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Use of Neural machine Translation in Multimodal Translation 6

Use of Neural Machine Translation in Multimodal Translation

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Nair, Manavi Tanwar, Sarvesh Badotra, Sumit Kukreja, Vinay Amity Institute of Information Technology Amity University Uttar Pradesh Noida India School of Computer Science and Engineering Bennett University U.P Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350304480

Multimodal Neural machine Translation (MNMT) is a type of machine Translation that allows the translation of source language that contains various forms of information, such as images, video, audio, and signs, into the target language while maintaining the intended meaning of the source language. In MNMT, the use of deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are required to process different modalities and to generate an appropriate translation. the use of attention mechanisms, early and late fusion techniques, and hierarchical fusion have significantly improved the accuracy and fluency of translation. the performance of different MNMT systems can be evaluated in terms of various metrics such as BLEU score, accuracy, and fluency. MNMT has the potential to revolutionise the way we communicate by bridging the gap between different languages and cultures. the integration of MNMT in various industries, such as e-commerce, tourism, and healthcare, can bring significant benefits in terms of customer experience and accessibility. It states how Neural machine Translation plays a significant role in processing different modalities in Multimodal Neural machine Translation. More research work in this field can produce effective and innovative ideas to improve Multimodal Neural machine Translation. © 2023 IEEE.

关键词： machine Translation Multimodal Neural machine Translation Natural Language processing Introduction Neural machine Translation

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A machine learning and Deep learning-Based Web Application for Crop and Fertilizer Recommendation and Crop Disease Prediction 6th

A Machine Learning and Deep Learning-Based Web Application f...

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6th international conference on Soft Computing and signal processing, ICSCSP 2023

作者： Srinidhi, Amuri Jahnavi, Veeramachinani Dholvan, Mohan Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819984503

India is a country that is majorly dependent on agriculture and farming for livelihood. the country’s diverse soil lets farmers produce various crops throughout the year. Depending on the characteristics of soil such as the composition of nitrogen, phosphorus, potassium and the pH of the soil and environmental factors such as rain, humidity, and temperature, it is important to know which crops should be grown and what fertilizers to use, to maximize the yield. It is also important to detect any crop diseases at an early stage to prevent major loss. this paper presents an all-in-one platform web application that performs all three tasks, and it uses ML algorithms such as KNN, Decision Tree, Gaussian Naive Bayes, SVM, Logistic Regression, and Random Forest to recommend crops and fertilizers by analyzing soil and environmental factors. For crop recommendation, Random Forest is chosen as it provided the highest accuracy of 99.09%. Random Forest is deployed for fertilizer recommendation as well, as it provided the highest accuracy of 100%. the Deep learning model ResNet50 is used to detect crop diseases, and the accuracy for training and validation was 95.52% and 87.36%, respectively. Flask framework is used to build the backend of the web application. the web application is enabled to send an SMS of the results to user’s mobile phone using the SMS API, Twilio. the goal of the project is to provide an ideal solution to farmers or the users of the web application, via a text message, which can be accessed even in remote areas and can be understood by the majority of people. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crops

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Multi-Layer Visual Perception for No-Reference Image Quality Assessment 6

Multi-Layer Visual Perception for No-Reference Image Quality...

引用

6th IEEE international conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Qi, Junwei Wang, Yingzhen Wang, Qingchun Li, Yingsong College of Computer Science and Technology Harbin Engineering University Harbin150001 China College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Shanghai Yanding Information Technology Co. Ltd. Shanghai201210 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Hefei230601 China

ISBN: (纸本)9798350399059

In this work, we propose a no-reference image quality evaluation approach, aiming to solve the problem that the traditional convolutional neural network is insufficient to express the global information of the image. therefore, in order to make the captured image features have the coherence of global context information, we combine a Transformer structure widely used in NLP (natural language processing) with the traditional CNN to realize the attention mechanism in the human visual perception characteristics. Firstly, the self-attention mechanism of Transformer is used to learn the global representation of the image from the multi-layer features extracted from different levels of CNN. Additionally, to forecast the ultimate image quality score, the global and local attributes are combined. Finally, the proposed algorithm is evaluated on three public datasets commonly used in CSIQ [1], TID2013 [2] and LIVE [3]. Compared with eight traditional no-reference image quality evaluation algorithms and five methods based on deep learning, the proposed algorithm achieves competitive results. © 2023 IEEE.

关键词： Convolutional neural networks

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Implicit Chinese Binary Compound Sentence Relation Recognition Based on DERIA 6

Implicit Chinese Binary Compound Sentence Relation Recogniti...

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6th international conference on machine learning and Natural Language processing, MLNLP 2023

作者： Lu, Haiying Li, Yuan Zhang, Yuan Lv, Jiacheng Central China Normal University China

ISBN: (纸本)9798400709241

the relationship of a complex sentence refers to the logical connection and semantic relationship between clauses in the sentence. Implicit complex sentences have certain relationships between clauses, but lack obvious conjunctions, requiring inference and identification through retrieval of other semantic information between the clauses. therefore, the identification of complex sentence relationships is a challenging problem in the field of natural language processing. this paper proposes a model based on deep enhanced representation for recognizing the relationships between implicit binary clauses. the model utilizes the BERT model to transform the input sequence into word vectors, which are then fed into a multi-layer encoder module to capture the contextual features between sentences. this module is composed of stacked Bi-LSTM layers, with residual connections between each layer. In addition to being fed into the next layer encoder, the output of each layer also enters an interaction attention layer, aiming to better capture the relevance between two arguments. the outputs of the interaction attention layers are concatenated and then passed through a fully connected layer and softmax layer to perform the task of identifying implicit complex sentence relationships. the model was tested on a dataset extracted from the Chinese Complex Sentence Corpus (CCCS) and the Chinese Discourse Treebank (CDTB), achieving a highest accuracy of 79.34% and a highest macro-Average F1 value of 78.77%, indicating the effectiveness of the method proposed in this paper. © 2023 ACM.

关键词： signal encoding

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Watch Your English Language: Text Normalization from Written Expressions to Spoken Forms 6

Watch Your English Language: Text Normalization from Written...

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6th international conference on machine learning and Natural Language processing, MLNLP 2023

作者： Jiang, Yufeng Xidian University Shaanxi Xi'an China

ISBN: (纸本)9798400709241

Based on the existing research results for speech-To-Text and methods based on statistical modelling, machine learning, etc., this paper applied the feature engineering method based on machine learning to speech-Totext, converting English text from written expressions into spoken forms. First, I preprocessed the data, including calculating the file structure and contenthad, missing values as well as reformating features. Next I had a look at normalized sentences and overview visualizations. And then I researched the impact of the normalization. Moreover, I researched token classes and their text normalization, which compose of the "PLAIN"(modifications), the "PUNCT"(punctuation), numerical classes, the "LETTERS"(acronyms anhd initials), the "VERBATIM"(special symbols) as well as the "ELECTRONIC"(websites) class, with the most frequent transformations of each class. Meanwhile, I did a next-neighbour analysis to prove that context matters a lot, inlcuding token class overview-previous vs next, treemap overviews relative percentages, exploration of all neighbour relations. then, I analysed the transformation statistics, such as sentence length and classes, as well as normalized tokens and where to found them. On the basis of these foundings, I did the feature engineering composing of string length, class-specific token distance, numeric tokens and what's in a name. I also did a case study-A tale of four twos, including the class "CARDINAL", "ORDINAL", "LETTERS", and "PLAIN". Ultimately, I evaluated the results with 11.11 MB size test data, getting a extremely high accuracy score of 99.776Additional Key Words and Phrases: Text normalization, Text-To-speechFeature engineering, Tok © 2023 ACM.

关键词： machine learning

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A Review on machine learning Techniques in IoT-Based Smart Grid Applications 6th

A Review on Machine Learning Techniques in IoT-Based Smart...

引用

6th international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2023

作者： Goyal, Samarth Goyal, Ishita Ahmed, Tanveer School of CSET Bennett University Greater Noida India

ISBN: (纸本)9783031530814

the deployment of machine literacy (ML) in the electric structure has been innovated by the arrival of the coming-generation power networks known as the smart grid. A detailed analysis related to smart grids has been performed by this article. the Internet of things (IoT) offers access and is at the heart of these advanced grid architectures. the system’s inter connectivity and requirement for ongoing communication resulted in a vast data flow that necessitates technologies considerably superior to traditional methods for appropriate data analysis and interpretation. In the modern world, cyber security has become a crucial concern in smart grid sophisticated networks, with sensor nodes and the data constituting key targets of cyberattacks. the article critically examines such security challenges and their remedies. the reviews are summarised to provide a succinct image of the practice and prospective future disciplines for industry developments, including existing constraints and feasible remedies, as well as their applicability. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart power grids

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Research on multimodal modal fusion application in pulmonary embolism diagnoses

Research on multimodal modal fusion application in pulmonary...

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5th international conference on signal processing and machine learning (CONF spml 2025)

作者： Haonan Deng Xinyu Zhang Yiyi Lu Zhongxu Ye Institute of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054 People's Republic of China Department of Computing Science University of Glasgow Glasgow G11 6QT UK Duke Kunshan University Suzhou 215316 People's Republic of China Department of Computer and Information Technology Purdue University West Lafayette 47907 USA

ISBN: (数字)9781837243143

Pulmonary embolism is a life-threatening illness caused by blockages in the pulmonary arteries, usually due to blood clots. this condition requires accurate diagnosis on time in order to prevent critical complications like cardiac arrest or chronic pulmonary hypertension. As a result, this study explores the capability of data fusion in a multimodal scenario to improve diagnostic accuracy in PE through the integration of Electronic Medical Records with imaging data from CT Pulmonary Angiography. Deep learning models such as PENet, ResNet3D_50, ResNext3D_101, and DenseNet were used for the analysis of imaging data. the EMR data were analyzed using logistic regression and other machine learning methods. A Late Fusion approach that combined the outputs from the EMR and imaging models, using averages, resulted in a diagnostic accuracy with an AUC of 0.94. this is a perfect example where combined clinical and imaging features addressed the shortcomings of one-modality approaches. Advanced fusion techniques, such as MISA, were also tried, but simpler strategies often performed better than them when the amount of data was limited. Overall, the results point out the promise of multimodal fusion in advancing PE diagnostics and also take note of the selection of an appropriate strategy for clinical application.

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Spiking Neural Network for Microseismic Events Detection Using Distributed Acoustic Sensing Data 6th

Spiking Neural Network for Microseismic Events Detection Usi...

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6th international conference on Soft Computing and Data Mining (SCDM)

作者： Bin Shahabudin, Mohd Safuwan Krishnan, Nor Farisha Binti Muhamad Mausor, Farahida Hanim Binti Univ Teknol PETRONAS Seri Iskandar 32610 Perak Malaysia

ISBN: (纸本)9783031669644;9783031669651

Microseismic detection events are critical in monitoring subsurface activities, including hydraulic fracturing, enhanced oil recovery, carbon dioxide, or natural gas geological storage and reservoir characterization, to guarantee safe and efficient energy extraction. this presents significant challenges as it generates large amounts of data. Despite the recent machine learning techniques being increasingly integrated into fiber-optic distributed acoustic sensor (DAS) systems to enhance their intelligent recognition capabilities, there is still a need to solve this. In some research, it has been observed that computational speed is time-consuming and overfitting, thus necessitating a more extensive investigation of this analysis. this study proposes a novel approach using DAS data to enhance microseismic event detection's precision, overfitting issue, and interpretability. this approach utilizes a specifically designed neural network architecture. the deep learning approach is highly effective for the real-time management of the substantial amounts of data recorded by DAS equipment. three phases of research methodology are proposed. the contribution of this research is that spiking neural network architecture for microseismic detection will bring advancements in microseismic monitoring.

关键词： Deep learning signal processing Microseismic Event Detection Spiking Neural Network Distributed Acoustic Sensing

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Optimizing System Performance in Punjabi Language processing: Revealing Linguistic Nuances and Advancing Computational Solutions 6th

Optimizing System Performance in Punjabi Language Processing...

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6th international conference on Recent Innovations in Computing, ICRIC 2023

作者： thakral, Manish Devare, Manoj AIIT-AUM Amity University Navi Mumbai India

ISBN: (纸本)9789819734412

this research addresses the optimization of system performance in Punjabi language processing. Recognizing the distinctive linguistic challenges inherent in Punjabi, we employ advanced natural language processing and machine learning techniques to enhance the accuracy and efficiency of Punjabi language applications. through a comprehensive dataset, we systematically identify linguistic nuances and contextual elements specific to Punjabi, providing valuable insights into the language's intricacies. these extracted features serve as the foundation for developing tailored algorithms, specifically designed to tackle the complexities associated with Punjabi language processing. Our study's primary focus is on Punjabi, a language that has been underexplored in this domain. By incorporating state-of-the-art methodologies, we aim to significantly improve the performance of Punjabi language applications. the outcomes of our research are expected to make noteworthy contributions to advancements in information retrieval, sentiment analysis, and machine translation within the digital landscape. the proposed model, driven by the identified linguistic features, promises to be a key factor in elevating the overall accuracy and efficiency of Punjabi language processing systems. this research thus plays a crucial role in bridging the gap and addressing the specific challenges posed by Punjabi, ultimately enhancing the capabilities of language applications in this linguistic context. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Natural language processing systems

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Content Moderation System Using machine learning Techniques 6th

Content Moderation System Using Machine Learning Techniques

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6th international conference on Innovative Computing and Communication, ICICC 2023

作者： Gulati, Gaurav Jha, Harsh Anand Jain, Rajat Sharma, Moolchand Chaudhary, Vikas CSE Department Maharaja Agrasen Institute of Technology New Delhi India AI & DS Department GNIOT Greater Noida India

ISBN: (纸本)9789819940707

With the ever so growing internet, its influence over the society has deepened, and one such example is social media as even children are quite active on social media and can be easily influenced by it, social media can be a breeding ground for cyberbullying, which can lead to serious mental health consequences for victims. To counter such problems, content moderation systems can be an effective solution. they are designed to monitor and manage online content, with the goal of ensuring that it adheres to specific guidelines and standards. One such system based on natural language processing is described in the following paper, and various algorithms are compared to increase accuracy and precision. After testing the application, logistic regression yielded maximum precision and accuracy among the other algorithms. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Natural language processing systems

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