7 research outputs found
The Significance of Emotional Intelligence on the Innovative Work Behavior of Managers as Strategic Decision-Makers
No full textAbstractIn recent years, the importance of innovation and emotional intelligence on strategic decision making has increased owing to the dynamic changes in the global world. That is why; managers as strategic decision makers play a crucial role and they need emotional intelligence and innovative work behavior together which provide them to make decisions effectively. Additionally the ideas and studies supporting the effect of innovation and emotional intelligence which is as crucial as IQ led to produce new studies to measure the emotional intelligence. In this scope, this paper aims to analyze to what extent emotional intelligence is used by managers and mainly how significant it is on strategic decision making in relation to innovative work behaviors
A Performance Evaluation of the Turkish Banking Sector after the Global Crisis via CAMELS Ratios
No full textAbstractAfter the crisis in November 2000 and February 2001, an important structural change occurred in financial sector especially in terms of banking in Turkey. It was tried to revise flaws with structural regulations of banking and financial supervision in the banking sector. Besides performance of banking field in the respective change process, the reactions of banking sector have become a significant analysis issue as a result of regulations and 2008 global economic crisis. Despite the fact that there are a lot of studies on the banking performance evaluation, CAMELS ratios which are one of the important analysis types for performance assessment in banking sector comprise important parameters reflecting results of banking sector performance
Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images
No full textCountries the world over have focused on protecting human health and combatting the COVID-19 outbreak. It has had a destructive effect on human health and daily life. Many people have been infected and have died. It is critical to control and prevent the spread of COVID-19 disease by applying quick alternative diagnostic techniques. Although laboratory tests have been widely applied as diagnostic tools, findings suggest that the application of X-ray and computed tomography images and pretrained deep convolutional neural network (CNN) models can help in the accurate detection of this disease. In this study, we propose a model for COVID-19 diagnosis, applying a deep CNN technique based on raw chest X-ray images of COVID-19 patients, which can be accessed publicly on GitHub. Fifty positive and 50 negative COVID-19 X-ray images for training and 20 positive and 20 negative images for testing phases are included. Because the classification of X-ray images needs a deep architecture to cope with the complicated structure of images, we apply five different architectures of well-known pretrained deep CNN models: VGG16, VGG19, ResNet, DenseNet, and InceptionV3. The pretrained VGG16 model can detect COVID-19 from non-COVID-19 cases with the highest classification performance of 80% accuracy among the other four proposed models, and it can be used as a helpful tool in the department of radiology. In the proposed model, a limited dataset of COVID-19 X-ray images is used that can provide more accurate performance when the number of instances in the dataset increases
Handbook of research on applied intelligence for health and clinical informatics/ Anuradha Thakare, Sanjeev Wagh, Manisha Bhende, Ahmed Anetr, and Xiao-Zhi Gao, editors.
No full textIncludes bibliographical references and index."This book focuses on the study of resources and methods for the management of healthcare infrastructure and information highlighting health and clinical data structure, behavior, and interactions of natural and engineered computational systems to helps researchers and practitioners learn further investigation and solutions"--Automated ICD Coding Using Deep Learning / Sagar Dhobale -- A Review on Social Distance & Face Mask Detector / Lokesh Giripunje, Arpita Patra, Riya Chaudhari, Aniket Sagar -- Decision Support Proposal for Imbalanced Clinical Data / Kevser Sahinbas -- Bone Tumor Detection Using Machine Learning / Deepak Mane -- A Novel Approach of Lung Tumor Segmentation using a 3D Deep Convolutional Neural Network / Shweta Tyagi, Sanjay Talbar, Abhishek Mahajan -- Ultrasonic Detection of Down Syndrome Using Multiscale Quantiser With Convolutional Neural Network / Michael Simon, Kavitha A.R. -- Detection of Lung Diseases Using Machine Learning and Deep Learning Techniques : An Analytical Study / Shravani Nimbolkar, Anuradha Thakare, Subhradeep Mitra, Omkar Biranje, Anant Sutar -- M2UNet++ A Modified Multi-Scale UNet++ Architecture for Automatic Liver Segmentation in Computed Tomography Images / Devidas Kushnure, Sanjay Talbar.1 online resourc
Assessment Approach with Mahara and Moodle in E-Learning
No full textVarious approaches regarding the assessment and evaluation system which is still effective today have been emerging. In recent years, portfolio (individual development files) evaluation approach have emerged with the necessity of alternative assessment and evaluation systems including learning, learning materials, and styles. In addition to classical evaluation methods of students, portfolio, a new teaching and evaluation system, is a method in which performance of students is evaluated with studies and projects they have carried out throughout their lives. Portfolio have been introduced since it is believed that portfolio shall have an importance role in terms of evaluating students in distance learning system in which communication is limited. With this aim, suggestions were proposed by touching on the subject what benefits a portfolio to be created through learning management system used in distance learning will have for distance learning students
Malicious URL Detection Using Machine Learning
No full textRecently, with the increase in Internet usage, cybersecurity has been a significant challenge for computer systems. Different malicious URLs emit different malicious software and try to capture user information. Signature-based approaches have often been used to detect such websites and detected malicious URLs have been attempted to restrict access by using various security components. This chapter proposes using host-based and lexical features of the associated URLs to better improve the performance of classifiers for detecting malicious web sites. Random forest models and gradient boosting classifier are applied to create a URL classifier using URL string attributes as features. The highest accuracy was achieved by random forest as 98.6%. The results show that being able to identify malicious websites based on URL alone and classify them as spam URLs without relying on page content will result in significant resource savings as well as safe browsing experience for the user
