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Boosting CRM Chatbot Solutions with Flash Attention and Probabilistic Inference 4th

Boosting CRM Chatbot Solutions with Flash Attention and Prob...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Gunay, Ahmet Can Ozara, Muhammet Furkan Celik, Ahmet Erkan Akbulut, Akhan Next4biz R&D Ctr Pakpen Plaza 40-4 TR-34734 Istanbul Turkiye Istanbul Kultur Univ Dept Comp Engn TR-34536 Istanbul Turkiye

ISBN: (纸本)9783031821493;9783031821509

In an effort to deliver exceptional customer service, organizations are increasingly recognising the critical role that advanced language models (LLMs) play in the integration of CRM systems. this article explores the forefront of chatbot technology, with a particular focus on the essential function they play in contemporary consumer relationship management. the ability of chatbots to provide personalized and contextually pertinent responses to customer inquiries has been significantly enhanced by the rapid development of LLMs, specifically in the areas of sentiment, intent, and context comprehension. Organizations have the opportunity to enhance customer contentment and optimize operations by leveraging the capabilities of these advanced AI systems. In addition, offline capabilities guarantee continuous support, thereby enhancing customer confidence and loyalty in a time when connectivity fluctuations continue to be a challenge. the present study introduces an innovative offline chatbot system that aims to overcome the limitations of traditional cloud-based counterparts. By efficiently integrating data from various sources, such as project documentation and social media, this chatbot is capable of operating independently and delivering assistance to users, even in locations with sporadic internet connectivity. the experimental outcomes illustrate that our newly developed chatbot model outperforms established benchmarks, as evidenced by its 4.1-second inference time, 90.2-point BLEU score, and 9.7-point WER score. these metrics underscore the model's effectiveness, precision, and timeliness in handling user inquiries and producing responses of exceptional quality. the experiments we conduct are designed to validate the effectiveness of our model in improving customer service experiences within CRM systems through comprehensive performance testing.

关键词： Natural Language processing NLP Chatbot Flash Attention Probabilistic Inference Data ingestion

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Domain adaptation, Explainability & Fairness in AI for Medical image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans

Domain adaptation, Explainability & Fairness in AI for Medic...

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IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Dimitrios Kollias Anastasios Arsenos Stefanos Kollias Queen Mary University of London UK National Technical University of Athens Greece National Infrastructures for Research and Technology Greece

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

the paper presents the DEF-AI-MIA COV19D Competition, which is organized in the framework of the ’Domain adaptation, Explainability, Fairness in AI for Medical image Analysis (DEF-AI-MIA)’ Workshop of the 2024 computer vision and pattern recognition (CVPR) conference. the Competition is the 4th in the series, following the first three Competitions held in the framework of ICCV 2021, ECCV 2022 and ICASSP 2023 international conferences respectively. It includes two Challenges on: i) Covid-19 Detection and ii) Covid-19 Domain Adaptation. the Competition use data from COV19-CT-DB database, which is described in the paper and includes a large number of chest CT scan series. Each chest CT scan series consists of a sequence of 2-D CT slices, the number of which is between 50 and 700. Training, validation and test datasets have been extracted from COV19-CT-DB and provided to the participants in both Challenges. the paper presents the baseline models used in the Challenges and the performance which was obtained respectively, together with the best corresponding performances of the methods submitted and evaluated in the Challenges.

关键词： COVID-19 Training computer vision image analysis Databases Computed tomography conferences

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Garbage Classification System Based on image recognition and Sensor Network technology 4

Garbage Classification System Based on Image recognition and...

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2022 4th international conference on Electronic Engineering and Informatics, EEI 2022

作者： Yang, Yuan-Ge Zhai, Shu-Ying Zhang, Xuan-Jia School of Information Engineering Xi’an Mingde Institute of Technology Shanxi Xi’an China

ISBN: (纸本)9783800759231

With the development of computer science and technology, the advent of the internet age has promoted the development of the entire society, but it is accompanied by the serious problem of garbage pollution in people's production and life. However, at this stage, low recycling efficiency and traditional recycling methods limit economic development and environmental protection. this paper designs an intelligent recycling system based on image recognition and multi-sensor coordination, which has the functions of real-time detection of garbage classification, cellphone and voice interaction, the remaining volume of the recycling bin detection. the machine vision is used to obtain the recognition confidence of the object, and the main control unit gets the data of the sensor. Under the comprehensive judgment of the recognition confidence and the detection data, the recycling bin classifies the renewable resources which contain plastic bottles, glass bottles and cans. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： image recognition

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vision Anomaly Detection Using Self-Gated Rectified Linear Unit 4

Vision Anomaly Detection Using Self-Gated Rectified Linear U...

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4th international conference on Artificial Intelligence in Information and Communication, ICAIIC 2022

作者： Jahan, Israt Ali, Md. Osman Rahman, Md. Habibur Chung, Byungdeok Jang, Yeong Min Kookmin University Department of Electronics Engineering Seoul02707 Korea Republic of Department of Electrical and Electronic Engineering Daffodil International University Dhaka1341 Bangladesh Deptment of Electrical and Electronic Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh ENS. Co. Ltd. Ansan15655 Korea Republic of

ISBN: (纸本)9781665458184

In the area of image processing and computer vision, visual anomaly detection is a critical and difficult task. For anomaly detection in surface image data, a customized neural network incorporating self-gated rectified linear unit (SGReLU) was designed, and the SGReLU-based model excelled other activation function-based models with a top-20 average test accuracy of 99.84%. the computational time needed for the operation is 10533 s for 20 epochs and the top-20 average test loss is 0.0125 using SGReLU, both of them were comparatively less than other activation functions. © 2022 IEEE.

关键词： computer vision

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Modified Barnacles Mating Optimization with Deep Learning based Tomato Disease Detection and Classification 4

Modified Barnacles Mating Optimization with Deep Learning ba...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Sharmila, M. Natarajan, M. Annamalai University Department of Computer and Information Science India

ISBN: (纸本)9798350300093

Tomatoes are the second largest crop grown in India. they are more commonly known vegetables and be possible to find in all kitchens. It is the most popularly produced crop after potato and sweet potato. On the other hand, various diseases might damage the quantity and quality of tomato crops. Leaf disease detection and classification employs different DL algorithms which is a cutting-edge image processing technique yet produces comparatively new and proficient outcomes. this study develops a new Modified Barnacles Mating optimization with Deep Learning based Tomato Disease Detection and Classification (MBMODL-TDDC) method. the MBMODL-TDDC method accurately determines the effectual identification of different kinds of tomato diseases. To accomplish this, the presented MBMODL-TDDC method uses a Wiener Filtering (WF) based noise removal process at the preprocessing step. In addition, the MBMODL-TDDC technique employs a RetinaNet object detector for tomato disease detection. Moreover, Hybrid Deep Learning (HDL) algorithm was applied for classifying tomato diseases into different types. Eventually, the MBMO algorithm is used to adjust the hyperparameter values of the HDL model. the simulation results of the MBMODL-TDDC technique are tested on the tomato disease dataset. the result analysis portrayed the improvement of the MBMODL-TDDC algorithm over other current methodologies. © 2023 IEEE.

关键词： computer vision

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Oracle Bone Inscription image Retrieval Based on Improved ResNet Network 27th

Oracle Bone Inscription Image Retrieval Based on Improved Re...

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27th international conference on pattern recognition, ICPR 2024

作者： Ding, Jun Wang, Jiaoyan Aysa, Alimjan Xu, Xuebin Ubul, Kurban School of Computer Science and Technology Xinjiang University Ürümqi830046 China Xinjiang Multilingual Information Technology Key Laboratory Xinjiang University Ürümqi830046 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Urumqi830046 China

ISBN: (纸本)9783031783043

Research on oracle bone inscription image retrieval is important for applications in academic and cultural heritage areas. the current oracle bone dataset faces problems such as the low similarity between the same category, the high similarity between the different categories, and imbalanced sample distribution. In addition, due to the complex background of oracle bone images, existing network models have certain limitations in extracting image features. To address these challenges, this study first adopts a Siamese network-based image retrieval method to learn feature representations of similar and dissimilar images. Subsequently, the existing dataset was partitioned, providing a practical and usable retrieval dataset for the oracle bone image retrieval field. Finally, an improved network model based on ResNet is proposed and integrated into the Siamese network framework. the model achieves the highest retrieval MP and MAP values of 83.26% and 90.68%, respectively, which is better than the current research. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Content based retrieval

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Estimated Posture for Human Elbow Flexion in Acoustic Environments 4

Estimated Posture for Human Elbow Flexion in Acoustic Enviro...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Selvi, P. Pandi Sangeetha, V. Sathish Kumar, L. Saravanan, D. Manikandan, J. Mangayarkarasi of Organization Department of Computer Science Tamil Nadu Paravai India Karpagam Academy of Higher Education TamilNadu Coimbatore India School of Computing Science and Engineering Vit Bhopal University MP Kotrikalan India

ISBN: (纸本)9798350300888

the present study proposes a novel approach to address the challenge of identifying smoking and phone usage behaviours among individuals amidst the intricate surroundings of construction sites. Specifically, the method involves the recognition of elbow flexor behaviours through posture estimation. the localization and detection of key points pertaining to the human upper body have been achieved through the application of AlphaPose. Subsequently, the present study introduces the HEFBD model, which is a mathematical framework that discriminates human elbow flexion behaviour. the model is constructed using human key points and is designed to identify the area that is relevant for recognising tiny objects while mitigating the impact on the complicated background. A method for image reconstruction with super-resolution is employed as a pre-processing technique for images that exhibit blurring. Furthermore, the performance of You Only Look Once (YOLO) has been enhanced through the incorporation of a tiny object identification layer and the integration of a convolutional inhibitory attention model, thereby improving its detection capabilities. the method has demonstrated an improvement in detection precision of 6.7% and a reduction in false identification rates due to complex backgrounds of 12%. these outcomes outperform existing detect approaches and meet the requirements for immediate results. © 2023 IEEE.

关键词： image reconstruction

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ArtDiff: Artwork Generation via Conditional Diffusion Models 23

ArtDiff: Artwork Generation via Conditional Diffusion Models

引用

15th international conference on Digital image processing, ICDIP 2023

作者： Liu, Wenhui Liu, Bin Lin, Xiao Li, Bo He, Zhifen Wang, Kang School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Jiangxi Nanchang China Department of Supercomputing Management Jiangxi Science and Technology Infrastructure Center Jiangxi Nanchang China

ISBN: (纸本)9798400708237

Artwork Generation is an important research area of computer vision. Recently, kinds of generative models have achieved great success in natural image generation. However, artwork generation has rarely been studied due to the unfixed structure of artworks. Combined with prevailing diffusion models, we propose a simple yet effective framework, named as ArtDiff, for artwork generation. Given a name of an artist, we can generate diverse and novel artworks which reflecting the style of the artist. To the best of our knowledge, ArtDiff is the first diffusion model that generate artworks with the guidance of artist's name. Experimental results demonstrate that ArtDiff is able to recognize the artist's preference and generate artworks with reasonable structures and fine-grained details. © 2023 ACM.

关键词： Diffusion

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A Moving Object Detection Technology for Intelligent Police Based on ViBe Algorithm

A Moving Object Detection Technology for Intelligent Police ...

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computer vision, image and Deep Learning (CVIDL), international conference on

作者： LongFei. Dang Miao. Yang Kai. Yi Binghe. Si Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China Department of Computer Science and Technology Sichuan Police College LuZhou China Department of Criminal Technology Sichuan Police College LuZhou China

the detection of moving targets in the field of public security and policing involves processes such as image processing, background modeling, and object segmentation to identify objects of interest from videos or images, such as people, vehicles, and objects. the current location and size of the target image are determined, preparing for subsequent pedestrian and vehicle recognition. this article uses MATLAB software to detect moving objects using the ViBe algorithm, providing a theoretical basis for the intelligence of police technology.

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PSTNet: A Progressive Sparse Transformer Network for image Deraining 27th

PSTNet: A Progressive Sparse Transformer Network for Image ...

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27th international conference on pattern recognition, ICPR 2024

作者： Shaik, Raj Ahamed Singh, Navjot Verriboina, Antony Shandilya, Debesh Kumar Computer Vision and Biometrics Lab Department of Information Technology Indian Institute of Information Technology Uttar Pradesh Allahabad India

ISBN: (纸本)9783031781247

image deraining aims to transform a rainy input image into an image of high quality. Transformer-based techniques have demonstrated remarkable efficacy in image deraining because of their capacity to represent non-local information, a crucial element for high-quality image reconstruction. Our findings indicate that most transformers used for feature aggregation nowadays leverage all token similarities between query-key pairs. If the query tokens differ from the key tokens, the quality of the recovered image is compromised by the self-attention values derived from these tokens if these are considered during feature aggregation. For this purpose, we propose the Progressive Sparse Transformer Network (PSTNet), which progressively learns to restore degraded inputs by retaining the helpful self-attention information during feature aggregation and discarding the remaining values which obstruct the restoration. Transformer blocks help to capture interactions between distant pixels. thorough experimental outcomes on widely used benchmarks show that the suggested approach performs better than most existing techniques. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image quality

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