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IAENG International Journal of computer science 2025年第4期52卷 1187-1195页

作者： Zhu, Hang Zhao, Ji School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

With the development of artificial intelligence, deep learning has been increasingly used to achieve automatic detection of geographic information, replacing manual interpretation and improving efficiency. However, remote sensing images themselves have the issue of slight inter-class variance and significant intra-class variance, making it challenging to extract valuable information. Additionally, the increasing resolution and size of remote sensing images in recent years have introduced more complexity in the types of information, further increasing the difficulty of extracting valuable data. This paper proposes an improved Unet semantic segmentation network (referred to as RAUnet). First, in the encoder, continuous convolutional blocks are enhanced to extract features. At the same time, the EMAM multi-scale attention module is employed for cross-channel learning, capturing information from different feature channels of the target and using the surrounding feature information to assist in distinguishing target information. To capture multi-directional long-range dependencies, the Lo2 module is used for long-range modeling, which captures not only local contextual information but also long-range dependencies. In the decoder, a Dysample upsampling module is used to restore feature details, and in the skip connection layer, features are added for feature fusion. Experimental results show that compared to mainstream models, the proposed method achieves superior segmentation results on the Potsdam and Vihingen datasets. © (2025), (International Association of Engineers). All rights reserved.

关键词： Semantic Segmentation

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Learning Salient Features for Speech Emotion Recognition using Attention-based Residual Bidirectional LSTM with Federated Learning 4

Learning Salient Features for Speech Emotion Recognition usi...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kaur, Dilsheen Misra, Anuradha Vyas, O.P. Amity University Lucknow Campus Amity School of Engineering and Technology Department of Computer Science and Engineering India Indian Institute of Information Technology Allahabad Department of Information Technology India

ISBN: (纸本)9798350343670

In human communication, expressing human emotion plays an essential role in transferring information to the other individual. The expression forms of human communication are very rich, in different patterns like facial expressions, voice tone, eye contact, body language and laughter. Moreover, the languages used by individuals in the entire world are different, yet without understanding the communication language, the individuals can realize the message that other individuals require to convey as emotional expressions. Among all the human emotional expressions, the conveyed expressions through human voice are experimented with in different studies. However, identifying the emotional situation of the presenter is complicated for the machine learning techniques, which gained more attraction in Speech Emotion Recognition (SER). Here, the SER framework plays an efficient role in different real-world applications in observing human actions, virtual reality, emergency centers and robot communication to observe the emotional phase of the speakers. Thus, an innovative federated learning-based SER is presented in this paper. The essential speech signals are initially collected from the standard database. After that, the collected signal is fed to the feature extraction stage. Here, the deep features in the input signal are obtained by the Variational Autoencoder (VAE). Further, the deep features obtained from the VAE are passed to the "Attention-based Residual Bidirectional Long Short-Term Memory (A-RBiLSTM)"to identify the emotions in the speech signal. At last, different experimental analyses are executed to observe the effectualness of the implemented framework. © 2024 IEEE.

关键词： Attention-based Residual Bidirectional Long Short-Term Memory Deep Features Feature Extraction Federated Learning Speech Emotion Recognition Variational Autoencoder

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Network Intrusion Detection for IoT Devices Using Deep Learning 10

Network Intrusion Detection for IoT Devices Using Deep Learn...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Kumar, Mohit Dubey, Sanjay Kumar Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350382471

Cybersecurity has seen widespread adoption across various domains, encompassing critical business infrastructure, residential settings, personal devices, and machinery. This has led to the development of innovative capabilities to effectively address security challenges within the realm of Internet of Things (IoT) devices. Notably, new security measures, including the application of deep learning for intrusion detection, have been introduced. Recent research primarily focuses on enhancing algorithms for a deeper understanding of IoT security. This research explores intrusion detection techniques employing deep knowledge mining. It involves a comprehensive analysis of various deep knowledge mining methods, comparing their overall performance. The objective is to identify a precise approach for implementing intrusion detection in the IoT context. The research leverages deep learning methodologies, specifically convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU). Furthermore, it incorporates an extensive dataset tailored for IoT intrusion detection. The empirical results of this study are thoroughly examined and compared against existing IoT intrusion detection techniques. Notably, the proposed approach demonstrates superior accuracy when compared to other methods. © 2023 IEEE.

关键词： accuracy convolutional neural network deep knowledge acquisition gated recurrent unit intrusion detection IoT long short-term memory

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SEEMA: An Automation Framework for Vulnerability Assessement and Penetration Testing 2

SEEMA: An Automation Framework for Vulnerability Assessement...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Tyagi, Yash Shekhar, Shubham Abhishek, P. Bhardwaj, Shweta Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350347982

The world has witnessed an exponential increase in the number of Internet Connected computers due to various causes. This exponential increase and the resulting dependence have had far reaching cybersecurity implications. The primary implication being exponential rise in cybercrime which necessitates the vulnerability assessment and penetration testing (VAPT). The vulnerability assessment and penetration testing aids in offsetting any liability associated with a successful attack on an organization's asset. The current frameworks and tool available for VAPT require very high expert knowledge and are predominantly manual. Moreover, some tools available automate only certain phases of VAPT but not the whole process. This mismatch provides the opportunity to create a solution by automating the whole process while lowering the knowledge threshold. Thus, this paper provides a revamped automated framework for a novice individual and a cybersecurity professional's use. It also provides a framework for commercial use which leverages multi-agent system and certain aspects of full stack monitoring. © 2023 IEEE.

关键词： Multi agent systems

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Transformer-Based Person Re-Identification: A Comprehensive Review

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第7期9卷 1-19页

作者： Sarker, Prodip Kumar Zhao, Qingjie Uddin, Md. Kamal School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Bangladesh

In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of accurately matching and identifying persons across several camera views that do not overlap with one another. This is of utmost importance to video surveillance, public safety, and person-tracking applications. However, vision-related difficulties, such as variations in appearance, occlusions, viewpoint changes, cloth changes, scalability, limited robustness to environmental factors, and lack of generalizations, still hinder the development of reliable person re-ID methods. There are few approaches have been developed based on these difficulties relied on traditional deep-learning techniques. Nevertheless, recent advancements of transformer-based methods, have gained widespread adoption in various domains owing to their unique architectural properties. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, there are few studies that consider transformer-based techniques for further investigation. This review proposes recent literature on transformer-based approaches, examining their effectiveness, advantages, and potential challenges. This review is the first of its kind to provide insights into the revolutionary transformer-based methodologies used to tackle many obstacles in person re-ID, providing a forward-thinking outlook on current research and potentially guiding the creation of viable applications in real-world scenarios. The main objective is to provide a useful resource for academics and practitioners engaged in person re-ID. IEEE

关键词： Cameras

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Secure vehicular data communication in Named Data Networking

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Digital Communications and Networks 2023年第1期9卷 203-210页

作者： Xiaonan Wang Xilan Chen Xingwei Wang School of Computer Science and Engineering Changshu Institute of TechnologyChangshu215500China School of Computer Science and Technology Northeastern UniversityShenyang110169China

Vehicular data misuse may lead to traffic accidents and even loss of life,so it is crucial to achieve secure vehicular data *** paper focuses on secure vehicular data communications in the Named Data Networking(NDN).In NDN,names,provider IDs and data are transmitted in plaintext,which exposes vehicular data to security threats and leads to considerable data communication costs and failure *** paper proposes a Secure vehicular Data Communication(SDC)approach in NDN to supress data communication costs and failure *** constructs a vehicular backbone to reduce the number of authenticated nodes involved in reverse *** the ciphtertext of the name and data is included in the signed Interest and Data and transmitted along the backbone,so the secure data communications are *** is evaluated,and the data results demonstrate that SCD achieves the above objectives.

关键词： Named data networking Reverse path Secure data communication

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Enhancing Driver Safety with Transfer Learning based Deep Learning Models for Drowsiness Detection

Enhancing Driver Safety with Transfer Learning based Deep Le...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Dutta, Stuti Arora, Deepak Tiwari, Shivam Amity University Amity School of Engineering and Technology Uttar Pradesh Lucknow India Sharda University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350378092

In response to the escalating concern over road accidents due to driver drowsiness, this research delves into enhancing drowsiness detection systems using advanced deep learning techniques and transfer learning. Prior investigations have explored conventional methods, yet they often lack the precision demanded by real-world scenarios. Leveraging convolutional neural networks (CNNs) alongside model fine-tuning and transfer learning, our methodology aims to optimize drowsiness detection accuracy. Through rigorous experimentation, we achieved compelling results, showcasing significant enhancements in accuracy across various CNN architectures. For instance, with Max Pooling and 2 Dense layers, one of our CNN models achieved an accuracy score of 1 at epoch 2, with a remarkably low loss of 6.622934e-12, all while taking only 2.6648 seconds for training. These outcomes underscore its potential to revolutionize driver safety systems, offering promising avenues for future research and implementation in real-world contexts. © 2024 IEEE.

关键词： Convolutional neural networks

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Detection and Classification of Bone Fracture Using Digital Image Processing 10

Detection and Classification of Bone Fracture Using Digital ...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Makwane, Simran Kiran, Anjali Bhardwaj, Shweta Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350382471

This research paper presents an innovative approach for automated bone fracture discovery and bracket using digital image processing ways and the Scale Invariant point Transform (SIFT) algorithm. The proposed system excerpts distinctive original features from radiographic images, enabling robust identification of fracture patterns. Through a multi-step process, including pre-processing, point birth, and bracket, the system achieves high delicacy in differencing between colourful fracture types. Experimental results demonstrate the effectiveness of the system on a different dataset, showcasing its eventuality for enhancing clinical diagnostics and expediting treatment planning. This exploration contributes to the advancement of computer- backed medical imaging systems, offering a promising tool for accurate and effective fracture opinion. © 2023 IEEE.

关键词： classification Digital Image processing fracture Machine learning SIFT X-ray

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Understanding and improving fairness in cognitive diagnosis

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science China(Information sciences) 2024年第5期67卷 99-114页

作者： Zheng ZHANG Le WU Qi LIU Jiayu LIU Zhenya HUANG Yu YIN Yan ZHUANG Weibo GAO Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology& School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence School of Computer Science and Information Engineering Hefei University of Technology

Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive diagnosis, which aims to gauge the level of proficiency among students on specific knowledge concepts(e.g., Geometry). To the best of our knowledge, most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues; for instance, the diagnosis of students may be affected by various sensitive attributes(e.g., region). In this paper,we aim to explore fairness in cognitive diagnosis and answer two questions:(1) Are the results of existing cognitive diagnosis models affected by sensitive attributes?(2) If yes, how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results? To this end, we first empirically reveal that several wellknown cognitive diagnosis methods usually lead to unfair performances, and the trend of unfairness varies among different cognitive diagnosis models. Then, we make a theoretical analysis to explain the reasons behind this phenomenon. To resolve the unfairness problem in existing cognitive diagnosis models, we propose a general fairness-aware cognitive diagnosis framework, FairCD. Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency. To achieve this, we divide student proficiency in existing cognitive diagnosis models into two components: bias proficiency and fair *** design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result. Extensive experiments on the Program for International Student Assessment(PISA) dataset clearly show the effectiveness of our framework.

关键词： fairness intelligent education cognitive diagnosis psychometrics adversarial learning

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A comprehensive review and open issues on energy aware resource allocation in cloud

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International Journal of Web engineering and technology 2023年第4期17卷 353-371页

作者： Singh, Shubham Singh, Pawan Tanwar, Sudeep Department of Computer Science and Engineering Amity University Uttar Pradesh Lucknow Campus India Department of Computer Science and Engineering Institute of Technology Nirma University India

Cloud computing is an on-demand service resource that includes applications to data centres on a pay-per-use basis. While allocating resources, the node failure causes the cloud service failures. This reduces the quality of service along with ‘extra’ energy consumption. This survey makes an evaluation on 65 papers regarding energy aware resource allocation (RA) techniques in cloud. The performances and associated highest attainments of each contribution are described in this review. In addition, the tools used for implementing the RA techniques in each paper along with type of workloads were considered. We examine a number of current or developed energy-aware RA approaches for cloud settings in this study. We also provide a thorough analysis of the various energy-conscious RA and selection strategies for cloud-based virtual machines. Lastly, the clear sketch of issues and weakness are determined that may be supportive to resolve the existing problems in this aspect. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Energy utilization

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