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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Zhang, Xing Guo, Yucan Jin, Xiaolong Liang, Haozhe Guan, Saiping School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Computing Technology Chinese Academy of Sciences Cas Key Laboratory of Network Data Science and Technology China Institute of Systems Engineering Academy of Military Science Australia

ISBN: (纸本)9798350358780

Existing works in event extraction typically extract event arguments within the sentence scope. However, besides the sentence level, events may also be naturally presented at the document level. A document-level event usually reflects, to some extent, the theme (i.e., the main content) of the document (e.g., electronic medical records and news articles), which is thus referred to as the thematic event. Thematic Event Extraction (TEE) aims to extract the arguments of thematic events. TEE faces a major challenge, i.e., the sparsity and dispersion of arguments, which means that the arguments of a thematic event are dispersed in different sentences of the document. To overcome this challenge, we propose an Event-related Sentence Detection based TEE model, called ESDTEE, which first detects the sentences related to the thematic event and then extracts the arguments only within these detected sentences using existing models. Extensive experiments with comprehensive analyses demonstrate the effectiveness of ESDTEE. © 2023 IEEE.

关键词： Medical computing

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AI-Driven Assessment and Evaluation: Enhancing Sustainability in Educational Practices 3

AI-Driven Assessment and Evaluation: Enhancing Sustainabilit...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Chinnasamy, P. Pushpalatha, M. Nirmala, V. Vidhya, V. Ayyasamy, Ramesh Kumar Priya, Divya Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of MCA Annamacharya PG College of Computer Studies Rajampet Kadapa India India Kampar Perak Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331532420

AI-powered educational technologies are emerging as transformative forces in the quickly changing field of education, where innovation is essential to keeping ahead of the competition. Assessments are one area that is undergoing revolutionary transformation. Artificial Intelligence (AI) is redefining the way we evaluate learning outcomes as traditional evaluation approaches struggle to keep up with the demands of the digital age. The field of artificial intelligence-powered learning assessments has a lot of promising future developments ahead of it. This research proposes novel technique in assessment and evaluation based on online education system using Artificial intelligence in machine learning techniques. Here, the input has been analysed as student performance data based on online education and processed for removing of missing values with noise removal. Then this data has been analysed using spatio LSTM convolutional fuzzy neural network for classification of evaluation and assessment model. the experimental analysis has been carried out for various student performance analysis dataset in terms of training accuracy, average precision, specificity, F-measure and recall. Proposed method training accuracy 97%, Average precision 94%, F-measure 90%, RECALL 93%, SPECIFICITY 96%. © 2024 IEEE.

关键词： Adversarial machine learning

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Review of Deep Speech Recognizer using Transcriber 6

Review of Deep Speech Recognizer using Transcriber

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6th IEEE International Conference on Advances in science and technology, ICAST 2023

作者： Tivarekar, Rahulkumar P. Khadye, Ruchita Mahesh Chavande, Saloni Ravindra Talkatkar, Pranita Sachin Mumbai University Finolex Academy of Management and Technology Computer Science and Engineering Department Ratnagiri India

ISBN: (纸本)9798350359817

This research presents the development of a 'Deep Speech Recognizer using Transcriber,' aimed at streamlining the process of capturing and converting live speech into text. The primary problem addressed by this system is the need for efficient and accurate transcription in various contexts, such as meetings, lectures, and interviews. The methodology employed in this project utilizes Jupyter widgets for user interaction, PyAudio for microphone audio capture, Vosk for speech recognition, and recasepunc for punctuation enhancement. Key findings of this research include the successful implementation of a user-friendly system capable of recording and transcribing live speech in real-time. The system exhibits commendable accuracy in speech recognition, making it a valuable tool for professionals and researchers in fields requiring transcription services. Furthermore, the integration of recasepunc significantly improves the readability and usability of the transcribed text. This research contributes to the automation of speech transcription, offering a practical solution to the time-consuming and error-prone process of manual transcription. The system's versatility and ease of use make it a promising tool for enhancing productivity and accessibility in various domains. Future work may involve further optimization and the exploration of additional features to expand the system's capabilities and applications. © 2023 IEEE.

关键词： Speech recognition

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Behaviour of micro-and nano-alumina-reinforced Mg-3Zn-0.7Zr-1Cu alloy composites processed at different sintering temperatures

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Rare Metals 2021年第1期40卷 143-155页

作者： Eacherath Suneesh Murugesan Sivapragash Department of Production Engineering Vidya Academy of Science and TechnologyThrissur 680501.India Department of Mechanical Engineering PSN College of Engineering and TechnologyTirunelveli 627152Ind

This study aims to develop a magnesium-based hybrid composite via a powder metallurgy(PM)technique by simultaneously reinforcing the matrix(Mg-3 Zn-0.7 Zr-1 Cu)alloy with micro-alumina(3.0 wt%)and nano-alumina(0.5 wt%and 1.0 wt%)*** extensive processing involved two different sintering temperatures(400 and 450℃)followed by hot extrusion and a heat treatment *** study aimed to add to our understanding of the effects of sintering temperature and alumina content on the physical characteristics of Mg-based *** was revealed that increasing sintering temperature improves the density of composites by increasing their diffusion *** addition,significant improvements in the hardness,tensile properties and compressive properties of the composites were observed at higher sintering *** was also discovered that composites with higher alumina concentrations experienced agglomeration and were more porous than other composites.A micro-structural examination showed that composites with higher concentrations of nano-alumina had a finer grain structure than other composites and underwent a marginal reduction in grain ***,the tensile and compressive properties of composites decreased when the nano-alumina content was increased to 1.0 wt%.The magnesium hybrid composite containing 3.0 wt%micro-alumina and 0.5 wt%nano-alumina sintered at 450℃displayed the greatest tensile strength properties in all composites studied.A transformation from brittle to mixed-mode failure,with sufficient evidence of increased plastic deformation,was also noted in the hybrid *** present study suggests that adding up to 0.5 wt%nano-alumina and employing a higher sintering temperature enhances the overall characteristics of magnesium/alumina microcomposites.

关键词： Hybrid composite Powder metallurgy Alumina particulates Sintering temperature Mechanical characteristics

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ReFuSeg: Regularized Multi-modal Fusion for Precise Brain Tumour Segmentation 9th

ReFuSeg: Regularized Multi-modal Fusion for Precise Brain T...

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9th International Workshop on Brain Lesion workshop, BrainLes 2023 and 3rd Stroke Workshop on Imaging and Treatment CHallenges, SWITCH 2023 Held in Conjunction with 26th Medical Image Computing and computer Assisted Intervention, MICCAI 2023

作者： Kasliwal, Aditya Sagaram, Sankarshanaa Srivastava, Laven Seth, Pratinav Khan, Adil Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Mechanical Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031761591

Semantic segmentation of brain tumours is a fundamental task in medical image analysis that can help clinicians in diagnosing the patient and tracking the progression of any malignant entities. Accurate segmentation of brain lesions is essential for medical diagnosis and treatment planning. However, failure to acquire specific MRI imaging modalities can prevent applications from operating in critical situations, raising concerns about their reliability and overall trustworthiness. This paper presents a novel multi-modal approach for brain lesion segmentation that leverages information from four distinct imaging modalities while being robust to real-world scenarios of missing modalities, such as T1, T1c, T2, and FLAIR MRI of brains. Our proposed method can help address the challenges posed by artifacts in medical imagery due to data acquisition errors (such as patient motion) or a reconstruction algorithm’s inability to represent the anatomy while ensuring a trade-off in accuracy. Our proposed regularization module makes it robust to these scenarios and ensures the reliability of lesion segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Semantic Segmentation

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Research on Image Subject Accessing Model under Foggy Weather 14

Research on Image Subject Accessing Model under Foggy Weathe...

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14th International Conference on Electronics, Communications and Networks, CECNet 2024

作者： Li, Zhiyi Zhang, Songtao Meng, Fanlei Zhang, Lijuan School of Instrument Science and Electrical Engineering Jilin University Jilin China Computer Science and Technology Guohao Academy Tongji University Shanghai200092 China College of Internet of Things Engineering Wuxi University Wuxi214105 China School of Computer Science and Engineering Changchun University of Technology Changchun130012 China

ISBN: (纸本)9781643685632

Imaging results under outdoor conditions are easily affected by foggy weather which may produce blur, low contrast, intensity shift and other visual degradation effects. This paper proposed an accessing model of fog density based on discrete wavelet transform features of multi-channel mean subtracted contrast normalized coefficients. Besides, corner feature intensity is utilized to extract highlevel features of foggy images. The accessing model used support vector regression to predict fog density with the help of a fog density accessing dataset collected from human eyes. The experimental results show that the subjective accessing method proposed in this paper has a high correlation with human eyes accessing results on the testing dataset, and is better than the general referenceless image spatial quality evaluator model. The accessing model can reflect the image quality as human eyes in foggy scenes. © 2024 The Authors.

关键词： Support vector regression

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Developing a Convolutional Neural Model for Segmentation of Medical Images 1

Developing a Convolutional Neural Model for Segmentation of ...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Arunachalam, M. Kumar, P. Mohan Selvam, L. Kumar, S. Senthil Arunkumar, B. Sri Krishna College of Engineering and Technology Department of Information Technology Coimbatore India Hindusthan Institute of Technology & Sciences Department of Computer Science and Engineering Chennai India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Sethu Institute of Technology Department of Computer Science and Engineering Kariapatti India

ISBN: (纸本)9798350359756

Medical image segmentation is a new biomedical image processing method that has made a significant contribution to sustainable health care. It has now become a major study area in the realm of computer vision research. Medical image processing based on deep convolutional neural networks has become a research hotspot due to the fast development of deep learning techniques. An additional cascade component is included to the architecture in this article to better replicate spatial closure tags' direct dependences. It is also utilised for post-segmentation processing, resolving the conflict between classification performance and network intensity and number of mixing times in a typical convolutional net. Numerous state-of-the-art deep recognition models, and a multi-atlas segmentation methodology, are outperformed by our methodology. © 2023 IEEE.

关键词： Image segmentation

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Solar Observation with the Fourier Transform *** Results of Solar Spectrum near the CO 4.66μm and Mg I 12.32μm

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Research in Astronomy and Astrophysics 2023年第12期23卷 128-137页

作者： Xianyong Bai Zhiwei Feng Zhiyong Zhang Yuanyong Deng Xiaoming Zhu Kaifan Ji Song Feng Xiao Yang Yongliang Song National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 101408China Yunnan Observatories Chinese Academy of SciencesKunming 650011China Faculty of Information Engineering and Automation/Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and TechnologyKunming 650500China

An infrared solar spectrum observed by ground-based telescopes is seriously affected by the background radiation both from the telescope and sky,relative to the visible *** accuracy is also influenced by the spectral resolution of the Fourier transform *** the paper,we developed a CO_(2)gas cell and installed it in the sample compartment to calibrate the spectral resolution of the Bruker IFS-125HR at infrared *** measured spectral resolution is 0.00342±0.00086 cm^(-1)and 0.0059±0.00024 cm^(-1)at the wavenumbers of798 cm^(-1)and 2136 cm^(-1),*** also updated a fully reflective sunlight feeding system to observe the solar spectrum near CO 4.66μm and Mg I 12.32μ*** quickly pointing the sunlight feeding system about1 degree away from the solar disk center,we are able to measure the background radiation from the telescope and the sky at Huairou Solar Observing *** removing the background radiation,our observed solar spectrum at CO 4.66μm is consistent with that from the National Solar *** Mg I 12.32μm working line selected by the Accurate Infrared Magnetic Field Measurements of the Sun(AIMS)project is also *** method is helpful not only for the spectral resolution calibration and background radiation correction of AIMS but also for other infrared astronomical telescopes.

关键词： Sun infrared-methods observational-instrumentation interferometers

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An Effective Counterfeit Medicine Authentication System Using Blockchain and IoT 4

An Effective Counterfeit Medicine Authentication System Usin...

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4th International Conference for Emerging technology, INCET 2023

作者： Shalini, S. Sheela, S. Abhishek, S. Bhavyashree, P. Gunashree, C. Rohan, K.S. VTU University Global Academy of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350335750

The current pharmaceutical sector is dealing with several issues. The pharmaceutical sector nowadays is dominated by counterfeit medications. Health research funding groups estimate that between 10 and 30 percent of the pharmaceuticals in the market are counterfeited. Globally, this fatal issue is creating several serious health risks. WHO estimates that 30% of all medications worldwide are fake. In practically every region of the world, this issue is becoming worse by the second. As drugs travel across several scattered networks, it is challenging to identify fakes. By integrating Blockchain technology into the network, the pharmaceutical supply chain may be made safer. Every area of the network keeps track of the medications through a record, making it possible to identify any introduction of fake medications. System transparency and dependability are improved with Blockchain security. By employing Blockchain technology, the initiative seeks to guarantee medicine quality, transaction security, and data protection. By eliminating information about expired and fake medications from the system, this effort increases system dependability and helps to make the Blockchain a stable and widely used technology. To determine whether or not counterfeit medications have been distributed, IOT technology is also employed. © 2023 IEEE.

关键词： Smart contract

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LPCC Based Music Genrefication Using Hybrid Computational Model 7

LPCC Based Music Genrefication Using Hybrid Computational Mo...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Sul, Astha Sarda, Harshit Billa, Aishwarya Mishra, Tusar Kanti School of Computer Science and Engineering Vellore Institute of Technology Vellore India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Dept. of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350304268

This work deals with spontaneous music genrefication through computational models which in the recent times has been gaining importance rapidly. Through these hybrid computational models implemented users get an enhanced level of satisfaction when their choice of music files genre with least latency is gained. The paper includes the use of LPCC attributes to obtain the features of the music files along with robust classification models such as neural networks. For this purpose, one thousand audio files are taken as the sample set. The input audio file is pre-processed and suitable LPCC features are computed and stored into final vector. The pool of vectors of numerous audio samples are finally utilized to train a neural model. The proposed work trains the model for ten very distinct categories of music such as hip-hop, jazz, metal, pop, blues, classical, country, disco, reggae, and rock. Further, a comparison is also made between all the classifiers such as SVM, ANN and random forest (RF). Comparatively the best accuracy rate of 85.53\% has been achieved for the proposed work that validates its effectiveness. © 2023 IEEE.

关键词： ANN gtzan dataset LPCC Music Genre

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