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automated Student Emotion Analysis During Online Classes Using Convolutional Neural Network 1

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11th international conference on Soft Computing for Problem Solving, SocProS 2022

作者： Mukherjee, Sourish Suhakar, Bait Yash Kamma, Samhitha Barukula, Snehitha Agarwal, Purab Singh, Priyanka Department of Computer Science and Engineering SRM University-AP Amaravati522502 India

ISBN: (数字)9789811965258

ISBN: (纸本)9789811965241

In non-verbal communication, facial emotions play a very crucial role. Facial recognition can be useful in various ways, such as understanding people better and using the collected data in various fields. In an e-learning platform, students’ facial expressions determine their comprehension levels. Students’ facial emotions can have a favorable or unfavorable impact on their academic performance. As a result, instructors need to create a positive, emotionally secure classroom environment to optimize student learning. In this paper, a novel Facial Emotion Recognition for improving our understanding of students during e-learning is proposed. Suggested model detects different students’ facial emotions such as anger, disgust, fear, happiness, sadness, surprise, and neutral and utilizing them for better teaching and learning during a lecture in an e-learning platform. Convolutional neural networks (CNNs) have been used for detecting facial emotions of students in e-learning platforms, and the proposed model shows an outcome of test accuracy of 67.5%. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Fuzzy Linguistic Summaries for Explaining Online Semi-Supervised learning 11

Fuzzy Linguistic Summaries for Explaining Online Semi-Superv...

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11th IEEE international conference on intelligent Systems, IS 2022

作者： Kaczmarek-Majer, Katarzyna Casalino, Gabriella Castellano, Giovanna Leite, Daniel Hryniewicz, Olgierd Polish Academy of Sciences Systems Research Institute Warsaw Poland University of Bari Aldo Moro Computer Science Dept. Bari Italy Universidad Adolfo Ibáñez Faculty of Engineering and Science Santiago Chile

ISBN: (纸本)9781665456562

intelligent systems for the medical domain often require processing data streams that evolve over time and are only partially labeled. At the same time, the need for explanations is of utmost importance not only due to various regulations, but also to increase trust among systems' users. In this work, an online data-driven learning method with focus on the explainability of evolving models equipped with incremental semi-supervised learning algorithms is considered. the proposed method combines: (i) the Dynamic Incremental Semi-Supervised Fuzzy C-Means (DISSFCM) algorithm to incrementally classify subsets of data;with (ii) Linguistic Summarization, which provides explanations of the classification results in terms of short sentences in a natural language. the approach has been illustrated for streaming data collected from voice calls of patients affected by Bipolar Disorder. the results show the effectiveness of the proposed method in classifying instances belonging to healthy and affective states, and explaining the approximate reasoning behind the classification of new acoustic data related to patients. © 2022 IEEE.

关键词： intelligent systems

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Improving Obfuscated Malware Classification: A Machine learning Approach with Feature Selection and data Balancing 11

Improving Obfuscated Malware Classification: A Machine Learn...

引用

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

作者： Bhavana, Gundu Pudota, Mokshagna Bhuvan Chowdary Saravanan, S. Deepak, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379136

Obfuscated malware refers to malicious software that has been deliberately modified to hide its malicious purpose and avoid detection by conventional security mechanisms. Classifying obfuscated malware poses a significant challenge due to its deceptive nature and the dynamic techniques used by attackers to obfuscate code. Existing approaches in malware analysis often struggle to accurately classify obfuscated malware, leading to potential security vulnerabilities and data breaches. In this research, we investigate the application of machine learning algorithms for malware analysis, specifically focusing on multi-class classification of obfuscated malware using the CIC-MalMem-2022, a latest dataset. We apply data preprocessing techniques, including Synthetic Minority Over-sampling Technique (SMOTE) and normalization, to enhance the dataset's balance and consistency. Subsequently, we train and evaluate various machine learning algorithms, including Random Forest (RF), XGBoost, K-Nearest Neighbors (KNN), LightGBM, and Decision Trees (DT), on different datasets comprising SMOTE, normalized, and SMOTE+normalized instances. Our study also explores the effect of feature selection process on the performance of the model. We identify the top 35 features for optimal accuracy, addressing the dimensionality challenge inherent in large-scale datasets. Our findings reveal that the Random Forest (RF) model achieves the highest accuracy of 96.23% on the dataset that is balanced by SMOTE with top 35 features. © 2024 IEEE.

关键词： Adversarial machine learning

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Design of UAV-Based Information Acquisition and Environmental Monitoring System for Dangerous Goods Warehouse 11th

Design of UAV-Based Information Acquisition and Environmenta...

引用

11th international conference on Traffic and Transportation Studies, ICTTS 2024

作者： Yao, Jiachen Gao, Xinyu Liu, Huaqiong Department of Transportation and Logistics Engineering Shandong Jiaotong University Jinan250300 China

ISBN: (纸本)9789819796434

Due to the special nature of the dangerous goods warehouse itself, for safety reasons, the monitoring requirements for the dangerous goods storage environment are more stringent than those for ordinary warehouses. However, the internal environment of the dangerous goods warehouse is highly hazardous to human beings, prone to personal injury and death accidents, and not suitable for personnel on-site task operations. this paper intends to combine the characteristics of UAV space flight flexibility, easy to carry ancillary equipment, as well as the wireless transmission of data from wireless sensor networks, low power consumption and other characteristics, the application of UAV technology, Internet of things technology, research and design a set of UAV cargo information collection and environmental monitoring system solutions suitable for dangerous goods warehouses, to carry out real-time recording and monitoring of a variety of environmental parameters for the dangerous goods in the storage of the warehouse, and realize the intelligent collection of dangerous goods information and real-time monitoring and management of environmental parameters in dangerous goods warehouses, thereby improving the safety of dangerous goods storage, and at the same time improving the efficiency of storage inventory and inspection and the intelligent level of environmental monitoring. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Zigbee

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data-Driven Analysis of Bicycle Lane Safety in Mexican Cities: Towards a Real-Time Route Recommendation System for Cyclists 11

Data-Driven Analysis of Bicycle Lane Safety in Mexican Citie...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Domínguez-Báez, Carlos Alberto Mendoza-González, Ricardo Luna-García, Huizilopoztli Tecnológico Nacional de México IT Aguascalientes Av. Adolfo López Mateos 1801 Ote. Aguascalientes Mexico Universidad Autónoma de Zacatecas Ramón López Velarde 801 Zacatecas Mexico

ISBN: (纸本)9789897587450

this study initiated a project to identify urban cycling routes with a focus on cyclist safety in Mexican cities. A data-Driven Analysis approach was implemented to map the riskiest and safest cycling routes by analysing traffic accident data from national, state, and local datasets. the accident hotspots were visually integrated into the urban map of Guadalajara city (Jalisco, Mexico), to identify high-risk zones for cyclists. the integration of diverse data sources and geospatial analysis allowed for an accurate characterization of accident patterns, providing a clear identification of critical areas. Key results from this initial stage of the project included an accurate risk-zones identification, a replicable methodology for data integration, and a first approach to developing algorithms for cyclist accident analysis. these preliminary findings hold promise for enhancing urban cycling safety and supporting urban eco-mobility strategies in Mexican cities. Additionally, the results served as a foundation for future exploration of machine learning techniques to refine data processing and develop a real-time safe bicycle lane recommender prototype aimed at guiding cyclists toward safer alternatives. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Recommender systems

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Deep Reinforcement learning-based Computing Offloading and Resource Allocation Algorithm in Internet of Vehicles 11

Deep Reinforcement Learning-based Computing Offloading and R...

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2023 IEEE 11th international conference on Information, Communication and Networks, ICICN 2023

作者： Yuan, Yiping Chen, Xin Jiao, Libo Wang, Yijie Beijing Information Science & Technology University School of Control Science and Engineering Beijing China Beijing Information Science & Technology University School of Computer Science Beijing China

ISBN: (纸本)9798350314014

With the rapid emergence of the Internet of Vehicles (IoV) and 6G networks, the exponential growth in data traffic has presented a formidable challenge to the limited computational capabilities of in-vehicle devices. To overcome this challenge, mobile edge computing (MEC) offers a viable solution by offloading computationally intensive tasks to the edge servers. In this study, we design a novel joint optimization approach that leverages deep reinforcement learning (DRL) to inform decisions on computational offloading, energy consumption, and resource allocation. Our proposed scheme considers the involvement of both the base station (BS) and road side units (RSUs) in providing computing resources to the user while simultaneously minimizing energy consumption and reducing latency of computational tasks. Our simulations unequivocally establish the efficacy of the Deep Reinforcement learning-based computing offloading and resource allocation (DCORA) algorithm. the proposed DCORA algorithm outperforms alternative baseline schemes by approximately 15% in direct comparison. © 2023 IEEE.

关键词： Reinforcement learning

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A Comparative Analysis on Fashion Cloth Feature Extraction Methods using Text and Image data 11

A Comparative Analysis on Fashion Cloth Feature Extraction M...

引用

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

作者： Attoor, Lemya Sainudeen Chitra, R. Anusha Bamini, A.M. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350379136

Modern methods of extracting relevant features from images and texts are crucial to the recovery of fashionable garments. In this research, we compare and contrast a number of different approaches to extracting features from images and text. We utilize Generative Pre-trained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) to efficiently process and represent textual descriptions of fashion products for text feature extraction. this allows us to take advantage of GPT's generative capabilities and contextual understanding. In order to extract features from photographs of fashion sketch images, we use a mix of Residual Networks (ResNet) and Visual Geometry Group (VGG) networks. this allows us to pick up complex visual patterns and details. Our method is designed to enhance the accuracy of text and image matching by combining the best features of these state-of-the-art models. Accuracy, recall, F1-score, and retrieval efficiency are some of the assessment metrics that were examined on a wide dataset that included different types of fashion clothing. the hybrid model improves feature extraction capabilities, producing robust and discriminative text and picture representations, according to preliminary findings. More intelligent and user-friendly retrieval systems in digital fashion platforms and e-commerce might be possible thanks to the study's useful insights into the possibilities of advanced deep learning architectures in the field of fashion product retrieval. © 2024 IEEE.

关键词： Image matching

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D-LeDe: A data Leakage Detection Method for Automotive Perception Systems 11

D-LeDe: A Data Leakage Detection Method for Automotive Perce...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Babu, Md Abu Ahammed Pandey, Sushant Kumar Durisic, Darko Koppisetty, Ashok Chaitanya Staron, Miroslaw Research and Development Volvo Car Corporation Gothenburg Sweden Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands Department of Computer Science and Engineering University of Gothenburg Chalmers University of Technology Gothenburg Sweden

ISBN: (纸本)9789897587450

data leakage is a very common problem that is often overlooked during splitting data into train and test sets before training any ML/DL model. the model performance gets artificially inflated with the presence of data leakage during the evaluation phase which often leads the model to erroneous prediction on real-time deployment. However, detecting the presence of such leakage is challenging, particularly in the object detection context of perception systems where the model needs to be supplied with image data for training. In this study, we conduct a computational experiment to develop a method for detecting data leakage. We then conducted an initial evaluation of the method as a first step on a public dataset, "Kitti", which is a popular and widely accepted benchmark dataset in the automotive domain. the evaluation results show that our proposed D-LeDe method are able to successfully detect potential data leakage caused by image similarity. A further validation was also provided to justify the evaluation outcome by conducting pair-wise image similarity analysis using perceptual hash (pHash) distance. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Leak detection

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Study on the Self-Explosion Defect Detection Methods in Glass Insulators Based on Modified YOLOv7 5

Study on the Self-Explosion Defect Detection Methods in Glas...

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5th IEEE international conference on Power, intelligent Computing and Systems, ICPICS 2023

作者： Yifan, Huo Likui, Yi School of Electric Power Shenyang Institute of Engineering Liaoning Shenyang China

ISBN: (纸本)9798350333442

With the increasing utilization rate of glass insulators in transmission lines, in order to adapt to the current mode of unmanned aerial vehicle (UAV) -assisted inspection, a modified YOLOv7 glass insulator self-explosion defect detection method is proposed to meet the demand for glass insulator defect detection in the process of transmission line inspection. ST attention mechanism was introduced, and its module was added to the 11th layer of YOLOv7 network model to strengthen the detection effect of small targets and incomplete insulators. the coordinate loss function was modified, the formula of SIOU loss function was defined, and the position of detection box was corrected. Some glass insulator images in the data set CPLID and the glass insulator images taken by the UAV are used to train the modified network. the detection results show that the detection accuracy and speed are significantly modified. © 2023 IEEE.

关键词： Defects

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learning CONTROLLABLE ADAPTIVE SIMULATION FOR MULTI-RESOLUTION PHYSICS 11

LEARNING CONTROLLABLE ADAPTIVE SIMULATION FOR MULTI-RESOLUTI...

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11th international conference on learning Representations, ICLR 2023

作者： Wu, Tailin Maruyama, Takashi Zhao, Qingqing Wetzstein, Gordon Leskovec, Jure Stanford University United States NEC Corporation United States

Simulating the time evolution of physical systems is pivotal in many scientific and engineering problems. An open challenge in simulating such systems is their multi-resolution dynamics: a small fraction of the system is extremely dynamic, and requires very fine-grained resolution, while a majority of the system is changing slowly and can be modeled by coarser spatial scales. Typical learning-based surrogate models use a uniform spatial scale, which needs to resolve to the finest required scale and can waste a huge compute to achieve required accuracy. In this work, we introduce learning controllable Adaptive simulation for Multi-resolution Physics (LAMP) as the first full deep learning-based surrogate model that jointly learns the evolution model and optimizes appropriate spatial resolutions that devote more compute to the highly dynamic regions. LAMP consists of a Graph Neural Network (GNN) for learning the forward evolution, and a GNN-based actor-critic for learning the policy of spatial refinement and coarsening. We introduce learning techniques that optimizes LAMP with weighted sum of error and computational cost as objective, allowing LAMP to adapt to varying relative importance of error vs. computation tradeoff at inference time. We evaluate our method in a 1D benchmark of nonlinear PDEs and a challenging 2D mesh-based simulation. We demonstrate that our LAMP outperforms state-of-the-art deep learning surrogate models, and can adaptively trade-off computation to improve long-term prediction error: it achieves an average of 33.7% error reduction for 1D nonlinear PDEs, and outperforms MeshGraphNets + classical Adaptive Mesh Refinement (AMR) in 2D mesh-based simulations. Project website with data and code can be found at: http://***/lamp. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Errors

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