Pub Date : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099522
Manahil, Raed M. T. Abdulla, Muhammad Ehsan Rana
The Internet of Things (loT) has lately grown across various applications, drawing much attention to its development. This research proposes to use loT to improve women's safety and ensure a much safer environment for their protection. It proposes to use an loT-based safety system designed to help women in danger or stress by alerting authorities and emergency contacts. The final artefact was derived based on experimental calculations. Multiple testing techniques were applied to check the accuracy and effectiveness of the proposed artefact.
{"title":"Architectural Design and Recommendations for a Smart Wearable Device for Women's Safety","authors":"Manahil, Raed M. T. Abdulla, Muhammad Ehsan Rana","doi":"10.1109/DeSE58274.2023.10099522","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099522","url":null,"abstract":"The Internet of Things (loT) has lately grown across various applications, drawing much attention to its development. This research proposes to use loT to improve women's safety and ensure a much safer environment for their protection. It proposes to use an loT-based safety system designed to help women in danger or stress by alerting authorities and emergency contacts. The final artefact was derived based on experimental calculations. Multiple testing techniques were applied to check the accuracy and effectiveness of the proposed artefact.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"60 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":"126539257","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/DeSE58274.2023.10099662
Muhammad Ehsan Rana, Lai Zhen Ji
The introduction of cloud computing has altered how IT demands are satisfied. Every chief information officer is focused on cloud computing because it has ushered in a new era in IT. Many banks are now utilising cloud technologies to accomplish their ever-demanding goals. Cloud computing offers business models for providing cutting-edge customer experiences, successful teamwork, improved IT efficiency and faster speed to market. Banks can react to changing business requirements rapidly by using cloud computing. The paper offers a valuable perspective on the potential applications of cloud computing in the banking sector and its various business models, cloud service providers, and adoption challenges.
{"title":"The Role and Potential Applications of Cloud Computing in the Banking Industry","authors":"Muhammad Ehsan Rana, Lai Zhen Ji","doi":"10.1109/DeSE58274.2023.10099662","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099662","url":null,"abstract":"The introduction of cloud computing has altered how IT demands are satisfied. Every chief information officer is focused on cloud computing because it has ushered in a new era in IT. Many banks are now utilising cloud technologies to accomplish their ever-demanding goals. Cloud computing offers business models for providing cutting-edge customer experiences, successful teamwork, improved IT efficiency and faster speed to market. Banks can react to changing business requirements rapidly by using cloud computing. The paper offers a valuable perspective on the potential applications of cloud computing in the banking sector and its various business models, cloud service providers, and adoption challenges.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"24 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":"127949714","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/DeSE58274.2023.10099758
Mohammed K. Awsaj, Y. A. Mashhadany, L. Chaari
Health monitoring systems have achieved great popularity and great importance, especially with the presence of pandemics, large numbers of patients, and a lack of health staff. The presence of sensors on the patient's body to measure blood pressure, body temperature, and heart rate, in addition to room temperature and humidity, constantly supports the specialized medical staff in measuring these indicators. The advantage of these devices is that they are with the patient all the time, and a nurse cannot accompany a patient for this period. In this paper, a health care system is designed and implemented to measure vital signs and room environment temperature and humidity. ESP32 receives the vital signs data from the patient and his room. This information has been sent to the Raspberry Pi 4 where the information was compared to the information provided by the doctor to ensure obtaining the alarm when the measure has a large difference. The system obtains two types of alarms; the first is a medical alarm that accrues when vital signs are high or low from the normal measurements. This alarm calls the medical staff, while the second alarm occurs during a hardware malfunction. The second type of alarm call the technical staff. Testing the system shows that the two types of alarm have been recognized on their occurrence. All these measurements and alarms have been stored in the cloud for patient health monitoring.
卫生监测系统已经获得了极大的普及和重要性，特别是在出现流行病、大量患者和缺乏卫生人员的情况下。除了室内温度和湿度外，患者身上还存在测量血压、体温和心率的传感器，不断支持专业医务人员测量这些指标。这些设备的优点是它们一直和病人在一起，而护士不能在这段时间陪伴病人。本文设计并实现了一套测量人体生命体征和室内环境温湿度的卫生保健系统。ESP32接收来自病人及其房间的生命体征数据。该信息被发送到Raspberry Pi 4，并与医生提供的信息进行比较，以确保在测量值差异较大时获得警报。系统获取两种类型的告警;第一种是当生命体征高于或低于正常测量值时产生的医疗警报。此警报呼叫医务人员，而第二个警报在硬件故障期间发生。第二类报警呼叫技术人员。系统测试表明，两种类型的报警在发生时都能被识别出来。所有这些测量和警报都存储在云中，用于患者健康监测。
{"title":"Real- Time Healthcare Monitoring and Treatment System Based Microcontroller with IoT","authors":"Mohammed K. Awsaj, Y. A. Mashhadany, L. Chaari","doi":"10.1109/DeSE58274.2023.10099758","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099758","url":null,"abstract":"Health monitoring systems have achieved great popularity and great importance, especially with the presence of pandemics, large numbers of patients, and a lack of health staff. The presence of sensors on the patient's body to measure blood pressure, body temperature, and heart rate, in addition to room temperature and humidity, constantly supports the specialized medical staff in measuring these indicators. The advantage of these devices is that they are with the patient all the time, and a nurse cannot accompany a patient for this period. In this paper, a health care system is designed and implemented to measure vital signs and room environment temperature and humidity. ESP32 receives the vital signs data from the patient and his room. This information has been sent to the Raspberry Pi 4 where the information was compared to the information provided by the doctor to ensure obtaining the alarm when the measure has a large difference. The system obtains two types of alarms; the first is a medical alarm that accrues when vital signs are high or low from the normal measurements. This alarm calls the medical staff, while the second alarm occurs during a hardware malfunction. The second type of alarm call the technical staff. Testing the system shows that the two types of alarm have been recognized on their occurrence. All these measurements and alarms have been stored in the cloud for patient health monitoring.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"21 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":"114408669","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/DeSE58274.2023.10100137
Terence. Chee, Muhammad Ehsan Rana
Blockchain presents many new examples of delivering a robust audit trail using the distributed ledger. However, some potential issues, especially scalability and privacy, are still ongoing in the blockchain. Fortunately, these issues can be settled by using cloud computing. Cloud computing is an on-demand service that enables consumers to purchase and use IT resources. It saves the consumer's cost as consumers don't have to spend extra on building their on-premises server and providing extra security to the application. Enterprises interested in joining the blockchain flow can run their blockchain in the cloud. Cloud Service Providers (CSPs) provide multiple services such as IaaS, PaaS, SaaS and BaaS that can help a business start its blockchain application. But there are still risks that should be carefully noted. Data leaks and breaching are still possible despite the safety and privacy guarantees. The failure to achieve proper security will have to face massive potential fines. In this research, the authors addressed the impact of utilising the cloud in the blockchain and provided an in-depth understanding of the relationship between blockchain technology and cloud computing.
{"title":"An Exploratory Study on the Impact of Hosting Blockchain Applications in Cloud Infrastructures","authors":"Terence. Chee, Muhammad Ehsan Rana","doi":"10.1109/DeSE58274.2023.10100137","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100137","url":null,"abstract":"Blockchain presents many new examples of delivering a robust audit trail using the distributed ledger. However, some potential issues, especially scalability and privacy, are still ongoing in the blockchain. Fortunately, these issues can be settled by using cloud computing. Cloud computing is an on-demand service that enables consumers to purchase and use IT resources. It saves the consumer's cost as consumers don't have to spend extra on building their on-premises server and providing extra security to the application. Enterprises interested in joining the blockchain flow can run their blockchain in the cloud. Cloud Service Providers (CSPs) provide multiple services such as IaaS, PaaS, SaaS and BaaS that can help a business start its blockchain application. But there are still risks that should be carefully noted. Data leaks and breaching are still possible despite the safety and privacy guarantees. The failure to achieve proper security will have to face massive potential fines. In this research, the authors addressed the impact of utilising the cloud in the blockchain and provided an in-depth understanding of the relationship between blockchain technology and cloud computing.","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":"114952333","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/DeSE58274.2023.10099479
M. Mahyoub, Thomas Coombs, M. Jayabalan, J. Mustafina, A. Hussain
This research proposes a computer vision-based solutions to identify whether a patient is covid19/normal/Pneumonia infected with comparable or better state-of-the-art accuracy. Proposed solution is based on deep learning technique CNN (Convolutional Neural networks) with multiple approaches to cover all open issues. First approach is based on CNN models based on pre-trained models; second approach is to create CNN model from scratch. Experimentation and evaluation of multiple approaches helps in covering all open points and gaps left unattended in related work performed to solve this problem. Based on the experimentation results of both the approaches and study of related work done by other researchers, Both the approaches are equally effective can be recommended for multi-class classification of lung disease.
{"title":"Identify Type of Lung Infection from Lung Patients X-RAY Image LIVERAGING Computer Vision","authors":"M. Mahyoub, Thomas Coombs, M. Jayabalan, J. Mustafina, A. Hussain","doi":"10.1109/DeSE58274.2023.10099479","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099479","url":null,"abstract":"This research proposes a computer vision-based solutions to identify whether a patient is covid19/normal/Pneumonia infected with comparable or better state-of-the-art accuracy. Proposed solution is based on deep learning technique CNN (Convolutional Neural networks) with multiple approaches to cover all open issues. First approach is based on CNN models based on pre-trained models; second approach is to create CNN model from scratch. Experimentation and evaluation of multiple approaches helps in covering all open points and gaps left unattended in related work performed to solve this problem. Based on the experimentation results of both the approaches and study of related work done by other researchers, Both the approaches are equally effective can be recommended for multi-class classification of lung disease.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"69 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":"117090091","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/DeSE58274.2023.10100044
Moo Chi Yuen, Muhammad Ehsan Rana, Kamalanathan Shanmugam, Raed M. T. Abdulla
This research proposes a smart entrance system to cope with the COVID-19 pandemic in public places. The system can help automate standard operating procedures (SOPs) for checking. The paper focuses on exploring the problem context related to the COVID-19 SOPs for public places. The research on technologies involves using thermal cameras, fingerprint recognition, face recognition, iris recognition, object detection and cloud computing. These technologies can be integrated to provide a more versatile and effective solution. The technological solutions proposed by contemporary researchers are also critically analysed by investigating their advantages and disadvantages.
{"title":"Technology-Driven Implementation of Smart Entrances in Public Places During the COVID-19","authors":"Moo Chi Yuen, Muhammad Ehsan Rana, Kamalanathan Shanmugam, Raed M. T. Abdulla","doi":"10.1109/DeSE58274.2023.10100044","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100044","url":null,"abstract":"This research proposes a smart entrance system to cope with the COVID-19 pandemic in public places. The system can help automate standard operating procedures (SOPs) for checking. The paper focuses on exploring the problem context related to the COVID-19 SOPs for public places. The research on technologies involves using thermal cameras, fingerprint recognition, face recognition, iris recognition, object detection and cloud computing. These technologies can be integrated to provide a more versatile and effective solution. The technological solutions proposed by contemporary researchers are also critically analysed by investigating their advantages and disadvantages.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"479 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":"123112077","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/DeSE58274.2023.10100226
K. Alheeti, Abdulkareem Alzahrani, Omar Hammad Jasim, Duaa Al-Dosary, Hamsa M. Ahmed, M. Al-Ani
Cyber-attacks involve stifling processes and activities, conciliating data, or restricting data access by carefully modifying computer systems and networks with malware. There has been a significant increase in these types of attacks over time. Due to the rise in complexity and structure, advanced defensive methods are needed. In the face of growing security threats, traditional methods of identifying cyber-attacks are ineffective. In this paper, the intelligent of intrusion a detection system is suggested. Moreover, the suggested system attempts to evaluate the capability of the k-nearest neighbour's algorithm (KNN) in terms of distinguishing between authentic and tampered data. A reliable dataset named the Multi-Step Cyber-Attack Dataset (MSCAD) is utilized to determine the behavior Among the new sorts of attacks. Moreover, 60% of the dataset was utilized for training the model, and a remaining 40% was used for testing. Evaluation metrics like accuracy, precision, recall, and F1 score are used. Experiments suggest that the proposed system-based KNN could enhance detection performance. Moreover, the suggested approach increases detection accuracy while minimizing false alarms.
{"title":"Intelligent Detection System for Multi-Step Cyber-Attack Based on Machine Learning","authors":"K. Alheeti, Abdulkareem Alzahrani, Omar Hammad Jasim, Duaa Al-Dosary, Hamsa M. Ahmed, M. Al-Ani","doi":"10.1109/DeSE58274.2023.10100226","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100226","url":null,"abstract":"Cyber-attacks involve stifling processes and activities, conciliating data, or restricting data access by carefully modifying computer systems and networks with malware. There has been a significant increase in these types of attacks over time. Due to the rise in complexity and structure, advanced defensive methods are needed. In the face of growing security threats, traditional methods of identifying cyber-attacks are ineffective. In this paper, the intelligent of intrusion a detection system is suggested. Moreover, the suggested system attempts to evaluate the capability of the k-nearest neighbour's algorithm (KNN) in terms of distinguishing between authentic and tampered data. A reliable dataset named the Multi-Step Cyber-Attack Dataset (MSCAD) is utilized to determine the behavior Among the new sorts of attacks. Moreover, 60% of the dataset was utilized for training the model, and a remaining 40% was used for testing. Evaluation metrics like accuracy, precision, recall, and F1 score are used. Experiments suggest that the proposed system-based KNN could enhance detection performance. Moreover, the suggested approach increases detection accuracy while minimizing false alarms.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"483 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":"123007485","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/DeSE58274.2023.10100293
Piers Blackmun, Sahar Al-Sudani, D. Al-Jumeily
The act of trading in the financial markets from a discretionary standpoint comes with a vast number of pitfalls that lead to participants achieving poor returns on their investments. With trading being a psychologically intense activity, the paper presents development of trading algorithms that will not only eliminate the psychological barriers to trading but do so in a way that ensures that significant returns on investments are made, with these returns being evaluated by testing the strategies on past historical price data of various assets. Findings noted that the algorithms performances varied depending on the market circumstances with certain strategies only being applicable to either strong or weak market conditions. The implication of these findings opens the door to new discussions since the algorithms developed resided outside of the traditional high frequency trading model which are the most prominent trading applications found on the markets. This unconventional algorithmic approach to the markets verifies a way of obtaining significant returns without the requisite of having low latency, thus enabling one to compete with the more sophisticated algorithms developed and used by the major financial institutions without the need for human intervention or any additional resources.
{"title":"Design and Implementation of Algorithmic Stock Trading","authors":"Piers Blackmun, Sahar Al-Sudani, D. Al-Jumeily","doi":"10.1109/DeSE58274.2023.10100293","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100293","url":null,"abstract":"The act of trading in the financial markets from a discretionary standpoint comes with a vast number of pitfalls that lead to participants achieving poor returns on their investments. With trading being a psychologically intense activity, the paper presents development of trading algorithms that will not only eliminate the psychological barriers to trading but do so in a way that ensures that significant returns on investments are made, with these returns being evaluated by testing the strategies on past historical price data of various assets. Findings noted that the algorithms performances varied depending on the market circumstances with certain strategies only being applicable to either strong or weak market conditions. The implication of these findings opens the door to new discussions since the algorithms developed resided outside of the traditional high frequency trading model which are the most prominent trading applications found on the markets. This unconventional algorithmic approach to the markets verifies a way of obtaining significant returns without the requisite of having low latency, thus enabling one to compete with the more sophisticated algorithms developed and used by the major financial institutions without the need for human intervention or any additional resources.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"106 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":"132003332","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/DeSE58274.2023.10099815
Brentton Wong Swee Kit, M. Joseph
Voice of the Customer (VoC) has gained traction over the past few years to understand the consumers' opinion, preferences, and expectation. Reviews that are posted online are one of the methods of communication between the company and the consumers. Therefore, companies can analyse the reviews posted online to identify the aspects and sentiments that are mentioned in the reviews. However, the process of analysing the reviews manually is inefficient and is prone to bias. One of the methods of tackling manually analysing is by using machine learning. This process is called aspect-based sentiment analysis, there are many aspect-based sentiment analysis studies and research has been done previously. However, majority of the previous studies focuses on other domains such as product reviews or restaurant reviews. Therefore, this research will focus on the movie industry where movie reviews will be used to train and predict the aspects and sentiment of the movie review using machine learning models. This research will perform both aspect prediction and sentiment prediction on different models. The aspect prediction will be done using Logistic Regression and Decision Tree whist the Sentiment Analysis will be done using Logistic Regression and Multinomial Naïve Bayes. Based on the findings of the study, Decision Tree was able to achieve a higher accuracy of 98% while Logistic Regression was able to score an accuracy of 92%. Additionally, Logistic Regression was able to score a better accuracy for Sentiment Prediction with an accuracy of 93% when compared to Multinomial Naïve Bayes which achieved an accuracy of 91 %. Therefore, Decision Tree is more suitable for Aspect Prediction whilst Logistic Regression is more suitable for Sentiment Analysis.
{"title":"Aspect-Based Sentiment Analysis on Movie Reviews","authors":"Brentton Wong Swee Kit, M. Joseph","doi":"10.1109/DeSE58274.2023.10099815","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099815","url":null,"abstract":"Voice of the Customer (VoC) has gained traction over the past few years to understand the consumers' opinion, preferences, and expectation. Reviews that are posted online are one of the methods of communication between the company and the consumers. Therefore, companies can analyse the reviews posted online to identify the aspects and sentiments that are mentioned in the reviews. However, the process of analysing the reviews manually is inefficient and is prone to bias. One of the methods of tackling manually analysing is by using machine learning. This process is called aspect-based sentiment analysis, there are many aspect-based sentiment analysis studies and research has been done previously. However, majority of the previous studies focuses on other domains such as product reviews or restaurant reviews. Therefore, this research will focus on the movie industry where movie reviews will be used to train and predict the aspects and sentiment of the movie review using machine learning models. This research will perform both aspect prediction and sentiment prediction on different models. The aspect prediction will be done using Logistic Regression and Decision Tree whist the Sentiment Analysis will be done using Logistic Regression and Multinomial Naïve Bayes. Based on the findings of the study, Decision Tree was able to achieve a higher accuracy of 98% while Logistic Regression was able to score an accuracy of 92%. Additionally, Logistic Regression was able to score a better accuracy for Sentiment Prediction with an accuracy of 93% when compared to Multinomial Naïve Bayes which achieved an accuracy of 91 %. Therefore, Decision Tree is more suitable for Aspect Prediction whilst Logistic Regression is more suitable for Sentiment Analysis.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"33 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":"132380396","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/DeSE58274.2023.10100050
Shyamala Rajasekar
Predictive maintenance is one of the main trends noted in Industry 4.0, the ongoing era of automation and digitization in the manufacturing sector. Condition monitoring, a widely prevalent technique in Predictive Maintenance involves constant monitoring of systems through sensors and technologies enabling timely intervention to prevent sudden/unplanned breakdown that affects production, man-hours, inventory, and in worst cases, safety. This paper uses a data-driven approach to identify and classify faults in a multi-component hydraulic rig. Different Feature extraction/selection methods from the historical data with multiple sensor readings of different sampling frequencies (asynchronous data) were explored and compared. Supervised Learning models were built on these features to distinguish and detect the different levels of components' degradation. In addition, given the challenge of lack of annotated data in Industrial setups, unsupervised Clustering and anomaly detection algorithms were also examined to detect faults in the system.
{"title":"Prediction of Component Level Degradation in a Hydraulic Rig using Machine Learning Methods","authors":"Shyamala Rajasekar","doi":"10.1109/DeSE58274.2023.10100050","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100050","url":null,"abstract":"Predictive maintenance is one of the main trends noted in Industry 4.0, the ongoing era of automation and digitization in the manufacturing sector. Condition monitoring, a widely prevalent technique in Predictive Maintenance involves constant monitoring of systems through sensors and technologies enabling timely intervention to prevent sudden/unplanned breakdown that affects production, man-hours, inventory, and in worst cases, safety. This paper uses a data-driven approach to identify and classify faults in a multi-component hydraulic rig. Different Feature extraction/selection methods from the historical data with multiple sensor readings of different sampling frequencies (asynchronous data) were explored and compared. Supervised Learning models were built on these features to distinguish and detect the different levels of components' degradation. In addition, given the challenge of lack of annotated data in Industrial setups, unsupervised Clustering and anomaly detection algorithms were also examined to detect faults in the system.","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":"132208386","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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