A Survey Study of the Current Challenges and Opportunities of Deploying the ECG Biometric Authentication Method in IoT and 5G Environments

The environment prototype of the Internet of Things (IoT) has opened the horizon for researchers to utilize such environments in deploying useful new techniques and methods in different fields and areas. The deployment process takes place when numerous IoT devices are utilized in the implementation phase for new techniques and methods. With the wide use of IoT devices in our daily lives in many fields, personal identification is becoming increasingly important for our society. This survey aims to demonstrate various aspects related to the implementation of biometric authentication in healthcare monitoring systems based on acquiring vital ECG signals via designated wearable devices that are compatible with 5G technology. The nature of ECG signals and current ongoing research related to ECG authentication are investigated in this survey along with the factors that may affect the signal acquisition process. In addition, the survey addresses the psycho-physiological factors that pose a challenge to the usage of ECG signals as a biometric trait in biometric authentication systems along with other challenges that must be addressed and resolved in any future related research.

Utilizing ECG Waveform Features as New Biometric Authentication Method

In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects‟ raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%

Medical E-Consultation System (MECS)

This study aims at establishing a medical consultation system using web-based tools such as PHP and MYSQL. The development of the system will be associated with the linking the website to a knowledge base rather than an ordinary database, using some popular framework like MYSQL. In addition, some expert system techniques are included through the application of intelligent database terms during the matching process of the symptoms provided by users. Features of the system include providing survey-based online diagnoses for patients, such as that produced by doctors. Furthermore, the system will provide information about various diseases, presenting them in the format of online materials. Moreover, it will also provide an online forum where specialists, trainees and patients can interact with each other using questions-answers information sharing technique. On the other hand, users of the system will be able to take advantage of online system-access. Consequently, the system will be meeting the user needs through better understanding of system requirements from the developers' perspectives according to the user needs in the correct and proper way

Smart farming application using knowledge embedded-graph convolutional neural network (KEGCNN) for banana quality detection

The appearance of fruits is crucial in their quality grading and consumer choices. Colour, texture, size, and shape determine fruit quality. Existing computer vision systems have been implemented for external quality control, relying on observations for fruit grading and classification. Banana quality detection systems, which employ advanced algorithms and sensors to evaluate the ripeness and general quality of bananas throughout their life cycle, are an innovative application of smart farming technology. In this proposed system, Knowledge Embedded-Graph Convolutional Neural Networks (KEGCNNs) are employed to classify and grade banana fruit. The approach aims to detect banana fruit quality by converting banana images into a knowledge graph, applying knowledge embedding to transform them into a continuous vector space, and using Graph Convolution Neural Networks (GCNNs) to analyze the graph structure and make accurate detections. KEGCNNs are especially useful for detecting the quality of banana fruits because they provide a form for capturing the contextual interactions between distinct nodes. KEGCNNs can learn from the data within the graph in an unsupervised manner, allowing them to use the knowledge inherent in the graph structure. KEGCNNs enable more accurate and efficient diagnosis of banana quality as they can discover patterns in data that conventional machine learning algorithms cannot. The suggested technique demonstrates an impressive performance score, indicating its suitability for detecting the quality or grade of banana fruit

A Novel Hybrid Prairie Dog Algorithm and Harris Hawks Algorithm for Resource Allocation of Wireless Networks

Enhancing the performance of wireless networks and communication systems requires careful resource allocation. Resource allocation optimization, however, is regarded as a mixed-integer non-linear programming (MINLP) problem, which is NP-hard and non-convex. Due to the serious limitations of conventional procedures, solving such optimization problems requires specialized approaches. For instance, no optimal performance can be guaranteed using the heuristic algorithms; besides, the global optimization systems suffer from exponential computation complexity and considerable training duration. This paper introduces an improved version of the Prairie dog optimization (PDO) algorithm by the Harris Hawks optimization (HHO) algorithm. The developed technique, namely HPDO, relies on using the HHO operators to improve the exploitation capability of PDO during the searching procedure. The significance of the presented HPDO is examined and analyzed using 23 mathematical benchmark functions and CEC-2019 with several dimension sizes to show the ability to solve different numerical problems. In addition to the resource allocation problem, the HPDO is evaluated using three engineering problems: The spring design issue, The pressure vessel design issue, and the Welded beam design issue. The experimental and simulation results demonstrated that the exploration and exploitation search method of HPDO and its convergence rate had remarkably increased. The experimental results of the resource allocation of the wireless network with different numbers of users 10, 50, and 100 achieve superior results compared to other algorithms with 0.136, 2.75, and 3.64, respectively.The results showed the supremacy of the HPDO over the traditional HHO, PDO, and several with state-of-the-art algorithms