http://localhost:8080/xmlui/handle/123456789/6765 2026-04-12T22:49:51Z http://localhost:8080/xmlui/handle/123456789/9843 Emotional intelligence for cognitive internet of things based Smart environments Abdurahman, Mehamed Ahmed In today’s extravagant era, the capacity to perceive feeling is one of the signs of passionate insight, a part of human knowledge that has been contended to be significantly more imperative than scientific and verbal intelligence. Due to gradual enrichment in IoT technology for smart environment level, Technology disruptions and degradation of performance in the industries, workers have lost their interest or concentration in work activity and have also lost their focus or performance in the working environment. In addition, despite the rapid growth of IoT, In the field of modern intelligent service, the current IoT based systems significantly lacks cognitive intelligence this implies cannot fulfill the requirements for industrial services. Deep learning is become one of the most popular technique that takes place in many machine learning related applications and studies. While it is put in the practice mostly on content based image retrieval, there is still room for improvement by employing it in diverse computer vision applications. As per the rigorous theoretical and practical analysis, it has been found that an immediate need to address this issue by developing an emotional intelligent approach, Machine learning (deep learning, CIoT), which will mentor and counsel workers by monitoring their behavior in the work environments. In this study, we aimed to construct a CNN model based emotional intelligence System (EIS), in order to automatically classify expressions presented in Facial Expression Recognition (FER2013) and kaggel image database. Our presented model achieved % 81.1, success rate on FER2013 database. For Full Thesis Kindly contact to respective Library 2019-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/9842 Machine Learning for Cactus (Beles) Diseases Detection Berhe, Hailay Beyene Machine learning is very important technology that can support people in different disciplines (Agriculture, health centers, household, transportation, etc) and different levels of life. Machine learning increases accuracy of performance (prediction). It uses various types of data (image, video, audio and text) for different purposes and applications. Our work has focused on cactus diseases detection to early prevent the reduction of productivity (quantitatively and qualitatively) of the cereal. To do this, we have used unhealthy and healthy cactus images. The images were enhanced, noises were removed and images were segmented to create better model using imadjust, guided filter and K-means clustering techniques respectively. These image preprocessing techniques were selected from many techniques after implementing each technique and measuring their performances. As part of creating the model, feature extraction techniques (Color histogram, Bag of features and GLCM) were applied to extract color, bag of features and texture features respectively. After testing the model applying these features, bag of features were found to be best for creating better model and they were selected as features of our model. We created our machine learning model using bag of features applying linear SVM. Other machine learning algorithms were used to train and test the model for detecting the diseases, but linear SVM was found with best performance (97.2%). In this task, 75% of each class was used for training and 25% was used for testing the model. Finally, the similarity for classification was checked using linear kernel, RBF kernel and Polynomial kernel and an average accuracy of 94% was achieved though linear kernel was the best classifying method with an accuracy of 98.951%. For Full Thesis Kindly contact to respective Library 2019-12-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/9832 Feature Extraction Techniques For Detection Of Rule Violation(S) On Highway At Toll Centres Using Raspberry Pi Hardware Purohit, Manishkumar NA For Full Thesis Kindly contact to respective Library 2020-09-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/9831 An Efficent Load Balancing Approach in Cloud Computing Shah, Jaimeel NA For Full Thesis Kindly contact to respective Library 2020-05-01T00:00:00Z