Pub Date : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100157
Ng Wei Shen Jackson, Jullisha Sasikumar, Wong Yok Hung, Osama Rasheed Khan, Vivian Ng Zhi Hui, Sahar Al-Sudani, Huaqun Guo, Zhiyuan Zhang, Zhengkui Wang
COVID-19's impacts have spread widely in all directions such as economy, people's lifestyles and well-being. Though existing studies have highlighted such an impact, it remains unclear how the current COVID-19 situation has affected the retrenchment, vaccination and global happiness. In this paper, we present an automated tool enables the public to view various insight. In particular, we integrate and analyze the data from various data sources and show how the COVID19 has impacted Singapore and globally. We employ the regression models to identify the correlation between Human Development Index, Stringency Index, Gross Domestic Product per Capita, Total Deaths from COVID-19, and Total Cases of COVID-19; the rate of vaccination and vaccine hesitancy; and the factors to positively correlate to the global happiness. The insight provided adds values to better fight against the COVID-19 pandemic and future global crisis.
{"title":"The Impact of the COVID-19 Pandemic on Retrenchment, Vaccinations, and Global Happiness","authors":"Ng Wei Shen Jackson, Jullisha Sasikumar, Wong Yok Hung, Osama Rasheed Khan, Vivian Ng Zhi Hui, Sahar Al-Sudani, Huaqun Guo, Zhiyuan Zhang, Zhengkui Wang","doi":"10.1109/DeSE58274.2023.10100157","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100157","url":null,"abstract":"COVID-19's impacts have spread widely in all directions such as economy, people's lifestyles and well-being. Though existing studies have highlighted such an impact, it remains unclear how the current COVID-19 situation has affected the retrenchment, vaccination and global happiness. In this paper, we present an automated tool enables the public to view various insight. In particular, we integrate and analyze the data from various data sources and show how the COVID19 has impacted Singapore and globally. We employ the regression models to identify the correlation between Human Development Index, Stringency Index, Gross Domestic Product per Capita, Total Deaths from COVID-19, and Total Cases of COVID-19; the rate of vaccination and vaccine hesitancy; and the factors to positively correlate to the global happiness. The insight provided adds values to better fight against the COVID-19 pandemic and future global crisis.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133608413","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}
Pub Date : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100288
Hanan R. Salih, Talal H. Fadil, A. Mahmoud
This study aimed to evaluate the permanent deformation of asphalt mixtures employing two testing methods: the cyclic and static loading tests. In the cyclic loading test, the creep rate was determined using a locally manufactured SHRL machine, according to the BS EN 12647-25-2016 standard specification. While in the static loading test, the strength against deformation (SD) was investigated utilizing the Kim testing procedure was adopted. Four wearing course asphalt mixture were prepared using two types of aggregates (Thumail and Al-Nibaie) and two types of asphalt binders (Erbil and Al-Dora). Results from both tests demonstrated that the resistance to permanent deformation varies based on the type of asphalt and aggregate used in the mixture. Mixtures prepared by Thumail aggregates showed higher resistance to deformation than that prepared using Al-Nibaie aggregate. There was a good agreement between the results from both tests. From the result, it is proposed a new design equation that can be used in design the surface layer depend on the test type.
本文采用循环和静载试验两种试验方法对沥青混合料的永久变形进行了研究。在循环加载试验中，根据BS EN 12647-25-2016标准规范，使用本地制造的SHRL机器确定蠕变速率。在静载试验中，采用Kim试验程序进行抗变形强度(SD)试验。采用两种集料(Thumail和Al-Nibaie)和两种沥青粘结剂(Erbil和Al-Dora)制备了四种耐磨层沥青混合料。两项试验的结果表明，根据混合料中使用的沥青和骨料的类型，抗永久变形的能力有所不同。与Al-Nibaie骨料相比，用Thumail骨料制备的混合料具有更高的抗变形能力。两次试验的结果很吻合。根据试验结果，提出了一种新的设计公式，可根据试验类型对表层进行设计。
{"title":"Estimation the Design Paramters of Surface Course Asphalt Concrete by Cyclic and Static Loading","authors":"Hanan R. Salih, Talal H. Fadil, A. Mahmoud","doi":"10.1109/DeSE58274.2023.10100288","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100288","url":null,"abstract":"This study aimed to evaluate the permanent deformation of asphalt mixtures employing two testing methods: the cyclic and static loading tests. In the cyclic loading test, the creep rate was determined using a locally manufactured SHRL machine, according to the BS EN 12647-25-2016 standard specification. While in the static loading test, the strength against deformation (SD) was investigated utilizing the Kim testing procedure was adopted. Four wearing course asphalt mixture were prepared using two types of aggregates (Thumail and Al-Nibaie) and two types of asphalt binders (Erbil and Al-Dora). Results from both tests demonstrated that the resistance to permanent deformation varies based on the type of asphalt and aggregate used in the mixture. Mixtures prepared by Thumail aggregates showed higher resistance to deformation than that prepared using Al-Nibaie aggregate. There was a good agreement between the results from both tests. From the result, it is proposed a new design equation that can be used in design the surface layer depend on the test type.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"264 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128278596","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}
Pub Date : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100180
M. Alsumaidaie, K. Alheeti, Abdul-Kareem A. Al-Aloosy
With a surge in the usage of systems that largely depend on networking and programming, the need for cybersecurity has grown as well. Cyberattacks are a rising threat to companies and people. The Distributed Denial of Service (DDoS) attack is one of the destructive hacks that have swiftly acquired appeal among hackers. In this work, a security system is proposed to prevent DDoS. In other words, it has the ability to protect external and internal communication systems from attacks. The primary contribution of this work is to acquire the best accuracy based on time series. Multiple machine learning algorithms are applied and compared between them. The Random Forest accuracy is 100% and the XGBoost was 91% using the same data set.
{"title":"Intelligent Detection System for a Distributed Denial-of - Service (DDoS) Attack Based on Time Series","authors":"M. Alsumaidaie, K. Alheeti, Abdul-Kareem A. Al-Aloosy","doi":"10.1109/DeSE58274.2023.10100180","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100180","url":null,"abstract":"With a surge in the usage of systems that largely depend on networking and programming, the need for cybersecurity has grown as well. Cyberattacks are a rising threat to companies and people. The Distributed Denial of Service (DDoS) attack is one of the destructive hacks that have swiftly acquired appeal among hackers. In this work, a security system is proposed to prevent DDoS. In other words, it has the ability to protect external and internal communication systems from attacks. The primary contribution of this work is to acquire the best accuracy based on time series. Multiple machine learning algorithms are applied and compared between them. The Random Forest accuracy is 100% and the XGBoost was 91% using the same data set.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133480350","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}
Pub Date : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100065
A. Badr, L. Chaari, S. Ayed
Blockchain is emerging as one of the most promising and resourceful security technologies for cloud infrastructures. In a distributed database system, blockchain is used to store, read, and validate transactions. It can improve security, trustworthiness, and privacy by using an unchallengeable, shared distributed ledger on cloud nodes. Cloud-based healthcare systems (CHS) are vulnerable to various threats and attacks such as identity theft, medical fraud, insurance fraud, and alteration of critical patient data. Secure retrieval, access, and storage of data on CHS are necessary to protect critical medical data. Accordingly, the integrated cloud and BlockChain (ICBC) architecture emerge as a potential solution for shaping the next era of a healthcare system while providing efficient, secure, and effective patient care. In this context, this paper presents an in-depth exploration of advanced approaches to securing cloud-based healthcare data management systems using blockchain technologies. It provides a taxonomy and highlights the benefits and limitations of the approaches examined.
{"title":"Investigation on the Integrated Cloud and BlockChain (ICBC)Technologies to Secure Healthcare Data Management Systems","authors":"A. Badr, L. Chaari, S. Ayed","doi":"10.1109/DeSE58274.2023.10100065","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100065","url":null,"abstract":"Blockchain is emerging as one of the most promising and resourceful security technologies for cloud infrastructures. In a distributed database system, blockchain is used to store, read, and validate transactions. It can improve security, trustworthiness, and privacy by using an unchallengeable, shared distributed ledger on cloud nodes. Cloud-based healthcare systems (CHS) are vulnerable to various threats and attacks such as identity theft, medical fraud, insurance fraud, and alteration of critical patient data. Secure retrieval, access, and storage of data on CHS are necessary to protect critical medical data. Accordingly, the integrated cloud and BlockChain (ICBC) architecture emerge as a potential solution for shaping the next era of a healthcare system while providing efficient, secure, and effective patient care. In this context, this paper presents an in-depth exploration of advanced approaches to securing cloud-based healthcare data management systems using blockchain technologies. It provides a taxonomy and highlights the benefits and limitations of the approaches examined.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130927348","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}
Pub Date : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100118
Muhammad Ehsan Rana, Lin Yanyu, Vazeerudeen Abdul Hameed, K. B. Nowshath
This study elaborated on the importance of fitness in the contemporary environment, put forward the problems in traditional fitness, and conducted a series of discussions according to the questions. It conducted an in-depth analysis of fitness data utilising appropriate data analysis techniques to explore the relationship between different fitness data. Moreover, this study explores the processes and tools needed for analysis and explains the difficulties and resistance that may be encountered in future research. The literature section provides a detailed discussion on muscle gain and weight loss in fitness, the elaboration of big data frameworks, and machine learning methods that may be applied in this field. However, the regression models were only conducted on calorie burning for weight loss due to the lack of suitable muscle data. The optimal Mean Absolute Error and coefficient of determination were obtained as 8.307 and 0.967. The final section also concludes the process and results of this study and puts forward the shortcomings and the direction for future improvement.
{"title":"Improved Traditional Fitness Model by Applying Big Data Analysis","authors":"Muhammad Ehsan Rana, Lin Yanyu, Vazeerudeen Abdul Hameed, K. B. Nowshath","doi":"10.1109/DeSE58274.2023.10100118","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100118","url":null,"abstract":"This study elaborated on the importance of fitness in the contemporary environment, put forward the problems in traditional fitness, and conducted a series of discussions according to the questions. It conducted an in-depth analysis of fitness data utilising appropriate data analysis techniques to explore the relationship between different fitness data. Moreover, this study explores the processes and tools needed for analysis and explains the difficulties and resistance that may be encountered in future research. The literature section provides a detailed discussion on muscle gain and weight loss in fitness, the elaboration of big data frameworks, and machine learning methods that may be applied in this field. However, the regression models were only conducted on calorie burning for weight loss due to the lack of suitable muscle data. The optimal Mean Absolute Error and coefficient of determination were obtained as 8.307 and 0.967. The final section also concludes the process and results of this study and puts forward the shortcomings and the direction for future improvement.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131676456","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}
Pub Date : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099775
Eugene Tye Wee Chin, Intan Farahana Binti Kamsin, S. Amin, Nur Khairunnisha Binti Zainal
Privacy and security of sensitive health information represents a significant issue within electronic health (e-Health). With breakthroughs in security and privacy in recent decades, the application of cloud technologies on health services have progressed forward. The aim of this research paper is to introduce an appropriate access control model for use in e-Health. To determine the requirements of a modern access control method, research was carried out on numerous scholarly articles sourced from the Google Scholar search engine. A survey which utilized sampling techniques will also be done to affirm the validity of the research. The target audience of the survey are large to medium scale healthcare providers. Qualitative data will be gathered as it better describes the different types of data obtained. As a result, the paper proposed a combination of Role-based Access Control and Attribute-based Access Control which utilizes zero-knowledge SNARK to ensure privacy of patients. Recommendations for future research include experimentation with other encryption algorithms in the proposed system, assessment on the use of different zero-knowledge proof methods for better efficiency and scalability, as well as modern access control methods that embrace expansions and simple authorization.
{"title":"Hybrid Zero-knowledge Access Control System in e-Health","authors":"Eugene Tye Wee Chin, Intan Farahana Binti Kamsin, S. Amin, Nur Khairunnisha Binti Zainal","doi":"10.1109/DeSE58274.2023.10099775","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099775","url":null,"abstract":"Privacy and security of sensitive health information represents a significant issue within electronic health (e-Health). With breakthroughs in security and privacy in recent decades, the application of cloud technologies on health services have progressed forward. The aim of this research paper is to introduce an appropriate access control model for use in e-Health. To determine the requirements of a modern access control method, research was carried out on numerous scholarly articles sourced from the Google Scholar search engine. A survey which utilized sampling techniques will also be done to affirm the validity of the research. The target audience of the survey are large to medium scale healthcare providers. Qualitative data will be gathered as it better describes the different types of data obtained. As a result, the paper proposed a combination of Role-based Access Control and Attribute-based Access Control which utilizes zero-knowledge SNARK to ensure privacy of patients. Recommendations for future research include experimentation with other encryption algorithms in the proposed system, assessment on the use of different zero-knowledge proof methods for better efficiency and scalability, as well as modern access control methods that embrace expansions and simple authorization.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129450224","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}
Pub Date : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099731
Mustafa Mahmoud Ibrahim, F. S. Mubarek
Many problems and accidents are becoming increasingly occurring due to the increased number of vehicles on the streets. Therefore, much research has been submitted to help reduce vehicle problems such as accidents, congestion, and others, such as predicting taxi requests in the regions. Taxis are currently a high percentage of the street's number of vehicles, and if they are directed correctly to their target (passengers), this will contribute to reducing the congestion in the streets. Relying on developed technology such as Vehicular Social networks (VSN) can provide the necessary data for drivers to update their data continuously when there is a network connection. Some previous related works are criticized according to this task. This paper suggests improving taxi demand prediction in the regions based on data preprocessing. This study focuses on a comparison among four machine learning algorithms used for taxi request prediction and finding the best one in terms of execution time and error rates. Finally, Recent data was used for the first three months of 2021 and 2022, where 70% for training and 30% for testing for the year 2021, while the year 2022 is all data for testing. The results show that the Random Forest model outperforms LSTM, ANN, and linear regression in terms of error rates, and it obtained MSE 4.3 * 10−4 and RMSE 2.09 * 10−2.
{"title":"Improving Prediction for taxi demand by using Machine Learning","authors":"Mustafa Mahmoud Ibrahim, F. S. Mubarek","doi":"10.1109/DeSE58274.2023.10099731","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099731","url":null,"abstract":"Many problems and accidents are becoming increasingly occurring due to the increased number of vehicles on the streets. Therefore, much research has been submitted to help reduce vehicle problems such as accidents, congestion, and others, such as predicting taxi requests in the regions. Taxis are currently a high percentage of the street's number of vehicles, and if they are directed correctly to their target (passengers), this will contribute to reducing the congestion in the streets. Relying on developed technology such as Vehicular Social networks (VSN) can provide the necessary data for drivers to update their data continuously when there is a network connection. Some previous related works are criticized according to this task. This paper suggests improving taxi demand prediction in the regions based on data preprocessing. This study focuses on a comparison among four machine learning algorithms used for taxi request prediction and finding the best one in terms of execution time and error rates. Finally, Recent data was used for the first three months of 2021 and 2022, where 70% for training and 30% for testing for the year 2021, while the year 2022 is all data for testing. The results show that the Random Forest model outperforms LSTM, ANN, and linear regression in terms of error rates, and it obtained MSE 4.3 * 10−4 and RMSE 2.09 * 10−2.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124357914","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}
Pub Date : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099735
Mohammad Al-Ameen A. Hameed, Khalid Shaker, H. A. Khalaf
Sentiment analysis extracts people's feelings and attitudes about a certain subject. It has recently received a lot of interest in a variety of applications. In general, the sentiment analysis of healthcare, especially of drug experiences of users, might give substantial importance to how to enhance public health and make sound judgments. In this paper, new approaches have been developed that are based on patient reviews to predict sentiment to improve data analysis. Then, use Term Frequency-Inverse Document Frequency (TF-IDF) to extract the features. The experimental findings show that the Random Forest Classifier (RFC) beats all results of other existing models from the literature in terms of Precision, Recall, F1-Score, and Accuracy of 93 % accuracy.
{"title":"Sentiment Classification of Drug Reviews Using Machine Learning Techniques","authors":"Mohammad Al-Ameen A. Hameed, Khalid Shaker, H. A. Khalaf","doi":"10.1109/DeSE58274.2023.10099735","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099735","url":null,"abstract":"Sentiment analysis extracts people's feelings and attitudes about a certain subject. It has recently received a lot of interest in a variety of applications. In general, the sentiment analysis of healthcare, especially of drug experiences of users, might give substantial importance to how to enhance public health and make sound judgments. In this paper, new approaches have been developed that are based on patient reviews to predict sentiment to improve data analysis. Then, use Term Frequency-Inverse Document Frequency (TF-IDF) to extract the features. The experimental findings show that the Random Forest Classifier (RFC) beats all results of other existing models from the literature in terms of Precision, Recall, F1-Score, and Accuracy of 93 % accuracy.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115885648","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}
Pub Date : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099981
S. W. Nourildean, Mustafa Dhia Hassib, Y. A. Mohammed
Future Internet, described as an “Internet of Things,” is planned to be a global network of connected items, each with a unique address, based on industry-standard protocol. It is an important growing technology for environmental monitoring and future enterprises. IoT could be described as linking commonplace objects to the Internet, such as smart phones, actuators and sensors to enable new communication forms between objects themselves as well as between objects and people. Internet of Things (IoT) and wireless sensor networks (WSN) can be used to perform smart home technologies. This research presented Ad hoc routing protocols in IoT -based WSN in smart home system using Riverbed Modeler simulation platform. The simulation of WSN based on Mesh topology-ZigBee (IEEE 802.15.4) standard. Different applications such as Data Access, File transfer, Peer - to peer File sharing, Voice and Video, Mobile Messaging were applied in different number of scenarios of IoT based Wireless Sensor Network with three routing protocols (AODV, OLSR and GRP hybrid routing protocol) were taken in this study. In different modeled scenarios of this study, the sensing nodes (sensors) sense the environmental condition and send the collected data to the WSN controller which it is represented by ZigBee coordinator. The controller sent the sensor's data to the WiFi which act a gateway, so that this data could be monitored and controlled by the user via the Internet. The research outcomes showed that ad hoc routing protocol played an important role to improve the network's performance in terms of QoS parameters (delay, throughput and data dropped) due to the network deficiency which occurs because of interference between WSN and WiFi since they utilize free frequency band 2.4GHz. in this study, AODV investigated better improvement on the throughput and delay network performance with acceptable improvement in data dropped.
{"title":"AD-Hoc Routing Protocols in WSN-WiFi based IoT in Smart Home","authors":"S. W. Nourildean, Mustafa Dhia Hassib, Y. A. Mohammed","doi":"10.1109/DeSE58274.2023.10099981","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099981","url":null,"abstract":"Future Internet, described as an “Internet of Things,” is planned to be a global network of connected items, each with a unique address, based on industry-standard protocol. It is an important growing technology for environmental monitoring and future enterprises. IoT could be described as linking commonplace objects to the Internet, such as smart phones, actuators and sensors to enable new communication forms between objects themselves as well as between objects and people. Internet of Things (IoT) and wireless sensor networks (WSN) can be used to perform smart home technologies. This research presented Ad hoc routing protocols in IoT -based WSN in smart home system using Riverbed Modeler simulation platform. The simulation of WSN based on Mesh topology-ZigBee (IEEE 802.15.4) standard. Different applications such as Data Access, File transfer, Peer - to peer File sharing, Voice and Video, Mobile Messaging were applied in different number of scenarios of IoT based Wireless Sensor Network with three routing protocols (AODV, OLSR and GRP hybrid routing protocol) were taken in this study. In different modeled scenarios of this study, the sensing nodes (sensors) sense the environmental condition and send the collected data to the WSN controller which it is represented by ZigBee coordinator. The controller sent the sensor's data to the WiFi which act a gateway, so that this data could be monitored and controlled by the user via the Internet. The research outcomes showed that ad hoc routing protocol played an important role to improve the network's performance in terms of QoS parameters (delay, throughput and data dropped) due to the network deficiency which occurs because of interference between WSN and WiFi since they utilize free frequency band 2.4GHz. in this study, AODV investigated better improvement on the throughput and delay network performance with acceptable improvement in data dropped.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122459890","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}
Pub Date : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099689
Palash Aich, Ali Al Ataby, M. Mahyoub, J. Mustafina, Y. Upadhyay
The United States is the second largest producer of apples in the world with an estimated $21 billion downstream revenue. Since agriculture in the USA is highly mechanized, it is critical that latest advancements in technology are always integrated to the agricultural sector to not only improve efficiency but also improve quality, quantity, and to ensure faster distribution. Crop disease hampers the overall agricultural productivity and for a temperature-controlled crop like apple trees, identification of diseases at beginning stage is of paramount importance. There are two ways to identify and rectify issues relating to apple tree diseases, firstly by engaging expert biologists and secondly via automated identification through image processing. The biggest challenges with identification of diseases via biologist are accuracy, time constraints in case of bigger farms and budgetary limits. This research proposes the use of Machine Learning (ML) technique to aid and assist in automated disease detection and identification, and hence, making it affordable. It proposes the use of an ensemble (via weighted average) over single models, thereby improving performance and robustness by utilizing augmentations (positional and colour) which were not present in earlier studies. The proposed work surely creates an impact on the current plant disease diagnosis field by making the classification mode accurate and robust since it reaches accuracy of ~95% for all the classes.
{"title":"Automated Plant Disease Diagnosis in Apple Trees Based on Supervised Machine Learning Model","authors":"Palash Aich, Ali Al Ataby, M. Mahyoub, J. Mustafina, Y. Upadhyay","doi":"10.1109/DeSE58274.2023.10099689","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099689","url":null,"abstract":"The United States is the second largest producer of apples in the world with an estimated $21 billion downstream revenue. Since agriculture in the USA is highly mechanized, it is critical that latest advancements in technology are always integrated to the agricultural sector to not only improve efficiency but also improve quality, quantity, and to ensure faster distribution. Crop disease hampers the overall agricultural productivity and for a temperature-controlled crop like apple trees, identification of diseases at beginning stage is of paramount importance. There are two ways to identify and rectify issues relating to apple tree diseases, firstly by engaging expert biologists and secondly via automated identification through image processing. The biggest challenges with identification of diseases via biologist are accuracy, time constraints in case of bigger farms and budgetary limits. This research proposes the use of Machine Learning (ML) technique to aid and assist in automated disease detection and identification, and hence, making it affordable. It proposes the use of an ensemble (via weighted average) over single models, thereby improving performance and robustness by utilizing augmentations (positional and colour) which were not present in earlier studies. The proposed work surely creates an impact on the current plant disease diagnosis field by making the classification mode accurate and robust since it reaches accuracy of ~95% for all the classes.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121649585","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}
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