A Survey on Critical Thinking in Education Scenario

Critical thinking has been a controversial issue among philosophers, researchers and educationalists, although there is no general consensus on a definition. Everyone thinks; it is our nature to do so. But much of our thinking, left to it-self, is biased, distorted, partial, uninformed or down-right prejudiced. Yet the quality of our life and that of what we produce, make, or build depends precisely on the quality of our thought. Excellence in thought, however, must be systematically cultivated. Critical thinking is that mode of thinking - about any subject, content, or problem - in which the thinker improves the quality of his or her thinking by skillfully taking charge of the structures inherent in thinking and imposing intellectual standards upon them. Critical thinking is not a matter of accumulating information. A person with a good memory and who knows a lot of facts is not necessarily good at critical thinking. A critical thinker is able to deduce consequences from what he/she knows, and he/she knows how to make use of information to solve problems, and to seek relevant sources of information to inform himself / herself. Critical thinking should not be confused with being argumentative or being critical of other people. Although critical thinking skills can be used in exposing fallacies and bad reasoning, critical thinking can also play an important role in cooperative reasoning and constructive tasks. Critical thinking can help us acquire knowledge, improve our theories, and strengthen arguments. It is self-guided, self-disciplined thinking which attempts to reason at the highest level of quality in a fair-minded way

Network Intrusion Detection System using Spark's Scalable Machine Learning Library

In this paper, considering that the serious network security situation we are facing and the problem of an increasing amount of data generated by the network, we proposed an Intrusion Detection System based on Spark's scalable machine learning library,In this paper we are showing that performance of Intrusion Detection system using sparks machine learning library is high in compare to hadoop. Fro IDS we will use K-Means algorithm

Intrusion Detection System Using Feature Selection and classifier based Algorithm

With the enlargement of web, there has been a terrific increases in the number of attacks and therefore Intrusion Detection Systems (IDS�s) has become a main topic of information security. The purpose of IDS is to help the computer systems to deal with attacks. The feature selection used in IDS helps to reduce the classification time. In this paper, the IDS for detecting the attacks efficiently has been proposed. We have proposed an algorithm based on associan rule to detect intrusion. We have combined algorithm with feature selection to improve efficiency of IDS.The proposed feature selection and associan rule algorithms enhance the performance of the IDS in detecting the attacks

An Efficient Data Analytics and Optimized Algorithm for Enhancing the Performance of Image Segmentation Using Deep Learning Model

Image segmentation is the key topic in computer vision and image processing with applications like robotic perception, scene understanding, video surveillance, image compression, medical image analysis, and augmented reality among many others. There are numerous algorithms are developed in the literature for image segmentation. This paper provides a broad spectrum of pioneering works for instance and semantic level segmentation with mask Region based Convolution Neural Network with Monarch butterfly Optimization (RCNN-MBO) architecture. The system is initially constructed in a Python environment with images of people and animals being input. Remove the unnecessary data from the gathered datasets during the pre-processing stage. Next, use a stochastic threshold function to segment the image. Then update the segmented images into a designed model for detecting and classifying a group of images. The main goal of the designed approach is to attain accurate prediction results also improve the performance of the designed model by attaining better results. To enhance the performance, two activation functions were used and MBO fitness is updated in the classification layer. It improves the prediction results and takes less time to detect and classify images. Finally, the experimental outcomes show the reliability of the designed approach by other conventional techniques in terms of accuracy, precision, sensitivity, specificity, F-measure, error rate, and computation time

Parameter Enhancement and Size Reduction using DGS of L Band Antenna

Proposed research is the outcome of the detailed literature review and intensive research carried out in the field of parameter improvement of antenna using defected ground structure. In this paper a patch antenna is proposed with the introduction of a slit ring DGS structure to modify its parameters and reduce the size of antenna. Antenna was designed and simulated at 1.95GHz initially but after implementing DGS its radiation efficiency is shifted to 1.58GHz which theoretically a sign of size reduction of antenna. DGS is actually a cut made in the ground plane of the antenna which create a disturbance in radiating power, this disturbance in the ground basically distributes the radiating frequency and make antenna more efficient than ever before

Remote Health Monitoring IoT Framework using Machine Learning Prediction and Advanced Artificial Intelligence (AI) Model

Real intervention and treatment standards drew attention to remote health monitoring frameworks. Remote monitoring frameworks for disease detection at an early stage are opposed by most conventional works. Even so, it ran into issues like increased operational complexity, higher resource costs, inaccurate predictions, longer data collection times, and a lower convergence rate. A remote health monitoring framework that uses artificial intelligence (AI) to predict heart disease and diabetes from medical datasets is the goal of this project. Patients' health data is collected via smart devices, and the resulting data is then combined using a variety of nodes, including a detection node, a visualisation node, and a prognostic node. People with long-term illnesses (such as the elderly and disabled) are in such greater demand than ever before that a new approach to healthcare delivery is essential. In the evolved paradigm, conventional physical medical services foundations like clinics, nursing homes, and long haul care offices will be old. Due to recent advancements in modern technology, such as artificial intelligence (AI) and machine learning (ML), the smart healthcare system has become increasingly necessary (ML). This paper will discuss wearable and smartphone technologies, AI for medical diagnostics, and assistive structures, including social robots, that have been created for the surrounding upheld living climate. The review presents programming reconciliation structures that are urgent for consolidating information examination and other man-made consciousness instruments to develop brilliant medical care frameworks (AI)

Notes on the discovery and ecology of the invasive armoured catfish Pterygoplichthys disjunctivus (Weber, 1991) and the exotic cichlid Amphilophus trimaculatus (Gunther, 1867) from Southern West Bengal, India

This paper documents the first occurrence of the exotic, highly invasive, South American armoured sucker mouth catfish Pterygoplichthys disjunctivus (Weber, 1991) from the brackish waters of the Sundarban Tiger Reserve, West Bengal, India and the Central American cichlid Amphilophus trimaculatus (Gunther, 1867) from Southern Bengal, India. Notes on the possible threats due to invasion, sources of introduction, extent of spread and management of these and other invasive species are discussed in the paper

Enhanced Ai-Based Machine Learning Model for an Accurate Segmentation and Classification Methods

Phone Laser Scanner becomes the versatile sensor module that is premised on Lamp Identification and Spanning methodology and is used in a spectrum of uses. There are several prior editorials in the literary works that concentrate on the implementations or attributes of these processes; even so, evaluations of all those inventive computational techniques reported in the literature have not even been performed in the required thickness. At ToAT that finish, we examine and summarize the latest advances in Artificial Intelligence based machine learning data processing approaches such as extracting features, fragmentation, machine vision, and categorization. In this survey, we have reviewed total 48 papers based on an enhanced AI based machine learning model for accurate classification and segmentation methods. Here, we have reviewed the sections on segmentation and classification of images based on machine learning models

Designing a wind energy harvester for connected vehicles in green cities

Electric vehicles (EVs) have recently gained momentum as an integral part of the Internet of Vehicles (IoV) when authorities started expanding their low emission zones (LEZs) in an effort to build green cities with low carbon footprints. Energy is one of the key requirements of EVs, not only to support the smooth and sustainable operation of EVs, but also to ensure connectivity between the vehicle and the infrastructure in the critical times such as disaster recovery operation. In this context, renewable energy sources (such as wind energy) have an important role to play in the automobile sector towards designing energy-harvesting electric vehicles (EH-EV) to mitigate energy reliance on the national grid. In this article, a novel approach is presented to harness energy from a small-scale wind turbine due to vehicle mobility to support the communication primitives in electric vehicles which enable plenty of IoV use cases. The harvested power is then processed through a regulation circuitry to consequently achieve the desired power supply for the end load (i.e., battery or super capacitor). The suitable orientation for optimum conversion efficiency is proposed through ANSYS-based aerodynamics analysis. The voltage-induced by the DC generator is 35 V under the no-load condition while it is 25 V at a rated current of 6.9 A at full-load, yielding a supply of 100 W (on constant voltage) at a speed of 90 mph for nominal battery charging

A hybrid supervised machine learning classifier system for breast cancer prognosis using feature selection and data imbalance handling approaches

Nowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification models for the prognosis of breast cancer are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Search, and Greedy Stepwise has been used to identify the important features. On these selected features popular machine learning classifiers Support Vector Machine, J48 (C4.5 Decision Tree Algorithm), Multilayer-Perceptron (a feed-forward ANN) were used in the system. The methodology of the proposed system is structured into five stages which include (1) Data Pre-processing; (2) Data imbalance handling; (3) Feature Selection; (4) Machine Learning Classifiers; (5) classifier's performance evaluation. The dataset under this research experimentation is referred from the UCI Machine Learning Repository, named Breast Cancer Wisconsin (Diagnostic) Data Set. This article indicated that the J48 decision tree classifier is the appropriate machine learning-based classifier for optimum breast cancer prognosis. Support Vector Machine with Particle Swarm Optimization algorithm for feature selection achieves the accuracy of 98.24%, MCC = 0.961, Sensitivity = 99.11%, Specificity = 96.54%, and Kappa statistics of 0.9606. It is also observed that the J48 Decision Tree classifier with the Genetic Search algorithm for feature selection achieves the accuracy of 98.83%, MCC = 0.974, Sensitivity = 98.95%, Specificity = 98.58%, and Kappa statistics of 0.9735. Furthermore, Multilayer Perceptron ANN classifier with Genetic Search algorithm for feature selection achieves the accuracy of 98.59%, MCC = 0.968, Sensitivity = 98.6%, Specificity = 98.57%, and Kappa statistics of 0.9682.Web of Science106art. no. 69