2021 8th NAFOSTED Conference on Information and Computer Science (NICS)最新文献

{"title":"Automated Attendance System in the Classroom Using Artificial Intelligence and Internet of Things Technology","authors":"Duy Dieu Nguyen, X. Nguyen, T. Than, Minh-Son Nguyen","doi":"10.1109/NICS54270.2021.9700991","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9700991","url":null,"abstract":"Computer vision is recently developing and applying in the utility apps serving people, facial recognition is one of its applications. Although the accuracy of the facial recognition is less than when compared to fingerprint recognition, iris recognition and Radio Frequency Identification (RFID) card recognition. But it is still widely used because the recognition process does not contact the device. With the advantage of the facial recognition method, we propose an automated attendance solution which uses embedded device integrated Artificial Intelligence technology (AI) and Internet of Things technology (IoT) in the smart classrooms. The highlight of the system is the ability to take attendance automatically and continuously throughout the learning period. When the students enter the class, the management department and the parents can know the student’s participation status by viewing the report in the real-time system. The system consists of the main components: embedded device component with attached camera sensor, which is used for the process recognition and interacts with the Cloud server via IoT infrastructure; the Cloud server stores and provides data analysis devices for the administrator and parents. At the beginning of the roll call, the embedded device will receive an instruction to replace the old data with the new recorded data, which contains the characteristics and identifier code of the objects to be attended. The new data goes from the Cloud server in the respective classroom to the embedded device and then it compares with the data collected in the classroom. When the results are available, the embedded device interacts with the Cloud server to update the status of the students. The experimental results of the proposal system achieve accuracy per frame is 89%. The recognition speed of 82ms per face with a distance in the 4 - 15 meter range. The system which is an embedded system-based application solution has low operating costs and rapid deployment.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132908604","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}

{"title":"On the Improvement of Machine Learning Based Intrusion Detection System for SDN Networks","authors":"Long Tan Le, T. N. Thinh","doi":"10.1109/NICS54270.2021.9701522","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701522","url":null,"abstract":"Software-Defined Networking (SDN) is seen as a next-generation paradigm promising to build a vendor-neutral networking environment. By decoupling control plane from data plane, SDN shifts network intelligent logic into a logically centralized controller, thereby helping address many thorny problems in conventional network architecture. Despite of offering immense benefits, SDN has shown to be vulnerable to cyber attacks; meanwhile, Machine Learning (ML) has come into being the most powerful weapon to deal with those of security issues. In this paper, we proposed an improved solution of ML-based network intrusion detection system for better protecting SDN from malicious activities. The proposed solution is formed from a combination of ML techniques including Deep Sparse Autoencoder for reducing dimension and learning meaningful feature representation in network data; Conditional Generative Adversarial Network for solving data imbalance problem in intrusion detection datasets; and Ensemble Learning methods for classifying anomaly network traffic. Moreover, we leverage NetFPGA, a high-speed networking platform, to accelerate the packet processing task for the proposed system. By evaluating on empirical datasets, we show that our proposed system is capable of fast classification network traffic with high detection accuracy rate and relatively low false negative/positive rate.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121368538","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}

{"title":"Advanced Terahertz Devices And Systems Toward 6G And Beyond","authors":"M. Fujita","doi":"10.1109/NICS54270.2021.9701534","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701534","url":null,"abstract":"A wide untapped region exists between radio waves and light in the electromagnetic spectrum: terahertz (THz) waves. THz frequencies combine the penetration of radio waves and the large bandwidth of light, which makes them excellent candidates for next-generation information communication technology, 6G and beyond, such as ultra-broadband wireless communication, spectroscopic sensing, nondestructive imaging, and high-resolution ranging. However, THz frequencies are at the upper limit of the capabilities of conventional electronics, and the development of THz devices and systems is a challenging field of interdisciplinary research. In particular, it is difficult to generate a significant amount of power from THz sources. THz devices must, therefore, be as efficient as possible to conserve limited power. Resonant tunneling diodes are a major candidate for both THz transmitters and receivers because of their simple and low-power electronic devices. In addition, a low-loss platform for integrating THz devices is essential for various practical systems. However, the propagation loss of transmission lines based on conventional electronics is high in the THz region, mainly owing to the high ohmic loss in metals. Thus, an alternative, metal-free integrated platform based on THz silicon photonics or photonic crystals is necessary to manipulate THz waves.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126781639","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}

{"title":"An Adaptive Method for Classification of Noisy Respiratory Sounds","authors":"Khanh Nguyen-Trong","doi":"10.1109/NICS54270.2021.9701460","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701460","url":null,"abstract":"Respiratory sounds (RSs) contain essential information about the physiology and pathology of lungs and airways obstruction. Therefore, RS understanding has a critical role in diagnosing respiratory patients. However, the external noise in the respiratory sound signal is a large restriction for this study. In this paper, we propose a method to classify noisy respiratory signals. Firstly, four adaptive filtering algorithms (RLS, LMS, NLMS, and Kalman) are applied and evaluated for noise reduction. Then, we extract features of filtered sounds, using Mel Frequency Cepstral Coefficient. Finally, the SVM method is used to classify respiratory sounds. We also conducted experiments on a dataset consisting of 1980 breath events, collected from 16 healthy volunteers. The obtained results show that, the investigated methods, SVM and Kalman achieves the highest accuracy of 95.5%.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126830372","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}

{"title":"Exploring the Performances of Stacking Classifier in Predicting Patients Having Stroke","authors":"Tasnimul Hasan, M. M. Nishat, Fahim Faisal, Abrar Islam, Abdullah Al Mehadi, Sarker Md. Nasrullah, Mohammad Tausiful Islam","doi":"10.1109/NICS54270.2021.9701526","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701526","url":null,"abstract":"Stroke refers to a spectrum of clinical manifestations with underlying neurological dysfunctions of the brain. It is a medical condition which is often misdiagnosed and commonly misclassified, leading to a delay in the initiation of disease-specific treatment in patients. Rapid and precise detection of stroke is the key to the effective management of the patients and alleviate possible disabilities. Machine learning techniques are being adopted for their capabilities of identifying hidden patterns from the obtained data of patients. In this study, a stacking classifier is constructed by utilizing Random Forest (RF), Extra Tree (ET) and Gradient Boosting Classifier (GBC) as well as the performances are observed in terms of various performance metrics. A detailed comparative analysis is portrayed where it is observed that the accuracies of RF, ET and GBC are 94.63%, 94.62% and 94.72% respectively whereas the proposed stacking classifier outperformed the individual classifiers’ performances with an accuracy of 95%. The hyperparameter tuning is accomplished for all the classifiers by which the performances are enhanced. Hence, the investigative analysis can significantly contribute to predict patients having a stroke and aid in developing an automated diagnosis for e-healthcare systems.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114995065","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}

{"title":"ODLIE: On-Demand Deep Learning Framework for Edge Intelligence in Industrial Internet of Things","authors":"Khanh-Hoi Le Minh, Kim-Hung Le","doi":"10.1109/NICS54270.2021.9701568","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701568","url":null,"abstract":"Recently, we have witnessed the evolution of Edge Computing (EC) and Deep Learning (DL) serving Industrial Internet of Things (IIoT) applications, in which executing DL models is shifted from cloud servers to edge devices to reduce latency. However, achieving low latency for IoT applications is still a critical challenge because of the massive time consumption to deploy and operate complex DL models on constrained edge devices. In addition, the heterogeneity of IoT data and device types raises edge-cloud collaboration issues. To address these challenges, in this paper, we first introduce ODLIE, an on-demand deep learning framework for IoT edge devices. ODLIE employs DL right-selecting and DL right-sharing features to reduce inference time while maintaining high accuracy and edge collaboration. In detail, DL right-selecting chooses the appropriate DL model adapting to various deployment contexts and user-desired qualities, while DL right-sharing exploits W3C semantic descriptions to mitigate the heterogeneity in IoT data and devices. To prove the applicability of our proposal, we present and analyze latency requirements of IIoT applications that are thoroughly satisfied by ODLIE.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129771770","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}

{"title":"Rotation Forest Ensemble Classifier to Improve the Cardiovascular Disease Risk Prediction Accuracy","authors":"K. Reddy, I. Elamvazuthi, A. A. Aziz, S. Paramasivam, Hui Na Chua, S. Pranavanand","doi":"10.1109/NICS54270.2021.9701455","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701455","url":null,"abstract":"Heart disease risk prediction is very important as it is one of the primary causes of sudden death in the world. Early-stage prediction can save the lives by undergoing appropriate diagnosis steps or making necessary changes in their lifestyles. Recent studies have focused on the use of data mining and machine learning in the detection of diseases based on specific features of a person. The Rotation Forest, a tree-based ensemble classifier that uses Principal Component Analysis for feature extraction, is proposed to improve the prediction accuracy of heart disease risk. The Statlog heart dataset has been selected from the publicly available UCI machine learning repository in this research work. The dataset was trained with a Rotation Forest ensemble classifier with default base classifier J48, and then, Random Forest on full features and selected features obtained from One Rule and Support Vector Machines attribute evaluators. The performance of the Rotation Forest was compared with the standard machine learning classifiers, Naïve Bayes, Logistic Regression, Support Vector Machines, K-Nearest Neighbors, AdaBoostM1, and Bagging. The Rotation Forest algorithm with Random Forest provided the highest accuracy of 94.44% and area under the ROC curve 0.980 on selected features of the Statlog dataset from the One Rule method.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122804569","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}

{"title":"In-bed posture classification using pressure sensor data and spiking neural network","authors":"Hoang Phuong Dam, Nguyen Duc Anh Pham, Hung-Manh Pham, Ngoc Phu Doan, Duc Minh Nguyen, Huy Hoang Nguyen","doi":"10.1109/NICS54270.2021.9701531","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701531","url":null,"abstract":"Observing and evaluating sleeping positions is crucial in the treatment of cardiovascular episodes, pressure ulcers and respiratory diseases. Therefore, in-bed posture recognition systems become necessary at home as well as in hospitals. Many studies have shown that the use of gravity sensors in combination with the second generation of neural network (NN) architectures are extremely effective in assessing and classifying sleeping positions. However, the disadvantage of the second generation NN architecture is that it is quite energy-intensive. While the third NN generation - Spiking Neural Network (SNN) is projected to solve the power consumption problem while providing an equal performance or even better performance than the old ones. Surprisingly, none of the studies consider combining SNN in sleeping position classification based on pressure sensor assessment. In this paper, we propose the development of a converted CNN-to-SNN network for sleeping posture recognition algorithm supported by preprocessing technique. Experimental results confirm that our proposed method can achieve an accuracy of nearly 100% in 5-fold as well as 10-fold cross-validation and 90.56% in the Leave-One-Subject-Out (LOSO) cross-validation for 17 sleeping postures, which greatly surpasses the previous method performing the same task. Furthermore, the power consumption of our SNN model is 140 times lower than that of the published CNN model.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132317640","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}

{"title":"Deep Feature Rotation for Multimodal Image Style Transfer","authors":"S. Nguyen, N. Tuyen, Nguyen Hong Phuc","doi":"10.1109/NICS54270.2021.9701465","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701465","url":null,"abstract":"Recently, style transfer is a research area that attracts a lot of attention, which transfers the style of an image onto a content target. Extensive research on style transfer has aimed at speeding up processing or generating high-quality stylized images. Most approaches only produce an output from a content and style image pair, while a few others use complex architectures and can only produce a certain number of outputs. In this paper, we propose a simple method for representing style features in many ways called Deep Feature Rotation (DFR), while not only producing diverse outputs but also still achieving effective stylization compared to more complex methods. Our approach is representative of the many ways of augmentation for intermediate feature embedding without consuming too much computational expense. We also analyze our method by visualizing output in different rotation weights. Our code is available at https://github.com/sonnguyen129/deep-feature-rotation.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133509700","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}

{"title":"Tire Wear Detection for Accident Avoidance Employing Convolutional Neural Networks","authors":"S.M. Mynul Karim, Yeaminur Rahman, Md. Abdul Hai, Rezwana Mahfuza","doi":"10.1109/NICS54270.2021.9701504","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701504","url":null,"abstract":"Tires are one of the most essential components of a vehicle, as they actively contribute to driving dynamics. However, they are often among the most overlooked when it comes to proper scrutiny and maintenance. More often than not, the general masses are found to be negligent of the condition of their tires. Treadwear and sidewall damage occur in abundance, and not tending to these problems can have devastating consequences in the long run. There is an innumerable number of road accident cases reported which were found to have been caused due to use of damaged and worn-out tires, and these occurrences are more prevalent in highways and during the rainy season. Despite being a widespread issue, many people are unable to identify good usable tires from worn-out ones, increasing their likelihood of using dangerous unsafe tires on roads. This paper introduces a model that can differentiate between good and worn-out tires, which has been implemented using Image Processing. The model takes external pictures of tires provided by the user as input and provides a verdict on its condition after comparing them with the model’s dataset using the machine learning algorithms DenseNet and MobileNet. This model has been made keeping in mind that it can be further used with appropriate hardware for implementing in real-life applications. By enforcing said implementation by the concerned regulatory bodies, tire-related accidents can be sharply reduced and damage to human life and property can be prevented on public roads.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129227416","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}