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MATLAB-Based Real-Time Data Acquisition Tool for Multimodal Biofeedback and Arduino-Based Instruments: Arduino Firmata Data Acquisition (AfDaq) 基于matlab的多模态生物反馈实时数据采集工具和基于Arduino的仪器:Arduino Firmata数据采集(AfDaq)
J. Inf. Technol. Res. Pub Date : 2022-01-01 DOI: 10.4018/jitr.299922
Kulbhushan Chand, A. Khosla
{"title":"MATLAB-Based Real-Time Data Acquisition Tool for Multimodal Biofeedback and Arduino-Based Instruments: Arduino Firmata Data Acquisition (AfDaq)","authors":"Kulbhushan Chand, A. Khosla","doi":"10.4018/jitr.299922","DOIUrl":"https://doi.org/10.4018/jitr.299922","url":null,"abstract":"AfDaq is an open-source, plug and play, MATLAB based tool that offers the capabilities of multi-channel real-time data acquisition, visualization, manipulation, and local saving of data for offline analysis. The MATLAB Arduino package suffers from serious timing jitter during real-time data acquisition. This timing jitter associated with four main commands (Analog Read, Digital Read, Digital Write and PWM Set) available in MATLAB Arduino package is statistically analyzed and a simple post-hoc timing jitter correction mechanism is proposed to acquire data points with high timing accuracy. The benchmark of the final program is conducted at various sampling rates for multichannel acquisition with 10 Hz comes as the maximum sampling rate for 5 channel recording. In the end, a use case of the developed tool for physiological data acquisition in multimodal biofeedback is presented. The software tool, data, and analysis scripts that support the findings of this study are released as an open-source project to support the replicability and reproducibility of the research.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124048763","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
Machine Learning for Android Scareware Detection Android恶意软件检测的机器学习
J. Inf. Technol. Res. Pub Date : 2022-01-01 DOI: 10.4018/jitr.298326
S. Bagui, Hunter Brock
{"title":"Machine Learning for Android Scareware Detection","authors":"S. Bagui, Hunter Brock","doi":"10.4018/jitr.298326","DOIUrl":"https://doi.org/10.4018/jitr.298326","url":null,"abstract":"With the steady rise in the use of smartphones, specifically android smartphones, there is an ongoing need to build strong Intrusion Detection Systems to protect ourselves from malicious software attacks, especially on Android smartphones. This work focuses on a sub-group of android malware, scareware. The novelty of this work lies in being able to detect the various scareware families individually using a small number of network attributes, determined by a recursive feature elimination process based on information gain. No work has yet been done on analyzing the scareware families individually. Results of this work show that the number of bytes initially sent back and forth, packet size, amount of time between flows and flow duration are the most important attributes that would be needed to classify a scareware attack. Three classifiers, Decision Tree, Naïve Bayes and OneR, were used for classification. The highest average classification accuracy (79.5%) was achieved by the Decision Tree classifier with a minimum of 44 attributes.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116010024","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
Target Sentiment Analysis Ensemble for Product Review Classification 面向产品评论分类的目标情感分析集成
J. Inf. Technol. Res. Pub Date : 2022-01-01 DOI: 10.4018/jitr.299382
Rhoda Viviane Achieng Ogutu, R. Rimiru, C. Otieno
{"title":"Target Sentiment Analysis Ensemble for Product Review Classification","authors":"Rhoda Viviane Achieng Ogutu, R. Rimiru, C. Otieno","doi":"10.4018/jitr.299382","DOIUrl":"https://doi.org/10.4018/jitr.299382","url":null,"abstract":"Abstract— Machine learning can be used to provide systems the ability to automatically learn and improve from experiences without being explicitly programmed. It is fundamentally a multidisciplinary field that draws on results from Artificial intelligence, probability and statistics, information theory and analysis, among other fields that impact the field of Machine Learning. Ensemble methods are techniques that can be used to improve the predictive ability of a Machine Learning model. An ensemble comprises of individually trained classifiers whose predictions are combined when classifying instances. Some of the currently popular ensemble methods include Boosting, Bagging and Stacking. In this paper, we review these methods and demonstrate why ensembles can often perform better than single models. Additionally, some new experiments are presented to demonstrate the computational ability of Stacking approach.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126296811","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
Coronavirus Pneumonia Classification Using X-Ray and CT Scan Images With Deep Convolutional Neural Network Models 基于深度卷积神经网络模型的x射线和CT扫描图像冠状病毒肺炎分类
J. Inf. Technol. Res. Pub Date : 2022-01-01 DOI: 10.4018/jitr.299391
Menaouer Brahami, Zoulikha Dermane, Nour El Houda Kebir, Sabri Mohammed, Nada Matta
{"title":"Coronavirus Pneumonia Classification Using X-Ray and CT Scan Images With Deep Convolutional Neural Network Models","authors":"Menaouer Brahami, Zoulikha Dermane, Nour El Houda Kebir, Sabri Mohammed, Nada Matta","doi":"10.4018/jitr.299391","DOIUrl":"https://doi.org/10.4018/jitr.299391","url":null,"abstract":"Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans. There are mainly two types of pneumonia: bacterial and viral. Likewise, patients with coronavirus can develop symptoms that belong to the common flu, pneumonia, and other respiratory diseases. Chest X-rays are the common method used to diagnose coronavirus pneumonia and it needs a medical expert to evaluate the result of X-ray. Furthermore, DL has garnered great attention among researchers in recent years in a variety of application domains such as medical image processing, computer vision, bioinformatics, and many others. In this paper, we present a comparison of Deep Convolutional Neural Networks models for automatically binary classification query chest X-ray & CT images dataset with the goal of taking precision tools to health professionals based on fined recent versions of ResNet50, InceptionV3, and VGGNet. The experiments were conducted using a chest X-ray & CT open dataset of 5856 images and confusion matrices are used to evaluate model performances.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129828605","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
Utilizing the Internet of Things in the Public Sector 在公共领域利用物联网
J. Inf. Technol. Res. Pub Date : 2022-01-01 DOI: 10.4018/jitr.299915
Mai Al-Sebae, E. Abu-Shanab
{"title":"Utilizing the Internet of Things in the Public Sector","authors":"Mai Al-Sebae, E. Abu-Shanab","doi":"10.4018/jitr.299915","DOIUrl":"https://doi.org/10.4018/jitr.299915","url":null,"abstract":"This study investigated the utility of the Internet of Things in the public sector and the factors influencing the satisfaction of its users. The study followed two directions, the first investigated managers’ perceptions and their satisfaction with using sensors for tracking vehicles. The second direction investigated drivers’ satisfaction with the system used. Results collected from 20 interviews conducted with managers revealed that cost reduction and more control over drivers’ behaviors are the contributions expected from the system. They reported the dissatisfaction of drivers based on violation of their privacy, inequity of implementation, and the low awareness of its utility. Surveys collected from drivers supported the role of trust and privacy, but failed to support the role of usefulness. The qualitative and quantitative nature of this research revealed valuable insights and concluded to important recommendations and future work.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122790337","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 Ensemble Learning Model Combined XGBoost With Deep Neural Network for Credit Scoring 一种结合XGBoost和深度神经网络的信用评分集成学习模型
J. Inf. Technol. Res. Pub Date : 2022-01-01 DOI: 10.4018/jitr.299924
X. He, Siqi Li, X. He, Wenqiang Wang, Xiang Zhang, Bin Wang
{"title":"A Novel Ensemble Learning Model Combined XGBoost With Deep Neural Network for Credit Scoring","authors":"X. He, Siqi Li, X. He, Wenqiang Wang, Xiang Zhang, Bin Wang","doi":"10.4018/jitr.299924","DOIUrl":"https://doi.org/10.4018/jitr.299924","url":null,"abstract":"Credit scoring, aiming to distinguish potential loan defaulter, has played an important role in financial industry. To further improve the accuracy and efficiency of classification, this paper develops an ensemble model combined extreme gradient boosting (XGBoost) and deep neural network (DNN). In the method, training set is divided into different subsets by bagging sampling at first. Then, each subset is trained as a feature extractor by DNN and the extracted features is taken as the input of XGBoost to construct the base classifier. At last, the prediction result is the average of outputs of different base classifiers. In the training verification process, three credit datasets from the UCI machine learning repository are used to evaluate the proposed model. The outcome shows that this model is superior with a significant improvement.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124422445","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 Dynamic Strategy for Classifying Sentiment From Bengali Text by Utilizing Word2vector Model 基于Word2vector模型的孟加拉语文本情感分类动态策略
J. Inf. Technol. Res. Pub Date : 2022-01-01 DOI: 10.4018/jitr.299919
Mafizur Rahman, Md. Rifayet Azam Talukder, Lima Akter Setu, A. Das
{"title":"A Dynamic Strategy for Classifying Sentiment From Bengali Text by Utilizing Word2vector Model","authors":"Mafizur Rahman, Md. Rifayet Azam Talukder, Lima Akter Setu, A. Das","doi":"10.4018/jitr.299919","DOIUrl":"https://doi.org/10.4018/jitr.299919","url":null,"abstract":"In today's world, around 230 million people used the Bengali or Bangla language to communicate. These individuals are progressively associated with online exercises on famous micro-blogging and long-range interpersonal communication locales, imparting insights what's more, musings, and also the vast majority of articles are in the Bengali language. Thus, Bengali people express their emotions using the Bangla language by reviewing, commenting, or recommendations. Sentiment analysis helps determine the people's emotions expressed on social media or several online platforms. Therefore, this study focused on extracting their emotion from a Bengali text by utilizing Word2vector, Skip-Gram, and Continuous Bag of Words (CBOW) with a new Word to Index model by focusing on three individual classes happy, angry, and excited. The authors achieved the highest accuracy of 75% by utilizing the skip-gram model to classify those three types of emotions. This study also outperformed other existing works with LSTM, CNN model with existing datasets.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132756625","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: 3
Local Binary Pattern Regrouping for Rotation Invariant Texture Classification 旋转不变纹理分类的局部二值模式重组
J. Inf. Technol. Res. Pub Date : 2022-01-01 DOI: 10.4018/jitr.299945
Asma Zitouni, B. Nini
{"title":"Local Binary Pattern Regrouping for Rotation Invariant Texture Classification","authors":"Asma Zitouni, B. Nini","doi":"10.4018/jitr.299945","DOIUrl":"https://doi.org/10.4018/jitr.299945","url":null,"abstract":"This paper represent a deep study of the Local Binary Pattern (LBP) method and its variants of patterns regrouping , which is largely used in texture classification as well in other domain. The analysis of LBP’s two hundred fifty-six patterns has led us to propose a new organization of uniform and no uniform patterns into twenty-eight groups; each group assembled a number of patterns varied according to specific terms. The principal idea is to preserve the low complexity of LBP and simultaneously increase the method robustness against quality degradation caused by image operations like rotation, grey level changes, illumination and mirror effects. The experiments are done with the two texture databases Outex and Brodatz; the tests are proving the robustness of Local Binary Pattern Regrouping (LBPG) under circumstances.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133154051","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
Security-Enabled Retransmission and Energy Conservation Architecture With Cluster-Based Multipath Routing in Heterogeneous Wireless Networks 异构无线网络中基于集群的多径路由的安全重传和节能架构
J. Inf. Technol. Res. Pub Date : 2022-01-01 DOI: 10.4018/jitr.299951
G. Asha, S. Srivatsa
{"title":"Security-Enabled Retransmission and Energy Conservation Architecture With Cluster-Based Multipath Routing in Heterogeneous Wireless Networks","authors":"G. Asha, S. Srivatsa","doi":"10.4018/jitr.299951","DOIUrl":"https://doi.org/10.4018/jitr.299951","url":null,"abstract":"The primary requirements of a heterogeneous wireless network topology, adaptive and smart resource allocation to users, protocols for routing and lifetime enhancement, access to the network with security and appropriate network selections. Routing algorithms deliberate on the performance of the network to evenly distribute load and thus enhance the lifespan of individual nodes, clustering algorithm decides on allowing the right nodes into the network for enhanced security feature, and finally the ability to analyse, predict the context of individual nodes/sensors in the network. Architecture of the proposed network includes the parameters such as decision making ability to sustain the clusters, decision on members of the clusters until the communication process is completed, local network abilities and disabilities, price, preferences of individuals, terminal and access points of the service providers. Network lifetime of the entire network is observed to be enhanced up to 91% with triple layer architecture.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126464494","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 Robust Authentication System With Application Anonymity in Multiple Identity Smart Cards 多身份智能卡中具有应用匿名的鲁棒认证系统
J. Inf. Technol. Res. Pub Date : 2022-01-01 DOI: 10.4018/jitr.2022010107
Varun Prajapati, B. Gupta
{"title":"A Robust Authentication System With Application Anonymity in Multiple Identity Smart Cards","authors":"Varun Prajapati, B. Gupta","doi":"10.4018/jitr.2022010107","DOIUrl":"https://doi.org/10.4018/jitr.2022010107","url":null,"abstract":"User Authentication plays a crucial role in smart card based systems. Multi-application smart cards are easy to use as a single smart card supports more than one application. These cards are broadly divided into single identity cards and Multi-identity cards. In this paper we have tried to provide a secure Multi-identity Multi-application Smart Card Authentication Scheme. Security is provided to user’s data by using dynamic tokens as verifiers and nested cryptography. A new token is generated after every successful authentication for next iteration. Anonymity is also provided to data servers which provides security against availability attacks. An alternate approach to store data on servers is explored which further enhances the security of the underlying system.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133808950","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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