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Research on facial expression recognition based on multimodal data fusion and neural network

International Journal of Wireless and Mobile Computing 2024年第1期27卷 47-55页

作者： Han, Yi Wang, Xubin Lu, Zhengyu School of Mechanical Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China Anyang Institute of Technology Department of Computer Science and Information Engineering Henan Province Anyang China School of Computer Science and Information Engineering Shanghai Institute of Technology FengXian District Shanghai China

Facial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadvantages of low accuracy and low robustness. In this paper, a neural network algorithm of facial expression recognition based on multimodal data fusion is proposed. The algorithm is based on the multimodal data, and it takes the facial image, the histogram of oriented gradient of the image and the facial landmarks as the input, and establishes Convolutional Neural Network (CNN) designed to extract features from facial image, neural network designed to extract features from facial Landmarks and Neural Network (LNN) designed to extract features from Histogram of gradient (HNN), three sub-neural networks to extract data features, using multimodal data feature fusion mechanism to improve the accuracy of facial expression recognition. Experimental results show that, the algorithm has a great improvement in accuracy, robustness and detection speed. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Data fusion

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Anomaly Detection Model of Time Segment Power Usage Behavior Using Unsupervised Learning

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Journal of Internet Technology 2024年第3期25卷 455-463页

作者： Ho, Wen-Jen Hsieh, Hsin-Yuan Tsai, Chia-Wei Software Technology Institute Institute for Information Industry Taiwan Department of Computer Science and Information Engineering National Taitung University Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taiwan

In Taiwan, the current electricity prices for residential users remain relatively low. This results in a diminished incentive for these users to invest in energy-saving improvements. Consequently, devising strategies to encourage residential users to adopt energy-saving measures becomes a vital research area. Grounded in behavioral science, this study introduces a feasible approach where an energy management system provides alerts and corresponding energy-saving recommendations to residential users upon detecting abnormal electricity consumption behavior. To pinpoint anomalous electricity usage within specific time segments, this research employs an unsupervised machine learning method, developing an anomaly detection model for the overall electricity consumption behavior of residential users. The model focuses on analyzing 2-hour intervals of electricity consumption, enabling more effective detection of abnormal usage patterns. It is trained using power consumption data collected from five actual residential users as part of an experimental study. The results indicate that the proposed anomaly detection model achieves performance metrics such as Precision, Recall, and F1-score of 0.90 or above, showcasing its potential for practical implementation. © 2024 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Energy conservation

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CAED: A Comprehensive Android Emulator Detection Framework with Data Augmentation

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IEEE Internet of Things Journal 2025年第13期12卷 22847-22861页

作者： Yan, Ran Niu, Weina Hou, Qinsheng Su, YuChi Gong, Jiacheng Zhang, Xiaosong School of Computer Science and Engineering Institute for Cyber Security Chengdu611731 China Shanghai Jiao Tong University School of Cyber Science and Engineering China

Anti-emulation is crucial for Android and IoT security as it helps apps determine whether they are running on a real mobile device or in an emulation environment. This prevents apps from being analyzed, debugged, or reverse-engineered in emulators, ultimately stopping criminals from making illegal profits. Current emulator detection methods cannot balance accuracy, universality, robustness, and compatibility. Their universality is often hindered by limited data diversity and accessibility. To address these issues, we propose the Comprehensive Android Emulator Detection (CAED) framework. The Preprocessing Module of CAED collects and normalizes data from both phones and emulators. We propose the first data augmentation method for emulator detection, EDA-GAN, which is tailored to the characteristics of our data and effectively enhances data diversity. The classifier module MFBoost employs an adaptive imputation algorithm and multiple Classification and Regression Trees (CART) for precise classification. Experiments on 324 devices show that CAED improves detection rate by at least 12.5% and up to 44.71% over state-of-the-art (SOTA) methods. The EDA-GAN data augmentation method boosts classifier accuracy, achieving a performance of up to 99.62%. Additionally, CAED’s unique loss function and imputation algorithm enhance the robustness and compatibility of CAED, with a 24% smaller accuracy drop than other methods when features are modified or unavailable. This study presents the CAED framework as an effective solution for protecting apps against real-world security threats in Android and IoT environments. © 2014 IEEE.

关键词： Crime

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Hierarchical blockchain-based secure data storage in cloud using Merkle tree approach

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International Journal of Cloud Computing 2024年第6期13卷 605-626页

作者： Bruno, Judy Flavia Chelliah, Balika J. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India

An authenticated manager must reinforce huge applications and operating systems, keeping information in the cloud while resisting potentially unreliable service providers. This article explores the presence of multiple service providers based on the cloud to store and manage customer information. It has investigated the creation of an optimal system for shared cloud storage to facilitate flexible operations and information migration. This paper uses the Merkle tree approach to describe the experimental outcomes and designs of security systems provided by blockchain in cloud computing. We proposed that good authenticity and security be implemented with minimal significant degradation. The method was based on the position of the conscious Merkle tree that employs a three-tuple technique that has proven reliable in terms of authentication and encryption of security functions. The proposed system performance was measured by parameters such as retrieval time of information in blockchain technology, cost of storage, and average time with conventional information storage methods. The results indicate that the proposed blockchain technology solution has a response time almost 50% faster than conventional methods. Also, it claims that the storage cost would be around 20% lower. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Cloud storage

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Flu-Net: two-stream deep heterogeneous network to detect flu like symptoms from videos using grey wolf optimization algorithm

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7733-7745页

作者： Gupta, Himanshu Imran, Javed Sharma, Chandani Department of Computer Science and Engineering Quantum University Roorkee India Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India

The outbreak of COVID-19 (also known as Coronavirus) has put the entire world at risk. The disease first appears in Wuhan, China, and later spread to other countries, taking a form of a pandemic. In this paper, we try to build an artificial intelligence (AI) powered framework called Flu-Net to identify flu-like symptoms (which is also an important symptom of Covid-19) in people, and limit the spread of infection. Our approach is based on the application of human action recognition in surveillance systems, where videos captured by closed-circuit television (CCTV) cameras are processed through state-of-the-art deep learning techniques to recognize different activities like coughing, sneezing, etc. The proposed framework has three major steps. First, to suppress irrelevant background details in an input video, a frame difference operation is performed to extract foreground motion information. Second, a two-stream heterogeneous network based on 2D and 3D Convolutional Neural Networks (ConvNets) is trained using the RGB frame differences. And third, the features extracted from both the streams are combined using Grey Wolf Optimization (GWO) based feature selection technique. The experiments conducted on BII Sneeze-Cough (BIISC) video dataset show that our framework can 70% accuracy, outperforming the baseline results by more than 8%. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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ediction Model for a Good Learning Environment Using an Ensemble Approach

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computer Systems science & engineering 2023年第3期44卷 2081-2093页

作者： S.Subha S.Baghavathi Priya Department of Computer Science and Engineering Rajalakshmi Institute of TechnologyChennaiTamil NaduIndia Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennaiTamil NaduIndia

This paper presents an efficient prediction model for a good learning environment using Random Forest(RF)*** consists of a series of modules;data preprocessing,data normalization,data split andfinally classification or prediction by the RF *** preprocessed data is normalized using minmax normalization often used before *** the input data or variables are measured at different scales,it is necessary to normalize them to contribute equally to the ***,the RF classifier is employed for course selection which is an ensemble learning method and k-fold cross-validation(k=10)is used to validate the *** proposed Prediction Model for Course Selection(PMCS)system is considered a multi-class problem that predicts the course for a particular learner with three complexity levels,namely low,medium and *** is operated under two modes;locally and *** former considers the gender of the learner and the later does not consider the gender of the *** database comprises the learner opinions from 75 males and 75 females per category(low,medium and high).Thus the system uses a total of 450 samples to evaluate the performance of the PMCS *** show that the system’s performance,while using locally i.e.,gender-wise has slightly higher performance than the global *** RF classifier with 75 decision trees in the global system provides an average accuracy of 97.6%,whereas in the local system it is 97%(male)and 97.6%(female).The overall performance of the RF classifier with 75 trees is better than 25,50 and 100 decision trees in both local and global systems.

关键词： Machine learning ensemble learning random forest data mining prediction system

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A moment-based pooling approach in convolutional neural networks for breast cancer histopathology image classification

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Neural Computing and Applications 2025年第2期37卷 1127-1156页

作者： Kumar, Arvind Singh, Chandan Sachan, Manoj Kumar Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal Punjab Sangrur148106 India Department of Computer Science Punjabi University Punjab Patiala147002 India

Automated analysis of breast cancer (BC) histopathology images is a challenging task due to the high resolution, multiple magnifications, color variations, the presence of image artifacts, and morphological variability in the tissue images, often resulting in misdiagnosis. We propose four CNN models using a novel Zernike moments (ZMs)-based pooling approach, which replaces the existing global average pooling for the effective classification, referred to as CZP-CNN, MZP-CNN, SPMZP-CNN, and SPCZP-CNN. We utilize the ZMs-based pooling at three intermediate and last convolution layers. Of the four models, the two models, SPMZP-CNN and SPCZP-CNN, employ spatial pyramidal structures of feature maps to represent global and local characteristics of the feature maps. They effectively integrate the ZMs of various orders derived from the pyramidal representation of a feature map, representing the global and local aspects of shape and fine morphological details. The ability of ZMs is fully utilized to capture shape and morphological information available in the feature map using only a few low-order moments, which adds to the robustness of the proposed approach. Detailed experimental results on the publicly available BreaKHis dataset with four magnification levels and on the high-resolution BACH dataset demonstrate the proposed models’ effectiveness, robustness, and generalization ability. Among these proposed models, the complex ZMs-based SPCZP-CNN model achieves an impressive mean classification accuracy of 94.16% on the BreaKHis dataset and overall best binary and multiclass classification accuracy of 96.25% and 93.75% on the BACH dataset. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolution

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An efficient schedulability analysis based on worst-case interference time for real-time systems

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science China(Information sciences) 2024年第9期67卷 88-104页

作者： Hongbiao LIU Mengfei YANG Lei QIAO Xi CHEN Jian GONG School of Computer Science and Technology Xidian University China Academy of Space Technology Beijing Institute of Control Engineering

Real-time systems are widely implemented in the Internet of Things(IoT) and safety-critical systems, both of which have generated enormous social value. Aiming at the classic schedulability analysis problem in real-time systems, we proposed an exact Boolean analysis based on interference(EBAI) for schedulability analysis in real-time systems. EBAI is based on worst-case interference time(WCIT), which considers both the release jitter and blocking time of the task. We improved the efficiency of the three existing tests and provided a comprehensive summary of related research results in the field. Abundant experiments were conducted to compare EBAI with other related results. Our evaluation showed that in certain cases, the runtime gain achieved using our analysis method may exceed 73% compared to the stateof-the-art schedulability test. Furthermore, the benefits obtained from our tests grew with the number of tasks, reaching a level suitable for practical application. EBAI is oriented to the five-tuple real-time task model with stronger expression ability and possesses a low runtime overhead. These characteristics make it applicable in various real-time systems such as spacecraft, autonomous vehicles, industrial robots, and traffic command systems.

关键词： five-tuple real-time task model real-time system spacecraft Internet of Things exact schedulability analysis worst-case interference time

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Hybrid RMDL-CNN for speech recognition from unclear speech signal

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International Journal of Speech Technology 2025年第1期28卷 195-217页

作者： Bhargava, Raja Arivazhagan, N. Babu, Kunchala Suresh Research Scholar Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering Potti Sriramulu Chaluvadi Mallikarjuna Rao college of Engineering and Technology Andhra Pradesh Vijayawada India

ASR is an effectual approach, which converts human speech into computer actions or text format. It involves extracting and determining the noise feature, the audio model, and the language model. The extraction and determination of the noise feature is a crucial aspect of speech recognition, serving as both a process of information compression and signal deconvolution. ASR schemes are mostly employed in smart homes, smart appliances, and biometric schemes. Yet, traditional approaches offer very low performance because of a noisy environment. Moreover, local differences and accents negatively influence the ASR scheme execution during the conversion of the speech signals. This paper introduces a hybrid RMDL-CNN method to address these challenges. At first, the input of unclear speech is carried out by the dataset. Then, signal pre-processing is done by employing a Gaussian filter. After that, voice enhancement is accomplished by employing nonlinear spectral subtraction. Later, the speech word is segmented from the enhanced output based on the Attentional Encoder-Decoder approach and finally, the speech is recognized using the proposed RMDL-CNN. The RMDL-CNN method is devised by the combination of RMDL and CNN. Furthermore, the established RMDL-CNN is accessed for its efficiency based on several values of k-group value, as well as learning data. In addition, the introduced RMDL-CNN approach for speech recognition achieved better accuracy, PPV, as well as NPV of 0.909, 0.947, and 0.917 for dataset 1. Moreover, the RMDL-CNN has achieved the highest accuracy of 0.909, PPV of 0.926 and NPV of 0.888 for dataset 2. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Protection of Hazardous Places in Industries using Machine Learning 5

Protection of Hazardous Places in Industries using Machine L...

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5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Sankarasubramanian, Praveen Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665475242

Extreme precautions must be observed to handle toxic wastes, radioactive substances, chemical raw materials, chemical wastes, and bio-products in different industries. Any malfunction in a dangerous traffic network can lead to serious accidents, deaths and / or serious damage. Direct monitoring and analysis, and preventive measures to prevent the spread of failures, can significantly reduce the recurrence of adverse effects. Current research suggests that detailed publicity and information on the latest developments in pipeline monitoring and research may help modernize the oil industry in the future. We also propose a framework to detect timely leakage in pipelines, especially in oil and gas sector. © 2023 IEEE.

关键词： Toxic materials

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