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Handwritten Character Recognition of Telugu Characters

Handwritten Character Recognition of Telugu Characters

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international conference for Convergence of Technology (I2CT)

作者： Yash Prashant Wasalwar Kishan Singh Bagga PVRR Bhogendra Rao Snehlata Dongre Dept. of Computer Science and Engineerng G H Raisoni College of Engineering Nagpur India Scientist-G Defence Research and Development Laboratory DRDO Kanchanbagh Hyderabad India

Given the cursive structure of the writing and the similarity in shape of the letters, Telugu handwritten character identification is an interesting topic. the lack of Telugu-related handwritten datasets has slowed the development of handwritten word recognizers and forced researchers to compare various approaches. Modern deep neural networks find it difficult because they often need hundreds or thousands of photos per class. It has been demonstrated that learning important aspects of machine learning systems can be computationally expensive and challenging when there is a limited amount of data available. this research analysis work proposes a use case on the pre-existing model called EfficientNet and on top of that a custom pooling layer is added to check the trend as the dataset size increases of Telugu characters. the dataset has been divided into three categories, namely, Vowels only dataset, Consonant only dataset, and All character dataset. Proposed model was trained with a considerable amount of dataset containing half a thousand of handwritten Telugu characters and has produced some fascinating results which were worth observing. the accuracies had followed a certain trend. the model was tested on the dataset collected, which were filtered out to record any performance improvement and improvement was observed, where average accuracy went from 55% to 92%.

关键词： Deep learning Handwriting recognition Analytical models Shape Computational modeling Neural networks Writing

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AI Approach for Minimizing the Energy Consumption of Servers Using Deep-Q-Learning

AI Approach for Minimizing The Energy Consumption of Servers...

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international conference for Convergence of Technology (I2CT)

作者： Anant Kaulage Shraddha Shaha Tanaya Naik Khushi Nikumbh Vedant Jagtap Dept. of Computer Engineering Vishwakarma Institute of Technology Pune India

this paper focuses on minimizing energy consumption by servers in data centers. Server’s energy consumption can be impacted by numerous factors, such as the number of connected devices, the workload being processed, and the energy efficiency of the *** energy consumption can be serious because of several reasons as it can impact the reliability of servers because high temperatures generated by energy consumption can lead to hardware failure and other technical issues. therefore, reducing energy consumption in servers is important for improving the cost-effectiveness, sustainability, scalability, and reliability of data center operations. A type of reinforcement learning called Deep Q-Learning (DQL) can be used to address issues with server energy consumption. the basic idea behind DQL is to train an artificial agent, such as a neural network, to make decisions about energy consumption in real time. the agent is trained by frequently performing actions in a setting and earning rewards depending on the amount of energy consumed by specific actions. Over time, the agent learns which actions are most likely to lead to energy savings, and it can then be deployed to make real-time decisions about energy consumption in a server. Experimental results of the proposed research show an average of 66% power saving in the server’s consumption of energy using Deep Q-Learning (DQL).

关键词： Energy consumption Data centers Q-learning Temperature Scalability Real-time systems Servers

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Automation of Health Monitoring System for Elderly and COVID-19 Patients using IOT

Automation of Health Monitoring System for Elderly and COVID...

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international conference for Convergence of Technology (I2CT)

作者： Kanisetty Swathi S. Bhargavi Latha Muniraju Naidu Vadlamudi Mohammed Ayad Alkhafaji Karrar Shareef Mohsen K Saikumar Department of ECE Vignan's Institute of Information Technology (A) Visakhapatnam A.P India Department of CSE Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering VNR Vignana Jyothi Institute Of Engineering And Technology Hyderabad Department of Medical Device Industry Engineering College of Engineering Technology National University of Science and Technology Dhi Qar Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Department of ECE Koneru Lakshmaiah Education Foundation Hyderabad Telangana

the healthcare industry has been getting advanced scientifically and technologically through wireless-sensing devices. Automation of the healthcare system with the Internet of things has been implemented & attains accurate results. Due to the panic situation of COVID-19, this automation of health monitoring systems plays an important role in the medical sector. In this research work, an advanced IoT-based, fully automated health monitoring system has been implemented for elderly & COVID-19 patients. the proposed design is verified on the python 3.6.0 software tool. the Lack of early treatment may cause patients to get unexpected risk failure, including death. this research work monitors elderly & older COVID-19 patients monitorization remotely using IoT technologies, even in crucial conditions. Wearable IoT devices have saved healthcare workers time and effort, according to medical regulations. the following mechanism helps with early diagnosis, quarantine, and patient monitorization has been performed with 68% saving time. this investigation aims to keep physicians and researchers can be updated about elderly patients covid 19 health issues while protecting their safety and health. Temperature and pulse sensors are used to check primary patient information. If heart rate or body temperature exceeds a safe level, IOT warning devices can give an indication. the designed application has achieved a sensitivity of 98.53%, an accuracy of 98.63%, a throughput of 97.98%, and a monitoring rate of 98.21%. the employed application is very helpful to researchers and doctors for easy treatment & covid 19 prevention. the proposed IoT-based automated health monitoring application strives with present technology and better method.

关键词： Temperature sensors COVID-19 Temperature measurement Automation Medical services throughput Internet of things

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ROMOT: An Online One-Shot Framework for Roadside multiple Object Tracking

ROMOT: An Online One-Shot Framework for Roadside multiple Ob...

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ieee international conference on Intelligent Transportation engineering (ICITE)

作者： Qi Shen Yuyang Yao Ping Wang Electronic and Information Engineering Tongji university Shanghai China

ISBN: (数字)9798350314212

ISBN: (纸本)9798350314229

Roadside-based multiple object tracking(MOT) is a crucial and fundamental research field in computer vision, providing abundant information for autonomous driving and intelligent transportation systems. However, in real-world tracking applications, frequent movements of traffic participants and variations in lighting conditions caused by different weather conditions pose significant challenges for roadside tracking. To overcome these challenges, we propose an online one-shot framework for roadside multiple object tracking, called ROMOT. we firstly propose the DLA-SA network in order to efficiently extract vehicle features. In addition, we introduce a fusion matrix called EC matrix and leverage low score detection information through a cascade matching method to address the correlation problem of long-term lost trajectories. We conduct experiments on the UA-DETRAC dataset to evaluate the performance of ROMOT, where ROMOT achieves a remarkable MOTA of 71.0% for the tracked objects, while maintaining an inference speed of 25Hz, meeting the real-time requirements.

关键词： Strips Target tracking Accuracy Transportation Lighting Feature extraction Real-time systems Trajectory Object tracking Meteorology

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Performance Analysis of Voltage Source Sine PWM Inverter for Micro Grid Topology

Performance Analysis of Voltage Source Sine PWM Inverter for...

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international conference for Convergence of Technology (I2CT)

作者： Priyanka Mane Sushant Suryawanshi Vilas Bugade Electrical Engineering Department Kolhapur Institute of Technology’s College of Engineering (Autonomous) Kolhapur India

A research work is being carried out on Micro grid system operation and control. Also, analysis, design and implementation of laboratory scale Micro Grid (MG) are much optimized work for the researchers. It is well documented in many publications, literatures and group discussions about the design analysis, battery design issues, design of Inverter, and Buck converter problem. the actual implementation of MG laboratory test bed leads to different issues mentioned earlier and has to overcome by doing actual software simulation before implementation. the scheme MG comprises of Solar PV panel, Wind Turbine, Energy Storage, Converter, Inverter, Charge Controller and several electrical loads which are fed through a Voltage Source Inverter (VSI).the Inverter which is proposed here is with PWM control with Proportional Resonant (PR) Controller. Whenever some disturbances occur in the system, Static Transfer Switch (STS) has to open & should isolate the Utility and MG within a very short duration. By this time the Distributed Energy Sources (DERs) is able to supply to the connected load maintains the good voltage regulation. Now, the DERs must deliver the power to connected loads and when fault clears and system is healthy, the MG should be able to re-synchronize with utility grid smoothly from Islanding mode of operation. the designed MG lab test bed will be undergone the different analysis about Total Harmonic Distortion (thD), transient behavior and other analysis regarding power system such as improvement of Power Factor on load side. By the use of DERs in the MG topology it is very beneficial to the environment as there is massive use of Renewable Energy Resources (RES).

关键词： Total harmonic distortion Voltage source inverters systems operation Loading Switches Wind turbines Topology

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VulSAT: Source Code Vulnerability Detection Scheme Based on SAT Structure

VulSAT: Source Code Vulnerability Detection Scheme Based on ...

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ieee international conference on Signal and Image Processing (ICSIP)

作者： Jintao Xue Zihan Yu Yubo Song Zhongyuan Qin Xin Sun Wen Wang School of Cyber Science and Engineering Southeast University Nanjing China Internet Technology Center State Grid Zhejiang Electric Power Co. Ltd. Research Institute Hangzhou China Internet Department State Grid Zhejiang Electric Power Co. Ltd Hangzhou China

the number of software vulnerabilities have increased rapidly, and their forms have shown the characteristics of complexity and diversity, which has brought severe challenges to software systems. Deep learning can automatically learn the features of code and is widely used in source code vulnerability detection. Some studies treat codes as text sequences, ignoring the structural relationship between lines of code. Other studies extract the code as a graph structure, but ignore the order relationship of code lines and do not make full use of the dependencies between code lines. this paper proposes a C/C++ source code vulnerability detection scheme based on SAT (Structure-Aware Transformer) structure. It first standardizes the source code, then extracts the PDG (program dependency graph) representation of source code, next convert each node into a vector representation using sentence embedding, which retains the dependencies between nodes. Finally input it into the SAT architecture model for training, and judge whether the input program contains vulnerabilities according to the output results. We compared VulSAT with a variety of vulnerability detection methods (i.e., Devign, SySeVR, VulCNN, Checkmarx, VulDeePecker), and selected C/C++ functions in the SARD vulnerability dataset as experimental objects. the performance of accuracy rate and false positive rate has been improved to a certain extent.

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Design and Implementation of AI Machine Vision Inspection System with Multi-Optical Images for OLED Film Manufacturing

Design and Implementation of AI Machine Vision Inspection Sy...

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Big Data, Cloud Computing, Data Science & engineering (BCD)

作者： Tae-Yong Kim Seongjin Oh So-Min Lee Sungkook Lee Sangyoon Hwang Jongpil Jeong Dept. of Smart Factory Convergence Sungkyunkwan University Suwon Republic of Korea Dept. of Culture and Technology Sungkyunkwan University Suwon Republic of Korea Dept. of Mechanical Engineering Sungkyunkwan University Suwon Republic of Korea Dept. of Chemical Engineering Sungkyunkwan University Suwon Republic of Korea

Machine vision technology is one of the main areas of rapid development in the modern world and is becoming increasingly important in various industries. this innovative technology uses the fusion of computer vision and artificial intelligence to process and analyze visual information, enabling the development of automated inspection systems and the automation of processes. In particular, machine vision technology is playing an increasingly important role in OLED film production and quality control. Currently, OLED film production is mainly based on visual inspection, but this method has various problems. It is labor-intensive and relies on humans, which limits the accuracy and consistency of the inspection. It is also difficult to identify defective elements with irregular shapes or sizes. To overcome these issues and revolutionize the field of OLED film production and quality control, we propose an OLED Film Inspection Architecture with Machine vision. this architecture utilizes advanced cameras and sensors to analyze the surface of OLED films in detail and automatically identify defective elements. In addition, by integrating multiple optical systems and AI deep learning algorithms, it can perform accurate inspections under various lighting conditions and detect defects in real time. By utilizing these advanced technologies, quality control and production efficiency can be greatly improved, which is expected to drive innovation in OLED film production. this research provides a high level of technical content and provides a bright outlook for the future of machine vision technology in the industrial field.

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Hyperspectral Image Classification Using Multi-feature Fusion Residual Hypergraph Convolution Network

Hyperspectral Image Classification Using Multi-feature Fusio...

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ieee international conference on Signal and Image Processing (ICSIP)

作者： Tianxing Zhu Qin Liu Lixiang Zhang School of Software Engineering Tongji University Shanghai China School of Computer Science Northwestern Polytechnical University Xi’an China

Convolutional neural network (CNN) has been broadly adopted on hyperspectral image (HSI) processing due to its impressive feature extraction capabilities. Nevertheless, it is still a challenge for CNN-based hyperspectral image classification methods to extract more effective spectral-spatial features considering all spectral bands. the recent advances of graph convolutional neural networks (GCN), a combination of CNN and graph representation learning, have shown great potential in the HSI. However, the existing GCN-based methods have lower accuracy than the mature CNN methods due problems such as overdependence on the adjacency matrix, usage of a single modal feature. In this paper, we propose the Multi-Feature Fusion Residual Hypergraph Convolution Network (MF-RHCN) for HSI classification. We present a novel Hypergraph Convolution Network that fuses multimodal features effectively, and automatically updates the hyperedge weights, which solves the difficulties encountered by the previous GCN-based hyperspectral classification methods. MF-RHCN first generates hyperedges from features of different modalities to construct a hypergraph representing multimodal features in HSI. then, the HSI and the extracted hypergraph are input into the hypergraph convolutional neural network for learning. We enhance the hypergraph convolutions with residual connection and input mapping, which helps reduce the over-smoothing issue associated with deep hypergraph models. Sufficient experiments on three datasets have proved the effectiveness of MF-RHCN.

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EFM: A Negative Network Public Opinion Early Warning Model Based on Evolutionary Feature Mining

EFM: A Negative Network Public Opinion Early Warning Model B...

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ieee international conference on Data Science in Cyberspace (DSC)

作者： Fanfeixue Meng Jingchen Wu Feifan Song Xu Wu Lan Hu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security BUPT Beijing University of Posts and Telecommunications Beijing China School of Computer Science (National Pilot Software Engineering School) BUPT School of Economics and Finance Changchun Finance College

With the popularity of the Internet and the increase in the number of netizens, it is of great significance to effectively monitor and predict the negative network public opinion on social media platforms. Most mainstream negative network public opinion early warning models mainly focus on mining textual features, while neglecting the response and dissemination among netizens during the process of negative network public opinion fermentation. this is one of the important reasons for the accuracy bottleneck of them. this article proposes a negative network public opinion early warning model based on evolutionary feature mining. Firstly, by using entity linking techniques based on knowledge graphs, domain prior knowledge is supplemented for the text need to be analyzed for negative network public opinion. then, the text sentiment features extracted based on long short-term memory networks and the evolutionary features extracted based on graph convolutional networks are fused through an enhanced-feature fusion method. Finally, the features are used for classification and early warning. Experimental results based on the Weibo public dataset show that the EFM model has higher early warning performance for negative network public opinion compared to other baseline models. the accuracy of this model is 0.959.

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Novel Cooperative Particle Swarm Optimization with Interactive Learning Strategy 1

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wang, Qiliang Liu, Yiheng Xu, Hao Zhang, Pinggai Wu, Qinlin Wei, Lisheng Anhui Key Laboratory of Electric Drive and Control School of Electrical Engineering Anhui Polytechnic University Anhui Wuhu241000 China Industrial Process Control Optimization and Automation Engineering Research Center School of Electronic Engineering Chaohu University Anhui Chaohu238024 China School of Computer Science and Artificial Intelligence Chaohu University Anhui Chaohu238024 China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

Particle Swarm Optimization (PSO) is an optimization method grounded in swarm intelligence, designed to mimic the social behaviors of bird flocks during hunting. By sharing information about their positions, the entire swarm can explore and search diverse areas within a given space. this method features rapid convergence, minimal parameters, and straightforward implementation, making it a potent and efficient metaheuristic technique. Nevertheless, conventional PSO algorithms, despite their simplicity and efficiency, often become trapped in local optima when tackling complex multimodal problems, which limits their global search capability. To mitigate these limitations, a new Cooperative Particle Swarm Optimization algorithm with Interactive Learning (CPSOIL) is introduced. this approach incorporates an interactive learning strategy, allowing particles to learn from their neighbors by perceiving the surrounding particles during updates. Moreover, a mechanism has been added to modify the particle velocity update when the algorithm encounters local optima, thereby preserving population diversity, enhancing the likelihood of escaping local optima, and improving algorithm accuracy. Comparisons with other leading algorithms on the CEC2005 benchmark functions indicate that, in most cases, the enhanced algorithm outperforms its counterparts. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Particle swarm optimization (PSO)

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