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Isolated Word recognition Based on Power Normalized Cepstrum and machine learning Clusters 1

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23rd international conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Revathi, A. Lohith, Maddirala Venkata Sai Reddy, Karnati Dharani Kumar Kalyan, Mallisetty Pavan School of EEE SASTRA Deemed University Thanjavur India

ISBN: (数字)9783031647796

ISBN: (纸本)9783031647789

Speech recognition is the ability of a machine to convert the words spoken by humans to text. Speech recognition is necessary for communication between the human and machine. This project is about recognizing isolated words such as digits from one to nine, enter, erase, no, go, start, stop, yes, etc. Speech recognition is done in two phases: Training and Testing. This work is done in several steps, such as collection of data, feature extraction, clustering of feature vectors and storing the models. Decision-making is done by pattern matching. Collected data is divided into train data and test data for each word. During the training phase, the feature extraction is performed using the Power Normalized Cepstral Coefficients (PNCC) algorithm and grouping all these features by a clustering algorithm. For the testing phase, the PNCC features are extracted. The distance between the training data clusters and testing data features is determined for each word. The minimum length is determined for each test vector, and an average of all the minimum distances is calculated. recognition accuracy is a performance metric, and the system’s overall accuracy is 90%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Speech recognition

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Robust Obstacle Avoidance for UAVs in Narrow Corridor Environment Based on Deep Reinforcement learning 3rd

Robust Obstacle Avoidance for UAVs in Narrow Corridor Enviro...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Feng, Ruiqi Lu, Minfang Liu, Zhihong National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819624676

As the use of UAVs becomes more prevalent, there is a growing need for increased autonomy. In this paper, we propose a robust collision avoidance method based on deep reinforcement learning in an unknown narrow corridor environment. In particular, we consider corridor environments with widths constrained to 1.6 times the UAV’s wingspan, making it difficult for the UAV to avoid obstacles. The proposed method is based on end-to-end control pattern, which takes airborne LiDAR measurements combined with the UAV state and destination information as inputs, and generates UAV control commands directly. Besides, we present the design of the deep reinforcement learning network and policy update strategy based on the PPO algorithm. In addition, we conduct extensive experimental validation both in 2D Stage scene and 3D Gazebo scene with external disturbance. The experimental results demonstrate that our method achieves robust and successful obstacle avoidance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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A method for improving YOLOv5s-based plant leaf cells recognition rate using transfer learning 3

A method for improving YOLOv5s-based plant leaf cells recogn...

引用

3rd international conference on Computer Vision and data mining, ICCVDM 2022

作者： Wei, Gaixing Yi, Wenlong Zhao, Yingding Liu, Yucheng Xu, Yilu Yin, Hua School of Software Jiangxi Agricultural University Nanchang China

ISBN: (纸本)9781510661363

In view of the small sample set of plant cell images, unclear cell boundaries and artifacts, the deep learning model has a low recognition rate. In this study, a yolov5s parameter transfer method is used to improve the recognition accuracy of plant cells. First, perform data enhancement operations such as denoising, rotation, translation and scaling on the plant cell training samples;then, pre-train the yolov5s network on the BCCD dataset, transfer the model parameters to the network, and then carry out model parameter update training on the plant cell data set. The experimental results showed that the transfer learning method was faster than the original yolov5s network on the model training and validation set in terms of algorithm convergence, with a smaller loss function value. This method can provide a precise cell positioning solution to measure the plant cell geometric parameters. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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An Efficient Model for Identifying Human Behaviour using machine learning 3

An Efficient Model for Identifying Human Behaviour using Mac...

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3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Bansal, Neha Bansal, Atul Gupta, Manish Gla University Department of Electronics & Communication Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798331528201

The extensive domain of Human Activity recognition (HAR) is dedicated to the identification of specific human actions or movements through the use of sensor data. Furthermore, it is capable of detecting and distinguishing human activity, as well as disseminating information about the detected motion or activity. Skeleton coordinates are employed by the HAR program to identify human body activity or motion. To be more precise, this application serves as an instrument for the analysis of human activity and is capable of automatically detecting the following activities: standing, sitting, lying down, walking, stairs, and walking. This paper introduces a hybrid model that employs deep learning to detect human activity. The model includes a diverse array of layer counts and the six most common activities. Furthermore, the proposed model will recognize behaviours such as walking, standing, sitting, lying down, falling, walking, and walking on staircases. The individual's activities will be consistently monitored by the proposed model, which will also issue appropriate notifications. However, this model is capable of detecting human activity in real time using even the most basic computer, in contrast to other models that require extremely potent CPU specifications to function in real time and rely on sensors to detect activity. Therefore, this paper introduces a novel hybrid deep learning model for Human Activity recognition (HAR) that effectively detects six common activities using skeletal coordinates on fundamental computing systems without the need for sensors. It operates across a variety of camera angles without the need for code adjustments, as it seamlessly integrates with CCTV systems. The model is deployable online, guaranteeing accessibility through web browsers, and its accuracy can be improved by utilising real-world datasets such as CCTV footage. This method expands the applicability of HAR to a wider range of applications, including healthcare, smart homes,

关键词： Active learning

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Performance Review of Various Classification Methods in machine learning for IVF dataset Using Python 3rd

Performance Review of Various Classification Methods in Mach...

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3rd international conference on data Science, machine learning and Applications, ICDSMLA 2021

作者： Gowramma, G.S. Nayak, Shantharam Rakshitha, K. Don Bosco Institute of Technology Bengaluru India

ISBN: (纸本)9789811959356

data mining is the method of analyzing and summarizing data in terms of useful knowledge, based on various perspectives. Classification is one of the approaches which is commonly used in medical data analysis. The goal here is to uncover new patterns in order to generate realistic results and knowledge about users. Currently, data mining approaches are applying to healthcare datasets to test effective strategies and this paper involves the application of various processes, steps, analyses and comparisons for the classifications of IVF datasets. A thorough performance analysis and comparative review of these approaches is conducted and can be used to choose the appropriate algorithm for future analysis for the given dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Python

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Research on Improving Accuracy of machine learning Power Prediction System Based on Semi-Supervised Strategy 3

Research on Improving Accuracy of Machine Learning Power Pre...

引用

3rd international conference on Energy, Power and Electrical Engineering, EPEE 2023

作者： Wen, Desheng Lu, Denglong Fan, Lifeng Zhang, Jinghui Wang, Yahua Cgn New Energy Holdings Co. Ltd. Huabei Branch Shijiazhuang China

ISBN: (纸本)9798350318180

The accuracy of wind and solar renewable energy power forecasting not only impacts the assessment of 'dual guidelines' for each wind and solar energy site, but also affects the electricity market-based transactions and power generation revenue of these facilities. Employing deep learning models, we amalgamate meteorological grid characteristics, temporal features, weather pattern scenarios, and refined wind speed and power observation data. Utilizing machine learning techniques, we integrate diverse numerical forecasting outcomes and refine wind speed predictions. Furthermore, transitioning from wind to electricity models, we directly forecast wind power outcomes, either through wind speed adjustments or directly generating wind power predictions. In summation, based on the geographical location, meteorological attributes, node consumption conditions, frequency, and types of extreme weather events in the current subsidiary's renewable energy site distribution area, we undertake the research and development of a high-precision power prediction and electricity generation estimation system rooted in a semi-supervised strategic machine learning framework. © 2023 IEEE.

关键词： generation income machine learning new energy Power prediction

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Prediction of Smart Grid Stability Utilizing machine learning Algorithms 3

Prediction of Smart Grid Stability Utilizing Machine Learnin...

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3rd international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2023

作者： Bayomi, Ghada Hamdy, Alaa Hasheem, Mohamed Ali, Ahmed.A. Misr International University Cairo Aast Arab Academy for Science Technology & Maritime Transport Egypt Helwan University Cairo Egypt

ISBN: (纸本)9798350306231

The smart grid encountered numerous issues when attempting to continually monitor participant usages, bidirectional data flow, and utility costs as a result of the rise in the use of renewable energy sources. Specialists must detect system failure before it occurs and ascertain whether the smart grid is stable in order to achieve a robust, dependable smart grid. Conventionally in a smart grid, data on consumer demand is gathered and centrally evaluated against current supply conditions where consumers are then given the resulting recommended pricing information so they can decide how much to use. Because the entire traditional process is time-sensitive, dynamically evaluating grid stability becomes not only a concern but also an absolute necessity. Power supply and demand need to be balanced in order to maintain grid stability. Thus, machine learning might significantly aid in classifying whether or not the smart grid is stable by applying contemporary data mining approaches. In this research, different machine learning algorithms will be applied to assess and predict grid stability. The algorithms used in this paper are XG-classifier, Random Forest, Neural Network, KNN, SVM. The experiment results show that the XG-classifier achieved the highest performance yielding Classification accuracy, AUC, F1 and Recall of 97.79%, 96.7%, 95.6%, 95.4%, 94.5% respectively. © 2023 IEEE.

关键词： Smart power grids

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The Future of DDoS Mitigation: An Exploration of machine learning Algorithms 3

The Future of DDoS Mitigation: An Exploration of Machine Lea...

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Chadha, Ravleen Kaur Sharma, Gautam Jain, Pushkar Apex Institute of Technology Chandigarh University Punjab Mohali India

ISBN: (纸本)9798350342338

DDOS attacks are currently among of the most frequent online crimes. Its prevalence does not, however, make it any simpler to deal with. The resources of the impacted server, such as broadband and buffering size, are lowered down as a result of being unable to give resources to legitimate clients. In this research study, evaluation of many models would be carried out and the one which has the most accuracy would be determined. To identify assaults and typical situations, numerous datasets will be used to train and test the machine learning algorithms. This study will use weak data mining platforms, and the outcomes of those platforms will be compared and studied. © 2023 IEEE.

关键词： Cyber Attacks DDoS Attacks Decision Tree Logistic Regression machine learning MLP Classifier Random Forest

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Text Classification Experiments on Contextual Graphs Built by N-Gram Series 4th

Text Classification Experiments on Contextual Graphs Built b...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Sen, Tarik Uveys Yakit, Mehmet Can Gumus, Mehmet Semih Abar, Orhan Bakal, Gokhan Abdullah Gul Univ TR-38080 Kayseri Turkiye Osmaniye Korkut Ata Univ TR-80000 Osmaniye Turkiye

ISBN: (纸本)9783031821493;9783031821509

Traditional n-gram textual features, commonly employed in conventional machine learning models, offer lower performance rates on high-volume datasets compared to modern deep learning algorithms, which have been intensively studied for the past decade. The main reason for this performance disparity is that deep learning approaches handle textual data through the word vector space representation by catching the contextually hidden information in a better way. Nonetheless, the potential of the n-gram feature set to reflect the context is open to further investigation. In this sense, creating graphs using discriminative ngram series with high classification power has never been fully exploited by researchers. Hence, the main goal of this study is to contribute to the classification power by including the long-range neighborhood relationships for each word in the word embedding representations. To achieve this goal, we transformed the textual data by employing n-gram series into a graph structure and then trained a graph convolution network model. Consequently, we obtained contextually enriched word embeddings and observed F1-score performance improvements from 0.78 to 0.80 when we integrated those convolution-based word embeddings into an LSTM model. This research contributes to improving classification capabilities by leveraging graph structures derived from discriminative n-gram series.

关键词： text-graph transformation graph convolution network deep learning text mining

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Enhancing Forest Fire Detection and Monitoring Through Satellite Image recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 data 3rd

Enhancing Forest Fire Detection and Monitoring Through Satel...

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3rd international conference on Optimization, learning Algorithms and Applications (OL2A)

作者： Brito, Thadeu Pereira, Ana I. Costa, Paulo Lima, Jose Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal Inst Politecn Braganca Lab Sustentabilidade & Tecnol Reg Montanha SusTEC Campus Santa Apolonia P-5300253 Braganca Portugal INESC TEC INESC Technol & Sci Porto Portugal Univ Porto Fac Engn Porto Portugal

ISBN: (纸本)9783031530357;9783031530364

Worldwide, forests have been harassed by fire in recent years. Either by human intervention or other reasons, the history of the burned area is increasing considerably, harming fauna and flora. It is essential to detect an early ignition for fire-fighting authorities can act quickly, decreasing the impact of forest damage impacts. The proposed system aims to improve nature monitoring and improve the existing surveillance systems through satellite image recognition. The soil recognition via satellite images can determine the sensor modules' best position and provide crucial input information for artificial intelligence-based systems. For this, satellite images from the Sentinel-2 program are used to generate forest density maps as updated as possible. Four classification algorithms make the Tree Cover Density (TCD) map, consisting of the Gaussian Mixture Model (GMM), Random Forest (RF), Support Vector machine (SVM), and K-Nearest Neighbors (K-NN), which identify zones by training known regions. The results demonstrate a comparison between the algorithms through their performance in recognizing the forest, grass, pavement, and water areas by Sentinel-2 images.

关键词： machine learning Classification algorithm Satellite Imagery Wildfires Tree Cover Density

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