A Blood Bank Management System-Based Internet of Things and Machine Learning Technologies

2020 ◽  
pp. 184-222
Author(s):  
Ahmed Mousa ◽  
Ahmed El-Sayed ◽  
Ali Khalifa ◽  
Marwa El-Nashar ◽  
Yousra Mancy Mancy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Undergraduate Students ◽  
Management System ◽  
Main Idea ◽  
Learning Technologies ◽  
Bank Management ◽  
Academic Researchers ◽  
Blood Banks

Nearly all of the Egyptian hospitals are currently suffering from shortage in rare blood types (e.g., -AB, -B, +AB), which are needed to perform vital surgeries. This leads them (hospitals or doctors) to ask patients' relatives to donate the amount of the required blood. The alternative is that they are forced to pay for the blood if the required type and amount is already available in these hospitals or the blood banks. The main idea of this work is solving problems related to the blood banks from collecting blood from donators to distributing blood bags for interested hospitals. This system is developed in order to enhance the management, performance, and the quality of services for the management of blood banks, which will be positively reflected on many patients in hospitals. This chapter targets undergraduate students, academic researchers, development engineers, and course designers and instructors.

scholarly journals MACHINE LEARNING IMPLEMENTATION FOR THE CLASSIFICATION OF ATTACKS ON WEB SYSTEMS. PART 1

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
K. Smirnova ◽  
A. Smirnov ◽  
O. Olshevska
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Web Application ◽  
Main Idea ◽  
Approximate Model ◽  
Training Sample ◽  
Network Layers ◽  
Entire Network

The possibility of applying machine learning is considered for the classification of malicious requests to a Web application. This approach excludes the use of deterministic analysis systems (for example, expert systems), and based on the application of a cascade of neural networks or perceptrons on an approximate model to the real human brain. The main idea of the work is to enable to describe complex attack vectors consisting of feature sets, abstract terms for compiling a training sample, controlling the quality of recognition and classifying each of the layers (networks) participating in the work, with the ability to adjust not the entire network, But only a small part of it, in the training of which a mistake or inaccuracy crept in.  The design of the developed network can be described as a cascaded, scalable neural network.  The developed system of intrusion detection uses a three-layer neural network. Layers can be built independently of each other by cascades. In the first layer, for each class of attack recognition, there is a corresponding network and correctness is checked on this network. To learn this layer, we have chosen classes of things that can be classified uniquely as yes or no, that is, they are linearly separable. Thus, a layer is obtained not just of neurons, but of their microsets, which can best determine whether is there some data class in the query or not. The following layers are not trained to recognize the attacks themselves, they are trained that a set of attacks creates certain threats. This allows you to more accurately recognize the attacker's attempts to bypass the defense system, as well as classify the target of the attack, and not just its fact. Simple layering allows you to minimize the percentage of false positives.

scholarly journals Pellet Roasting Management System Based on Deep Learning and Internet of Things

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Weixing Liu ◽  
Liyan Zhang ◽  
Jiahao Wang ◽  
Yiming Yang ◽  
Jie Li ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Rotary Kiln ◽  
Management System ◽  
Production Management ◽  
Image Data ◽  
Control Mode ◽  
Enterprise Information ◽  
Blast Furnace Ironmaking ◽  
Use Of The Internet

Pellet is widely used in blast furnace ironmaking. Pellet quality affects the effect of ironmaking, the existing control system of grating-rotary kiln mainly adopts manual control mode, and the quality of pellet production largely depends on the experience, fatigue, and sense of responsibility of the site operators. The use of the Internet of things (IoT) technology in the integration and improvement of enterprise information level, to achieve fine, intelligent production management, at the same time, is conducive to promoting steel enterprises to reduce costs and increase efficiency, energy conservation and emission reduction, transformation and upgrading, and taking a new road to industrialization. According to the working principle and technological characteristics of the grate-rotary kiln at all stages, this paper designs the management system of firing pellets based on convolutional neural network (CNN) and IoT technology, so as to realize automatic recognition of image data obtained by the perceptual layer and make an intelligent analysis of it. The system can classify the working conditions of the current equipment, so as to judge whether the production process parameters of the grate-rotary kiln are up to the standard, thus achieving the goal of controlling the quality of the finished pellet.

scholarly journals Design of Data Mining Techniques for Online Blood Bank Management by CNN Model

2021 ◽  
Vol 3 (3) ◽  
pp. 213-228
Author(s):  
I. Jeena Jacob ◽  
P. Ebby Darney
Keyword(s):  
Machine Learning ◽  
Blood Donors ◽  
Blood Pressure Level ◽  
Blood Bank ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Model Classification ◽  
Bank Management ◽  
Learning Techniques ◽  
Blood Banks

A blood bank is the organisation responsible for storing blood to transfuse it to the patients in need. The primary goal of a blood bank is to be reliable and ensure that patients get the relevant non-toxic blood to avoid transfusion-related complications since blood is a critical medicinal resource. It is difficult for the blood banks to offer high levels of precision, dependability, and automation in the blood storage and transfusion process if blood bank administration includes many human processes. This research framework is proposing to maintain blood bank records using CNN model classification method. In the pre-processing of CNN method, the datasets are tokenized and set the donor’s eligibility. It will make it easier for regular blood donors to donate regularly to charitable people and organizations. A few machine learning techniques offer the automated website updation. Jupyter note book has been used to analyze the dataset of blood donors using decision trees, neural networks, and von Bays techniques. The proposed method operates online through a website. Moreover, the donor's eligibility status with gender, body mass index, blood pressure level, and frequency of blood donations is also maintained. Finally, the comparison of different machine learning algorithms with the suggested framework is tabulated.

scholarly journals IoT for Smart Environment Monitoring Based on Python: A Review

2021 ◽  
pp. 57-70
Author(s):  
Saad Hikmat Haji ◽  
Amira B. Sallow
Keyword(s):  
Water Pollution ◽  
Machine Learning ◽  
Internet Of Things ◽  
Environmental Monitoring ◽  
Sensor Technology ◽  
The Internet ◽  
Smart Environment ◽  
Environment Monitoring ◽  
The Internet Of Things

Air pollution, water pollution, and radiation pollution are significant environmental factors that need to be addressed. Proper monitoring is crucial with the goal that by preserving a healthy society, the planet can achieve sustainable development. With advancements in the internet of things (IoT) and the improvement of modern sensors, environmental monitoring has evolved into a smart environment monitoring (SEM) system in recent years. This article aims to have a critical overview of significant contributions and SEM research, which include monitoring the quality of air , water pollution, radiation pollution, and agricultural systems. The review is divided based on the objectives of applying SEM methods, analyzing each objective about the sensors used, machine learning, and classification methods. Moreover, the authors have thoroughly examined how advancements in sensor technology, the Internet of Things, and machine learning methods have made environmental monitoring into a truly smart monitoring system.

An Intelligent-Internet of Things (IoT) Outbound Logistics Knowledge Management System for Handling Temperature Sensitive Products

2018 ◽  
Vol 9 (1) ◽  
pp. 23-40 ◽  
Author(s):  
Joseph S.M. Yuen ◽  
K.L. Choy ◽  
H.Y. Lam ◽  
Y.P. Tsang
Keyword(s):  
Knowledge Management ◽  
Internet Of Things ◽  
Customer Satisfaction ◽  
Management System ◽  
Knowledge Management System ◽  
Case Based Reasoning ◽  
High Quality ◽  
Logistics Strategy ◽  
Environmentally Sensitive

A comprehensive outbound logistics strategy of environmentally-sensitive products is essential to facilitate effective resource allocation, reliable quality control, and a high customer satisfaction in a supply chain. In this article, an intelligent knowledge management system, namely the Internet-of-Things (IoT) Outbound Logistics Knowledge Management System (IOLMS) is designed to monitor environmentally-sensitive products, and to predict the quality of goods. The system integrates IoT sensors, case-based reasoning (CBR) and fuzzy logic for real-time environmental and product monitoring, outbound logistics strategy formulation and quality change prediction, respectively. By studying the relationship between environmental factors and the quality of goods, different adjustments or strategies of outbound logistics can be developed in order to maintain high quality of goods. Through a pilot study in a high-quality headset manufacturing company, the results show that the IOLMS helps to increase operation efficiency, reduce the planning time, and enhance customer satisfaction.

Internet of Things in Healthcare

2020 ◽  
pp. 23-39
Author(s):  
Rajasekaran Thangaraj ◽  
Sivaramakrishnan Rajendar ◽  
Vidhya Kandasamy
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Deep Learning ◽  
Internet Of Things ◽  
Healthcare Systems ◽  
Wearable Devices ◽  
Healthcare Research ◽  
Learning Technologies ◽  
Learning Approaches ◽  
And Performance

Healthcare motoring has become a popular research in recent years. The evolution of electronic devices brings out numerous wearable devices that can be used for a variety of healthcare motoring systems. These devices measure the patient's health parameters and send them for further processing, where the acquired data is analyzed. The analysis provides the patients or their relatives with the medical support required or predictions based on the acquired data. Cloud computing, deep learning, and machine learning technologies play a prominent role in processing and analyzing the data respectively. This chapter aims to provide a detailed study of IoT-based healthcare systems, a variety of sensors used to measure parameters of health, and various deep learning and machine learning approaches introduced for the diagnosis of different diseases. The chapter also highlights the challenges, open issues, and performance considerations for future IoT-based healthcare research.

Study of Dynamic Management in Digital Harbor Based on Internet of Things

2012 ◽  
Vol 203 ◽  
pp. 212-215
Author(s):  
Cheng Hu ◽  
Jiang Yi Du
Keyword(s):  
Internet Of Things ◽  
Dynamic Analysis ◽  
Management System ◽  
Main Idea ◽  
The Internet ◽  
Dynamic Management ◽  
Virtual Scene ◽  
The Internet Of Things

The business of the harbor expands enormously nowadays and gives new challenge to the harbor management. This paper analyzes the Internet of Things (IOT) based dynamic management system with the correlative key theory and techniques in detail, then proposes the architecture of the digital harbor system and gives the main idea of construction in technique which provides 2D&3D virtual scene to demonstrate the dynamic analysis and forecast the development of harbor.

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