The rapid development of social media, and special websites with critical reviews of products have created a huge collection of resources for customers all over the world. These data may contain a lot of information including product reviews, predicting market changes, and the polarity of opinions. Machine learning and deep learning algorithms provide the necessary tools for intelligence analysis in these challenges. In current competitive markets, it is essential to understand opinions, and sentiments of reviewers by extracting and analyzing their features. Besides, processing and analyzing this volume of data in the cloud can increase the cost of the system, strongly. Fewer dependencies on expensive hardware, storage space, and related software can be provided through cloud computing and Natural Language Processing (NLP). In our work, we propose an integrated architecture of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to identify the polarity of words on the Google cloud and performing computations on Google Colaboratory. Our proposed model based on deep learning algorithms with word embedding technique learns features through a CNN layer, and these features are fed directly into a bidirectional LSTM layer to capture long-term feature dependencies. Then, they can be reused from a CNN layer to provide abstract features before final dense layers. The main goal for this work is to provide an appropriate solution for analyzing sentiments and classification of the opinions into positive and negative classes. Our implementations show that found on the proposed model, the accuracy of more than 89.02% is achievable.

During pandemic periods, there is an intense flow of patients to hospitals. Depending on the disease, many patients may require hospitalization. In some cases, these patients must be taken to intensive care units and emergency interventions must be performed. However, finding a sufficient number of hospital beds or intensive care units during pandemic periods poses a big problem. In these periods, fast and effective planning is more important than ever. Another problem experienced during pandemic periods is the burial of the dead in case the number of deaths increases. This is also a situation that requires due planning. We can learn some lessons from Covid 19 pandemic and be prepared for the future ones. In this paper, statistical properties of the daily cases and daily deaths in Turkey, which is one of the most affected countries by the pandemic in the World, are studied. It is found that the characteristics are nonstationary. Then, random forest regression is applied to predict Covid-19 daily cases and deaths. In addition, seven other machine learning models, namely bagging, AdaBoost, gradient boosting, XGBoost, decision tree, LSTM and ARIMA regressors are built for comparison. The performance of the models are measured using accuracy, coefficient of variation, root-mean-square score and relative error metrics. When random forest regressors are employed, test data related to daily cases are predicted with an accuracy of 92.30% and with an r² score of 0.9893. Besides, daily deaths are predicted with an accuracy of 91.39% and with an r² score of 0.9834. The closest rival in predictions is the bagging regressor. Nevertheless, the results provided by this algoritm changed in different runs and this fact is shown in the study, as well. Comparisons are based on test data. Comparisons with the earlier works are also provided.

Face recognition systems are built on the idea that each person has a particular face structure, and using the facial symmetry, computerized face-matching is possible. The work on face recognition has begun in the 1960‟s, the results of which are being used for security in various institutions and firms throughout the world. The images must be processed correctly for computer based face recognition. The face and its structural properties should be identified carefully, and the resulting image must be converted to two dimensional digital data. An efficient algorithm and a database which consists of face images are needed to solve the face recognition problem. In this paper, Eigenfaces method is used for face recognition. In the recognition process, an eigenface is formed for the given face image, and the Euclidian distances between this eigenface and the previously stored eigenfaces are calculated. The eigenface with the smallest Euclidian distance is the one the person resembles the most. Simulation results are shown. Simulations have been done using the Matlab program. The success rate for the large database used is found to be 94.74%

A license plate recognition system employs image processing techniques, to help to identify the vehicles through their plates. License plate recognition is a process, where first the license plate region is localized in a car image supplied by one camera or by multiple cameras, and then the characters on the plate are identified by a character recognition system. There are many applications of the license plate recognition systems, both public and private. The algorithms, hardware and the network structure for recognition are designed according to the specific application. Recently, thanks to the advances in science and technology, the algorithms and hardware of higher quality have been designed, and license plate recognition systems are now widely used. The recognition can be done in three major steps: Localization of the plate, extraction of the plate characters, and recognition of0 the characters using a suitable identification method. In this paper, an algorithm is designed that can recognize plates using the pictures taken at various angles, various distances and different times of the day, thus under various illumination conditions. The plate is localized using Otsu's thresholding method and the plate features. Vertical and horizontal histograms are used for character segmentation. Finally, character recognition is done by Probabilistic Neural Networks. Simulation results are included and performance analyses are tabulated. MATLAB program is used in the simulations

Natural disasters are becoming more frequent and more severe as a result of global warming. It is critical to take precautions before disasters, to gather and analyze information simultaneously while they are happening, and to make accurate assessments after them given that the deaths and injuries brought on by such disasters both leave lasting traumas in the life of society and damage the economy. Internet of Things (IoT) technology, is a young field that can assist intelligent safety-critical systems with data collection, processing in cloud edge data centers, and application of prediction methodologies for discovering key points and unexpected patterns using 5G technology. With the use of a cloud-based prediction algorithm for disaster management in the IoT environment, this study seeks to quickly process the data that is gathered during disasters and to speed up the analysis that will be done both during and after the disasters. An Optimized Ensemble Bagged Tree (OEBT) algorithm with ANOVA-based feature selection is developed for this aim. The experimental results show that accuracy, F1-Score, precision, and recall of the proposed OEBT algorithm utilizing the US Natural Disasters Dataset are 97.9%, 78.3%, 98.7%, and 78.9%, respectively. Comparisons with decision tree, logistic regression, and the traditional ensemble techniques are made. The suggested algorithm outperforms them all in terms of success rates.