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2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Nagdiya, Jagrati Goyal, Rajeev Amity School of Engineering and Technology Computer Science and Engineering Gwalior India

ISBN: (纸本)9798350388800

Thereis an urgent need for strong security measures to protect Body Area Networks (BANs) from malicious threats, as their use in health monitoring and telemedicine continues to grow. To improve BAN security, this research presents a new method that makes use of deep learning approaches. Data breaches, unauthorised access, and other cyberattacks that target the particular weaknesses of BANs are among the many cybersecurity threats that our approach uses sophisticated neural network designs to detect and mitigate. Typical network traffic events and simulated adversarial attacks are both included in the extensive dataset that our deep learning model is trained on. The model can then learn to identify suspicious behaviour by observing complicated patterns and outliers. The design of the device is such that it can run smoothly on very little power while still achieving excellent efficiency. © 2024 IEEE.

关键词： Adversarial machine learning

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Network Anomaly Detection using Autoencoder on Various Datasets: A Comprehensive Review

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Recent Patents on engineering 2024年第9期18卷 63-77页

作者： Singh, Richa Srivastava, Nidhi Kumar, Ashwani Amity Institute of Information Technology Amity University Lucknow Campus Uttar Pradesh Lucknow India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

The scientific community is currently very concerned about information and communication technology security because any assault or network anomaly can have a remarkable collision on a number of areas, including national security, the storage of private data, social welfare, economic concerns, and more. As a result, many strategies and approaches for this goal have been developed over time, making the anomaly detection domain a large research subject. The primary concern of this patent study is to review the most crucial elements relating to anomaly detection, including an overview of background analysis and a core study on the most important approaches, procedures, and systems in the field. To make the structure of this survey easier to understand, the domain of anomaly detection was examined along with five dimensions: Detection methods in network traffic, objectives of the patent paper, various datasets used, accuracy, and open issues/gaps. The gap which has been identified after the survey can be extended as a future scope might be helpful for the researcher. © 2024 Bentham science Publishers.

关键词： Anomaly detection

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An Enhanced Aspect-Based Sentiment Analysis Model Based on RoBERTa For Text Sentiment Analysis

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 193-202页

作者： Chauhan, Amit Sharma, Aman Mohana, Rajni Jaypee University of Information Technology HP Solan173234 India Department of Computer Science Amity School of Engineering and Technology Amity University Punjab Punjab Mohali140306 India

Using an aspect-based sentiment analysis task, sentiment polarity towards specific aspect phrases within the same sentence or document is to be identified. The process of mechanically determining the underlying attitude or opinion indicated in the text is known as sentiment analysis. One of the most important aspects of natural language processing is sentiment analysis. The RoBERTa transformer model was pretrained in a self-supervised manner using a substantial corpus of English data. This means it was pretrained solely with raw texts and an algorithmic process to generate inputs and labels from those texts. No human labelling was involved, allowing it to utilise a vast amount of publicly available data. The authors of this work provide a thorough investigation of aspect-based sentiment analysis with RoBERTa. The RoBERTa model and its salient characteristics are outlined in this work, followed by an analysis of the model's optimisation by the authors for aspect-based sentiment analysis. The authors compare the RoBERTa model with other state-of-the-art models and evaluate its performance on multiple benchmark datasets. Our experimental results show that the RoBERTa model is effective for this important natural language processing task, outperforming competing models on sentiment analysis tasks. Based on the SemEval-2014 variant benchmarking datasets, the restaurant and laptop domains have the highest accuracy, scoring 92.35 % and 82.33 %, respectively. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Natural language processing systems

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Mitigating Cross-Site Request Forgery Vulnerabilities: An Examination of Prevention Systems 18

Mitigating Cross-Site Request Forgery Vulnerabilities: An Ex...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Singh, Yuvraj Goel, Paranjay Aggarwal, Shubhani Chaudhary, Rajat Budhiraja, Ishan Amity University Amity School of Engineering & Technology Department of Computer Science & Engineering Punjab India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350391725

Cross-Site Request Forgery (CSRF) remains a pervasive vulnerability in web applications, appearing regularly in the OWASP Top 10 lists, posing significant threats to user data and system integrity despite the existence of numerous rectification methodologies. This study undertakes a thorough, comprehensive analysis of the present environment of CSRF prevention systems, investigating their operational mechanisms, effectiveness, and associated limitations. Hence, this paper explores the role of CSRF tokens in the prevention of cyberattacks, including the detection of token absence and the implementation of token-based countermeasures to mitigate CSRF risks. The system model utilizing simple JavaScript and Flask Applications that aim to utilize the token-based countermeasure to identify the existence of tokens and generate and append them to requests, which ensures security of user data and system integrity. © 2024 IEEE.

关键词： CSRF attacks CSRF tokens Mitigation Prevention

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Modified Blowfish Encryption Standard with Optimization Strategy for Secured Cloud Storage with Data Dynamics

Modified Blowfish Encryption Standard with Optimization Stra...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Singh, Prabhdeep Singh, Pawan Agarwal, Abhay Kumar Kumar, Anil Computer Science and Engineering Amity School of Engineering and Technology Lucknow India Computer Science and Engineering Kamla Nehru Institute of Technology Sultanpur India Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India

ISBN: (纸本)9798350382693

Organizations and individuals can store, transport, and backup a rising quantity of data at an affordable cost through the assistance of cloud storage service providers, who also give them access to other cloud services. Cloud service providers use the most popular separation approach to offer effective data storage as it reduces upload capacity and storage expenses by allowing the storage of only one instance of data while eliminating multiple copies. When customers upload data to the cloud, their primary concerns are related to data security, integrity, privacy, and confidentiality. It is not advised to use traditional encryption, which is typically used to encrypt data when outsourcing, since it interferes with the data dynamics technique. This study suggested a new security architecture for cloud data storage using modified blowfish encryption to rectify these kinds of problems. Secure storage, outsourcing, and the proxy-re-encryption phase are the three stages. During the secure storage phase, a modified version of the blowfish technique is suggested for data encryption. The Kookaburra optimization algorithm is utilized for obtaining the optimal key. Auditing, proof, verification, File Access request, and file download are the five processes that occur throughout the outsourcing phase. Ultimately, the final stage of the suggested work is proxy re-encryption. In this case, the data user requests a key for a proxy, where the data owner has already transmitted the encrypted data key and the proxy provides the data user access to the encoded key. Lastly, a comparison of the suggested approach with various security methods validates its superiority. © 2024 IEEE.

关键词： Cloud storage

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Predictive Data Analysis to Support Decision-Making Based on Long-Term Impacts of Disasters 1

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Mishra, Shailendra Kumar Rahamatkar, Surendra Amity School of Engineering and Technology Department of Computer Science and Engineering Amity University Chhattisgarh Raipur India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

Research into disaster relief is a field with a lot of potential, and predictions made about its future are based on historical data. It is a difficult task to analyze the long-term consequences of a disaster. Governments, NGOs, and other organizations struggle to make sense of disaster data. It is hard to assess the impact of different factors, such as education, employment, agriculture, and the social sector, on people living in affected areas. This research paper represents a framework to evaluate the long-term effects of disasters, by means of machine learning algorithms. The algorithms mentioned in this paper are Random Forest, Decision Tree, Gradient-boosted Decision Tree (GBDT), and XGBoost. It seeks to analyze historical disaster data, identify impacts, and predict the effects of similar disasters in the future. The framework also suggests preemptive measures and the impact on those involved. Finally, the proposed framework will be able to make long-term analyses and recommendations, using data from past events. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Disasters

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C-SPIN: Classification of Satellite Images for Prediction Network 14

C-SPIN: Classification of Satellite Images for Prediction Ne...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Kumar, Sumit Mittal, Devanshu Nehra, Vibha Amity School of Engineering and Technology Department of Computer Science Engineering Noida India

ISBN: (纸本)9798350344837

Satellite image classification and prediction are crucial, with applications in domains ranging from environmental monitoring to urban planning, geological exploration, mapping, disaster response management, and agriculture. The paper provides a comprehensive analysis of current methods and techniques for object classification and prediction through satellite image data. The current work introduces the Classification of Satellite Images for Prediction Network (C-SPIN), which is meant to use both the spatial-geographical and temporal information inherent in satellite data. Image resolutions, compression, bit depth, sensor types, and spectral bands are among some of the variables, datasets, and factors considered in the research. Accurate object classification, detection, and prediction from satellite images are crucial for realising the full potential of the proposed model. The study gives insight on the strengths and limits of current models in various circumstances and highlights the potential capabilities of C-SPIN to enhance satellite image processing, image classification, and object prediction. © 2024 IEEE.

关键词： Forecasting

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Machine Learning Classification of Shoulder Pain Pathologies: A Scoping Review 15

Machine Learning Classification of Shoulder Pain Pathologies...

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

作者： Shukla, Garima Bhagat, Khushi Chaudhari, Vedant Navaneeth Krishnan, E. Bhadauria, Mansi Chauhan Chouksey, Yash Amity University Mumbai Amity School of Engineering and Technology Department of Computer Science and Engineering Mumbai India

ISBN: (纸本)9798350370249

Shoulder problem is a widely known musculoskeletal disorder that impacts heavily on the life of patients. Effective treatment for this must be preceded by accurate and timely diagnosis but traditional diagnostic techniques have always been subjective and less reliable. In recent years, machine learning (ML) techniques have shown promising results in improving the accuracy of diagnosing shoulder pain, addressing the limitations of traditional diagnostic methods. Nonetheless, there is a failure to translate these ML models into practical clinical tools that can easily be employed by healthcare professionals. In addition, this paper will present an overview of current understanding and methodologies employed for diagnosing and managing shoulder pain along with reviewing the use of ML techniques in medical image diagnostics. It also suggests development of a user-friendly and accessible diagnosis tool for shoulder pathologies aimed at linking modern research and daily practice in the medical field. © 2024 IEEE.

关键词： Patient treatment

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Live Audio Recognition and Text Conversion System 2

Live Audio Recognition and Text Conversion System

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Sharma, Pratyaksha Gupta, Sumita Bhavya, Boddu Asmitha Sharma, Khushi Dhir, Saru Gambhir, Sapna Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP India Department of Information Technology Amity School of Engineering & Technology Amity University UP India Dept. of Computer Science College of Engineering and Computing George Mason University United States

ISBN: (纸本)9798331530389

With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for individuals with physical disabilities or those requiring efficient documentation of spoken content. The goal of this study is to review recent advancements in this field and highlight gaps in current research, with a focus on accuracy, language adaptability, ease of use, and the integration of various technologies. We also suggest future directions for enhancing and developing this technology further. © 2025 IEEE.

关键词： Hidden Markov models

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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