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Islanding Detection in PV-Grid System by Random Forest and Spectrogram Based LBP

Islanding Detection in PV-Grid System by Random Forest and S...

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Electrical Power Engineering, Communication and computing Technology(ODICON), Odisha international conference on

作者： Ritu Singh S. P. Shukla Archana Panda Subhendu Sekhar Sahoo Subhendu Pati Basant K Panigrahi Department of Electrical Engineering Bhilai Institute of Technology Durg India Department of Electronics and Communication Engineering SOA Deemed to be University Bhubaneswar India Department of Electrical Engineering Gandhi Institute for Technology Bhubaneswar India Department of Electrical Engineering ITER SOA University Bhubaneswar India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

Islanding is a significant obstacle along with excessive penetration of DG sources. The islanding could cause harm to the customers and their equipment's. The IEEE 1547 DG interconnection regulations stipulate that the DG must be disconnected and the islanding must be observed within two seconds. Basically, this paper presents a novel method for detecting islanding based upon image pattern recognition using random forest (RF) classification technique is proposed. Images are utilized to derive the local binary pattern (LBP) characteristics so as to identify non-islanding as well as islanding events. Hence, the time series signal is obtained and transformed into spectrogram images at the point of common connection. These images are utilized to extract the features using histogram of oriented gradient, which is then utilized as a feature vector input for the RF classifier for training as well as testing. It uses rate of change of negative sequence voltage parameter to derive spectrogram image. The RF classifier's effectiveness is assessed with 5-fold cross-validations. The results from classification demonstrates that detection of islanding using RF classifier and LBP feature being used for image pattern recognition achieved excellent results with 98.25% accuracy and detection time of 182 milliseconds.

关键词： Training Accuracy Time series analysis Feature extraction Vectors Regulation pattern recognition Spectrogram Classification tree analysis Testing

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Emotion Detection Using pattern recognition from Speech 4th

Emotion Detection Using Pattern Recognition from Speech

引用

4th international conference on Frontiers in computing and Systems, COMSYS 2023

作者： Somolu, Harshita Dutta, Suchibrota Ghosal, Arijit Support Analyst Cognizant Technology Solutions West Bengal Kolkata India Department of Information Technology and Mathematics Royal Thimphu College Thimphu Bhutan Department of Information Technology St. Thomas’ College of Engineering and Technology West Bengal Kolkata700023 India

ISBN: (纸本)9789819726103

Detection of emotion from speech has immense importance in our present life. Emotion detection plays an important role in music therapy of human being. But detection of emotion is a problematic task. The objective of this work is to detect the underlying emotion in the audio speech along with a high accuracy. Emotion is a very important factor in communication. It has an impact in making decisions. In the virtual world, where people depend on artificial approaches, analyzing a person’s emotion plays an important role. Thus, objective is to detect the underlying emotion in a the audio speech and assign it to either joy, anger, boredom, sadness, fear, frustration, annoyance, satisfaction or neutral or any other emotion. This work considers neutral, happy, sad, and angry emotions only from speech signal. Emotions within speech mostly vary in the frequency domain. MFCC (mel-frequency cepstral coefficients) is a very good aural facet for capturing different emotions within speech. MFCC is supported by some other popular frequency domain aural facets as well as some time domain aural facets. ZCR (zero crossing rate) and STE (short time energy) have been used as time domain aural facets, and spectral roll-off, skewness, and spectral flux have been used as frequency domain aural facets. For precise observation, co-occurrence-based facets have also been considered for spectral flux as well as MFCC. For classification task, multi-layer perceptron (neural network), Random Forest, Naïve Bayes, and Logistic Regression have been used. Experimental result exhibits superiority of the work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Emotion recognition

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Efficient Regular Expression Matching and Hardware-Accelerated Finite Automata pattern recognition in NIDS

Efficient Regular Expression Matching and Hardware-Accelerat...

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international conference on Recent Trends in Advance computing (ICRTAC)

作者： Krishna Kishore Thota R. Jeberson Retna Raj Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamilnadu India

To protect network infrastructure from the ever-changing cyber threats, Intrusion Detection Systems (IDS) perform important duties. This study introduces a sophisticated algorithm aimed at overcoming key challenges in IDS. These challenges include efficient regular expression matching, hardware implementation, and improved pattern recognition. The algorithm employs a unique technique to reduce memory specifications by using controlled faults into the pattern matching process. It builds upon perfect hashing, ensuring precise and efficient regular expression matching, making it ideal for high-speed processing in IDS applications. Additionally, the central component of a hardware-based Network Intrusion Detection System (NIDS), the technique comprises a signature match processor that is based on a field-programmable gate array (FPGA). For effective string matching, this processor makes use of a CAM-based cellular architecture. For multiple signature matches, it generates priority-encoded addresses using a binary tree structure. To enhance IDS capabilities further, the algorithm introduces an efficient method for detecting regular expression matches within network traffic text using Field-Programmable Gate Arrays (FPGAs). It presents a hardware implementation approach based on Deterministic Finite Automata (DFA), enhancing both speed and accuracy in pattern matching. This proposed algorithm provides a comprehensive solution to optimize intrusion detection by integrating innovative techniques in regular expression matching, hardware acceleration, and pattern recognition, thereby enhancing network security against springing cyber threats.

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High-performance computing system and smart speech pattern findings for intangible cultural heritage art analysis considering visual information 3

High-performance computing system and smart speech pattern f...

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3rd international conference on Inventive Research in computing Applications, ICIRCA 2021

作者： Zhou, Mingyong Fang, Nannan School of Humanities Xi'an Shiyou Univercity Shaanxi Xian710065 China

ISBN: (纸本)9780738146270

This paper studies the visual application of highperformance computing systems and artificial language recognition in intangible cultural heritage art design. First, this article introduces the advantages, disadvantages, problems and performance evaluation of high-performance computing systems in the new era. Then, a high-performance computer-based artificial language recognition algorithm is used to classify and recognize intangible cultural heritage art. Finally, use the network database and artificial language recognition algorithm to visualize the intangible cultural heritage art design. © 2021 IEEE.

关键词： Visual languages

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Modified Artificial Hummingbird Algorithm with Deep Learning for Pest Detection through Leveraging pattern recognition-based Fusion Approach

Modified Artificial Hummingbird Algorithm with Deep Learning...

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international conference on Circuit, Power and computing Technologies (ICCPCT)

作者： B. Rubadevi Helina Rajini Suresh Ibrahim Abdullah Prashant Kumar Choudhary Minu Choudhary Piyush Mathurkar Department of Computer Science and Engineering Arunai Engineering College Tiruvannamalai Tamilnadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Computer Science Department Al-Turath University Baghdad Iraq Department of Electronics and Communication Engineering Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Phagwara India Department of Electronics and Telecommunication Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

The limitations of manual inspection and simple algorithms highlight the need for advanced insect detection systems in agriculture. The study presents the Modified Artificial Hummingbird Algorithm with Deep Learning (MAHADL), a significant breakthrough in pest detection. MAHADL combines the speed and efficacy of the Artificial Hummingbird Algorithm (AHA) with deep learning models such as Recurrent and Convolutional Neural Networks. Combining pattern recognition with the fusion approach improves pest detection accuracy while reducing false positives and negatives. Accurate pest identification is essential for effective agricultural management, which MAHADL provides through data collection, preprocessing, and algorithmic integration. MAHADL outperforms existing systems in terms of accuracy (0.94), recall (0.95), precision (0.92), computational economy (12 hours of training time, 0.5 seconds/image inference time), and decrease in false positives and negatives. The innovative technique provides enhanced agricultural output and sustainability, representing a significant leap in intelligent pest management.

关键词： Deep learning Training Accuracy Insects Manuals Inference algorithms pattern recognition

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Recurrent and Mixture Density Architecture-based Driver pattern recognition

Recurrent and Mixture Density Architecture-based Driver Patt...

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IEEE international conference on Wireless and Mobile computing, Networking And Communications (WiMob)

作者： Djamila Zamouche Kahina Ouazine Mohamed Benaissa Djalal Bouarouri Sofiane Aissani LIMED Laboratory University of Bejaia Bejaia Algeria LIMED Laboratory Estin of Bejaia Bejaia Algeria LIST3N Laboratory University of Technology of Troyes Troyes France

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

Within the framework of Intelligent Transportation Systems (ITS), recognizing driving patterns is becoming of crucial importance, given the alarming rise in accident rates resulting from imprudent conduct behind the wheel. As vehicles become more advanced, the ability to identify and deal with risky behavior have become a key factor in improving road safety. In this work, we propose an approach that distinguishes between safe and unsafe driving, enabling self-control and adaptation of the driver's actions if necessary. The main idea of our solution is the use of LSTM-based Recurrent Neural Networks (RNN) combined with the Gaussian Mixture Method (GMM), which enables classification of driving styles into three distinct categories. The performance of the proposed approach has been evaluated using important evaluation metrics. The results show that the proposal offers encouraging performance, achieving an accuracy of over 93% for different driving styles.

关键词： Wireless communication Wireless sensor networks Recurrent neural networks Accuracy Computational modeling Wheels Probabilistic logic pattern recognition Proposals Vehicles

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Role of Machine Learning in Acute Stroke Imaging: A Technical Review

Role of Machine Learning in Acute Stroke Imaging: A Technica...

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Advanced computing and Emerging Technologies (ACET), international conference on

作者： Gurmeet Singh Mahender Singh Akansha Gupta Deepjyot Singh Department of Electronics and Communication Engineering Guru Tegh Bahadur Institute of Technology (GGSIPU) New Delhi India Department of Computer Science and Engineering Guru Tegh Bahadur Institute of Technology (GGSIPU) New Delhi India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

The utilization of machine learning (ML) and pattern recognition algorithms, which is used for analysis of complex datasets to detect similarities, differences, or trends, which can be particularly useful in medical diagnostics, has become essential in enhancing the prognostics of various neurological diseases such as Acute Ischemic Stroke (AIS). These advanced techniques have particularly transformed the evaluation and exploitation of neuroimaging data in the assessment and management of Acute Ischemic Stroke (AIS). In addition, these techniques also enable rapid and accurate analysis of complex imaging data, aiding clinicians in timely diagnosis, personalized treatment planning, and outcome prediction for stroke patients. This paper focuses on a comprehensive examination of machine learning algorithm strategies to enhance the accuracy and effectiveness of predicting strokes. The author has explored and reviewed different ML algorithms and has also delved into stroke prevention to advance our understanding.

关键词： Neuroimaging Machine learning algorithms Accuracy Prevention and mitigation Imaging Machine learning Stroke (medical condition) Prediction algorithms Planning pattern recognition

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Element-Wise Layer Normalization for Continuous Signal Representation 23

Element-Wise Layer Normalization for Continuous Signal Repre...

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15th international conference on Digital Image Processing, ICDIP 2023

作者： Chen, Weifeng Ding, Hui He, Zhifen Li, Bo Liu, Bin Wang, Kang School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Jiangxi Science and Technology Infrastructure Center Jiangxi Computing Center Jiangxi Computing Center Nanchang China

ISBN: (纸本)9798400708237

Implicit neural representation (INR), sometimes also referred to coordinate-based representation or fitting, has gained the state-of-the-art performance in numerous research fields including computer vision and computer graphics due to the powerful continuous representation ability. Recent researches indicate that Fourier embedding is critical for INR to fit realistic images with high-frequency details. However, is Fourier embedding all we need for high-frequency coordinate (image) fitting? In this paper, we revisit the problem of coordinate fitting from a novel perspective of distribution mapping. Fourier embedding, as a preprocessing step of coordinate fitting, essentially performs the operation of mapping the uniform coordinate distribution to a normal distribution, and makes the learning of mapping function between two similar smooth distribution become easier. However, the number of discrete Fourier basis function affects the fitting performance dramatically and cannot be determined automatically. Based on the above analysis, a simple yet efficient INR coordinate fitting method is proposed in this paper, which demonstrates that the Fourier embedding is not the only way to improve INR. The proposed method only adds an element-wise layer normalization (ELN) module to the vanilla multi-layer perception (MLP) with ReLU activation. Experimental results on public database demonstrate that the proposed method outperforms the state-of-the-art methods using Fourier embedding. © 2023 ACM.

关键词： Mapping

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A Novel ML classifier Using Modern Hopfield Neural Networks

A Novel ML classifier Using Modern Hopfield Neural Networks

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computing and Machine Intelligence (ICMI), international conference on

作者： Mohamed Abdelkarim Hanan Ahmed Kamal Doaa Shawky Siemens Digital Industries Software Faculty of Engineering Cairo University Zewail City of Science and Technology

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

State-of-the-art classification neural networks, used for images and various data types, are complex and require significant energy and computational resources relying on super-vised gradient back-propagation. In contrast, the Hopfield Neural Network (HNN) is simpler, being a single-layer, fully connected network that mimics the human brain's associative memory network, making it easy to implement and computationally efficient. Its compatibility with oscillatory neural networks (ONNs) makes it ideal for lightweight machine learning applications in the Internet of Things (IoT) era. Normally, HNN has been primarily used for associative memory aiding in image processing, pattern recognition, and more, but this paper introduces it as a classifier. The proposed HNN classifier is adaptable to various datasets, including images and tabular data, and requires zero training time, making it suitable for resource-limited environments. It represents a significant leap in classification, with the highest accuracy reported for HNN classifiers to the best of our knowl-edge, achieving 96% accuracy on the MNIST dataset, a 36% percentage improvement over previous models.

关键词： Training Associative memory Accuracy MIMICs Hopfield neural networks Machine learning pattern recognition

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SAI-ChileanDiet: A multi-label food dataset with self-acquired images of the Chilean diet

SAI-ChileanDiet: A multi-label food dataset with self-acquir...

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pattern recognition Systems (ICPRS), international conference on

作者： Roberto Morales Bastián Muñoz Angela Martínez-Arroyo Eduardo Aguilar Dept. of Computing and Systems Engineering Universidad Católica del Norte Antofagasta Chile Escuela Nutrición y Dietética Universidad de Valparaíso Valparaíso Chile

ISBN: (数字)9798350375657

ISBN: (纸本)9798350375664

The lack of data representative of the Chilean diet precludes providing accurate applications based on real foods consumed by Chilean society. Moreover, for the multi-label food recognition problem, there are few datasets available, which is mandatory for food analysis considering the great variability of dishes that can be made by combining different foods, which do not have a particular name and therefore cannot be treated as a single-label recognition problem. In this paper, we introduce a new challenging multi-label food dataset, SAI-ChileanDiet, focused on the Chilean diet. The dataset includes foods of popular dishes from Chilean cuisine and others included in the Chilean diets. SAI-ChileanDiet considers different sources of data for training and test sets. The training set comprises images from the web and the test set comprises self-acquired images. This feature of the dataset allows us to assess the representativeness of public images in real-world Chilean foods and encourages the generation of advanced methods capable of dealing with the distribution shift of these sets. We evaluate on the proposed SAI-ChileanDiet dataset the performance of several state-of-the-art deep networks and AI platforms for multi-label food recognition. The experiments demonstrate how challenging the dataset is and the importance of validating self-acquired data to be aware of the performance it could offer in real-life conditions.

关键词： Training Image recognition Accuracy pattern recognition Artificial intelligence

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