The rapid developments observed in the field of Internet of Things (IoT), along with the recently increasing dependence on this technology in home and financial applications, have made it necessary to pay attention to the security of information sent through these IoT applications. The present article proposes a new encryption method for important messages that are sent via IoT applications. The proposed method provides four levels of security for the confidential message (in this case, an image). The first level is represented by applying the Conformal Mapping on the secret image. The second level is represented by encoding the resulting image from the first level using the encryption and decryption (RSA) method, while the third level is the use of Less Significant Bit (LSB) as the hiding method to hide the message inside the cover image. The compression of the stego image using GZIP is the last level of security. The peak signal-to-noise (PNSR) metric was used to measure the quality of the resulting image after the steganography process. The results appear promising and acceptable. Therefore, it is suggested that this method can be applied to send secret messages through applications of special importance across the IoT.
The limitation of traditional iris recognition systems to process iris images captured in unconstraint environments is a breakthrough. Automatic iris recognition has to face unpredictable variations of iris images in real-world applications. For example, the most challenging problems are related to the severe noise effects that are inherent to these unconstrained iris recognition systems, varying illumination, obstruction of the upper or lower eyelids, the eyelash overlap with the iris region, specular highlights on pupils which come from a spot of light during captured the image, and decentralization of iris image which caused by the person's gaze. Iris segmentation is one of the most important processes in iris recognition. Due to the different types of noise in the eye image, the segmentation result may be erroneous. To solve this problem, this paper develops an efficient iris segmentation algorithm using image processing techniques. Firstly, the outer boundary segmentation of the iris problem is solved. Then the pupil boundary is detected. Testes are done on the Chinese Academy of Sciences' Institute of Automation (CASIA) database. Experimental results indicate that the proposed algorithm is efficient and effective in terms of iris segmentation and reduction of time processing. The accuracy results for both datasets (CASIA-V1 and V4) are 100% and 99.16 respectively.
Heart disease is one of the worst life-threatening conditions. Correct and early diagnosis of this disease is crucial for saving patients’ life and avoiding other complications. On the other hand, keeping the patient’s data, diagnosis process, and treatment plan secured is equally important to the defactomedical procedure. This research proposes a system that is consisting of two phases: security provision and patients’ condition diagnosis. Typically, the first phase exercises a security protocol, called three-pass protocol, to ensure that the people who can access the patient's information are authorized. In order to obtain a high accuracy level in the diagnosis process, artificial intelligence with machine learning methods are employed in the later phase. The proposed system relies on a data set which includes a number of vital indicators, by which the patient's status can be classified as having heart disease or not. The KNN algorithm and the random forest tree algorithm are applied to carry out the classification task. The accuracy scale results reveals that the randomforest tree algorithm (99%) gave higher accuracy than KNN (97%).
In the past two years, the world witnessed the spread of the coronavirus (COVID-19) pandemic that disrupted the entire world, the only solution to this epidemic was health isolation, and with it everything stopped. When announcing the availability of a vaccine, the world was divided over the effectiveness and harms of this vaccine. This article provides an analysis of vaccinators and analysis of people's opinions of the vaccine's efficacy and whether negative or positive. Then a model is built to predict the future numbers of vaccinators and a model that predicts the number of negative opinions or tweets. The model consists of three stages: first, converting data sets into a synchronized time series, that is, the same place and time for vaccination and tweets. The second stage is building a prediction model and the third stage was descripting analysis of the prediction results. The autoregressive integrated moving averages (ARIMA) method was used after decomposing the components of ARIMA and choosing the optimal model, the best results obtained from seasonal ARIMA (SARIMA) for both predictions, the last stage is the descriptive analysis of the results and linking them together to obtain an analysis describing the change in the number of vaccinators and the number of negative tweets.
Background: Handwriting recognition is an important issue nowadays, where handwriting can be a image, document, etc., the ability of a computer to recognize handwritten numbers is very important in more than one application such as translation, reading and number recognition applications. The proposed project provides a system that recognizes handwritten English numbers, the input data being images downloaded from a global dataset. The proposed system consists of a number of stages. The first stage is the preprocessing, which includes resizing of the images to be one size (28 * 28), and then a step (data mapping) is applied. As for the classification stage, it relied on the use of two algorithms, the KNN algorithm and the neural network (error backpropagation). To start the process of training the selected algorithms, the data was divided into two sets, the training setand the test set. Two algorithms were used for the purpose of choosing the best of them, by evaluating their performance using a number of evaluation metrics. Accuracy and Precision were used for the purpose of evaluating the performance of the algorithms. The performance of the KNN algorithm was 0.94 and 0.942 respectively when k = 4. While the best performance reached by the neural network mechanism was 0.98673333 and 0.9698, respectively, at epoch = 15. The neural network (error backpropagation) is shows the best result in the recognation stage Materials and Methods: K-Nearest Neighbors (KNN) technique makes no assumptions about the basic dataset. It is recognized for its effectiveness and ease of use. It is a supervised learning algorithm. To estimate the category of the unlabeled data, a labeled training set containing data points separated into many groups is supplied. Results: The performance of the KNN model with various values for "K." Since the high value of model accuracy was "0.94", the "4" parameter value is the one that provides the best results and precision was "0.94". Conclusion: The problem of handwritten recognition needs high accuracy and precision indicators show an accurate description of the performance of the algorithms that were employed in the proposed system. The two indicators described the performance of the algorithm (KNN), which gave results (0.94 and 0.942).
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