2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)最新文献_第4页

Comparison of 4G and 5G Cellular Network Architecture and Proposing of 6G, a new era of AI 4G与5G蜂窝网络架构对比及人工智能新时代6G的提出

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371846

Bibi Mariat Shah, Mohsin Murtaza, M. Raza

{"title":"Comparison of 4G and 5G Cellular Network Architecture and Proposing of 6G, a new era of AI","authors":"Bibi Mariat Shah, Mohsin Murtaza, M. Raza","doi":"10.1109/CITISIA50690.2020.9371846","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371846","url":null,"abstract":"In this 21st Century, the deployment of wireless technology has created a spur amongst technologists about the future progression of wireless communication. It has not only transformed the way of communication but also paved a way for many multi-functional devices and technology. The essence of modern culture is the digital economy, which is also the base for a wireless system of connections. There was a time when wireless networks evolved, from 0G towards 4G along with their architectures and new features. The correlation is contrasting between 4G and 5G with their architectures, coverage, speed quality of service, bandwidth, and latency rates. But later the 5G system architecture conceptualized a full-bodied network, which introduced an architecture that worked intensively while the petulant analysis and consultations are stimulated to bring an advanced evolvement in the wireless community, hence 5G was slowly and gradually deployed all over the world. Meanwhile, the idea of 6G is being looked into and the researchers and engineers are working on its architecture and development, which will make it significantly different from the preceding generations some of which have made a mark and some that have not. This paper presents a complete understanding of variations between 4G and 5G wireless network architectures, the comparisons, and the evolvement through all these years. The analytical and factual results help in advancing an evolved 6G architecture and the change, enhancement, and transformation it will bring in the future of Wireless Cellular Networks.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127638514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Study of Security and Privacy Issues in Internet of Things 物联网安全与隐私问题研究

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371828

M. Khalid, Mohsin Murtaza, Mostafa Habbal

{"title":"Study of Security and Privacy Issues in Internet of Things","authors":"M. Khalid, Mohsin Murtaza, Mostafa Habbal","doi":"10.1109/CITISIA50690.2020.9371828","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371828","url":null,"abstract":"With the advancement of technology and the internet, almost every device is connected to internet. Internet of Things (IoT) can be explained as an extension of the internet in which smart devices are interconnected with each other. While each smart device has unique identifiers, which makes every device unique. Now, IoT has helped in developing smart architectures whether its home, healthcare, financial institutions or industries. There are smart devices everywhere. These smart devices can also communicate to each other in a network and they work together thus eliminating the need for human interaction. IoT is relatively new and is still developing rapidly. So, there are numerous privacy concerns in IoT. IoT can be divided into different architectural layers depending where the IoT is being used. The IoT model discussed in this research report is Service Oriented Architecture (SOA) which is divided into three layers, application, Network and perception layer. In 2020, during the COVID pandemic the reliability on IoT has increased as people are working from home and many of the tasks has been automated using IoT. The number of security attacks on IoT has also been increased in 2020 alone, which has affected many IoT devices. The objective of this research report is to discuss a number of security and privacy challenges in IoT based on the three SOA layers, the objective of this report also covers discussion on the above mentioned three layers, different technologies used in each layer for communication and different attacks and methods which target each specific layer also discussing different security attacks on IoT which occur ed in 2020 during the COVID pandemic phase. This topic is chosen because Internet of Things is becoming important and is impacting everything around us. It is expected that the total number of IoT devices will cross 20 Billion in 2020 and will have an impact of more than $11 Trillion by 2025. Thus, the security of IoT is to be discussed.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116380503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Classification of Melanoma (Skin Cancer) using Convolutional Neural Network 卷积神经网络在黑色素瘤(皮肤癌)分类中的应用

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371829

Shoman Gurung, Yifan Robin Gao

{"title":"Classification of Melanoma (Skin Cancer) using Convolutional Neural Network","authors":"Shoman Gurung, Yifan Robin Gao","doi":"10.1109/CITISIA50690.2020.9371829","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371829","url":null,"abstract":"Background and Aim: The current state of art solution for detecting melanoma using Convolutional Neural network has not considered selection of only affected areas from the input images of skin lesion which has resulted in the unnecessary processing of non-affected skin parts and thus less accuracy. The aim of this research is to propose a new solution to solve the above issue by creating a bounding box around the affected areas and decrease the search space by regression technique which results in more accuracy for classification.Methodology: The proposed system consists of three parts. i) data augmentation ii) boundary extraction and iii) DCNN feature extraction and selection. In the boundary extraction part, exclusive or (XOR) is used with regression technique which creates the bounding box around the affected areas of skin lesion. It helps to reduce search space, improve the accuracy in terms of classification and reduce the processing time to extract the features.Results: The proposed system here is tested on PH2, ISBI 2016 and 2017 datasets which has increased approx. 1.2 % of accuracy compared to state-of-art solution.Conclusions: The proposed system has outperformed the current best solution. Whereas, the difference is quite low, so can be further improve by testing other type of CNN network and classifiers.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123638611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Blockchain for data sharing in the rational use of coastlines and seaport demands in inter-organizational networks: Development of a new intelligent decision support system 在组织间网络中合理利用海岸线和海港需求的数据共享区块链:开发新的智能决策支持系统

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371837

A. Halabi-Echeverry, H.L. Nino-Vergara, N. Obregón-Neira, S. Islam

引用次数: 1

Review of gesture recognition technique using cloud-assisted wearable devices for real-time healthcare 使用云辅助可穿戴设备进行实时医疗保健的手势识别技术综述

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371838

A. Atif, Jinfeng Su

{"title":"Review of gesture recognition technique using cloud-assisted wearable devices for real-time healthcare","authors":"A. Atif, Jinfeng Su","doi":"10.1109/CITISIA50690.2020.9371838","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371838","url":null,"abstract":"with the development of technology, the use of robotic assemblies is increasing in different sectors including healthcare. In accurate surgeries, early and effective diagnosis, robots are becoming crucial for healthcare workers. However, one of the major challenges in the healthcare sector is the interaction complexity that restricts the use of robotic assemblies. The complexity in interaction with these assemblies does not allow their effective use and this is a major challenge in the smart healthcare system. The expertise requirements to operate such a complex system in the medical domain create additional challenges. Also, the collection of real-time healthcare data with accuracy is challenging with the available techniques. Therefore, in order to overcome these challenges, a system is developed in this research that uses cloud-assisted wearable devices to recognise gestures and help healthcare system in real time. This reduces the interaction complexity with the robotic assemblies in the healthcare system by providing effective control over these assemblies and automation through recognised gestures. Classification of the developed system is given in the research as components ‘SGR’ and the system is developed based on the review of current techniques, and further evaluated, validated, and verified. This will be of great help in the medical domain reducing the interaction challenges and helping in real-time monitoring and diagnosis in the medical field.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125505670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chromosome Encoding Schemes in Genetic Algorithms for the Flexible Job Shop Scheduling: A State-of-art Review Useful for Artificial Intelligence Applications 柔性作业车间调度遗传算法中的染色体编码方案:人工智能应用研究进展

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371789

Hu Xuewen, Sardar M N Islma, Yuxun Zhuo

{"title":"Chromosome Encoding Schemes in Genetic Algorithms for the Flexible Job Shop Scheduling: A State-of-art Review Useful for Artificial Intelligence Applications","authors":"Hu Xuewen, Sardar M N Islma, Yuxun Zhuo","doi":"10.1109/CITISIA50690.2020.9371789","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371789","url":null,"abstract":"This paper undertakes an innovative review and organization of the relevant issues of the FJSP in the genetic algorithm to provide some systematic way of organizing its issues and provide useful insights in this method of the genetic algorithm Flexible Job-shop Scheduling Problem (FJSP) is a type of scheduling problem with a wide range of application backgrounds. In recent years, genetic algorithms have become one of the most popular algorithms for solving FJSP problems and have attracted widespread attention. In this paper, a comprehensive review of chromosome coding methods of the genetic algorithm for solving the FJSP and three standards are used to compare the advantages and disadvantages of each coding method. The results show that MSOS-I coding is a better chromosomal encoding method for solving FJSP problems, whose chromosome structure is simple, feasibility and larger storage. The main contribution of this paper is to fill the literature gap, because No such comprehensive review of the FJSP in the GA prevails in the existing literature. This comprehensive review will be useful for scholars and practical applications of the FJSP and the genetic algorithm for artificial intelligence and machine learning implementations and applications.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116586290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A review of data analytics techniques for effective management of big data using IoT 回顾使用物联网有效管理大数据的数据分析技术

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371818

Owais Khalid, Suntharalingam Senthilananthan

{"title":"A review of data analytics techniques for effective management of big data using IoT","authors":"Owais Khalid, Suntharalingam Senthilananthan","doi":"10.1109/CITISIA50690.2020.9371818","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371818","url":null,"abstract":"IoT and big data are energetic technology of the world for quite a time, and both of these have become a necessity. On the one side where IoT is used to connect different objectives via the internet, the big data means having a large number of the set of structured, unstructured, and semi-structured data. The device used for processing based on the tools used. These tools help provide meaningful information used for effective management in different domains. Some of the commonly faced issues with the inadequate about the technologies are related to data privacy, insufficient analytical capabilities, and this issue is faced by in different domains related to the big data. Data analytics tools help discover the pattern of data and consumer preferences which is resulting in better decision making for the organizations. The major part of this work is to review different types of data analytics techniques for the effective management of big data using IoT. For the effective management of the ABD solution collection, analysis and control are used as the components. Each of the ingredients is described to find an effective way to manage big data. These components are considered and used in the validation criteria. The solution of effective data management is a stage towards the management of big data in IoT devices which will help the user to understand different types of elements of data management.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132510521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Heart disease monitoring and predicting by using machine learning based on IoT technology 利用基于物联网技术的机器学习进行心脏病监测和预测

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371772

Qingyun He, Angelika Maag, A. Elchouemi

{"title":"Heart disease monitoring and predicting by using machine learning based on IoT technology","authors":"Qingyun He, Angelika Maag, A. Elchouemi","doi":"10.1109/CITISIA50690.2020.9371772","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371772","url":null,"abstract":"The major disease caused by human death nowadays is heart disease, due it happens suddenly and without significant symptoms, leads patient to miss the best time for first aid. With the development of IoT technology combined with the healthcare industry. It is providing technical support for clinic staff to predict and monitor heart disease patients remotely. In this paper, the main goal is to review the most relevant and latest papers to find the advantages and disadvantages and gaps in this area. Furthermore, compare the different proposed method’s performance and present the best framework for heart disease continuous prediction and monitoring. Many researchers have been already providing the use of different types of machine learning algorithms to predict and diagnose heart disease. However, most of the previous researchers use the data collected from the dataset. As well know, to process the data collected from IoT sensors is harder than data collected from the dataset, because it may contain more noise and missing values in IoT sensor collected data. Dealing with those issues is the main challenge in the whole prediction system. Therefore, in this paper, we expect to reduce the research gap to find the best way to continuously monitoring and predicting patient ECG signals collected from IoT sensor devices in the meantime achieved acceptable prediction accuracy.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133241026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Enhancing Local Binary Patterns for higher accuracy in Fatty Liver classification using Deep Learning 利用深度学习增强局部二值模式以提高脂肪肝分类的准确性

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9397491

Muhammad Arslan Javed, A. Alsadoon, P. Prasad, Tarik A. Rashid, Angelika Maag, Yahini Murugesan

{"title":"Enhancing Local Binary Patterns for higher accuracy in Fatty Liver classification using Deep Learning","authors":"Muhammad Arslan Javed, A. Alsadoon, P. Prasad, Tarik A. Rashid, Angelika Maag, Yahini Murugesan","doi":"10.1109/CITISIA50690.2020.9397491","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9397491","url":null,"abstract":"In deep learning, local binary patterns (LBP) are inefficient for the textural feature-based classification of the fatty liver because they lose some of the relevant features. The purpose of this study is to enhance classification accuracy. We analyze accuracy and processing time. The proposed system con-sists of a convolutional neural network with curvelet local binary pattern for feature extraction which improves accuracy and can also now determine the size of the fatty liver. Accuracy is measured using probability scores and processing time is measured with total execution time, using sample image groups from CT/MRI images. Results shows that the proposed solution has improved the classification accuracy to 98% from 94% on average and reduced the processing time to 0.313 seconds compared to the existing 0.561 seconds. Moreover, the proposed system has added a volume feature, a, green border represents the volume of the fatty liver. Overall, the proposed system has improving accuracy and processing time required for fatty liver detection whilst leaving desirable features of the best current solution intact.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132175181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning For Minimally Invasive Computer Assisted Surgery 微创计算机辅助手术的深度学习

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371813

Aravinth Sivarasa, Oday D. Jerew

{"title":"Deep Learning For Minimally Invasive Computer Assisted Surgery","authors":"Aravinth Sivarasa, Oday D. Jerew","doi":"10.1109/CITISIA50690.2020.9371813","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371813","url":null,"abstract":"Detection of surgical instrument has been implemented in minimally invasive computer assisted surgery domain but detection of desired parts of surgical instrument has not been implemented properly. Previous researches have divided surgical instrument into two parts: End-effector and Shaft [12], which are not adequate to detect the components clearly. In this paper, we propose solution to improve accuracy and processing time of instrument detection. The novel detection has been implemented using deep learning algorithms-Convolutional Neural Network (CNN). The CNN uses kernel to perform feature extraction. The feature extraction includes convolution, batch normalisation, ReLu, max pooling and drop. In addition, selective kernel has been used during convolution to detect the parts of surgical instrument. There are four different types of datasets have been used for the execution. The proposed solution has giving promised results as there are nearly 2% improvement in accuracy and nearly 2s drop-in processing time. ReLu activation in convolution network and 20% dropout from output of convolution, not only reduces the processing time but also improved accuracy of detection.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"415 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133193050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0