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Design of Animal Myocardial Contractile Force Detection System Based on Tissue Engineering

Design of Animal Myocardial Contractile Force Detection Syst...

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7th IFIP WG 5.14 international conference,CCTA2013

作者： Guiqing Xi Ke Han Ming Zhao Caojun Huang Feng Tan College of Information and Technology Heilongjiang Bayi Agricultural University Harbin University of Commerce

this paper uses the sensor convert animal myocardial contractile force into a voltage signal,which is collect by a MCU acquisition,transferred to the computer,using the PC visualization language VB compiled a data acquisition and processing platform,collected data can be stored into a computer,and real-time curve of the data changes is plotted at any *** testing the tissue engineered cardiac tissue strips micro contractility,it was proved that the platform operation is of stable performance,accurate data acquisition,processing method effective,the system can also be applied in other similar signal detection and acquisition and other fields.

关键词： tensile testing data acquisition tissue engineering real-time plotting

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Detection of Unstructured Sensitive Data Based on a Pre-Trained Model and Lattice Transformer

Detection of Unstructured Sensitive Data Based on a Pre-Trai...

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international conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Feng Jin Shaozhi Wu Xingang Liu Han Su Miao Tian Yangtze River Delta Research Institute of University of Electronic Science and Technology of China (Quzhou) University of Electronic Science and Technology of China Quzhou China University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350385106

ISBN: (纸本)9798350385113

Sensitive data often contains a significant amount of unstructured information, posing great challenges to its processing. When dealing with unstructured data, effectively utilizing the structure of this sensitive data without a fixed format can greatly enhance the accuracy of sensitive data detection. therefore, in this paper, we have developed and evaluated a Chinese sensitive data detection method for unstructured data using a pre-trained model and a Lattice-Transformer structure. this method leverages the lattice structure to fully utilize the structural information of sensitive data, overcomes the complexity and uncertainty of unstructured information through the powerful parallel computing capability of the transformer, and benefits from the strong language representation ability of the pre-trained model, enabling the method to possess strong detection capabilities for unstructured sensitive data. the outcomes of the experiment demonstrate that the model is capable of adeptly managing sensitive data in Chinese, regardless of its unstructured nature, type, or provenance.

关键词： Training Uncertainty Computational modeling Lattices Parallel processing Big Data Transformers

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Machine Learning Approaches for Sentiment Analysis in Social Media Data

Machine Learning Approaches for Sentiment Analysis in Social...

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international conference of Electronics, Communication and Aerospace technology (ICECA)

作者： S. thenappan Anju R Krishnan P. Sundara Bala Murugan A.S. Valarmathy P. Chitra Hifajatali Sayyed Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Telecommunication Engineering Karpagam College of Engineering Coimbatore Tamilnadu India Department of Management Studies St. Joseph's Institute of Technology Chennai Tamilnadu India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamilnadu India Department of Electronics and Communication Engineering Agni College of Technology Kanchipuram Tamilnadu India Department of Law Symbiosis Law School Hyderabad Symbiosis International Deemed University Pune India

As a crucial component of Natural language processing, sentiment analysis autonomously identifies and categorizes the positive and negative emotions reflected in social media text data. Here, the proposed work examines the viability of using Support Vector Machines (SVM) as a technique for machine learning to decipher online comments. Analysis of public opinion is crucial in the information age. Due to its nonlinear separability and efficacy with high-dimensional data, SVM is well-suited for this task. the bag-of-words technique is utilized for data acquisition, cleaning, and feature extraction. the SVM model is trained using cross-validation and hyperparameter adjustment, resulting in a 97% accuracy rate. Compared to other algorithms, the proposed SVM technique is preferable to the current method. the proposed work sheds light on how to assess the attitudes of social media users, whose findings may have significant implications for industries, including advertising, politics, and brand public opinion.

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ELDA: Enhancing Multi-Modal Machine Translation via Large language Model-Driven Data Augmentation

ELDA: Enhancing Multi-Modal Machine Translation via Large La...

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Machine Learning and Natural language processing (MLNLP), international conference on

作者： Yusong Wang Dongyuan Li Jialun Shen Yicheng Xu Shuai Zhong Mingkun Xu Tokyo Institute of Technology The University of Tokyo Guangdong Institute of Intelligence Science and Technology

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

Multi-modal machine translation (MMT) leverages auxiliary information to reduce ambiguity and semantic distortion compared to traditional neural machine translation. However, a significant challenge for current MMT methods is their inability to surpass strong pre-trained machine translation methods, mainly due to the scarcity of triplet training data. A common approach involves incorporating extensive parallel and monolingual data to train the text and visual models separately. However, collecting external data is labor-intensive and time-consuming, often leading to distribution deviations. To address this challenge, we introduce ELDA, a novel low-cost data augmentation method driven by large language models, which can automatically expand the dataset. We introduce a carefully crafted prompt based on multi-attributes, combined with in-context learning, to effectively guide the GPT-3.5 model in generating diverse yet consistent-style samples. Extensive experiments on three benchmark datasets show obvious improvement up to 3.26 BLEU score and 2.64 METEOR score on the current leading MMT methods in downstream MMT tasks, confirming the effectiveness of ELDA.

关键词： Visualization Translation Semantics Training data Neural machine translation Manuals Benchmark testing Data augmentation Data models Meteors

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A Graphical DSL for Accelerating Chatbot Development

A Graphical DSL for Accelerating Chatbot Development

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international conference on advanced Communication Technologies and Networking (CommNet)

作者： Charaf Ouaddi Lamya Benaddi Lahbib Naimi El Mahi Bouziane Abdeslam Jakimi Software Engineering and Information Systems Engineering Team Faculty of Sciences and Technologies Errachidia Moulay Ismail University Morocco

ISBN: (数字)9798350367027

ISBN: (纸本)9798350367034

the development of chatbots utilizing artificial intelligence (AI) techniques represents a significant advancement in Natural language processing (NLP). Numerous studies employ deep learning and NLP methodologies to construct sophisticated chatbot systems. Additionally, developers and companies often utilize APIs provided by intent recognition services like Dialogflow and Amazon Lex to easily create chatbots using graphical forms, which enhance chatbot functionality. However, these APIs have limitations, such as potential dependency on specific NLP service providers and associated high costs. To address these limitations, the proposed work addresses critical gaps in chatbot development tools by constructing a Domain-Specific language (DSL) for chatbot development. this DSL aims to simplify the chatbot development process and reduce costs. Specifically, the paper introduces a graphical DSL designed to accelerate chatbot development. We use code generation templates to easily generate the source code for chatbots of the Rasa framework, simplifying the development process and reducing the time spent on manual coding.

关键词： Deep learning Costs Codes Intent recognition Source coding Manuals Chatbots DSL Artificial intelligence Domain specific languages

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Petidentityapp: A Mobile Application With Emergency Rescue And Domestic Pet Adoption For Pet Paradise Ph 23

Petidentityapp: A Mobile Application With Emergency Rescue A...

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Proceedings of the 2023 7th international conference on Natural language processing and information Retrieval

作者： Jefferson Costales Melody Gabas Allen Paul Olguera Bernadette Macabebe Information Technology Eulogio “Amang” Rodriguez Insititute of Sceience and Technology Philippines

ISBN: (纸本)9798400709227

the study's goal is to develop a system that anyone may use to rescue and adopt pets. the application displays and spotlights pets for attention using the power of social media. the program includes numerous animal-centric components but also contains a section where you may interact with others. the survey form was used to collect the data of the respondents, and based on the findings of the analysis, the study's respondents obtained high percentage results on the questionnaire given by the researcher, and it was evaluated using international Standardization Organization (ISO/IEC) 25010:2011 system and software quality requirements. Additionally, there is no significance in the perception of respondents to the app. the primary task of this application is to accomplished process satisfaction with regards to its goal to also life the organizational image. Most organization both nonprofit and profit generating, adapt and transition from the traditional or manual web-based these days. It is to take advantage of the resources and emerging technologies of today's context, as it is main solution for faster and smooth communication, transaction and data collection. the mean of communication for the animal shelter is the interface itself replacing physical transactions or phone calls to mobile application for its convenience and availability, a mobile application transaction of an animal shelter is a great advantage for its always available.

关键词： adopt pets domestic pet mobile application social media

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Mood Detection and Emoji Classification using Tokenization and Convolutional Neural Network

Mood Detection and Emoji Classification using Tokenization a...

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international conference on ent Computing and Control Systems (ICICCS)

作者： Kuldeep Vayadande Ubed Shaikh Rajan Ner Sanket Patil Omkar Nimase Tejas Shinde Department of Information Technology Vishwakarma Institute of Technology Pune India

Tokenization and convolutional neural networks (CNNs) is being used for the task of mood detection, emoji generation, and classification. Tokenization is helpful to break down text into individual sentences, words as well as phrases which is the important step in natural language processing (NLP). It allows the model to focus on individual words and phrases. the CNNs are then trained on labeled text datasets for mood detection, text-emoji pair datasets for emoji generation and emoji-label pair datasets for emoji classification. the results of the study indicate that tokenization and CNNs can be effectively used to understand the sentiment or emotion expressed in text data, to acquire high accuracy in classifying text as having a negative and positive or neutral sentiment, generating emojis that match the sentiment or emotion expressed in the text and classifying emojis as expressing a certain sentiment or emotion. For sentiment analysis, a CNN can be trained on a dataset of labeled text where the labels indicate the sentiment or emotion expressed. Once trained, the CNN can be used to classify new text as expressing a negative, neutral, or positive sentiment. As far as emoji generation is concerned, a CNN can be trained on a dataset that pairs text with corresponding emojis and labels. Once trained, the model can be used to generate emojis that align with the sentiment or emotion conveyed in new text inputs. As for emoji classification. this research study discusses about various techniques involved in tokenization emoji as well as text generation and classification.

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Malicious Domain Detection using NLP Methods — A Review

Malicious Domain Detection using NLP Methods — A Review

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： G. Pradeepa R. Devi Department of Computer Applications Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (纸本)9781665487351

Cyber security has emerged as a serious problem in today's business world. Attackers use different techniques to deceive the internet user to perform illegal activities such as stealing sensitive information, injecting malware, using the victim's system for further attack, etc. the majority of attacks are disseminated via social media posts, emails, and SMS messages. the message or webpage has embedded malicious links that will lead the user to the attacker's nest. Consequently, the issue of identifying malicious domain names has sparked significant concern. Researchers employ a variety of methods to find such malicious pages, including black or white lists, rules, machine or deep learning, visual similarity, and language processing (NLP). the attacker's employment of various tricks to confuse internet users, detecting or blocking an attack through a malicious domain is a difficult task. NLP with Machine Learning and NLP with Deep Learning methodologies receive the most interest from researchers due to their processing power. this paper provides an overview of several NLP text processing techniques as well as recent domain-related research.

关键词： Deep learning Uniform resource locators Visualization Social networking (online) Employment web pages Market research

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Arabic Sentiment Analysis with Noisy Deep Explainable Model 23

Arabic Sentiment Analysis with Noisy Deep Explainable Model

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Proceedings of the 2023 7th international conference on Natural language processing and information Retrieval

作者： Md. Atabuzzaman Md Shajalal Maksuda Bilkis Baby Alexander Boden Department of Computer Science Virginia Tech USA Fraunhofer Institute for Applied Information Technology FIT Germany Department of Computer Science UITS Bangladesh

ISBN: (纸本)9798400709227

Sentiment Analysis (SA) is an essential task for numerous real-world applications. However, the majority of SA research focuses on high-resource languages such as English and Chinese, while limited-resource languages like Arabic and Bengali receive less attention. Additionally, existing Arabic sentiment analysis methods based on advanced artificial intelligence (AI) approaches tend to operate as black boxes, making it challenging to comprehend the reasoning behind their predictions. this paper proposes an explainable sentiment classification framework for the Arabic language. We introduce a noise layer to different deep learning (DL) models, including BiLSTM and CNN-BiLSTM, to address the issue of overfitting. the proposed framework enables the explanation of specific predictions by training a local surrogate explainable model, shedding light on the reasons behind each sentiment prediction (positive or negative). Experiments were conducted on publicly available benchmark Arabic SA datasets, and the results demonstrated that the inclusion of noise layers in the DL model improves performance for the Arabic language by mitigating overfitting. Our method also outperformed several state-of-the-art approaches. Moreover, the introduction of explainability with the noise layer enhances transparency and accountability, making the model suitable for practical adoption in AI-enabled systems.

关键词： Arabic Sentiment Analysis BiLSTM CNN Explainable AI (XAI) Interpretability Noise layer

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Development of Indonesian language Speech Recognition Algorithm Model in Knowledge Database

Development of Indonesian Language Speech Recognition Algori...

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international conference on information technology, Computer and Electrical Engineering (ICITACEE)

作者： Emny Harna Yossy Syaeful Karim Dannys Dian Rachmantyo BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

ISBN: (数字)9781728172262

ISBN: (纸本)9781728172279

Research on humanoid robots in Indonesia is developing very rapidly, marked by research on humanoid robots having the ability to learn independently using Indonesian and vocabulary to receive and provide responses from humans based on Natural language processing. the general objective of research focuses on speech recognition systems to be able to interact with people naturally. Humanoid robots must be able to consider the question style and infer answers through automated, unreliable conversations in noisy environments. In development, there are still shortcomings that is if the answer is not in the knowledge base, the robot will look for the context that is most similar to the knowledge base, instead of giving an answer, the robot gives an answer `not found'. Sometimes, the wrong answer will be displayed because the cosine value between the question and the wrong answer is influenced by the same context, that the truth is not the actual answer. Based on these problems, this study was conducted with the aim of producing structured data and information knowledge according to the topic of the question, producing knowledge storage designs, optimizing models based on design and knowing the performance of knowledge representation storage models. Based on these objectives, this research will design algorithms and modeling representation of Indonesian language knowledge that will be applied to humanoid robots. the expected output is an algorithm design and storage of knowledge representation on humanoid robots.

关键词： Vocabulary Knowledge based systems Humanoid robots Speech recognition Knowledge representation Data models Noise measurement

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