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2024 International Conference on Intelligent Algorithms for Computational intelligence Systems, IACIS 2024

作者： Kumar, S. Vinod Sunil, G. Hussein, Ramy Riad Manju Vidhya, S. Sundaram, S. Meenakshi Department of Computer Science and Engineering Secunderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Excel Engineering College Department of Artificial Intelligence and Data Science Komarapalayam India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (纸本)9798350360660

Facial Emotion Recognition (FER) plays a crucial role in analysing and understanding individual's emotions in the form of non-verbal communication. Existing Deep Learning (DL) based FER methods offered diverse contribution in analysing human emotions but several challenges like extracting accurate spatial and temporal features for accurate classification of emotions is a major concern. To address this, a DL based Facial Recognition and Emotion Detection (FRED) system is proposed by using extended Cohn-Kanade (CK+) dataset as the input. The data is pre-processed first where the Histogram Equalization (HE) is applied and then Convolutional Neural Network (CNN) is used to extract the spatial features while reducing the dimensionality. The extracted features are given to Recurrent Neural Network (RNN) to learn long term dependencies from a temporal sequence of data. The final classification is performed where the Attention Mechanism (AM) is integrated in the RNN to determine the higher weights for better outcome. The results of proposed AMConVnet-RNN showed a superior classification accuracy of 99.54%, precision of 98.63%, and f1-score of 97.39% when compared to existing FER methods like Residual Network-50 (ResNet-50) and Channel Based Attention Mechanism (CBAM) based ResNet50. © 2024 IEEE.

关键词： Convolutional neural networks

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Multi-Scale Feature Fusion and Advanced Representation Learning for Multi Label Image Classification

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Computers, Materials & Continua 2025年第3期82卷 5285-5306页

作者： Naikang Zhong Xiao Lin Wen Du Jin Shi Institute of Artificial Intelligence on Education Research College of InformationMechanical and Electrical EngineeringShanghai Normal UniversityShanghai200234China Lab for Educational Big Data and Policymaking Ministry of EducationShanghai Normal UniversityShanghai200234China Shanghai Intelligent Education Big Data Engineering Technology Research Center Shanghai Normal UniversityShanghai200234China Shanghai Online Education Research Base for Primary and Secondary Schools Shanghai200234China DS Information Technology Co. Ltd.Shanghai200032China Faculty of Innovation Engineering Macao university of Science and TechnologyMacao999078China

Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in *** class-specific precise representations at different scales is a key aspect of feature ***,existing methods often rely on the single-scale deep feature,neglecting shallow and deeper layer features,which poses challenges when predicting objects of varying scales within the same *** some studies have explored multi-scale features,they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different *** address these issues,we propose a two-stage,three-branch Transformer-based *** first stage incorporates multi-scale image feature extraction and hierarchical scale *** design enables the model to consider objects at various scales while enhancing the flow of information across different feature scales,improving the model’s generalization to diverse object *** second stage includes a global feature enhancement module and a region selection *** global feature enhancement module strengthens interconnections between different image regions,mitigating the issue of incomplete represen-tations,while the region selection module models the cross-modal relationships between image features and ***,these components enable the efficient acquisition of class-specific precise feature *** experiments on public datasets,including COCO2014,VOC2007,and VOC2012,demonstrate the effectiveness of our proposed *** approach achieves consistent performance gains of 0.3%,0.4%,and 0.2%over state-of-the-art methods on the three datasets,*** results validate the reliability and superiority of our approach for multi-label image classification.

关键词： Image classification multi-label multi scale attention mechanisms feature fusion

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Lightweight Attention-CycleGAN for Nighttime-Daytime Image Transformation 1

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2nd International Conference on artificial intelligence Security and Privacy, AIS and P 2024

作者： Huang, Junhao Xiao, Xiangjun Zhou, Haojun Yasin, Affan Zhou, Zhili School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia School of Artificial Intelligence Guangzhou University Guangzhou China

ISBN: (数字)9789819611485

ISBN: (纸本)9789819611478

With the rapid development of deep learning in the field of computer vision, the performance of core vision tasks such as image recognition has achieved significant improvement. In nighttime environment, due to the low-light condition and reduced visibility, cross-domain transformation of nighttime images based on Generative Adversarial Network (GAN) model can effectively improve the accuracy of nighttime recognition models. However, the existing GAN models are difficult to be effectively deployed on resource-constrained devices due to the requirement of high storage space and computational resource. To this end, this paper proposes a shared attention network based on the attention mechanism with the CycleGAN structure, and designs an online knowledge distillation method to compress and optimize the model, so as to obtain a lightweight model to achieve the nighttime-daytime cross-domain image transformation. Experimental results demonstrate that the proposed model achieves the state-of-the-art performance in the task of Nighttime-Daytime Image Transformation. This is of great significance for edge devices to solve the problem of recognition at night. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Distributed Dimensionality Reduction Filtering for CPSs Under DoS Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1080-1082页

作者： Xiaoyuan Zheng Hao Zhang Xindi Yang Huaicheng Yan School of Artificial Intelligence and Data Science Hebei University of TechnologyTianjin 300401China Department of Control Science and Engineering Tongji UniversityShanghai 200092China Key Laboratory of Smart Manufacturing in Energy Chemcical Process of Ministry of Education East China University of Science and TechnologyShanghai 200237 School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China

Dear Editor,This letter concerns about the distributed dimensionally reduction filtering for a class of cyber physical systems with denial of service(DoS)*** each sensor exchanging measurements with neighbors subjecte... 详细信息

关键词： DoS service filtering

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Application of artificial Neural Network in Impact and Crashworthiness: A Review 2nd

Application of Artificial Neural Network in Impact and Crash...

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2nd International Conference on Modern Research in Aerospace engineering, MRAE 2023

作者： Nath, Dipjyoti Ankit Neog, Debanga Raj Gautam, Sachin Singh Department of Mechanical Engineering Indian Institute of Technology Guwahati Guwahati781039 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute Technology Guwahati Guwahati781039 India

ISBN: (纸本)9789819713059

The paper presents an extensive review of the application of machine learning (ML) techniques, especially artificial neural networks (ANNs), in the realm of crashworthiness, hypervelocity impacts, and inverse analysis across various structural and aerospace domains. It delves into the prediction of stiffness matrices, contact stiffness, impact forces, and failure conditions, showcasing the potential of ML to streamline complex structural analysis processes. While the review highlights the predictive accuracies and computational efficiencies achieved by these methods, it also underscores the need for a more critical evaluation of model robustness, uncertainties, and real-world applicability. The review offers valuable insights into how ML can transform inverse analysis yet emphasizes the importance of addressing challenges and limitations to ensure the reliability and practicality of these innovative approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural networks

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Beware of data Leakage from Protein LLM Pretraining 19

Beware of Data Leakage from Protein LLM Pretraining

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19th Conference on Machine Learning in Computational Biology Meeting, MLCB 2024

作者： Hermann, Leon Fiedler, Tobias Nguyen, Hoang An Nowicka, Melania Bartoszewicz, Jakub M. Hasso Plattner Institute Digital Engineering Faculty University of Potsdam Germany Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States

Pretrained protein language models are becoming increasingly popular as a backbone for protein property inference tasks such as structure prediction or function annotation, accelerating biological research. However, related research oftentimes does not consider the effects of data leakage from pretraining on the actual downstream task, resulting in potentially unrealistic performance estimates. Reported generalization might not necessarily be reproducible for proteins highly dissimilar from the pretraining set. In this work, we measure the effects of data leakage from protein language model pretraining in the domain of protein thermostability prediction. Specifically, we compare two different dataset split strategies: a pretraining-aware split, designed to avoid similarity between pretraining data and the held-out test sets, and a commonly-used naive split, relying on clustering the training data for a downstream task without taking the pretraining data into account. Our experiments suggest that data leakage from language model pretraining shows consistent effects on melting point prediction across all experiments, distorting the measured performance by an average 11.1% compared to the pretraining-aware split. The source code and our dataset splits are available at https://***/dacshpi/pretraining-aware-hotprot. © 2024 Proceedings of Machine Learning Research. All rights reserved.

关键词： Information leakage

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Solarsense: Enhancing Energy Efficiency Through Iot-Enabled Solar Tracking 2

Solarsense: Enhancing Energy Efficiency Through Iot-Enabled ...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Thopate, Kaushalya Gawade, Mayuri Kakade, Surabhi Bahir, Suvarna Kulkarni, Mukund Jaiswal, Krishna Nilesh Computer Engineering Vishwakarma Institute of Technology Pune India Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India Computer Engineering Trinity Academy of Engineering Pune India Electronics and Telecommunication Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350360523

The surging global demand for energy is primarily fueled by a burgeoning world population and elevated living standards. However, with traditional fossil fuels like oil dwindling in availability, there is an urgent need to shift towards renewable energy sources. Given the rapid progress in renewable energy technologies, a key objective of the project is to develop a solar energy tracking panel that can rotate for enhanced power generation. This innovative panel, capable of adjusting its position to optimize energy output by tracking the sun, proves invaluable in monitoring power consumption. The design of this rotatable solar energy tracking panel, incorporating four essential components (solar energy tracking panels, light-dependent resistors (LDRs), an Arduino, and a motor), is intricately explained in this study. This configuration not only serves as an advanced educational tool but also provides engineering technology students with a comprehensive understanding of alternative energy sources like solar power. © 2024 IEEE.

关键词： Fossil fuels

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Illumination Effects on Deep Fake Detection: Performance Comparison of CNN Architectures

Illumination Effects on Deep Fake Detection: Performance Com...

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2024 International Conference on artificial intelligence and Emerging technology, Global AI Summit 2024

作者： Yangoti, Sai Bhavya Dumpala, Rohith Punyamurthy, Vijay Jatoth, Ravi Kumar Indian Institute of Information Technology Design and Manufacturing Dept. of Artificial Intelligence and Data Science Kurnool India National Institute of Technology Dept. of Electronics and Communication Engineering Warangal India

ISBN: (纸本)9798350379716

While AI has many advantages, it also has a few drawbacks associated with 'deep fakes.' Deep fakes involve the creation of very realistic videos and images realized through technologies that work with facial and voice modalities for deceptive purposes. Researchers have developed various techniques to detect these manipulated videos and images, employing complex neural network models such as convolutional neural networks (CNNs). However, deep fakes are very difficult to identify due to lighting and shadow effects that may hide some signs of manipulation. No one technique of detection could have the ability of identifying the tampered images under all kinds of illumination conditions. This paper compares different CNN models-ResNet50, EfficientNet, and MobileNetV2-to improve detection accuracy so as to be more resistant to new deep fake techniques, in particular under different illumination conditions. The importance of the study lies in arriving at which of the considered CNN models turns out to be most accurate under a specific illumination effect. Such an understanding could, therefore, improve the results of detection even when applied to cases of tampered faces in fake Aadhaar detection and fake id detection, thereby clearly bringing out the usefulness of the research in practice. © 2024 IEEE.

关键词： Convolutional neural networks

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Toward Optimal Periodic Crowd Tracking via Unmanned Aerial Vehicle

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Computer Modeling in engineering & sciences 2023年第10期137卷 233-263页

作者： Khalil Chebil Skander Htiouech Mahdi Khemakhem Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj11942Saudi Arabia Data Engineering and Semantics Research Unit Faculty of Sciences of SfaxUniversity of SfaxSfaxTunisia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Mathematics and Business Intelligence College of Electronics and Telecommunications Engineering of SfaxUniversity of SfaxSfaxTunisia

Crowd management and analysis(CMA)systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles(UAVs)*** tracking using UAVs is among the most important services provided by a *** this paper,we studied the periodic crowd-tracking(PCT)*** consists in usingUAVs to follow-up crowds,during the life-cycle of an open crowded area(OCA).Two criteria were considered for this *** first is related to the CMA initial investment,while the second is to guarantee the quality of service(QoS).The existing works focus on very specified assumptions that are highly committed to CMAs applications *** study outlined a new binary linear programming(BLP)model to optimally solve the PCT motivated by a real-world application study taking into consideration the high level of *** closely approach different real-world contexts,we carefully defined and investigated a set of parameters related to the OCA characteristics,behaviors,and theCMAinitial infrastructure investment(e.g.,UAVs,charging stations(CSs)).In order to periodically update theUAVs/crowds andUAVs/CSs assignments,the proposed BLP was integrated into a linear algorithm called PCTs *** main objective was to study the PCT problem fromboth theoretical and numerical *** prove the PCTs solver effectiveness,we generated a diversified set of PCTs instances with different scenarios for simulation *** empirical results analysis enabled us to validate the BLPmodel and the PCTs solver,and to point out a set of new challenges for future research directions.

关键词： Unmanned aerial vehicles periodic crowd-tracking problem open crowded area optimization binary linear programming crowd management and analysis system

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Air quality and dust level monitoring systems in hospitals using IoT

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Discover Internet of Things 2025年第1期5卷 1-18页

作者： Parkavi, A. Sowmya, B.J. Alex, Sini Anna Supreeth, S. Shruthi, G. Rohith, S. Chatterjee, Sudipta Lingaraj, K. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India Department of Electronics and Communication Engineering Brainware University Barasat West Bengal Kolkata700125 India Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Bellary583104 India

This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that focus on fewer parameters. The use of IoT technology enables real-time monitoring and alerts, allowing hospital staff to respond immediately to any issues. Additionally, predictive analytics anticipate future changes, giving staff time to take preventive measures. The integration with a mobile app and cloud-based data storage ensures easy access to data, convenient monitoring, and the ability to scale the system across multiple hospital facilities, catering specifically to the unique needs of different healthcare environments. © The Author(s) 2025.

关键词： Indoor air pollution

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