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检索条件"任意字段=International Conference on Advanced Research on Computer Science and Information Engineering"

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Vision Transformer-Based Breast Mass Diagnosis in Mammography Using Bilateral information 6

Vision Transformer-Based Breast Mass Diagnosis in Mammograph...

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6th IEEE international conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Zeng, Tianyu Zhang, Zhang Zeng, Yuwen Zhang, Xiaoyong Ichiji, Kei Homma, Noriyasu Graduate School of Medical Tohoku University Sendai Japan Tohoku University Center for Data-Driven Science and Artificial Intelligence Sendai Japan National Institute of Technology Sendai College Department of General Engineering Sendai Japan

ISBN: (纸本)9798350389692

In response to the high incidence of breast cancer and the need to enhance automatic diagnosis to assist radiologists, there is an urgent need to develop a system that can aid in the accurate diagnosis of breast cancer. computer-aided Diagnosis systems have been proposed for the task of breast cancer detection;however, they often encounter challenges in effectively utilizing bilateral images for accurate diagnosis of breast masses. To address this limitation, this study introduces a novel method that leverages a vision transformer to fuse information from both breasts to classify cancerous and normal mammograms. Our study contributes by utilizing bilateral information to diagnose the presence or absence of masses, thereby enhancing the sensitivity and accuracy of disease diagnosis, using the rich breast imaging data in the Digital Database for Screening Mammography. Compared with traditional convolutional neural network-based models, our method improves diagnostic performance and shows better robustness in avoiding registration problems. Experimental results show that our proposed method achieves satisfactory accuracy and reliability in breast mass diagnosis. This improvement leads to more valuable results for clinical decision-making and enhances the diagnostic capabilities of computer-aided diagnosis systems. © 2024 IEEE.

关键词： Lung cancer

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Deep Learning-Based Facial Emotion Recognition for advanced Human-computer Interaction 1

Deep Learning-Based Facial Emotion Recognition for Advanced ...

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1st international conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Chindiyababy, U. Kakkar, Preetish Vedula, Jagadeesh Yunus, Jumaniyozov Umidbek, Abdalov Sharma, Subhash SRM Valliammai Engineering College Dept. of Information Technology Chennai India India Digital Transformation Lead Meghaz Inc. HoustonTX United States Urgench state university Department of psychology and pedagogy Urgench Uzbekistan Mamun university Department of History Khiva Uzbekistan Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331518578

Deep learning technology establishes advanced methods for enhancing Human-computer Interaction systems by providing machines with the ability to sense and understand human emotional states. The capability's value stems from its applications, including healthcare situations alongside virtual assistants, gaming interfaces, and educational platforms seeking personalized functions. We present a deep learning framework that unites multimodal data fusion with Convolutional Neural Network(CNN) to enhance emotion recognition systems while improving performance and interpretability. The proposed framework undergoes testing against three benchmark emotion datasets: FER2013, AffectNet, and CK+ dataset. Our experimental findings show that the proposed system delivers a 92.5% accuracy rate for emotion recognition which exceeds traditional unimodal models by a maximum of 12% improvement. Context-aware modeling achieves a 15% performance boost during real-world applications while lightweight architectural design reduces system processing requirements by 25% to support edge device runtime operations. Through explainable AI integration users can see how the model functions to make decisions therefore establishing both trust and ethical practices in use. This research delivers substantial progress in emotion recognition study through an approach that establishes a deployable system that maintains both operational effectiveness and ethical operations across multiple Human-computer Interaction application domains. © 2025 IEEE.

关键词： Convolutional neural networks

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research Trends of Human Machine Interaction Studies in Mechanical Equipment Design: A Bibliometric Review 26th

Research Trends of Human Machine Interaction Studies in Mech...

引用

26th international conference on Human-computer Interaction (HCII)

作者： Wang, Yuwen Gan, Yan Li, Yanwei Ding, Zijie Wu, Qiuyuan Zang, Enyu Huazhong Univ Sci & Technol Luoyu Rd 1037 Wuhan Peoples R China Jereh Environm Protect Technol Co Ltd 9 Jereh Rd Yantai Peoples R China

ISBN: (纸本)9783031621093;9783031621109

The fourth industrial revolution has advanced artificial intelligence and the Internet of Things. It has also transformed mechanical equipment into intelligent, integrated systems by changing human-computer interaction. However, current academics have not comprehensively studied the evolution of human-machine interaction in recent mechanical technology. Bibliometric techniques were used to evaluate 223 relevant Web of science and Scopus papers from 2012 to 2022 to determine this subject's research progress, significant subjects, and future directions. The findings show that user-centeredness is the foundation of HMI research and that over the past decade, human-computer interaction research on mechanical equipment has closely integrated with physiological signals and begun to use artificial intelligence to process data and improve analysis results to develop new human-computer interaction methods. This paper concludes with an overview of human-computer interaction research for mechanical equipment and future directions in multimodal interfaces, naturalistic interaction, intelligent decision help, and ethics.

关键词： Human-computer Interation Mechanical Equipment Industry 4.0 bibliometrics

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Performance Analysis of Liver Disease Prediction Using Supervised Machine Learning Classification Models 1

Performance Analysis of Liver Disease Prediction Using Super...

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1st international conference on advanced Computing and Emerging Technologies, ACET 2024

作者： Singh, Ramveer Sharma, Rahul Chaudhary, Shalbha Yadav, Deepika Jain, Jaishree Kumar, Pawan Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Raj Kumar Garg Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India R.D. Engineering College Department of Management Studies UP Ghaziabad India Bharati Vidyapeeth's College of Engineering Department of Computer Science & Engineering New Delhi India Teerthanker Mahaveer University Department of Computer Science & Engineering UP Moradabad India Satyug Darshan College of Engineering Department of Computer Science Faridabad India

ISBN: (纸本)9798350367720

Liver is most fundamental part of human body. It is highly dependable to performing several body functions such as helping in food digestion, waste filtering, enzyme activation, detoxification, nutrition and glycogen storage, carbohydrate metabolism, vitamin and mineral storage etc. It has been found from various survey reports that across the world, 58 million people are suffering from liver disorders and nearly 01 million liver disorder patients are present in India. The growth rate of liver disorder patients is increasing rapidly worldwide due to a sharp rise in obesity, eating more junk foods, more alcohol consumption and unhealthy lifestyles. People who have liver disorders problem need medical treatment at the correct time and early disease prediction helps to reduce the number of deaths. The research focuses on designing an intelligent system that helps for prediction liver disorder by using various classification models of supervised machine learning and it also helps to prevent becoming India a global hub of liver diseases by 2030. The research emphasis on comparative performance analysis of different classification algorithms and ensemble methods. It also helps to select best accurate model for prediction of the liver diseases. © 2024 IEEE.

关键词： Self-supervised learning

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Towards True Decentralization: Development, Testing and Evaluation of a Novel Blockchain Consensus Protocol 1

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56th international conference on Asia Pacific advanced Network, APANConf 2023

作者： Wimal, Kesara Liyanage, Geethapriya School of Computer Science and Engineering University of Westminster London United Kingdom Department of Computer Science and Engineering University of Moratuwa Moratuwa Sri Lanka

ISBN: (数字)9783031511356

ISBN: (纸本)9783031511349

The blockchain technology that powers the Bitcoin network was unveiled to the world in 2008. After more than a decade of research and development, a number of blockchain systems have been put out by both industry and academics. This research undertaking brings attention to a significant issue that impacts both blockchain users and cryptocurrency investors. The concern regarding true decentralization in existing blockchain platforms has long been a prominent topic within the blockchain community. Although Proof of Work (PoW), Proof of Stake (PoS), and hybrid approaches are commonly employed in blockchain systems, both methods exhibit limitations concerning decentralization. Despite the emergence of alternative consensus protocols, none have thus far achieved complete decentralization. This project aims to devise and implement an innovative consensus protocol that circumvents tendencies toward centralization, accommodates any number of network participants, eliminates the requirement for specialized hardware, reduces energy consumption, and fosters network activities. The proposed protocol advocates for a democratic and equitable approach to blockchain consensus. A comprehensive evaluation is conducted to assess the degree of achieved decentralization. The solution successfully demonstrates genuine decentralization. The test results also indicate enhanced security, and high performance, while obviating the need for specialized hardware and reducing energy consumption. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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research on the Application of Artificial Intelligence in computer Networks 6

Research on the Application of Artificial Intelligence in Co...

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6th international conference on computer information science and Application Technology, CISAT 2023

作者： Ma, Li Wuhan Donghu University China

ISBN: (纸本)9781510668546

The effective use of high technology in all sectors of society can achieve the purpose of technological empowerment and facilitate the development of education, medical, aerospace, biomedical and other fields to overcome the difficulties. The rational use of advanced technology to achieve business development goals is one of the issues that all sectors are concerned about and must study. Artificial intelligence is a technology developed based on computer technology, through which computers can imitate human consciousness and improve their "cognitive" ability, enabling them to deal with various situations in a continuous cognitive manner. The article explores the application strategies of artificial intelligence in computer networks with a view to providing references for effective optimisation of computer networks. © 2023 SPIE.

关键词： Expert systems

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A Color-Aware Unsupervised Segmentation Network for Nuclei in Histopathology Images 4

A Color-Aware Unsupervised Segmentation Network for Nuclei i...

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4th international conference on Electronic information engineering and computer Communication, EIECC 2024

作者： Ruan, Hongyan University of Science and Technology of China School of Biomedical Engineering Division of Life Sciences and Medicine Hefei China University of Science and Technology of China Suzhou Institute for Advanced Research Suzhou China

ISBN: (纸本)9798331534622

Precise nuclei segmentation is essential in digital pathology, yet it faces difficulties due to the wide existence and morphological diversity of nuclei, staining protocols, and imaging conditions. The process of annotating nuclei instances demands skilled pathologists to carefully trace the outlines of the nuclear structures, which is both labor-intensive and expensive. Consequently, deep learning-based segmentation techniques, which heavily depend on large datasets for training, face challenges in maximizing their effectiveness in this domain. Here our study introduces an unsupervised method for segmenting nuclei instances in whole slide histopathology images. Our approach is divided into two primary phases: self-supervised training and clustering-based segmentation. In the first phase, mask pre-training allows the encoding network to capture the essential color features of pathology images, enabling it to extract more comprehensive and detailed information about the nuclei. In the second stage, the encoder backbone that has been trained is used to derive clustering features from the images. The network learning and clustering processes are optimized jointly, and the final results of instance segmentation are achieved by means of subsequent post-processing techniques. The results indicate that the proposed method exhibits enhanced performance compared to other unsupervised nuclei segmentation approaches. © 2024 IEEE.

关键词： Image segmentation

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Quantum-Powered Autonomous Deep-Sea Explorer Using AI, Microbiofuels, and advanced Sensors for Oceanic Discovery

Quantum-Powered Autonomous Deep-Sea Explorer Using AI, Micro...

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2025 international conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Chouhan, Sandeep Sandhu, Ramandeep Yadav, Om Prakash Islam, Sarder M. N. Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India 2 Department of Applied Information Melbourne Australia

ISBN: (纸本)9798331513894

The ocean's depths are among the most enigmatic regions on Earth, with geographical features, temperatures, and marine life remaining widely unexplored. The traditional forms of exploration confront water pressure, cold-temperature conditions, and a lack of energy resources. This research suggests a novel apparatus, that integrates quantum technology, artificial intelligence (AI), and cutting-edge sensors to address these issues and enhance ocean floor mapping, gas detection and element *** energy apparatus of the device is central to ensuring its functionality for a prolonged period. It employs thermoelectric generators exploiting the temperature difference between the cold waters located at depth and warmer surface layers and the microbial fuel cells (MFCs) that convert organic materials in ocean sediments into electricity. This hybrid energy combined cycle, therefore sustainable operation without external power *** sensors such as quantum-enhanced spectroscopy and mass spectrometers will be used for the detection and analysis of gases, trace elements and organic compounds. The balanced engineering systems integrated into the automated equipment will be used with the power distribution control and information processing in the real-time mode performed by the quantum neural networks (QNN). The system's adaptability, and accuracy and that system may function unattended for fairly long periods, this system presents an unprecedented device that should change the course of deep-sea studies as it will provide access to previously unreachable areas. © 2025 IEEE.

关键词： Microbial fuel cells

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Signature-Based Batch Auditing Verification in Cloud Resource Pool 1

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1st international conference on advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Goswami, Paromita Gogoi, Munmi Debnath, Somen Khan, Ajoy Kumar Department of Computer Engineering and Applications GLA University Mathura India Department of Information Technology Mizoram University Aizawl India Department of Computer Engineering Mizoram University Aizawl India

ISBN: (数字)9783031472244

ISBN: (纸本)9783031472237

Online cloud resource pool provides cloud users with a multitude of appealing developments in highly sought-after online scalable storage services for them to eager new creative and investment business benefits. Today, the majority of cloud data security research focuses on ways to increase the accuracy of outsourced data audits rather than internal and foreign foes who could hack a cloud server. Even though the data owner enviously envies the data auditing work of stored data to a trustworthy Third Party Auditor(TPA) to avoid communication as well as computational overhead of data when outsourcing to a reputable Cloud Service Provider, TPA is unreliable in a realistic setting. In this research, we offer a model for an effective data integrity system based on the ZSS signature, which secures data privacy in cloud storage. Finally, the actual results of the performance of the proposed prototype of the system model were tested and indicated greater efficiency of provided model in comparison to BLS signature. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lakes

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Multiresource allocation in dynamic MEC systems: a continual learning-based approach 3

Multiresource allocation in dynamic MEC systems: a continual...

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3rd international conference on Electronic information engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Zhan, Shutao Mao, Jin Chu, Zhipeng Xiong, Ke Fan, Pingyi Letaief, Khaled B. Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China Department of Electronic Engineering Tsinghua University Beijing100084 China Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781510680449

Recently, mobile edge computing (MEC) has been considered as a promising solution to address the challenges arising from the growing computationally intensive applications and limited computation capability of mobile devices. Thus, the resource allocation is crucial for ensuring the system performance of MEC. Recent deep learning-based resource allocation (DLRA) methods achieve preferable performance in real-time. However, in practical scenarios, the number of users often changes, which leads to a decrease in the performance of DLRA methods. Thus, once the number of users changes, the traditional DLRA methods need to retrain their neural networks, which consumes a lot of computing resources. To address this issue, this paper proposes a continual learning-based resource allocation (CLRA) method to jointly optimize multiple resource including offloading decision, power control, time slot assignment and computation resource distribution in MEC system. Our CLRA method is able to avoid forgetting previous knowledge when the number of users in the MEC system changes by selecting a small number of the most representative samples from the past and relearning. Experimental results show that, our proposed CLRA method achieves a better performance than the traditional DLRA method in terms of the higher quality of service's (QoS's) satisfaction rate and the less energy consumption. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Resource allocation

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