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Usage of Parallelization Techniques for Video Summarisation: State-of-the-art, Open Issues, and Future Research Avenues 7

Usage of Parallelization Techniques for Video Summarisation:...

引用

7th IEEE international conference for Convergence in technology, I2CT 2022

作者： Karale, Sonali Saini, Jatinderkumar R. Faculty of Computer Studies Pune India Symbiosis Institute of Computer Studies and Research Pune India

ISBN: (纸本)9781665421683

Videos are one of the prime and important sources of information. they can be availed in huge numbers. Whenever something is to be searched for in the video, the whole video clip has to be searched for which is a very time-consuming process. the search time could be reduced using video summarisation technique which is used to extract the relevant frames or the key-frames from the video in order to get the quick summary of the event. the relevant frames are extracted based on the particular object detection, face recognition, event detection, movements in the frames, etc. Supervised and unsupervised approaches are used to achieve the desired result. As the research got deeper in this area, new advanced and efficient technologies were invented. Deep learning feature extraction algorithms were found to be more efficient as compared to the conventional feature extraction algorithms. To make the video summarisation process faster, a parallel processing approach was also used by the researchers. this paper presents a review of different techniques used for video summarisation available in the literature. We have specifically presented a comparative study of video summarisation techniques implemented using parallel processing and multi-core CPUs. While the state-of-the-art of the field is presented through a comprehensive and systematic study, the paper also presents open issues, challenges, as well as open research avenues in the area. It is concluded that there is a particular scope of improvement in the processing time and use of parallelization in various stages of video summarisation. It is also concluded that the empirical study in the field should make use of a large size of video data set collected from multiple cameras rather than a small dataset collected using a single camera. © 2022 IEEE.

关键词： Face recognition

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Direction-of-arrival estimation with new versions of Min-Norm algorithm based on Nyström method 7

Direction-of-arrival estimation with new versions of Min-Nor...

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7th international conference on Image and Signal processing and their Applications, ISPA 2022

作者： Naceur, Aounallah Ali, Khalfa Kasdi Merbah University Faculty of New Information Technologies and Communication Department of Electronic and Telecommunications Ouargla30000 Algeria Mohamed Boudiaf University Faculty of Technologies Department of Electronic M'Sila28000 Algeria

ISBN: (纸本)9781665480420

Direction-of-arrival estimation is a significant problem in many telecommunication systems which their architecture is composed with antenna arrays. In literature, several algorithms were proposed as a challenge, for different scenarios, to detect rapidly and accurately the direction of impinging signals on the antenna array. In this paper firstly, we use the Nyström method to develop a low computational Min-Norm version for solving the problem of direction-of-arrival estimation by spectral peak searching. then, we generalize the root version of the proposed algorithm. Finally, we give some simulation results for demonstrating the efficiency and the performances of the new proposed algorithm versions. © 2022 IEEE.

关键词： Array processing

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Empowering Medical Image Analysis: Unveiling Anomalies through GANs and BiGAN’s Models 7th

Empowering Medical Image Analysis: Unveiling Anomalies Throu...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Sharma, Vatsal Kumar Jakhar, Aryan Vats, Aaroh Singh, Gurwinder Computer Science Engineering with AIML Chandigarh University Mohali India

ISBN: (纸本)9783031699818

In many researches, the field of medical image analysis has made noteworthy strides, thanks to the implementation of sophisticated machine learning methods. this project, titled "Enhancing Medical Image Analysis: Uncovering Abnormalities through Innovative Models", stands as a modern game powerup to utilize rapidly progressing computer algorithms known as Generative Adversarial Networks (GANs) and Bidirectional GANs (BiGANs) for the automated identification and categorization of anomalies within medical images. Medical image analysis plays a crucial role in diagnosing and treating a range of health conditions, in this case particularly of 2 types, Bacterial and Viral. However, the precision and accuracy of image analysis often depend upon the expertise and execution of radiologists, which can introduce the potential for human errors and result in time-consuming processes. To tackle these challenges, this project introduces a fresh approach that makes use of GANs and BiGANs to improve the identification and categorization of abnormalities in medical images, thereby providing valuable insights to healthcare professionals and ultimately enhancing patient diagnosis and treatment outcomes. © IFIP international Federation for information processing 2024.

关键词： Generative adversarial networks

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Artificial Intelligence for Digital Business Performance 7

Artificial Intelligence for Digital Business Performance

引用

7th international conference on Digital Arts, Media and technology, DAMT 2022 and 5th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering, NCON 2022

作者： Meepung, Tippawan Kannikar, Phudit Rajamangala University of Technology Tawan-Ok Chakrabongse Bhuvanarth Faculty of Business Administration and Information Technology Bangkok Thailand

ISBN: (纸本)9781665495103

Unique and different consumer behaviors that are important for the decision to purchase products or services of the business, as well as how data is collected and used for effective marketing. Digital marketing tools, including understanding customer journey, affect digital marketing in different ways. Learn consumer behavior and customer journey, as well as how to use digital marketing tools for effective marketing. For marketing around the world, the ability to use AI and machine learning to help with marketing is getting clearer, and there are many marketing technology tools that bring AI and machine learning capabilities to the application of marketing in an interesting way. It saves time searching for information, getting faster results. It can be used to create content like follower to increase sales, followers, and engagement a lot more, or for businesses with websites, it uses AI to track consumer behavior and help segment people who visit your website. Use data to increase sales and connect to other businesses. © 2022 IEEE.

关键词： Commerce

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Research on image anomaly detection technique for pipeline leakage 24

Research on image anomaly detection technique for pipeline l...

引用

7th international conference on Signal processing and Machine Learning, SPML 2024

作者： Guo, Jikun Zheng, Canpeng School of Electronic and Information Engineering Guangdong University of Petrochemical Technology China

ISBN: (纸本)9798400717192

With the increasing demand of the whole society for oil and gas resources, the safety and reliability of pipelines cannot be ignored, and the abnormal detection of pipeline leakage becomes especially important. the traditional manual inspection method is not only time-consuming and labor-intensive, but also susceptible to the influence of subjective factors, and unable to realize efficient and accurate anomaly detection. therefore, with the rapid advancement of artificial intelligence for the society, this paper is dedicated to the development of advanced computer vision techniques to automate the discovery of abnormal regions from large-scale oil and gas pipeline image data. In this paper, we propose a semi-supervised anomaly detection method based on teacher-student network, which is applied to the detection of abnormal regions in oil and gas pipeline images. the key of the method is to use the student network to learn the normal patterns from a small amount of labeled data, and at the same time, through the mutual guidance with the teacher-student network, we can fully explore the potential knowledge of a large amount of unlabeled data, and further optimize the performance of anomaly detection. We have specially designed the network structure and loss function for the characteristics of oil and gas pipeline images in order to improve the extraction ability of image anomaly features. © 2024 ACM.

关键词： Students

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A method for encrypting images based on the AES algorithm 7

A method for encrypting images based on the AES algorithm

引用

7th international conference on Image information processing, ICIIP 2023

作者： Sharma, Nidhi Dhiman, Panakj Chauhan, Amit Sharma, Neha Kumar, Sanjay Shalu Lovely Professional University Department of Cse Jalandhar India JUIT Waknaghat Department of Cse & It Himachal Pradesh Solan India Jaypee University of Information Technology Computer Science And Engineering Himachal Pradesh Solan India University Institute of Technolog Hpu Department of Electrical Engineering Himachal Pradesh India

ISBN: (纸本)9798350371406

Every day, the use of devices such as PCs, laptops, and a multitude of other different devices for correspondence and the associated information and transmission capacity has evolved. As a result, the customer base has expanded. Along with these clients, there is an increase in the number of unauthorized clients attempting to illegally gain access to information. Hence the problem of data security. the photos are transmitted by unknown means from numerous sources;Some of the information in the image contains confidential information;and some images themselves are extremely secret;It is therefore important to protect them from attacks. To solve this problem, we apply the AES equation for image encoding and decoding. this encrypted data is incomprehensible to the unauthorized client. this encrypted information is often sent over the network and can be decrypted using AES at the least desirable end. It thus guarantees the secure transmission of images. © 2023 IEEE.

关键词： Cryptography

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Rumor detection and combat through Machine Learning 7

Rumor detection and combat through Machine Learning

引用

7th international conference on Image information processing, ICIIP 2023

作者： Agarwal, Ruchi Pradhan, Rahul Gla University Department of Computer Science And Engineering U.P. Mathura India

ISBN: (纸本)9798350371406

this research paper gives a complete overview of the current state of studies on fake news detection, together with the strategies and techniques which have been advanced, the effects performed to this point, and the challenges that continue to be. We survey the present literature on fake information detection, masking quite a number methods, together with truth-checking, natural language processing, machine mastering, and crowd-sourcing. We also speak the strengths and limitations of these techniques, as well as the possibilities for destiny research on this area. Our overview exhibits that at the same time as a few strategies for fake news detection have proven promise, there may be nonetheless a lot work to be done to develop effective and reliable techniques for detecting fake news at scale. One of the main demanding situations dealing with researchers on this field is the evolving nature of fake news, which could exchange swiftly in reaction to new trends and advances in era. © 2023 IEEE.

关键词： Machine learning

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Depth Control of a Biomimetic Manta Robot via Reinforcement Learning 7th

Depth Control of a Biomimetic Manta Robot via Reinforcement ...

引用

7th international conference on Cognitive Systems and information processing, ICCSIP 2022

作者： Zhang, Daili Pan, Guang Cao, Yonghui Huang, Qiaogao Cao, Yong School of Marine Science and Technology Northwestern Polytechnical University Xi’an710072 China Unmanned Vehicle Innovation Center Ningbo Institute of NPU Ningbo315103 China Key Laboratory of Unmanned Underwater Vehicle Technology of Ministry of Industry and Information Technology Xi’an710072 China

ISBN: (纸本)9789819906161

this paper proposes a model-free biomimetic manta robot depth control method based on reinforcement learning. Different from the traditional control method, the reinforcement learning method does not need to establish a mathematical model of the control object, and autonomously learns the control law through data training. Based on the classical Q algorithm, the state space, the action space, and reward function of the depth control of the bionic manta robot are designed. the state-action function is trained offline using the experience replay mechanism and random sampling strategy. Finally, the trained function is transplanted to the biomimetic manta robot prototype to establish a controller. the effectiveness of the proposed control method is verified by experiments. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Multi-band discriminant speech synthesis analysis based on Natural Language processing 7

Multi-band discriminant speech synthesis analysis based on N...

引用

7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Liu, Songjia Yao, Yuancheng Liu, Binyu Zhu, Rui Ren, Jing Southwest University of Science and Technology School of Information Engineering Mianyang China

ISBN: (纸本)9781665478571

With the rapid development of neural networks and deep learning, speech synthesis technology has been significantly improved. the end-to-end speech synthesis systems based on deep learning have been able to synthesize speech with naturalness close to the original human pronunciation. However, the existing end-to-end speech synthesis system model is complex, and it is impossible to achieve real-time speech synthesis on devices with low computing power. In this paper, a multi-band discriminative autoregressive speech synthesis model is proposed based on natural language processing. the model uses an encoder-decoder architecture with attention mechanism, which is mainly composed of DSC-GRN modules. Stacking multiple convolutions with different expansion coefficients by gating the residual structure can increase the receptive field so that the encoder and decoder can pay attention to the context information with a longer time span, which can improve the performance of the model. the whole model uses full convolution architecture and can be trained in parallel. Compared with the existing autoregressive model, the number of parameters of the model is greatly reduced. the synthesis speed is improved, and the quality of synthesized speech is ensured. © 2022 IEEE.

关键词： Deep learning

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Enhancing Content-Based Image Retrieval for Lung Cancer Diagnosis: Leveraging Texture Analysis and Ensemble Models 7

Enhancing Content-Based Image Retrieval for Lung Cancer Diag...

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7th international conference on Image information processing, ICIIP 2023

作者： Jayabalaji, K.A. Laxmaiah, Bagam thatipudi, Jayendra Gopal Chandraprabha, K. Badhe, Pravin Balaram, Allam Sri Krishna Arts And Science College Department of Software Systems Tamilnadu Coimbatore India Cmr Technical Campus Department of Cse Telangana Kandlakoya India University of North Texas Department of Computer Science DentonTX76203 United States Bannari Amman Institute of Technology Department of Information Technology Tamilnadu Sathyamangalam India Sinhgad College of Pharmacy Swalife Biotech Department of Pharmacology Pune India Mlr Institute of Technology Department of Computer Science And Engineering Telangana Hyderabad500043 India

ISBN: (纸本)9798350371406

In image retrieval systems, many search engines rely on text-based methods, extracting information from descriptions and tags to retrieve relevant images. However, this conventional approach often overlooks the optical or semantic information present in photographs, leading to errors in the retrieved results. To address this issue, it is essential to consider content-based information such as intensities, edges, and pixel values when designing image retrieval systems, especially in domains like medicine. this study focuses on developing a Content-Based Image Retrieval (CBIR) system for the domain of medicine, with a particular emphasis on lung cancer detection. Early identification and treatment of lung cancer are critical for patient outcomes, making suitable computer-Aided Diagnosis (CAD) systems essential for assisting clinicians. the proposed ensemble model incorporates various feature extraction methods, including image processing techniques and wavelet transform, to harness the valuable information from images. these features are then fed into a deep learning model to assess their effectiveness in image retrieval tasks. By combining machine learning and image processing techniques, this work presents a comprehensive CBIR system capable of efficiently retrieving relevant medical images. Such advancements in image retrieval technology hold significant promise for aiding medical professionals in diagnosing and treating diseases like lung cancer promptly. © 2023 IEEE.

关键词： computer aided diagnosis

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