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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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the Aberrant Behavioral Spectrum of Internet Addiction in University Students

The Aberrant Behavioral Spectrum of Internet Addiction in Un...

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international conference on Educational and Information Technology (ICEIT)

作者： Jie Liu Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9781665422956

ISBN: (纸本)9781665446471

A large number of behaviors, collected by self-reporting questionnaires, have been demonstrated associative with Internet addiction (IA). Assume that these behaviors are informative for distinguishing IA subjects from normal controls (NCs). Rather than analyzing each behavior separately, a classification model is presented for discriminant analysis of behaviors jointly at a subject level. In conjunction with support vector machine (SVM) classifier, a forward behavior selection technique is presented to select behaviors for constructing the most discriminative behavioral spectrum. the discriminant analysis model has been applied to 8 types of behaviors based on IA study with 27 IA subjects and 27 NCs. these 8 types of behaviors were average online days per week, average online time per day, anxiety level, depression level, self-control ability, positive self-confidence, negative self-confidence, and self-confidence, which were closely associated with AI. Experimental results indicated that the aberrant behavioral spectrum were properly arranged, which identified behavioral markers with different levels of importance for IA. the presented classification model also achieved an excellent performance for differentiating IA subjects from NCs (average area under the receiver operating characteristic curve [AUROC]: 0.96, average accuracy: 84%, average specificity: 92%, average sensitivity: 76%).

关键词： Analytical models Support vector machine classification Receivers Depression Internet pattern recognition Information technology

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Feature Fool Exploitation for Lightweight Anomaly Detection in Respiratory Sound 10th

Feature Fool Exploitation for Lightweight Anomaly Detection...

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10th international conference on Future Data and Security Engineering, FDSE 2023

作者： Le, Kim-Ngoc T. Bang, Sammy Yap Xiang Le, Duc-Tai Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Seoul Korea Republic of College of Computing and Informatics Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9789819982950

Respiratory sound auscultation with digital stethoscopes is a common technique for identifying lung disorders, however, it requires a qualified medical expert to interpret the sounds, and inter-listener variability results from subjectivity in interpretations. To improve diagnostic accuracy and enhance patient treatment, there is a growing need for automated detection of lung diseases. Deep neural networks (DNNs) have demonstrated substantial potential in addressing such challenges. However, DNNs demand a significant amount of data, and the largest available respiratory dataset, ICBHI, comprises only 6898 breathing cycles, which is insufficient to train a satisfactory DNN model. To address the issue, we propose a robust and lightweight model that employs a feature fool exploitation technique to identify respiratory anomalies. Next, we deploy two evaluation approaches to evaluate its performance: random 60/40 splitting and 5-fold cross-validation, against state-of-the-art methods, using the ICBHI dataset. Remarkably, our scheme outperforms existing approaches up to 18.26%, achieving impressive accuracy rates of 72.36% and 89.46%, using the two respective train/test splitting methods. the results show a significant improvement from our method over existing approaches, suggesting its promise for future respiratory healthcare technology research. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Anomaly detection

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Understanding Power Transformer Current-Voltage (I-V) Lissajous pattern Under Different Faults

Understanding Power Transformer Current-Voltage (I-V) Lissaj...

引用

international conference on Condition Monitoring and Diagnosis, CMD

作者： Nanda Redha Arsya Suwarno A. Abu-Siada School of Electrical Engineering and Informatics ITB PT. PLN (Persero) Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia School of Electrical Engineering Computing and Mathematical Science Curtin University Perth Australia

ISBN: (数字)9788986510225

ISBN: (纸本)9798350353877

Condition-based maintenance (CBM) is a robust maintenance strategy suitable for high-criticality assets, including Power Transformers. Various measurements and tests are conducted by current industry practice to assess the condition of power transformers. One common method used to assess the mechanical integrity of transformers is Sweep Frequency Response Analysis (SFRA). SFRA testing is performed to detect any changes due to windings or core deformations. However, the main drawback of this method is its offline nature and the requirement for high level of expertise to analyze the results. Several studies have been conducted to address these challenges. One proposed solution in the literature is to employ Current-Voltage (I-V) Lissajous pattern as a fingerprint for operating transformers. the method requires voltage and current measurements at both sides of each phase at the power frequency. Hence, it has the potential to be implemented online whereas, any deviation from the fingerprint raises an alarm for potential internal faults. However, the literature lacks of a comprehensive study on the detailed impact of various faults on the proposed fingerprint. this paper is aimed to investigate the impact of various fault types and levels on the transformer I-V Lissajous pattern. In this regard, simulations have been conducted on a detailed high-frequency power transformer equivalent circuit using PSIM software tool. Results are aimed to provide insights to understand power transformers I-V Lissajous patterns under different faults. With clear understanding, the I-V Lissajous method can be widely applied for other critical assets in the power grid in the future.

关键词： Electric potential Deformation Current measurement Fingerprint recognition Capacitance Maintenance Power transformers Circuit faults Integrated circuit modeling Equivalent circuits

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A Model for Fostering Learning Interaction in Hybrid Classroom based on Constructivism theory

A Model for Fostering Learning Interaction in Hybrid Classro...

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Mohammad Nehal Hasnine Mahmoud Mohamed Hussien Ahmed Hiroshi Ueda Research Center for Computing and Multimedia Studies Hosei University Koganei Japan South Valley University Qena Egypt

ISBN: (纸本)9781665424219

In higher education, the pandemic has caused a dramatic disruption. Due to the disruption, educators are changing their conventional perspectives to reshape the teaching practices. Educators are now relying more on technologies for delivering lecture materials, interaction design, assessment, and engagement detections; and therefore, the conventional active learning models for learning interaction design became hard to implement, particularly in hybrid classrooms. this paper kept three key aspects of education: active learning, learning interaction, and hybrid classroom in the center spots; and explored the pedagogical changes that the higher educational institutions worldwide are experiencing. this article discusses the critical aspects of education under pandemic, including distance learning technologies, learning interaction, hybrid classroom, and learner-centric education. Based on the constructivism learning theory, a model called AL-TST (Active Learning-theory, Strategy, Technology) for increasing learning interaction and bridging the gaps between real space (i.e., in-class learning), virtual space (i.e., electronic learning), and hybridization space (i.e., hybrid learning) is proposed and discussed.

关键词： Electronic learning Pandemics Education Hybrid learning Informatics

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Fill-in-the-blank Questions for Objected-Oriented Programming Education

Fill-in-the-blank Questions for Objected-Oriented Programmin...

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Miyuki Murata Naoko Kato Tetsuro Kakeshita Kumamoto College National Institute of Technology Yatsushiro Japan Ariake College National Institute of Technology Omuta Japan Computing Division Saga University Saga Japan

ISBN: (纸本)9781665424219

Object-oriented technology is important to improve software quality from various perspectives. We have developed pgtracer, a programming education tool that provides fill-in-the-blank questions for the C programming language. By analyzing the data collected by using pgtracer in actual classes, we have obtained useful knowledge for C programming education. In this paper, we develop fill-in-the-blank questions for Java programs to extend pgtracer for object-oriented programming. the fill-in-the-blank question consists of a set of programs and trace tables. A program and a trace table respectively correspond to a Java class and an instance. A trace table contains message sendings between instances, which are important for understanding the behavior of object-oriented programs. Furthermore, we introduce blanks that students do not need to fill. this provides more flexibility in setting the difficulty level while reducing the student workload to fill the blanks. the proposed fill-in-the-blank questions will be used in actual classes, and the collected learning data will be analyzed to understand students' answering behavior and their weak points. the obtained findings are expected to be used to measure the learning outcome and improve object-oriented programming education.

关键词： Java Computer languages Education Software quality Behavioral sciences Object oriented programming Informatics

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Diabetes Foot Ulcer Detection Using Inception V3 Deep Learning Technique

Diabetes Foot Ulcer Detection Using Inception V3 Deep Learni...

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： S. Geeitha Aravinth. S Rishikesh. K Nishanth. J P. Renuka Department of Information Technology M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Diabetes problems like Diabetic Foot Ulcers (DFU) can lead to frequent hospitalizations and costly treatments. Ulcers in the feet caused by diabetes are extremely prevalent. Mellitus adverse effect, and if left untreated, they can lead to amputations. Diabetic feet impact 15-25% of diabetic individuals worldwide. People who suffer from diabetic foot ulcers (DFU) have a more than twice higher death risk than non-ulcerated diabetic individuals. the present investigation offers a new particle swarm optimization approach that combines deep learning to identify and classify diabetic foot ulcers. the described technique's purpose is to recognize and categorize DFU. To do this, the suggested approach generates feature vectors using the Inception V3, Squeeze net, VGG-16, and VGG-19 model. Among these four techniques Inception v3 technique attains highest accuracy. the particle swarm optimization approach optimizes hyper parameter selection for the Inception-v3 model, improving DFU classification results by eliminating the need for human trial-and-error tweaking. the Inception v3 model works well, and our approach outperforms it with an accuracy of 93.3%. DFU is classified utilizing the Deep stacked sparse auto encoder method. this method outperforms other deep learning algorithms, according to thorough experimental results.

关键词： Deep learning Representation learning Accuracy Systematics Medical services Vectors Diabetes pattern recognition Particle swarm optimization Optimization

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Estimation of Abundance and Distribution of Salt Marsh Plants from Images Using Deep Learning

Estimation of Abundance and Distribution of Salt Marsh Plant...

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international conference on pattern recognition

作者： J. Parashar S. M. Bhandarkar J. Simon B. M. Hopkinson S. C. Pennings Institute for AI University of Georgia Athens Georgia USA University of Georgia Athens Georgia USA University of Houston Houston Texas USA

Recent advances in computer vision and machine learning, most notably deep convolutional neural networks (CNNs), are exploited to identify and localize various plant species in salt marsh images. three different approaches are explored that provide estimations of abundance and spatial distribution at varying levels of granularity defined by spatial resolution. In the coarsest-grained approach, CNNs are tasked with identifying which of six plant species are present/absent in large patches within the salt marsh images. CNNs with diverse topological properties and attention mechanisms are shown capable of providing accurate estimations with > 90 % precision and recall for the more abundant plant species and reduced performance for less common plant species. Estimation of percent cover of each plant species is performed at a finer spatial resolution, where smaller image patches are extracted and the CNNs tasked with identifying the plant species or substrate at the center of the image patch. For the percent cover estimation task, the CNNs are observed to exhibit a performance profile similar to that for the presence/absence estimation task, but with an ≈ 5%-10% reduction in precision and recall. Finally, fine-grained estimation of the spatial distribution of the various plant species is performed via semantic segmentation. the DeepLab-V3 semantic segmentation architecture is observed to provide very accurate estimations for abundant plant species, but with significant performance degradation for less abundant plant species; in extreme cases, rare plant classes are seen to be ignored entirely. Overall, a clear trade-off is observed between the CNN estimation quality and the spatial resolution of the underlying estimation thereby offering guidance for ecological applications of CNN-based approaches to automated plant identification and localization in salt marsh images.

关键词： Location awareness Graphical models Semantics Estimation pattern recognition Task analysis Spatial resolution

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Handwritten Mathematical Expression recognition with Self-Attention 21

Handwritten Mathematical Expression Recognition with Self-At...

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Proceedings of the 2021 4th international conference on Algorithms, computing and Artificial Intelligence

作者： Xueke Chi Da-Han Wang Yuefeng Wu Yun Wu Fujian Key Laboratory of Pattern Recognition and Image Understanding China and School of Computer and Information Engineering Xiamen University of Technology China

ISBN: (纸本)9781450385053

Attention-based encoder-decoder models have made great success on handwritten mathematical expression recognition in recent years. However, this kind of method has the problem of attention drift, because under the local attention mechanism based on RNN, the high similarity between coding features can cause attention confusion. To settle this problem, we propose an encoder-decoder model with self-attention, which captures the global information of the feature map and fuses the local information of the CNN as complementary features. Experiments are conducted on the CROHME2014 and CROHME 2016 competition datasets. the experimental results show that, when only using the official training dataset, the proposed method achieves recognition accuracies of 51.98% and 50.74% on the CROHME2014 and CROHME2016 competition datasets, respectively, which outperforms the other methods significantly. the improvements demonstrate the effectiveness of the self-attention module.

关键词： handwritten mathematical expression offline recognition non-local self-attention

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An Analysis of Indian Election Outcomes using Machine Learning

An Analysis of Indian Election Outcomes using Machine Learni...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Harmanjeet Singh Anand Kumar Shukla University Institute of Computing Chandigarh University Mohali

ISBN: (纸本)9781665439718

What other people think" has always been makes us curious while indulge in decision making process. Earlier, many of us asked our personal acquaintances to recommend a political candidate or political party to whom we can vote or consult a broker to decide in which share we should invest our money. With the invent of Internet and world wide web have now let it happen to find out large pool of online users to share recommendation, opinion, views about an event. Although these online users are never our personal acquaintance nor professional critics. An irony of situation is many people are sharing opinions available to completely unknown person via Internet. In the beginning of 21st century, social networking websites taken exponential growth in popularity among online users by providing them a platform for discussion and exchange information. Apart from posting photos and videos for event, now online users are posting their comments, views, opinion about event. With this platform, most researchers are analyzing posts or opinion, performing computational methods to come to conclusion about popularity of event. this analysis part is becoming more powerful day by day to gauge user’s opinion and would play a significant role for someone to make major decision in future. the social microblogging sites such as Pinterest, Tumblr, Apsense, Scoop and specially Twitter are leading pools of online users and contains massive information that can be useful by extracting knowledge from information and that knowledge become asset for business specialist, political specialist, marketing specialist etc.

关键词： Sentiment analysis Social networking (online) Voting Blogs Transfer learning Predictive models Market research

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Identifying Quality Attributes of FODA and DSSA Methods in Domain Analysis using a Case Study

Identifying Quality Attributes of FODA and DSSA Methods in D...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Megha Bhushan Ashok Kumar Piyush Samant Sakshi Bansal Sharad Tiwari Arun Negi School of Computing DIT University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India AIT-CSE Apex Chandigarh University Gharuan Mohali Punjab India NIT Silchar Assam India ECED Vignan Foundation of Science and Technology Andra Pradesh India Deloitte USI Hyderabad India

ISBN: (纸本)9781665439718

the concept of reuse is addressed in software product line engineering (SPLE) by distinguishing between two types of processes for development: application and domain engineering. In domain engineering (DE), the focus is to define and manage all the valid combinations of reusable artefacts participating in the product line (PL) and the relationships between them. Domain analysis (DA) is the activity in DE, which describes the variability and commonalities in a domain. Instead of being applicable to a single software system, DA is applicable to multiple related software systems. Each DA method supports different quality attributes which are preserved while reuse. In this paper, two DA methods namely domain specific software architecture (DSSA) ad feature oriented domain analysis (FODA) are explained to model common and variable requirements of PL(s). A case study on Automated Teller Machine (ATM) is discussed using these two methods to identify quality attributes supported by these methods. this paper also discuss two different DA methods, which allows reusability for the identification, organization and knowledge modelling regarding the domain solution, so as to provide reuse among each element of domain.

关键词： Analytical models Online banking Software architecture Organizations Software systems Market research Software reliability

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