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检索条件"机构=Computer Science and Engineering Institute of Aeronautical Engineering"

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Classification and Segmentation of Leaf Images based on Deep Learning for Peanut Plant Disease Detection 4

Classification and Segmentation of Leaf Images based on Deep...

引用

4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Ebenezer, V. Daniel, Arsha Anna Getzi Jeba Lillipushpam, P. Edwin, E.Bijolin Roshni Thanka, M. Devassy, Rosebel Karunya Institute of Technology and Science Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350300093

Peanut is a key food commodity, and diseases of its leaves can have a negative impact on the quantity and quality of the product. The seed has high levels of potassium, magnesium, calcium, riboflavin, niacin, folic acid, vitamin E, resveratrol, and amino acids. It also contains 36% to 54% oil, 16% to 36% protein, and 10% to 20% carbs. Early, late, and rusty leaf spots, among other frequent illnesses, are recognized by this equipment. Techniques for image augmentation have been used, such as twisting, rotating, and scaling. The variant employs a Multi Class Convolutional neural network with 5 output which include Normal Leaf, Images Without Leaf and the images with the leaf been infected by the diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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A Secure Remote Monitoring as a Service (MaaS) for Solar Power Plant 1

A Secure Remote Monitoring as a Service (MaaS) for Solar Pow...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Sophy Priya, C. Thangakumar, J. Sambath, M. Hindustan Institute of Technology and Science Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350336009

In the field of Renewable energy, data processing is occurring in the photovoltaic plant using the IoT data logger. A data logger is a piece of technology that records and stores data over time, where SCADA systems are also indulged in data transfer and monitoring. Using these systems, we are transferring data from the data logger to cloud where a cloud-based platform is allowed users to manage and monitors the data loggers and as the result of analysis of various plants, the issues will be reported in the ticketing system and the further storage will be done in another cloud. This will result a cloud-cloud data flow where we are concentrating in ensuring the security of the data logger, Ethernet, WIFI, router connectivity, external web pages where data is viewed should be protected and the data backup, storage should be a secured area. RS485/RS422 bus provides the highest data transmission rate in data logger. Data encryption technique that involves Internet-cloud data transfers. This project mainly focus on introducing some new technologies in MaaS, where MaaS is a collection of tools and apps used to monitor a specific aspect of an application, server, system, or IT component. In the solar industry, this implies combining hardware, software, and services to monitor, evaluate, and troubleshoot solar plant faults throughout the solar array's lifetime, where we can monitor the amount of energy and control it securely. And this will help to introduce a secure wide range of global monitoring and reporting techniques in the renewable energy sector where data and data loggers are rising along with the risks. Cyber threats have a significant impact on smart grid performance due to the fast proliferation of real system in power electronic systems for connecting renewable energy sources with cyber frameworks. These electrical equipment in systems are linked by communication networks, which may be vulnerable to major cyber-attacks by malicious attackers. We have shown the d

关键词： Data transfer

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A Review of Intelligent Traffic Management Systems 7

A Review of Intelligent Traffic Management Systems

引用

7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Ramanathan, R Ashwath BalaMurugan, R. Bama Krishna, R Sathya Selvan, Mercy Paul Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397284

Traffic congestion is a significant issue that is becoming increasingly prevalent in modern society, particularly in major metropolitan areas. The negative impacts of traffic congestion include increased air and noise pollution, heightened risks of accidents, and longer travel times for residents. The traditional method of managing traffic, through the use of pre-determined traffic lights, is no longer adequate for the demands of a rapidly growing urban population. In response to this issue, various efforts have been made to develop an automated, intelligent traffic management system that utilizes sensors or video cameras to detect the density of vehicles on the road and adjust traffic signals accordingly. This study provides a comprehensive review of the different methods that have been proposed for intelligent traffic management, including a comparison of their pros and cons. © 2023 IEEE.

关键词： Object detection

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ExpACVO-Exponential Anti Corona Virus Optimization Enabled Hybrid Deep learning for Tongue Image Segmentation Towards Diabetes Mellitus Detection Via CNN-LSTM Network 1

ExpACVO-Exponential Anti Corona Virus Optimization Enabled H...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Mathew, Jimsha K Sathyalakshmi, S. Hindustan Institute of Technology & Science Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350330915

The surprising headways in biotechnology and public medical services foundations have prompted a pivotal creation of basic and touchy medical care information. By applying canny information examination methods, many fascinating examples are distinguished for the early and beginning discovery and avoidance of a few deadly infections. Diabetes Mellitus (DM) is a very dangerous illness since it adds to other deadly infections, i.e., heart, kidney, and nerve harm. The following stages have been proposed in this paper for the order, beginning phase ID, and expectation of diabetes: a) Data collection from tongue dataset of many images, b) Preprocessing using the gaussian filter for noise removal, c) Segmentation using U-Net-CRFRNN for segmenting region of interest and finally d) classification using the integration of Exponential Weighted Moving Average (EWMA) and Anti Corona Virus Optimization (ACVO) and also to increase the efficiency of the network, use of CNN-LSTM. The suggested architecture is more effective than other state-of-the-art models on many different parameters, based on the outcomes (accuracy: 0.94, sensitivity: 0.96, specificity: 0.96) © 2023 IEEE.

关键词： Anti Corona Virus Optimization Classification CNN-LSTM Deep learning Diabetes Mellitus Exponential Weighted Moving Average Tongue Segmentation

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Aggregative Dynamics and Revenue Structuring in Energy Internet User-Prosumer Ecosystems

Aggregative Dynamics and Revenue Structuring in Energy Inter...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Singh, Anil Pratap Veena, Ch. Anandhi, R.J. Singla, Atul Parmar, Ashish Alabdely, Haider Ies Insititute of Technology and Management Ies University Department of Computer Science & Engineering M.P Bhopal India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India The Islamic University Najaf Iraq

ISBN: (纸本)9798350373783

This study explores income structure and group dynamics in complicated energy internet consumer-prosumer ecosystems.a DRORA (Dynamic Revenue Optimization and Aggregation) is proposed after studying energy consumers and prosumers throughout time. PDPA, EDOA, and RFO are in the system. These boost energy market efficiency, transparency, and fairness. As a way to promote fair pay and customer cost, the PDPA lets prosumers have a say in how prices are set. The EDOA makes sure that resources are used efficiently by distributing power in the best way possible to cut down on costs and improve system performance. With the RFOA's accurate income predictions, stakeholders can make smart decisions that will help them make the most money. Simulations and real-world studies have shown that the suggested model is better than traditional methods at getting stable and fair income sharing in the Energy Internet environment, which is always changing. © 2024 IEEE.

关键词： Signal modulation

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Cybersecurity for Industrial IoT: A Review on Vulnerabilities and Attacks 8

Cybersecurity for Industrial IoT: A Review on Vulnerabilitie...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kumari, Reetu Kumar, Sunil Amandeep Dutta, Kamlesh Kumari, Aparna Guru Jambheshwar University of Science and Technology Dept. of Computer Science & Engineering Haryana Hisar India Guru Jambheshwar University of Science and Technology Dept. of AI & Data Science Haryana Hisar India National Institute of Technology Hamirpur Dept. of Computer Science & Engineering Himachal Pardesh India Institute of Technology Nirma University Dept. of Computer Science & Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798331520762

Internet has evolved from a network of connecting people to a network of connecting things, leading to a more complex and sophisticated network of Industrial Things, known as Industrial IoT (IIoT) today. This evolution has revolutionized the traditional Industrial system into real-time smarter systems that enhanced overall efficiency and productivity. Despite its benefits, the interconnected nature of IIoT, expanded the attack surface, exposing the systems to new crucial and complex cybersecurity threats. Several real time attacks on various industries as mentioned in this paper and the increasing cybersecurity cost over the years, clearly states the need of robust security solutions. To deeply understand the cybersecurity aspect, we provided a comprehensive study of attacks and vulnerabilities in this paper. In this paper, we did a indepth study of various IIoT related severe attacks and aspects related to these attacks, like vulnerabilities, impacts, potential solutions etc. We also provided future research directions to help the research community, to work upon and develop more robust and efficient cybersecurity solutions. © 2025 IEEE.

关键词： attacks cybersecurity etc IIoT Industrial IoT IoT vulnerabilities

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Skin Lesion Decode A CNN-SVM Convergence for Enhanced Diagnostic Accuracy

Skin Lesion Decode A CNN-SVM Convergence for Enhanced Diagno...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Suryavanshi, Ankita Mehta, Shiva Rana, Samir Manwal, Manika Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Sharda University Computer Science and Engineering Sharda School of Engineering and Technology U.P. Greater Noida India

ISBN: (纸本)9798350361537

This research study investigates the development and evaluation of a merged CNN and SVM model for detecting and classifying skin lesions. The research systematically divides skin lesions into five categories that allow a thorough analysis of the model's diagnostic skills. The CNN-SVM model is developed and tested using a high-quality dataset, and its accuracy, recall, and F1 scores provide insights into specific performance aspects of the model. The results show a high level of precision for all the classes that vary from 92.95% for Class 1 to 95.95% for Class 5, which suggests that the model is good at making optimistic predictions. The recall measure, a sensitivity measure of the model, varies from 91.44% for Class 3 to 98.47% for Class 1, indicating the model's capability to recognize true positives correctly. The F1-score, which balances precision and recall, is always high, fluctuating from 93.00% for Class 3 to 96.28% for Class 5, reflecting the model's well-balanced categorization. Several types of averages are computed to assess the model's performance thoroughly. The macro average indicates accuracy of 94.70%, recall of 94.81%, and F1-score of 94.73%, with equal weight given to each class. With class distribution, the weighted average is as follows: accuracy - 94.74%, recall - 94.72%, F1-score - 94.71%. The micro average indicating the sum of contributions from all classes is 94.72% for all three parameters across the board. The model is entirely accurate, with an overall accuracy of 94.72397, showing the model's strong ability to generalize across different skin lesion types. The statistical measures confirm the CNN-SVM model as a practical approach to accurate and reliable classification of skin lesions, revealing high clinical potential. The paper's impressive experimental and statistical analyses demonstrate the power of the model and its ability to improve dermatological diagnostic procedures. © 2024 IEEE.

关键词： Neural networks

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Unveiling Hidden Patterns: Exploration of Data Dependencies in Disease Prediction

Unveiling Hidden Patterns: Exploration of Data Dependencies ...

引用

2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Durga Devi, P. Akila, K. Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani India

ISBN: (纸本)9798350394580

In the current scenario, the possibility of getting affected by disease is high so it is important to predict in advance. Due to technological advancement, it is for early prediction. Hence, this paper is focused on analysis of various DL algorithms for prediction system. This work provides a comprehensive report for the disease prediction system with the analysis of test input parameters for different disease. Further, different algorithms useful for the prediction framework has been discussed. Studying the interdependence of these risk factors in patients' medical records and comprehending their respective contributions to stroke prediction is critical. Deep learning techniques are crucial in overcoming these issues since they enable automatic disease identification in healthcare facilities. The prediction framework is analyzed using data from the Electronic Health Record dataset. Parametric statistics are used to evaluate the results. © 2023 IEEE.

关键词： Electroencephalogram(EEG) Electronic Health Record(EHR) Gated Recurrent Unit(GRU) Long Short Term Memory(LSTM) Recurrent Neural Network(RNN)

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Increasing The Energy Efficiency of Gallium Arsenide Photovoltaic Cells Employing a Phase Change Material Cooling Methodology

Increasing The Energy Efficiency of Gallium Arsenide Photovo...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Srivastava, Akriti Al-Jawahry, Hassan M. Kumar, Yogendra Rawat, Ruchira Aswini, S. Gupta, Manish Boob, Nandini Shirish Srivastava, Arun Pratap Lloyd Institute of Engineering & Technology Greater Noida India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical Engineering GLA University Mathura India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India

ISBN: (纸本)9798350355338

These findings emphasize the relationship between the operating temperature of a solar cell and its performance parameters. The open circuit voltage and short circuit current of solar cells exhibit a direct proportionality to the operating temperature of the cell due to their nature as semiconductor devices. According to the panel's characteristics, the open circuit voltage decreases and the short circuit current increases as the temperature increases. Applying water cooling on both sides of the panel produces the most favorable outcomes compared to other cooling methods. This leads to a reduction of approximately 31 degrees Celsius and an efficiency improvement of around 2.7%. The panels experienced a 1.9% increase in efficiency when they were submerged at a particular depth in water, where the temperature difference between the water and the panels was approximately 25°C. After analyzing the above results, it can be inferred that the efficiency of the panel will improve as the operating temperature approaches the ambient temperature. Although nanofluid cooling technology is a viable alternative, its non-toxic nature poses a challenge to its widespread adoption. The integration of PV panels with phase change materials has demonstrated promising outcomes owing to the extended durability of phase change materials (approximately 2000 cycles) and the reduced temperature decrease (around 15 oC) that ensues. This paper concludes that employing water cooling techniques on both sides of the panel enhances its efficiency, thereby maximizing the juice extracted from the system. W/M2 of irradiance and 25°C ambient temperature. © 2024 IEEE.

关键词： III-V semiconductors

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Notes Sharing and Student performance Analysis Web Application 7

Notes Sharing and Student performance Analysis Web Applicati...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Swasha, K. Gracia, S. Steffie Maheswari, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

Students frequently experience issues while taking notes, which interferes with their ability to study. There are many reasons students miss to take notes in college. Without notes, they might miss on important information and it would be really hard for the students during their exam times. Students will have a place to congregate and engage in collaborative learning through our application. It is possible for students to their materials (both written notes and documents in PPT, PDF, DOC, etc. format) for free access by other students in any branch or semester. Each user will log in to the application using their individual account. This application will also help the students to earn money. Students who wish to earn money while studying can lock their notes and the other students who want to download the notes have to pay the uploader. This way the application will benefit students to earn part time and also encourage students to take notes in class. There are few existing models for the said application but users face many difficulties like access to notes only after payment, does not accept hand written notes, must upload notes regularly, allows upload only for a particular stream. The proposed method provide solutions like students from different streams can upload their notes, notes can be downloaded for free or paid version based on how the uploader has set the download option. The other important feature is that Student Performance Analysis is done using Random Forest Regressor. The future work of this application is to enable student credit other students for their work by including credit system which will motivate the students to do a better job. This application is intended to be an Educate and Share application. © 2023 IEEE.

关键词： Students

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