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TTST: A Top-k Token Selective Transformer for Remote Sensing Image Super-Resolution

IEEE Transactions on Image Processing 2024年 33卷 738-752页

作者： Xiao, Yi Yuan, Qiangqiang Jiang, Kui He, Jiang Lin, Chia-Wen Zhang, Liangpei Wuhan University School of Geodesy and Geomatics Wuhan430079 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu300 Taiwan Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China

Transformer-based method has demonstrated promising performance in image super-resolution tasks, due to its long-range and global aggregation capability. However, the existing Transformer brings two critical challenges for applying it in large-area earth observation scenes: (1) redundant token representation due to most irrelevant tokens;(2) single-scale representation which ignores scale correlation modeling of similar ground observation targets. To this end, this paper proposes to adaptively eliminate the interference of irreverent tokens for a more compact self-attention calculation. Specifically, we devise a Residual Token Selective Group (RTSG) to grasp the most crucial token by dynamically selecting the top-k keys in terms of score ranking for each query. For better feature aggregation, a Multi-scale Feed-forward Layer (MFL) is developed to generate an enriched representation of multi-scale feature mixtures during feed-forward process. Moreover, we also proposed a Global Context Attention (GCA) to fully explore the most informative components, thus introducing more inductive bias to the RTSG for an accurate reconstruction. In particular, multiple cascaded RTSGs form our final Top-k Token Selective Transformer (TTST) to achieve progressive representation. Extensive experiments on simulated and real-world remote sensing datasets demonstrate our TTST could perform favorably against state-of-the-art CNN-based and Transformer-based methods, both qualitatively and quantitatively. In brief, TTST outperforms the state-of-the-art approach (HAT-L) in terms of PSNR by 0.14 dB on average, but only accounts for 47.26% and 46.97% of its computational cost and parameters. The code and pre-trained TTST will be available at https://***/XY-boy/TTST for validation. © 1992-2012 IEEE.

关键词： Remote sensing

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Anomaly Detection in Univariate Time Series: HOT SAX vesus LSTM-Based Method 12th

Anomaly Detection in Univariate Time Series: HOT SAX vesus L...

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12th EAI International Conference on Context-Aware Systems and Applications, ICCASA 2023

作者： Anh, Duong Tuan Hoai, Tran Long Department of Information Technology HCMC University of Foreign Languages and Information Technology Ho Chi Minh City Viet Nam Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology Ho Chi Minh City Viet Nam

ISBN: (纸本)9783031588778

Anomaly detection in time series has been an important and challenging research topic. There have been several methods proposed for detecting anomaly subsequences in a time series. The majority of these methods is classified into the window-based category, which applies a sliding window with a fixed length to extract subsequences before finding out anomaly subsequences. A well-known algorithm in this category is HOT SAX algorithm. Recently, deep neural network models, especially Long Short Term Memory (LSTM) network, are also applied for time series anomaly discovery. LSTM-based methods for time series anomaly detection belong to prediction-based category. So far, there has been no research work to compare the performance of LSTM-based method to that of any traditional window-based method in time series anomaly detection. The research question investigated in this paper is that whether the newly developed LSTM-based method for time series anomaly detection is superior to the traditional algorithms, such as HOT SAX or not. In this study, we give an empirical comparison between LSTM-based method and HOT SAX in time series anomaly detection. Extensive experiments on seven benchmark time series indicate that LSTM-based method is not superior to HOT SAX since it brings out the same detection accuracy as HOT SAX while it incurs much higher computational overhead. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Deep neural networks

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Moving Towards Nano-Devices in the Context of Smart Bio-Tech Wearables

Moving Towards Nano-Devices in the Context of Smart Bio-Tech...

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

作者： Ranganathaswamy, M.K. Srivastav, Vaibhav Choudhary, Deeksha Department of Mechanical Engineering Faculty of Engineering and Technology Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineeing & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

Because of the development of wireless capabilities, the network and, in specific, data centers have become a significant problem in our everyday lives. In order to maximize miniaturization and lower the opportunity burden among this equipment, the Network of Things (IoT) attempts to incorporate sensor and actuators into commonplace things. The idea is to use wireless and satellite networks to link these parts to the Internet, producing hard numbers that can then be saved and processed later. The Internet and pcs all provide innovative elements, but Interface TV (iTV) mixes conventional TV with a comparable level of involvement. With the advancement of Smart tvs, computational capability has been added to the device, enabling it to provide services beyond the norm, execute programmes, and realise its networking potential. This study proposes a methodology that comprises and unifies an IoT platform, a genuine tv Streaming show, and a network of wireless sensors. It includes everything from wireless sensor network implementation and transmission to web content accessibility guidelines and finished products through a genuine interactive television application. Hard data provision has now been trialled in a housing area with the goal of enhancing residents' life experiences. Additionally, it combines the potential for using this data to build procedures aimed at lowering energy consumption, enhancing longevity, and promoting the effective use of already available resources. For any implementation with comparable features, the conceptual approach serves as the foundation. © 2023 IEEE.

关键词： cloud computing data centers IoT IoT platform microdevices network of things Next-gen biotechnology Sensors and ThinkSpeak smart TV UN Wearable Devices wearables wireless sensors

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Detection and Prevention of Malicious Activities in Vulnerable Network Security Using Deep Learning

Detection and Prevention of Malicious Activities in Vulnerab...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Wadibhasme, Rashmi Nilesh Chaudhari, Arpit U. Khobragade, Prashant Mehta, Hina D. Agrawal, Rahul Dhule, Chetan Yeshwantrao Chavan College of Engineering Departemnt of Information Technology Nagpur India Prof. Ram Meghe Institute of Research Badnera Department of Computer Science and Engineering Amravati India G H Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India Nagpur College of Pharmacy Nagpur India Nagpur India

ISBN: (纸本)9798350319019

In the digital age, network security is very important because bad people are always coming up with new ways to break into systems that are weak. Most of the time, standard security methods aren't good enough to find and stop these complex threats. By using its ability to look at and learn from huge amounts of data, deep learning has become a useful tool for making networks safer. A thorough study of how deep learning techniques can be used to find and stop harmful actions in networks with weak security is presented in this paper. In this paper, a deep learning-based method that uses recurrent neural networks (RNNs), LSTM and convolutional neural networks (CNNs) to look at network traffic trends and spot strange behavior that could be a sign of hostile activity. The suggested system can accurately tell the difference between good and bad actions by training the model on a set of datasets that contain both normal and harmful network traffic. The paper discussed about setting up a real-time tracking system that checks network data all the time and lets managers know about possible threats. Several efficiency measures are used to judge how well the system works, showing that it can greatly improve network security by finding and stopping hostile actions in weak systems. The paper results show that methods based on deep learning are a hopeful way to deal with the problems of network security, offering a proactive way to protect against new threats. At the end, we talk about where future study should go and how important it is to include deep learning in current security systems to make sure they are strong against online risks © 2024 IEEE.

关键词： Anomaly detection

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Automated Detection of Type 2 Diabetes with Imbalanced and Machine Learning Methods 3rd

Automated Detection of Type 2 Diabetes with Imbalanced and M...

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3rd International Conference on Machine Learning, Image Processing, Network Security and Data sciences, MIND 2021

作者： Anirudh, G. Talukdar, Upasana Department of Data Science and Analytics Central University of Rajasthan Rajasthan Ajmer India Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India

ISBN: (纸本)9789811958670

Diabetes mellitus is an abominable disease, if left untreated, may turn fatal. This paper presents rigorous experimentation to ascertain a machine learning model that can implement an accurate diagnosis of diabetes mellitus. While many research efforts have been publicized till date to detect diabetes, none of them addresses the class imbalance problem. This paper carries out the experiments on Pima Indian Diabetes in three categories a. experiments with traditional machine learning algorithms, b. experiments with ensemble machine learning methods, and c. experiments with machine learning algorithms after imbalanced data pre-processing techniques. The performance of the solutions has been evaluated using F1-score, Precision, Recall, Area Under Precision-Recall curve, Area Under Receiver Operating Characteristic curve, and Classification Accuracy. The experimental results show improved performance with the proposed approach as compared to the state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Research and Innovation in Next Generation Security and Privacy in Industry 5.0 Iot 6

Research and Innovation in Next Generation Security and Priv...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Navale, Geeta S. Madala, Ravikiran Managuli, Manjunath Jayalakshmi, N. Kadiravan, G. Rawat, Ruchira Sinhgad Institute of Technology and Science Department of Computer Engineering Maharashtra Pune India Ph.D Information Technology Network and Data Center Engineer University of Cumberlands DurhamNC United States KLS Gogte Institute of Technology Department of Electronics and Communication Engineering Karnataka Belagavi India PSNA College of Engineering and Technology Master of Computer Applications Tamilnadu Dindigul India KL Deemed to Be University Department of Computer Science and Information Technology Andhra Pradsh India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India

ISBN: (纸本)9798350304480

The objective of the newest manufacturing paradigm, known as 'Industry 5.0,'is to combine human intelligence with cutting-edge technology like the Internet of Things (IoT), artificial intelligence (AI), and robots to develop production systems that are incredibly effective and adaptable. But as we rely more on these technologies, we face additional privacy and security issues that might affect how dependable and trustworthy these systems are. In this report, we examine the most recent advancements in security and privacy for Industry 5.0 IoT and pinpoint the newest trends, obstacles, and possibilities. We also cover how these changes may affect manufacturing in the future and provide advice to scholars, practitioners, and policymakers. © 2023 IEEE.

关键词： artificial intelligence cyber-physical systems human-machine collaboration Industry 5.0 Internet of Things IoT manufacturing privacy robotics security

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RvXmBlendNet: A Multi-architecture Hybrid Model for Improved Skin Cancer Detection

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Human-Centric Intelligent Systems 2024年第4期4卷 545-570页

作者： Prity, Farida Siddiqi Hasan, Ahmed Jabid Anik, Md Mehedi Hassan Hossain, Rakib Hossain, Md. Maruf Bhuiyan, Sazzad Hossain Islam, Md. Ariful Lavlu, Md Tousif Hasan Department of Computer Science and Engineering Shanto-Mariam University of Creative Technology Dhaka Bangladesh Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali Bangladesh Department of Information and Communication Engineering Pabna University of Science and Technology Pabna Bangladesh Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh

Skin cancer, one of the most dangerous cancers, poses a significant global threat. While early detection can substantially improve survival rates, traditional dermatologists often face challenges in accurate diagnosis, leading to delays in treatment and avoidable fatalities. Deep learning models like CNN and transfer learning have enhanced diagnosis from dermoscopic images, providing precise and timely detection. However, despite the progress made with hybrid models, many existing approaches still face challenges, such as limited generalization across diverse datasets, vulnerability to overfitting, and difficulty in capturing complex patterns. As a result, there is a growing need for more robust and effective hybrid models that integrate multiple architectures and advanced mechanisms to address these challenges. Therefore, this study aims to introduce a novel multi-architecture hybrid deep learning model called "RvXmBlendNet," which combines the strengths of four individual models: ResNet50 (R), VGG19 (v), Xception (X), and MobileNet (m), followed by "BlendNet" to signify their fusion into a unified architecture. The integration of these models is achieved through a synergistic combination of architectures, incorporating self-attention mechanisms using attention layers and adaptive content blocks. This study used the HAM10000 dataset to refine dermoscopic image preprocessing and enhance deep learning model accuracy. Techniques like OpenCV-based hair removal, min–max scaling, and adaptive histogram equalization were employed to improve image quality and feature extraction. A comparative study between the proposed hybrid "RvXmBlendNet" and individual models (CNN, ResNet50, VGG19, Xception, and MobileNet) demonstrated that "RvXmBlendNet" achieved the highest accuracy of 98.26%, surpassing other models. These results suggest that the system can facilitate earlier interventions, improve patient outcomes, and potentially lower healthcare costs by reducing the need for inv

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Enhancing Data Security and Privacy in Cloud Computing: A Survey of Modern Techniques 3

Enhancing Data Security and Privacy in Cloud Computing: A Su...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Suganya, N. Gouthami, P. Sathiya, R. Sivaranjani, S. Murugesan, M. Department of Computer Science and Business Systems Dr. N.G.P. Institute of Technology Coimbatore India Department of Information Technology Thangavelu Engineering College Chennai India Departement of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Departement of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (纸本)9798331527549

A computing system that is based on the internet and offers users all resources as on-demand services. Servers, storage, databases, software, and networking are among these on-demand services. Usually, the user must pay for the use of the resources;they are not their property. The number of people using this technology is increasing dramatically as a result of these features. Although it has benefits, cloud computing poses certain security and privacy risks. The authorized user’s data can be stolen easily. This current research thoroughly investigates modern strategies to these problems, highlighting identity management protocols, effective encryption and blockchain-based solutions. The article identifies research gaps, focuses attention to the inadequacies of current approaches, and recommends the possible ways to improve the efficiency, scalability and trustworthiness. © 2025 IEEE.

关键词： Cloud security

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Assessing a BERT-based model for analyzing subjectivity and classifying academic articles

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Multimedia Tools and Applications 2024年第42期83卷 90511-90532页

作者： Mehmood, Atif Shahid, Farah Khan, Rizwan Ahmed, Shahzad Ibrahim, Mostafa M. Zheng, Zhonglong School of Computer Science and Technology Zhejiang Normal University Jinhua321002 China Zhejiang Institute of Photoelectronics & Zhejiang Institute for Advanced Light Source Zhejiang Normal University Zhejiang Jinhua321004 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt

The metaverse concept extends beyond virtual worlds and can be applied to collaborative analysis environments. Data analysts worldwide may read academic article extracts in real-time in a shared digital workplace to mine and analyze data using the metaverse. Furthermore, a semantic metaverse in natural language processing might also involve creating a digital environment with linguistic and semantic connections. Many dedicated researchers navigating the complexities of natural language processing in the metaverse era have spent considerable time searching for relevant papers. However, online reviews and evaluations of articles are helpful for their assistance and may save the researcher time. In this work, human specialists manually produced a dataset from four conferences and evaluated subjectively using rule-based techniques. Subsequently, we aim to evaluate the effectiveness of pre-trained word embeddings and pre-trained BERT models seamlessly integrated with convolutional neural networks. This endeavor focuses on the subjective analysis and classification of contributions and previous work sentences extracted from academic literature. For comparison, various deep learning architectures were systematically employed, including long short-term memory-GloVe and bi-directional long short-term memory-GloVe, alongside classical machine learning methods. Our findings show that the proposed BERT model achieved state-of-the-art performance in classification and subjective analysis tasks with an accuracy of 91.50% and F1 score of 91.00%. Finally, we plan to utilize sentence similarity to identify contributions within abstracts, thus outlining potential avenues for future research. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Natural language processing systems

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Customer Prioritization for Medical Supply Chain During COVID-19 Pandemic

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computers, Materials & Continua 2022年第1期70卷 59-72页

作者： Iram Mushtaq Muhammad Umer Muhammad Imran Inzamam Mashood Nasir Ghulam Muhammad Mohammad Shorfuzzaman Department of Management Sciences Sir Syed CASE Institute of Technology45230IslamabadPakistan Department of Operation&Supply Chain Management NUST Business SchoolNational University of Sciences&Technology45200IslamabadPakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

During COVID-19,the escalated demand for various pharmaceutical products with the existing production capacity of pharmaceutical companies has stirred the need to prioritize its customers in order to fulfill their *** study considers a two-echelon pharmaceutical supply chain considering various pharma-distributors as its suppliers and hospitals,pharmacies,and retail stores as its *** studies have generally considered a balanced situation in terms of supply and demand whereas this study considers a special situation of COVID-19 pandemic where demand exceeds supply Various criteria have been identified from the literature that influences the selection of customers.A questionnaire has been developed to collect primary data from pharmaceutical suppliers pertaining to customerselection *** criteria have been prioritized with respect to eigenvalues obtained from Principal Component Analysis and also validated with the experts’domain-related knowledge using Analytical Hierarchy *** potential appeared to be the most important criteria of customer selection followed by trust and service convenience brand loyalty,commitment,brand awareness,brand image,sustainable behavior,and ***,Multi Criteria Decision Analysis has been performed to prioritize the customerselection criteria and customers with respect to selection *** experts with seven and three and ten years of experience have participated in the *** of the study suggest large hospitals,large pharmacies,and small retail stores are the highly preferred ***,findings of prioritization of customer-selection criteria fromboth Principal Component Analysis and Analytical Hierarchy Process are ***,this study considers the experience of three experts to calculate an aggregate score of priorities to reach an effective *** traditional supply chain problems of supplier selection,this study considers a selection

关键词： Pandemic customer prioritization pharmaceutical supply chain principal component analysis multi-criteria decision-making

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