2023 15th International Conference on Developments in eSystems Engineering (DeSE)最新文献_第8页

Improving Prediction for taxi demand by using Machine Learning 利用机器学习改进出租车需求预测

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10099731

Mustafa Mahmoud Ibrahim, F. S. Mubarek

{"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}

引用次数: 0

Hybrid Zero-knowledge Access Control System in e-Health 电子医疗中的混合零知识访问控制系统

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10099775

Eugene Tye Wee Chin, Intan Farahana Binti Kamsin, S. Amin, Nur Khairunnisha Binti Zainal

{"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}

引用次数: 0

Predicting the Effectiveness of ‘Stop and Search’ Police Interventions Using Advanced Data Analytics 使用高级数据分析预测“拦截和搜索”警察干预措施的有效性

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10100242

Bradley Marimbire, Abdulaziz Al-Nahari, Waris Khan Ahmadzai, D. Al-Jumeily, Wasiq Khan

{"title":"Predicting the Effectiveness of ‘Stop and Search’ Police Interventions Using Advanced Data Analytics","authors":"Bradley Marimbire, Abdulaziz Al-Nahari, Waris Khan Ahmadzai, D. Al-Jumeily, Wasiq Khan","doi":"10.1109/DeSE58274.2023.10100242","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100242","url":null,"abstract":"Predicting the criminals' behaviour is a difficult task to accomplish. It is unexpected in most cases and can possibly transpire at any time, which is challenging for police agencies and victims being affected by the offences. The proposed work presents a crime prediction model using the stop & search dataset and the demographic of those charged with possession of a weapon. The study is first of its kind using multiple publicly available datasets to predict the effectiveness of ‘stop & search’ interventions by the police. We employ multiple machine learning algorithms to predict whether a ‘further action’ is required following the stop & search by the police. We utilise several data science techniques mainly including pre-processing, feature engineering and appropriate use of model selection. The proposed model produced 93.20% accuracy using Random Forest classifier. The outcomes of this research can be useful by relevant authorities to anticipate the crime at a specific time and location through the analysis of patterns that will support decision-making and help on deterrent effective strategies to lower offences being committed.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"42 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":"133984113","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

Data Augmentation Using Generative Adversarial Networks to Reduce Data Imbalance with Application in Car Damage Detection 基于生成对抗网络的数据增强减少数据不平衡在汽车损伤检测中的应用

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10100274

M. Mahyoub, F. Natalia, S. Sudirman, P. Liatsis, A. Al-Jumaily

{"title":"Data Augmentation Using Generative Adversarial Networks to Reduce Data Imbalance with Application in Car Damage Detection","authors":"M. Mahyoub, F. Natalia, S. Sudirman, P. Liatsis, A. Al-Jumaily","doi":"10.1109/DeSE58274.2023.10100274","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100274","url":null,"abstract":"Automatic car damage detection and assessment are very useful in alleviating the burden of manual inspection associated with car insurance claims. This will help filter out any frivolous claims that can take up time and money to process. This problem falls into the image classification category and there has been significant progress in this field using deep learning. However, deep learning models require a large number of images for training and oftentimes this is hampered because of the lack of datasets of suitable images. This research investigates data augmentation techniques using Generative Adversarial Networks to increase the size and improve the class balance of a dataset used for training deep learning models for car damage detection and classification. We compare the performance of such an approach with one that uses a conventional data augmentation technique and with another that does not use any data augmentation. Our experiment shows that this approach has a significant improvement compared to another that does not use data augmentation and has a slight improvement compared to one that uses conventional data augmentation.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"89 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":"134398254","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

AD-Hoc Routing Protocols in WSN-WiFi based IoT in Smart Home 基于WSN-WiFi的智能家居物联网中的AD-Hoc路由协议

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10099981

S. W. Nourildean, Mustafa Dhia Hassib, Y. A. Mohammed

{"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}

引用次数: 0

BrandTrend: Understanding the Trending Games and Gaming Influencers for Better Gaming Peripheral Promotion BrandTrend:了解流行游戏和游戏影响者，以便更好地推广游戏外围设备

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10100248

Tan Wen Zheng Ashley, Lim Jo Han, Derrick, Kowit Tan, Rong Kai Tech Avin, Ashlinder Kaur, Sahar Al-Sudani, Zhengkui Wang

引用次数: 0

Using Simulation for Investigating Emergency Traffic Situations 利用模拟技术调查紧急交通状况

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10099681

I. Makarova, G. Yakupova, P. Buyvol, E. Mukhametdinov, A. Abashev, J. Mustafina

引用次数: 0

A Complete Log Files Security Solution Using Anomaly Detection and Blockchain Technology 使用异常检测和区块链技术的完整日志文件安全解决方案

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10100200

Tshun Kong Chan, I. F. Kamsin, S. Amin, N. Zainal

{"title":"A Complete Log Files Security Solution Using Anomaly Detection and Blockchain Technology","authors":"Tshun Kong Chan, I. F. Kamsin, S. Amin, N. Zainal","doi":"10.1109/DeSE58274.2023.10100200","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100200","url":null,"abstract":"Tamper-proof log files has always been desired in any business settings as it is usually the prime target of bad actors to eliminate their presence in a cyber-attack, while the current log files solutions are mostly insufficient when it comes to practicality and efficiency. The research aims to propose a complete log files solution to prevent hackers from tampering with a system log record using blockchain technology and minimizes the scalability issues of current blockchain-based log files solution with anomaly detection frameworks. The research will focus on gathering data using purposive sampling method by distributing surveys to carefully selected populations to draw conclusions based on the information gathered. In conclusion, the proposed system will feature a blockchain-based log files security solution with anomaly detection built on top to minimize the scalability issues of blockchain technology and to act as a secondary intrusion detection system to achieve defense-in-depth. Future recommendations for the proposed system involve the use of a better anomaly detection framework or more efficient blockchain technology.","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":"124775598","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

A Novel Predictive Model for Housing Loan Default using Feature Generation and Explainable AI 基于特征生成和可解释人工智能的住房贷款违约预测模型

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10099796

M. Mahyoub, Shatha Ghareeb, J. Mustafina

{"title":"A Novel Predictive Model for Housing Loan Default using Feature Generation and Explainable AI","authors":"M. Mahyoub, Shatha Ghareeb, J. Mustafina","doi":"10.1109/DeSE58274.2023.10099796","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099796","url":null,"abstract":"Home Loan plays a pivotal role in today's age when one steps into purchasing their home. It has been witnessed that in many cases users are unable to pay the after taking the loan and thus the loan is slipped to NPA(Non-Performing Asset) from Standard Asset for the bank or any lending institution. The revenue generation is ceased. As the housing loan is taken against property the lenders have right to sell the property and close the dues, but the process is lengthy as judicial procedures are involved. In most cases, the property value is much less than the calculated loan amount (Principal + Interest). In this study we examined the several ML methods to identify the loan default before disbursing the loan to the applicant. This matter has been studied widely and used the predictive analytics to find out the relationship between attributes and the target variable. Predictive Analytics enables us to feed optimal set of features to the ML models. The study started with 122 attributes and ended up with around 30% of features as the ideal subset for housing loan default prediction. Then, five ML models were fit into the dataset and the champion model came up with roc score 0.94, Recall 0.90 and Precision 0.94. LIME and SHAP were applied on the champion model along with the dataset for global and local interpretability. The experimental procedure concluded that ML models along with predictive analytics can arrest the loan disbursal to the ineligible applicants and will also provide the insight of such prediction with the help of model interpretability.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"17 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":"115243839","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

CNN Aided Surface Inspection for SMT Manufacturing CNN辅助表面检测用于SMT制造

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10099694

Mee Chun Loo, R. Logeswaran, Zailan Arabee bin Abdul Salam

{"title":"CNN Aided Surface Inspection for SMT Manufacturing","authors":"Mee Chun Loo, R. Logeswaran, Zailan Arabee bin Abdul Salam","doi":"10.1109/DeSE58274.2023.10099694","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099694","url":null,"abstract":"Automated optical inspection (AOI) is a visual defect inspection system. The semiconductor industry has a strong dependency on AOI for defects screening. Conventional AOI is inadequate for some inspections, especially surface defects like crack, chip and void, and the algorithms are inefficient in isolating the defects from product variants. Convolutional Neural Network (CNN) had been broadly studied and adopted to replace the conventional AOI in surface inspection. There are many CNN architectures developed in the past decade for image classification, such as AlexNet, GoogLeNet, ResNet, VGGNet, etc.; each with its own strength in terms of accuracy and speed. The training process could be speeded up too using techniques such as transfer learning from pre-trained CNN models. Newer techniques in vector programming on kernels, e.g., Single Instruction Multiple Data (SIMD) and depth wise separable method can further increase the efficiency of convolutional layer activation functions. CNN algorithms for surface inspection are found to be very promising, with defect classification able to achieve accuracies of 91-99% on the wide range of products. The CNN result outperforms conventional surface inspection methods like edge detection and machine learning algorithms.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"54 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":"115249592","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