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Audio to Sign Language conversion using Natural Language processing

Audio to Sign Language conversion using Natural Language Pro...

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Communication, Computing and Industry 4.0 (C2I4), international conference on

作者： Nayana J Suparna Bhat Rekha R Nair Tina Babu Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Bengaluru India

ISBN: (数字)9781665474979

ISBN: (纸本)9781665474986

People who are deaf never get to enjoy things that hearing folks do, whether it's conversation, playing the game, going to seminars, or participating in virtual meetings, for example. The major challenge they have while interacting among regular people is connection, because every regular person has no concept what sign language is or how it works.. Our project's objective is to help those who have hearing loss develop a system for communication. The spoken message is converted into signs. It converts the spoken word into signs. This system receives voice as inputs, converts the soundtrack into a text, and displays the key symbols used in Indian language using well before graphics or GIFs. The use of this technology makes communication between those who have normal hearing and those who are difficult to hear easier This takes into account how your arms, body, and appearance are combined. Around the universe, there really are 135 different varieties of hand signals. There are numerous different sign languages, including American Sign Language (ASL), Indian Sign Language (ISL), British Sign Language (BSL), Australian Sign Language (Auslan), and many more In this initiative, Indian Sign Language is being used. The deaf community may take advantage of all the activities that hearing people engage in, from social engagement to accessing information, thanks to this technology. NLP is used to pre-process the text (Natural Language processing). With the help of this technology, hearing-impaired people may participate in a wide range of activities, from socialising to accessing information NLP is used to post the text (Natural Language processing). This programme converts audio into text, displays Indian sign language, and then accepts speech as input. Accuracy rate for the planned project work is 99%, and Validation accuracy is 96%.

关键词： Seminars Program processors Symbols Gesture recognition Auditory system Oral communication Games

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Analyzer for Heterogeneous Peer Server Nodes with ADCD Technique for Load Diversion

Analyzer for Heterogeneous Peer Server Nodes with ADCD Techn...

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Communication, Computing and Industry 4.0 (C2I4), international conference on

作者： V.S. Prakash Vennila J Shiva Kumar B. N Shriidhar P J Department of Computer Science Kristu Jayanti College Bengaluru Karnataka India Department of statistics Kristu Jayanti College Bengaluru Karnataka India Department of Mathematics CMR Institute of Technology Bengaluru Karnataka India Department of Computer Science Engineering VMKV Engineering College Salem Tamil Nadu India

ISBN: (数字)9781665474979

ISBN: (纸本)9781665474986

Peer-to-peer distributed computer architecture are taken into consideration in order to distribute computer assets like storage, comfortable, and CPU cycles openly deprived of consuming a middle scheme or a central server. It can be difficult to Upkeep of load balancing on diverse peer-to-peer networks, and various previous research have provided state balancing algorithms for the demand aspect of load balance. For effective load balancing in dispersed heterogeneous peer networks, the prior work developed processing Heterogeneous Data using Time - Peer Nodes. Using the Dynamic Time Warping (DTW) technique, load-demand balancing factors for peer servers are determined to a certain degree. The prior work solely discussed the peer node's liveliness in the heterogeneous network. In this work, we will provide the subsequent load-demand element generated as of DTW, peer server giving out cycle requirements are recognized utilizing Appropriation of Duty Cycle Data(ADCD) Technique in order to improve the previous study. In its active state, peer node servers examine their processing cycle needs and distribute data to other nodes in accordance with their duty cycles. If there is a load imbalance, peer node servers are used to distribute the load using a node collection policy created on ADCD abundant demonstration? Performance of the proposed Diverse cycle of a peer node server analyzer with an existing time variant utilizing DCPS-ADCD & NSS and the ADCD approach Using data from diverse peer networks that have been taken from major internet service providers, a P2P network that uses the Dynamic Time Warping algorithm is experimentally assessed. The efficiency is calculated based on the duty cycle count, energy usage, and load.

关键词： Knowledge engineering Heuristic algorithms Simulation Web and internet services Load management Heterogeneous networks Peer-to-peer computing

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Expression of Concern for: Fog-Cloud-IoT Enabled WSN Architecture

Expression of Concern for: Fog-Cloud-IoT Enabled WSN Archite...

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Intelligent Engineering and Management (ICIEM), international conference on

作者： P Anitha Christy Angelin. P.V Samuel Devakumar. Liwa H. Al-Farhani Gandam Vijay Kumar Shaurya Deep Shoaib Mohammad PSNA College of Engineering and Technology Dindigul Tamilnadu India Karunya Institute of Technology and Sciences Coimbatore Tamilnadu India System Analysis Control and Information Processing Academy of Engineering RUDN University Moscow Russia Computer Science and Engineering Career Point University Rajasthan India IIMT College of Management Greater Noida India Department of Law IMS Unison University Dehradun Uttarakhand India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467575

Remote Because of the exciting innovation it introduces, the WSN likely to be a subject of research in recent time. Because of the impromptu distant connections, adaptability, and simplicity of execution, this has all the makings of becoming the most maintainable innovation for ecological detecting, whether it's about limited or huge scope watching. In any case, the main drawbacks stem from the limited capacity, processing, and accessibility of organization centers for information. Virtual machines resources have been adopted to overcome such constraints, devices to expanded capacity, computation, and easy-to-understand availability. This was a natural progression from traditional WSN designs among pattern in recent theories evolved along advent of Internet-of-Things advancements. Against the growing popularity of WSN associated cloud checking frameworks, still the issues are detected because of the distributed comp ting's disadvantages, like dormancy with the capacity charges. In this paper, the advancements achieved to a fog-cloud-IoT associated with WSN architecture of putting a bit of computing at the network part are elaborated, a process, which follows the smart Fog associated with Cloud-computing concept of dissecting also following up on IoT data. A similar investigation was carried out to illustrate the improvements made possible by processing at the organization's edge.

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A Multi-scale Framework for Visual Grounding 2021

A Multi-scale Framework for Visual Grounding

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2021 3rd international conference on Video, signal and Image processing

作者： Baosheng Li Peng Qi Jian Wang Chong Tan Runze Qi Xiaolei Li State Grid Shandong Electric Power Company Laiwu Power Supply Company China School of Control Science and Engineering Shandong University China

ISBN: (纸本)9781450385886

Visual Grounding aims to ground the right region of the image according to the query that describes the target. Generally, this task can be formalized as selecting the target with the highest matching score between the image regions and the language queries. This task is based on object detection, word embedding and multi-modal data fusion. At present, this task has a wide range of needs in real life. For example, it is necessary to search the matching content of surveillance video based on text information to check whether certain violations have occurred. Based on the current advanced one-stage algorithm ReSC, this paper studies the algorithm details of the object detection algorithm, word embedding and multi-modal data fusion. On this basis, this paper exploits multi-scale features to improve performance.

关键词： Word Embedding Object Detection Visual Grounding Multi-scale features

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Attention-based Multi-View Stereo Network 2021

Attention-based Multi-View Stereo Network

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2021 3rd international conference on Video, signal and Image processing

作者： Liufeng Chen Ruifang Zhai Hui Peng Wanneng Yang College of Informatics Huazhong Agricultural University China National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural University China and College of Plant Science&Technology Huazhong Agricultural University China

ISBN: (纸本)9781450385886

The convolutional neural networks (CNNs) is a basic component in deep learning-based multi-view stereo (MVS) network for feature extraction, which effectively extract feature representations on single image scale. A broad range of prior research has exploited feature pyramids to compute a multi-scale feature representation, seeking to strengthen semantics at all scales. In this paper, we focus to improving the quality of representations by explicitly modelling the interdependencies between channels throughout its feature hierarchy. We propose to add a lightweight attention module to the feature pyramids, allowing us inferring high resolution depth maps to achieve better reconstruction results. Using attention module in Cas-MVSNet, our method only slightly increases the complexity and computational burden of the model, and achieves a more complete point cloud and less noise results on the DTU benchmark.

关键词： Reconstruction Feature Pyramid Attention Multi-View Stereo CNNs

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Image Deep Steganography Detection based on Knowledge Distillation in Teacher-Student Network 2021

Image Deep Steganography Detection based on Knowledge Distil...

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2021 3rd international conference on Video, signal and Image processing

作者： Huifang Hao Shangping Zhong Kaizhi Chen College of Mathematics and Computer Science Fuzhou University China

ISBN: (纸本)9781450385886

The current hot deep steganography can hide confidential information of an image into a carrier image of the same size. It also has the characteristics of a high steganography rate and is often used for private communication within specific organizations. However, the existing deep steganography detection network is complex and time-consuming. Thus, rapid steganography detection for massive image information is urgently needed. In this study, a typical distillation algorithm is applied to the existing classical deep steganography detection network (Ye-Net and Yedroudj-Net). We constructed a student network, taking Ye-Net and Yedroudj-Net as teacher networks respectively. Appropriate parameters T and α are also selected in accordance with the effect of knowledge distillation. With these parameters, a fast training model of deep steganography detection is established. The proposed deep steganography detection network is compared with Ye-Net and Yedroudj-Net through experiments. Three spatial steganography algorithms (Wow,S-uniward and Hill) and different steganography rates are used. The experimental results show that the proposed network can shorten the training and average detection time by about 70% and 24% when the detection accuracy is slightly lower. Overall, compared with Ye-Net and Yedroudj-Net, the proposed method can achieve fast detection of deep steganography. And code will be available at: https://***/hhfshiqi/KD-Ye-net

关键词： Model rapid detection Deep steganography detection Teacher-student network Knowledge distillation

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Robustness Investigation of FOC of PMSG Based Variable-Speed Wind Turbine Connected on Grid

Robustness Investigation of FOC of PMSG Based Variable-Speed...

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international conference on Advanced Electrical Engineering (ICAEE)

作者： Mohammed AOUMRI Abdelkader HARROUZ Ibrahim YAICHI Seddik BAALA Patrice WIRA Laboratoire de Développement Durable et Informatique (LDDI) University of Ahmed Draïa Adrar Algeria Institute for Research in Computer Science Mathematics Automation and Signal University of Haute Alsace Mulhouse France

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349368

Wind energy has emerged as an increasingly viable renewable energy source, driven by rising fossil fuel costs, energy security concerns, and environmental considerations. In 2022, wind energy production grew by 9% compared to 2021, maintaining an annual rate exceeding 12% since 2012. This study focuses on the application of Field Oriented Control (FOC), a prevalent control strategy in wind power generation systems, to a permanent magnet synchronous generator (PMSG) in a variable speed wind turbine configuration. The research methodology involved implementing FOC and conducting robustness analyses through PMSG parameter variations. The primary objectives were to maximize wind energy capture efficiency while ensuring sustained control performance. MATLAB/Simulink was employed to simulate and evaluate the various components of the wind turbine system (WTS), facilitating comprehensive analysis of the results.

关键词： Wind energy generation Systematics Wind speed Robustness Synchronous generators Permanent magnets Stability analysis Wind turbines Security Wind energy conversion

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The improvement of hand gesture recognition based on sEMG by moving average filtering for features 2021

The improvement of hand gesture recognition based on sEMG by...

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2021 3rd international conference on Artificial Intelligence and Advanced Manufacture

作者： Cai Chen Lijuan Li Xikun Zhang Xingwei Wang Changming Han Tian Xia Wenchao Li Fulai Peng Yang Shen Jianpeng An Chinese Academy of Sciences China Huazhong University of Science and Technology China Integrated Traditional Chinese and Western Medicine Hospitial of Tongzhou District China

ISBN: (纸本)9781450385046

Surface electromyographic signal (sEMG) is a kind of bioelectrical signal, which records the data of muscle activity intensity. Hand gesture recognition based on sEMG plays an important role in the hand rehabilitation robotics. Improving the recognition accuracy of hand gesture is the goal always pursued by researchers. This study purposes to evaluate the performance of moving average filtering for features in improving the accuracy of hand gesture recognition based on sEMG. Firstly, empirical mode decomposition was applied on the effective motion segments extracted by sliding windows with different size and stride. Afterward, 21 features were extracted from original signal and each of the intrinsic mode function. Subsequently, moving average filtering with different orders were used to further suppress the noise in each feature series to improve the recognition performance. Finally, two representative machine learning methods (including decision tree and K nearest neighbor) were used to evaluate the performance. Compared with the original features, the intra-subject recognition accuracy rate after feature processing for KNN and DT increase by 17%, and 6%, respectively. The accuracy of the inter-subject across five subjects after feature processing increase by 28% (KNN), and 12% (DT), respectively. The above results prove that the moving average filtering for features could effectively improve the performance of hand gesture recognition.

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Sentiment Analysis on Drug Reviews Using Diverse Classification Techniques

Sentiment Analysis on Drug Reviews Using Diverse Classificat...

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Communication, Computing and Industry 4.0 (C2I4), international conference on

作者： Premkumar Duraisamy Yuvaraj Natarajan K.R.Sri Preethaa K. Mouthami Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Research Professor Intelligent Construction Automation Center Kyungpook National University Deagu South Korea

ISBN: (数字)9781665474979

ISBN: (纸本)9781665474986

Free text reviews are abundantly distributed over the internet among the standard population of the world. Although online doctors were consulted during the pandemic, many drugs are taken without prescription by ordinary people. Reviews are distributed about the medicines through online feeds, posts and blogs, which are merely helpful in identifying the side effects and their adverse reactions in combination with another drug or standalone adverse reactions. Spelling mistakes, jargon and short names are normalized throughout the medical terminologies using Natural language processing (NLP) and RxNorm from the reviews extracted from these sources. The concept of Bidirectional Encoder Representations from Transformers (BERT) is used to mine the association rules for the drug interactions when used in conjugation with another drug, and the idea of Adaptive Fuzzy logic neural networks is used to validate the results of association rules derived from the BERT. Inconsistencies identified by the regulations generated were rejected efficiently by the Support vector machines (SVM), Radial Basis Function (RBF)and the system shows 60.11 percentage of accuracy with 10 cross-fold validation, whereas the results are equivalent to the accuracy obtained using SVM with RBF kernel. The data set is reduced from its dimension using Linear discriminant analysis (LDA) by applying the mining process and BERT on two drugs, namely “Azithromycin” in conjugation with Buprenorphine has Type ‘A’ side effects, and Prozac in conjugation with Metronidazole shows different side effects. Deplin and Zoloft give very positive symptoms and are independent of dosage interactions.

关键词： Support vector machines Drugs Analytical models Terminology Bit error rate Support vector machine classification Transformers

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Text Guided Image Manipulation using LiT and StyleGAN2

Text Guided Image Manipulation using LiT and StyleGAN2

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Computing and Machine Intelligence (ICMI), international conference on

作者： Shantanu Todmal Tanmoy Hazra Computer Science and Engineering Indian Institute of Information Technology Pune (2022) Pune India Department of Artificial Intelligence Sardar Vallabhbhai National Institute of Technology Surat Surat India

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

The convergence of generative adversarial networks and advanced natural language processing models in recent years has paved the way for crucial developments in text-based image manipulation. However, the conditional generative models rely heavily on the annotated text-image data for accomplishing this task. In addition, the quality of manipulation depends upon the accuracy of the textual descriptions present in the dataset. To address these challenges, we propose a novel framework for text-based image manipulation that eradicates the dependency on the text annotations for training. We leverage the characteristic of a well-aligned distribution between the differences in LiT's image features of two images and the differences in LiT's textual embeddings of two texts. The difference between the LiT encodings of the source and target image is mapped to the edit directions in the StyleGAN2 inversion space W+ using a mapper, during the training stage. The trained mapper then predicts the StyleGAN2 edit directions using the differences in the LiT's text encodings of the provided text prompts, during the inference phase. As a result, the mapper is trained without using text and generalizes well to a variety of text-based prompts. The proposed approach is able to achieve an FID score of 33.67 and IDS score of 71.89, better than the other state-of-art approaches like TediGAN, StyleCLIP, StyleMC, HairCLIP, CLIPInverter etc, which signifies superior image quality and precise manipulation.

关键词： Training Hair Image quality Image coding Image color analysis Aerospace electronics Transformers

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