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image Schemas and image Schematic Complexes: Enhancing neural Machine Translation networks 1

4th International Conference on Computational and Corpus-Based Phraseology (Europhras)

作者： Haddad, Amal Haddad Univ Granada Granada 18071 Spain

ISBN: (数字)9783031159251

ISBN: (纸本)9783031159251;9783031159244

Machine Translation (MT) is a Natural Language processing (NLP) application which has taken off and reported considerable progress in recent years. Most recent applications of MT employ neural networks imitating the principles of human understanding and creation of meaning at conceptual and cognitive levels (Nerlich and Clarke 2000: 141). They are based on techniques which try to simulate the mechanisms of learning in biological organisms carried out through the neurons (Aggarwal 2018: 1;Theordoris 2020: 903). However, human intelligence and the cognitive models which humans use through language should be subject to more examination in order to have the capacity to unveil more basic cognitive features. For this reason, a more holistic understanding of certain aspects of human intelligence based on cognitive models is still required in order to take machine learning a step further (Goertzel et al. 2012: 124). image schemas and image schematic complexes are among the cognitive issues which would benefit from further studies as its basic structure is fundamental in natural language processing and conceptualisation (Hedblom et al. 2019). They are common in all languages and all cultures but their use is not always universal. This variation influences the quality of MT, as in some cases, this variance is not taken into consideration while feeding the neural networks of MT. This preliminary study has the objective of studying the novel idea of image schemas and image schematic complexes and proposing an applied methodology to use them in MT.

关键词： image schemas Cognitive models Machine translation

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ActivMedica—MRI Brain image Detection & Captioning with ActivGPT Integration

ActivMedica—MRI Brain Image Detection & Captioning with Act...

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International Conference on Advances and applications of artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Malayil, Shanid Krishna, P. Sudeep Pattupara, Rabeen Mohamed, P. M. Rishil Faris, Salman Suhair, K. Muhammed Ahmed, P. Jemsheer MEA Engineering College Kerala Perinthelmanna India

ISBN: (纸本)9789819795062

ActivMedica is an innovator in brain tumor diagnosis in the rapidly changing healthcare industry, employing cutting edge AI tools including deep learning and natural language processing (NLP). The shortcomings of existing manual approaches for medical imaging data analysis include difficulty in identifying diverse brain tumors and increasing medical expenditures. Our approach, which at first is based on transfer learning and GIT architecture, uses the GIT convolutional neural network, or CNN, model. A carefully chosen and preprocessed diverse dataset, enhanced for variety and improved by transfer learning from the imageNet dataset for feature extraction, is essential for successful model training. Using the GIT-based methodology, ActivGPT, an AI chatbot built on OpenAI, enables real-time image analysis through natural language conversations. In addition to brain tumor detection, our research generates brief medical reports based on detected brain tumors, providing medical experts with comprehensive information. Brain tumor diagnosis is ethically improved by ActivMedica, a comprehensive healthcare innovation that combines AI, deep learning, and natural language processing. By doing this, diagnosis accuracy is greatly increased while moral and ethical requirements for AI use in healthcare are maintained. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Unlocking the Potential of Zebrafish Research with artificial Intelligence: Advancements in Tracking, processing, and Visualization

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2023年第11期61卷 2797-2814页

作者： Fan, Yi-Ling Hsu, Fang-Rong Wang, Yuhling Liao, Lun-De Natl Hlth Res Inst Inst Biomed Engn & Nanomed 35 Keyan Rd Miaoli Cty 35053 Taiwan Feng Chia Univ Dept Informat Engn & Comp Sci Taichung 407 Taiwan Natl United Univ Dept Elect Engn 2 Lien Da Miaoli 360302 Taiwan

Zebrafish have become a widely accepted model organism for biomedical research due to their strong cortisol stress response, behavioral strain differences, and sensitivity to both drug treatments and predators. However, experimental zebrafish studies generate substantial data that must be analyzed through objective, accurate, and repeatable analysis methods. Recently, advancements in artificial intelligence (AI) have enabled automated tracking, image recognition, and data analysis, leading to more efficient and insightful investigations. In this review, we examine key AI applications in zebrafish research, including behavior analysis, genomics, and neuroscience. With the development of deep learning technology, AI algorithms have been used to precisely analyze and identify images of zebrafish, enabling automated testing and analysis. By applying AI algorithms in genomics research, researchers have elucidated the relationship between genes and biology, providing a better basis for the development of disease treatments and gene therapies. Additionally, the development of more effective neuroscience tools could help researchers better understand the complex neural networks in the zebrafish brain. In the future, further advancements in AI technology are expected to enable more extensive and in-depth medical research applications in zebrafish, improving our understanding of this important animal model. This review highlights the potential of AI technology in achieving the full potential of zebrafish research by enabling researchers to efficiently track, process, and visualize the outcomes of their experiments.

关键词： Zebrafish Machine learning Deep learning Trajectory tracking image recognition Data analysis

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PE-YOLO: Pyramid Enhancement Network for Dark Object Detection 1

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32nd International Conference on artificial neural networks (ICANN)

作者： Yin, Xiangchen Yu, Zhenda Fei, Zetao Lv, Wenjun Gao, Xin Univ Sci & Technol China Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China Anhui Univ Hefei Peoples R China Qufu Normal Univ Qufu Shandong Peoples R China China Univ Min & Technol Beijing Peoples R China

ISBN: (数字)9783031441950

ISBN: (纸本)9783031441943;9783031441950

Current object detection models have achieved good results on many benchmark datasets, detecting objects in dark conditions remains a large challenge. To address this issue, we propose a pyramid enhanced network (PENet) and joint it with YOLOv3 to build a dark object detection framework named PE-YOLO. Firstly, PENet decomposes the image into four components of different resolutions using the Laplacian pyramid. Specifically we propose a detail processing module (DPM) to enhance the detail of images, which consists of context branch and edge branch. In addition, we propose a low-frequency enhancement filter (LEF) to capture low-frequency semantics and prevent high-frequency noise. PE-YOLO adopts an end-to-end joint training approach and only uses normal detection loss to simplify the training process. We conduct experiments on the low-light object detection dataset ExDark to demonstrate the effectiveness of ours. The results indicate that compared with other dark detectors and low-light enhancement models, PE-YOLO achieves the advanced results, achieving 78.0% in mAP and 53.6 in FPS, respectively, which can adapt to object detection under different low-light conditions. The code is available at https://***/XiangchenYin/PE-YOLO.

关键词： Object detection Low-light perception Pyramid enhancement

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Hardware Acceleration Schemes for Convolutional neural networks 5

Hardware Acceleration Schemes for Convolutional Neural Netwo...

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2023 5th International Conference on artificial Intelligence Technologies and applications, ICAITA 2023

作者： Xie, Zhiqing Jiang, Xue Peng, Meihua Yang, Ziheng Electronic Engineering College Heilongjiang University Harbin150080 China

This paper presents a hardware acceleration design for convolutional neural networks. Floating-point fixed-point operations, pipeline interlayer parallel acceleration, and design space exploration are the three key areas of optimization, and optimized modules can be used to build various networks with convolutions according to specifications for the application scenario, thus achieving a universal design. The experimental results show that the optimization of hardware resources improves the speed and performance of the algorithm, and can withstand larger data volumes and higher real-time requirements. The system achieves an accuracy of 95.09% and an inference speed of 0.237 ms per image, with a high processing speed. As a result, convolutional neural networks may now be used in a wider variety of application scenarios and manage larger datasets and higher real-time demands thanks to the design solutions presented in this research. © Published under licence by IOP Publishing Ltd.

关键词： Digital arithmetic

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DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition

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SN Computer Science 2024年第6期5卷 770页

作者： Shianios, Demetris Kolios, Panayiotis S. Kyrkou, Christos KIOS Research and Innovation Center of Excellence University of Cyprus Department of Computer Science University of Cyprus

The integration of Unmanned Aerial Vehicles (UAVs) with artificial intelligence (AI) models for aerial imagery processing in disaster assessment, necessitates models that demonstrate exceptional accuracy, computational efficiency, and real-time processing capabilities. Traditionally Convolutional neural networks (CNNs), demonstrate efficiency in local feature extraction but are limited by their potential for global context interpretation. On the other hand, Vision Transformers (ViTs) show promise for improved global context interpretation through the use of attention mechanisms, although they still remain underinvestigated in UAV-based disaster response applications. Bridging this research gap, we introduce DiRecNetV2, an improved hybrid model that utilizes convolutional and transformer layers. It merges the inductive biases of CNNs for robust feature extraction with the global context understanding of Transformers, maintaining a low computational load ideal for UAV applications. Additionally, we introduce a new, compact multi-label dataset of disasters, to set an initial benchmark for future research, exploring how models trained on single-label data perform in a multi-label test set. The study assesses lightweight CNNs and ViTs on the AIDERSv2 dataset, based on the frames per second (FPS) for efficiency and the weighted F1 scores for classification performance. DiRecNetV2 not only achieves a weighted F1 score of 0.964 on a single-label test set but also demonstrates adaptability, with a score of 0.614 on a complex multi-label test set, while functioning at 176.13 FPS on the Nvidia Orin Jetson device. © The Author(s) 2024.

关键词： Convolutional neural networks image classification Multi-label classification Natural disaster recognition UAV (Unmanned Aerial Vehicle) Vision Transformers

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Deep Learning: From Big Data to artificial Intelligence with R

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2023年

作者： Stephane Tuffery

ISBN: (数字)9781119845034;9781119845027

ISBN: (纸本)9781119845010

DEEP LEARNING A concise and practical exploration of key topics and applications in data science In Deep Learning: From Big Data to artificial Intelligence with R, expert researcher Dr. Stéphane Tufféry delivers an insightful discussion of the applications of deep learning and big data that focuses on practical instructions on various software tools and deep learning methods relying on three major libraries: MXNet, PyTorch, and Keras-TensorFlow. In the book, numerous, up-to-date examples are combined with key topics relevant to modern data scientists, including processing optimization, neural network applications, natural language processing, and image recognition. This is a thoroughly revised and updated edition of a book originally released in French, with new examples and methods included throughout. Classroom-tested and intuitively organized, Deep Learning: From Big Data to artificial Intelligence with R offers complimentary access to a companion website that provides R and Python source code for the examples offered in the book. Readers will also find:

A thorough introduction to practical deep learning techniques with explanations and examples for various programming libraries
Comprehensive explorations of a variety of applications for deep learning, including image recognition and natural language processing
Discussions of the theory of deep learning, neural networks, and artificial intelligence linked to concrete techniques and strategies commonly used to solve real-world problems

Perfect for graduate students studying data science, big data, deep learning, and artificial intelligence, Deep Learning: From Big Data to artificial Intelligence with R will also earn a place in the libraries of data science researchers and practicing data scientists.

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Deep Learning for Sentiment Analysis with Adaptive neural networks for Emotion Classification 5

Deep Learning for Sentiment Analysis with Adaptive Neural Ne...

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5th International Conference on Sustainable Communication networks and Application, ICSCNA 2024

作者： Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Deepa, R. Surendran, R. SRM Institute of Science and Technology Department of Computer Science and Applications Tamilnadu Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of Science and Technology Department of Computing Technologies Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

In understanding human emotions, sentiment analysis has been invaluable in social media monitoring, consumer comment analysis, and psychological evaluations. Customer service, human-computer interface, and public mood comprehension depend on appropriate emotion categorisation. Despite its potential, sentiment analysis struggles with sarcasm, multimodal data integration, and emotion processing. Implementing real-time applications requires improving deep learning model computational complexity. Multimodal Optimised Emotion Analysis using Deep Learning (MOEA-DL) uses CNNs for image processing, RNNs for text., and LSTM networks for sequential audio data. This improves emotion recognition by combining modality strengths. Marketing sentiment, mental health monitoring, social media sentiment, and consumer feedback use MOEA-DL. Multimodal solutions are better than text-based ones for communicating emotion. Customer contacts, film reviews, and social media platforms are used to evaluate the proposed strategy. According to simulations, MOEA-DL categorises emotions more accurately and robustly than previous sentiment analysis methods. Its adaptive neural network lets the system learn and perform well in many cultural and language environments. © 2024 IEEE.

关键词： Emotion Recognition

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The Research of the Relationship Between artificial Intelligence and Human Brain 3

The Research of the Relationship Between Artificial Intellig...

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3rd International Conference on artificial Intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Xin, Qiangwei School of Computer Science Xianyang Normal University Shaanxi Xianyang712000 China

ISBN: (纸本)9781643684444

artificial intelligence is a technology and method that utilizes computers and algorithms to simulate and implement human intelligence. It can learn and optimize through a large number of data and algorithms to achieve various intelligent tasks and functions, such as natural language processing, image recognition, intelligent decision-making, etc. artificial intelligence technology has been widely applied in fields such as healthcare, finance, transportation, and manufacturing, becoming an important force driving economic and social development. artificial intelligence is closely related to the human brain. This article analyzes the relationship between artificial intelligence and the human brain, including their connections and differences, imitation mechanisms, neural networks, and deep learning. © 2023 The Authors.

关键词： Brain

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A Low-Cost Device for the Monitoring and Detection of Falls in Older Adults

A Low-Cost Device for the Monitoring and Detection of Falls ...

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International Conference on Communication and Applied Technologies (ICOMTA)

作者： Calva-Bravo, B. Rodas-Perez, W. Robles-Bykbaev, V. Leon-Gomez, P. Univ Politecn Salesiana GI IATa Catedra UNESCO Tecnol Apoyo Inclus Educ Cuenca Ecuador

ISBN: (纸本)9789811963469;9789811963476

According to the World Health Organization (WHO), falls are the second cause of death due to accidental injuries, and older adults are the ones who suffer the most from them. In Ecuador, there are about 1,300,000 older adults, and falls are a major problem for their quality of life. For this reason, in this article, we present a low-cost prototype system for the monitoring and detection of falls, with the aim of providing support for the care of older adults. This tool is based on a module that applies computer vision and image processing techniques, convolutional neural networks (CNNs) and Web and mobile applications. They allow the monitoring and control of falls. To test the operation of the system, tests were carried out with fifteen volunteers. It was determined that the system managed to correctly detect 80% of fall-related events.

关键词： Convolutional neural networks Elderly people Fall detection Computer vision

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