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An Enhanced Deep learning Model for Handwritten Tamil Character Identification

An Enhanced Deep Learning Model for Handwritten Tamil Charac...

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international conference on Smart Systems and Inventive Technology (ICSSIT)

作者： R Babitha Lincy J Jency Rubia C Sherin Shibi M Kavitha CCE department Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department ECE K.Ramakrishnan College of Engineering Trichy Tamil Nadu India Department of CI School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department ECE K.Ramakrishnan College of Technology Trichy Tamil Nadu India

In the data processing industry, data entry is regarded as a great bottleneck. An eminent Character recognition (OCR) system acts as a key solution for this concern. the OCR is being familiaized in universal level for different languages. Till now, only a limited explores were made for the Tamil Handwritten character recognition (HCR). Existing research works provides best performances for many languages, but for the work towards Tamil language is not satisfactory in the recognition area. To resolve this demerit this paper proposed an enhanced CNN for Tamil HCR utilizing Adam optimizer. Initially, encode the target variable using “one-hot4ncode” method. then, the encoded image is preprocessed using RGB to grey conversion, image resizing, slant removal and array conversion. Subsequently, word and characters are segmented as of the preprocessed image. Next the segmented images are provided as input to the CNN classifier. the CNN is enhanoed by utilizing Adam optimizer algorithm (AOA) which optimizes the weight value for minimizing its loss function. finally, the experiential outcomes of the proposed CNN are contrasted to the prevailing SVM and some other machine learning algorithms. the proposed recognition system shows preeminent performance when comparing with the prevailing methods.

关键词： Industries Image segmentation machine learning algorithms Image resolution Optical character recognition Support vector machine classification Writing

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Deep learning Framework for Solar Panel Fault Detection: Combining GAN and EfficientNet

Deep Learning Framework for Solar Panel Fault Detection: Com...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： V. Manimegalai B. Oviya V. Mohanapriya S. Umesh Kargvel R. Tejuaswene Mr. P. Ravi Raaghav Department of Electrical and Electronics Engineering Sri Krishna College of Technology Department of Electrical and Electronics Engineering Bannari Amman Institute of Technology

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

the increasing adoption of solar energy systems necessitates robust solutions for addressing efficiency challenges posed by environmental conditions and operational anomalies in photovoltaic (PV) panels. this study emphasizes the importance of real-time monitoring to mitigate power losses caused by surface contamination, shading discrepancies, and thermal variations. these factors contribute to hotspot formation and accelerated degradation of solar panels. By leveraging advanced machine learning techniques, this work integrates Generative Adversarial Networks (GANs) for data augmentation and pattern recognition with EfficientNet for efficient feature extraction and classification. the combination enables accurate detection of common PV faults, such as cracks, dust accumulation, shading irregularities, and thermal hotspots, from both visible light and infrared images. this approach achieves enhanced fault detection accuracy while reducing computational overhead, demonstrating its suitability for large-scale solar installations. the proposed deep learning framework provides a scalable and reliable solution to improve the performance and lifespan of solar energy systems while minimizing maintenance efforts and operational interruptions.

关键词： Deep learning Accuracy Fault detection Surface contamination Neural networks Solar energy Generative adversarial networks Real-time systems Maintenance Solar panels

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Toward data-Driven Glare Classification and Prediction for Marine Megafauna Survey 1

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26th international conference on pattern recognition, ICPR 2022

作者： Power, Joshua Jacoby, Derek Drouin, Marc-Antoine Durand, Guillaume Coady, Yvonne Meng, Julian University of New Brunswick FrederictonNBE3B 5A3 Canada University of Victoria VictoriaBCV8P 5C2 Canada National Research Council Canada OttawaONK1A 0R6 Canada

ISBN: (数字)9783031377310

ISBN: (纸本)9783031377303

Critically endangered species in Canadian North Atlantic waters are systematically surveyed to estimate species populations which influence governing policies. Due to its impact on policy, population accuracy is important. this paper lays the foundation towards a data-driven glare modelling system, which will allow surveyors to preemptively minimize glare. Surveyors use a detection function to estimate megafauna populations which are not explicitly seen. A goal of the research is to maximize useful imagery collected, to that end we will use our glare model to predict glare and optimize for glare-free data collection. To build this model, we leverage a small labelled dataset to perform semi-supervised learning. the large dataset is labelled with a Cascading Random Forest Model using a naïve pseudo-labelling approach. A reflectance model is used, which pinpoints features of interest, to populate our datasets which allows for context-aware machine learning models. the pseudo-labelled dataset is used on two models: a Multilayer Perceptron and a Recurrent Neural Network. With this paper, we lay the foundation for data-driven mission planning;a glare modelling system which allows surveyors to preemptively minimize glare and reduces survey reliance on the detection function as an estimator of whale populations during periods of poor subsurface visibility. © 2023, Springer Nature Switzerland AG.

关键词： Large dataset

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A Detailed Review of Implementation of Deep learning Approaches for Industrial Internet of things with the Different Opportunities and Challenges 5

A Detailed Review of Implementation of Deep Learning Approac...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Saxena, Akash Pant, Bhasker Alanya-Beltran, Joel Akram, Shaik Vaseem Bhaskar, Bandi Bansal, Rohit Compucom Institute of Information Technology and Management Rajasthan Jaipur India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Universidad Tecnológica Del Perú Peru Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India S R University School of CS & AI Telangana Warangal India Vaish College of Engineering Department of Management Studies Rohtak India

ISBN: (纸本)9798350398267

the Industrial Internet of things (IIoT) has become one of the most rapidly developing innovative technologies in recent years, with the potential to digitize and connect numerous industries for enormous commercial prospects and growth of the global GDP. Industry, logistics, shipping, petroleum and natural gas, mining and metals, power utilities, and aviation are just a few of the diverse industries that employ IIoT. Even while the IIoT offers exciting potential for the creation of many industrial applications, these applications must meet more stringent security standards and are vulnerable to cyberattacks. Since there are so many sensors there is a tone of data in the IIoT networkis produced, which has caught the attention of hackers all around the world. the intrusion detection system (IDS), which monitors network traffic and identifies network activity, is one of the main security solutions for defending IIoT applications from threats. Recent research has shown that deep learning and machine learning techniques can enhance intrusion detection effectiveness and lessen a range of security risks. In this work, we provide an overview of IIoT-focused IDS techniques based on deep learning. Providing a range of deep learning-based IDS detection techniques, datasets, and comparative studies is the major goal of this effort. Ultimately, the goal of the study is to highlight the shortcomings and difficulties of past *** well as possible alternatives and potential trends. © 2022 IEEE.

关键词： Internet of things

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A Systematic Literature Survey on Credit Card Fraud Detection using machine learning Techniques

A Systematic Literature Survey on Credit Card Fraud Detectio...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Mayank Pathak Rohit Beniwal Department of Computer Science and Engineering Delhi Technological University Delhi Delhi India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Credit card fraud transactions are a major problem in the banking industry. this study explores the possible ways to detect fraud transactions using Deep learning (DL) as well as machine learning (ML) models. this study tries to attempt to discuss multiple ways to detect the fraud transaction. A better understanding of all approaches to make a better decisionmaking model for detecting a fraud transaction. this paper reviews fraud transactions, challenges, and their solutions. this study highlights strategies for detecting both established and emerging fraud patterns. Academics and businesses looking to improve their fraud detection efforts might benefit greatly from its description of important data mining techniques and identification of the nations most vulnerable to financial fraud. Alongside summarizing significant contributions by various researchers in the financial fraud domain, this review expands the scope of analyzed articles beyond earlier studies. In order to improve financial security measures, the research also emphasis how important it is to continuously innovate and adapt.

关键词： Industries Deep learning Surveys Systematics Social networking (online) Finance Banking Credit cards Fraud Systematic literature review

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Improved Feature Extraction for Time Series data Using Sliding Window: A Case Study of Carnatic Tala

Improved Feature Extraction for Time Series Data Using Slidi...

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Advancements in Computing (ICAC), international conference on

作者： Rukshani Puvanendran Oshani Erunika Dayarathna thangathurai Kartheeswaran Department of ICT University of Vavuniya Sri Lanka Department of Statistics and Computer Science University of Peradeniya Sri Lanka Department of Physical Science University of Vavuniya Sri Lanka

Human action patterns are typically temporal. Since the data collected is periodic, processing time series data into a feature matrix for further learning and pattern recognition is a challenging task. Periodic data must be processed in a sequential manner. Here we have tested the case study to identify the patterns related to the Carnatic Sapta Tala (the Seven Tala System) used in Carnatic music. Accelerometer, gyroscope, gravity sensor, and rotation vector are some of the sensors embedded in smartphones and used for data collection. the minimum window size to extract distinct characteristics of the selected Sapta tala was determined to be 20 through the experiment performed. this study employed a feature extraction method with a 20-second window and 99.95% overlap to create a feature matrix for identifying Sapta tala patterns in the Carnatic music system. the findings demonstrate an improved association between these rhythmic patterns of movement and can be tracked using smartphone sensors when utilizing a method that involves 20-frame overlapping windows. Future work could incorporate more advanced machine learning algorithms and data from additional sensors to further enhance the proposed methodology to categorize tala patterns. the optimal window size of 20 frames can be further enhanced during the application of classification algorithms, subject to the relevant metric.

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D-CBAMFi: A Lightweight Multi-Scale Fusion Network Based on Depthwise Separable Convolution and Dilated Convolution

D-CBAMFi: A Lightweight Multi-Scale Fusion Network Based on ...

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Intelligent Computing and Human-Computer Interaction (ICHCI), international conference on

作者： Zhen Li Zhibiao Zhao Yulang He Yan Shi Qi Zhou Tianjin University of Technology and Education School of Automation and Electrical Engineering Tianjin China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Gesture recognition is crucial for the development of new human-computer interaction paradigms that will change the interaction between humans and their surroundings. Most deep learning-based techniques use neural networks with a single backbone architecture, which can only capture a single feature and cannot meet the requirements of real-world applications for recognition tasks in resource-limited and complex scenarios. therefore, we propose a lightweight multi-scale fusion network, D-CBAMFi, whose core component is a D-CBAM network consisting of parallel depth-wise separable convolution and dilated convolution and CBAM. the proposed system achieves an average accuracy of 84.30% on the Widar dataset, validating the effectiveness of the model.

关键词： Human computer interaction Convolution Computational modeling Neural networks Gesture recognition Feature extraction Spatiotemporal phenomena Sensors data mining Wireless fidelity

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A new network intrusion detection system based on auto-encoding and variance analysis

A new network intrusion detection system based on auto-encod...

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IEEE international conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Dequ Chen Ming Zhang Fujian Provincial Key Laboratory of Big Data Mining and Applications/School of Computer Science and Mathematics Fujian University of Technology Fuzhou China

In today's big data era, ensuring the network security of information technology (IT) communication facilities is one of the most challenging issues. In fact, with progress in technology, hackers have developed increasingly complex and risky network attacks, making malicious access identification a very tired task. Under various threats, the existing analysis methods encounter many challenges in detecting and mitigating these illegal accesses. this work presents a new network intrusion detection system (IDS) which combines deep learning method and mathematical strategy. Indeed, our IDS makes full use of data analysis, mathematical modeling, and deep learning to select and optimize features that are more relevant for classification. We use NSL-KDD dataset to test the performance of the IDS. the experimental results show that our proposed method has better performance compared with traditional deep learning, machine learning and recently proposed advanced methods.

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SOC Estimation of Lithium Batteries Based on Deep learning and Particle Filtering 5

SOC Estimation of Lithium Batteries Based on Deep Learning a...

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5th international conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Zhang, Wenjin Liu, Yao Li, Lei Li, Xiangyu Chang, Jiantao School of Communications and Information Engineering Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

State of Charge (SOC) estimation of Li-ion batteries is important to ensure safe and reliable battery operation. the SOC estimation method based on mechanism knowledge and data fusion can take advantage of the mechanism model and data-driven method to obtain more accurate prediction results. However, it is difficult to determine the accurate degradation mechanism model of Li-ion battery under complex operating conditions. In this paper, we propose a deep learning and particle filter (PF) fusion method for Li-ion battery SOC estimation, solving the dependence of PF method on mechanism model problem by establishing the agent model of Li-ion battery degradation mechanism through deep learning. the specific steps are as follows. Firstly, extract the degradation trend of SOC by Convolutional Neural Networks (CNN) to fit the degradation trajectory of Li-ion battery SOC using the selected health features, constructing the state space model of PF algorithm, and then update and correct the output of CNN model by PF method to achieve the accurate estimation of Li-ion battery SOC. the proposed method is validated by two different lithium battery charge/discharge data sets, and the experimental results show that the proposed method has the features of strong tracking ability of degradation trajectory, high prediction accuracy and stability, and the method does not depend on the mechanism model of the battery, which has strong generalization ability in the practical application scenarios. © 2022 ACM.

关键词： Lithium-ion batteries

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Prediction of House Pricing Using machine learning

Prediction of House Pricing Using Machine Learning

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Kavita Saini Ramvachan Piyush Kashyap Praveen Yadav School of Computing Science & Engineering Galgotias University Delhi NCR India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

data mining is increasingly frequently used in the housing market. In order to estimate house prices, important housing qualities, and many other things, data mining is quite helpful since it can extract pertinent knowledge from raw *** to research, the real estate market and home owners are frequently concerned about price variations in housing. a study analysis of the pertinent characteristics and the best models for predicting property values have been developed. the analysis’s findings demonstrated that, when compared to other models, ANN, can SVM, and XGBoost. After going through the data in this paper we came across that characteristics structures are the deciding factors for predicting house prices. this research will be Finding the most important factors that influence housing prices and identifying the most effective machine learning model to utilise in this project area will be of enormous help, particularly to housing developers and academics.

关键词： Support vector machines Analytical models Computational modeling Pricing machine learning Predictive models data mining

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