2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)最新文献_第3页

Overlapped Speech Detection for Improved Speaker Diarization on Tamil Dataset 基于泰米尔语数据集的改进说话人分类的重叠语音检测

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002438

S.T. Jarashanth, K. Ahilan, R. Valluvan, T. Thiruvaran, A. Kaneswaran

引用次数: 0

Short-Term Network Traffic Prediction with Multilayer Perceptron 基于多层感知机的短期网络流量预测

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002431

Agnieszka Ganowicz, Bartosz Starosta, Aleksandra Knapińska, K. Walkowiak

{"title":"Short-Term Network Traffic Prediction with Multilayer Perceptron","authors":"Agnieszka Ganowicz, Bartosz Starosta, Aleksandra Knapińska, K. Walkowiak","doi":"10.1109/SLAAI-ICAI56923.2022.10002431","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002431","url":null,"abstract":"The constantly increasing internet traffic and rising network requirements trigger fast development and implementation of new networking architectures and technologies. Predictability of network traffic can bring significant benefits in many areas, such as network planning, network security, dynamic bandwidth allocation, and predictive congestion control. This paper studies the problem of short-term traffic forecasting in application-aware backbone optical networks. The proposed method is based on the Multilayer Perceptron (mlp). Multiple neural network architectures are evaluated using three datasets with diverse characteristics, representing different types of internet traffic in a real-world backbone network. An extensive examination is performed to find the best neural network architecture for each traffic type. The proposed method revealed high prediction quality, achieving the mean absolute percentage errors between 2% and 10%, depending on the traffic type. The proposed neural networks outperform the baseline regression model in all considered types of traffic.","PeriodicalId":308901,"journal":{"name":"2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"47 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":"128746854","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

Evaluating Deep Neural Network-based Speaker Verification Systems on Sinhala and Tamil Datasets 在僧伽罗语和泰米尔语数据集上评估基于深度神经网络的说话人验证系统

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002663

S. P. D. Anuraj, S.T. Jarashanth, K. Ahilan, R. Valluvan, Tharmarajah Thiruvaran, A. Kaneswaran

{"title":"Evaluating Deep Neural Network-based Speaker Verification Systems on Sinhala and Tamil Datasets","authors":"S. P. D. Anuraj, S.T. Jarashanth, K. Ahilan, R. Valluvan, Tharmarajah Thiruvaran, A. Kaneswaran","doi":"10.1109/SLAAI-ICAI56923.2022.10002663","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002663","url":null,"abstract":"Speaker verification, a biometric identifier, determines whether an input speech belongs to the claimed identity. The existing models for speaker verification have reported performances mainly in English, and no study has experimented with Sinhala and Tamil datasets. This study proposes a semi-automated pipeline to curate datasets for Sinhala and Tamil from videos on YouTube filmed under noisy and unconstrained conditions which represent real-world scenarios. Both Sinhala and Tamil datasets include utterances for 140 persons of interest (POIs) with more than 300 utterances per POI under one or more genres: interviews, speeches, and vlogs. Moreover, this study investigates how domain mismatch affects a speaker verification model trained in English and applied to Sinhala and Tamil. Two deep neural network models trained in English show significant performance drops on Sinhala and Tamil datasets compared to an English dataset as expected due to domain mismatch, however, it is observed that AM-softmax performed better than vanilla softmax. In the future, robust speaker verification models with domain adaptation techniques will be built to improve performance on Sinhala and Tamil datasets.","PeriodicalId":308901,"journal":{"name":"2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"7 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":"133993107","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

Comparing the Behaviour of Ensemble Algorithms for Route Optimization in Last-Mile Deilivery Considering the Weather Condition and Holiday Effect 考虑天气条件和假日效应的最后一英里配送路径优化集成算法性能比较

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002604

B.D.E. Kodikara, Amila Thibotuwa, H. Perera, P. Gamage

{"title":"Comparing the Behaviour of Ensemble Algorithms for Route Optimization in Last-Mile Deilivery Considering the Weather Condition and Holiday Effect","authors":"B.D.E. Kodikara, Amila Thibotuwa, H. Perera, P. Gamage","doi":"10.1109/SLAAI-ICAI56923.2022.10002604","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002604","url":null,"abstract":"Delivery Time Prediction (DTP) is a crucial factor in last mile logistics. A variety of studies were conducted under this domain using statistics, machine learning and deep learning approaches. The main intention of these kinds of systems is to measure the accuracy and the computational time. However, these improvements come at the cost of significantly increased implementation and operation expenses which are not affordable for small and medium scale businesses. Moreover, DTP considering dynamic factors such as weather, traffic conditions and holidays remains a challenge. Considering the above factors, this paper proposes a novel method of DTP based on the origin, destination geographical points (OD DTP) which is fitting for short distances. According to the case study analysis conducted on the delivery time of New York yellow cab data set using Light Gradient Boosting (LGB), Extreme Gradient Boosting (XGB), Cat Boosting Algorithms and Random Forest (RF), proved that Booting algorithms are much more capable of building DTP model with the exogenous factors such as weather conditions and holiday effect. The feature importance data explained that temperature, humidity, and wind directions are the most important factors within other selected climate criteria). Overall, the trip distance and the trip direction are the most important features when predicting short distance delivery time. The detailed analysis of the selected algorithm behavior concludes that, in terms of evaluation criteria (computational time, overfitting, accuracy, feature importance) LGB is good for model training which has short iteration rounds with small data sets, and the XGB is good for more complex predicting model which deal with large and complex data.","PeriodicalId":308901,"journal":{"name":"2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"71 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":"123972929","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

Conference Partner 合作伙伴会议

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/slaai-icai56923.2022.10002455

引用次数: 0

Predicting the Class of a Bachelor’s Degree Student Using an Artificial Neural Network 用人工神经网络预测本科学生的班级

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002545

N.M. Siyad, L.A.K.U. Liyanarachchi, A.S.R.A. Ranawaka, H. Ratnayake

{"title":"Predicting the Class of a Bachelor’s Degree Student Using an Artificial Neural Network","authors":"N.M. Siyad, L.A.K.U. Liyanarachchi, A.S.R.A. Ranawaka, H. Ratnayake","doi":"10.1109/SLAAI-ICAI56923.2022.10002545","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002545","url":null,"abstract":"The academic status of a student following a degree programme is very useful to the university and the student as well in many ways. Through the degree programme most of the students aim to graduate with a class, but very few students achieve their goal. Therefore, both university administration and students are very concerned about their academic status. Through the proposed Undergraduate’s Class Prediction System, we hope to predict a student’s class at an early stage of their respective degree programme using an Artificial Neural Network (ANN). In this work, a multi-layered feedforward neural network is used to classify the class of a student’s degree into first, second-upper, second-lower or general degree. Using the feedforward algorithm, we were able to achieve the best performance with an accuracy of 76.27%. Through this system the university can identify underperforming students at the early stages and help them with the difficulties they face, can help talented students to finish their degree with a good GPA leading to a better class and will be able to identify the potential dropouts and counsel them with academic guidance.","PeriodicalId":308901,"journal":{"name":"2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"35 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":"123198457","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

Prediction of Age Based on Development of Mandibular Third Molars in Sri Lankan Population 基于斯里兰卡人群下颌第三磨牙发育的年龄预测

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002451

H. Bandara, Lakshika S. Nawarathna, P. Hettiarachchi, R. Jayasinghe

{"title":"Prediction of Age Based on Development of Mandibular Third Molars in Sri Lankan Population","authors":"H. Bandara, Lakshika S. Nawarathna, P. Hettiarachchi, R. Jayasinghe","doi":"10.1109/SLAAI-ICAI56923.2022.10002451","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002451","url":null,"abstract":"Age estimation is fundamental to forensic expertise and clinical medicine. The third molar offers one of the unique benefits that proceed over a more extended period. Demirjian’s method is used to classify the third molar development based on eight stages. The stages were allocated a biologically weighted score for each gender. The main objective of this study is to predict the age of subadults based on the third molar development stages. Each third molar development stage was analyzed according to their side and gender. In this study, 1643 left lower third molars and 1665 right lower third molars are considered for analysis, and the third molars’ development stages were recorded in the age group from 10 to 28. Generalized Linear Mixed Model (GLMM), classification and regression tree algorithm (CART), Ridge regression, and Elastic net regression were used to predict the age. Results were validated using the cross-validation technique. Root mean squared error (RMSE), mean absolute error (MAE), and R-squared values were used to select the best model. There were significant differences between the male and female third molars, and there were no significant differences between the left and right lower third molars. Weighted Demirjian’s stages and gender were the significant variables of the fitted models for predicting age. The best model for the prediction of age was the classification and regression tree algorithm (CART), which gave the highest accuracy (70.6%) with the minimum root mean squared error (RMSE = 2.27). Therefore, the classification and regression tree algorithm (CART) can be used to predict the age using the development stages of third molars.","PeriodicalId":308901,"journal":{"name":"2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"53 2 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":"123460575","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

Prediction of Cardiac Diseases with Dobutamine Stress Echocardiography 多巴酚丁胺应激超声心动图预测心脏疾病

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002453

W.K. Uthsuka, Lakshika S. Nawarathna

{"title":"Prediction of Cardiac Diseases with Dobutamine Stress Echocardiography","authors":"W.K. Uthsuka, Lakshika S. Nawarathna","doi":"10.1109/SLAAI-ICAI56923.2022.10002453","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002453","url":null,"abstract":"The heart is one of the essential organs in the human body. People are suffering from ‘Myocardial Infraction’, ‘Angioplasty’ and ‘Bypass surgery’ or sudden death. Stress Echocardiography involves raising patients’ heart rates through exercise. Then, take various measurements by pressuring the heart. Dobutamine can be used to pressure the heart, called Dobutamine Stress Echocardiography. Therefore, the main objective of this study is to propose models to predict cardiac diseases that can happen after giving the Dobutamine drug. This study was performed on a sample of 558 patients. This sample was taken by the Adult Cardiac Imaging and Hemodynamics Laboratories officers at the University of California, Los Angeles (UCLA). The study fits the statistical and machine learning models such as K-Nearest Neighbors (KNN), Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, Bagging methods with SVM, Gradient Boost, Extreme Gradient Boost (XG Boost), and Feedforward Neural Network (FFNN). Moreover, the hyperparametric tuning with the help of K-Fold Cross Validation techniques and Boosting methods were used to validate the fitted models and obtain better predictions. Furthermore, scaling methods such as Min-Max Scaling, Standard Scaling, and Quantile Scaling were used and handled the outliers to get better predictions without wasting much time. This study proposed five models corresponding to three diseases, sudden death, and any of these events. Myocardial infarction, angioplasty, bypass surgery, cardiac death, and any of these events can predict with 94.98%, 96.43%, 94.27%, 95.7%, and 84.44% accuracies.","PeriodicalId":308901,"journal":{"name":"2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"29 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":"131695811","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

Operations that Allow the Manipulation of Basic Belief Assignment(BBAs)Defined on Product Space 允许操纵产品空间上定义的基本信念赋值(BBAs)的操作

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002474

Najat H. Qasim

引用次数: 0

Towards Machine Learning Approaches for Predicting Risk Level of Postpartum Depression 预测产后抑郁症风险水平的机器学习方法研究

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2022-12-01 DOI: 10.1109/SLAAI-ICAI56923.2022.10002477

T. H. K. R. Prabhashwaree, N. Wagarachchi

{"title":"Towards Machine Learning Approaches for Predicting Risk Level of Postpartum Depression","authors":"T. H. K. R. Prabhashwaree, N. Wagarachchi","doi":"10.1109/SLAAI-ICAI56923.2022.10002477","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002477","url":null,"abstract":"This Postpartum depression (PPD) is approaching epidemic rates in many South Asian countries. It occurs in some mothers after giving childbirth because of changes in their physical, behavioral, and emotional development. The main objective of this research is to identify factors that reason for PPD based on the mother’s family, social background, and other data related to the status of the mother and develop a model to predict postpartum depression risk levels. Here, based on a postnatal period of Sri Lankan mothers at 6 months, risk levels have been classified into 4 classes mild, moderate, severe, and profound using the Edinburgh Postpartum Depression Scale (EPDS). After reviewing past literature has identified Feed-Forward Neural Network (FFANN), Adaptive Neuro-Fuzzy Inference System, Genetic Algorithm (ANFIS - GA), Random Forest (RF), and Support Vector Machine (SVM) best for building the proposed models. Finally, supposed to identify which model has good performance when predicting depending on the model’s performance. After model training and testing, as classification and regression types of models, the FFANN model (97.08% accuracy) and the ANFIS - GA model (testing error: 0.0496) have good performance. Finally, comparing the performance of both models for predicting PPD risk levels, it is concluded that FFANN has the best performance with multi classification. It has given great help to identify more influencing factors for PPD.","PeriodicalId":308901,"journal":{"name":"2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"28 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":"127737782","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