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40th National Radio science Conference, NRSC 2023

作者： Naeem, Mohamed Aboul-Dahab, Mohamed Technology and Maritime Transport Computer Networks and Data Centre-Cairo Arab Academy for Science Cairo11799 Egypt Technology and Maritime Transport Department of Electronics and Communications Engineering. Arab Academy for Science Cairo11799 Egypt

ISBN: (纸本)9798350311440

Smart agriculture requires field sensing, especially video and photo transmissions, to detect severe issues in this field. Although the most suitable communication infrastructure is wireless transmission, the agriculture field suffers from severe diffraction and scattering of the wirelessly propagated waves. This paper proposes an optimization technique for improving wireless transmission in the agricultural environment by utilizing the most appropriate wireless impairment mitigation methods. The proposed technique adopts a joint wireless transmission optimization approach for both the client and the network sides. The amendment in the wireless transmission from the client-side perspective utilized the concurrent load balance over the heterogeneous wireless connections. While the refinement of the heterogeneous wireless network achieved better coverage by minimizing the number of propagating nodes by optimizing the propagating nodes' placement within the agricultural plot. The simulation, experiment, and results proved the effectiveness of the proposed solution for achieving better wireless video transmission performance. © 2023 IEEE.

关键词： Heterogeneous networks

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Multi-view Event Recognition in Crowd Scenes using Convolutional Neural Networks 15

Multi-view Event Recognition in Crowd Scenes using Convoluti...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Garg, Avni Vanaja, T. Dev, Sudhanshu Kumar, R. Thanga Ingle, Yashwant S. Balaji, N.V. Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Ug Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India

ISBN: (纸本)9798350370249

Convolutional Neural Networks (CNNs) have been gaining big interest in packages related to video reputation. This abstract focuses on their application to the recognition events in crowd scenes. Multi-view event popularity is proposed, with the CNN receiving a concatenated two-flow input contemporary pictures and optical waft-primarily based 3-D data from a couple of digi cam views. The CNN parameters are trained and tuned with the use of a multi-view low-stage feature aggregation level. The experiments performed on benchmark datasets demonstrate that the proposed approach gives advanced performance over current algorithms. The improved overall performance is attributed to the blended impact of state-of-the-art multi-view information and stepped forward CNN schooling. Moreover, it is verified that the low-level feature aggregation considerably improves the event's reputation and overall performance. © 2024 IEEE.

关键词： Convolutional neural networks

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FusionINN: Decomposable Image Fusion for Brain Tumor Monitoring

arXiv

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arXiv 2024年

作者： Kumar, Nishant Tao, Ziyan Singh, Jaikirat Li, Yang Sun, Peiwen Zhao, Binghui Gumhold, Stefan Chair of Computer Graphics and Visualization Faculty of Computer Science Technische Universität Dresden Dresden Germany School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Radiology Shanghai Tenth People’s Hospital Tongji University Medical School Shanghai China

Image fusion typically employs non-invertible neural networks to merge multiple source images into a single fused image. However, for clinical experts, solely relying on fused images may be insufficient for making diagnostic decisions, as the fusion mechanism blends features from source images, thereby making it difficult to interpret the underlying tumor pathology. We introduce FusionINN, a novel decomposable image fusion framework, capable of efficiently generating fused images and also decomposing them back to the source images. FusionINN is designed to be bijective by including a latent image alongside the fused image, while ensuring minimal transfer of information from the source images to the latent representation. To the best of our knowledge, we are the first to investigate the decomposability of fused images, which is particularly crucial for life-sensitive applications such as medical image fusion compared to other tasks like multi-focus or multiexposure image fusion. Our extensive experimentation validates FusionINN over existing discriminative and generative fusion methods, both subjectively and objectively. Moreover, compared to a recent denoising diffusion-based fusion model, our approach offers faster and qualitatively better fusion results. The source code of the FusionINN framework is available at: https://***/nish03/FusionINN. © 2024, CC BY.

关键词： Image fusion

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Recent Advancements in Hyperspectral Face Recognition: A Review 13

Recent Advancements in Hyperspectral Face Recognition: A Rev...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Das, Samiran Chatterjee, Sujoy JIS University Centre for Data Science JIS Institute for Advanced Studies and Research Kolkata India Amity University Department of Computer Science and Engineering Kolkata India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

The increasing demand for high-accuracy face recognition methods and the rapid advancement in instrumentation has made hyperspectral face recognition a viable alternative to traditional face recognition. The high-security biometric application scenarios, which demand high accuracy of recognition are switching to hyperspectral imaging due to its robustness against spoofing, illumination, pose variation, etc. The hundreds of visible and near infrared (NIR) images contained in a hyperspectral face cube provide sufficient information content, leading to more accurate recognition. However, the gigantic volume of information and the computational challenges call for efficient learning strategies for the recognition task. Researchers have introduced several feature extraction, supervised learning, deep learning, and pattern recognition approaches to carry out face recognition from hyperspectral face cubes. This work summarized the state-of-the-art approaches for hyperspectral face recognition in detail and highlight their pros and cons. The work also discusses future directions and challenges in this domain. © 2022 IEEE.

关键词： Biometrics

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Remaining Useful Life Prediction of Degraded Systems Based on Multiple Performance Indicators Constructed by Multiple Features

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IEEE Sensors Journal 2025年第11期25卷 19613-19631页

作者： Wu, Bin Shi, Hui Zeng, Jianchao Zhang, Xiaohong Wang, Zhijian Wang, Zuolu Taiyuan Institute of Technology Department of Computer Engineering Taiyuan030008 China Taiyuan University of Science and Technology School of Electronic Information Engineering Taiyuan030024 China Institute for Big Data and Visual Computing North University of China Taiyuan030051 China Taiyuan University of Science and Technology School of Economics and Management Taiyuan030024 China North University of China School of Mechanical Engineering Taiyuan030051 China University of Huddersfield Centre for Efficiency and Performance Engineering HuddersfieldHD1 3DH United Kingdom

The health status assessment of industrial equipment is critical to fault prediction and health management technologies. To fully reflect system degradation and accurately predict the remaining useful life (RUL) of a system, the construction of multiple performance indicators is important. Thus, a new state-space model and RUL prediction method are proposed herein based on multiple indicators, considering the three correlation relationships among features and degradation indicators. First, according to the extracted and selected feature signals, various relationships such as the correlations among multiple features, multiple features and indicators, and multiple indicators are analyzed. Then an intuitive multi-indicator state-space model is established considering the three correlations and the identified degradation patterns. On this basis, three failure modes of the system are defined according to the multi-indicator complex correlation conditions, and the corresponding probability density function (pdf) of the system RUL is calculated. Finally, the PHM2012 Challenge and XJTU-SY datasets are used as examples for experimental verification. Compared with other prediction methods, the proposed method can intuitively reflect the correlation between features and indicators, to solve the problem caused by the hidden random correlation. © 2001-2012 IEEE.

关键词： Prediction models

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MedSay-Tamil: A Pharmacological-Translator Mobile Application for the aid of Native Tamil Speakers

MedSay-Tamil: A Pharmacological-Translator Mobile Applicatio...

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2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Joshua Alfred, J. Sai Srivathsan, V. Sasithradevi, A. Mohamed Mansoor Roomi, S. Vellore Institute of Technology School of Electronics Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Centre for Advanced Data Science Vellore Institute of Technology Chennai India Thiagarajar College of Engineering Department of Electronics and Communication Engineering Madurai India

ISBN: (纸本)9798350312126

Medication error is one of the major causes of death, as victims intake the wrong medicine or dosage which can cause chronic effects. One such cause of medication error comes from the misinterpretation of information due to language barrier. There is a need for translation of information in medicinal strips to create awareness to the user. For this purpose, we propose an Innovative Mobile Application Using Flutter and Dart, empowering all native-Tamil speakers who find difficulty in acquiring information from tablet strips. The app is designed to revolutionize the way people obtain information from tablet strips such as general information, dosage, side effects, etc. with the help of web scraping. With its advanced Optical Character Recognition (OCR) engine, the app can accurately recognize text from images and convert it into editable text. The app's user-friendly interface with its state-of-art innovation mainly focusing native-Tamil speakers, built using the Flutter framework, allows users to quickly and easily scan and process strings. The application then translates and displays the information present in the Tamil language along with a text-to-speech feature. Upon testing, the application displayed a 90% accuracy in retrieving relevant information about the tablet present in images. © 2023 IEEE.

关键词： Optical character recognition

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A Novel Framework for Detection of Digital Face Video Manipulation using Deep Learning 3

A Novel Framework for Detection of Digital Face Video Manipu...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Chandra, Sanskriti Saxena, Shivam Kumar, Santosh Chaube, Mithilesh Kumar Srinivas, K.G. Alsamhi, Saeed Hamood Curry, Edward Saif, Abdu IIIT-Naya Raipur Department of Data Science and Artificial Intelligence Chhattisgarh India IIIT-Naya Raipur Department of Computer Science and Engineering Chhattisgarh India IIIT-Naya Raipur Department of Mathematical Sciences Chhattisgarh India University of Galway Insight Sfi Research Centre for Data Analytics Galway Ireland Faculty of Engineering & Information Technology Taiz Yemen

ISBN: (纸本)9798350321487

Digital face manipulation and classification have recently attracted the attention of academia and industry worldwide. Researchers have developed deep learning and computer vision techniques for detecting face manipulations, and it has become a challenging task to differentiate between authentic and manipulated face images manually. The challenge results in the decline of authenticity in digital media content. In this paper, we propose a framework for the classification of manipulating face images using the EfficientNet learning model. The proposed framework takes four digital facial forgeries: Face-Swap, Face-2-Face, DeepFakes, and neural textures. Multiple manipulation techniques are used to process manipulated faces, such as the Blaze-face tracking method, to determine the locations of the face images and pixel coordinates. The proposed framework is used first to identify the type of face manipulation and then to perform detection of the tampered regions in the face images. The proposed framework provided an automated benchmark that considers all four modification techniques in a realistic situation. The results show that the proposed framework outperforms existing approaches regarding accuracy and efficiency. Furthermore, the proposed framework is suitable for detecting digital face video manipulation in various applications, including forensics and security. © 2023 IEEE.

关键词： Textures

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Assessing the Feasibility of Incorporating AI for Efficient data Access Strategies

Assessing the Feasibility of Incorporating AI for Efficient ...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Yadav, Dhyan Geetha, G. Elackiya, R. Kumar, Santosh D. Sharma, Ritu Jain, Romil Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karnataka Bangalore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350329773

The use of AI in statistics gets entry to techniques has recently emerged as a critical topic in the discipline of the information era. AI and gadgets gaining knowledge of strategies can enhance the performance and accuracy of facts to get entry to strategies. Also, they can offer extra blessings, including advanced scalability, privacy, and protection. This paper examines the feasibility of incorporating AI for efficient information entry techniques. An in-depth review of present studies inside the place of AI for statistics gets entry is supplied, highlighting the challenges and possibilities related to its use. The paper also examines the practical factors of incorporating AI for information get entry to techniques and assesses the price, scalability, and complexity of such procedures. We concluded that incorporating AI for records access techniques is viable and might benefit information customers significantly. However, similar studies are wanted to decide the most incredible layout and implementation of such techniques. © 2023 IEEE.

关键词： Scalability

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AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection 37

AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis o...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Chen, Haoyang Liu, Shuai Lu, Feng Li, Wei Sheng, Bin Li, Mi Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)9781577358800

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Large dataset

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Multi-agent Scheduling for Shared Manufacturing Systems

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Procedia computer science 2025年 253卷 727-736页

作者： Ege Duran Cemalettin Ozturk Barry O’Sullivan SFI Centre for Research Training in AI School of Computer Science & IT University College Cork Ireland Department of Process Energy & Transport Engineering Munster Technological University Cork Ireland Insight SFI Research Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

Advances in digitization and resource-sharing business models have created new opportunities for manufacturing companies, enhancing competitiveness and resilience. However, these benefits bring computational challenges in efficiently planning and scheduling shared resources. Therefore, there is a need for scalable and quick solutions for practical applications. Shared manufacturing systems share characteristics with parallel machine scheduling, allowing for the application of advancements from this domain. This research focuses on Multi-Agent Parallel Machine Scheduling (MAPMS) in shared manufacturing, specifically addressing scenarios involving two parallel machines and distinct agents managing exclusive, set of non-overlapping orders. The study introduces a novel multi-objective mixed integer programming (MIP) model for order scheduling across multiple facilities, accounting for sequence-dependent setup times between orders. It also proposes a new heuristic method designed for industrial use. Benchmark instances demonstrate the practicality of both the MIP model and heuristic, contributing valuable insights into MAPMS challenges in shared manufacturing environments.

关键词： Multiagent systems Scheduling planning parallel machine

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