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Machine Learning-Enhanced Voice Interaction: Revolutionizing Windows Desktop Applications 1

Machine Learning-Enhanced Voice Interaction: Revolutionizing...

1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Vora, Raj Chakrawarti, Rajesh Kumar Raghuwanshi, Namrata Sushila Devi Bansal College of Technology Computer Science and Engineering Indore India

ISBN: (纸本)9798331518578

The use of voice interaction is changing the way human being communicate with devices as it offers a simple and effective way of interaction. To implement voice interaction within desktop applications and make it more effective as it is integrated with the operating system, there is a various need models, to particularly improve Convolutional its neural quality. Networks This (CNNs), paper Recurrent considers Neural how Networks (RNNs), and transformers, can enhance the effectiveness of voice interaction in the context of Windows desktop platforms. The research focuses on several major issues: enhancing the speech recognition performance in the presence of background noise, handling the differences in user's accent and speech manner, and real-time operation with reduced delay. It also examines issues of privacy relating to handling of sensitive voice information and ways of reducing these vulnerabilities. Using and enhanced user machine intent learning and results operating encompass connecting detection strategies, the indicate system the in this user's and that voice possibility developing voice research experience provides the interaction the of recognition in is an application with next increasing systems. Different designed better of other these generation the practical user to machine current models of multilingual applications experience. Improve learning and are desktop capabilities, including the enhances future assessed apps. Implementing productivity current The AI from the tools, limited paper effectiveness solutions, the ability accessibility capabilities also and thus perspective to features of provides quality stating of recognize and contextual recommendations of that performance, users' intelligent understanding for voice flexibility emotions, assistants. Future control is and the research, commands the which on key them to Windows. © 2025 IEEE.

关键词： Speech recognition

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Kinmen Wind Lion Face Generation Based on the Deep Convolutional Generative Adversarial Network 13

Kinmen Wind Lion Face Generation Based on the Deep Convoluti...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Wu, Chia-Huan Zhao, Yu-Xiang Chou, Cheng-Hou Hsieh, Yi-Zeng National Taiwan University of Science and Technology Department of Electrical and Computer Engineering Taiwan National Quemoy University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350355079

Under the advancements of science and technology at present, artificial intelligence has become widely applied in daily life. Hence, deep learning has attracted much attention in recent years and has been widely used in recognition, classification and generation. The results generated by generative adversarial networks have become increasingly realistic and are now being applied to human face generation and reconstruction. Among the various technologies, the deep convolutional generative adversarial network (DCGAN) is more widely used. Based on the trained DCGAN of human and cat faces, this paper collected Kinmen wind lion data by YOLOv3 and crawler technology and transferred the data to the DCGAN of wind lions through network-based transfer learning to generate complete wind lion faces. The experimental process consisted of four parts. The wind lion dataset was directly used to train the DCGAN in the first two parts, and the DCGAN of the human and cat faces was used for transfer learning in the second two parts. According to the results, the training effect of human face-based transfer learning was the best and was the closest to Kinmen's wind lions. © 2024 IEEE.

关键词： Generative adversarial networks

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Facial emotion recognition using convolutional neural networks

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Materials Today: Proceedings 2023年 80卷 3560-3564页

作者： Sarvakar, Ketan Senkamalavalli, R. Raghavendra, S. Santosh Kumar, J. Manjunath, R. Jaiswal, Sushma Information Technology Department U.V. Patel College of Engineering Ganpat University Gujarat Mehsana384012 India CSE Department East Point College of Engineering Bangalore India Department of Computer Science and Engineering School of Engineering and Technology Christ Deemed to be University Bengaluru560074 India Department of Computer Science and Engineering K S School of Engineering and Management Bengaluru56019 India Department of Computer Science and Engineering R R Institute of Technology Bengaluru560090 India Bilaspur495009 India

Emotional expressivity has always been a simple job for people, but computer programming is much harder to accomplish. Image emotions may be recognised by recent developments in computer vision and machine learning. In this article, we present a new method to detect face emotion. Use neural networks convolutionary (FERC). The FERC is based on a CNN network of two parts: the first portion removed the backdrop of the image, the second part removed the face vector. The expressional vector (EV) is utilised in the FERC model to detect the fve different kinds of regular facial expressions. The double-level CNN is continuous and the weights and exponent values of the final perception layer vary with each iteration. In that it improves accuracy, FERC varies from widely utilised CNN single-level technology. Moreover, EV generation prevents the development of a number of issues before a new background removal process is used (for example distance from the camera). © 2021

关键词： Emotion Recognition

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A Comprehensive Framework for Electronic Medical Records (EMRs) Leveraging Blockchain technology 1st

A Comprehensive Framework for Electronic Medical Records (EM...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Pandey, Shraiyash Alazzawi, Esraa Mohammed Pandey, Surendra Bhushan, Bharat Jamil, Akhtar Baniya, Pashupati Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Computer Engineering University of Baghdad Al-Jadriya Baghdad10071 Iraq Computer and Information Technology Tata Consultancy Services MariettaGA United States Department of Computer Science National University of Computer and Engineering Sciences Islamabad44000 Pakistan

ISBN: (纸本)9783031823763

In today’s healthcare landscape, where participation is widespread, the need for secure and efficient record management is evident. However, many healthcare organizations, particularly in regions like India, still rely on outdated paper-based methods for handling medical records. Unfortunately, these traditional approaches fall short in meeting the rigorous requirements for security, confidentiality, and efficiency in today’s digital age. This work presents a comprehensive framework that integrates modern technologies such as Smart Contracts, Ethereum Blockchain, and the Interplanetary File System (IPFS) to ensure secure storage, and seamless management of Electronic Medical Records (EMRs). By leveraging these emerging technologies, our goal is to lead a shift in electronic medical record management. Additionally, we explore recent advancements in the healthcare sector, with a focus on enhancing security protocols within Electronic Medical Records management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Assesment of Cloud Security Posture Management Scenarios 23

Assesment of Cloud Security Posture Management Scenarios

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Leaua, Melina Silvia Chis, Alexandru Balan, Titus-Constantin Ilca, Lucian Florin Transylvania University Faculty Electrical Engineering and Computer Science Braşov Romania

ISBN: (纸本)9798331540388

The dynamic growth of the digital ecosystem presents evolving cybersecurity considerations, highlighting the constant change and emergence of new security concerns. While cloud providers offer robust security for the infrastructure, the user configurations of the cloud services can lead to misconfigurations, vulnerabilities, and compliance issues due to human error. The aim of the paper is to explore Cloud Security Posture Management solutions that can help mitigate these security risks. In this scope, two solutions are analyzed: A managed posture management solution using cloud native services and an open-source alternative solution. This comparison provides essential insights to understand their respective advantages and disadvantages helping to determine which solution is more suitable for specific cloud environments, taking into consideration various factors such as ease of use, complexity, costs, and dependency. © 2024 IEEE.

关键词： Cloud security

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A novel low-rank blind spot reconstruction network based on multi-scale mask convolution for hyperspectral anomaly detection

A novel low-rank blind spot reconstruction network based on ...

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2024 International Conference on Remote Sensing, Mapping, and Geographic Information Systems, RSMG 2024

作者： Yang, Zhiwei Zhao, Rui Meng, Xiangchao Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China

ISBN: (纸本)9781510685826

With the emergence of blind spot reconstruction networks in hyperspectral anomaly detection, a series of new challenges have arisen. The most significant issues include local anomaly identity mapping and inaccurate detection of large anomaly targets in deep learning networks. Therefore, we propose a busy point reconstruction network for hyperspectral anomaly detection based on multi-scale mask convolution. We creatively design a multi-scale mask convolution module that uses an inner window mask to isolate the center block and edge background for reconstruction. To further enhance space-spectral information interactivity, we introduce a joint space-spectral module. Additionally, to increase the network's focus on global low-rank backgrounds, we incorporate a block low-rank penalty to guide global low-rank attention. Finally, by considering the scale difference in detection performance between different mask convolutions, we develop a dynamic learnable fusion module that performs adaptive fusion of two mask convolution results for optimal detection performance on two real data sets. © 2024 SPIE.

关键词： Anomaly detection

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Traditional and parameter-efficient fine-Tuning of LLMs for sentiment analysis in the English and Serbian language 11

Traditional and parameter-efficient fine-Tuning of LLMs for ...

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11th International Conference on Electrical, Electronic and Computing engineering, IcETRAN 2024

作者： Dordevic, Nikola Stojkovic, Suzana University of Niš Faculty of Electronic Engineering Department of Computer Science Niš Serbia

ISBN: (纸本)9798350386998

Fine-Tuning of large language models is often demanding in terms of computational resources and memory. Consequently, there is a need to explore new methods that can effectively fine-Tune these models without compromising performance. In this paper, we compared traditional and parameter-efficient fine-Tuning of large language model XLM-RoBERTa, across both high-resourced languages like English and low-resourced languages such as Serbian. The model is fine-Tuned for sentiment analysis when it is conducted using text classification. The experiments have shown that parameter-efficient fine-Tuning can produce results that are on par with traditional fine-Tuning, both for the English and the Serbian language. Furthermore, parameter efficient fine-Tuning approach significantly reduces the computational time and memory requirements associated with model training, thereby offering a promising avenue for resource-efficient large language models fine-Tuning. In experiments with the English language, we achieved accuracy using the PEFT approach that was only 3% lower than the accuracy obtained through the traditional approach, while reducing training time by 35% and memory requirements by 42%. In experiments involving the Serbian language, we achieved accuracy using the PEFT approach that was only 1% lower than the accuracy obtained through the traditional approach, while reducing training time by 42% and memory requirements by 69%. © 2024 IEEE.

关键词： Modeling languages

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Comparative Analysis of Deep Learning and Machine Learning Analysis for Kidney Tumor Analysis 7

Comparative Analysis of Deep Learning and Machine Learning A...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Yazhini, A. Kalpana Devi, M. Anitha, M. Praveena, R. Mol, Maheswari K Lawrence, Jinsha Anna University Faculty of Electrical Engineering College of Engineering Chennai600025 India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore10 India KIT - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore641402 India Jai Shriram Engineering College Department of Computer Science and Engineering Tirupur638660 India Veterinary College and Research Institute Thanjavur614625 India Karpagam Academy of Higher Education Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350372816

In order to lower death risks, provide the most effective course of treatment, and improve community healthcare, the majority of recent research has concentrated on examining prevalent illnesses in the population. One of the prevalent ailments that has impacted our society is kidney disease. Around the world, kidney tumors (KT) rank tenth in terms of frequency in both men and women. The organization of cancer is a problem that is attracting increasing attention in the domains of bioinformatics and computational biology. This study presents a comparative analysis of the latest developments in deep learning (DL) and machine learning (ML). While several methods have been proposed to tackle the cancer classification challenge, new research indicates that supervised and deep learning-based methods are the most effective. Healthcare has advanced as a result of the creation of numerous machine learning techniques for awareness evaluation in cancer categorization. It seems that there is a high requirement for the ongoing development of efficient categorization algorithms in order to handle the growth in healthcare applications. The comparative analysis of the proposed classification shows the accuracy as 0.92%. © 2024 IEEE.

关键词： Adversarial machine learning

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A Hybrid Fault Diagnosis Method Based on Transformer and CNN 7

A Hybrid Fault Diagnosis Method Based on Transformer and CNN

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Gao, Zhi He, Meixuan Zhang, Bangcheng Shao, Yubo Liu, Jingru Que, Shaohua Changchun University of Technology School of Mechatronic Engineering Changchun China Changchun University of Technology College of Computer Science and Engineering Changchun China Changchun University of Technology Faculty of Mechanical and Electrical Engineering Faculty of Mechanical and Electrical Engineering Changchun Institute of Technology Changchun China Faw Tooling Die Manufacturing Changchun China

ISBN: (纸本)9798350363173

The elevator data of the welding production line has significant timing characteristics. There are more normal data and less abnormal data. Therefore, it becomes more complicated to diagnose the abnormality of the welding production line hoisting system through data analysis. This paper proposes a hybrid fault diagnosis method that combines a Transformer with a convolutional neural network. Firstly, the Transformer is used to extract the long-range dependencies in the data and identify complex faults that may not be adjacent. Then, the convolutional neural network extracted local or spatial features from the data. Finally, the multilayer perceptron maps the extracted features to the output space. The model innovatively merges the Transformer's global context analysis with the convolutional neural network's local feature detection precision, boosting fault diagnosis accuracy and efficiency. The proposed hybrid model was applied to the actual vibration data set of the hoists condition monitoring system of the white welding production line, and the results were contrasted with those of the support vector machine method, the long short-term memory network method, and the back propagation neural network method. Experimental findings demonstrate that the hybrid model achieves an accuracy of 99.98%, which is suitable for the fault diagnosis of the driving end bearing of the body of the white welding production line. © 2024 IEEE.

关键词： Failure analysis

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An Approach for Metadata Enrichment on Open Data Portals 11

An Approach for Metadata Enrichment on Open Data Portals

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11th International Conference on Electrical, Electronic and Computing engineering, IcETRAN 2024

作者： Gligorijević, Milena Frtunic Bogdanović, Milos Stoimenov, Leonid University of Niš Faculty of Electronic Engineering Department for Computer Science Niš Serbia

ISBN: (纸本)9798350386998

Open data initiatives have resulted in a large amount of data being published on open data portals. In order to make published data more accessible these portals provide search mechanisms based on metadata like category, tags, format, and publisher. However, due to the lack of metadata information, like category values, search results are incomplete, and data is harder to be found. Therefore, this paper presents an approach for metadata enrichment on open data portals. In particular, the presented approach is focused on suggesting categories one dataset should belong to, based on the combination of tags that is used for its description. This approach relies on the tags usage for describing datasets within categories and the Formal Concept Analysis method for creating a hierarchical order of tags within categories. The presented proposal has been evaluated using data from Ireland's open data portal. © 2024 IEEE.

关键词： FCA formal concept analysis metadata enrichment open data open data portals

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