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Convolutional-Neural Network Bases Speaker Emotion Detection Based on Human-computer Interaction

Convolutional-Neural Network Bases Speaker Emotion Detection...

Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Nithya B Saranya R Mary Joseph R. Josphineleela Arunkumar Thirunagalingam Arun Kumar Kodali Dept of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai Department of Computer Science and Engineering V.S.B. Engineering College Karur Department of IT St. Joseph's Institute of Technology OMR Chennai Department of Computer Science and Engineering Panimalar Engineering College Data and Operations Mckesson Corporation Mphasis Corporation Data Architect

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The present investigation proposes a Convolutional Neural Network (CNN)-based method for human-computer interaction (HCI) speaker emotion identification. Optimising the user experience through clearer and sympathetic connections is made possible by emotional knowledge. Conventional techniques for detecting emotions depend on manually created characteristics, which are frequently laborious and less resilient to speech variability. By using CNNs' automatic feature extraction capabilities, the suggested system analyses speech signal spectrograms and extracts crucial emotional indicators. The method obtains notable gains in emotion classification accuracy by retraining the system on a varied collection of emotional speech samples. Important emotional states like happiness, anger, sadness, and neutrality are all successfully distinguished using the CNN model. This study demonstrates how deep learning can help develop emotion-sensitive HCI systems and promote more organic and contextually aware human-machine interactions. Applications in customer service, healthcare, and virtual assistants can benefit from the technique's adaptability and versatility.

关键词： Human computer interaction Emotion recognition Virtual assistants Medical services Feature extraction User experience Convolutional neural networks Robots System analysis and design Spectrogram

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Evolving Perceptions of Mental Health on Social Media and their Medical Impacts

Evolving Perceptions of Mental Health on Social Media and th...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Reagan, Kenna Varde, Aparna S. Xie, Lei Montclair State University Computational Linguistics MontclairNJ United States Data Science Computational Linguistics Montclair State University Computer Science Department NJ United States Cuny Hunter Neuroscience Weill Cornell Medical Computer Science ManhattanNY United States

ISBN: (纸本)9781665480451

Mental health is an ever-growing issue of concern, especially in light of the COVID pandemic. In this context, we study big data from social media over a 7-year time span to gauge evolving perceptions of mental health, and discuss our research findings, potentially useful for decision support in healthcare. We deploy topic modeling and sentiment analysis to estimate public perceptions of mental health issues, focusing on Twitter as the social media site. We claim that it is important to consider polarity as well as subjectivity in sentiment analysis to comprehend two different aspects of sentiment, i.e. orientation in the emotion, and extent of fact vs. opinion. We assert that ranking via topic modeling is beneficial to fathom the relative importance of issues over the years. We harness tools/techniques from natural language processing and data mining to discover knowledge from big data on social media, related to mental health. Some of our findings reveal that the sentiment around mental health has remained positive overall, but has decreased since the beginning of the COVID pandemic. Major events, such as elections and the pandemic, greatly impact the conversation surrounding mental health. Some topics have remained consistent throughout the years. In other topics, the tone of the public discussions has shifted. The outcomes of our study would be useful to a variety of professionals, ranging from data scientists to epidemiologists and psychologists. This work impacts big healthcare data in general. © 2022 IEEE.

关键词： data Analytics

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DFA-GNN: forward learning of graph neural networks by direct feedback alignment 24

DFA-GNN: forward learning of graph neural networks by direct...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Gongpei Zhao Tao Wang Congyan Lang Yi Jin Yidong Li Haibin Ling Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education China and School of Computer Science & Technology Beijing Jiaotong University China Department of Computer Science Stony Brook University

ISBN: (纸本)9798331314385

Graph neural networks (GNNs) are recognized for their strong performance across various applications, with the backpropagation (BP) algorithm playing a central role in the development of most GNN models. However, despite its effectiveness, BP has limitations that challenge its biological plausibility and affect the efficiency, scalability and parallelism of training neural networks for graph-based tasks. While several non-backpropagation (non-BP) training algorithms, such as the direct feedback alignment (DFA), have been successfully applied to fully-connected and convolutional network components for handling Euclidean data, directly adapting these non-BP frameworks to manage non-Euclidean graph data in GNN models presents significant challenges. These challenges primarily arise from the violation of the independent and identically distributed (i.i.d.) assumption in graph data and the difficulty in accessing prediction errors for all samples (nodes) within the graph. To overcome these obstacles, in this paper we propose DFA-GNN, a novel forward learning framework tailored for GNNs with a case study of semi-supervised learning. The proposed method breaks the limitations of BP by using a dedicated forward training mechanism. Specifically, DFA-GNN extends the principles of DFA to adapt to graph data and unique architecture of GNNs, which incorporates the information of graph topology into the feedback links to accommodate the non-Euclidean characteristics of graph data. Additionally, for semi-supervised graph learning tasks, we developed a pseudo error generator that spreads residual errors from training data to create a pseudo error for each unlabeled node. These pseudo errors are then utilized to train GNNs using DFA. Extensive experiments on 10 public benchmarks reveal that our learning framework outperforms not only previous non-BP methods but also the standard BP methods, and it exhibits excellent robustness against various types of noise and attacks.

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A Hybrid Approach to Rice Leaf Disease Detection Using Transfer Learning and SVM

A Hybrid Approach to Rice Leaf Disease Detection Using Trans...

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Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Raman Kumar Pal Sibarama Panigrahi Sneha Pratihar Intelligent Data Analytics Laboratory Computer Science& Engineering National Institute of Technology Rourkela India Computer Science & Engineering Kalinga Institute of Industrial Technology Bhubaneshwar India

ISBN: (数字)9798331522612

ISBN: (纸本)9798331522629

Rice is a prominent crop in India, with the country ranking second globally in rice production. However, rice cultivation is severely affected by several diseases, impacting crop yield and quality. Early detection of these diseases can significantly mitigate their adverse effects. This study focuses on detecting primary rice leaf diseases: brown spot, tungro, bacterial blight, and blast. We explore various models, including recent publications, and develop hybrid approaches employing deep features from VGG16, VGG19, and ResNet50 models, alongside handcrafted features such as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). These features combined with shallow machine learning classifiers like Random Forest and Support Vector Machine (SVM), are evaluated for rice leaf disease classification. Different combinations of these extracted features, with and without Principal Component Analysis (PCA), are tested using various classifiers to assess their impact on classification accuracy. The models that stack deep features using ResNet50 and handcrafted features using LBP, followed by dimensional reduction via PCA and transformation using SVM, achieved near-perfect metrics across F1 score, precision, accuracy, recall, and AUC. These results indicate strong generalization and balanced performance, outperforming methods published in recent literature.

关键词： Support vector machines Deep learning Accuracy Feature extraction Robustness Medical diagnosis Monitoring Diseases Principal component analysis Residual neural networks

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SMDNet: A Pulmonary Nodule Classification Model Based on Positional Self-Supervision and Multi-Direction Attention

SMDNet: A Pulmonary Nodule Classification Model Based on Pos...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Xiong, Jun Wang, Chengliang Wu, Xing Wang, Peng Wang, Haidong Wang, Hongqian College of Computer Science Chongqing University Chongqing China Southwest Hospital of Army Medical University Medical Big Data Center Chongqing China Southwest Hospital of Army Medical University Department of Thoracic Surgery Chongqing China

ISBN: (纸本)9781665410205

Accurate classification of pulmonary nodules holds importance in the early diagnosis of lung cancer. Unlike 2D models, 3D models can simultaneously utilize multiple slices as input to capture features. However, 3D models face challenges in capturing nodule features in different directions and discerning feature differences in various positions of computed tomography (CT). We introduce a pulmonary nodule classification model, SMDNet. Firstly, a multi-direction attention is proposed to capture nodule features from sagittal, coronal, and axial axes. Secondly, distinct labels are assigned to the cubes at different cropping positions from CT for binary classification to capture local differences. Besides, gradient boosting decision tree (GBDT) is employed to combine shallow features with deep features to improve accuracy. Comparative experimental results on the largest publicly available dataset of pulmonary nodules, LIDC-IDRI, showed that SMDNet achieves a 4.81% improvement in accuracy under identical data processing. © 2024 IEEE.

关键词： computerized tomography

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Real-time Stream Processing in IoT Environments 9

Real-time Stream Processing in IoT Environments

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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Rajasekar, P. Bhosale, Rupali Shekar Indhumathi, C. Sandeep, K.V. Prasanthi Kumari, N. Rajendiran, M. School of Computing Srm Institute of Science And Technology Department of Data Science And Business Systems Tamil Nadu Kattankulathur603203 India Bharati Vidyapeeth Institute of Technology Department of Electronics And Telecommunication Engineering Maharashtra Navi Mumbai400614 India Rmk College of Engineering And Technology Department of Computer Science And Business Systems Tamil Nadu Puduvoyal601206 India Dayananda Sagar College of Engineering Department of Electronics & Telecommunication Engineering Karnataka Bengaluru560111 India School of Computer Sciences University of Petroleum And Energy Studies Uttarakhand 248007 Dehradun India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

New IoT devices generate massive real-time data. Fast, real-time stream processing is needed for IoT data insight extraction. There are pros and downsides to real-time stream processing for the Internet of Things (IoT). This article analyzes present methods and technologies. The inquiry begins with large-scale, real-time IoT data analysis. Real-time analytics and bulk processing limits are important for IoT decision-making. We then investigate IoT stream processing frameworks, methodologies, and systems' complexity. We explore Internet of Things technology selection and tradeoffs between scalability, accuracy, latency, and real-time processing system scalability. It covers real-time stream processing system design and deployment and risk mitigation. Case studies and use cases highlight real-time IoT stream processing. Healthcare, smart city, and industrial IoT operational efficiency and proactive decision-making improve with real-time data. Finally, the report suggests the Internet of Things real-time stream processing research. New IoT data analytics protocols, methods, and technologies are highlighted. We teach real-time stream processing to IoT academics, practitioners, and decision-makers. They could then develop more efficient and scalable IoT data stream optimization solutions. © 2024 IEEE.

关键词： Edge computing

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On Differentially Private Linear Algebra

arXiv

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arXiv 2024年

作者： Kaplan, Haim Mansour, Yishay Moran, Shay Stemmer, Uri Tur, Nitzan School of computer science Tel Aviv University Google Research Departments of Mathematics Computer Science and Data and Decision Sciences Technion and Google Research Google Research United States

We introduce efficient differentially private (DP) algorithms for several linear algebraic tasks, including solving linear equalities over arbitrary fields, linear inequalities over the reals, and computing affine spans and convex hulls. As an application, we obtain efficient DP algorithms for learning halfspaces and affine subspaces. Our algorithms addressing equalities are strongly polynomial, whereas those addressing inequalities are weakly polynomial. Furthermore, this distinction is inevitable: no DP algorithm for linear programming can be strongly polynomial-time efficient. Copyright © 2024, The Authors. All rights reserved.

关键词： Integer linear programming

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Semantic consistency assisted change detection in remote sensing images

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National Remote Sensing Bulletin 2025年第4期29卷 1031-1045页

作者： Wu, Hucheng Wang, Renfang Qiu, Hong Wang, Feng Gao, Guang Wu, Dun College of Computer Science and Technology Ocean University of China Qingdao266100 China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo315100 China Co. Ltd. Ningbo315042 China

Semantic change detection of remote sensing images plays an important role in ecological environment, land use, and land cover monitoring. In recent years, deep learning-based change detection methods are the hotspot of remote sensing intelligent interpretation concern;however, the existing three-branch semantic change detection methods lack consistency of change branch and semantic branches, which leads to the self-contradiction of the bitemporal semantic change detection. To address this problem, this study proposes a remote sensing image semantic change detection algorithm based on Siamese CNN and Transformer. In the encoding stage, the Siamese ResNet34 network is first designed to extract the multiscale features of the image, and the difference enhancement module is embedded to increase the attention of the change information;then, the semantic tokenizer is used to map the feature map into compact semantic tokens, and the Transformer encoder is used to combine the bitemporal semantics and the change information to model the"semantic change"consistency. In the decoding stage, different fine-grained semantic information is fused by the Transformer decoder using a hopping connection to generate a refined semantic feature map. Finally, the result of bitemporal semantic change is obtained after upsampling recovery and mask multiplication. Experimental results on the remote sensing semantic change detection public dataset SECOND and Landsat-SCD show that the proposed algorithm can effectively focus on the change region, maintain the consistency between the change results and the semantic results, and achieve excellent evaluation indexes and visual effects. We can draw the following conclusions: (1) The proposed difference enhancement module can enhance the difference characteristics of bitemporal remote sensing images and improve the network’s focus on change information. (2) The proposed bitemporal Transformer module maps the difference information and bitemporal sem

关键词： Change detection

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral Image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Pei Wang, Dong Wu, Chanyue Yang, Jing Kang, Lei Bai, Zongwen Li, Ying Shen, Qiang School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Yan'an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd. Qingdao China Department of Computer Science Aberystwyth University Aberystwyth United Kingdom

ISBN: (纸本)9798350368741

Hyperspectral Image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques. © 2025 IEEE.

关键词： Cross-scene classification Diffusion model Hyperspectral image Transformer

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CMNEE: A Large-Scale Document-Level Event Extraction dataset based on Open-Source Chinese Military News

arXiv

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arXiv 2024年

作者： Zhu, Mengna Xu, Zijie Zeng, Kaisheng Xiao, Kaiming Wang, Mao Ke, Wenjun Huang, Hongbin Laboratory for Big Data and Decision National University of Defense Technology China School of Computer Science and Engineering Southeast University Bangladesh Computer Science and Technology Tsinghua University China

Extracting structured event knowledge, including event triggers and corresponding arguments, from military texts is fundamental to many applications, such as intelligence analysis and decision assistance. However, event extraction in the military field faces the data scarcity problem, which impedes the research of event extraction models in this domain. To alleviate this problem, we propose CMNEE, a large-scale, document-level open-source Chinese Military News Event Extraction dataset. It contains 17,000 documents and 29,223 events, which are all manually annotated based on a pre-defined schema for the military domain including 8 event types and 11 argument role types. We designed a two-stage, multi-turns annotation strategy to ensure the quality of CMNEE and reproduced several state-of-the-art event extraction models with a systematic evaluation. The experimental results on CMNEE fall shorter than those on other domain datasets obviously, which demonstrates that event extraction for military domain poses unique challenges and requires further research efforts. Our code and data can be obtained from https://***/Mzzzhu/CMNEE. Copyright © 2024, The Authors. All rights reserved.

关键词： data mining

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