2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)最新文献_第2页

Directional Forecast with Dynamic Volatility and Time Regime Classification: An Evaluation on EUR/USD 动态波动和时间制度分类的方向预测:对欧元/美元的评价

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002484

Ramindu P. de Silva, H. Pathberiya

{"title":"Directional Forecast with Dynamic Volatility and Time Regime Classification: An Evaluation on EUR/USD","authors":"Ramindu P. de Silva, H. Pathberiya","doi":"10.1109/SLAAI-ICAI56923.2022.10002484","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002484","url":null,"abstract":"Predicting the directional movement of price is of utmost importance to remain profitable in financial markets. This is particularly important for the Foreign Exchange (Forex) market owing to its high volatility which is affected by both internal and external market factors. Volatility is considered to be one of the significant obstacles for accurate directional prediction in the forex market. Although research efforts have been made to couple dynamic volatility with trend prediction models, most previous studies have been conducted subjected to unrealistic assumptions pertaining to volatility which have led to unsatisfactory results. This indicates that traders still face serious challenges when deriving more accurate predictions on the direction of the forex market, in order to remain profitable in the market. This study presents a directional prediction model for the forex market incorporating the dynamic volatility inherent to the market using intraday data. This was achieved by identifying different volatility levels that exist in the market using techniques such as change point analysis and clustering while Support Vector Machines (SVMs) are utilized to capture the directional movement of the market. The proposed solution is validated using different metrics and the results indicate that it outperforms the standard trend prediction method subjected to the nature of the input variables used when constructing the SVM models.","PeriodicalId":308901,"journal":{"name":"2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127791590","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

Faster RCNN Algorithm for Object Detection and Thereby provides a way for Tile Grading 更快的RCNN算法用于目标检测，从而为瓷砖分级提供了一种方法

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002633

D.A.I.S Sewwandi, D. Vidanagama

引用次数: 0

A data-driven approach for ranking entry and exit points in UAV-assisted firefighting missions 一种数据驱动的方法，用于对无人机辅助消防任务中的入口和出口点进行排名

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002537

Mohamed El Yafrani, Peter Nielsen, Inkyung Sung, Amila Thibbotuwawa

引用次数: 0

Identifying Medicinal Plants and Their Fungal Diseases 鉴定药用植物及其真菌病害

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002624

M. Senanayake, N. M. T. De Silva

{"title":"Identifying Medicinal Plants and Their Fungal Diseases","authors":"M. Senanayake, N. M. T. De Silva","doi":"10.1109/SLAAI-ICAI56923.2022.10002624","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002624","url":null,"abstract":"Today, with the development of technology, most manual methods are replaced by automated computer systems for the easiness of human beings. Plant identification and disease classification are two major agricultural research areas, focusing on introducing computerized systems rather than manual methods. Many researchers used various identification and classification techniques using computer-based systems as human classification errors lead to risk and high cost. Medicinal plant identification needs an expert to correctly identify plants because misidentifying poisonous plants as medicinal plants causes fatal cases. Further, taking diseased medicinal plants to prepare medicines and herbal products may have adverse effects. Therefore, this study proposed a computerized method to identify medicinal plants and classify their diseases to overcome such shortcomings. In this work, a comparison is done with Convolutional Neural Network (CNN) architecture from scratch and Transfer Learning with several experiments. Transfer learning models achieved higher accuracy than CNN architectures for medicinal plant identification with 99.5 % accuracy and medicinal plant disease classification with 90% accuracy, respectively.","PeriodicalId":308901,"journal":{"name":"2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133578032","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

BRAINWAVE: EEG Based Brain and Voice Controlled Hybrid Smart Multi-Plug 脑波:基于脑电图的脑和语音控制混合智能多插头

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002496

I.P.A. Kalindu, Grl. Kodikara, R. Hirshan, W. Kumara

{"title":"BRAINWAVE: EEG Based Brain and Voice Controlled Hybrid Smart Multi-Plug","authors":"I.P.A. Kalindu, Grl. Kodikara, R. Hirshan, W. Kumara","doi":"10.1109/SLAAI-ICAI56923.2022.10002496","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002496","url":null,"abstract":"The purpose of this study is to develop a hybrid Brain-Computer Interface (BCI) system for home automation (smart multiplug), which can be controlled through both mind (brain) and voice (vocals). BCIs have emerged as a viable prospect in the fields of medicine (e.g., neuronal rehabilitation), education, mind reading, and distant communication over the last decade. However, because of the challenges of the uncomfortable head equipment, reduced classification accuracy, high expense, and complex operation, BCI is still difficult to utilize in daily life. In this work, the Fast Fourier transform (FFT) and the Convolution Neural Network (CNN) are the algorithms that were used for feature extraction and classification respectively. Four home appliances will be controlled by the BCI system in our work. Besides that, we propose to make a Smart Multi-plug that can be controlled by both brain and voice from anywhere in the world, with links to Google Assistant, Alexa, and a Virtual Private Server(VPS) through WEMOS D1 Mini board and Sinric Pro API. With this WEMOS D1 Mini project, four home appliances will be controlled with Google Assistant, Alexa, and manual switches.","PeriodicalId":308901,"journal":{"name":"2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121690969","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

Improving the Visibility at Night for Advanced Driver Assistance Systems Using Night-to-Day Image Translation 利用夜间图像转换提高高级驾驶辅助系统的夜间能见度

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002695

H. Lakmal, M. B. Dissanayake

{"title":"Improving the Visibility at Night for Advanced Driver Assistance Systems Using Night-to-Day Image Translation","authors":"H. Lakmal, M. B. Dissanayake","doi":"10.1109/SLAAI-ICAI56923.2022.10002695","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002695","url":null,"abstract":"Automobile manufacturers are targeting to increase the safety of drivers and passengers by incorporating different Advanced Driver Assistance Systems (ADAS). Most of these ADAS are vision-based and in order to operate them properly, these systems require a clear vision which is challenging to acquire during the night. Considering this limitation, the study presented explores the possibility of translating night-time images to clear and visible day-time images which can be used for ADAS instead of poor-quality night-time images. Even though there exist many deep-learning-based techniques to transform images between two domains, most of them highly depend on pixel-to-pixel paired datasets during training. It is challenging to develop such a dataset, particularly in dynamic roadside environments. Hence, this study proposes unsupervised deep learning with the popular Cycle-GAN model to cater the problem. Another challenging task is accessing the quality of the Cycle-GAN generated images. Since there do not exist pixel-to-pixel paired images, to compare the quality of the regenerated images, Blind Referenceless Image Spatial Quality Evaluator (BRISQUE), a referenceless image quality evaluation technique, is utilised to evaluate the performance of the model. The synthesized output of the trained Cycle-GAN indicated an average BRISQUE score of 28.0416, whereas that of the original day-time images was 26.2156. This exhibits that the Cycle-GAN was able to generate synthesised day-time images with unpaired night images with significant similarity to the actual day-time images. The source code along with the dataset of this study is publicly available at https://www.github.com/isurushanaka/Unpaired-N2D.","PeriodicalId":308901,"journal":{"name":"2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125698049","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

Emergency Vehicle Detection using Vehicle Sound Classification: A Deep Learning Approach 基于车辆声音分类的紧急车辆检测:一种深度学习方法

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002605

S. Sathruhan, O. K. Herath, T. Sivakumar, Amila Thibbotuwawa

{"title":"Emergency Vehicle Detection using Vehicle Sound Classification: A Deep Learning Approach","authors":"S. Sathruhan, O. K. Herath, T. Sivakumar, Amila Thibbotuwawa","doi":"10.1109/SLAAI-ICAI56923.2022.10002605","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002605","url":null,"abstract":"Emergency vehicles are equipped with audio and visual warning devices, which are meant to help them navigate through traffic by demanding manoeuvring space from other vehicles. Notable deaths happen due to the delay in reaching their destinations by ambulance and fire engine vehicles. Depending on local legislation, vehicles on the road may be compelled to cede the right of way to emergency responders utilizing their warning devices. Emergency vehicles happen to wait at signalized intersections with fixed cycle timing. Though there are DL-based vehicle classification techniques that support intelligent traffic light systems, this study discusses the Emergency vehicle sound detection model based on Deep Learning techniques as additional prop data to improve the accuracy of existing vehicle detection. The Convolutional Neural Network (CNN) model was trained based on short audio signals. The sound was processed using the Mel-frequency Cepstral Coefficients (MFCC) feature extraction technique to transform into an image. The model successfully reached 93% accuracy.","PeriodicalId":308901,"journal":{"name":"2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116530315","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

Non-Verbal Communication Based Emotion Detection in Online Interviews 基于非语言交流的在线访谈情绪检测

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002669

V.A.S.A. Ranasinghe, Dilruk Ranasinghe

{"title":"Non-Verbal Communication Based Emotion Detection in Online Interviews","authors":"V.A.S.A. Ranasinghe, Dilruk Ranasinghe","doi":"10.1109/SLAAI-ICAI56923.2022.10002669","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002669","url":null,"abstract":"Online interviews has become the norm at present due to the requirement of maintaining social distance as well as an efficient means for avoiding other prevailing limitations. In online interviews the candidate faces the interview panel virtually, limiting the opportunity of the panel to observe facial expressions, body language and other soft skills of the candidate. Analyzing the required soft skills and attitudes for a particular job is a good parameter in the long term to judge the suitability of a candidate to hold on for the job. Yet, selecting the most suitable candidate who is good ‘on paper’ as well as good ‘in person’ is very challenging. This research proposes a method of identifying emotions of candidates based on a captured video sequence of the candidate during the interview. Finally the developed model will be able to calculate the most prevalent emotion of the candidate during the interview. Thus, it is expected that fine-grained speaker-specific continuous emotion recognition system developed in this research will help online interview panels to select the most suitable candidate by giving extra information about the candidates in online interviews. The system consists of four main modules for image preprocessing, feature extraction, identification of feature occurrences and intensities and classification of emotions. The system is capable of classifying the emotions with 68% accuracy using the Mini_Xception method. The model can be further improved by having a uniform training data set, which is a challenge. As future work it was identified to develop a prediction model for the suitability of each candidate for the advertised post.","PeriodicalId":308901,"journal":{"name":"2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115909208","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

Time Series and Neural Network Approaches for Accurate Forecasting of Monthly Natural Rubber Prices in Sri Lanka 斯里兰卡天然橡胶月价格准确预测的时间序列和神经网络方法

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002480

Jocelyn Erandi, L. Dilshan, K.N.T Piyasena, N. Chandrasekara

{"title":"Time Series and Neural Network Approaches for Accurate Forecasting of Monthly Natural Rubber Prices in Sri Lanka","authors":"Jocelyn Erandi, L. Dilshan, K.N.T Piyasena, N. Chandrasekara","doi":"10.1109/SLAAI-ICAI56923.2022.10002480","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002480","url":null,"abstract":"Rubber is the most significant and widely used material in the industry. Sri Lanka is well renowned for production of quality rubber. This study is undertaken to forecast natural rubber (NR) prices for upcoming years in Sri Lanka. Since rubber is a storable intermediate good, current population heavily depends on future prices. Before the year 2011, rubber has good price scale, but due to the lack of government intervention the rubber price has been decreased after 2011. With change of rubber price, the accurate forecast is extremely important in executing policies and making decisions for the future of rubber industry. Moreover, there is no research studies can be found which attempts to forecast monthly NR prices in Sri Lanka by using machine learning techniques. Monthly rubber prices from June 2005 to January 2019 was considered for this study. 80% of the data was used to fit the model and the rest was used for model performance evaluation. All the unit root tests confirmed that the first difference of log series was stationary. ARIMA (1,1,1) model was selected as the best model with lower Akaike Information Criterion (AIC) among the other candidate models which exhibits RMSE of 22.5039 and MAE of 16.6923. To find a better model which cater the instability of the NR prices properly, a dynamic machine learning technique, Time Delay Neural Network (TDNN) was employed. The architecture of the identified TDNN model with the hyper-parameter tunning consists of one hidden layer with sixteen neurons, and 1:16 time delays and exhibits lower errors: RMSE of 0.01347 and MAE of 0.0074. It can be concluded that TDNN perform better than the ARIMA model in forecasting NR prices Sri Lanka.","PeriodicalId":308901,"journal":{"name":"2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130960865","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

Study on Using Signal Filtering Techniques for Machine Learning-based Indoor Positioning Systems(IPS) 基于机器学习的室内定位系统信号滤波技术研究

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002655

Rhns Jayathissa, Mwp Maduranga

{"title":"Study on Using Signal Filtering Techniques for Machine Learning-based Indoor Positioning Systems(IPS)","authors":"Rhns Jayathissa, Mwp Maduranga","doi":"10.1109/SLAAI-ICAI56923.2022.10002655","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002655","url":null,"abstract":"Internet of Things (IoT) systems, along with Machine Learning (ML) and Artificial Intelligence (AI), performed well in present systems. For location-based IoT systems, it is vital to accurately estimate the object’s geographical position to differentiate objects in an indoor environment. In this research study, Received Signal Strength Indicators (RSSI) and ML-based solutions are proposed for indoor localization. Although the RSSI-based position techniques are much more interested in position estimation, as it does not require any additional hardware, the precision remains a significant issue because of the considerable fading effects, multipath propagation, and different parameters in the indoor environments. This research study examines ML-based Indoor Positioning Systems (IPS) using different signal filtering techniques. In this work, RSSI signals are filtered separately using three filters, Moving Average, Gaussian and Median, and the impact on position estimation is observed. To examine each filter’s performance, the error is compared in terms of statistical figures of RMSE (Root Mean Squared Error) and R2 (Coefficient of Determination). Most widely used Random Forest Regression (RFR) and Extra Tree Regressor (ETR) have been used as the Supervised ML techniques, and results are compared. According to the experimental results, the above filters can reduce the position estimation error to a maximum of 12 cm, which is negligible in many IPS applications with the ETR ML technique.","PeriodicalId":308901,"journal":{"name":"2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116876236","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