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A Deep CNN Self-Attention Model for Multidimensional Speech Quality Prediction Using Crowdsourced datasets 5

A Deep CNN Self-Attention Model for Multidimensional Speech ...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Gummadi, Annapurna Kaur, Chamandeep Gupta, Deepak Rao, Vuda Sreenivasa Tripurana, Santosh Nagaraj, P. Hyderabad India Jazan University Computer Science & Information Technology Department Jazan Saudi Arabia Graphic Era Hill University Department of Mechanical Engineering Uttarakhand Dehradun India Graphic Era Deemed to be University Department of Mechanical Engineering Uttarakhand Dehradun India Koneru Lakshmaiah Education Foundation Department of Cse A.P. Vaddeswaram India Vignan's Institute of Engineering for Women Dept of Ece Andhra Pradesh Visakhapatnam India K. Ramakrishnan College of Engineering Department of Ai & Ml Tamil nadu Trichy India

ISBN: (纸本)9798350356236

Predicting voice quality is critical for improving communication systems and the user experience. In this article, we offer a unique technique to multidimensional speech quality prediction using a Deep Convolutional Neural Network (CNN) enriched with self-attention techniques and crowdsourced datasets. The proposed model incorporates both spatial and temporal information from speech signals via numerous convolutional layers, enabling it to accurately represent the intricate patterns and features seen in speech data. Furthermore, self-attention methods are used to let the algorithm to focus on relevant parts of the input data, increasing its discriminative capacity and tolerance to interference. Transfer learning is utilized to leverage pre-trained representations from large-scale speech datasets, enabling the model to generalize well to diverse speech characteristics and environments. By fine-tuning the pre-trained model on the specific crowdsourced dataset, we adapt it to the task of speech quality prediction while mitigating the need for large annotated datasets, which are often costly and time-consuming to obtain. Experimental results demonstrate the effectiveness of the proposed approach, achieving state-of-the-art performance on various metrics for multidimensional speech quality prediction tasks. Furthermore, extensive ablation studies are conducted to analyze the contributions of different components of the model, highlighting the importance of both the CNN architecture and self-attention mechanisms. Overall, our findings suggest that the proposed deep CNN self-attention model, combined with transfer learning from crowdsourced datasets, presents a promising approach for accurate and efficient speech quality prediction in diverse real-world scenarios © 2025 IEEE.

关键词： Convolutional neural networks

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Real-Time Anomaly Detection in Industrial Automation Using Generative Adversarial Networks 5

Real-Time Anomaly Detection in Industrial Automation Using G...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Changala, Ravindra Khan, Shamim Ahmad Kumar, Vijay Rani, V. Kanpur Dhilli, K. Raj, I. Infant Department of Cse Guru Nanak Institutions Technical Campus Hyderabad India Glocal School of Science & Technology Glocal University Department of Ece Uttar Pradesh India Department of Physics Graphic Era Hill University Dehardun India Graphic Era Deemed to be University Dehradun India Hindustan Institute of Technology and Science Department of Eee Chennai India Vignan's Institute of Engineering for Women Dept of Ece Visakhapatnam India K. Ramakrishnan College of Engineering Department of Cse Tamil Nadu Trichy India

ISBN: (纸本)9798350356236

The study presents the use of GANs in anomaly detection for real-time industrial automation. A new model is proposed and implemented in Python, taking into consideration the dual nature of the algorithm known as GANs, which work on a dual framework including a generator and a discriminator. The normal behavior of industrial automation must be generated by a generator;meanwhile, the discriminator distinguishes between normal and anomalous data. It would facilitate the effective identification of anomalies, through adversarial training, based on learning normal operational patterns in real time, with the advantage of being adaptable in very dynamic and complex industrial environments. The ability to be timely about intervention, given its real-time capability, significantly enhances such attributes as predictive maintenance, improved quality control, and safe and efficient operations. This has been found in the study that the right identification of anomalies will prevent expensive downtime and will be a sign of smooth industrial process. In the proposed GAN model, 99.12% accuracy is achieved compared with other approaches, such as DBN, AnoGAN, and BiGAN, with an impressive gap of 4.05%. Thus, superior performance indicates that GANs are robust and reliable for dealing with real-time industrial automation challenges. A critical characteristic of this Python implementation model is its adaptability, flexibility, and amenability to a practitioner in the industry as much as a researcher. Overall, this study further provides significant additions to the overall understanding of anomaly detection in an industrial setting with its demonstration capabilities toward the resiliency enhancement and efficiency potential in modern systems of industry through GAN applications. © 2025 IEEE.

关键词： Office automation

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Integrated Energy System for an Integrated Park of Cloud Pipe Edge Device Based on Carbon Emission Flow: System Framework, Key technologies and Prospects

Integrated Energy System for an Integrated Park of Cloud Pip...

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international conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Wang, Yun Ke, Ji Yin, Yaohua Xing, Xueshu College of Energy and Electrical Engineering Chang’an University Shaanxi Xi’an710064 China Power China Guiyang Engineering Co. Ltd Guiyang China

ISBN: (纸本)9789819618637

The "double carbon" strategy has brought attention to the importance of integrated energy system management in parks for improving energy utilization efficiency, reducing energy consumption costs, and achieving intelligent management. This paper introduces an integrated energy system framework for a park that uses cloud-based edge devices and focuses on managing carbon emissions and operational efficiency based on carbon emission flow. This framework aims to enhance data processing and energy management efficiency, decrease carbon emissions, and improve economic performance by utilizing resource pre-allocation through edge computing and centralized management via cloud platforms. The paper summarizes critical technologies associated with this framework, including carbon emission prediction, data security assurance, operational efficiency maintenance, and pressure reduction on energy storage facilities to optimize carbon emission solutions. These technologies support the low-carbon development and optimal resource utilization of integrated energy systems, providing a novel approach to sustainable development and a reference for future technological advancements. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Carbon capture and utilization

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LLM-Based Automating Product Information Retrieval for Industry Analysis: A Real-World Application 8th

LLM-Based Automating Product Information Retrieval for Indu...

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8th international conference on Cognitive computing, ICCC 2024, Held as Part of the Services conference Federation, SCF 2024

作者： Liao, Chen Cheng, Gang Huang, Shilei Yao, Lin IMSL Shenzhen Key Lab PKU-HKUST Shenzhen Hong Kong Institution ShenZhen China Shenzhen Raisound Technologies Co. Ltd. ShenZhen China

ISBN: (纸本)9783031779534

In the rapidly evolving digital landscape, effective retrieval of product information from enterprise websites is crucial for enterprise research, industry analysis, and strategic planning, which rely on accurate and comprehensive data. In this context, a "product" is defined as any tangible item, a solution, or a service. This paper proposes a novel method for extracting such product data-such as product name, category, description, and specifications-directly from company websites. Our approach leverages the capabilities of Large Language Models (LLMs) to enhance the accuracy and automation of the web-page information retrieval process. The adoption of LLMs allows for a more sophisticated extraction and organization of data, overcoming the limitations of conventional methods. Currently, there is a notable absence of open-source or commercial databases that comprehensively cover enterprise products, making it challenging to conduct comparative studies. Our proposed method aims to fill this gap, providing a tool for gathering product information that can be used to assess competitive differences. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data accuracy

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Diabetic Foot Ulcer Classification Using Deep Learning Approach 8th

Diabetic Foot Ulcer Classification Using Deep Learning Appro...

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8th IFIP TC 12 international conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Sivakamasundari, P. Niranjana, G. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9783031736162

Podiatrist diagnosis and lesion localization are used as current testing approaches for Diabetic Foot ulcers (DFU). Current systems for automation concentrate on either categorization or division. One of the most common metabolic conditions, Diabetes (also called diabetes mellitus), is brought on by the pancreas incapacity to generate sufficient anti hyper-glycemic hormone to control glycemia levels. It may cause "Diabetic Peripheral Neuropathy," a collection of nerve diseases that can develop if the blood glucose level is left uncontrolled and unmanaged. Blood glucose levels may be efficiently maintained by recognising diabetes initially through constant surveillance and screenings. Using image processing methods such as data stretching with Deep Learning, image reduction and improvement, image division, and feature extraction, this paper offers a thorough approach for analysing thermal scans of the diabetic foot. By using these techniques, doctors may assess and track a patient's condition with fewer examinations. In this work, EfficientNetB3 architecture is compared with other Deep Learning models for accuracy in DFU identification. To create a powerful deep learning model, we gathered an extensive collection of 1,775 DFU photos. The basis of belief for this data gathering was created by two medical experts using annotator software to define the DFU region of interest. It took 48 ms to figure out a single image and 57.2 MB to validate the model. EfficientnetB3 got an average mean accuracy of 93%. This work demonstrates the effectiveness of Deep Learning for DFU localization in real-time, which could have improved with the use of a larger data set. © IFIP international Federation for Information Processing 2025.

关键词： Diagnosis

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Adaptive Gradient data Partition and Route Selection for Distributed DNN Training 20th

Adaptive Gradient Data Partition and Route Selection for D...

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Chai, Bo Tan, Xiaobin Yuan, Shenzhi Jia, Guangge Meng, Qiushi Wang, Weifeng Zhu, Shiyin University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China H3C Technologies Co. Limited Beijing China

ISBN: (纸本)9789819628636

As the scale of distributed training for Deep Neural Network (DNN) increases, communication has become a critical performance bottleneck in data center networks. In-Network Aggregation (INA) can accelerate aggregating gradients process, the critical communication bottleneck in distributed DNN training. Due to the limited processing capacity of network switch, existing aggregation approaches have turned to a combination of INA and Parameter Server (PS) methods. However, these approaches employ a fixed partitioning of the gradient data, leading to suboptimal performance. This paper proposes GPRS, an adaptive gradient aggregation framework for distributed DNN training, which adaptively combines the line-rate processing of INA and large memory capacity of PS. We present an adaptive gradient data partition scheme, which divides the gradient data into INA and PS aggregation. We propose a heuristic algorithm that determines the aggregation route selection by assigning priority to the available network paths. To validate our approach, we build a real-world experimental testbed and implement the proposed framework. Experimental results show that it can overcome the limitations of traditional INA-only methods and reduce communication time by 15% ∼ 52% for DNN training of varying scales. © IFIP international Federation for Information Processing 2025.

关键词： data aggregation

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Development of an Arabic Sign Language Recognition System Utilizing Deep Convolutional Neural Network 5

Development of an Arabic Sign Language Recognition System Ut...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Kale, Meenal R. Kaur, Chamandeep Kumar, Indrajeet Mahdi, Haider Sharif Uprikar, Vijay Sankari, V. Yeshwantrao Chavan College of Engineering Department of Humanities Nagpur India Jazan University Department of Computer Science & Information Technology Jizan Saudi Arabia Department of Cse Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun India Al-Farahidi University Department of Biomedical Engineering Baghdad Iraq Datta Meghe Institute of Management Studies Nagpur India K. Ramakrishnan College of Engineering Department of Ai&ds Trichy India

ISBN: (纸本)9798350356236

The ability to recognize sign language movements, especially Arabic Sign Language (ArSL), is necessary to increase the conversation accessibility for the those who are hard of hearing or deaf. However, there are a number of drawbacks to the current ASLR techniques, such as their dependency on manually created features, lack of datasets, and inefficiencies in real-time applications. This study suggests a novel method that utilize Deep Convolutional Neural Network (DCNN) model designed especially for ASLR applications to overcome these difficulties. The suggested DCNN overcomes the drawbacks of conventional handmade feature-based techniques by automatically extracting discriminative features from unprocessed sign language images using hierarchical feature learning. Preprocessing techniques such as normalization and scaling are applied to improve the 7,857 completely labeled images in the RGB Arabic Alphabet Sign Language (AASL) the data set, which is provided for ASLR model evaluation and training. The results of the study demonstrate how successful the recommended DCNN model is, as evidenced by its astounding 99% accuracy on the AASL dataset. Comparison with similar approaches like VGG-16, ResNet-18, and other Efficient Net based architecture, shows the accuracy to which the proposed method can classify Arabic sign language alphabets. The findings presented evidence for how the chosen architecture for DCNNs can significantly enhance ASLR improvement, open up opportunities for improving communication interaction with Deaf and hard-of-hearing people and their integration. In doing the proposed work, we use Python. © 2025 IEEE.

关键词： Convolutional neural networks

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Advanced Facial Recognition with Behavioural Analysis for Intelligent Security Monitoring 5

Advanced Facial Recognition with Behavioural Analysis for In...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Changala, Ravindra Yasaswi, K. RaviKiran Farooq, Mansoor Khan, Shamim Ahmad Kiran, Ajmeera Muthuperumal, S. Department of Cse Guru Nanak Institutions Technical Campus Hyderabad India Jntuk Department of Master of Business Administration Andhra Pradesh India American University of Bahrain Department of Cybersecurity Bahrain Glocal School of Science & Technology Glocal University Department of Ece Uttar Pradesh India Mlr Institute of Technology Dept. of Cse Telangana Hyderabad India K. Ramakrishnan College of Engineering Department of Mathematics Trichy India

ISBN: (纸本)9798350356236

Facial recognition systems has been adopted widely now in security systems of today's world but the drawbacks methodologies lack capability to identify suspicious activities. Other approaches, including CNNs applied to face recognition and hybrid CNN-LSTM models for behavior recognition, have some issues with recognizing efficient features of human appearance based on sequence-to-sequence temporal analysis, as well as the long-term dependencies inherent to the data. In this research, a new advanced hybrid ViT-LSTM model is developed to improve facial recognition and behavior anomaly detection to support smart security surveillance. The Vision Transformer (ViT) acquires the high-dimensional facial features with better accuracy as well as it gains the global spatial information which CNNs failed to do in large-scale scenarios. These features are processed by LSTM networks over time to identify behavioral patterns, in order to provide for a sound temporal handling of human activities. The mixed structure connects both components with the fusion layer so that multitask classification can be achieved for identity authentication and anomaly identification. The proposed model efficacy is demonstrated by training it and validating it on the benchmark dataset, which establishes increased face recognition and higher precision of behavioral anomaly detection as compared to the conventional CNN-LSTM models. Third, the system has less latency and high real-time performance to be implemented in security critical areas. This novel hybrid approach therefore presents a scalable, accurate and efficient solution for next-generation security monitoring systems. © 2025 IEEE.

关键词： Network security

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Optimal Scheduling for Integrated Energy System via Model-Based Reinforcement Learning 24

Optimal Scheduling for Integrated Energy System via Model-Ba...

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2024 international conference on Artificial Intelligence of Things and computing, AITC 2024

作者： Cheng, Kexin Wan, Jianxiong Liu, Chuyi Sun, Pengfei Li, Leixiao Zhang, Huaqin School of Data Science and Application Inner Mongolia University of Technology Inner Mongolia Autonomous Region Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Inner Mongolia Autonomous Region Hohhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Republic of China Inner Mongolia Autonomous Region Hohhot China

ISBN: (纸本)9798400709869

The Integrated Energy System (IES) integrates energy consumption, conversion, and storage to increase the utilization of renewable energy sources and fossil fuels. When using heuristic algorithms and mathematical optimization methods for optimal scheduling of IES, these methods often face challenges of high computational cost and insufficient model accuracy, for which Reinforcement Learning (RL) offers a promising solution. However, most studies have used Model-Free Reinforcement Learning (MFRL) for optimal scheduling, which is limited by problems of low sampling efficiency and long training time. Model-Based Reinforcement Learning (MBRL) is an effective alternative to accelerate learning and facilitate RL applications. Therefore, this paper compares the performance gap between MFRL and MBRL in the optimal scheduling problem of IES to determine the superiority of MBRL. Experimental results show that the MBPO algorithm significantly improves sampling efficiency and learning speed at high prediction accuracy, outperforming the Soft Actor-Critic (SAC) algorithm. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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Optimized Hybrid Cryptographic Technique Using Symmetric and Asymmetric Encryption for Cloud computing

Optimized Hybrid Cryptographic Technique Using Symmetric and...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： R. Suresh T.R. Nisha Dayana Department of Computer Science Vel's Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Cloud computing (CC) is an expanding computing concept in which computing resources are given as Internetbased services. Security and confidentiality of cloud storage services are critical and pose a difficulty in cloud computing owing to loss of control over data and reliance on the cloud computing provider. While there is a big number of transmitting data in the cloud system, the risk of data access by attackers raises. CC requires encryption to maintain the privacy and security of data stored, processed, and transported on the cloud. Encryption ensures that confidential information saved in the cloud stays private and accessible only to authorized parties. The field of cryptography is continuously evolving to deal with current challenges in cybersecurity, confidentiality, and computing power. Optimizing encryption algorithms entails enhancing security, performance, and efficiency while reducing resource usage. This study suggests a optimized framework for encryption using RSA, an asymmetric key cryptography for key exchange and Advanced / Encryption Standard (AES), a symmetric key cryptography for bulk message encryption to improve the efficiency of encryption. The suggested models's performance evaluated with RSA with data Encrytpion Standard(DES) and RSA with BlowFish algorithms and the efficiency of RSA with AES is found to more in terms of Execution time, Throughput, memory usage and latency.

关键词： Cloud computing data privacy Memory management Public key Throughput data models Encryption Blockchains Computer security Standards

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