jamal zarepour ahmadabadi

Ischemic heart diseases are one of the most common diseases that cause high mortality worldwide. This article has identified various factors affecting heart disease and identified susceptible people using various machine learning methods.

The current research was conducted on the Yazd Health Study (YaHS) database. YaHS was conducted on adults aged 20-70 years who were residents of Yazd Greater Area and collected information on the health and various diseases of nearly 10,000 people in the form of a questionnaire with 300 different questions. In this research, by using the correlation of questions with heart disease, the most important factors of heart disease have been identified. By using the identified factors and machine learning algorithms, susceptible people with heart disease have been identified.

The results of the evaluations have shown that factors such as age, family history of heart disease, blood pressure, diabetes, blood cholesterol, stress, anxiety, depression, quality of life, quality of sleep, physical activity, smoking, and diet have an effect on heart disease. Likewise, among the different machine learning methods, the nearest neighbor method, the deep neural network method, and the multi-layer perceptron method with a recall criterion of 99.94%, 99.88%, and 99.11% have performed the best in the identifying sick people, respectively.

According to the findings of the research, it can be understood that by controlling factors such as blood pressure, diabetes, blood cholesterol, stress, anxiety, and depression, changing factors such as quality of life, sleep status, physical activity, and eating patterns of people and quitting smoking, it is possible to move towards improving the health of society. On the other hand, the identification of people prone to heart disease using machine learning methods is done faster and at a lower cost than the traditional methods that are done by referring to medical centers and doctors and performing various tests.

in visual secret sharing, the secret image is transformed into several share images and distributed among different people. The share images do not contain any information about the original image, and these images are similar to the noise image. When needed, the original image can be retrieved in the presence of all stakeholders and by stacking the share images. In this regard, the appearance of noisy images may attract the attention of the attackers. To solve this problem, meaningful visual secret sharing was presented. In this article, a method for sharing the image with meaningful shares is introduced. There are a number of hyper parameters in the proposed algorithm. To improve performance, an attempt has been made to determine these hyper parameters using a genetic algorithm. The cost function of the genetic algorithm is the difference between the two values of the correct number of bits of the recovered image and the original image and the correct number of integer bits between the cover image and the shares. The proposed method was evaluated using PSNR, MSE, BCR criteria and presented good results on different images with different number of shares.

Due to increasing population and decreasing energy sources, this research studies the consumption of domestic energy. The purpose of this study is to predict the factors affecting household energy consumption. To do this, we use 3 algorithms, M5Rules, K-nearest neighbor and random forest, available in Weka software. In this study, the feature correlation algorithm is used to select the most important factors affecting energy consumption and their impact. The results show that lights and fixtures, temperature of the living room, outside temperature, temperature outside of Chievres Station, wind speed, humidity in the kitchen and the temperature in the laundry area have the most impact on household energy consumption. Among the methods, random forest algorithm presented the best results.