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International Conference on Signal Processing and Communication (ICSC)

作者： Abdullah M Alqarni Faiz S Alshehri Majed S Alharbi Nitin Talreja Sajal Bhatia School of Computer Science and Engineering Sacred Heart University Fairfield USA Principal Data Engineer Optum Services Inc. (United Health Group) Hartford USA

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

As our world continues to embrace digital technology, cyber threats have become increasingly prevalent, especially botnet attacks. Ensuring cybersecurity is a significant concern, and to address this, we need Intrusion Detection Systems (IDS) that can automatically adapt to new threats using advanced machine learning (ML) and deep learning (DL) techniques. Our project aims to develop an advanced anomaly detection and recognition system for IoT botnet attacks using hyper-dimensional models namely Random Forest, Decision Tree, Gradient Boosting, Multi-layer Perceptron (MLP), and K-Nearest Neighbor (KNN) and makes uses of hyperparameter tuning for performance optimization. By integrating an ensemble voting classifier that combines these five algorithms using a soft-voting mechanism, our results indicate an improved detection accuracy and adaptability. The proposed ensemble-based model is tested on a publicly available dataset which offers comprehensive coverage of botnet attack patterns and outperforms all individual classifiers, achieving an accuracy of 99.88% and an ROC AUC score of 0.999. The proposed approach highlights the advantages of combining hyper-dimensional classification models in effectively detecting IoT botnet attacks.

关键词： Adaptation models Accuracy Botnet Intrusion detection Signal processing algorithms Nearest neighbor methods Radar signal processing Anomaly detection Tuning Random forests

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AI Enhanced Ontology Driven NLP for Intelligent Cloud Resource Query Processing Using Knowledge Graphs

arXiv

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arXiv 2025年

作者： Sunkara, Krishna Chaitanya Narukulla, Krishnaiah Independent Researcher Engineering Manager AI Data Center and Cloud Engineering IEEE Senior Member RaleighNC United States Independent Researcher Principal Software Engineer AI/ML Data Engineering IEEE Senior Member San JoseCA United States

The conventional resource search in cloud infrastructure relies on keyword-based searches or GUIDs, which demand exact matches and significant user effort to locate resources. These conventional search approaches often fail to interpret the intent behind natural language queries, making resource discovery inefficient and inaccessible to users. Though there exists some form of NLP based search engines, they are limited and focused more on analyzing the NLP query itself and extracting identifiers to find the resources. But they fail to search resources based on their behavior or operations or their capabilities or relationships or features or business relevance or the dynamic changing state or the knowledge these resources have. The search criteria has been changing with the inundation of AI based services which involved discovering not just the requested resources and identifiers but seeking insights. The real intent of a search has never been to just to list the resources but with some actual context such as to understand causes of some behavior in the system, compliance checks, capacity estimations, network constraints, or troubleshooting or business insights. This paper proposes an advanced Natural Language Processing (NLP) enhanced by ontology-based semantics to enable intuitive, human-readable queries which allows users to actually discover the intent-of-search itself. By constructing an ontology of cloud resources, their interactions, and behaviors, the proposed framework enables dynamic intent extraction and relevance ranking using Latent Semantic Indexing (LSI) and AI models. It introduces an automated pipeline which integrates ontology extraction by AI powered data crawlers, building a semantic knowledge base for context aware resource discovery. © 2025, CC BY.

关键词： Knowledge graph

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Development of Multi Objective Approach for Validation of PID Controller for Buck Converter

Development of Multi Objective Approach for Validation of PI...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Arun Kumar K Gopi Chand Vegineni C. Suresh Bhagath Chandra Chowdari Marella Sireesha Addanki Palak Chimwal Big Data Architect India Sr.Software Engineer Enrollment and Eligibility Team India Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Samayapuram Trichy Department of Financial Services Insights & Data Capgemini India Principal software developer System soft Technologies llc Information Technology India Department of Electrical Engineering Bipin Tripathi Kumaon Institute of Technology Dwarahat Almora India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Buck converters are a common converter of DCDC used for high energy conversion and power sources. These regulators are utilized in electronic gear to alter the result voltage because of changes in the heap current and information voltage. Therefore, in order to react to enquiries in real time, an efficient control system is required. In this work, an upgraded Sine Cosine Algorithm (ISCA) with proportional-integral-derivative (PID) controller is proposed for Mult objective optimization of a DC-DC buck converter. Through two distinct modifications, the limitations of the traditional SCA were therefore addressed, leading to the innovative use of nonlinear equations in conjunction with instrumental mechanisms to update the average location. By contrasting its response for frequency, response for increment, uncertainties of parametric, statistics of time-space indispensable error execution, objective capability, and resistance from aggravation with the previous methods-based PID for buck converters of DC-DC, the suggested ISCA-PID technique's robustness was *** results obtained later revealed that the suggested approach outperformed its algorithmic predecessors in terms of the DC-DC buck converter's remarkably improved transient responsiveness.

关键词： Buck converters Regulators Machine learning algorithms Uncertainty Heuristic algorithms Velocity control Power system stability Real-time systems Voltage control Optimization

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NLP Based TAG Algorithm for Enhancing Customer data Platform and Personalized Marketing 5

NLP Based TAG Algorithm for Enhancing Customer Data Platform...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Gummadi, Venkata Udayaraju, Pamula Kolasani, Dilip Kotaru, Chakravarthy Sayana, Raghavendra Neethika, Alluri Software Engineer Expedia Group Austin United States School of Engineering and Sciences Department of CSE SRM University AP India Principal Data Engineer Expedia Group Austin United States Data Infrastructure Expedia Group Austin United States Principal Software Engineer Expedia Group Austin United States SRKR Engineering College Department of IT India

ISBN: (纸本)9798331518097

As businesses strive to enhance decision-making and personalized marketing, the challenge of extracting valuable insights from vast datasets becomes paramount. Recent advancements in natural language processing (NLP), particularly Table-Augmented Generation (TAG), present a transformative approach by merging structured data with unstructured text generation. This integration enables businesses to derive actionable insights that improve marketing precision and operational strategies. This paper explores how TAG can effectively analyze extensive datasets, allowing organizations to create more targeted marketing initiatives and enhance customer engagement. By harnessing the power of TAG, businesses can generate personalized information that drives growth and fosters competitive advantages. The implications of TAG for strategic decision-making are examined, highlighting its role in optimizing resource utilization and facilitating data-driven storytelling. The findings of this research contribute to a deeper understanding of how advanced analytics can significantly enhance business effectiveness, laying the groundwork for future applications and research in the rapidly evolving landscape of personalized marketing and customer relationship management. © 2024 IEEE.

关键词： Sales

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NLP Based TAG Algorithm for Enhancing Customer data Platform and Personalized Marketing

NLP Based TAG Algorithm for Enhancing Customer Data Platform...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Venkata Gummadi Pamula Udayaraju Dilip Kolasani Chakravarthy Kotaru Raghavendra Sayana Alluri Neethika Software Engineer Expedia Group Austin United States Department of CSE School of Engineering and Sciences SRM University AP India Principal Data Engineer Expedia Group Austin United States Data infrastructure Expedia Group Austin United States Principal Software Engineer Expedia Group Austin United States Department of IT SRKR Engineering College

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

关键词： Technological innovation Decision making Merging Organizations Medical services data models Natural language processing Real-time systems Pattern recognition Resource management

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Sustainable data centers

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XRDS: Crossroads, The ACM Magazine for Students 2011年第4期17卷 8–12页

作者： Bill Weihl Erik Teetzel Jimmy Clidaras Chris Malone Joe Kava Michael Ryan Green Energy Czar at Google Google's Greenkeeper a Staff Technical Program Manager Principal Datacenter Engineer at Google Datacenter Research & Development group Director of Data Center Operations at Google Datacenter Research and Development Mechanical Engineer at Google

How Google addresses energy and environment issues as they pertain to its global data centers.

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Forecasting Future Sales Using Linear Regression Approach

Forecasting Future Sales Using Linear Regression Approach

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Jain, Ashish Gupta, Pankaj Saran, Hemlatha Kaur Parmar, Devendra Singh Bhati, Jai Prakash Rawat, Daksh CSE AI IIMT College of Engineering India Manager Data Engineer Discover Financial Services United States Independent Researcher India Discover Financial Services Principal Product Owner United States Greater Noida India Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331542344

In this age of digital information, theoretical data generation has grown to a level that no person can handle. Thus, several machine learning models are available. Therefore, our aim is to test forecast sales in various locations with different machine learning methods and select the best algorithm of all for our problem statement. We utilize both traditional regression strategies as well as boosting techniques to evaluate whether boosting algorithms outperform traditional regression ones. Thus, it is necessary to apply intelligent data mining methods based on accurate forecasting models that can produce highly reliable results in order to carry out effective sales prediction analysis. It is important in this process to understand and to be skilled about many market segments as possible the projection of demand for business-to- business sales data analysis big data along with the accuracy of sales forecasting presents significant challenges for old-fashioned forecasting systems. This article will discuss the possibility of predicting sales volumes using different machine learning methodologies. In conclusion, this study provides realistic, accurate and valuable forecast data for future sale planning purposes. © 2024 IEEE.

关键词： Prediction models

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SOUR-WATER STRIPPER EFFICIENCY.

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Plant/operations progress 1983年第2期2卷 108-113页

作者： Won, Kwang W. Fluor Engineers Advanced Technology Div. Irvine Calif. 92714 Kwang (Ray) Won joined Fluor in 1974 and is now Principal Process Engineer in the Process Methods and Data group of the Advanced Technology Division. He graduated from Seoul National University with a B.S. from Wayne State University with a M.S. and from the University of California Berkeley with a PhD all in Chemical Engineering. In his current position he is responsible for providing up-to-date and reliable methods and data for high pressure phase equilibria thermo physical properties non conventional separation processes and reaction engineering. He is a member of the American Institute of Chemical Engineers and the American Chemical Society. He is Fluor's representative to Fractionation Research Inc. and is a registered Professional Engineer in Calif.

Sour-water stripping is an important part of integrated industrial aqueous waste management. This paper explores the use of multi-component-dependent stage efficiencies for sour-water stripper calculations. The component-dependent stage efficiencies calculated from the performance data of real sour water strippers appear to exhibit a trend in a mixture.

关键词： INDUSTRIAL PLANTS

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OOPSLA distributed object management 90

OOPSLA distributed object management

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Proceedings of the European conference on object-oriented programming on Object-oriented programming systems, languages, and applications

作者： John R. Rymer Richard Mark Soley William Stephen Andreas Ian Fuller Neal Jacobson Richard A. Demers Seybold Office Computing Group Moderator Vice President Object Management Group Staff Software Engineer Distributed Applications Architecture Data General Project Manager Distributed New Wave Application Architecture Hewlett-Packard Principal Engineer Distributed Information Systems Digital Equipment Corp. Distributed Data Management Architecture IBM

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Trust engineering for Human-AI Teams

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2019年第1期63卷 322 - 326页

作者： Neta Ezer Sylvain Bruni Yang Cai Sam J. Hepenstal Christopher A. Miller Dylan D. Schmorrow Chief Technologist for Human-Machine Teaming Northrop Grumman Corporation Principal Human Systems Engineer Aptima Inc Director of Visual Intelligence Studio Carnegie Mellon University Data Scientist Defence Science and Technology Laboratory Chief Scientist Smart Information Flow Technologies Vice President and Chief Scientist Soar Technology Inc

Human-AI teaming refers to systems in which humans and artificial intelligence (AI) agents collaborate to provide significant mission performance improvements over that which humans or AI can achieve alone. The goal is faster and more accurate decision-making by integrating the rapid data ingest, learning, and analyses capabilities of AI with the creative problem solving and abstraction capabilities of humans. The purpose of this panel is to discuss research directions in Trust engineering for building appropriate bi-directional trust between humans and AI. Discussions focus on the challenges in systems that are increasingly complex and work within imperfect information environments. Panelists provide their perspectives on addressing these challenges through concepts such as dynamic relationship management, adaptive systems, co-discovery learning, and algorithmic transparency. Mission scenarios in command and control (C2), piloting, cybersecurity, and criminal intelligence analysis demonstrate the importance of bi-directional trust in human-AI teams.

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