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A Deep Auto Encoder Based Security Mechanism for Protecting Sensitive Data Using AI Based Risk Assessment

Journal of Harbin institute of Technology(New series) 2024年

作者： Lavanya M Mangayarkarasi S Department of Computer Science， Vels Institute of Science Technology & Advanced Studies (VISTAS)

Big data has ushered in an era of unprecedented access to vast amounts of new， unstructured data， particularly in the realm of sensitive information. This data presents unique opportunities for enhancing risk alerting systems， but also poses challenges in terms of extraction and analysis due to its diverse file formats. This paper proposes the utilization of a DAE-based (Deep Auto Encoders) model for projecting risk associated with financial data. The research delves into the development of an indicator assessing the degree to which organizations successfully avoid displaying bias in handling financial information. Results from simulation experiments demonstrate the superior performance of the DAE algorithm， showcasing fewer false positives， improved overall detection rates， and a noteworthy 9% reduction in failure jitter. The optimized DAE algorithm achieves an accuracy of 99%， surpassing existing methods， thereby presenting a robust solution for sensitive data risk projection.

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Intention and Face in Dialog 30

Intention and Face in Dialog

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Soubki, Adil Rambow, Owen Department of Computer Science Department of Linguistics Institute for Advanced Computational Science Stony Brook University United States

ISBN: (纸本)9782493814104

The notion of face described by Brown and Levinson (1987) has been studied in great detail, but a critical aspect of the framework, that which focuses on how intentions mediate the planning of turns which impose upon face, has received far less attention. We present an analysis of three computational systems trained for classifying both intention and politeness, focusing on how the former influences the latter. In politeness theory, agents attend to the desire to have their wants appreciated (positive face), and a complementary desire to act unimpeded and maintain freedom (negative face). Similar to speech acts, utterances can perform so-called face acts which can either raise or threaten the positive or negative face of the speaker or hearer. We begin by using an existing corpus to train a model which classifies face acts, achieving a new SoTA in the process. We then observe that every face act has an underlying intention that motivates it and perform additional experiments integrating dialog act annotations to provide these intentions by proxy. Our analysis finds that dialog acts improve performance on face act detection for minority classes and points to a close relationship between aspects of face and intent. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computation theory

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Ensemble of Deep Learning with Crested Porcupine Optimizer Based Autism Spectrum Disorder Detection Using Facial Images

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computers, Materials & Continua 2025年第5期83卷 2793-2807页

作者： Jagadesh Balasubramani Surendran Rajendran Mohammad Zakariah Abeer Alnuaim Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105Tamil NaduIndia Department of Computer Science and Engineering College of Applied Studies and Community ServiceKing Saud UniversityP.O.Box 22459Riyadh11495Saudi Arabia

Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several *** all autistic children remain undiagnosed before the age of *** problems affecting face features are often associated with fundamental brain *** facial evolution of newborns with ASD is quite different from that of typically developing *** recognition is very significant to aid families and parents in superstition and *** facial features from typically developing children is an evident manner to detect children analyzed with ***,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic *** study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)*** overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal *** the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed *** the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)*** extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI *** simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.

关键词： Autism spectrum disorder ensemble learning crested porcupine optimizer facial images computeraided diagnosis

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Lung Cancer Analysis and Classification using Enhanced Efficient Net V3 6

Lung Cancer Analysis and Classification using Enhanced Effic...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Ramya, J. Poongodi, A. Department of Software Applications Agurchand Manmull Jain College Chennai India Department of Computer Sciences Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

Lung cancer is one of the main causes of death globally and adds to the burden of disease and mortality. Early detection of lung cancer may reduce the chance of developing lung cancer. Artificial intelligence (AI) in diagnostic imaging has garnered a lot of attention in recent years, particularly for the detection of lung cancer. On the other hand, manual CT scan analysis takes a lot of time and is inaccurate or prone to mistakes. Given these limitations, the accurate categorization of CT images as either malignant or non-cancerous is accelerated by the application of computer approaches, including machine learning and deep learning algorithms. In this work, we provide a new and improved CNN model architecture with FCN and Deeplab V3+ for segmentation and classification using hybrid efficient net B0 to investigate lung cancer. The suggested model achieves superior results in both segmentation and classification. © 2025 IEEE.

关键词： Lung cancer

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Review of DDoS Attack Detection in Big Data with Cloud using Machine Learning 2

Review of DDoS Attack Detection in Big Data with Cloud using...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Radhika, P. Kamalakkannan, S. Kavitha, P. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798350340235

Cloud Computing (CC) is a massive breakthrough in Information Technology (IT) that provides end users to access flexible andvirtualizedsources at affordable infrastructure cost and management. One of the most significant technologies in the big-data era is the CC Data Centres (DCs). The Distributed Denial of Service (DDoS) attacks are one of the most serious issues when it comes to the privacy of DC. DDoS attacks using Transmission Control Protocol (TCP) traffic are taken into consideration, which are becoming more prevalent but challenging to identify. The DDoS attack is the focus of this study, along with the technique used to prevent it and lessen the vulnerability of the big data server side. The system entails the delivery of packets in the form of DDoS attacks to cloud-based websites and even addresses the real-time prediction of software layer DDoS attacks using various Machine Learning (ML) and Deep Learning (DL) techniques. As a result, it stands apart among numerous hosts. Additionally, the objective of this paper was to offer a succinct introduction to attack detection approaches for early researchers working on cloud-based big data applications. As a result, these approaches are categorised according to how they function, their strengths and shortcomings are reviewed, and finally, several research papers that used each method are examined. © 2023 IEEE.

关键词： Big Data Cloud computing Deep learning Distributed Denial of Service attacks Machine Learning

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A Comprehensive Exploration of Storage Attacks in Information-Centric Networking Environments 10

A Comprehensive Exploration of Storage Attacks in Informatio...

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10th International Conference on advanced Computing and Communication Systems, ICACCS 2024

作者： Sethu, S. Manikandan, A. Vels Institute of Science Technology & Advanced Studie Department of Computer Science and Engineering Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

To help users overcome the limitations of current IP networks, Data Networking (NDN) and Content-Centric Network (CCN) architectures have been created to move communication from host-centric to content-centric. NDN is essentially a network for the dissemination of content. In NDN, a user's request for material will be the only reason for contact. The descriptive capabilities of NDN technology leverage the globally unique names that encryption and content-based security provide. As part of its design, it upholds content validity and reliability. We examine several storage risks that NDN caches face, including content poisoning attacks, cache pollution attacks, and man-in-the-middle attacks. © 2024 IEEE.

关键词： Internet protocols

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EFS-XGBoost: A robust framework for precision classification of COVID-19 cases

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Intelligent Data Analysis 2025年第1期29卷 171-185页

作者： Abidi, Mustufa Haider Khare, Neelu D, Preethi Alkhalefah, Hisham Umer, Usama Advanced Manufacturing Institute King Saud University Riyadh Saudi Arabia School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram Chennai India

The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine learning techniques have emerged as a promising avenue for augmenting the capabilities of medical professionals in disease diagnosis and classification. In this research, the EFS-XGBoost classifier model, a robust approach for the classification of patients afflicted with COVID-19 is proposed. The key innovation in the proposed model lies in the Ensemble-based Feature Selection (EFS) strategy, which enables the judicious selection of relevant features from the expansive COVID-19 dataset. Subsequently, the power of the eXtreme Gradient Boosting (XGBoost) classifier to make precise distinctions among COVID-19-infected patients is *** EFS methodology amalgamates five distinctive feature selection techniques, encompassing correlation-based, chi-squared, information gain, symmetric uncertainty-based, and gain ratio approaches. To evaluate the effectiveness of the model, comprehensive experiments were conducted using a COVID-19 dataset procured from Kaggle, and the implementation was executed using Python programming. The performance of the proposed EFS-XGBoost model was gauged by employing well-established metrics that measure classification accuracy, including accuracy, precision, recall, and the F1-Score. Furthermore, an in-depth comparative analysis was conducted by considering the performance of the XGBoost classifier under various scenarios: employing all features within the dataset without any feature selection technique, and utilizing each feature selection technique in isolation. The meticulous evaluation reveals that the proposed EFS-XGBoost model excels in performance, achieving an astounding accuracy rate of 99.8%, surpassing the efficacy of other prevailing feature selection techniques. This research not only advances the field of COVI

关键词： COVID-19

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A class imbalance aware hybrid model for accurate rice variety classification

International Journal of Cognitive Computing in Engineering

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International Journal of Cognitive Computing in Engineering 2025年 6卷 170-182页

作者： Vaidya, Hanumesh Prasad, K.V. Rajashekhar, C. Tripathi, D. S, Renuka Shetty, Jyoti Swamy, Kumar Y, Shobha Department of Studies in Mathematics Vijayanagara Sri Krishnadevaraya University Karnataka Ballari India Department of Mathematics Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India Department of Studies in Computer Science Vijayanagara Sri Krishnadevaraya University Karnataka Ballari India Karnataka Ballari India Department of Studies in Computer Science Kishkinda University Karnataka Ballari India Department of Mathematics National Institute of Technology Uttarakhand Srinagar India

Rice classification plays a critical role in global agriculture, impacting the food security of over 3.5 billion people worldwide. As the complexity of this task has grown, advanced Machine Learning (ML) and Deep Learning (DL) models have emerged as powerful tools for accurate variety identification. These sophisticated algorithms excel in processing extensive datasets, leveraging key characteristics such as grain size, color, and texture to make precise predictions. However, a persistent challenge in this field is a class imbalance, where certain rice varieties are significantly underrepresented in datasets. This imbalance can severely impact model performance, particularly for minority classes, leading to biased predictions that favor more abundant varieties. Many existing models struggle to effectively address this issue, often prioritizing majority classes at the expense of overall generalization across all rice types. To tackle this critical gap in rice classification, we introduce a novel hybrid model: the XGBoost Multi-Layer 33Perceptron (XGB-MLP). This innovative approach is specifically designed to handle class imbalance, ensuring fair and accurate classification across all rice varieties, regardless of their representation in the dataset. Our model demonstrates remarkable versatility, effectively accommodating binary and multi-class scenarios while maintaining robust performance in the face of imbalanced data. As part of our contribution, we have also developed a new dataset intentionally incorporating class imbalance. This dataset serves as a rigorous benchmark for evaluating our model's performance against existing works in the field. The results of our comprehensive evaluation are compelling, with the XGB-MLP model achieving outstanding accuracy across various classification tasks: 99.86 % for binary class, 99.95 % for multi-class, and 98.46 % for a challenging multi-class scenario using a merged dataset. These impressive results not only surpass the pe

关键词： Adversarial machine learning

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Denoising Diffusion Models for High-Resolution Microscopy Image Restoration

Denoising Diffusion Models for High-Resolution Microscopy Im...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Osuna-Vargas, Pamela Wehrheim, Maren H. Zinz, Lucas Rahm, Johanna Balakrishnan, Ashwin Kaminer, Alexandra Heilemann, Mike Kaschube, Matthias Frankfurt Institute for Advanced Studies Germany Goethe University Frankfurt Department of Computer Science and Mathematics Germany Institute of Physical and Theoretical Chemistry Goethe University Frankfurt Germany

ISBN: (纸本)9798331510831

Advances in microscopy imaging enable researchers to visualize structures at the nanoscale level thereby unraveling intricate details of biological organization. However, challenges such as image noise, photobleaching of fluo-rophores, and low tolerability of biological samples to high light doses remain, restricting temporal resolutions and experiment durations. Reduced laser doses enable longer measurements at the cost of lower resolution and increased noise, which hinders accurate downstream analyses. Here we train a denoising diffusion probabilistic model (DDPM) to predict high-resolution images by conditioning the model on low-resolution information. Additionally, the probabilistic aspect of the DDPM allows for repeated generation of images that tend to further increase the signal-to-noise ratio. We show that our model achieves a performance that is better or similar to the previously best-performing methods, across four highly diverse datasets. Importantly, while any of the previous methods show competitive performance for some, but not all datasets, our method consistently achieves high performance across all four data sets, suggesting high generalizability. Our code and datasets are available at https://githubcom/kaschube-lab/ddpm_highres_microscopy, https://***/10.5281/zenodo.14178277, https://***/10.5281/zenodo.14215837. © 2025 IEEE.

关键词： Fluorescence microscopy

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Energy-aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

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Software - Practice and Experience 2024年第2期54卷 109-148页

作者： Hashemi, Sayed Mohsen Sahafi, Amir Rahmani, Amir Masoud Bohlouli, Mahdi Department of Computer Engineering Qeshm Branch Islamic Azad university Qeshm Iran Department of Computer Engineering South Tehran Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan Department of Computer Science and Information Technology Institute for Advanced Studies in Basic Sciences Zanjan Iran Research and Innovation Department Petanux GmbH Bonn Germany Zanjan Iran

The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location-aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study. © 2023 John Wiley & Sons Ltd.

关键词： Quality of service

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