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4th International Conference on Electrical Engineering and Informatics, ICon EEI 2024

作者： Dash, Annada Mksss Cummins College of Engineering for Women Data Science and Ml Pune India

ISBN: (纸本)9798331540791

This paper explores the emerging cybersecurity challenges in the oil and gas sector, a vital part of the global energy ecosystem that heavily relies on data. The study underscores the need to secure both Information Technology (IT) and Operational Technology (OT) systems within the industry. It highlights the reliance on legacy systems and the critical role that Windows-based Operating Systems play in the industry's infrastructure. Moreover, the paper examines how the adoption of Industry 4.0 technologies, while increasing operational efficiency, has also introduced new security vulnerabilities. Special attention is given to the security of OT systems due to their integral role in the sector's operations. The paper concludes by proposing strategic approaches to enhance data security and protect valuable assets in the petroleum industry. © 2024 IEEE.

关键词： cybersecurity data security Industry 4.0 information technology legacy systems oil and gas operational technology

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Deep Learning in Medical Imaging: Image Processing - From Augmenting Accuracy to Enhancing Efficiency 24

Deep Learning in Medical Imaging: Image Processing - From Au...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Lakshmi Vara Prasad, G. Murali, A. Bhaskar Reddy, Y.V. Marturi, Haribabu Paul, Valeru Vision Centre for Data Science AI and ML QIS College of Engineering and Technology Andhra Pradesh Ongole India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798400709586

Deep learning has transformed medical imaging by significantly improving accuracy and efficiency in image processing tasks such as disease detection, segmentation, and classification. This paper explores the role of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other advanced architectures in enhancing image interpretation across various medical imaging modalities. By analyzing MRI, CT, and ultrasound data, this study demonstrates the efficacy of deep learning in automating critical diagnostic tasks, reducing human error, and improving patient outcomes. We propose a comprehensive framework for applying deep learning techniques in medical imaging, providing insights into future advancements in the field. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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ControlVideo: conditional control for one-shot text-driven video editing and beyond

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science China(Information sciences) 2025年第3期68卷 150-162页

作者： Min ZHAO Rongzhen WANG Fan BAO Chongxuan LI Jun ZHU Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML CenterTsinghua Laboratory of Brain and Intelligence Lab Tsinghua University ShengShu Technology Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Pazhou Laboratory (Huangpu)

This paper presents ControlVideo for text-driven video editing — generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions(such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r.t. the source content, and temporal consistency, all while aligning with the text. By incorporating low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing(e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous studies achieved. The code is available at https://***/thu-ml/controlvideo.

关键词： diffusion models controllable generation text-driven editing video editing long video editing

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Monitoring Industrial Protection Gear Using Intelligent System 2

Monitoring Industrial Protection Gear Using Intelligent Syst...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Yakubraj, M. Rajkumar, J.S. Sophas Samuel, S. Matthew Palmer, G. Ancy Jenifer, J. Karunya Institute of Technology and Sciences Data Science & Cyber Security Coimbatore India Karunya Institute of Technology and Science Artificial Intelligence & ML Coimbatore India Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

In this article, the problem of using an intelligent system to ensure the use of personal protective equipment (PPE) in industrial safety is discussed. The real-time solution provides fast feedback and easy-to-use monitoring to safety managers and users alike by combining sensors and machine learning algorithms. The analysis shows how it can improve industrial safety regulations and assesses its efficacy. Strategically placed sensors gather copious amounts of data about PPE compliance, and the system integrates wearable, vision-based, and environmental technologies. Novel techniques such as Computer vision algorithm is used object detection and recognition. SSD single-shot multi-box detectors and (YOLO) enable efficient object detection and quick PPE item identification. Furthermore, it has been observed that Region-based Convolution Neural Network(R-CNNs) increase workplace safety standards by improving the system's adaptability to variations in the usage of safety gear over time. © 2024 IEEE.

关键词： Intelligent systems

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Securing Oil and Gas Infrastructure: An Analysis of Emerging Cybersecurity Threats

Securing Oil and Gas Infrastructure: An Analysis of Emerging...

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International Conference on Electrical Engineering and Informatics (ICon EEI)

作者： Annada Dash Data Science and ML MKSSS Cummins College of Engineering for Women Pune

ISBN: (数字)9798331540791

ISBN: (纸本)9798331540807

This paper explores the emerging cybersecurity challenges in the oil and gas sector, a vital part of the global energy ecosystem that heavily relies on data. The study underscores the need to secure both Information Technology (IT) and Operational Technology (OT) systems within the industry. It highlights the reliance on legacy systems and the critical role that Windows-based Operating Systems play in the industry’s infrastructure. Moreover, the paper examines how the adoption of Industry 4.0 technologies, while increasing operational efficiency, has also introduced new security vulnerabilities. Special attention is given to the security of OT systems due to their integral role in the sector’s operations. The paper concludes by proposing strategic approaches to enhance data security and protect valuable assets in the petroleum industry.

关键词： Operating systems Oils Focusing Safety Fourth Industrial Revolution Stakeholders Petroleum industry Information technology Informatics Monitoring

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Impact of Ensemble of Vector Embeddings on Speculative Retrieval Augmented Generation 5

Impact of Ensemble of Vector Embeddings on Speculative Retri...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Kukreja, Sanjay Kumar, Tarun Bharate, Vishal Singh, Rajat Dasgupta, Abhijit Guha, Debashis Sp Jain School of Global Management Department of Machine Learning Mumbai India EClerx Services Ltd. Coe AI-ML Chandigarh India EClerx Services Ltd. Coe AI-ML Pune India EClerx Services Ltd. Coe AI-ML Mumbai India Sp Jain School of Global Management Department of Data Science Mumbai India

ISBN: (纸本)9798350389609

Recent developments in Natural Language Processing (NLP) have impacted all fields. Retrieval Augmented Generation (RAG) has played significant part in resent advancements in NLP applications. RAG architecture combines the powers of retrieval-based methods and generative models to increase the relevance and accuracy of generated texts. This paper deals with the performance comparison on speculative RAG, recent development in RAG systems, with different state-of-the-art vector embedding models and their ensembles. Two approaches were adopted to carry out this research: one-to-one mapping context with response-rationale and mapping of clustered contexts with response and rationale. The performance measures semantic answer similarity (SAS), and Rouge score on descriptive-type NCERT books dataset. State-of-the-art vector embedding models, BAAI/bge-base-en, thenlper/gte-base and intfloat/e5-base-v2, were used individually and to create average and weighted average ensemble *** responses of the systems corresponding to the 200 questions used for evaluation were validated against the NCERT Solution dataset. The result showed that the ensemble of vector embedding models outperforms state-of-the-art embedding models with weighted average ensemble achieving the highest accuracy. Further, the clustered context approach consistently outperforms the one-to-one mapping approach with noticeably higher SAS and Rouge metric values corresponding to each vector embedding method employed. For weighted average ensemble, the clustering approach improves the semantic answer similarity value by 8.24% over the one-to-one mapping approach. The performance of the systems may be improved by optimizing the weights of the weighted average ensemble and better clustering approaches. Various institutions using RAG systems for descriptive question-answer solutions may benefit from employing advancement in RAG systems. © 2024 IEEE.

关键词： Semantics

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Enhancing Carbon Emission Tracking with Gemini: An AI Solution for Sustainable Development 1

Enhancing Carbon Emission Tracking with Gemini: An AI Soluti...

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1st International Conference on AIml-Applications for Engineering and Technology, ICAET 2025

作者： Dash, Annada Mathur, Bhavini Patil, Piyusha B.Tech in Computer Engineering Honors in Data Science and ML Cummins College of Engineering for Women Pune India B.Tech in Computer Engineering Cummins College of Engineering for Women Pune India

ISBN: (纸本)9798350355611

This research explores the integration of advanced AI technologies, specifically Google's Gemini-1.5-Flash-001 model and Vertex AI, into the development of EcoTrack-a mobile application that helps users monitor and reduce their carbon emissions. At the core of this innovation is EcoBot, an AI-powered chatbot embedded within EcoTrack, designed to deliver personalized, actionable insights on sustainability. By leveraging Gemini's capabilities alongside real-time data through Vertex AI, EcoBot enhances user engagement by offering tailored advice on reducing their carbon footprint. The study illustrates how EcoBot processes various data points, including user input and environmental metrics, to generate precise recommendations for users looking to make more sustainable choices. The focus of this work is on the importance of scalable AI frameworks that can adapt to evolving sustainability goals while efficiently managing real-time data. Additionally, this paper addresses the ethical dimensions of using AI for environmental purposes, discussing its potential to drive meaningful action at both the individual and societal levels, contributing to the global effort to combat climate change. © 2025 IEEE.

关键词： Carbon sequestration

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Phishing URLs Using Machine Learning Hybrid Stacking Classifier Approach with XGBoost, Random Forest and Extra Trees

Phishing URLs Using Machine Learning Hybrid Stacking Classif...

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

作者： Chitteti, Chengamma Divya Sree, Tanguturi Sai Madhavi, K. Reddy Pooja, Pikkili Jayanth, Sannapaneni Reddy, Middi Harshavardhan Dept. of Data Science Tirupathi India Sree Vidyanikethan Engineering College Dept. of Information Technology Tirupathi India Mohan Babu University Dept. of Ai & Ml Tirupati India

ISBN: (纸本)9798350382693

Within the dynamic field of cybersecurity threats, phishing remains a prevalent and sophisticated tactic employed by malicious actors to deceive consumers. This paper presents a novel approach that leverages the strengths of Random Forest, ExtraTrees, and XGBoost in a machine learning framework to enhance the detection of phishing URLs through a hybrid stacking classifier. The suggested method combines the best features of all three ensemble learning techniques to provide a phishing detection model that is reliable and precise. An ensemble of classifiers is formed by combining XGBoost, which is well-known for its boosting abilities, with the highly randomized ExtraTrees method and the adaptable Random Forest algorithm. By using the various decision limits of each base learner, the hybrid stacking model combines their particular capabilities in the best possible way to provide higher prediction performance. Preprocessing is done on the training dataset to extract pertinent characteristics such as content, URL structure, and contextual data. The efficacy of the suggested method in detecting phishing URLs is assessed by extensive testing on an extensive dataset. The results indicate how well the hybrid stacking classifier performs in comparison to separate classifiers and highlight the possibility for improved phishing detection in practical situations. The study has importance as it presents a novel and efficient machine learning-based approach to counteract the rising danger of phishing assaults, hence propelling the area of cybersecurity forward. © 2024 IEEE.

关键词： Phishing

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Explaining Classifications to Non-experts: An XAI User Study of Post-Hoc Explanations for a Classifier When People Lack Expertise 1

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26th International Conference on Pattern Recognition, ICPR 2022

作者： Ford, Courtney Keane, Mark T. School of Computer Science University College Dublin Dublin Ireland Insight Centre for Data Analytics UCD Dublin Ireland ML Labs SFI CRT Dublin Ireland

ISBN: (数字)9783031377310

ISBN: (纸本)9783031377303

Very few eXplainable AI (XAI) studies consider how users’ understanding of explanations might change depending on whether they know more/less about the to-be-explained domain (i.e., whether they differ in their expertise). Yet, expertise is a critical facet of most high-stakes, human decision-making (e.g., understanding how a trainee doctor differs from an experienced consultant). Accordingly, this paper reports a novel, user study (N = 96) on how people’s expertise in a domain affects their understanding of post-hoc explanations-by-example for a deep-learning, black-box classifier. The results show that people’s understanding of explanations for correct and incorrect classifications changes dramatically, on several dimensions (e.g., response times, perceptions of correctness and helpfulness), when the image-based domain considered is familiar (i.e., MNIST) as opposed to unfamiliar (i.e., Kannada-MNIST). The wider implications of these new findings for XAI strategies are discussed. © 2023, Springer Nature Switzerland AG.

关键词： Deep learning

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Machine Learning based Remaining Useful Life Prediction of Lithium-ion Batteries in Electric Vehicle Battery Management System 2

Machine Learning based Remaining Useful Life Prediction of L...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Umayal, R.M. Darapaneni, Narayana Aditya, V. Paduri, Anwesh Reddy Ml and Data Science Pes University Bangalore India Aiml Great Learning Bangalore India Koneru Lakshmaiah Education Foundation Department of Commerce and Management Hyderabad India

ISBN: (纸本)9798350369960

The accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for the effective management and maintenance of electric vehicle (EV) battery systems. RUL prediction involves estimating the number of cycles or time until a battery's capacity degrades to a specified threshold, considering factors such as temperature, charging/discharging profiles, State-of-Charge(Q1), Voltage (C1, C2), and operational conditions. The RUL prediction of lithiumion batteries in EV battery management systems is a challenging task due to the complex interplay of degradation mechanisms and evolving battery technologies. data-driven approaches, leveraging machine learning algorithms and advanced data analytics techniques, have emerged as the primary method for RUL prediction. However, challenges remain in ensuring the scalability, reliability, and real -time applicability of prediction models. This paper aims to contribute to the field by developing state-of-the-art RUL prediction models that seamlessly integrate into existing EV battery management systems. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of Random Forest regression and classification models is higher than the accuracy of the other methods, and the prediction cycle is shorter than the average of the other methods. © 2023 IEEE.

关键词： accuracy comparison data- driven approaches Lithium-ion Batteries Random Forest models RUL prediction

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