Self-aware COVID-19 AI Approach (SIntL-CoV19) by Integrating Infected Scans with Internal Behavioral Analysis

In the Artificial intelligence (AI) field, intelligent social awareness is a quantifiable analysis that interacts with humans socially with other infected or non-infected COVID-19 (CoV19) humans. However, less importance is given in this direction. Clinically, there is a need for a social-awareness automated model design to quantify the self-awareness of infected patients and develop a social learning system. In this research paper, a new model of self-aware internal learning coronavirus 19 (SIntL-CoV19) model technique is presented with quantification measures to represent model requirements as an individual self-aware automated detection. Through this model, a human can communicate with the social environment and other humans with an accurate CoV19 infection diagnosis. SIntL-CoV19 model framework for implementation of self-aware architecture with this model is proposed making the diagnosis process compared with the existing architecture. The proposed model achieves improved accuracy Feature Classifier, which outperforms other learning algorithms for CoV19 and normal scans. The data from the investigation show that the proposed SIntL-CoV19 model method might be more effective than other methods

Comparative Analysis of Classification Models on Income Prediction

Predictive Analytics is the underlying technology that can simply be described as an approach to scientifically utilize the past to predict the future to help coveted results. It is the branch of cutting edge analytics which is utilized to make predictions about unfamiliar events. Predictive analytics utilizes different procedures from information mining, insights, modeling, machine learning and artificial Intelligence. It includes extraction of data from information and is utilized to predict patterns and behavior patterns. It can be connected to an unfamiliar event or interest whether past, present or future. It helps being used of statistical algorithms information and machine learning strategies to distinguish the probability of future results in light of chronicled information. Income Determination is an important application of predictive analytics where customer segmentation takes place based on different demographical data. In this paper, we attempt to identify this purpose with a novel approach using different classification techniques to minimize the risk and cost involved to predict certain income levels. Here we have demonstrated the performance of each algorithm particularly on identification of customers using classification techniques. In addition, we provide an investigation analysis on true positives, false negatives, scored labels and scored probabilities

Student Engagement Prediction in Online Session

The individuals who make up the globe constantly advance into the future and improve both their personal lives and the conditions in which they live. One ‘s education is the basis of one ‘s knowledge. Humans' education has a significant impact on their behavior and IQ. Through the use of diverse pedagogical techniques, instructors always play a part in changing students' ways of thinking and developing their social and cognitive abilities. However, getting students to participate in an online class is still difficult. In this study, we created an intelligent predictive system that aids instructors in anticipating students' levels of interest based on the information they learn in an online session and in motivating them through regular feedback. The level of students' engagement is divided into three tiers based on their online session activities (Not engaged, passively engaged, and actively engaged). The given data was subjected to the application of Decision Trees (DT), Random Forest Classifiers (RF), Logistic Regression (LR), and Long Short-Term Memory Networks are among the numerous machine learning approaches (LSTM). According to performance measurements, LSTM is the most accurate machine learning algorithm. The instructors can get in touch with the students and inspire them by improving their teaching approaches based on the results the system produces

A Novel Method of Fraud Detection of Credit Cards by Fuzzy, LSTM, and PSO Optimization

Since online shopping has become so popular, credit card theft has skyrocketed. This makes it hard to spot fake charges on accounts. In this research, credit card fraud detection is performed using a fuzzy database. It has been considered a data mining challenge to reliably identify whether or not a transaction is legitimate. This paper discusses the LSTM method and fuzzy logic. The learning process was sped up and made more precise by using a technique called particle swarm optimization (PSO). Performance values have been compared with those of the LSTM and fuzzy logic techniques, and a PSO-based neural network has been intensively trained by increasing the number of iterations and the population, or number of swarms. It has been shown that the PSO-based algorithm yields the best result for detecting fraudulent transactions. The goal of this method is to lower the mean square error (MSE) rate of the system. PSO is a popular optimization technique that can be used to locate the optimal set of features for the credit card fraud detection system. The proposed method PSO shows less mean squared error compared with Fuzzy and LSTM techniques

AODV (ST_AODV) on MANETs with Path Security and Trust-based Routing

The nodes of the MANET are connected by an autonomous that has no predetermined structure (Mobile ad hoc Network). When a node's proximity to other nodes is maintained dynamically via the use of relying nodes, the MANET network's node-to-node connection is un-trusted because of node mobility. If a node relies on self-resources at any point in time, it runs the risk of acting as a selfish or malicious node, the untrusted selfish or malicious node in the network. An end-to-end routing route that is secure has been presented to enhance the security of the path based on the AODV routing protocol using ST AODV (Secure and Trust ADV). To do this, we must first identify the selfish/malicious nodes in the network and analyse their past activity to determine their current trust levels. A node's stage of belief is indicated by the packet messages it sends. In order to resolve each route, trust must be identified and the path's metadata in RREP must be updated