检索结果-内蒙古大学图书馆

Improving K-Means Effectiveness and Efficiency with Initialization Estimates of Cluster Centroids 2

学校读者我要写书评

暂无评论

Improving K-Means Effectiveness and Efficiency with Initiali...

2nd International Conference on Smart Electronics and Communication, ICOSEC 2021

作者： Ojha, Rajesh Kumar Srivastava, Sandeep Goyal, Mohit Kumar, Lalan Kumar, Amit Prasad, Chitturi Sri Sri University Faculty of Science Odisha Cuttack753001 India Galgotias University School of Computer Science Engineering UP Greater Noida201306 India GL Bajaj Institute of Technology Management Department of MCA UP Greater Noida201306 India Graphic Era Hill University School of Computing Uttarakhand Dehradun248002 India Kiet Group of Institutions Department of Computer Applications UP Ghaziabad201206 India Koneru Lakshmaiah Education Foundation Department of CSE Department of CSE AP 522502 India

ISBN: (纸本)9781665433686

K-Means is known both for its usefulness in finding clusters of related data as well as its fragility with respect to initialization choices. This paper introduces a 95% more effective and 50% more efficient initialization methods, that could eliminate the need for multiple executions of K-Means to find high quality clustering. To initialize the centroids, it selects a multiple, m, of K real data points, computes (mK)2 distances and keeps only the K maximum( minimum( distance ) ) points. A consequence of this technique enables O(lnK) binary search to find the optimal K on 'linearly' separable clusters. The effectiveness claim applies both to separable and intertwined clusters although the efficiency is lost on intertwined clusters. © 2021 IEEE.

关键词： Support vector machines

Multi-Layered Ontology Models for Dynamic Knowledge Representation in Social Network Analysis

学校读者我要写书评

暂无评论

Multi-Layered Ontology Models for Dynamic Knowledge Represen...

Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Y Manohar Reddy Vijilius Helena Raj Saloni Bansal H Pal Thethi Ravi Kalra Mohammed Khalid Institute of Aeronautical Engineering Hyderabad Department of Applied Sciences New Horizon College of Engineering Bangalore Department of Computer Engineering and Applications GLA University Mathura Lovely Professional University Phagwara Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

In the rapidly evolving landscape of social networks, understanding the multifaceted nature of human interactions and the dissemination of information is pivotal. This paper introduces a novel approach to dynamic knowledge representation through the development of Multi-Layered Ontology Models tailored for Social Network Analysis (SNA). The core of this research lies in architecting a robust, scalable, and semantically rich framework that leverages the principles of data mining and web technologies to dissect and interpret the complex structure of social networks. The ontology layers are intricately designed to represent various dimensions of social interactions, including temporal dynamics, user behavior, and network evolution. Each layer is interlinked yet distinct, providing a granular view of the social fabric and enabling precise inference and prediction of patterns. The methodology adopts an innovative blend of semantic web technologies for ontology construction, coupled with advanced data mining techniques to efficiently process and analyze large volumes of social data. The result is a dynamic model that adapts to the ever-changing social context, offering valuable insights into trends, community structures, and influence dissemination. The implications of this research are vast, impacting areas ranging from targeted marketing to public opinion analysis. This paper sets a new precedent for interdisciplinary research in SNA, opening avenues for further exploration and refinement.

关键词： Semantic Web Adaptation models Analytical models Social networking (online) Simulation Ontologies Predictive models

Strategies for Machine Learning Applied to Noisy HEP Datasets: Modular Solid State Detectors from SuperCDMS

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Cushman, P.B. Fritts, M.C. Chambers, A.D. Roy, A. Li, T. School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States

Background reduction in the SuperCDMS dark matter experiment depends on removing surface events within individual detectors by identifying the location of each incident particle interaction. Position reconstruction is achieved by combining pulse shape information over multiple phonon channels, a task well-suited to machine learning techniques. Data from an Am-241 scan of a SuperCDMS SNOLAB detector was used to study a selection of statistical approaches, including linear regression, artificial neural networks, and symbolic regression. Our results showed that simpler linear regression models were better able than artificial neural networks to generalize on such a noisy and minimal data set, but there are indications that certain architectures and training configurations can counter overfitting tendencies. This study will be repeated on a more complete SuperCDMS data set (in progress) to explore the interplay between data quality and the application of neural networks. © 2024, CC BY.

关键词： Neural networks

Unified View of Damage leaves Planimetry & Analysis Using Digital Images Processing Techniques

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Kumar, Pijush Kanti Munjal, DeepKiran Rani, Sunita Voumik, Liton Chandra Dutta, Anurag Ramamoorthy, A. Department of Information Technology Government College of Engineering & Textile Technology Calcutta Serampore India Department of Computer Applications G. L. Bajaj Institute of Technology and Management Uttar Pradesh Greater Noida India Department of Engineering Administration Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of Economics Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science & Engineering Government College of Engineering & Textile Technology Calcutta Serampore India Department of Mathematics Velammal Engineering College Anna University Tamil Nadu Chennai India

The detection of leaf diseases in plants generally involves visual observation of patterns appearing on the leaf surface. However, there are many diseases that are distinguished based on very subtle changes in these visually observable patterns. This paper attempts to identify plant leaf diseases using image processing techniques. The focus of this study is on the detection of citrus leaf canker disease. Canker is a bacterial infection of leaves. Symptoms of citrus cankers include brown spots on the leaves, often with a watery or oily appearance. The spots (called lesions in botany) are usually yellow. It is surrounded by a halo of the leaves and is found on both the top and bottom of the leaf. This paper describes various methods that have been used to detect citrus leaf canker disease. The methods used are histogram comparison and k-means clustering. Using these methods, citrus canker development was detected based on histograms generated based on leaf patterns. The results thus obtained can be used, after consultation with experts in the field of agriculture, to identify suitable treatments for the processes used. © 2023, CC BY.

关键词： Graphic methods

Reliability-Redundancy Optimization of an Overspeed Protection System of a Gas Turbine by Modified Wild Horse Optimizer

学校读者我要写书评

暂无评论

Reliability-Redundancy Optimization of an Overspeed Protecti...

Disruptive Technologies (ICDT), International Conference on

作者： Shshank Chaube Sangeeta Pant B.P. Joshi Anuj Kumar Manoj K. Singh Ketan Kotecha Department of Physical Sciences Jaypee University Anoopshar Uttar Pradesh India Department of Applied Sciences Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Lavale Pune Maharashtra India Department of Mathematics Graphic Era Hill University Bhimtal India School of Computer Science Engg. & Applications D. Y. Patil International University (DYPIU) Akurdi Pune India School of Computer Science Engineering and Technology Bennett University Techzone II Greater Noida Uttar Pradesh India Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Lavale Pune Maharashtra India

This article addresses the persistent challenges involved in the complex system reliability redundancy optimization of the overspeed protection system of a gas turbine (OPSGT), which is an essential complex system for ensuring the turbine's safety. The optimal functionality of this complex reliability-redundancy is achieved by means of a novel nature-inspired computational approach named the Modified Wild Horse Optimization Algorithm (MWHO), inspired by the behavior of wild horses. The MWHO algorithm efficiently delivers optimal solutions to this complex NP-hard problem, showcasing its potential in solving various complex system reliability optimization problems. This efficiency is easily verified through extensive numerical simulations, demonstrating that the outcomes produced by MWHO exhibit outstanding performance when compared to various existing metaheuristics. Hence, the MWHO not only introduces a fresh perspective in tackling associated reliability-redundancy optimization challenges but also establishes itself as a competitive and efficient algorithm in the complex realm of system reliability optimization.

关键词：

Smart Thermal Management and Control of Interleaved Boost Converters in High-Power Photovoltaic Systems

学校读者我要写书评

暂无评论

Smart Thermal Management and Control of Interleaved Boost Co...

2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Dasari, Kavitha Asha, V. Kansal, Lavish Praveen Parashar, Ashish Kumar Alwan, Adil Abbas Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Master of Computer Applications Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Lloyd Institute of Management and Technology Plot No.-11 Knowledge Park-II Uttar Pradesh Greater Noida201306 India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350357769

In the realm of high-power photovoltaic (PV) systems, ensuring efficient and reliable performance demands precise thermal management. This study delves into the intricacies of thermal management and control strategies for interleaved boost converters (IBC), pivotal components in these systems. Interleaved topology, though recognized for its capability to reduce ripple currents and enhance efficiency, presents unique thermal challenges due to uneven current distribution among the parallel converters. This research introduces a smart thermal control methodology that amalgamates real-time temperature monitoring with adaptive control strategies, ensuring optimal thermal performance and preventing hotspot formation. Employing a combination of advanced sensors and sophisticated algorithms, the proposed system autonomously adapts to varying solar irradiances and ambient conditions, thereby ensuring consistent thermal stability. Experimental results demonstrate a significant reduction in temperature deviations among parallel converters and an overall enhancement in system reliability and efficiency. The findings from this research set the groundwork for future designs of PV systems, ensuring their sustainable and efficient operation under diverse conditions. © 2023 IEEE.

关键词： Adaptive Control Strategies Hotspot Prevention. Top of Form Interleaved Boost Converters Photovoltaic Systems Thermal Management

Machine Learning-Driven Hybrid Adaptive Beamforming for Efficient Interference Mitigation in Dense Wireless Sensor Networks

学校读者我要写书评

暂无评论

Machine Learning-Driven Hybrid Adaptive Beamforming for Effi...

Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： P. Loganathan A.T. Priyeshkumar K. Kodeeswari K Jose Reena I. Poonguzhali M. Balaji Department of Electronics and Communication Engineering Excel Engineering College(Autonomous) Namakkal Department of Electronics and Communication Engineering Mahendra College of Engineering Salem Department of Electronics and Communication Engineering Kongu Engineering College(Autonomous) Erode Department of Computer Applications B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Department of Electronics and Communication Engineering Vivekananda College of Technology for Women Tiruchenhode

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The Adaptive Beamforming Algorithm (ABA) represents a promising approach to suppress interference in dense wireless sensor networks (WSNs), enhancing signal fidelity and network throughput. In dense WSNs, the proximity of sensor nodes leads to high interference levels, which degrade energy efficiency and communication reliability. This is exacerbated by static beamforming methods that oversimplify time and frequency utilization. This work proposes a set of adaptive beamforming solutions, including digital, machine learning (ML)-empowered, and hybrid beamforming approaches. The Adaptive Array Signal Processing (AASP) method introduces real-time interference analysis with null steering to suppress unwanted signals and maximize the signal-to-interference-plus-noise ratio (SINR). Hybrid beamforming, combining analog and digital domains, balances computational cost with performance. Additionally, a deep learning-based beam steering module dynamically adjusts beam patterns based on interference forecasts. Extensive simulations demonstrate the proposed methods' superiority over conventional beamforming, achieving significant SINR improvement, enhanced network throughput, and improved energy efficiency. In ultra-dense environments, the results indicate a 10-fold reduction in interference power alongside better scalability. Proactive interference mitigation using ML models further ensures adaptability to varying network conditions. These techniques establish a baseline for deploying adaptive beamformers in dense WSNs, addressing scalability and real-time computational challenges. They also pave the way for implementing advanced 5G/6G-capable sensor systems, enabling resilient communication in high-interference environments. Key-words: signal to interference plus noise ratio(SINR), adaptive beamforming, interference mitigation, hybrid beamforming, null steering, beam steering, computational efficiency.

关键词： Wireless sensor networks Adaptive systems Array signal processing Scalability Prevention and mitigation Interference Throughput Real-time systems Energy efficiency Signal to noise ratio

How Good is ChatGPT at Audiovisual Deepfake Detection: A Comparative Study of ChatGPT, AI Models and Human Perception

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Shahzad, Sahibzada Adil Hashmi, Ammarah Peng, Yan-Tsung Tsao, Yu Wang, Hsin-Min The Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Academia Sinica Taipei Taiwan The Department of Computer Science National Chengchi University Taipei Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan The Institute of Information Science Academia Sinica Taiwan

Multimodal deepfakes involving audiovisual manipulations are a growing threat because they are difficult to detect with the naked eye or using unimodal deep learning-based forgery detection methods. Audiovisual forensic models, while more capable than unimodal models, require large training datasets and are computationally expensive for training and inference. Furthermore, these models lack interpretability and often do not generalize well to unseen manipulations. In this study, we examine the detection capabilities of a large language model (LLM) (i.e., ChatGPT) to identify and account for any possible visual and auditory artifacts and manipulations in audiovisual deepfake content. Extensive experiments are conducted on videos from a benchmark multimodal deepfake dataset to evaluate the detection performance of ChatGPT and compare it with the detection capabilities of state-of-the-art multimodal forensic models and humans. Experimental results demonstrate the importance of domain knowledge and prompt engineering for video forgery detection tasks using LLMs. Unlike approaches based on end-to-end learning, ChatGPT can account for spatial and spatiotemporal artifacts and inconsistencies that may exist within or across modalities. Additionally, we discuss the limitations of ChatGPT for multimedia forensic tasks. © 2024, CC BY.

关键词： Audiovisual

One-shot Generative Distribution Matching for Augmented RF-based UAV Identification

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Kazemi, Amir Basiri, Salar Kindratenko, Volodymyr Salapaka, Srinivasa National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Civil and Environmental Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

This work addresses the challenge of identifying Unmanned Aerial Vehicles (UAV) using radiofrequency (RF) fingerprinting in limited RF environments. The complexity and variability of RF signals, influenced by environmental interference and hardware imperfections, often render traditional RF-based identification methods ineffective. To address these complications, the study introduces the rigorous use of one-shot generative methods for augmenting transformed RF signals, offering a significant improvement in UAV identification. This approach shows promise in low-data regimes, outperforming deep generative methods like conditional generative adversarial networks (GANs) and variational auto-encoders (VAEs). The paper provides a theoretical guarantee for the effectiveness of one-shot generative models in augmenting limited data, setting a precedent for their application in limited RF environments. This research contributes to learning techniques in low-data regime scenarios, which may include atypical complex sequences beyond images and videos. The code and links to datasets used in this study are available at https://***/amir-kazemi/*** Codes 49Q22, 68T37 Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks