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International Journal of Software Innovation最新文献

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Machine Learning-Based Academic Result Prediction System 基于机器学习的学业成绩预测系统
IF 0.6
International Journal of Software Innovation Pub Date : 2023-12-15 DOI: 10.4018/ijsi.334715
Megha Bhushan, Utkarsh Verma, Chetna Garg, Arun Negi
{"title":"Machine Learning-Based Academic Result Prediction System","authors":"Megha Bhushan, Utkarsh Verma, Chetna Garg, Arun Negi","doi":"10.4018/ijsi.334715","DOIUrl":"https://doi.org/10.4018/ijsi.334715","url":null,"abstract":"Students' academic performance is a critical issue as it decides his/her career. It is pivotal for the educational institutes to track the performance record because it can help to enhance the standard of their quality education. Thus, the role of the academic result prediction system comes into existence which uses semester grade point average (SGPA) as a metric. The proposed work aims to create a model that can forecast the SGPA of students based on certain traits. It predicts the result in the form of SGPA of computer science students considering their past academic performance, study, and personal habits during their academic semester using different machine learning models, and to compare them based on different accuracy parameters. Some models that are widely used and are found effective in this field are regression algorithms, classification algorithms, and deep learning techniques. The results conclude that deep learning techniques are the most effective in the proposed work because of their high accuracy and performance, depending upon the attributes used in the prediction.","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":"146 2","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138998214","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 Spatial Data Pipeline for Orchestrating Apache NiFi/MiNiFi 一种用于编排Apache NiFi/MiNiFi的新型空间数据管道
International Journal of Software Innovation Pub Date : 2023-11-01 DOI: 10.4018/ijsi.333164
Chase D. Carthen, Araam Zaremehrjardi, Vinh Le, Carlos Cardillo, Scotty Strachan, Alireza Tavakkoli, Frederick C. Harris Jr., Sergiu M. Dascalu
{"title":"A Novel Spatial Data Pipeline for Orchestrating Apache NiFi/MiNiFi","authors":"Chase D. Carthen, Araam Zaremehrjardi, Vinh Le, Carlos Cardillo, Scotty Strachan, Alireza Tavakkoli, Frederick C. Harris Jr., Sergiu M. Dascalu","doi":"10.4018/ijsi.333164","DOIUrl":"https://doi.org/10.4018/ijsi.333164","url":null,"abstract":"In many smart city projects, a common choice to capture spatial information is the inclusion of lidar data, but this decision will often invoke severe growing pains within the existing infrastructure. In this article, the authors introduce a data pipeline that orchestrates Apache NiFi (NiFi), Apache MiNiFi (MiNiFi), and several other tools as an automated solution to relay and archive lidar data captured by deployed edge devices. The lidar sensors utilized within this workflow are Velodyne Ultra Puck sensors that produce 6-7 GB packet capture (PCAP) files per hour. By both compressing the file after capturing it and compressing the file in real-time; it was discovered that GZIP and XZ both saved considerable file size being from 2-5 GB, 5 minutes in transmission time, and considerable CPU time. To evaluate the capabilities of the system design, the features of this data pipeline were compared against existing third-party services, Globus and RSync.","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":"50 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135271393","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 an Elevated Signal-to-Noise Ratio in EEG Emotion Recognition 提高脑电情绪识别的信噪比评估
International Journal of Software Innovation Pub Date : 2023-11-01 DOI: 10.4018/ijsi.333161
Zachary Estreito, Vinh Le, Frederick C. Harris Jr., Sergiu M. Dascalu
{"title":"Evaluating an Elevated Signal-to-Noise Ratio in EEG Emotion Recognition","authors":"Zachary Estreito, Vinh Le, Frederick C. Harris Jr., Sergiu M. Dascalu","doi":"10.4018/ijsi.333161","DOIUrl":"https://doi.org/10.4018/ijsi.333161","url":null,"abstract":"Predicting valence and arousal values from EEG signals has been a steadfast research topic within the field of affective computing or emotional AI. Although numerous valid techniques to predict valence and arousal values from EEG signals have been established and verified, the EEG data collection process itself is relatively undocumented. This creates an artificial learning curve for new researchers seeking to incorporate EEGs within their research workflow. In this article, a study is presented that illustrates the importance of a strict EEG data collection process for EEG affective computing studies. The work was evaluated by first validating the effectiveness of a machine learning prediction model on the DREAMER dataset, then showcasing the lack of effectiveness of the same machine learning prediction model on cursorily obtained EEG data.","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135270960","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 Two-Stage Long Text Summarization Method Based on Discourse Structure 基于语篇结构的两阶段长文本摘要方法
International Journal of Software Innovation Pub Date : 2023-09-29 DOI: 10.4018/ijsi.331091
Xin Zhang, Qiyi Wei, Qing Song, Pengzhou Zhang
{"title":"A Two-Stage Long Text Summarization Method Based on Discourse Structure","authors":"Xin Zhang, Qiyi Wei, Qing Song, Pengzhou Zhang","doi":"10.4018/ijsi.331091","DOIUrl":"https://doi.org/10.4018/ijsi.331091","url":null,"abstract":"This paper proposes a two-stage automatic text summarization method based on discourse structure, aiming to improve the accuracy and coherence of the summary. In the extractive stage, a text encoder divides the long text into elementary discourse units (EDUs). Then a parse tree based on rhetorical structure theory is constructed for the whole discourse while annotating nuclearity information. The nuclearity terminal nodes are selected based on the summary length requirement, and the key EDU sequence is output. The authors use a pointer generator network and a coverage mechanism in the generation stage. The nuclearity information of EDUs is to update the word attention distribution in the pointer generator, which not only accurately reproduces the critical details of the text but also avoids self-repetition. Experiments on the standard text summarization dataset (CNN/DailyMail) show that the ROUGE score of the proposed two-stage model is better than that of the current best baseline model, and the summary achieves corresponding improvements in accuracy and coherence.","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135193707","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
Sentiment Analysis of Hybrid Network Model Based on Attention 基于注意力的混合网络模型情感分析
International Journal of Software Innovation Pub Date : 2023-08-04 DOI: 10.4018/ijsi.327364
Hongzhan Zhen, Wenqian Shang, Wanyu Zhang
{"title":"Sentiment Analysis of Hybrid Network Model Based on Attention","authors":"Hongzhan Zhen, Wenqian Shang, Wanyu Zhang","doi":"10.4018/ijsi.327364","DOIUrl":"https://doi.org/10.4018/ijsi.327364","url":null,"abstract":"The existing text sentiment analysis models based on deep learning and neural network usually have problems such as incomplete text feature extraction and failure to consider the impact of key information on text sentiment tendency. Based on the parallel hybrid network and the two-way attention mechanism, an improved text sentiment analysis model is proposed. The model first takes the word vector trained by the BERT language model as the input, and then extracts the global and local features of the context simultaneously through the parallel hybrid neural network constructed by the Convolution Neural Network (CNN) and The Bidirectional Gated Recurrent Unit (BiGRU), so as to improve the feature extraction ability of the model. It also integrates the dual-way attention mechanism to strengthen the key information in the global feature and local feature, and the feature vectors obtained by feature fusion are used for sentiment analysis.","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136119177","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
Vehicle Type Classification Using Hybrid Features and a Deep Neural Network 基于混合特征和深度神经网络的车型分类
International Journal of Software Innovation Pub Date : 2023-03-31 DOI: 10.4018/ijsi.297511
None Sathyanarayana N., Anand M. Narasimhamurthy
{"title":"Vehicle Type Classification Using Hybrid Features and a Deep Neural Network","authors":"None Sathyanarayana N., Anand M. Narasimhamurthy","doi":"10.4018/ijsi.297511","DOIUrl":"https://doi.org/10.4018/ijsi.297511","url":null,"abstract":"In this research, a framework incorporating hybrid features is proposed to improve the performance of vehicle type classification. The proposed model includes a camera response model to enhance the collected images and a Gaussian mixture model to localize the object of interest. The feature vectors are extracted from the pre-processed images using Gabor features, histogram of oriented gradients, and local optimal-oriented pattern. The hybrid set of features discriminate the classes better; further, an ant colony optimizer is used to reduce the dimension of the extracted feature vectors. Finally, deep neural network is used to classify the types of vehicles in the images. The proposed model was tested on the MIO vision traffic camera dataset and a real-world dataset consisting of videos of multiple lanes of a toll plaza. The proposed model showed an improvement in accuracy ranging from 0.28% to 8.68% in the MIO TCD dataset when compared to well-known neural network architectures like AlexNet, Inception V3, ResNet 50, VGG 19, Xception, and DenseNet.","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135822895","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
Cardiac Arrhythmia, CHF, and NSR Classification With NCA-Based Feature Fusion and SVM Classifier 基于nca特征融合和SVM分类器的心律失常、CHF和NSR分类
IF 0.6
International Journal of Software Innovation Pub Date : 2023-01-01 DOI: 10.4018/ijsi.315659
A. DeepakH., T. Vijayakumar
{"title":"Cardiac Arrhythmia, CHF, and NSR Classification With NCA-Based Feature Fusion and SVM Classifier","authors":"A. DeepakH., T. Vijayakumar","doi":"10.4018/ijsi.315659","DOIUrl":"https://doi.org/10.4018/ijsi.315659","url":null,"abstract":"","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":"11 1","pages":"1-24"},"PeriodicalIF":0.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70471049","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
SentiNeg: Algorithm to Process Negations at Sentence Level in Sentiment Analysis 句子:情感分析中句子级否定处理算法
IF 0.6
International Journal of Software Innovation Pub Date : 2023-01-01 DOI: 10.4018/ijsi.315741
Sandhya R. Savanur, R. Sumathi
{"title":"SentiNeg: Algorithm to Process Negations at Sentence Level in Sentiment Analysis","authors":"Sandhya R. Savanur, R. Sumathi","doi":"10.4018/ijsi.315741","DOIUrl":"https://doi.org/10.4018/ijsi.315741","url":null,"abstract":"","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":"11 1","pages":"1-27"},"PeriodicalIF":0.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70471114","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
Prediction of Air Quality using LSTM Recurrent Neural Network 基于LSTM递归神经网络的空气质量预测
IF 0.6
International Journal of Software Innovation Pub Date : 2022-01-01 DOI: 10.4018/ijsi.297982
{"title":"Prediction of Air Quality using LSTM Recurrent Neural Network","authors":"","doi":"10.4018/ijsi.297982","DOIUrl":"https://doi.org/10.4018/ijsi.297982","url":null,"abstract":"Rapid increase of Industrialization and Urbanization significantly draws the interest of researchers towards the prediction of air quality. Efficient modelling of air quality parameters using deep learning methods can facilitate the imminent implications of air pollution. However, existing methods weakens at consideration of long-term dependencies for multiple parameters. The present study aims prediction of air quality of New Delhi based on concentration of multiple parameters namely PM2.5, PM10, CO, O3, NO2 and SO2. The study uses long short-term memory (LSTM) approach due to its efficiency over other deep learning methods and referred it as A-LSTM prediction model. It supports multiple layers to add more linearity to the desired output. Performance of A-LSTM is evaluated for prediction of year 2019 data. Mean absolute error, root mean squared error, precision, recall and F1-score metrics are considered for comparison with other three prediction models namely support vector regressor (SVR), SVR with LSTM and I-LSTM.","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43344946","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
Use of machine learning to detect Lung Cancer 使用机器学习检测肺癌
IF 0.6
International Journal of Software Innovation Pub Date : 2022-01-01 DOI: 10.4018/ijsi.297988
{"title":"Use of machine learning to detect Lung Cancer","authors":"","doi":"10.4018/ijsi.297988","DOIUrl":"https://doi.org/10.4018/ijsi.297988","url":null,"abstract":"Lung cancer has become one of the most common causes of cancer in both men and women. A large number of people die every year due to lung cancer. The purpose of this project is to detect early signs of lung cancer and improve accuracy and sensitivity. Different features are extracted from the input image and based on the calculations, result from the support vector machine is obtained as cancerous cells are present or not. The stages included in this are pre-processing, segmentation, feature extraction and classification. In pre-processing the noise and blurriness of image removed. In segmentation the image is segmented using DWT techniques. The features extracted using GLCM matrix. The extracted features are Entropy, Co-relation, energy, contrast and Dissimilarities. SVM uses hyper plane algorithm to detect whether the given image is ‘Malignant’ or ‘Benign’","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46823098","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
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