Natalja Osintsev
Abstract
Air pollution is the biggest environmental hazard that cannot be ignored. Due to increase in number of industries and urbanization increases air pollutants concentrations in many areas because of this different changes are been happening in human life like health issues and as well as other living organisms ...
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Air pollution is the biggest environmental hazard that cannot be ignored. Due to increase in number of industries and urbanization increases air pollutants concentrations in many areas because of this different changes are been happening in human life like health issues and as well as other living organisms . we have some pollutant emission monitoring systems, like Opsis, Codel, Urac and TAS-Air metrics which are expensive. As well as these systems have limitations to be installed on chimney due to their principle of operation. In this work I like to propose a function that is easy to use and causes less cost compared to the other ones.That is an industrial air pollution monitoring system based on the technology of wireless sensor networks (. This system is integrated with the global system for mobile communications (GSM) and the protocol it uses is zigbee. The system consists of sensor nodes, a control center and data base through which sensing data can be stored for history and future plans. It is used to monitor carbon monoxide (CO), sulfur dioxide (SO2) and dust concentration caused by industrial emissions due to process.
Ibrahim Mekawy
Abstract
Household object detection is a brand-new computer technique that combines image processing and computer vision to recognize objects in the home. All objects stored in the kitchen, room, and other areas will be detected by the camera. Low-end device techniques for detecting people in video or images ...
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Household object detection is a brand-new computer technique that combines image processing and computer vision to recognize objects in the home. All objects stored in the kitchen, room, and other areas will be detected by the camera. Low-end device techniques for detecting people in video or images are known as object detection. With picture and video analysis, we've lost our way.
Aziza Algarni
Abstract
We all know forest is very important resource of oxygen. Saving our environmental resources is human beings responsibility. One of the techniques to save forests is forest fire detection. This is a technique used to detect the fire and prevent them in less time. Forest fire leads to death of wild life ...
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We all know forest is very important resource of oxygen. Saving our environmental resources is human beings responsibility. One of the techniques to save forests is forest fire detection. This is a technique used to detect the fire and prevent them in less time. Forest fire leads to death of wild life and trees. There are other techniques used to detect fire in forests like cameras, satellite system, manual monitoring but they take time to detect the fire whereas Forest fire detection system detects the fire within seconds and triggers the alarms. In this way we can save tress and wildlife in very less time.
Mingyue Wang
Abstract
Wireless sensor network technologies normally deploy a large number of small, low cost sensors, fairly densely that can observe and influence the physical world around them by gathering physical information, transform it into electrical signals, send it to a remote location to do some analysis and deploy ...
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Wireless sensor network technologies normally deploy a large number of small, low cost sensors, fairly densely that can observe and influence the physical world around them by gathering physical information, transform it into electrical signals, send it to a remote location to do some analysis and deploy the results in different applications. This means there is no need to build towers or set up complicated communication links such as; microwave and satellite. It can be deployed anywhere, even in inaccessible places. This technology can provide a real time monitoring for forest fire, where it can provide information at the ignition instance or at very small delay, depends on the node used wake up/sleep schedule. It’s more reliable because it can influence the world in the surrounded area, if it is used in appropriate methods, rather than expecting events over large distances and long delay like other satellite and camera towers techniques. In this work, all nodes only use temperature sensors and they are programmed on a certain threshold temperature, above it the node will send an alarm message to the sink. This concept relies solely on the node behavior to alert of crises possibility using simple node components to provide detection and information on whether this is a peaceful fire, or the beginning of wild fire. The key in this method is to make decisions by tracking the fire propagation and check the logic behind it.
Victoria Nozick
Abstract
In any other place in the world, people crave for protection during dark times (Night). Either it is man or women. Everyone is scared of being alone where dark looks like a haunting one to everyone. Where people are going MAD towards women during at that point. So, I am contributing this project to all ...
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In any other place in the world, people crave for protection during dark times (Night). Either it is man or women. Everyone is scared of being alone where dark looks like a haunting one to everyone. Where people are going MAD towards women during at that point. So, I am contributing this project to all the women who are hustling hard to get their independence and freedom. These DRONES helps them to keep secure and do all the patrol or Police work during nights.
Amir Hossein Hariri; Esmaeil Bagheri; Sayyed Mohammad Reza Davoodi
Abstract
Coronary artery heart failure is the leading cause of mortality among other cardiac diseases. In most of the cases, angiography is a reliable method for the diagnosis and treatment of cardiovascular diseases. However, it is a costly approach associated with various complications. The significant increase ...
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Coronary artery heart failure is the leading cause of mortality among other cardiac diseases. In most of the cases, angiography is a reliable method for the diagnosis and treatment of cardiovascular diseases. However, it is a costly approach associated with various complications. The significant increase in the prevalence of cardiovascular diseases and the subsequent complications and treatment costs have urged researchers to plan for the better examination, prevention, early detection, and effective treatment of these conditions. The present study aimed to determine the patterns of cardiovascular diseases using integrated classification techniques for analyzing the data of internal medicine patients who are at the risk of heart failure with 451 samples and 13 characteristics. Selecting characteristics and evaluating the influential factors are essential to the development of classifiers and increasing their accuracy. Therefore, we investigated the influential factors of the Gini index. In the classification phase, basic techniques were used, including a decision tree, a neural network, and different cumulative techniques such as gradient boosting, random forest, and the novel deep learning method. A comparison revealed that deep learning with the accuracy of 95.33%, disease class accuracy of 95.77%, and health class accuracy of 94.74% could enhance the presentation and results of the neural network. Out findings confirmed that cumulative methods and selecting influential factors are essential to increasing the accuracy of the diagnostic systems for heart failure. Furthermore, the reported practical tree rules emphasized the use of analytical methods to extract knowledge.
Agyan Panda; Sheila Maria Muniz
Abstract
Household object detection is a brand-new computer technique that combines image processing and computer vision to recognise objects in the home. All objects stored in the kitchen, room, and other areas will be detected by the camera. Low-end device techniques for detecting people in video or images ...
Read More
Household object detection is a brand-new computer technique that combines image processing and computer vision to recognise objects in the home. All objects stored in the kitchen, room, and other areas will be detected by the camera. Low-end device techniques for detecting people in video or images are known as object detection. With picture and video analysis, we've lost our way.
Fatemeh Mohades Deilami; Hossein Sadr; Mozhdeh Nazari
Abstract
Personality can be defined as the combination of behavior, emotion, motivation, and thoughts that aim at describing various aspects of human behavior based on a few stable and measurable characteristics. Considering the fact that our personality has a remarkable influence in our daily life, automatic ...
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Personality can be defined as the combination of behavior, emotion, motivation, and thoughts that aim at describing various aspects of human behavior based on a few stable and measurable characteristics. Considering the fact that our personality has a remarkable influence in our daily life, automatic recognition of a person's personality attributes can provide many essential practical applications in various aspects of cognitive science. Although various methods have been recently proposed for the task of personality recognition, most of them have mainly focused on human-designed statistical features and they did not make use of rich semantic information existing in users' generated texts while not only these contents can demonstrate its writer's internal thought and emotion but also can be assumed as the most direct way for people to state their feeling and opinion in an understandable form. In order to make use of this valuable semantic information as well as overcoming the complexity and handcraft feature requirement of previous methods, a deep learning based method for the task of personality recognition from text is proposed in this paper. Among various deep neural networks, Convolutional Neural Networks (CNN) have demonstrated profound efficiency in natural language processing and especially personality detection. Owing to the fact that various filter sizes in CNN may influence its performance, we decided to combine CNN with AdaBoost, a classical ensemble algorithm, to consider the possibility of using the contribution of various filter lengths and gasp their potential in the final classification via combining various classifiers with respective filter size using AdaBoost. Our proposed method was validated on the Essay dataset by conducting a series of experiments and the empirical results demonstrated the superiority of our proposed method compared to both machine learning and deep learning methods for the task of personality recognition.
Fatemeh Taghvaei; Ramin Safa
Abstract
As the construction sector accounts for the highest energy consumption worldwide, new solutions must be offered in buildings through the adoption of energy-efficient techniques. The main factors involved in energy consumption and residents' behaviors patterns considering environmentally-friendly lifestyle ...
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As the construction sector accounts for the highest energy consumption worldwide, new solutions must be offered in buildings through the adoption of energy-efficient techniques. The main factors involved in energy consumption and residents' behaviors patterns considering environmentally-friendly lifestyle changes must be clearly identified and modeled to provide such solutions. One of the most important topics in smart grids is managing energy consumption in buildings, and one way to optimize energy consumption by analyzing building energy data is to use personalized recommender systems. The Non-Intrusive Load Monitoring (NILM) technique is an important way to cost-effective real-time monitoring the energy consumption and time of use for each appliance. However, the combination of recommender systems and NILM has received less attention. In this paper, a personalized NILM-based recommender system is proposed, which has three main phases: DAE-based NILM, TF-IDF-based text classification, and personalized recommender system. The proposed approach is investigated using the Reference Energy Disaggregation Dataset (REDD). According to the results, the accuracy of the proposed framework is about 60%.
