Inspired by human dancers who can evaluate the aesthetics of their own dance poses through mirror observation, this paper presents a corresponding mechanism for robots to improve their cognitive and autonomous abilities. Essentially, the proposed mechanism is a brain-like intelligent system that is symmetrical to the visual cognitive nervous system of the human brain. Specifically, a computable cognitive model of visual aesthetics is developed using the two important aesthetic cognitive neural models of the human brain, which is then applied in the automatic aesthetics evaluation of robotic dance poses. Three kinds of features (color, shape and orientation) are extracted in a manner similar to the visual feature elements extracted by human brains. After applying machine learning methods in different feature combinations, machine aesthetics models are built for automatic evaluation of robotic dance poses. The simulation results show that our approach can process visual information effectively by cognitive computation, and achieved a very good evaluation performance of automatic aesthetics.

Cognitive Computing For Sustainable Agriculture

Cognitive computing in agriculture is going to be a big revolution like the green revolution. Agriculture is a big step that accompanied the humanity to evolve from the ancient times to the modern days and has fulfilled the basic need for food supply. Today still remains it’s at most importance. Cognitive computing uses cognitive technologies in agriculture that help to understand, learn from experiences and environment, reason, interact and thus increase the efficiency. Civilization has led to more urbanization. There are more people than available food. There is a great necessity to increase the per meter yield, So many techniques have been for seen in agriculture in terms of usage of pesticides and fertilizers, use of hybridization and green revolution to increase the production in agriculture. Now the use of modern technologies such as artificial intelligence and cognitive computation is going to bring a new big revolution for sustainable agriculture. The present paper focuses on the problems faced by the modern society in agriculture and how the cognitive computation provides an ultimate solution to the problems. We also discuss some illustrations for the usage of cognitive technologies and machine learning in the field of agriculture.