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A Combine CNN-RNN Based Approach for Augmenting the Performance of Speech Emotions Recognition

Traitement du Signal 2025年第2期42卷 647-662页

作者： Sayed, Nasir Husnain, Ghassan Shoaib, Muhammad Ghadi, Yazeed Yasin Alajmi, Masoud Qahmash, Ayman Department of Computer Science CECOS University of IT and Emerging Science Peshawar25000 Pakistan Department of Computer Science Islamia College Peshawar Peshawar25100 Pakistan Department of Computer Science and Software Engineering Al Ain University Al Ain64141 United Arab Emirates Department of Computer Engineering College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Department of Informatics and Computer Systems King Khalid University Abha61421 Saudi Arabia

Due to the advancement of neural networks and the increasing demand for accurate and real-time Speech Emotion Recognition (SER) in human-computer interactions, it is necessary to compare existing methods and databases in SER in order to arrive at feasible solutions and a complete understanding of this open-ended problem in SER. To detect and recognize the emotions expressed in speech, various techniques have been used in the literature, including well-established speech analysis and classification techniques. These techniques, including speech analysis and classification, have been used to extract emotions from signals. In this study, we propose a novel method for analyzing signals called Wavelet-Scaled Spectrogram which combines the frequency and scale spectrum of a signal using wavelet transform. This method is effective in analyzing signals at different scales and frequency content. In order to train models for speech emotion identification, a large number of handcrafted features and intermediary depictions i.e., frequency-time plot that have traditionally been utilized in data compilation, collection, and analysis. The development of end-to-end models which extract characteristics and learn directly from raw speech signals to improve speech recognition has recently been studied by researchers following the emergence of deep learning. After training and evaluation on the famous speech databases EmoDB, RAVDESS and IEMOCAP, the proposed model is evaluated on various speakers in both speaker-independent and speaker-dependent modes and on a variety of different voices. When advanced preprocessing techniques or data augmentation are omitted from the proposed architecture, the results demonstrate that it can produce products comparable to those produced by the current state of the art. Three concurrent CNN pipelines and a series of modified local features learning blocks (LFLBs) achieved the highest classification accuracy outperforming some advance state-of-the-approa

关键词： Recurrent neural networks

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Decentralized MIMO systems With Imperfect CSI Using LMMSE Receivers

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IEEE Journal on Selected Topics in Signal Processing 2025年第2期19卷 331-348页

作者： Zhuang, Zeyan Zhang, Xin Xu, Dongfang Song, Shenghui Eldar, Yonina C. The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong The Hong Kong University of Science and Technology Division of Integrative Systems and Design Hong Kong Hong Kong The Weizmann Institute of Science Department of Mathematics and Computer Science Rehovot7610001 Israel

Centralized baseband processing (CBP) is required to achieve the full potential of massive multiple-input multiple-output (MIMO) systems. However, due to the large number of antennas, CBP suffers from two major issues: 1) Extensive data interconnection between radio frequency (RF) circuitry and the central processing unit;and 2) high-dimensional computation. To this end, decentralized baseband processing (DBP) has been proposed, where the antennas at the base station are partitioned into clusters connected to separate RF circuits and equipped with separate computing units. However, the optimal fusion scheme that maximizes signal-to-interference-and-noise ratio (SINR) and the related performance analysis for DBP with general spatial correlation and imperfect channel state information (CSI) have not been studied. In this paper, we consider a decentralized MIMO system where all clusters adopt linear minimum mean-square error (LMMSE) receivers. We first establish an optimal linear fusion scheme that has high computational and data input/output costs. To reduce the cost, we then propose two suboptimal fusion schemes with reduced complexity. For all three schemes, we study the SINR performance by leveraging random matrix theory and demonstrate conditions under which the suboptimal schemes are optimal. Furthermore, we determine the optimal regularization parameter for the LMMSE receiver, identify the best antenna partitioning strategy, and prove that the SINR will decrease as the number of clusters increases. Numerical simulations validate the accuracy of the theoretical results. © 2025 IEEE.

关键词： Mean square error

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A decentralized and privacy-preserving framework for electronic health records using blockchain

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Alexandria engineering Journal 2025年 126卷 196-203页

作者： Alahmari, Saad Alshardan, Amal Al-Wesabi, Fahd N. Sorour, Shaymaa Alghushairy, Omar Alsini, Raed Khadidos, Alaa O. Duhayyim, Mesfer Al Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Management Information Systems College of Business Administration King Faisal University Al-Ahsa31982 Saudi Arabia Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj16273 Saudi Arabia

As healthcare services have become increasingly digitized, Electronic Health Records (EHRs) have become widely adopted, providing seamless data exchange among providers. Conventional EHRs, however, are extremely vulnerable to cyber threats because patients' sensitive data is centralized and transmitted electronically. The paper proposes a decentralized, privacy-preserving framework for managing EHRs on blockchains in order to address these security and privacy concerns. Using cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, the proposed system enhances security and ensures data integrity. Additionally, the model facilitates scalable, efficient, and secure access to patient records through the integration of cloud-based storage and blockchain. Using smart contracts, we also ensure compliance with healthcare regulations by regulating access control and authentication. As a result of performance evaluations, the proposed approach is demonstrated to be feasible, and the advantages it offers in terms of security, privacy, and efficiency are highlighted. © 2025 The Authors

关键词： Electronic health record

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IoT based Leakage Detection and Emergency Shutdown 6

IoT based Leakage Detection and Emergency Shutdown

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Abarna, P.S. Priya, S. Padma Pavalarajan, S. Sivatharani, S. Manshi, M. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331522667

The risk of gas leaks has grown significantly as a life threatening issue in industrial activities, cooking, and heating. This system integrates automatic reaction mechanisms, real-time monitoring capabilities, and advanced detection technologies in a multifaceted manner to address various dangers. The comprehensive gas leak detection and automatic cut off system is intended to improve safety in a variety of situations, including industrial and residential ones. To precisely identify the existence of dangerous gases like methane and propane, the proposed system combines infrared, electrochemical, and semiconductor types of gas sensors integrated with a mobile application, users may remotely administer and monitor the system in real-time. The system initiates a multi-tiered response upon detecting a gas leak;it sounds an alarm, notifies user-specified The mobile devices about the leak, and turns on an exhaust fan to release concentrated gas. A solenoid valve is used by the system to quickly shut off the gas supply and electricity from any location. The need of user-friendly controls and prompt notifications in emergency situations is highlighted in this paper's discussion of software architecture, sensor integration, and user interface design. Experimental results show that the system is reliable and capable of responding quickly under different circumstances. In future the application of machine learning algorithms for predictive maintenance and the possibility of integrating IoT to further strengthen security protocols. The results emphasize importance to deploy these kinds of extensive safety systems widely in order to reduce the risks associated with gas exposure. © 2025 IEEE.

关键词： Solenoid valves

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Recommendation System using Emotion Detection 3

Recommendation System using Emotion Detection

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Susindra, S. Sweta, B. Keerthikha Shri, R. Brindha, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver in decision making. The paper proposes a new framework in which the detection of emotions becomes part of the recommendation system through advanced facial expression analysis and sentiment evaluation. The paper discusses a hybrid architecture combining emotion recognition via CNN and a collaborative filtering technique for dynamic recommendations. The study outcome demonstrated an enormous improvement in user engagement and satisfaction. It further underlines the transformative potential of emotion-aware systems. © 2025 IEEE.

关键词： Emotion Recognition

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Android offloading computation over the cloud 1st

Android offloading computation over the cloud

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Prakash, Vijay Dudeja, Deepak Chhabra, Gunjan Garg, Lalit Department of Computer Information Systems University of Malta Msida Malta Department of Computer Science and Engineering MMEC MMDU Haryana Mullana India Department of Computer Science & Engineering Graphic Era Hill University Dehradun India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9781032874036

This paper analyzes whether Android apps may outsource computational activities to cloud servers. Due to the complexity of mobile apps, shifting computing operations to the cloud has become popular to improve performance and reduce energy consumption. This paper explains how to deploy an Android app on Cloudflare and tests it under various settings. The testing showed that cloud computing can boost Android app efficiency. The findings show that shifting computing workloads to the cloud reduces mobile device CPU utilization. This frees up device resources, allowing it to perform other tasks. However, cloud processing has limitations. Network latency between the mobile device and the cloud server may affect application performance. In addition, the cost of offloading computation to the cloud can be a concern for some users, especially in high data consumption scenarios. Our findings can be helpful for developers who want to optimize the performance of their Android applications and for researchers who wish to investigate this topic further. Future research could explore ways to minimize the impact of network latency on offloading computation and investigate the cost-effectiveness of this technique for different types of applications. © 2025 the Author(s).

关键词： Cloud security

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Deep Learning-Based Enhanced Object Detection for Humanoid Robots 1

Deep Learning-Based Enhanced Object Detection for Humanoid R...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Negi, Akshit Patel, Harshita Vellore Institute of Technology School of Computer Science Engineering & Information Systems Vellore India

ISBN: (纸本)9798331520540

In the contemporary world, humanoid robots are likely to play a key role in various fields, including health care, domestic service, hospitality, business, and military and security activities. The robots are employed to assist people in accomplishing specific missions or sometimes substitute human beings in hazardous situations. To accomplish these missions effectively, humanoid robots should be very good at recognition and subsequent processing of objects. The main goal of this research is to enhance the object recognition capabilities Leverage the advancements in deep learning to provide the control of Robotis-Op3 humanoid robots. In this work, the researchers analyzed popular deep neural models, among which are VGG16 and ResNet, highly promoted for their robust object recognition ability, as well as the more recent CNN architectures such as SSD MobileNetV3. This research improves object recognition capabilities on hu-manoid robots as such to enhance performance. in different domains, allowing them to better recognize and interact with the objects around them. This development will then allow more efficient and effective utilization of humanoid robots, extending their application and contribution in society. © 2025 IEEE.

关键词： Anthropomorphic robots

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Attention-Driven Emotion Recognition in EEG: A Transformer-Based Approach With Cross-Dataset Fine-Tuning

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IEEE Access 2025年 13卷 69369-69394页

作者： Ghous, Ghulam Najam, Shaheryar Alshehri, Mohammed Alshahrani, Abdulmonem AlQahtani, Yahya Jalal, Ahmad Liu, Hui Riphah International University Department of Electrical and Computer Engineering Islamabad44000 Pakistan Bahria University Department of Electrical Engineering Islamabad44000 Pakistan King Khalid University Department of Computer Science Abha61421 Saudi Arabia King Khalid University Department of Informatics and Computer Systems Abha61421 Saudi Arabia Air University Faculty of Computer Science and AI Islamabad44000 Pakistan Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of University of Bremen Cognitive Systems Laboratory Bremen28359 Germany Guodian Nanjing Automation Company Ltd. Nanjing210032 China

Emotion recognition from EEG (electroencephalogram) signals is crucial in mental health diagnostics and human-computer interaction but is often hindered by high dimensionality, noise, and complex temporal dependencies in the data. This paper presents a novel approach that integrates transformer models, attention mechanisms, and transfer learning to enhance emotion recognition accuracy from EEG signals. The proposed methodology consists of two phases: Attention Enhanced Base Model Development (AE-BMD) and Cross-Dataset Fine Tuning Adaptation (CD-FTA). In the AE-BMD phase, the base model is developed and trained on the SEED-IV dataset (15 participants, 62 EEG channels), achieving an accuracy of 84%, with an average precision of 84.75%, recall of 84% and F1-score of 84%. This phase employs optimized feature extraction from key EEG frequency bands (Delta, Theta, Alpha, Beta, Gamma) using techniques such as MFCC, GFCC, power spectral density, and Hjorth parameters. A transformer encoder with integrated spectral and temporal attention mechanisms captures intricate patterns and long-range dependencies within the EEG signals. In the CD-FTA phase, the model undergoes fine-tuning on the SEED-V dataset (20 participants, 62 channels) leading to an improved accuracy of 90%, with an average precision of 90.6%, recall of 90.6%, and F1-score of 90.6%. The model’s generalization is further validated on the MPED dataset (23 participants, 62 channels, seven emotion classes), achieving 79%, with an average precision of 79.3%, recall of 79.3% and F1-score of 79.1% across diverse emotional states. This cross-dataset adaptation leverages transfer learning to enhance the model’s generalization across different emotional states and EEG datasets. Experimental results show that the proposed approach outperforms traditional methods, achieving superior accuracy and robustness in emotion recognition tasks. This work advances emotion recognition systems by addressing challenges in EEG signal proc

关键词： Electroencephalography

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Cryptography machine: Encryption and decryption model for multimedia data 1st

Cryptography machine: Encryption and decryption model for mu...

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

ISBN: (纸本)9781032874036

The digital era has brought a surge in the amount of data generated, increasing the need for data security across individuals, organizations, and governments. Protecting sensitive information from unauthorized access is a top priority in today’s digital world. Cryptography is used to ensure secure communication over the internet, including email, instant messaging, and web browsing. Our paper’s primary goal is to develop a cryptography machine capable of encrypting and decrypting different data types, such as plain text, images, audio and videos using the cryptography of the symmetric key. The proposed model will be implemented using Python and Java. Developing the cryptography machine will significantly protect sensitive information and ensure its confidentiality and integrity. The machine’s ability to encrypt and decrypt different data types using symmetrickey cryptography will provide an added layer of security to individuals, organizations, and governments in the digital era. © 2025 the Author(s).

关键词： algorithms Cryptography decryption encryption keys machine

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Encryption and decryption framework using elliptic curve cryptography 1st

Encryption and decryption framework using elliptic curve cry...

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Prakash, Vijay Dudeja, Deepak Chhabra, Gunjan Garg, Lalit Department of Computer Information Systems University of Malta Msida Malta Department of Computer Science and Engineering MMEC MMDU Haryana Mullana India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9781032874036

Secure file encryption has become increasingly important in various disciplines, including finance, healthcare, and government, where protecting sensitive data is paramount. The proposed framework aims to meet this requirement by developing an encryption and decryption framework for files that employ Elliptic Curve Cryptography (ECC), a modern and practical cryptographic technique. The framework will employ ECC algorithms for key generation, encryption, and decryption, which offer several advantages over conventional methods such as RSA and AES. ECC provides equivalent security with shortened key lengths, resulting in faster encryption and decryption, decreased computational burden, and optimized resource utilization. A crucial aspect of the proposed framework is its user-friendly interface, making it easy for users to manage encrypted files. In addition, the framework will include key management and validation mechanisms to ensure the security and authenticity of encryption keys, thereby preventing unauthorized access to encrypted files. The proposed framework will be rigorously tested on a local machine and hosted on a server in the cloud to ensure its dependability and resiliency in real-world scenarios. The framework’s efficacy will be determined based on the encryption and decryption times for various file sizes. The outcomes will demonstrate the efficacy and efficiency of the proposed framework, demonstrating that it provides superior performance compared to conventional algorithms. © 2025 the Author(s).

关键词： decryption Elliptic Curve Cryptography Encryption

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