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CESFusion: Cross-Frequency Enhanced Spatial—Spectral Fusion Network for Hyperspectral and Multispectral Image Fusion

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Haozheng Yang, Yanhong Li, Chaoyang Lu, Yanjie Zhang, Guodao Chen, Shengyong Tianjin University of Technology School of Computer Science and Engineering Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China School of Computer Science and Engineering Tianjin300384 China Hangzhou Dianzi University Institute of Intelligent Media Computing Hangzhou310018 China

The fusion of hyperspectral and multispectral images (MSIs) involves integrating a high spectral resolution hyperspectral image (HSI) and a high spatial resolution MSI to generate an HSI with high-resolution HSI (HR-HSI). Existing HSI-MSI fusion methods primarily focus on information fusion within the spatial domain;however, few solutions have explored the employment of frequency analysis to enhance spatial resolution, limiting their capability for global perception. In this article, we propose an efficient and novel paradigm for HSI-MSI fusion through the cross-frequency enhanced spatial-spectral fusion network, named CESFusion, exploring the complementary fusion of information between the spatial and frequency domains. Specifically, we first present the cross-frequency fusion module (CFFM) to perform global analysis through the Fourier transform (FT) and effectively integrate and enhance the frequency domain information from both HSI and MSI. Subsequently, we propose the spectral modeling module (SpeMM) based on a state space model (SMM) to capture long-range spectral dependencies with linear complexity, and integrate it with the spatial residual block-based module (SRM) for joint spatial-spectral feature extraction. Finally, to enable sufficient interaction between the spatial and frequency domains, we adopt the cross-domain interaction module (CDIM), capturing and integrating complementary information from both domains. Moreover, a frequency-based loss function is purposely designed to further improve the restoration of global information. Extensive experiments conducted on both synthetic and real datasets demonstrate the superiority of our CESFusion, as evidenced by both quantitative and qualitative evaluation results. © 1980-2012 IEEE.

关键词： Image fusion

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Deepfakes: A Review on Technologies, Applications and Strategies

Deepfakes: A Review on Technologies, Applications and Strate...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kaswan, Kuldeep Singh Malik, Kiran Dhatterwal, Jagjit Singh Naruka, Mahaveer Singh Govardhan, D. School of Computing Science and Engineering Galgotias University UP Greater Noida India Mriem Department of Computer Science and Engineering Rohtak India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh India G L Bajaj Institute of Technology and Management Greater Noida India Institute of Aeronautical Engineering Department of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798350357769

Deepfakes are artificial intelligence-generated multimedia, primarily videos or audio, that effectively edit or invent information by superimposing one person's likeness or voice onto that of another. They use deep learning techniques such as GAN s to generate hyper-realistic simulations that blur the line between reality and fiction. Deepfakes encompass everything from political satire to possible concerns in the fields of disinformation, privacy invasion, and cybersecurity. Understanding deepfakes is critical in an age when manipulated media may influence public opinion and undermine confidence. The primary objective of this paper is to provide a holistic overview of deepfake technology, shedding light on both its creative potential and inherent risks. Through a systematic exploration of the evolution, applications, techniques, and detection strategies, we aim to equip readers with an informed perspective on this ever-evolving field. By synthesizing existing knowledge and insights, this paper intends to contribute to the ongoing discourse surrounding deepfakes, enabling stakeholders to make informed decisions, develop effective countermeasures, and anticipate the future trajectory of this technology. Ultimately, our work strives to foster awareness and preparedness in an age where discerning the truth has never been more vital. © 2023 IEEE.

关键词： AI CNN Deepfakes GAN Machine Learning RNN

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A Smart Waste Management System Using ESP8266

A Smart Waste Management System Using ESP8266

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Sustainable Expert Systems (ICSES), International Conference on

作者： S. Thangam Gurupriya M C.Sai Kushal Yadav M. Harshith M.Sunil Kumar Computer Science and Engineering Amrita school of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This research study presents a comprehensive smart waste management system designed to enhance collection efficiency and sustainability through the integration of Internet of Things (IoT) technologies. Key components include ultrasonic sensors for detecting waste bin fill levels, soil moisture sensors for monitoring waste composition, and the ESP8266 Wi-Fi module for real- time data transmission. The system offers continuous real-time monitoring and analytics, allowing waste management authorities to dynamically adjust collection routes based on actual waste levels. This optimization reduces operational costs by minimizing unnecessary collection trips, decreasing fuel consumption, and lowering vehicle wear and tear. Additionally, the system enhances waste segregation and recycling processes, promoting a circular economy and reducing landfill waste. Beyond operational benefits, the system contributes to environmental sustain ability by reducing greenhouse gas emissions associated with waste collection and disposal. It also improves public health by preventing overfilled bins and related hazards. This paper details the design, implementation, and advantages of this innovative IoT -based waste management system, highlighting its potential to transform traditional waste management practices.

关键词： Waste management Transforms Real-time systems Internet of Things Fuels Data communication Sustainable development Intelligent sensors Optimization Wireless fidelity

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The Design Innovation Space of Intelligent Personal Assistants (IPAs) in Healthy Buildings in the Era of Artificial Intelligence 26th

The Design Innovation Space of Intelligent Personal Assistan...

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26th International Conference on Human-computer Interaction, HCII 2024

作者： Liu, Yuqi Zhang, Jiawen Zeng, Yibei Liu, Yunlu Shidujamam, Mohammad Academy of Arts and Design Tsinghua University Beijing China Department of Service Design School of Design Royal College of Art London United Kingdom Fine Arts College Shanghai Normal University Shanghai China Faculty of Science Engineering and Computing Kingston University London United Kingdom Department of Computer Science and Engineering Independent University Dhaka Bangladesh

ISBN: (纸本)9783031768088

The implementation of Intelligent Personal Assistants (IPA) in healthy buildings cannot just elevate the buildings, intelligence level, but also boost their interactivity and interaction, enhance the quality of life for residents, and establish tailored health management approaches. Given China’s status as the world’s most populous country and a significant consumer market for emerging smart personal assistant products, this study focuses on the Chinese market. Utilizing a sample of 665 user data, it defines the demand, function, and design domains of IPA products in healthy buildings. Furthermore, it establishes a diversified design innovation space model for IPAs in healthy buildings amidst the artificial intelligence era. Initially, the study quantitatively assesses the impact of demographic variables on the acceptance of IPAs, offering a preliminary clarification and definition of the user profile of Chinese consumers. Additionally, by analyzing user needs and preferences, it explores the specific factors that guide users, purchasing decisions and categorizes these factors into four levels based on their significance. In terms of functional domains, this study has comprehensively gathered and categorized 30 service functions pertinent to Intelligent Personal Assistants (IPA), encompassing both current and anticipated functionalities. Utilizing questionnaire responses and statistical analysis, the study ranked the significance and precedence of these service functions, while employing principal component analysis to categorize user needs for smart speakers into seven distinct dimensions. Additionally, focusing on design domains, this research summarizes the diverse and innovative spatial models for intelligent personal assistants within architectural spaces, drawing insights from the functional domain analysis. This comprehensive study serves as a crucial foundation for the innovative design of IPA products in healthy buildings, while also facilitating the expans

关键词： Architectural design

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To Generate or to Retrieve? On the Effectiveness of Artificial Contexts for Medical Open-Domain Question Answering

arXiv

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arXiv 2024年

作者： Frisoni, Giacomo Cocchieri, Alessio Presepi, Alex Moro, Gianluca Meng, Zaiqiao Department of Computer Science and Engineering University of Bologna Italy School of Computing Science University of Glasgow United Kingdom

Medical open-domain question answering demands substantial access to specialized knowledge. Recent efforts have sought to decouple knowledge from model parameters, counteracting architectural scaling and allowing for training on common low-resource hardware. The retrieve-then-read paradigm has become ubiquitous, with model predictions grounded on relevant knowledge pieces from external repositories such as PubMed, textbooks, and UMLS. An alternative path, still under-explored but made possible by the advent of domain-specific large language models, entails constructing artificial contexts through prompting. As a result, "to generate or to retrieve" is the modern equivalent of Hamlet's dilemma. This paper presents MEDGENIE, the first generate-then-read framework for multiple-choice question answering in medicine. We conduct extensive experiments on MedQA-USMLE, MedMCQA, and MMLU, incorporating a practical perspective by assuming a maximum of 24GB VRAM. MEDGENIE sets a new state-of-the-art in the open-book setting of each testbed, allowing a small-scale reader to outcompete zero-shot closed-book 175B baselines while using up to 706× fewer parameters. Our findings reveal that generated passages are more effective than retrieved ones in attaining higher accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Hidden Emotion Detection from Speech and Text Using Deep Learning Approach

Hidden Emotion Detection from Speech and Text Using Deep Lea...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Ravula Tarun Reddy Suja Palaniswamy Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita VishwaVidyapeetham India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

—The value that comes from accurate detection and understanding human emotions stands across a ton of domains. This paper details a few well-tested models applied in a unique way for discovering hidden emotion through audio and text data with the help of a custom dataset. Every sample in the dataset has an audio snippet and a Q&A pair which tries to evoke a certain emotion. The main goal lies in creating a model that can identify emotional indicators from diverse data sources. To achieve this a custom dataset was developed. The audio samples have been annotated with descriptions of emotions, and their corresponding transcripts have been similarly labeled for enhanced clarity and were able to achieve 81.44% accuracy in identifying hidden emotions.

关键词： Deep learning Emotion recognition Accuracy Soft sensors Data models Information technology

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Internet of Medical Things: A CT Image Denoising in Tetrolet Domain 5

Internet of Medical Things: A CT Image Denoising in Tetrolet...

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5th International Conference on Information Systems and computer Networks, ISCON 2021

作者： Diwakar, Manoj Pandey, Richa Sharma, Ritik Saun, Sapna Singh, Prabhishek Pandey, Neeraj Kumar Graphic Era Deemed to Be University Department of Computer Science Engineering Uttarakhand Dehradun India Graphic Era Hill University Campus-Haldwani Department of Computer Science Engineering Uttarakhand India Amity University Uttar Pradesh Amity School of Engineering Technology Department of Computer Science Engineering Noida India DIT University School of Computing Dehradun India

ISBN: (纸本)9781665403412

The Internet of Medical Things (IoMT) is a huge community of linked medical equipment and technologies that communicate with a large number of Servers over the internet to provide a variety of services such as medical image denoising. Projection-based reconstructed images in computed tomography (CT) are noisy due to thermal noise and electrical noise, both of which are approximately AWGN in magnitude. Because of the variety of clinical features and textures included in CT reconstructed images, denoising is a difficult process. It is possible to estimate the true noise of a single image, and then to minimize both the real noise and the additional noise by reconstructing CT images. A unique modified approach is proposed in this work, in which the noise will be calculated actual as well as added, followed by a proposed method for denoising, which is based on thresholding in tetrolet domain. Different denoising schemes are used in the experiments, and the results are compared to one another. Experimentation has revealed that both in terms of PSNR and visual quality, the suggested technique yields promising results that are favorable to both. © 2021 IEEE.

关键词： Image denoising

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MSKI-Net: Towards modality-specific knowledge interaction for glioma survival prediction

MSKI-Net: Towards modality-specific knowledge interaction fo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Su, Ran Xiao, Jin Cui, Hui Xuan, Ping Feng, Xikang Wei, Leyi Jin, Qiangguo Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Shantou University School of Engineering Department of Computer Science China Northwestern Polytechnical University School of Software China Macao Polytechnic University AIDD Faculty of Applied Science China Xiamen University School of Information Xiamen China Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China

ISBN: (纸本)9798350386226

Gliomas hold a prominent position in neurooncology due to their high malignancy and poor survival rates. Accurately predicting the prognosis and survival risk of glioma patients is crucial for clinical treatment. Recent advances in survival prediction methods emphasize the importance of integrating complementary information from diverse modalities while neglecting the significant modality gap between pathological images and genomic data. To address this issue, we propose a modality-specific knowledge interaction network (MSKI-Net), which integrates whole slide images (WSI), RNA-Seq gene expression data, and copy number variation (CNV) data for glioma survival analysis. The MSKI-Net consists of a modality-specific feature enhancement (MSFE) module, a modality-interactive cross-attention (MICA) module, and a modality-specific knowledge-guided representation learning (MSKR) module. The three modules collaborate by complementing modality-specific features with modality-agnostic knowledge to improve the learning capability of MSKI-Net. Furthermore, we construct a dataset named TCGAmm, which combines WSI, RNA-Seq, and CNV data from The Cancer Genome Atlas (TCGA) to address the issue of data scarcity. Extensive experiments demonstrate that MSKI-Net achieves superior performance in predicting the survival risk of glioma cancer. © 2024 IEEE.

关键词： Gene expression

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Malpractice Detection in Examination Hall using Deep Learning

Malpractice Detection in Examination Hall using Deep Learnin...

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International Conference on Inventive computing and Informatics (ICICI)

作者： Aruna S K Madhumitha A Selvanayaki Kolandapalayam Shanmugam Senthil Kumar Thangavel Maiga Chang Department of Computer Science and Engineering School of Engineering and Technology Bangalore CHRIST University India Department of Mathematics and Computer Science Ashland University Ohia USA Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India School of Computing Information and Systems Athabasca University Canada

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

Various institutions administer tests at designated examination locations, chosen third-party and approved centers, and have established standards for installing CCTV cameras and conducting frisking under the supervision of designated personnel. Some institutions are using online proctoring, which enables students to take exams from any location. In all of the aforementioned scenarios, human monitoring is conducted, and maintaining a high level of vigilance may be challenging due to administrative oversight or intentional allowance of malpractice for personal gain. The malpractice detection may be attributed to acts like as plagiarism, unauthorized sharing of papers, and non-verbal communication. The study is conducted by capturing the dataset in the classroom of Christ University. The proposed approach is based on the YOLO framework. The movies are processed in real time to identify hand rotation, paper extraction, and classify the motion. The accuracy for the Head_right class is significantly higher than that of the Head_left class. The system is implemented using the programming language Python and has the potential for future expansion to provide real-time monitoring.

关键词： YOLO Accuracy Plagiarism Real-time systems Classification algorithms Object recognition Monitoring

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A Detailed Investigation on Potential Impact of Quantum computing on Improving Artificial Intelligence

A Detailed Investigation on Potential Impact of Quantum Comp...

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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Nagaraj, G. Upadhayaya, Niraj Matroud, Atheer Sabitha, N. Reshma, V.K. Nagaraju, Regonda Sethu Institute of Technology Department of Mechanical Engineering Tamilnadu India J.B. Institute of Engineering & Technology Department of Cse Hyderabad India School of Engineering and Computing De Montfort University Dubai United Arab Emirates Engineering College Department of Computer Science and Engineering Telangana India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India St. Martin's Engineering College Department of It Secunderabad Telangana Dhulapally India

ISBN: (纸本)9798350397208

Quantum computing is an emerging technology (QC). Several groups and research institutions are working to make quantum computing applications a reality. Artificial Intelligence (AI) is another emerging field that is becoming more established over time. The main goal of this research is to figure out how the advancement of quantum computing technology will affect AI applications. Computational methods are used to explore the growing influence of quantum computing development for a specific AI application. This study also discusses about the impact and potential of Quantum computing (QC) on Artificial Intelligence (AI). These can be accomplished by employing cutting-edge methods to deliver usable, economically viable, and scalable technology across a wide range of industries. On the other hand, the risk of committing errors in quantum algorithms, as well as the inability to function without being super-cooled, emerges as a major challenge. Macro-energy systems and the development of sustainable energy materials are just two areas of science and engineering that quantum computing has the potential to completely transform. © 2023 IEEE.

关键词： Artificial intelligence

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