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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Early vs. Late Multimodal Fusion for Recognizing Confusion in Collaborative Tasks 11

Early vs. Late Multimodal Fusion for Recognizing Confusion i...

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11th international conference on Affective Computing and intelligent Interaction (ACIIW)

作者： Ashwath, Anisha Peechatt, Michael Alm, Cecilia Bailey, Reynold Rochester Inst Technol Golisano Coll Comp & Informat Sci Rochester NY 14623 USA Rochester Inst Technol Coll Liberal Arts Rochester NY 14623 USA

ISBN: (纸本)9798350327458

there has been a rapid transformation in the medium of learning and communication due to the pandemic. Multitudes have adopted online video platforms to learn and work from any corner of the world. Emotion detection is vital for understanding how well instructions are communicated through online interactions and for building cognitive systems that can identify human behavior. Confusion is a key emotion that can impact online learning and can be used to verify whether students using an online platform understand the material being taught. Our research expands on previous work regarding confusion detection, focusing on data fusion techniques. We explore the impact of early fusion (feature-level) vs late fusion (decision-level) on modeling confusion identification during a collaborative block building task. Experimenting with different classifiers, our results show that late fusion performs better with larger time windows. this fusion approach can aid in model interpretability.

关键词： data fusion multimodal data affective computing early fusion late fusion

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automated Electrical Waveform Design for Cell-Type Selective Stimulation 11

Automated Electrical Waveform Design for Cell-Type Selective...

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11th international IEEE EMBS conference on Neural engineering (IEEE/EMBS NER)

作者： Goswami, Chaitanya Grover, Pulkit Carnegie Mellon Univ Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781665462921

Being able to selectively stimulate a particular neuron type while not activating other neuron types is key to many proposed treatments for several neurological diseases such as Parkinson's disease. Such selective stimulation has been achieved using optogenetic methods but owing to the hurdles present in the use of optogenetics in humans, there has been recent interest in achieving selectivity using more classical techniques such as current stimulation. Often, approaches for designing selective current stimulations are either model-based, thereby limiting their practical applicability, and/or use trial and error, which allows only a limited exploration of the waveform space. In this work, we propose SelStim, a systematic algorithm for designing electrical current waveforms to selectively stimulate different neuron types using only data. SelStim iteratively designs selective current waveforms by adaptively sampling waveforms with an increasing degree of selectivity. the adaptivity allows SelStim to design selective waveforms in a data-efficient manner. Using data obtained on computational neuron models, we show that SelStim designs current waveforms which achieve a high degree of selectivity in stimulating particular neuron types requiring as few as similar to 200 datapoints. this amounts to a greater than 300% reduction in data requirements compared to a naive brute-force search for achieving similar selectivity.

关键词： Neuromodulation Neural networks Healthcare Artificial Intelligence Machine learning

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Detection of Sleep Apnea: A Comparative Analysis of Advanced Machine learning and Deep learning Approaches 11

Detection of Sleep Apnea: A Comparative Analysis of Advanced...

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11th IEEE international conference on Advances in Computing and Communications, ICACC 2024

作者： Prajitha, M.V. Naveen Sundar, G. Narmadha, D. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350379136

Sleep apnea is a prevalent and serious sleep disorder caused by breathing interruptions during sleep, which is lead to critical health issues like cardiovascular diseases. Traditional diagnostic methods, such as polysomnography, are often very large process, costly, and not easily accessible, highlighting the need for automated and affordable detection solutions. this review paper examines the utilization of machine learning (ML) and deep learning (DL) methods for detecting and diagnosing sleep apnea. We offer a thorough overview of the current research, analyzing various studies that utilize different data types, including electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmography (PPG) signals. the review assesses the effectiveness of different algorithms, feature extraction techniques, and the integration of multiple signal types to improve detection accuracy. By summarizing key developments and identifying existing challenges, we aim to outline future research directions to enhance the robustness, interpretability, and real-time identification of sleep apnea using machine learning and deep learning techniques. © 2024 IEEE.

关键词： Sleep research

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Prediction of Reference Evapotranspiration Using Neural Networks 11

Prediction of Reference Evapotranspiration Using Neural Netw...

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11th international conference on Electrical, Electronic and Computing engineering, IcETRAN 2024

作者： Milić, Miljana Jeremić, Miljan Milojković, Jelena Mirković, Milena Stanojlović University of Niš Faculty of Electronic Engineering Niš Serbia

ISBN: (纸本)9798350386998

the goal of the study is the investigation of artificial neural networks (ANNs) for the estimation of reference evapotranspiration (ETo) at the meteorological station Niš, Serbia for twelve representative months of a year. datasets from twenty-five meteorological stations were employed. Available input data were mean temperature values (Tmean), sunshine hours (N), vapor pressure deficit (es-ea), and wind speed (u2). Models were tested and compared against the corresponding FAO-56 Penman-Monteith (FAO-PM) via statistical indices. Based on the literature standards, the results indicate that even ANNs with simple architecture can be very good predictive model of ETo for the city of Niš. © 2024 IEEE.

关键词： intelligent systems

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A Novel Load Transfer Method for Distribution Systems Based on Reinforcement learning 11

A Novel Load Transfer Method for Distribution Systems Based ...

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11th China international conference on Electricity Distribution, CICED 2024

作者： Zhang, Pei Chang, Weimeng Li, Changcheng School of Electrical and Information Engineering Tianjin University Tianjin China School of Electrical Engineering Guangxi University Nanning China

ISBN: (纸本)9798350368345

When a fault occurs in a radial distribution system, the lost load should be transferred to the non-fault branches for service restoration. However, current load transfer methods are difficult to develop the optimal load transfer scheme in a short time. this paper proposes a novel load transfer method for distribution systems based on the reinforcement learning algorithm. Firstly, the process of load transfer is analyzed and formulated as the Markov decision model. Secondly, the deep reinforcement learning is applied into the problem of load transfer. the interaction between the intelligent agent and the distribution system is modeled with the objective of maximizing the restored load. Key constraints such as voltage, current and radial topology are considered. Finally, the effectiveness of the proposed method is verified based on the distribution system of an urban power grid. © 2024 IEEE.

关键词： Adversarial machine learning

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Investigating Self-supervised learning for Predicting Stress and Stressors from Passive Sensing 11

Investigating Self-supervised Learning for Predicting Stress...

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11th international conference on Affective Computing and intelligent Interaction (ACIIW)

作者： Haresamudram, Harish Suh, Jina Hernandez, Javier Butler, Jenna Chaudhry, Ahad Yang, Longqi Saha, Koustuv Czerwinski, Mary Georgia Inst Technol Atlanta GA 30332 USA Microsoft Redmond WA USA Univ Illinois Champaign IL USA Microsoft Res Redmond WA USA

ISBN: (纸本)9798350327458

the application of machine learning (ML) techniques for well-being tasks has grown in popularity due to the abundance of passively-sensed data generated by devices. However, the performance of ML models are often limited by the cost associated with obtaining ground truth labels and the variability of well-being annotations. Self-supervised representations learned from large-scale unlabeled datasets have been shown to accelerate the training process, with subsequent fine-tuning to the specific downstream tasks with a relatively small set of annotations. In this paper, we investigate the potential and effectiveness of self-supervised pre-training for well-being tasks, specifically predicting both workplace daily stress as well as the most impactful stressors. through a series of experiments, we find that self-supervised methods are effective when predicting on unseen users, relative to supervised baselines. Scaling both data size and encoder depth, we observe the superior performance obtained by self-supervised methods, further showcasing their utility for well-being applications. In addition, we present future research directions and insights for applying self-supervised representation learning on well-being tasks.

关键词： self-supervised learning workplace stress prediction well-being

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BRIDGING thE GAP TO REAL-WORLD OBJECT-CENTRIC learning 11

BRIDGING THE GAP TO REAL-WORLD OBJECT-CENTRIC LEARNING

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11th international conference on learning Representations, ICLR 2023

作者： Seitzer, Maximilian Horn, Max Zadaianchuk, Andrii Zietlow, Dominik Xiao, Tianjun Simon-Gabriel, Carl-Johann He, Tong Zhang, Zheng Schölkopf, Bernhard Brox, thomas Locatello, Francesco Max-Planck Institute for Intelligent Systems Tübingen Germany Amazon Web Services United States Department of Computer Science ETH Zürich Switzerland

Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world. Allowing machine learning algorithms to derive this decomposition in an unsupervised way has become an important line of research. However, current methods are restricted to simulated data or require additional information in the form of motion or depth in order to successfully discover objects. In this work, we overcome this limitation by showing that reconstructing features from models trained in a self-supervised manner is a sufficient training signal for object-centric representations to arise in a fully unsupervised way. Our approach, DINOSAUR, significantly out-performs existing image-based object-centric learning models on simulated data and is the first unsupervised object-centric model that scales to real-world datasets such as COCO and PASCAL VOC. DINOSAUR is conceptually simple and shows competitive performance compared to more involved pipelines from the computer vision literature. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： learning algorithms

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Proceedings - 11th international conference on Information Technology, Computer and Electrical engineering, ICITACEE 2024

Proceedings - 11th International Conference on Information T...

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11th international conference on Information Technology, Computer and Electrical engineering, ICITACEE 2024

ISBN: (纸本)9798350389289

the proceedings contain 59 papers. the topics discussed include: Fjord Phantom malware process and execution analysis in mobile banking application;Internet of things network handover for hydroponic cultivation;PGV: an edge operator based random point localization algorithm for eye tracking system;the impact of online video streaming on customer engagement in social commerce;an automatic video timestamp technique for monitoring moving objects using YOLOv5;task scheduling for energy efficiency in data center: machine learning approach;design of Cebuano-English automatic signal recognition system using Hidden Markov models;study of fuel save controller (FSC) system in renewable energy: a case study Wangi-Wangi, Wakatobi, Indonesia;and solar panel installation analysis: a case study Wangi-Wangi, Wakatobi, Indonesia.

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Advancing Android Malware Detection with BioSentinel Neural Network using Hybrid Deep learning Techniques 18

Advancing Android Malware Detection with BioSentinel Neural ...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Rani, D. Sandhya Gnaneshwar, K. Sampurnima Pattem, K. Sekhar, Soma Krishna, G. Bala Kakarla, Shirisha Cvr College of Engineering Department of Computer Science Engineering Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engineering-Cyber Security Hyderabad India Vardhaman College of Engineering Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

the paper introduces the BioSentinel Neural Network (BSNN), a novel hybrid deep learning model designed to enhance malware detection, particularly focusing on zero-day threats. the BSNN model integrates diverse neural network architectures, leveraging Graph Neural Networks (GNNs) for feature extraction and Transformers for sequential data analysis. It demonstrates significant improvements in accuracy (93.16%), precision (90.89%), recall (88.19%), and F1-score (90.45%) over traditional methods. the model exhibits a high detection rate of 88% for zero-day malware from 1000 samples tested and shows computational efficiency with an average inference time of 30 milliseconds. this research addresses critical gaps in current malware detection approaches, highlighting the potential of hybrid deep learning techniques in the cybersecurity domain. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cybersecurity Deep learning Hybrid Neural Networks Malware Detection Real-Time Analysis Zero-Day threats

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TRANSFORMER-BASED MODEL FOR SYMBOLIC REGRESSION VIA JOINT SUPERVISED learning 11

TRANSFORMER-BASED MODEL FOR SYMBOLIC REGRESSION VIA JOINT SU...

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11th international conference on learning Representations, ICLR 2023

作者： Li, Wenqiang Li, Weijun Sun, Linjun Wu, Min Yu, Lina Liu, Jingyi Li, Yanjie Tian, Songsong Institute of Semiconductors Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China School of Integrated Circuits University of Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China

Symbolic regression (SR) is an important technique for discovering hidden mathematical expressions from observed ***-based approaches have been widely used for machine translation due to their high performance, and are recently highly expected to be used for *** input the data points, then output the expression skeleton, and finally optimize the ***, recent transformer-based methods for SR focus more attention on large scale training data and ignore the ill-posed problem: the lack of sufficient supervision, i.e., expressions that may be completely different have the same supervision because of their same skeleton, which makes it challenging to deal with data that may be from the same expression skeleton but with different ***, we present a transformer-based model for SR with the ability to alleviate this ***, we leverage a feature extractor based on pure residual MLP networks to obtain more information about data ***, the core idea is that we propose a joint learning mechanism combining supervised contrastive learning, which makes features of data points from expressions with the same skeleton more similar so as to effectively alleviates the ill-posed *** benchmark results show that the proposed method is up to 25% higher with respect to the recovery rate of skeletons than typical transformer-based ***, our method outperforms state-of-the-art SR methods based on reinforcement learning and genetic programming in terms of the coefficient of determination (R2). © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Genetic programming

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