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Home Care Assistance Solution based on a Software Multi-Agents System

Home Care Assistance Solution based on a Software Multi-Agen...

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international conference on Control Systems and Computer Science (CSCS)

作者： Mohammad Rasras Iuliana Marin Şerban Radu Doctoral School of Automatic Control and Computers University POLITEHNICA of Bucharest Bucharest ROMANIA Dept. of Engineering in Foreign Languages University POLITEHNICA of Bucharest Bucharest ROMANIA Dept. of Computer Science University POLITEHNICA of Bucharest Bucharest ROMANIA

A smart home allows automated technology to control systems and appliances to assist daily life activities. In the current work, a prototype of a designed smart home health-aware system is presented. three predefined ruled agents have been used to gather data, communicate between themselves, interpret the data based on several machine learning algorithms to predict abnormality for cardiac conditions. the paper also compares results obtained by running five machine learning algorithms. the agents function in accordance with predefined dataset describing residents ’ age, gender, height, weight, systolic and diastolic blood pressure, cholesterol, and glucose levels, smoking and alcohol consumption habits, and lastly, physical activity. Our plan is to simplify ubiquitous health monitoring for smart home’s residents. the proposed solution aims to monitor parametric measurements to alert health specialists in case of emergencies.

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A Hybrid Deep learning Architecture for Misinformation Detection on Social Media

A Hybrid Deep Learning Architecture for Misinformation Detec...

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IEEE international conference on Information Reuse and Integration (IRI)

作者： Amani Alzahrani Tahani Baabdullah Aeman Almotairi Danda B. Rawat Department of Electrical Engineering and Computer Science Howard University Washington DC USA

Social media has grown to become a popular source of news and information for users around the world. However, the strength of fast dissemination of information to users in diverse places also exposes social media platforms, including Twitter, to the spread of misinformation, such as rumors or false information. automated classification of such news on social media is a challenging task. In this study, we propose a hybrid deep learning model that utilizes a Features-Based model (FB) extracted from two levels: tweet level and user level, combined with pre-trained text embedding models such as Global Vectors for word representation (GloVe) and Universal Sentence Encoders (USE). the models were evaluated on a real-world dataset containing a collection of Twitter rumors and non-rumors. the experimental evaluation results reveal that our hybrid deep-learning model achieves higher accuracy in detecting rumors compared to the baseline learners and previous methods. Further, a hybrid model that combined a features-based model and text embedding model led to improve performance compared to use a single model.

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learning When to Defer to Humans for Short Answer Grading 24th

Learning When to Defer to Humans for Short Answer Grading

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Li, Zhaohui Zhang, Chengning Jin, Yumi Cang, Xuesong Puntambekar, Sadhana Passonneau, Rebecca J. Penn State Univ Dept Comp Sci & Engn State Coll PA 16801 USA Univ Wisconsin Madison Dept Educ Psychol Madison WI USA

ISBN: (纸本)9783031362712;9783031362729

To assess student knowledge, educators face a tradeoff between open-ended versus fixed-response questions. Open-ended questions are easier to formulate, and provide greater insight into student learning, but are burdensome. Machine learning methods that could reduce the assessment burden also have a cost, given that large datasets of reliably assessed examples (labeled data) are required for training and testing. We address the human costs of assessment and data labeling using selective prediction, where the output of a machine learned model is used when the model makes a confident decision, but otherwise the model defers to a human decision-maker. the goal is to defer less often while maintaining human assessment quality on the total output. We refer to the deferral criteria as a deferral policy, and we show it is possible to learn when to defer. We first trained an autograder on a combination of historical data and a small amount of newly labeled data, achieving moderate performance. We then used the autograder output as input to a logistic regression to learn when to defer. the learned logistic regression equation constitutes a deferral policy. Tests of the selective prediction method on a held out test set showed that human-level assessment quality can be achieved with a major reduction of human effort.

关键词： Selective prediction automated short answer grading

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the influence of sampling on imbalanced data classification 8

The influence of sampling on imbalanced data classification

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8th Brazilian conference on intelligent Systems, BRACIS 2019

作者： Barella, Victor H. Garcia, Luís P.F. De Carvalho, André C.P.L.F. ICMC/University of São Paulo São Carlos Brazil CIC/University of Brasilia Brasília Brazil

ISBN: (纸本)9781728142531

Classification tasks using imbalanced data are not challenging on their own. When the classes are linearly separable, a regular classification algorithm usually induces predictive models able to distinguish the classes properly. Imbalanced data poses difficulty for the minority class when the training sets have classes overlapping or a complex border decision. Assessing these characteristics is fundamental to understand the classification task difficulty and to choose adequate pre-processing techniques for imbalanced data. Measures able to identify the complexity of a classification task for a given dataset have been proposed. these measures use different criteria to identify how difficult it is to induce a classifier from a dataset. In this paper, we investigate the use of data complexity measures to estimate the best sample size for data imbalance pre-processing techniques. For such, this paper assesses the predictive performance and the data complexity of real datasets after applying pre-processing techniques using different sample sizes. According to experiments, the data complexity measures are a tool to help in choosing a proper sample size to improve the predictive performance of the classifiers. We also observe that only the difficulty of predicting the minority class is not enough when dealing with sampling. As an alternative to deal with this deficiency, we suggest a combination of the data complexity of both classes. © 2019 IEEE.

关键词： Sampling

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learning from Auxiliary Sources in Argumentative Revision Classification 24th

Learning from Auxiliary Sources in Argumentative Revision Cl...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Afrin, Tazin Litman, Diane Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (纸本)9783031363351;9783031363368

We develop models to classify desirable reasoning revisions in argumentative writing. We explore two approaches - multi-task learning and transfer learning - to take advantage of auxiliary sources of revision data for similar tasks. Results of intrinsic and extrinsic evaluations show that both approaches can indeed improve classifier performance over baselines. While multi-task learning shows that training on different sources of data at the same time may improve performance, transfer-learning better represents the relationship between the data.

关键词： Writing Revision Natural Language Processing

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Emotionally Adaptive intelligent Tutoring System to Reduce Foreign Language Anxiety 24th

Emotionally Adaptive Intelligent Tutoring System to Reduce F...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Ismail, Daneih Hastings, Peter DePaul Univ Chicago IL 60614 USA

ISBN: (纸本)9783031363351;9783031363368

Quality education is associated with two necessary factors: learners' cognitive and emotional states. An adaptive system that takes the emotional state into account can enhance learning. We implemented an emotionally adaptive intelligent tutoring system that detects foreign language anxiety and provides appropriate intervention as needed. Note that the current experiment is still ongoing, and the results reported here are based on the data we currently have. We compared the adaptive approach with fixed feedback strategies. Our preliminary results revealed a statistically significant effect of using adaptive feedback to reduce foreign language anxiety. Also, we found a statistically significant improvement in the learning gains with a moderate effect size when using adaptive feedback.

关键词： Affect detection Foreign language anxiety Machine learning Animated agent Emotional support Adaptive feedback

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Al-based Traffic System: A Novel Approach

Al-based Traffic System: A Novel Approach

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international Middle-East Power System conference, MEPCON

作者： Hesham. A. Sakr A. Yasser E. Elzimity M. Hazim M. Ahmed Y. Gammaz Y. Eid H. Reda S. Nasser A. Nasser A. Bdelnasser M. Meshref M. Hazem Magda I. El-Afifi Dept. of Electronics and Communication Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt

the notion of smart cities has recently gained popularity. We are experiencing traffic congestion as the number of automobiles in metropolitan areas grows. the development of an automated intelligent traffic system is critical. the recommended method is to digitalize roadways and create innovative systems to overcome the difficulties above. As part of this plan, the creation of an intelligent robot equipped with artificial intelligence (AI) and cutting-edge technologies is proposed. the robot would perform traffic management tasks such as verifying drivers’ personal licenses, checking vehicle registration and licenses, identifying wanted individuals, monitoring seat belts and cell phone usage while driving, and enabling electronic payment of highway fees. AI algorithms, image processing techniques, and other tools and procedures would be employed. Image recognition technologies, data analysis, and mobile application development are all areas of expertise.

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Investigation of different machine learning approaches for fault identification in microgrid

Investigation of different machine learning approaches for f...

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international Middle-East Power System conference, MEPCON

作者： Mohamed A. Elsaeed Amir Abdel Menaem Ibrahim I. Mansi Ebrahim A. Badran Electrical Engineering Department Mansoura University Mansoura Egypt Department of Automated Electrical Systems Ural Federal University Yekaterinburg Russia

the growing adoption of microgrids in modern energy systems has underscored the critical need for robust protection mechanisms to ensure their reliability and stability. With the dynamic nature of microgrids resulting from the large integration of renewable energy sources, conventional fault prediction schemes do not provide reliable protection. this paper investigates different protection models based on machine learning approaches: support vector machine (SVM), decision tree (DT), and random forest (RF), which accurately detect the variability in the microgrid operation under normal and fault conditions. A MATLAB Simulink environment is employed to model a comprehensive microgrid system, incorporating PV systems, diesel generators, and diverse loads. Subsequently, RMS values of three-phase voltage and current measurements are seamlessly integrated into a Python codebase as data inputs to train and test the protection models. By employing simulation data, the models based on the three techniques (SVM, DT, and RF) learn to accurately identify the mode of operation, the variations in PV generation, and the occurrence of a fault and its type in the microgrid. the practicability of the proposed schemes is tested on a five-bus AC microgrid. Comparisons among approaches in terms of accuracy and speed of operation results are highlighted. the results show the improved accuracy and adaptability of the machine learning-based protection system compared to conventional methods.

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Improving Accuracy of Machine learning Based Short-Term Load Forecasting Models with Correlation Analysis and Feature engineering

Improving Accuracy of Machine Learning Based Short-Term Load...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Nikita Sergeev Pavel Matrenin dept. of Industrial Power Supply Systems Novosibirsk State Technical University Novosibirsk Russia

Short-term load forecasting is an integral part of the electric power system management and is necessary to ensure the electricity market operation. Load forecasting of individual market participants, especially industrial enterprises, can be particularly challenging due to many external factors that influence the load pattern. these factors may include a change in weather conditions, holidays and days off, economic trends, etc. therefore, there is a need to develop new and more advanced forecasting models that are able to produce adequate predictions of future power consumption under such conditions. this study considers the task of day-ahead load forecasting of a mining and processing plant in Yakutia. the analysis of the exogenous data use impact on its load pattern was carried out. the forecasting models were built using two machine learning methods: decision tree ensembles (Adaptive Boosting, Gradient Boosting and Random Forest) and artificial neural networks (Deep Feedforward Neural Network). To improve the model performance, the following data preprocessing techniques were used: correlation analysis, feature engineering, and intelligent feature selection. the experiments show that the appropriate feature selection can significantly increase the accuracy of the forecasting models. In this case, taking into account the weather data allowed to reduce the average forecasting error by 22% for the neural network model, yielding the best result among the considered models.

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data Augmentation with GAN to Improve the Prediction of At-Risk Students in a Virtual learning Environment 24th

Data Augmentation with GAN to Improve the Prediction of At-R...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Volaric, Tomislav Ljubic, Hrvoje Dominkovic, Marija Martinovic, Goran Rozic, Robert Univ Mostar Trg Hrvatskih Velikana 1 Mostar Bosnia & Herceg Josip Juraj Strossmayer Univ Osijek Fac Elect Engn Comp Sci & Informat Technol Osijek Croatia

ISBN: (纸本)9783031363351;9783031363368

In this paper, we explore the use of data augmentation through generative adversarial networks (GANs) for improving the performance of machine learning models in detecting at-risk students in the context of e-learning institutions. It is well known that balancing datasets can have a positive effect on improving the performance of machine learning models, especially for deep neural networks. However, undersampling can potentially result in the loss of valuable data, so data augmentation seems to be more meaningful solution when the dataset is relatively small. One of the most popular data augmentation approaches is the use of GAN networks due to their ability to generate high-quality synthetic samples that belong to the distribution of the original dataset. On the other hand, detecting at-risk students is a hot topic in learning analytics, and ability to detect these students early with high accuracy enables e-learning institutions to take necessary steps to motivate and retain students during the course. We apply this approach to the OULA dataset, a commonly used dataset in learning analytics that includes labeled at-risk students. the OULA dataset is not highly-imbalanced, making it more challenging to improve model performance through these techniques.

关键词： data augmentation Generative adversarial networks learning analytics Virtual learning environment At-risk students

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