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Automatic emotion recognition using deep neural network

Multimedia Tools and Applications 2025年 1-30页

作者： Sujatha, R. Chatterjee, Jyotir Moy Pathy, Baibhav Hu, Yu-Chen School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of CSE Graphic Era University Dehradun India School of Electrical Engineering Vellore Institute of Technology Vellore India Department of Computer Science Tunghai University Taichung City Taiwan

Emotions are a vital semantic part of human correspondence. Emotions are significant for human correspondence as well as basic for human–computer cooperation. Viable correspondence between people is possibly achieved when both the importance and the emotion of the correspondence are perceived by all groups included. Understanding the significance of language has generally been concentrated on in natural language processing (NLP) as a semantic examination. In NLP, the text can be handled appropriately for classification. Emotion detection from facial emotion is the subfield of social signal processing applied in a wide assortment of regions, explicitly for human and PC collaboration. Many researchers have proposed various approaches, generally utilizing machine learning concepts. Automatic emotion recognition (AER) is significant for working with consistent intuitiveness between a person and a smart device toward fully acknowledging an intelligent society. Many researchers examined cross-lingual and multilingual speech emotion as a stage toward language-free emotion acknowledgment in natural speech. In the present work, we are proposing a deep learning-based AER system using four openly accessible datasets, namely Basic Arabic Vocal Emotions Dataset (BAVED), Acted Emotional Speech Dynamic Database (AESDD), Urdu written in Latin/Roman Script (URDU), and Toronto Emotional Speech Set (TESS), by utilizing the Jupyter notebook and a Python library for music and audio synthesis named Librosa. The experimental results exhibited that the proposed approach achieves better than the existing approaches, i.e., the accuracy of the proposed system with the URDU dataset is 96.24%, the TESS dataset is 99.10%, the AESDD dataset is 65.97%, and the BAVED dataset is 73.12%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantics

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Leveraging UAV-assisted communications to improve secrecy for URLLC in 6G systems

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Digital communications and Networks 2023年第6期9卷 1458-1464页

作者： Hemant Kumar Narsani Ali Ranjha Kapal Dev Fida Hussain Memon Nawab Muhammad Faseeh Qureshi Faculty of Engineering and Technology University of SindhPakistan Department of Electrical Engineering Ecole de Technologie Superieure Montreal QCCanada Department of Institute of Intelligent Systems University of JohannesburgSouth Africa Department of Electrical Engineering Sukkur IBA UniversityPakistan Department of Computer Education Sungkyunkwan UniversitySeoulRepublic of Korea

Unmanned Aerial Vehicles(UAVs)will be essential to support mission-critical applications of Ultra Reliable Low Latency communication(URLLC)in futuristic Sixth-Generation(6G)***,several security vulnerabilities and attacks have plagued previous generations of communication systems;thus,physical layer security,especially against eavesdroppers,is vital,especially for upcoming 6G *** this regard,UAVs have appeared as a winning candidate to mitigate security *** this paper,we leverage UAVs to propose two *** first method utilizes a UAV as Decode-and-Forward(DF)relay,whereas the second method utilizes a UAV as a jammer to mitigate eavesdropping attacks for URLLC between transmitter and receiver ***,we present a low-complexity algorithm that outlines the two aforementioned methods of mitigating interception,*** secrecy rate,and we compare them with the benchmark null method in which there is a direct communication link between transmitter and receiver without the UAV DF ***,simulation results show the effectiveness of such methods by improving the secrecy rate and its dependency on UAV height,blocklength,decoding error probability and transmitter-receiver separation ***,we recommend the best method to enhance the secrecy rate in the presence of an eavesdropper based on our simulations.

关键词： URLLC UAV-Assisted communications 6G systems Secrecy rate

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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

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computers, Materials & Continua 2024年第9期80卷 4791-4812页

作者： Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan Department of Accounting and Finance Faculty of BusinessMiddle East UniversityAmman11831Jordan Faculty of Engineering Al-Balqa Applied UniversitySalt19117Jordan Department of Mathematics Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo210-40601Kenya Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering Saveetha Engineering College(Autonomous)Chennai602105India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.

关键词： Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units

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Binary Particle Swarm Optimization for Design of a Reconfigurable Transmissive Unit Cell

Binary Particle Swarm Optimization for Design of a Reconfigu...

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2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Roy, Saurav Vinoy, K.J. Department of Electrical Communication Engineering Indian Institute of Science Bengaluru Karnataka Bengaluru India

ISBN: (纸本)9798350379693

Ensuring connectivity between pixels and the switching element on a pixelated unit cell is challenging using binary particle swarm optimization (BPSO). Here, a design approach is formulated to utilize BPSO for a reconfigurable unit cell design, and then a novel random walk-based initialization is proposed to reduce computation cost. The proposed initialization scheme has shown several-fold improvement in the computation time. This approach may be extended for metasurface unit cell designs for various other applications. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Islanding Detection in PV-Grid System by Random Forest and Spectrogram Based LBP 3

Islanding Detection in PV-Grid System by Random Forest and S...

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3rd Odisha International Conference on electrical Power engineering, communication and Computing Technology, ODICON 2024

作者： Singh, Ritu Shukla, S.P. Panda, Archana Sahoo, Subhendu Sekhar Pati, Subhendu Panigrahi, Basant K Bhilai Institute of Technology Department of Electrical Engineering Durg India Soa Deemed to Be University Department of Electronics and Communication Engineering Bhubaneswar India Gandhi Institute for Technology Department of Electrical Engineering Bhubaneswar India Soa University Department of Electrical Engineering Iter Bhubaneswar India

ISBN: (纸本)9798350354379

Islanding is a significant obstacle along with excessive penetration of DG sources. The islanding could cause harm to the customers and their equipment's. The IEEE 1547 DG interconnection regulations stipulate that the DG must be disconnected and the islanding must be observed within two seconds. Basically, this paper presents a novel method for detecting islanding based upon image pattern recognition using random forest (RF) classification technique is proposed. Images are utilized to derive the local binary pattern (LBP) characteristics so as to identify non-islanding as well as islanding events. Hence, the time series signal is obtained and transformed into spectrogram images at the point of common connection. These images are utilized to extract the features using histogram of oriented gradient, which is then utilized as a feature vector input for the RF classifier for training as well as testing. It uses rate of change of negative sequence voltage parameter to derive spectrogram image. The RF classifier's effectiveness is assessed with 5-fold cross-validations. The results from classification demonstrates that detection of islanding using RF classifier and LBP feature being used for image pattern recognition achieved excellent results with 98.25% accuracy and detection time of 182 milliseconds. © 2024 IEEE.

关键词： Spectrographs

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Multi-objective Cluster-head-based Energy-aware Routing using a Separable Convolution Neural Network in a Wireless Sensor Network

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IEIE Transactions on Smart Processing and Computing 2024年第2期13卷 105-112页

作者： Ather, Danish Mary, J. Prisca Singh, Pooja Garg, Kanika Priyadharshini, T.R. Princy, B. Anni Tiwari, Mohit Department of IT and Engineering Amity University in Tashkent Uzbekistan Department of Computer Science and Engineering NPR College of Engineering and Technology Tamil Nadu Dindigul India Department of Physics Faculty of Science SGT University Haryana Gurugram India Department of Computer Science and Engineering SRM Institute of Science and Technology NCR Campus Ghaziabad Modinagar India Department of Information Technology Hindusthan College of Engineering and Technology Pollachi main road Coimbatore India Department of Computer and Communication Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

Wireless sensor network (WSN) applications are added day by day owing to numerous global uses (by the military, for monitoring the atmosphere, in disaster relief, and so on). Here, trust management is a main challenge. Sensor nodes are important in wireless sensor networks, but they are easily depleted because of their short lifespan from continuous sensing activity and low battery capacity. So efficient energy utilization is a challenging task in a WSN. To minimize energy loss, clustering with an optimum path selection process is needed to retain energy in sensor nodes. This manuscript proposes multi-objective Pelican Optimization Algorithm (POA) routing to maintain energy efficiency and minimize transmission distances in wireless sensor networks. A cluster head (CH) is selected by using a Separable Convolution Neural Network (SCNN). Simulation outcomes prove the proposed technique attains 22.3% and 25.04% improvements in energy consumption when compared to the Multi-Objective CH Energy-aware Optimized Routing Approach at WSN (MOCH-EORA-WSN) and Multiple Optimum Cluster Head Multi-Objective Grasshopper Optimization with Harmony-search at WSN (MOCH-MOGOH-WSN), respectively. © 2024 institute of Electronics Engineers of Korea. All rights reserved.

关键词： Energy utilization

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O-RAN-Based NTN Architecture: Open NTN Concept and Optimization of Power Allocation and Fronthaul Compression

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 7950-7966页

作者： Lee, Junseok Kim, Hee Wook Ryu, Joon-Gyu Zikria, Yousaf Bin Jung, Bang Chul Yu, Heejung Korea University Department of Electronics and Information Engineering Sejong30019 Korea Republic of Electronics and Telecommunications Research Institute Satellite Communication Infra Research Section Daejeon34129 Korea Republic of SydneyNSW2000 Australia Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

Based on an open radio access network (O-RAN) architecture in terrestrial networks (TNs), this study proposed an open nonterrestrial network (open NTN) architecture, in which multiple service operators could share low Earth orbit (LEO) satellites for cost-effective network deployment in LEO-based NTNs. In the open NTN, a centralized unit of next-generation node B is located in a gateway, and the distributed units and radio units are in LEO satellites. Intersatellite links (ISLs) between LEOs function as wireless fronthaul links, where data and pilot symbols are delivered for further processing. Compared to O-RAN in TNs with wired fronthaul, the bandwidth constraints in wireless ISL fronthaul are more stringent, and signal compression in fronthaul cannot be avoided. Therefore, to improve the efficiency of open NTNs, a joint optimization problem of ISL fronthaul compression rate and power allocation to the data and pilot signals was formulated to maximize the sum of uplink rates of multiple user equipments under the bandwidth constraints of the ISL fronthauls. The original problem was divided into two subproblems: one is an optimization of the data and pilot compression rate and the other is that of the data and pilot power allocation. An alternating optimization approach that solved two subproblems in an iterative manner was adopted to find the convergent near-optimal solution. The convergence and performance of the proposed approach were verified with numerical results. © 1965-2011 IEEE.

关键词： Satellite links

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Stock Prediction by Signal Decomposition-driven Multivariate Feature Extractor and Executor-based Mixture of Experts

Stock Prediction by Signal Decomposition-driven Multivariate...

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2025 IEEE Symposium on Computational Intelligence for Financial engineering and Economics Companion, CiFer Companion 2025

作者： Tsai, Cheng-En Li, Shiou-Chi Huang, Jen-Wei Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Institute of Computer and Communication Engineering Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9798331519704

Stock market prediction is a complex and dynamic area of fintech, involving the assessment of risk, projecting returns, and analyzing trends. Researchers have demonstrated that signal decomposition can facilitate all of these tasks by untangling intricate data streams. This paper presents a novel approach to predicting short-term stock market returns. The proposed scheme integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and the Mixture of Experts (MoE) method within an ensemble learning framework. Decomposed signals are encoded into embeddings via attention mechanisms to facilitate feature extraction, and an innovative training target tailored for the unique MoE architecture was shown to significantly boost prediction accuracy. In simulations using two real-world stock markets, the proposed method outperformed existing state-of-the-art methods on all relevant metrics. © 2025 IEEE.

关键词： machine learning mixture of experts signal decomposition stock prediction

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A Comprehensive Investigation of GaSb/GaAs0.5Sb0.5/GaAs based Double Gate Source-Pocket Engineered Heterogeneous Gate Dielectric Vertical TFET for Optimized Low-Power Applications 6

A Comprehensive Investigation of GaSb/GaAs0.5Sb0.5/GaAs base...

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6th International Conference on electrical engineering and Information and communication Technology, ICEEICT 2024

作者： Mahbub, Lamiya Masud Badhon, Mehrab Ibne Sultana, Sunjida Saha, Riya Military Institute of Science and Technology Department of Electrical Electronic and Communication Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

Recognizing the simplified fabrication and advanced performance of Vertical Tunnel Field Effect Transistors (VTFET) compared to Lateral TFET, numerous research has been con- ducted. In this study, a comparative analysis was performed on two double gate heterojunction source-pocket engineered VTFETs with and without heterogeneous cascaded gate dielectric using ATLAS SILVACO TCAD software. Highly doped GaSb as source intensifies the tunneling rate. GaAs0.5Sb0.5 as source pocket reduces lattice mismatch and increases Ion. The research indicates that using High-? and & Low-? thick gate dielectrics at source and drain ends reduces average subthreshold swing (SSavg), enhances transconductance(gm), and amplifies Ion/Ioff ratio exponentially. This research examines Analog/RF and DC performances of VTFET to optimize crucial device parameters. The heterogeneous gate dielectric structure incorporating HfO2 showed an improved Ion of 1.1 10-5 A/ m, diminished leakage current Ioff of 1.05 10-17 A/ m, SSavg of 19.6 mV/dec, highest Ion/Ioff ratio of 1.04 1012 and gm of 45 S. The analysis revealed that the study is appropriate for assessing low-power applications with increased performance and also has a great potential for broadening the realm of vertical TFETs. © 2024 IEEE.

关键词： Electric fields

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MEMHD: Memory-Efficient Multi-Centroid Hyperdimensional Computing for Fully-Utilized In-Memory Computing Architectures

MEMHD: Memory-Efficient Multi-Centroid Hyperdimensional Comp...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Kang, Do Yeong Oh, Yeong Hwan Hwang, Chanwook Kim, Jinhee Jeon, Kang Eun Ko, Jong Hwan Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of College of Information and Communication Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9783982674100

The implementation of Hyperdimensional Computing (HDC) on In-Memory Computing (IMC) architectures faces significant challenges due to the mismatch between high-dimensional vectors and IMC array sizes, leading to inefficient memory utilization and increased computation cycles. This paper presents MEMHD, a Memory-Efficient Multi-centroid HDC framework designed to address these challenges. MEMHD introduces a clustering-based initialization method and quantization-aware iterative learning for multi-centroid associative memory. Through these approaches and its overall architecture, MEMHD achieves a significant reduction in memory requirements while maintaining or improving classification accuracy. Our approach achieves full utilization of IMC arrays and enables one-shot (or few-shot) associative search. Experimental results demonstrate that MEMHD outperforms state-of-the-art binary HDC models, achieving up to 13.69% higher accuracy with the same memory usage, or 13.25x more memory efficiency at the same accuracy level. Moreover, MEMHD reduces computation cycles by up to 80x and array usage by up to 71x compared to baseline IMC mapping methods when mapped to 128x128 IMC arrays, while significantly improving energy and computation cycle efficiency. © 2025 EDAA.

关键词： Memory architecture

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