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S3AM++: Integrating Differential Strategy into Spectral-Similarity-Based Spatial Attention Module for Hyperspectral Remote Sensing Image Classification 23

S3AM++: Integrating Differential Strategy into Spectral-Simi...

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15th International Conference on Digital Image Processing, ICDIP 2023

作者： Li, Ningyang Wang, Zhaohui Cheikh, Faouzi Alaya Ullah, Mohib Faculty of Computer Science and Technology Hainan University China Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology China

ISBN: (纸本)9798400708237

Algorithms based on deep learning, especially the attention mechanism, have been the preference for hyperspectral remote sensing image classification. Recently, a spectral similarity based spatial attention module (S3AM) was designed to solve the deficiency that the existing attention modules cannot take the center pixel into full account for the deduction of the spatial attention. However, two crucial processes, including the calculation and conversion of spectral similarity, of this module may be affected by its single policy, which is harmful to alleviate the spectral variability and the accurate spatial attention. In this article, an improved version called S3AM++ which integrates the differential strategy into the original attention module is proposed for hyperspectral image classification. The S3AM++ divides each neighborhood of the center pixel into the relevant pixel and the interfering pixel implicitly to make the individual optimization of the network for the two kinds of pixels possible. Benefit from this specific, the spectral variability can be relieved effectively for the similarity calculation and the relevant pixels and the interfering pixels can be preserved and restrained appropriately during the similarity conversion, respectively. Experimental results reveal that the spatial attention derived by the S3AM++ is more representative and the classification performance of the model adopting the new attention module is superior. © 2023 ACM.

关键词： Pixels

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Systematic Review on Recommendation Systems to select Trustworthy Cloud Services and ensure Data Integrity 2

Systematic Review on Recommendation Systems to select Trustw...

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2nd International Conference on Technological Advancements in Computational Sciences, ICTACS 2022

作者： Saxena, Urvashi Rahul Alam, Taj Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (数字)9781665476577

ISBN: (纸本)9781665476577

With unprecedented growth and popularity of cloud computing in due course of time, there has been a constant urge and continuous demand for cloud service providers to deliver cloud services. When it comes to opting for a cloud service, several crucial factors like availability, reliability, performance, scalability, etc., are taken into consideration. Yet, identifying the service that best meets the consumer requirements is still a challenge. Recommendations and feedbacks facilitate the users in making appropriate decisions in selection for the correct type of service. In recent times, various approaches like content-based filtering models, collaborative filtering, fuzzy logic-based models, k-means clustering technique have been used by different authors to predict cloud services. This paper presents a comparative study of different methods adopted to suggest suitable cloud services to cloud consumers. This work aims to unfold the strengths, limitations, handle issues and analyze the solutions proposed by the authors in making suitable recommendations for cloud service. © 2022 IEEE.

关键词： Collaborative filtering

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Collaborative decision making system in intelligent transportation system using distributed blockchain technology

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International Journal of Vehicle information and Communication Systems 2022年第1期7卷 64-81页

作者： Mohanta, Bhabendu Kumar Jena, Debasish Satapathy, Utkalika Ramasubbareddy, Somula Department of Computer Science and Engineering International Institute of Information Technology Odisha Bhubaneswar India Telangana Hyderabad India

Intelligent Transportation System (ITS) is one of the promising applications of the Internet of Things (IoT) as the IoT system provides an easy way to collect and monitor the system. One of the critical components to make a city smart is by managing the traffic. The mode of transportation in the city is different, like bike, car, bus, auto, and rickshaw. Most of the vehicles integrated with information Communication technology (ICT). As the vehicles share and access the information from the ITS infrastructure, some security issue exists. The security issues are trust management, privacy, data linking, and computational problems. In this paper, the authors first identify the security issue present in the ITS model, then propose a distributed architecture of the ITS system using Blockchain. Then, the Consensus algorithm is used to perform computations in a distributed platform. Ethereum platform used to create a distributed network. The implementation and security analysis are given at the end. © 2022 Inderscience Enterprises Ltd.

关键词： Decision making

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Effect of individual atmospheric parameters on beam spreading of higher-order Gaussian beam

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Optical and Quantum Electronics 2024年第12期56卷 1-18页

作者： kumar, Mukesh Khandelwal, Arpit Azeemuddin, Syed Center for VLSI and Embedded System Technologies International Institute of Information Technology Hyderabad India Department of Electrical Engineering Indian Institute of Technology Jodhpur India Department of Electrical and Computer Engineering Behrend College Penn State University EriePA United States

The beam spreading is essential for evaluating the higher-order Gaussian beam when it propagates through atmospheric turbulence. In this paper, we investigate the impact of individual atmospheric parameters such as jitter, turbulence, wind speed, and thermal blooming on beam spreading of Hermite Gaussian (HG), and Laguerre Gaussian (LG) beams. We also examine spot size variations due to beam quality on various HG and LG modes. It is seen that the impact of spreading caused by beam quality and turbulence is higher than that of jitter, wind speed, and thermal blooming. As the mode order increases, the effect of turbulence strength increases, leading to more spread for HG and LG beams. Spreading due to diffraction and beam quality on higher mode LG beam is higher than the HG beam in different modes. Beam spreading due to thermal blooming for the lower mode is higher compared to the higher mode for LG and HG beams. The sensitivity of higher mode LG beams to turbulence distortions is greater than HG beams as they experience more significant fractional increases in their spot size due to turbulence. The combined beam spread for the Laguerre Gaussian beam is larger than the Hermite Gaussian beam. This paper aims to understand better individual beam spreading in the atmosphere and its impact on the overall performance of higher-order laser propagation to develop optimized laser systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Gaussian beams

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Leveraging Automated Methods and Advanced Neural Networks for Accurate information Extraction

Leveraging Automated Methods and Advanced Neural Networks fo...

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

作者： Hemalatha, S. Deshpande, Leena A. Shrimal, Gajendra Neetha, S.S. Alam, Intekhab Karthigaikumar, P. Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore641032 India

ISBN: (纸本)9798350329773

This research investigates how computerized techniques and superior neural networks may be used to improve the accuracy of fact extraction (IE). This study uses a deep learning model to extract principles and activities from unstructured texts. The experiment uses widespread IE benchmarks to evaluate the model's capability regarding idea and event extraction accuracy. Effects show that the proposed approach rides on the faculty of deep getting to know, leveraging technique capabilities with the herbal language era. It allows for the progressed accuracy of IE over the modern-day baseline strategies. The model also achieves a precision price of 76.79% in concept extraction, with a keep-in-mind charge of 79.92%. In event extraction, the gadget reports a precision fee of 64.17% and a recall rate of 69.66%. The proposed technique is expected to be used to efficiently enhance the overall performance of IE tasks in a wide variety of programs. © 2023 IEEE.

关键词： Deep learning

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Modified Identify and Broadcast-based Encryption Scheme to Secure Cloud 1

Modified Identify and Broadcast-based Encryption Scheme to S...

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1st International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2022

作者： Rahul Saxena, Urvashi Alam, Taj Jaypee Institute of Information Technology Department of Computer Science and Engineering and Information Technology Noida India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

Cloud computing is foreseen as the lineage framework model for Distributed Computing Environment and has escalated itself in due course of time owing to the advantages it provides to its service users, and also due to the unfolding ease, it renders to its customers. In recent times, especially because of pandemics, there has been a tremendous acceleration and dependency on the cloud environment and a sudden outburst in the increase of cloud users. However, it is significant to mention that the Cloud environment is quite susceptible to numerous attacks from intruders, thereby resulting in jeopardizing the system security and user privacy. Hence, one of the biggest challenges encountered by the researchers in the cloud domain is to propose algorithmic approaches or frameworks that ensure cloud security and maintain data integrity. Service users often hesitate to synchronize and store their data to cloud since they are not sure if their data privacy and confidentiality on the cloud environment will be preserved or compromised. This vulnerability of cloud systems eventually hinders cloud storage development. The objective of this paper is to project the trajectory towards maintaining privacy of the user data by assigning a restricted grant of permission to the user data stored on the cloud using cryptographic techniques. This work demonstrates an approach through which Identity validation along with Broadcast based Encryption mechanism aims to secure cloud data. This work depicts the functionality of various modules like set-up, encryption, and decryption processes, and the work is then finally compared with its peer research works based on the revocation rate. © 2022 IEEE.

关键词： Access control

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Target Detection Algorithm for Drone Aerial Images based on Deep Learning

Target Detection Algorithm for Drone Aerial Images based on ...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Liu, Tao Zhang, Bohan Heilongjiang Institute of Technology College of Computer Science and Technology Harbin China China University of Geosciences School of Information Engineering Wuhan China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan China

ISBN: (纸本)9798350375442

In recent years, the rapid development of drones has brought tremendous changes to many fields. From refined management in agriculture and forestry to aerial surveying in urban planning, the popularity of UAVs provides efficient and accurate data support for various industries, promoting social intelligence and technological progress. Meanwhile, deep learning methods represented by computer vision provide powerful tools for research such as image processing and object recognition. The research results of HE-YOLOX-ASFF (Highly Efficient You Only Look Once eXtend with Adaptive Spatial Feature Fusion) algorithm for target detection in UAV aerial images in this article can help improve the recognition accuracy and efficiency of drone remote sensing images, and apply it to fields such as smart agriculture, urban management, and environmental monitoring. At present, detection technologies for large-scale targets have achieved good detection results in clear backgrounds. However, drone aerial images often have complex backgrounds, small target volumes, limited available features, and high target density, making them prone to occlusion. Due to the above reasons, existing target detection algorithms have low detection accuracy for weak and small targets in aerial images under complex backgrounds, and are prone to missed and false detections. Therefore, the detection method of weak aerial targets in complex backgrounds remains a challenging issue. The experimental results show that in identifying pedestrians and bicycles, the parameter values are relatively high, at 33.3 and 65.1, respectively, while the FPS (frames per second) is relatively low, at 63 and 38, respectively. This indicates that the algorithm requires more parameters to process these targets and may affect processing speed. © 2024 IEEE.

关键词： Drones

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Quality of Service-aware 6G-Enabled NB-IoT for Health Monitoring in Long Distance High-Speed Trains

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1136-1147页

作者： Roy, Bipasha Guha Roy, Deepsubhra Guha Dutta, Piyali Khan, Surbhi Bhatia Asiri, Fatima Ayadi, Manel Institute of Engineering and Management Kolkata India Institute of Engineering and Management Kolkata India University of Salford Chitkara University Institute of Engineering and Technology Chitkara University Department of Data Science United Kingdom University Centre for Research and Development Chandigarh University India Centre for Research Impact and Outcome Punjab Rajpura140401 India King Khalid University College of computer science Informatics and computer systems department Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia

Internet of Things connectivity in home health monitoring is a high-in-demand application area. The electronics industry and procedural researchers seek high-end, secured, on-time, cost-effective ways to build reliable quality of service (QoS) proved autonomous systems using existing wireless techniques. Also, the continuous availability of a traffic-free gateway, particularly in isolated places, is necessary for large-scale data gathering and real-time data update processes to function the sensor nodes in an Internet of Things (IoT) network. Improved Narrowband IoT (NB-IoT) is one of the most desirable networks offered by 6th generation (6G) connectivity in IoT-associated remote-monitoring proceedings. In this article, we propose a QoS-aware narrow bandwidth allocation-based prototype model for healthcare monitoring in long-distance high-speed trains during the patient transfer from home to the healthcare center or vice versa. This article demonstrates a possible enhancement in social aspects of cognitive IoT applications with large data systems in industrial informatics. © 1975-2011 IEEE.

关键词： Frequency allocation

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Data Analytics Technique of Different IP Address Utilized for Cloud Services 9

Data Analytics Technique of Different IP Address Utilized fo...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Wani, Arpita Mohite, Shahuraj Gonge, Sudhanshu Joshi, Rahul Vora, Deepali Kotecha, Ketan Department of Computer Science & Engineering Symbiosis Institute of Technology Pune India Department of Information Technology Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798350383201

The utilization of different IP addresses for cloud services has become a critical aspect of modern data analytics. As cloud computing continues to revolutionize the way organizations store, process, and manage data, the need to optimize data analytics techniques for diverse IP address utilization has grown exponentially. This research delves into the various methods and strategies employed to effectively harness the power of multiple IP addresses in the cloud environment. The abstracted study begins by exploring the significance of IP addresses in cloud services and how their dynamic allocation can impact data analysis. It delves into the challenges faced by data analysts in handling varying IP addresses, such as security concerns, network complexities, and data consistency. Subsequently, the research highlights innovative techniques and tools designed to efficiently manage and analyze data from different IP sources. These include load balancing algorithms, virtual IP mapping, and geo-distributed data processing. Moreover, the research assesses the benefits and limitations of each approach, considering factors like performance, scalability, and cost-effectiveness. It also examines real-world case studies and success stories of companies that have leveraged these techniques to gain a competitive edge in the cloud-based data analytics landscape. In conclusion, the study advocates for the adoption of optimized data analytics techniques that incorporate diverse IP address utilization to maximize the potential of cloud services and facilitate data-driven decision-making in modern enterprises. © 2023 IEEE.

关键词： Data Analytics

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Internet of Everything: Application and Various Challenges Analysis a Survey 1

Internet of Everything: Application and Various Challenges A...

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1st International Conference on Informatics, ICI 2022

作者： Murtuza, Shariq Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9781665437219

The Internet of Everything (IoE) is a fast-growing field with capabilities to revolutionize the whole industry. As per market trends, more than 20 billion smart devices will be deployed in the next 5 years. These interconnected devices will be generating sensitive data which needs to be protected. The field of IoE is making leaps and bounds technologically. In this survey we throw light on new domains that can benefit from IoE, along with presenting a comprehensive study of available hardware, software, infrastructure, communication protocols, attack classification, attack surfaces and individual layer security. © 2022 IEEE.

关键词： Internet of Everything

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