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A Fuzzy C-Means With Support Vector Regression for Calibration and Fault Detection in Modern Instruments 4

A Fuzzy C-Means With Support Vector Regression for Calibrati...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Habelalmateen, Mohammed I. Kasarla, Satish Reddy Sravanthi, Jajimoggala Kavitha, P. Sundaram, S. meenakshi College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Hyderabad Institute of Technology And Management Department of Electronics and Communication Engineering Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India

ISBN: (纸本)9798350352931

From the past few decades, Calibration and Fault Detection in modern instruments are crucial for ensuring accuracy, reliability, and safety by configuring instruments to provide accurate measurements and identifying anomalies or malfunctions. These processes prevent accidents, reduce downtime, and increase productivity, but also have drawbacks, including being time-consuming and labor-intensive, requiring specialized expertise and equipment, and being expensive, while also being susceptible to environmental factors and requiring frequent recalibration. To overcome these problems, a Fuzzy C-Means with Support Vector Regression (FCM-SVR) is proposed for detecting calibration and fault in modern instruments using Predictive Electrical Fault Analysis (PEFA) dataset. The collected data is undergone preprocessing using Label Encoder (LE) to normalize data without reducing the dimension. Then, Empirical Mode Decomposition and Sample Entropy are considered for feature extraction for robust fault detection and to reduce false alarm. At last, FCM-SVR is employed as classifier for enhancing computational efficiency, flexibility, and overall performance. From the results, it clearly shows that the proposed FCM-SVR obtained higher values in the context of precision, recall, F1 score and accuracy when compared to existing method K-Nearest Neighbor (KNN). © 2024 IEEE.

关键词： Calibration

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Intrusion Detection System in Mobile Cloud Computing Using Bat Optimization Algorithm-Support Vector Machine 2

Intrusion Detection System in Mobile Cloud Computing Using B...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Alabdeli, Haider Yamsani, Nagendar Anitha, D. Chaithra, K.N. Bindu, G. The Islamic University Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350317558

Mobile Cloud Computing (MCC) is provided in various industries to acquire cloud-based services by using mobile technology. MCC is employed to minimize mobile device limitations by enabling them to offload computations to remote cloud. It contains significant information about organizations;hence, to avoid this environment from attacks a security solution is required. An Intrusion Detection System (IDS) is a solution to these security problems. The Bat Optimization Algorithm-based Support Vector Machine (BOA-SVM) is developed for IDS by utilizing machine learning in MCC in this research. Firstly, data is acquired by UNSW-NB15 dataset and then min-max normalization is established for normalizing acquired data. GWO is employed for Feature Selection (FS) that determines relevant features for classification. At last, SVM is established to classify IDS effectively and accurately. BOA-SVM attains a better accuracy of 99.78% compared to existing techniques such as Edge-based Hybrid Intrusion Detection Framework (EHIDF), Information Gain-Chi-Square-Particle Swarm Optimization (IG-CS-PSO), and improved Self-adaptive Evolutionary Extreme Learning Machine (SaE-ELM) respectively. © 2024 IEEE.

关键词： Bat Optimization Algorithm Intrusion Detection System Machine Learning Mobile Cloud Computing Support Vector Machine

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A Bidirectional Encoder Representations from Transformers - Multi Head Attention for Predicting the Stock Market Volatility based on Textual Sentiment Analysis 2

A Bidirectional Encoder Representations from Transformers - ...

引用

2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Anjali, G. Almusawi, Muntather Zahira Jahan, N. Mumjitha, M. meenakshi Sundaram, S. Maisammaguda Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Erode India Department of Chemistry New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9798331504960

Recent days, stock market plays an important role in country’s economic landscape, serving as a vital conduit for companies and investors to facilitate investments. As a cornerstone of economic growth. The stock market offers unparalleled opportunities for businesses to access capital and expand their operations. The existing approaches for stock market prediction based on textual sentiment analysis has gradient vanishing problems and limited parallelization. Therefore, this paper proposes Bidirectional Encoder Representations from Transformers (BERT)- Multi Head Attention which effectively enabled parallel processing and the transformer minimizes the gradient vanishing problem. Initially, the data is taken from Xueqiu investors community and the processing of text is done in three steps they are web crawling, data cleaning and data transforming. Then, this data is used for measuring volatility which helps the investors assess market risk and potential losses. Finally, proposed BERT- Multi Head Attention is used for predicting and effectively enabled the parallel processing and minimizes the problem of vanishing gradient. The proposed BERT- Multi Head Attention achieved better results in terms of accuracy of 90.65% and F1-Score of 88.76% when compared with existing NB. © 2024 IEEE.

关键词： Marketplaces

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Supply chain tracking of drugs using Ethereum

Supply chain tracking of drugs using Ethereum

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2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Raghunandan, K.R. Dhanya Shetty, Avisha V Bhat, Akshata D Bangera, Devishree Nalini, N. NMAM Institute of Technology Deemed to be University Department of Computer Science and Engineering Udupi India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331527518

Counterfeiting, increased fake intermediate products, lack of transparency, unauthorized alteration of data, and other related problems throughout the supply chain have made it necessary for the modern pharmaceutical industry to ensure integrity and security. Thus, the above-mentioned problem can be solved by a decentralized blockchain-based system, which ensures security and traceability. In the proposed solution, there are seven stakeholders including manufacturers and consumers. Authorized manufacturers are allowed to store the drugs in the blockchain network, and corresponding QR and bar codes are generated. This generates a reliable record of drug movements by constructing an architecture that is decentralized to avoid alteration of the data by anyone. Consumers have the opportunity to scan QR codes to ensure that their products are genuine and trace their origin. By addressing the challenges of the conventional pharmaceutical supply chain that provides high transparency, minimal counterfeits, and assured data security, this system addresses the aforementioned disadvantages. © 2025 IEEE.

关键词： Network security

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Glioma Detection using Improved Artificial Neural Network in MRI Images 2

Glioma Detection using Improved Artificial Neural Network in...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Nandihal, Priya Shetty S, Vijaya Guha, Tapas Pareek, Piyush Kumar Global Academy of Technology Department of AI/ML Bengaluru India Nitte Meenakshi Institute of Technology Department of Computer Science Engineering Bangalore India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

The physical identification of tumors may be a laborious and time-consuming process for medical professionals because of the complex nature of the tumor and the noise involution that can occur in magnetic resonance (MR) imaging information. Therefore, determining the location of the tumor at an earlier stage is quite important. The medical scan may track and prognosticate the uncontrolled proliferation of cancer pretentious regions at different levels in order to deliver a felicitous diagnosis at an early time. This is accomplished via the utilisation of segmentation in conjunction with relegation procedures. In order to recognize the tissues of a brain tumor, segmentation of the picture obtained from the MRI is a crucial and challenging step. So, the proposed work includes the tumor segmentation process using Region Growth Algorithm with Gray-Level-Run-Length-Matrix and Centre-Symmetric-Local-Binary-Patterns texture feature extraction process. The segmented images undergo feature extraction process with higher level of accuracy. The performance metrics are measured using accuracy, sensitivity and specificity. The proposed work has 0.97% sensitivity, 0.85% specificity and 99.80% accuracy. © 2022 IEEE.

关键词： Tumors

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Supply chain tracking of drugs using Ethereum

Supply chain tracking of drugs using Ethereum

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Raghunandan K R Dhanya Avisha V Shetty Akshata D Bhat Devishree Bangera Nalini N Department of Computer Science and Engineering NMAM Institute of Technology (Deemed to be University) Udupi India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

关键词： Drugs Data security Supply chains QR codes Data engineering Blockchains Stakeholders Reliability Pharmaceutical industry Counterfeiting

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Customer Feedback and Sentiment Analysis for Hotel Services

Customer Feedback and Sentiment Analysis for Hotel Services

引用

Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Abhyudhay S R G M Aditya Achyuta K Upadya Archana Naik Ushashree P Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering Geethanjali College of Engineering and Technology Hydrabad India

In the era of digital transformation, the hospitality industry faces unique challenges and opportunities. With travellers increasingly relying on online reviews to make informed decisions, the role of sentiment analysis in the hotel sector has never been more critical. This comprehensive research aims to provide a profound understanding of sentiment analysis in the context of hotel reviews and delves into preprocessing and the application of state-of-the-art sentiment analysis techniques. The sentiment analysis model, based on SVM, achieved promising results in categorizing hotel reviews into positive, negative sentiments. The evaluation metrics indicate an accuracy of 82%. Visualizations such as the confusion matrix and ROC curve further demonstrate the model's effectiveness in distinguishing between positive and negative sentiments. The findings of this research shed light on the distribution of sentiments in hotel reviews, providing actionable insights for the hospitality industry.

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Malware Detection Using the Machine Learning Based Modified Partial Swarm Optimization Approach 1

Malware Detection Using the Machine Learning Based Modified ...

引用

1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Sivakumar, S. Saminathan, S. Ranjana, R. Mohan, M. Pareek, Piyush Kumar Department of Computer Science Perambalur India Srinivasan College of Arts and Science Department of Computer Science and IT Perambalur India Panimalar Engineering College ECE Department India AP/CSE KPR Institute of Engineering and Technology India Nitte Meenakshi Institute of Technology Department of AI and ML Bengaluru India

ISBN: (纸本)9798350323795

Malware analysis includes a crucial step after malware detection called malware categorization, which classifies dangerous files. There have been many reported static and dynamic methods for classifying malware up to this point. The ML-MD strategy presented in this study uses static methods to categorise various malware families and is based on machine learning. In order to detect malware, we create a new machine learning-based framework. The characteristics from the dataset are extracted in this case using principal component analysis (PCA). In order to offer the best malware detection solutions, introduce a machine learning-based Modified Particle Swarm Optimization (MPSO) algorithm. Improved Accuracy and detection rate using ML-based MPSO technique. The effectiveness of the suggested technique in detecting malware is demonstrated by the experimental results on several benchmark data sets, which greatly outperform alternative approaches. © 2023 IEEE.

关键词： Machine learning

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Image Captioner: Captioning for Visual Impact

Image Captioner: Captioning for Visual Impact

引用

Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： Shabareesh Aryan S Subrahmanya Pahel Jagtap Leander Mani Sowmya M R Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Image captioning is the process of generating a textual description that accurately represents the features of an image. In the realm of deep learning, this task is of utmost significance and has a wide range of uses. Image captioning involves converting an image, represented as a sequence of pixels, into a sequence of words that are relevant to the image. It can be seen as an end-to-end and sequence-to-sequence challenge, as both the language and visual aspects need to be processed. In this regard, recurrent neural networks are employed for the language processing, while convolutional neural networks are used to extract feature vectors from the images. The findings of this study highlight the effectiveness of this method and show how diverse applications in image processing and description generation are possible. This work promotes the incorporation of these methods into actual image captioning systems in order to provide more precise and contextually appropriate image descriptions.

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Obtain Better Accuracy Using Music Genre Classification Systemon GTZAN Dataset

Obtain Better Accuracy Using Music Genre Classification Syst...

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： SuriyaPrakash, J. Kiran, S. Jain University Department of Computer Science Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Mathematics Bengaluru India

ISBN: (纸本)9781665453424

The term 'music genre' refers to any method of categorization that divides musical compositions (or even individual musical phrases) into distinct musical forms or styles. Religious music, secular music, classical music, and pop music are only few of the many musical genres that exist. The amount of data available to us is increasing rapidly, making it infeasible for manual curation. In this work, we apply a variety of simple and basic machine learning algorithms namely Logistic Regression, K-Nearest Neighbor, Random Forest, Support Vector Machine and Artificial Neural Network, along with dimensionality reduction techniques namely, PCA, KPCA and LDA, on the GTZAN dataset. Further, we compared their accuracies and found that the combination model of KNN with PCA provides the highest accuracy of 77.41% among the compared models. © 2022 IEEE.

关键词： Principal component analysis

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