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Predicting Co-occurring Emotions from Eye-Tracking and Interaction data in MetaTutor 1

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22nd international conference on Artificial Intelligence in Education (AIED) - Mind the Gap - AIED for Equity and Inclusion

作者： Lalle, Sebastien Murali, Rohit Conati, Cristina Azevedo, Roger Univ British Columbia Vancouver BC Canada Univ Cent Florida Orlando FL 32816 USA

ISBN: (数字)9783030782924

ISBN: (纸本)9783030782924;9783030782917

Emotions in intelligent Tutoring Systems (ITS) are often modeled as single affective states, however there is evidence that emotions co-occur during learning, with implications for affect-aware ITS that need to have a comprehensive understanding of a student's affective state to react accordingly. In this paper we broaden the evidence that emotions co-occur in an educational context, and present a first attempt to predict these co-occurrences from data, using the MetaTutor ITS as a test-bed. We show that boredom+frustration, as well as curiosity+anxiety, frequently co-occur in MetaTutor, and that we can predict when these emotions co-occur significantly better than a baseline using eye-tracking and interaction data. These findings provide a first step toward building affect-aware ITS that can adapt to these complex co-occurring affective states.

关键词： Co-occurring emotions Eye-tracking Logs Classification

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A Novel Public database of Lymphoma for Whole Body Diffusion-Weighted MRI 22

A Novel Public Database of Lymphoma for Whole Body Diffusion...

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22nd international conference on Cyberworlds, CW 2023

作者： Ferjaoui, Radhia Boujnah, Sana Kraiem, Nour El Houda Kraiem, Tarek Khalifa, Anouar Ben Istmt Tunis Tunisia University of Tunis El Manar Tunisia National Engineering School of Tunis Tunisia Hospital Aziza Othmana of Tunis Radiology Department Tunis Tunisia King Abdulaziz Hospital Specialist Doctor Taif Saudi Arabia Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef7100 Tunisia

ISBN: (纸本)9798350315653

Lymphoma affects the human lymphatic system, it has become one of the leading causes of patient deaths. Hence, accurate diagnosis is essential to help doctors prescribe a suitable treatment. In this work, we propose a new database that contains 50 volunteer patients treated for lymphoma on Whole Body (head/neck, chest, abdomen, and pelvis regions). These collected databases contain some MRI sequences and medical information of patients which tells whether that lesion is evolutive lymphoma or residual masses. It is mainly composed of 100000 images on axial, coronal, and sagittal plans. To highlight the utility of this dataset, we used a computer-aided diagnosis (CAD) system to recognize evolutive lymphoma from residual masses. This recognition is very important from a medical point of view, as it helps identify patients who may need additional therapy. After successful steps of database preparation, features extraction and selection, we evaluated several Machine learning models such as Random Forest, Decision Tree, Naive Bayes, Extreme Gradient Boost, Logistic Regression, K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM). The metrics of Accuracy, Precision, Sensitivity, Specificity, confusion matrix, Positive Predictive Value, Negative Predictive Value, Fl-score, missed classification, and Recall was measured to evaluate our CAD system based on AI models. The best obtained results reach 95% accuracy for SVM with RBF kernel. In addition, the proposed approach was compared to three literature works, and it gives much better results that can correctly recognize more than 64 lesions out of 67 cases. Interested researchers could contact the author to acquire the database. © 2023 IEEE.

关键词： Classification (of information)

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Research of Gesture Recognition Algorithm Based on Acceleration Trajectory Image 2

Research of Gesture Recognition Algorithm Based on Accelerat...

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2nd international conference on Networking Systems of AI, INSAI 2022

作者： Zhu, Yaling Zheng, Gang Li, Xiangwei College of Software Engineering Lanzhou Institute of Technology GanSu Lanzhou China

ISBN: (纸本)9781665491785

According to the characteristics of neural network computing, this paper designs a neural network based on acceleration for gesture detection. First, the acceleration information is collected by using the acceleration sensor to extract the key point information, and convert the effective data into the acceleration track image data. Two neural networks with depth distribution of 50 and 101 are built and trained by ResNet algorithm. Matching image data features to obtain models with accuracy rates of 85% and 91% respectively, so as to achieve higher gesture recognition accuracy with less computational time and storage space complexity. © 2022 IEEE.

关键词： Gesture recognition

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Processing TIL-Script Constructions for Supervised Machine learning with Symbolic Representation 22

Processing TIL-Script Constructions for Supervised Machine L...

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22nd conference Information Technologies - Applications and Theory, ITAT 2022

作者： Michalovský, Tomáš Menšík, Marek Albert, Adam Silesian University in Opava Bezručovo nám. 13 Opava746 01 Czech Republic VSB - Technical University of Ostrava 17. listopadu 15 Ostrava Czech Republic

This paper deals with the processing of TIL-Script constructions. TIL-Script constructions are a computational variant of the Transparent Intensional Logic (TIL) constructions. In this paper, two main directions are addressed. The first area is the use of TIL-Script in supervised machine learning with a symbolic representation of facts in a search for explications of atomic concepts. Subsequently, the paper discusses the modifications that were necessary for use in the spatial data processing. The paper describes the theory necessary to understand TIL theory, Supervised machine learning, formal conceptual analysis, and spatial data processing issues specified in the TIL-Script language. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： data handling

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Leaf Lens: An intelligent Vision for Plant Disease Diagnosis using Deep learning

Leaf Lens: An Intelligent Vision for Plant Disease Diagnosis...

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Artificial Intelligence and Machine learning Applications Theme: Healthcare and Internet of Things (AIMLA), international conference on

作者： P. Yogendra Prasad M. Ramu Sanjamala Reshma Rayachoty Chandana Priya Pasupuleti Anusha B Likhith Reddy Dept of CSSE Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Dept of CSE Annamacharya Institute of Technology & Sciences Tirupati India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

In India, where 70% of the population is involved in agriculture, accurate recognition of botanical disorders is crucial to minimize crop losses. Manual monitoring of these diseases requires significant labor, expertise in plant pathology, and time investment. This paper presents a methodology for identifying plant diseases by analyzing leaves' images. Image processing extracts valuable properties and information from the images, while deep learning, [3] a part of artificial intelligence, which simplifies data preprocessing. The algorithms used can process unstructured data, including text and images, automating feature extraction and diminishing the dependance on human professionals. This automated approach facilitates informed decision-making and precise predictions based on extensive training data. The paper introduces an innovative approach to plant disease detection through deep learning, specifically using the VGG19 model, which can scrutinize intricate patterns in plant images, providing a reliable method for identifying diseases.

关键词： Deep learning Plant diseases Image recognition Computational modeling Training data Feature extraction Agriculture

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Reliability Enhancement of Hardware Trojan Detection using Histogram Augmentation Technique

Reliability Enhancement of Hardware Trojan Detection using H...

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international conference on VLSI Design

作者： Vaishnavi Sankar Balachander S Nirmala Devi M Jayakumar M Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

The growing complexity of integrated circuit (IC) design and constrained time to market make the IC supply chain spanned globally, involving multiple untrusted parties. The inclusion of malicious modules known as hardware Trojans (HT) can be established at any phase of IC design and manufacturing. Diversity in threat conditions and Trojan designs evolve continuously. However, the number of manually learned features limits conventional hardware Trojan detection (HTD) schemes using static features. On the other hand, machine learning allows the incorporation of various structural and functional features for enhanced Trojan detection. However, the lack of sufficient training datasets, design-specific bias, and class imbalance pose the most significant challenges to using ML algorithms for HT detection and significantly impact the detection performance. The proposed methodology develops a histogram-based augmentation (HAT) technique that generates data following the feature distribution of the original dataset. Furthermore, the scheme ensures the generation of reliable synthetic data. Experimental results on synthetic Trust-HUB datasets show that the presented technique achieves an average of 99.06 % true positive rate, 97.52 % true negative rate, 98.08% f-score, and 98.14% accuracy for the 13 circuits under test.

关键词： Training Supply chains Time to market Machine learning Very large scale integration Feature extraction Hardware

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Research on intelligent Sorting System

Research on Intelligent Sorting System

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international conference on Communication Technology (ICCT)

作者： Yanghong Mao Chuixin Chen Department of computer science and Engineering Guangzhou Institute of Science and Technology Guangzhou China Department of intelligent manufacturing and electrical engineering Guangzhou Institute of Science and Technology Guangzhou China

In order to achieve the goal that the manipulator can automatically obtain any position within its working range, and can adjust the end actuator for grasping, a vision sorting system is designed. The system passes through image processing, target recognition and positioning, and finally realizes the sorting effect through end control. The acquired image is enhanced and edge detected. Aiming at the problem that the deep learning algorithm takes a long time, The traditional image processing algorithm and deep learning target detection algorithm are organically combined to reduce the use delay of image processing in the system. Because the training of deep learning model requires a large number of data sets, but the amount of data in manufacturing factories is small, and data labeling is time-consuming and laborious, YOLO v3 algorithm is used to improve the efficiency of training model under a small number of labeled data sets. In the algorithm of position, the centroid is extracted as the positioning reference coordinate, the kinematics model of the manipulator is established. and sent to STM32 through the serial port. Then the STM32 completes the control command to grasp and place the workpiece and complete the sorting task. The experimental results show that the system meets the design requirements.

关键词： Deep learning Training Target recognition Machine vision Grasping Manipulators Production facilities

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Advancing Gait Analysis in Physiotherapy: Sensor-Based Insights from the Ten Meter Walk Test

Advancing Gait Analysis in Physiotherapy: Sensor-Based Insig...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Maykol Santos Paulo Jorge Coelho Ivan Miguel Pires Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal School of Technology and Management Polytechnic of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra Coimbra Portugal

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

The Ten Meter Walk Test is a mobile application designed to improve gait analysis in physiotherapy, particularly for older adults. The application uses sensor-based data collection from wearable *** devices attached to shin pads to collect comprehensive lower limb movement data. data was collected from 90 older adults aged 65–93 years. Machine learning techniques were applied to classify participant characteristics using Neural Networks, AdaBoost, Naive Bayes, and Decision Trees. Key findings showed up to 89% accuracy in gender classification, improved accuracy in disease classification to a binary task, and perfect accuracy in predicting walking aid usage. The study highlights the potential of integrating wearable sensors and mobile technology in clinical practice to enhance gait analysis.

关键词： Meters Limbs Accuracy Neural networks XML Machine learning Mobile applications Older adults Gait recognition Wearable sensors

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Directing Mutual Informative Active learning Model to Perform Panoptic Segmentation in Lung Images 22

Directing Mutual Informative Active Learning Model to Perfor...

引用

22nd OITS international conference on Information Technology, OCIT 2024

作者： Jyothsna Devi, Koppagiri Mandal, Gouranga School of Computer Science and Engineering VIT-AP University Andhra Pradesh 522237 India

ISBN: (纸本)9798331510404

Panoptic segmentation automatically delineates particular anatomical regions of interest and facilitates numerous subsequent tasks like shape analysis and volume measurement;image segmentation is crucial to medical image analysis. In particular, the performance of segmentation algorithms has been significantly improved by the recent rapid development of deep learning techniques, which effectively use massive volumes of labelled data to build complex models. Nevertheless, a significant barrier to the application of learning-based techniques for medical images may be the challenge of acquiring manual labels for training. Researchers have looked into mutual information-based learning strategies to loosen the labelling criteria to solve this issue. Researchers outline the fundamental concepts of deep learning-based segmentation along with some of the most advanced methods available today, arranged according to the type of supervision. Given a particular segmentation objective and dataset, we aim to offer the reader some potential options for model selection, training approaches, and data modification using the Mutual informative active learning (MIALA) model for lung image segmentation. To build a better trade-off, the functionality of the proposed model is compared with other existing alternatives. © 2024 IEEE.

关键词： Image correlation

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The Prediction of Global Spending Trends on Security Using Holt-Winters Model

The Prediction of Global Spending Trends on Security Using H...

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international Symposium on intelligent Systems and Informatics (SISY)

作者： Eva Beke László Hanka Bánki Donát Faculty of Mechanical and Safety Engineering Óbuda University Budapest Hungary

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

Over the years, threat actors have grown in their capabilities and diversified in their attack methods, increasing the ingenuity and pervasiveness of their threats. The massive data generation in modern-day digital interactions has enabled cyber security teams to amass large data to hand over to machine learning models for discerning patterns. As artificial intelligence (AI) becomes increasingly integrated into security frameworks, organizations face the dual challenge of mitigating AI-related threats while optimizing their security expenditure. This research delves into the complexities involved in managing these risks effectively. It explores the impact of emerging AI threats within cybersecurity landscapes and other relevant fields. It discusses the critical importance of efficiently forecasting the growth of allocated resources to bolster security measures. By emphasizing a balanced approach, this paper aims to provide insights into the intricate interplay between AI threats and security spending, offering a forecasting mathematical model to navigate this dynamic landscape in the future.

关键词： Training data privacy Face recognition Time series analysis Predictive models Market research Mathematical models data models Forecasting Computer crime

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