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Fault Diagnosis of Tenessee Eastman Process with Detection Quality Using IMVOA with Hybrid DL Technique in IIOT

SN computer science 2023年第5期4卷 1-10页

作者： Anitha, Cuddapah Rajesh Kumar, T. Balamanigandan, R. Mahaveerakannan, R. Computer Science and Engineering School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai 602105 India

This work presents a deep learning (DL) based approach to defect identification in continuous systems. The Tennessee Eastman Procedure was chosen as a use case for this study because it involves a dispersed network of sensors throughout a manufacturing facility. To improve detection quality for fault diagnosis, a hybrid DL approach is presented to choose the most representative sensors. Using an enhanced version of the Multi-Verse Optimization technique, the learning velocity of a Recurrent Neural Network is maximised (IMVOA). An exhaustive design space of solutions varying in sensing and detected quality has been made available by the suggested technique. It is an alternative to the standard Industry 4.0 setup, in which a plethora of decentralised sensor networks report their findings to a single cloud repository. In contrast, the suggested method takes a decentralised approach, which means that processing may occur closer to the sensors that create the data, or even at the advantage of the Internet of Things. Bandwidth, privacy, and latency issues, which often plague centralised methods, are circumvented here. Experimental results demonstrate that the suggested technique delivers fault finding solutions for the Tennessee Eastman Procedure at state-of-the-art detection quality levels. Solution times are 35 times faster than the application with the best detection quality, while feature counts are reduced by an average of 1.99 times. It is important to note that the framework’s scalability creates a design space from which the best possible implementation may be selected based on the requirements of the application. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Fault diagnosis Improved multi-verse optimization algorithm Recurrent Neural Network Tennessee Eastman process

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Enhanced Cross Z-Complementary Set and Its Application in Generalized Spatial Modulation

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 4674-4690页

作者： Huang, Zhen-Ming Pai, Cheng-Yu Liu, Zilong Chen, Chao-Yu Institute of Computer and Communication Engineering National Cheng Kung University Tainan701 Taiwan National Cheng Kung University Department of Engineering Science Tainan701 Taiwan National Cheng Kung University Department of Electrical Engineering Tainan701 Taiwan University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

Generalized spatial modulation (GSM) is a novel multiple-antenna technique offering flexibility among spectral efficiency, energy efficiency, and the cost of RF chains. In this paper, a novel class of sequence sets, called enhanced cross Z-complementary set (E-CZCS), is proposed for efficient and feasible training sequence design in broadband GSM systems. Specifically, an E-CZCS consists of multiple CZCSs possessing front-end and tail-end zero-correlation zones (ZCZs), whereby any two distinct CZCSs have a tail-end ZCZ when a novel type of cross-channel aperiodic correlation sums is considered. The theoretical upper bound on the ZCZ width is first derived, upon which E-CZCSs with maximum ZCZ width and flexible parameters are constructed. For optimal channel estimation over frequency-selective channels, we introduce and evaluate a novel GSM training framework employing the proposed E-CZCSs. Numerical results demonstrate that the proposed E-CZCS-based training can achieve the minimum channel estimation mean square error (MSE) and outperform other classes of sequences. © 2020 IEEE.

关键词： Mean square error

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Biotic Stress and Severity Classification of Pear Leaves Disease with Ensemble Learning

Biotic Stress and Severity Classification of Pear Leaves Dis...

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2024 IEEE International Conference on Communication, computing and Signal Processing, IICCCS 2024

作者： Sugunadevi, C. Maheswari, B. Uma Cmr Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru India Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350390759

Leaf disease frequently arises as pears plants mature. With a particular emphasis on categorizing the pear leaf disease, the proposed research work suggests the implementation of the CNN architecture that we use in this study to create this disease identification and classification method comprised of ResNet50, InceptionV3, VGG16, and VGG19. These neural networks have been pre-trained on the ImageNet dataset. The ensemble methodology that we describe produces final predictions for the test samples based on the recall, precision, accuracy, and f1-score scores of four standard evaluation metrics. Even though there is an increase in the amount of accuracy when the models are ensembled, the remarkable combination is InceptionV3 with VGG16 with an accuracy of 77.25%, precision of 79.34%, recall of 77.25% and F1 score of 76.50% and with the severity classification as accuracy 45.29%, precision 46.88%, recall 45.29% and F1 score 45.50% respectively. © 2024 IEEE.

关键词： Plant diseases

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Analyzing the Role of Time Series Analysis in Early Detection of Pancreatic Cancer 5th

Analyzing the Role of Time Series Analysis in Early Detectio...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Kavitha, R. Singh, Yudhveer Madhusudhan, M.V. Mishra, Awakash Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Pancreatic cancer is a vital disorder that is still the number one motive of lack of lifestyle, with a five-12 months survival fee of only 9. Early detection of pancreatic cancers is fundamental to beautifying outcomes. However, it could be tough to stumble on based totally on scientific and imaging outcomes myself. Time collection evaluation offers a progressive technique to sources in the early detection of pancreatic cancer. This paper proposes to research time series analysis for analysis and early detection of pancreatic cancers. Time series evaluation is a powerful series of techniques for analyzing statistics and temporal records, biological alerts, or healthcare facts. It uses a sequence of observations taken through the years to assess patterns and traits in facts, which could tell osteoporotic fracture hazards or provide early sickness signs and symptoms. Previous research has assessed time series evaluation's efficacy in detecting cardiovascular ailments and influenza. However, there may be a shortage of research examining the application of time series evaluation for pancreatic cancer detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Large Language Models for Data Annotation and Synthesis: A Survey

Large Language Models for Data Annotation and Synthesis: A S...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Tan, Zhen Li, Dawei Wang, Song Beigi, Alimohammad Jiang, Bohan Bhattacharjee, Amrita Karami, Mansooreh Li, Jundong Cheng, Lu Liu, Huan School of Computing and Augmented Intelligence Arizona State University United States Department of Electrical and Computer Engineering the University of Virginia United States Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891761643

Data annotation and synthesis generally refers to the labeling or generating of raw data with relevant information, which could be used for improving the efficacy of machine learning models. The process, however, is labor-intensive and costly. The emergence of advanced Large Language Models (LLMs), exemplified by GPT-4, presents an unprecedented opportunity to automate the complicated process of data annotation and synthesis. While existing surveys have extensively covered LLM architecture, training, and general applications, we uniquely focus on their specific utility for data annotation. This survey contributes to three core aspects: LLM-Based Annotation Generation, LLM-Generated Annotations Assessment, and LLM-Generated Annotations Utilization. Furthermore, this survey includes an in-depth taxonomy of data types that LLMs can annotate, a comprehensive review of learning strategies for models utilizing LLM-generated annotations, and a detailed discussion of the primary challenges and limitations associated with using LLMs for data annotation and synthesis. Serving as a key guide, this survey aims to assist researchers and practitioners in exploring the potential of the latest LLMs for data annotation, thereby fostering future advancements in this critical field. © 2024 Association for Computational Linguistics.

关键词： Data assimilation

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Detecting Electromagnetic Vulnerabilities in Networking Topologies 5th

Detecting Electromagnetic Vulnerabilities in Networking Topo...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Preethi, D. Verma, Upendra K. Arumugam, Jayachandran Alam, Intekhab Karnataka Bengaluru India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper describes a technique of detecting electromagnetic vulnerabilities in networking topologies, consisting of Wi-Fi communications. A green approach is offered which entails the mixture of electromagnetic measurements from multiple antennae, and the utility of parameterization, optimization and clustering algorithms. Effects are generated to offer a complete breakdown of the electromagnetic residences of a community topology, permitting identity of weaknesses or regions of vulnerability. The resulting vulnerabilities may be used to guide layout changes, or as a first indicator of potential security problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Topology

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Recent Advances in In-Memory computing:Exploring Memristor and Memtransistor Arrays with 2D Materials

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Nano-Micro Letters 2024年第7期16卷 1-30页

作者： Hangbo Zhou Sifan Li Kah-Wee Ang Yong-Wei Zhang Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis Way#16-16 ConnexisSingapore 138632Republic of Singapore Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Republic of Singapore Institute of Materials Research and Engineering Agency for ScienceTechnology and Research(A*STAR)2 Fusionopolis WaySingapore 138634Republic of Singapore

The conventional computing architecture faces substantial chal-lenges,including high latency and energy consumption between memory and processing *** response,in-memory computing has emerged as a promising alternative architecture,enabling computing operations within memory arrays to overcome these *** devices have gained significant attention as key components for in-memory computing due to their high-density arrays,rapid response times,and ability to emulate biological *** these devices,two-dimensional(2D)material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing,thanks to their exceptional performance driven by the unique properties of 2D materials,such as layered structures,mechanical flexibility,and the capability to form *** review delves into the state-of-the-art research on 2D material-based memristive arrays,encompassing critical aspects such as material selection,device perfor-mance metrics,array structures,and potential ***,it provides a comprehensive overview of the current challenges and limitations associated with these arrays,along with potential *** primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing,leveraging the potential of 2D material-based memristive devices.

关键词： 2D materials Memristors Memtransistors Crossbar array In-memory computing

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Clustering the Various Categorical Data: An Exploration of Algorithms and Performance Analysis 4

Clustering the Various Categorical Data: An Exploration of A...

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4th International Conference for Emerging Technology, INCET 2023

作者： Kumar, Raman Pati, Peeta Basa Deepa, K. Yanan, Suresh Amrita School of Computing Department of Computer Science and Engineering India Amrita School of Engineering Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham Bengaluru India University of Technology and Applied Sciences-Al Musannah Department of Engineering Oman

ISBN: (纸本)9798350335750

Clustering is a method of grouping data based on similarities, and is an unsupervised technique for discovering patterns in data. In this research paper, various clustering algorithms such as k-Means, DBSCAN, Spectral Clustering, Gaussian Mixture, and Agglomerative Clustering are compared and evaluated on Amazon Prime Video Movies and TV Shows, Netflix Movies and TV Shows, and Disney+ Movies and Tv Shows datasets. The results of the study indicate that the k-Means algorithm performed well in clustering the data for all datasets, with an overall high level of performance. Additionally, the study provides valuable insights into the genre distribution of the data, and highlights the advantages and limitations of each clustering algorithm. © 2023 IEEE.

关键词： K-means clustering

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Colour Detection Using HSV and RGB Colour Models 14

Colour Detection Using HSV and RGB Colour Models

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Harikumar, Akash Vinod, Amal Aravind, S. Abhishek, S. Anjali, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350335095

This paper presents a colour detection application that utilises a deep learning model to predict the name of a colour based on its HSV value. The use of the HSV values rather than the RGB values makes for a more intuitive representation of colours. The application supports two modes of operation, real-time mode and static mode, which cater to the needs of visually impaired individuals and content designers in the varying scenarios that they may find themselves in. This paper presents a design that leverages the advantages of deep learning and the stability of the HSV colour scheme to provide a model with good accuracy in detecting colours and labelling them accordingly. The application's integration with the Flutter Framework and Firebase database provides a scalable and robust platform for both managing the gathered data and collecting more relevant data for the improving the model further. © 2023 IEEE.

关键词： computer vision

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