hedhab khalid

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 ...

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 perfor...

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 compon...

Consumption of medicines for a particular disease can be an indicator of the spread of the disease, as the increase in the consumption of medicines implies an increase in the incidence of the disease. Acquired immunodeficiency syndrome (AIDS) is a chronic, potentially lifethreatening condition caused by the human immunodeficiency virus (HIV). AIDS is one of the deadliest diseases in human life. Therefore, monitoring the spread of AIDS through analyzing the consumption of its drugs and determining the places where the drugs are consumed geographically is an urgent necessity and brings useful information in the health sector. The main idea behind this paper is to employ a new approach of using deep learning as the main stage to predict the quantities of AIDS's drugs. Additionally, in the second stage the spatial concept is exploited to state the spread position of that disease. The deep neural network is a fully automated network that consists of a preprocessing layer, normalization layer and prediction layer depending on the state utilization drugs dataset of the USA for five consecutive years. Based on the results of the prediction process, the second stage represents the consumption of AIDS's drugs and produces a spatial map representing the disease Surveillance map. The results of the prediction process using the deep neural network are compared with the results of the linear regression method, as indicated by previous research. The deep network has given superior results by obtaining a very low value of (MSE).The results were encouraging and promising to rely on deep learning. The creation of a spatial map illustrated the spread of the disease and gave a clear vision based on the consumption of drugs.

The provision of pharmaceutical drugs in quantities appropriate to consumption is an important point in the pharmaceutical industry and storage of medicines, as the production of large quantities of unnecessary drugs leads to a longer storage of drugs. Meanwhile most medicines have a short shelf life. When the amount of production is less than required, this affects the satisfaction of the customer and the marketing of the drug. Time series analysis is the appropriate solution to this problem. Deep learning has been adapted for the purpose of time series analysis and a prediction of the required quantities drugs. A recurrent neural network with Long-Short Term Memory LSTM has been used by deep learning. The proposed methodology is based on the seasonal number of prescription required quantities with the number of quarters as indicators. The aim of the research is to forecast the drugs amount needed for one year. The proposed method is assessed using two types of evaluation. The firs...

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 quali...

This research presents a technique for protect the data through using cryptography and steganography. The cryptography stage is using Play fair cipher to encrypted the secret message. In steganography stage convert Playfair cipher text to binary and store the first bit in every letter in secret message in the LSB of pixel in the image and then the second bit in every letter embedded in the LSB of pixel and continue so until the last bit in last letter in the secret message. The greater size of the text more difficult to decode See's picture does not know what the image bits and what are bits of text.

Consumption of medicines for a particular disease can be an indicator of the spread of the disease, as the increase in the consumption of medicines implies an increase in the incidence of the disease. Acquired immunodeficiency syndrome (AIDS) is a chronic, potentially lifethreatening condition caused by the human immunodeficiency virus (HIV). AIDS is one of the deadliest diseases in human life. Therefore, monitoring the spread of AIDS through analyzing the consumption of its drugs and determining the places where the drugs are consumed geographically is an urgent necessity and brings useful information in the health sector. The main idea behind this paper is to employ a new approach of using deep learning as the main stage to predict the quantities of AIDS's drugs. Additionally, in the second stage the spatial concept is exploited to state the spread position of that disease. The deep neural network is a fully automated network that consists of a preprocessing layer, normalization layer and prediction layer depending on the state utilization drugs dataset of the USA for five consecutive years. Based on the results of the prediction process, the second stage represents the consumption of AIDS's drugs and produces a spatial map representing the disease Surveillance map. The results of the prediction process using the deep neural network are compared with the results of the linear regression method, as indicated by previous research. The deep network has given superior results by obtaining a very low value of (MSE).The results were encouraging and promising to rely on deep learning. The creation of a spatial map illustrated the spread of the disease and gave a clear vision based on the consumption of drugs.

The provision of pharmaceutical drugs in quantities appropriate to consumption is an important point in the pharmaceutical industry and storage of medicines, as the production of large quantities of unnecessary drugs leads to a longer storage of drugs. Meanwhile most medicines have a short shelf life. When the amount of production is less than required, this affects the satisfaction of the customer and the marketing of the drug. Time series analysis is the appropriate solution to this problem. Deep learning has been adapted for the purpose of time series analysis and a prediction of the required quantities drugs. A recurrent neural network with Long-Short Term Memory LSTM has been used by deep learning. The proposed methodology is based on the seasonal number of prescription required quantities with the number of quarters as indicators. The aim of the research is to forecast the drugs amount needed for one year. The proposed method is assessed using two types of evaluation. The first one is based on MSE and the visualization of the actual data and forecasted data. The proposed method has reached a low value of MSE and the visualization graph is semi-identical, whereas the second evaluation method compares the result of the proposed method with traditional forecasting method. Multiple linear regression is a traditional prediction method used with the data set, whose results are relatively good and promising compared to the results of the traditional method.

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 process of product development in the health sector, especially pharmaceuticals, goes through a series of precise procedures due to its directrelevance to human life. The opinion of patients or users of a particular drugcan be relied upon in this development process, as the patients convey their experience with the drugs through their opinion. The social media field provides many datasets related to drugs through knowing the user's ratingand opinion on a drug after using it. In this work, a dataset is used that includes the user’s rating and review on the drug, for the purpose of classifying the user’s opinions (reviews) whether they are positive ornegative. The proposed method in this article includes two phases. The first phase is to use the Global vectors for word representation model for converting texts into the vector of embedded words. As for the second stage, the deep neural network (Bidirectional longshort-termmemory) is employedin the classification of reviews. The...