Annals of Emerging Technologies in Computing最新文献_第5页

Privacy-preserved Secure Medical Data Sharing Using Hierarchical Blockchain in Edge Computing 边缘计算中使用分层区块链的隐私保护安全医疗数据共享

Annals of Emerging Technologies in Computing Pub Date : 2022-10-01 DOI: 10.33166/aetic.2022.04.005

Rasel Iqbal Emon, Md. Mehedi Hassan Onik, A. Hussain, Toufiq Ahmed Tanna, Md. Akhtaruzzaman Emon, Muhammad Al Amin Rifat, Mahdi H. Miraz

{"title":"Privacy-preserved Secure Medical Data Sharing Using Hierarchical Blockchain in Edge Computing","authors":"Rasel Iqbal Emon, Md. Mehedi Hassan Onik, A. Hussain, Toufiq Ahmed Tanna, Md. Akhtaruzzaman Emon, Muhammad Al Amin Rifat, Mahdi H. Miraz","doi":"10.33166/aetic.2022.04.005","DOIUrl":"https://doi.org/10.33166/aetic.2022.04.005","url":null,"abstract":"A distributed ledger technology, embedded with privacy and security by architecture, provides a transparent application developing platform. Additionally, edge technology is trending rapidly which brings the computing and data storing facility closer to the user end (device), in order to overcome network bottlenecks. This study, therefore, utilises the transparency, security, efficiency of blockchain technology along with the computing and storing facility at the edge level to establish privacy preserved storing and tracking schemes for electronic health records (EHRs). Since the EHR stored in a block is accessible by the peer-to-peer (P2P) nodes, privacy has always been a matter of great concern for any blockchain-based activities. Therefore, to address this privacy issue, multilevel blockchain, which can enforce and preserve complete privacy and security of any blockchain-based application or environment, has become one of the recent blockchain research trends. In this article, we propose an EHR sharing architecture consisting of three different interrelated multilevel or hierarchical chains confined within three different network layers using edge computing. Furthermore, since EHRs are sensitive, a specific data de-identification or anonymisation strategy is also applied to further strengthen the privacy and security of the data shared.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47143619","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

Computation and Optimization of Traffic Network Topologies Using Eclipse SUMO 基于Eclipse SUMO的交通网络拓扑计算与优化

Annals of Emerging Technologies in Computing Pub Date : 2022-10-01 DOI: 10.33166/aetic.2022.04.004

Y. H. Chow, K. Ooi, Mohammad Arif Sobhan Bhuiyan, M. Reaz, C. W. Yuen

引用次数: 0

Research on Music Signal Processing Based on a Blind Source Separation Algorithm 基于盲源分离算法的音乐信号处理研究

Annals of Emerging Technologies in Computing Pub Date : 2022-10-01 DOI: 10.33166/aetic.2022.04.003

Xiaomin Zhao, Qiang Tuo, Ruosi Guo, Tengteng Kong

引用次数: 0

Analysis of Intelligent English Chunk Recognition based on Knowledge Corpus 基于知识语料库的智能英语块识别分析

Annals of Emerging Technologies in Computing Pub Date : 2022-07-01 DOI: 10.33166/aetic.2022.03.002

Mei Zhang

引用次数: 0

An Edge Computing Environment for Early Wildfire Detection 面向野火早期检测的边缘计算环境

Annals of Emerging Technologies in Computing Pub Date : 2022-07-01 DOI: 10.33166/aetic.2022.03.005

Ahmed Saleem Mahdi, S. A. Mahmood

{"title":"An Edge Computing Environment for Early Wildfire Detection","authors":"Ahmed Saleem Mahdi, S. A. Mahmood","doi":"10.33166/aetic.2022.03.005","DOIUrl":"https://doi.org/10.33166/aetic.2022.03.005","url":null,"abstract":"Recently, an increasing demand is growing for installing a rapid response system in forest regions to enable an immediate and appropriate response to wildfires before they spread across vast areas. This paper introduces a multilevel system for early wildfire detection to support public authorities to immediately specify and attend to emergency demands. The presented work is designed and implemented within Edge Computing Infrastructure. At the first level; the dataset samples of wildfire represented by a set of video sequences are collected and labelled for training mode purposes. Then, YOLOv5 deep learning model is adopted in our framework to build a trained model for distinguishing the fire event against non-fire events in binary classification. The proposed system structure comprises IoT entities provided with camera sensor capabilities and NVIDIA Jetson Nano Developer kit as an edge computing environment. At the first level, a video camera is employed to assemble environment information received by the micro-controller middle level to handle and detect the possible fire event presenting in the interested area. The last level is characterized as making a decision by sending a text message and snapshot images to the cloud server. Meanwhile, a set of commands are sent to IoT nodes to operate the speakers and sprinklers, which are strategically assumed to place on the ground to give an alarm and prevent wildlife loss. The proposed system was tested and evaluated using a wildfire dataset constructed by our efforts. The experimental results exhibited 98% accurate detection of fire events in the video sequence. Further, a comparison study is performed in this research to confirm the results obtained from recent methods.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46093068","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: 7

Performance Limits of 433 MHz Quarter-wave Monopole Antennas due to Grounding Dimension and Conductivity 基于接地尺寸和电导率的433mhz四分之一波单极天线性能限制

Annals of Emerging Technologies in Computing Pub Date : 2022-07-01 DOI: 10.33166/aetic.2022.03.001

Jinfeng Li

{"title":"Performance Limits of 433 MHz Quarter-wave Monopole Antennas due to Grounding Dimension and Conductivity","authors":"Jinfeng Li","doi":"10.33166/aetic.2022.03.001","DOIUrl":"https://doi.org/10.33166/aetic.2022.03.001","url":null,"abstract":"Among antennas for Industrial, Scientific and Medical (ISM band) applications at 433 MHz, quarter-wave monopole is a reasonably good trade-off between size, gain, and cost. The electrical performance of the monopole is largely dependent on the quality of the ground plane (size and conductivity), which exhibits a practical limit on the achievable gain as most industrial user environments can provide only a finite ground plane of finite electrical conductivity. Establishing traceability in understanding the performance degradation due to such limits due to the grounding dimension and conductivity is becoming mandatory. To this end, this work leverages universal MATLAB in place of off-the-shelf software (HFSS or CST) for the quarter-wave monopole antenna simulation at 433 MHz parametrised with the ground plane’s dimension with respect to the wavelength (λ). Results indicate that by enlarging the ground plane’s size from 0.14 λ to 14 λ, the gain (directivity for PEC) from the 3D radiation pattern rises from 1.79 dBi, then starts levelling off at 6.7 dBi (5.78 λ), until saturating at 7.49 dBi (13 λ). The radiation efficiency and gain of various grounding conductivity scenarios (e.g., gold, aluminium, steel) are also quantified to inform antenna designers and engineers for commercial, industrial, defence and space applications.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45481436","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 Novel Data Aggregation Mechanism using Reinforcement Learning for Cluster Heads in Wireless Multimedia Sensor Networks 无线多媒体传感器网络中基于簇头强化学习的数据聚合机制

Annals of Emerging Technologies in Computing Pub Date : 2022-07-01 DOI: 10.33166/aetic.2022.03.006

J. Uddin

{"title":"A Novel Data Aggregation Mechanism using Reinforcement Learning for Cluster Heads in Wireless Multimedia Sensor Networks","authors":"J. Uddin","doi":"10.33166/aetic.2022.03.006","DOIUrl":"https://doi.org/10.33166/aetic.2022.03.006","url":null,"abstract":"Wireless multimedia sensor networks (WMSNs) are getting used in numerous applications nowadays. Many robust energy-efficient routing protocols have been proposed to handle multimedia traffic-intensive data like images and videos in WMSNs. It is a common trend in the literature to facilitate a WMSN with numerous sinks allowing cluster heads (CHs) to distribute the collected data to the adjacent sink node for delivery overhead mitigation. Using multiple sink nodes can be expensive and may incur high complexity in routing. There are many single-sink cluster-based routing protocols for WMSNs that lack in introducing optimal path selection among CHs. As a result, they suffer from transmission and queueing delay due to high data volume. To address these two conflicting issues, we propose a data aggregation mechanism based on reinforcement learning (RL) for CHs (RL-CH) in WMSN. The proposed method can be integrated to any of the cluster-based routing protocol for intelligent data transmission to sink node via cooperative CHs. Proposed RL-CH protocol performs better in terms of energy-efficiency, end-to-end delay, packet delivery ratio, and network lifetime. It gains 17.6% decrease in average end-to-end delay and 7.7% increase in PDR along with a network lifetime increased to 3.2% compared to the evolutionary game-based routing protocol which has been used as baseline.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42515815","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

Comparative Analysis of Intrusion Detection System Using Machine Learning and Deep Learning Algorithms 基于机器学习和深度学习算法的入侵检测系统的比较分析

Annals of Emerging Technologies in Computing Pub Date : 2022-07-01 DOI: 10.33166/aetic.2022.03.003

Johan Note, Maaruf Ali

{"title":"Comparative Analysis of Intrusion Detection System Using Machine Learning and Deep Learning Algorithms","authors":"Johan Note, Maaruf Ali","doi":"10.33166/aetic.2022.03.003","DOIUrl":"https://doi.org/10.33166/aetic.2022.03.003","url":null,"abstract":"Attacks against computer networks, “cyber-attacks”, are now common place affecting almost every Internet connected device on a daily basis. Organisations are now using machine learning and deep learning to thwart these types of attacks for their effectiveness without the need for human intervention. Machine learning offers the biggest advantage in their ability to detect, curtail, prevent, recover and even deal with untrained types of attacks without being explicitly programmed. This research will show the many different types of algorithms that are employed to fight against the different types of cyber-attacks, which are also explained. The classification algorithms, their implementation, accuracy and testing time are presented. The algorithms employed for this experiment were the Gaussian Naïve-Bayes algorithm, Logistic Regression Algorithm, SVM (Support Vector Machine) Algorithm, Stochastic Gradient Descent Algorithm, Decision Tree Algorithm, Random Forest Algorithm, Gradient Boosting Algorithm, K-Nearest Neighbour Algorithm, ANN (Artificial Neural Network) (here we also employed the Multilevel Perceptron Algorithm), Convolutional Neural Network (CNN) Algorithm and the Recurrent Neural Network (RNN) Algorithm. The study concluded that amongst the various machine learning algorithms, the Logistic Regression and Decision tree classifiers all took a very short time to be implemented giving an accuracy of over 90% for malware detection inside various test datasets. The Gaussian Naïve-Bayes classifier, though fast to implement, only gave an accuracy between 51-88%. The Multilevel Perceptron, non-linear SVM and Gradient Boosting algorithms all took a very long time to be implemented. The algorithm that performed with the greatest accuracy was the Random Forest Classification algorithm.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44013966","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

Dynamic Context Driven Re-configurable Business Process 动态上下文驱动的可重新配置业务流程

Annals of Emerging Technologies in Computing Pub Date : 2022-07-01 DOI: 10.33166/aetic.2022.03.004

P. Chakraborty, A. Sarkar

{"title":"Dynamic Context Driven Re-configurable Business Process","authors":"P. Chakraborty, A. Sarkar","doi":"10.33166/aetic.2022.03.004","DOIUrl":"https://doi.org/10.33166/aetic.2022.03.004","url":null,"abstract":"The building of a re-configurable business process (BP) has gained importance in business organizations. It helps the organization to adapt to the agility in business goals. A proper context-driven re-configurable BP should be capable of integrating dynamic context information. However, this is absent in the existing studies. As a result, providing a suitable, expressive and re-configurable BP to the business organization stakeholders has become a challenging issue. The prevailing research works lack the proper consideration and suitable incorporation of the context-driven services to make a BP re-configurable. And then it can quickly respond and change its behavior to adapt to the rapid and unpredictable changing business environment. In addition, those methods hardly come up with any appropriate technique to use the set of specified goals to extract context-driven services. Those business goals are determined by the group of stakeholders of a business organization. This paper proposes a new method of re-configuring context-driven from a defined goal to sort out these vital challenges. Present context data is included in an existing BP to achieve a modified goal which immensely benefits the end-users. Thus, this approach is intrinsically highly user-centric, reusable, fast and inexpensive. To achieve this, an algorithm called Context-driven Re-configurable Business Process Achievement Algorithm (CDRBPA) is introduced and implemented. Based on Primary Context (PC), three software metrics, namely, Degree of re-usability (DRUPC), Degree of re-appropriation (DRAPC) and Degree of re-configurability (DRPC) have been proposed to measure the modifications done to the existing BP. In conclusion, various case studies with different complexities have been performed to show the strength of the proposed algorithm.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41673484","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

Examining Mental Disorder/Psychological Chaos through Various ML and DL Techniques: A Critical Review 通过各种ML和DL技术检查精神障碍/心理混乱:综述

Annals of Emerging Technologies in Computing Pub Date : 2022-04-01 DOI: 10.33166/aetic.2022.02.005

Afra Binth Osman, Faria Tabassum, M. Patwary, Ahmed Imteaj, Touhidul Alam, Mohammad Arif Sobhan Bhuiyan, Mahdi H. Miraz

{"title":"Examining Mental Disorder/Psychological Chaos through Various ML and DL Techniques: A Critical Review","authors":"Afra Binth Osman, Faria Tabassum, M. Patwary, Ahmed Imteaj, Touhidul Alam, Mohammad Arif Sobhan Bhuiyan, Mahdi H. Miraz","doi":"10.33166/aetic.2022.02.005","DOIUrl":"https://doi.org/10.33166/aetic.2022.02.005","url":null,"abstract":"Mental soundness is a condition of well-being wherein a person understands his/her potential, participates in his or her community and is able to deal effectively with the challenges and obstacles of everyday life. It circumscribes how an individual thinks, feels and responds to any circumstances. Mental strain is generally recognised as a social concern, potentially leading to a functional impairment at work. Chronic stress may also be linked with several physiological illnesses. The purpose of this research stands to examine existing research analysis of mental healthiness outcomes where diverse Deep Learning (DL) and Machine learning (ML) algorithms have been applied. Applying our exclusion and inclusion criteria, 52 articles were finally selected from the search results obtained from various research databases and repositories. This literatures on ML and mental health outcomes show an insight into the avant-garde techniques developed and employed in this domain. The review also compares and contrasts amongst various deep learning techniques for predicting a person's state of mind based on different types of data such as social media data, clinical data, etc. Finally, the open issues and future challenges of utilising Deep learning algorithms to better understand as well as diagnose mental state of any individual were discussed. From the literature survey, this is evident that the use of ML and DL in mental health has yielded significant attainment mostly in the areas of diagnosis, therapy, support, research and clinical governance.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43128741","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