IAES International Journal of Artificial Intelligence (IJ-AI)最新文献_第5页

Design of a novel deep network model for spinal cord injury prediction 设计用于脊髓损伤预测的新型深度网络模型

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2131-2142

P. R. S. S. Venkatapathi Raju, Valayapathy Asanambigai, Suresh Babu Mudunuri

引用次数: 0

Determining community happiness index with transformers and attention-based deep learning 利用变换器和基于注意力的深度学习确定社区幸福指数

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1753-1761

Hilman Singgih Wicaksana, Retno Kusumaningrum, R. Gernowo

{"title":"Determining community happiness index with transformers and attention-based deep learning","authors":"Hilman Singgih Wicaksana, Retno Kusumaningrum, R. Gernowo","doi":"10.11591/ijai.v13.i2.pp1753-1761","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1753-1761","url":null,"abstract":"In the current digital era, evaluating the quality of people's lives and their happiness index is closely related to their expressions and opinions on Twitter social media. Measuring population welfare goes beyond monetary aspects, focusing more on subjective well-being, and sentiment analysis helps evaluate people's perceptions of happiness aspects. Aspect-based sentiment analysis (ABSA) effectively identifies sentiments on predetermined aspects. The previous study has used Word-to-Vector (Word2Vec) and long short-term memory (LSTM) methods with or without attention mechanism (AM) to solve ABSA cases. However, the problem with the previous study is that Word2Vec has the disadvantage of being unable to handle the context of words in a sentence. Therefore, this study will address the problem with bidirectional encoder representations from transformers (BERT), which has the advantage of performing bidirectional training. Bayesian optimization as a hyperparameter tuning technique is used to find the best combination of parameters during the training process. Here we show that BERT-LSTM-AM outperforms the Word2Vec-LSTM-AM model in predicting aspect and sentiment. Furthermore, we found that BERT is the best state-of-the-art embedding technique for representing words in a sentence. Our results demonstrate how BERT as an embedding technique can significantly improve the model performance over Word2Vec.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229084","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

Scaling effectivity in manifold methodologies to detect driver’s fatigueness and drowsiness state 检测驾驶员疲劳和昏昏欲睡状态的多种方法的缩放效应

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1227-1240

Gowrishankar Shiva Shankara Chari, Jyothi Arcot Prashant

{"title":"Scaling effectivity in manifold methodologies to detect driver’s fatigueness and drowsiness state","authors":"Gowrishankar Shiva Shankara Chari, Jyothi Arcot Prashant","doi":"10.11591/ijai.v13.i2.pp1227-1240","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1227-1240","url":null,"abstract":"The state of fatigueness and drowsiness relates to the stressed physical and mental condition of a driver that reduces the ability of a driver to drive safely leading to fatal consequences of road accidents. With a rising concerns about the road safety, the premium and modern vehicles are coming up with a sophisticated technology to detect and rise alarm during the positive case of fatigueness and drowsiness. Irrespective of availability of archives of literatures towards solving this problem, it is quite unclear about the strength and weakness of varied methodologies. Therefore, this paper presents a crisp and insightful discussion about the recent methodologies associated with detecting driver's attention, fatigueness, drowsiness along with highlights of commercial devices to realize various limiting factors and constraints associated with them. The paper contributes to introduce a well-structured flow of research trend to realize various patterns of current trend adopted towards solving varied problems and significant research gaps have been identified in this process. The outcome of this paper presents that still there is an open scope of an improvement towards accomplishing the agenda towards safer driving.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231939","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

FinTech forecasting using an evolving connectionist system for lenders and borrowers: ecosystem behavior 利用不断发展的出借人和借款人关联系统预测金融科技：生态系统行为

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2386-2394

A. Al-Khowarizmi, Michael J. Watts, Syahril Efendi, Anton Abdulbasah Kamil

{"title":"FinTech forecasting using an evolving connectionist system for lenders and borrowers: ecosystem behavior","authors":"A. Al-Khowarizmi, Michael J. Watts, Syahril Efendi, Anton Abdulbasah Kamil","doi":"10.11591/ijai.v13.i2.pp2386-2394","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2386-2394","url":null,"abstract":"Financial Technology (FinTech) which is included in the development of digitalization in the financial sector in the industrial era 4.0. Fintech can make any transactions anywhere with the pillars of Peet-to-Peer (P2P) lending, merchants and crowdfunding. In the P2P Lending pillar, there are borrowers and lenders who are digitized in Fintech devices. Fintech in Indonesia is controlled by a state agency called the Otoritas Jasa Keuangan or Financial Services Authority (OJK). In the movement of P2P Lending, there are borrowers and lenders who can be said to be investors where these activities are reported to the OJK. This data can be forecasted using a neural network approach such as ECoS, which is a method capable of forecasting with learning that develops in the hidden layer. In this research article, we present results on forecasting borrowers with a Mean Absolute Percentage Error (MAPE) of 0.148% and forecasting lenders with an accuracy measurement with MAPE of 0.209% with a learning rate 1 = 0.6 and a learning rate 2 = 0.3. So, this forecasting model can be said as an optimization in FinTech activities on the behavior of borrowers and lenders.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141228757","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

Enhancing aerial image registration: outlier filtering through feature classification 增强航空图像配准：通过特征分类过滤离群点

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1900-1912

Hayder Mosa Merza, Ihab Sbeity, M. Dbouk, Z. Ibrahim

{"title":"Enhancing aerial image registration: outlier filtering through feature classification","authors":"Hayder Mosa Merza, Ihab Sbeity, M. Dbouk, Z. Ibrahim","doi":"10.11591/ijai.v13.i2.pp1900-1912","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1900-1912","url":null,"abstract":"In the context of feature-based image registration, the crucial task of outlier removal plays a pivotal role in achieving precise registration accuracy. This research introduces an innovative binary classifier founded on an adaptive approach for effectively identifying and eliminating outliers. The methodology begins with the utilization of the scale invariant feature transform (SIFT) to extract features from two images, initially matched using the Euclidian distance metrics. Subsequently, a classification procedure is executed to segregate the feature points into two categories: genuine matches (inliers) and spurious matches (outliers), which is accomplished through the brute-force matcher (BFM) technique. To enhance this process further, a novel classifier rooted in the random forest algorithm is introduced. This classifier is trained and tested using a comprehensive dataset curated for this study. The newly proposed classifier plays a pivotal role in attenuating the influence of outliers, ultimately leading to refined image registration process characterized by enhanced accuracy. The effectiveness of this outlier removal approach is assessed through a meticulous analysis of positional and classification accuracy. Additionally, we offer comparative insights by evaluating the performance of selected algorithm on our dataset.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141234548","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 survey on planet leaf disease identification and classification by various machine-learning technique 利用各种机器学习技术识别和分类行星叶病的研究

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1187-1194

Premakumari Pujar, Ashutosh Kumar, Vineet Kumar

引用次数: 0

Effect of dataset distribution on automatic road extraction in very high-resolution orthophoto using DeepLab V3+ 数据集分布对使用 DeepLab V3+ 在超高分辨率正射影像图中自动提取道路的影响

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1650-1657

S. Sussi, E. Husni, Arthur Siburian, Rahadian Yusuf, Agung Budi Harto, D. Suwardhi

引用次数: 0

Face mask classification using convolutional neural networks with facial image regions and super resolution 利用具有面部图像区域和超分辨率的卷积神经网络进行人脸面具分类

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2423-2432

N. Wattanakitrungroj, W. Wettayaprasit, Peemakarn Rujirapong, Sasiporn Tongman

{"title":"Face mask classification using convolutional neural networks with facial image regions and super resolution","authors":"N. Wattanakitrungroj, W. Wettayaprasit, Peemakarn Rujirapong, Sasiporn Tongman","doi":"10.11591/ijai.v13.i2.pp2423-2432","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2423-2432","url":null,"abstract":"Face mask classification is relevant to public health and safety, so an approach for face mask classification using Multi-Task Cascaded Convolutional Networks (MTCNN) for face detection on image data, ResNet152 architecture for feature extraction, and super-resolution method, BSRGAN, for enhanced image quality was proposed. The classification model was trained by a fully connected layer of neural networks. The goal is to classify each facial image into three classes: the image with a mask, without a mask, or with an incorrectly worn mask. The performance of each classification model on two real-world datasets was evaluated by Accuracy, Precision, Recall, and F1 score for different sets of input patterns which were features extracted from the facial image regions including their combinations. Using multiple image regions, i.e. face, nose, and mouth, as resources for preparing input features showed the improved classification performance compared to using single image regions. In addition, the super-resolution technique applied to medium or large-sized images can improve the performance of the face mask classification model. Our findings may further guide the development for greater effective models and techniques on face mask classification contributing to practical scenarios.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231130","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

Enhancing service excellence: analyzing natural language question answering with advanced cosine similarity 提升卓越服务：利用高级余弦相似性分析自然语言问题解答

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1773-1781

R. Arifudin, Subhan Subhan, Yahya Nur Ifriza

{"title":"Enhancing service excellence: analyzing natural language question answering with advanced cosine similarity","authors":"R. Arifudin, Subhan Subhan, Yahya Nur Ifriza","doi":"10.11591/ijai.v13.i2.pp1773-1781","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1773-1781","url":null,"abstract":"Information related to student services in higher education must be produced and disseminated in various forms. Covid-19 pandemic, student services with a remote model related to this question and answer become very important. To carry out this automation process, the advanced cosine similarity method is used to check the similarity of the questions to the database and statistics to calculate the similarity value of each word. The proposed paper proceeds with three phases. The first stage to solve this problem is the data processed in question; the professional next step is word insertion. It converts alphanumeric words to vector format. Each word is a vector that represents a point in space with a certain dimension. The recommended advanced cosine similarity data still must be analyzed into a statistical approach. We will measure accuracy to get results so that optimal results and answers are obtained, research procedures are carried out based on literature study, initial data collection and observation, system development, system testing, system analysis, and system evaluation. This research implemented in universities with student chat automation applications providing an accuracy 83.90% given by natural language question answering system (NLQAS) so that it can improve excellent service in universities.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231354","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

Structure tensor-based Gaussian kernel edge-adaptive depth map refinement with triangular point view in images 基于结构张量的高斯核边缘自适应深度图细化与图像中的三角点视图

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1945-1953

H. Shalma, P. Selvaraj

{"title":"Structure tensor-based Gaussian kernel edge-adaptive depth map refinement with triangular point view in images","authors":"H. Shalma, P. Selvaraj","doi":"10.11591/ijai.v13.i2.pp1945-1953","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1945-1953","url":null,"abstract":"Image reconstruction is the process of restoring the image resolution. In 3D image reconstruction, the objects in different viewpoints are processed with the triangular point view (TPV) method to estimate object geometry structure for 3D model. This work proposes a depth refinement methodology in preserving the geometric structure of objects using the structure tensor method with a Gaussian filter by transforming a series of 2D input images into a 3D model. The computation of depth map errors can be found by comparing the masked area/patch with the distribution of the original image's greyscale levels using the error pixel-based patch extraction algorithm. The presence of errors in the depth estimation could seriously deteriorate the quality of the 3D effect. The depth maps were iteratively refined based on histogram bins number to improve the accuracy of initial depth maps reconstructed from rigid objects. The existing datasets such as the dataset tanks and unit (DTU) and Middlebury datasets, were used to build the model out of the object scene structure. The results of this work have demonstrated that the proposed patch analysis outperformed the existing state of the art models depth refinement methods in terms of accuracy.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141233854","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