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Quantum Machine Learning Techniques based on Nurturing Agri-Ontology Framework in Agricultural Science 3

Quantum Machine Learning Techniques based on Nurturing Agri-...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Bennet Prabhu, A. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Drbccc Hindu College Department of Computer Applications Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Kone Elevator India Technology & Engineering Centre Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

The absence of a consistent ontological framework often impedes efficient knowledge organization and sharing within the agricultural community, despite the quantity of data created in agricultural research. The lack of defined vocabulary and conceptual frameworks hinders data integration, agricultural research, and innovation by preventing interoperability between databases and systems. To address these issues, this research work proposes the Nurturing Agri-Ontology Framework (NAOF), which offers a systematic approach to creating and updating agricultural ontologies. Ontologies that faithfully portray agricultural ideas and connections can be more easily created with the help of NAOF, which combines ideas from knowledge management, agricultural science, and ontology engineering. NAOF aims to ensure that the ontologies produced reflect the wide range of viewpoints and terminology used in agricultural research by encouraging cooperation among stakeholders, ontology engineers, and domain specialists. In this study, the improvement of knowledge organization, sharing and integration in the agricultural area is shown with the application of NAOF. Using this research study, scientists can build ontologies that improve agricultural decision-making, data interoperability, and the discovery of new information. Using quantum machine learning techniques, a strong foundation of knowledge can be laid for several agricultural applications, such as precision agriculture, crop management, and the formulation of agricultural policies. Ontology coverage, interoperability, scalability, and usability are established evaluation parameters that are used to assess the efficacy of NAOF in this research work. The analysis shows that NAOF effectively solves the problems and creates a better agricultural knowledge ecosystem by thoroughly evaluating it using these standards. © 2024 IEEE.

关键词： Ontology

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Three-Port DC-DC Converter with Galvanic Isolation Using Multi-Winding Transformer 3

Three-Port DC-DC Converter with Galvanic Isolation Using Mul...

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3rd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2024

作者： Paul, Preenu Jose, Babita R. Shahana, T.K. Abraham, Chikku Mathew, Jimson School of Engineering Cochin University of Science and Technology Division of Electronics Engineering Kerala India Muthoot Institute of Technology and Science Department of Electrical and Electronics Engineering Kerala India Indian Institute of Technology Department of Computer Science and Engineering Patna India

ISBN: (纸本)9798350387193

A three port DC-DC converter that employs push-pull topology with isolation is presented. The converter transfers power from two DC input sources using a multi-winding transformer to the load. The high gain of the converter is achieved using quasi - switched boost (qSB) network, switched capacitor (SC) topology, multi winding transformer turn's ratio and voltage doubler circuit (VDR). The converter operation, steady state analysis deriving output voltage and comparison with similar topologies are presented. MATLAB/ Simulink results justify the converter operation and analysis. The underlined features of proposed converter are (i) high voltage conversion ratio (ii) galvanic isolation (iii) reduced utilization of semiconductor devices and (iv) capability to combine multiple input sources. © 2024 IEEE.

关键词： Capacitor bank

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Prototype Development of a Multilevel Solar Dryer for Agriculture

Prototype Development of a Multilevel Solar Dryer for Agricu...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Fernandez, Gian Carlo C. Luis, Jed Matthew A. San Peña, Robert Alfie S. MacAbebe, Erees Queen B. School of Science and Engineering Ateneo de Manila University Ateneo Energy Research Lab Department of Electronics Computer and Communications Engineering Quezon City Philippines

ISBN: (纸本)9798350350821

Basic food processing in the Philippine agricultural sector relies on drying, which makes food products less perishable. A common practice is traditional sun-drying, which is simple, but exposes the produce to contaminants. This study developed a multilevel solar dryer for agriculture to provide better drying conditions while keeping costs down. A temperature and humidity monitoring system was built into the dryer to control its cooling fans and the humidity inside. The dryer demonstrated better drying conditions with an average drying temperature of 47.38 °C compared to the traditional sun-drying's 39.38 °C. In addition, the solar dryer's inside humidity was also better with a recorded average of 31.29% RH compared to the traditional setup's 39.66% RH. The dryer successfully dried a hybrid of Capsicum frutescens and Capsicum annuum or siling tingala, as it is locally known, at a drying rate faster than the traditional sun-drying setup. Overall, the multilevel solar dryer was able to provide better drying conditions, a faster drying rate, and a protected enclosed space for drying food products. © 2024 IEEE.

关键词： Solar dryers

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Comparing a Novel Multilayer Perceptron's Performance Analysis with an Extreme Gradient Boosting Algorithm for Financial Fraud Transaction Detection 15

Comparing a Novel Multilayer Perceptron's Performance Analys...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Durga, Battu MahaveeraKannan, R. Department of Computer and Science Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Saveetha University Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350370249

Aim: This study is aimed at pinpointing instances of financial fraud through comprehensive computations, ultimately devising a system capable of effectively flagging online fraud and implementing XGBoosting to mitigate its occurrence. The escalating prevalence of fraudulent transactions has evolved into a pressing global concern. In this research, we introduce an innovative approach founded on the Novel Multilayer Perceptron for fraud detection. The implementation and model delineations of rival models are also illustrated, highlighting the substantial superiority of our proposed model. Methods and Materials:The study employed sample files specific to fraudulent transactions for a more accurate assessment of accuracy. Each of the two datasets comprises 130 participants, and the sample size determination utilized a G-power pretest score of 70%, a significance threshold of 0.05%, and a confidence interval of 94%. Result: The sample T-test indicated that there was no statistically significant difference between the performance of the Novel Multilayer Perceptron and the XGBoosting models, with a p-value of 0.000 (p>0.05). This suggests that both models performed similarly. The XGBoosting model demonstrated a slightly higher accuracy of 97.539% compared to the Novel Multilayer Perceptron's accuracy of 97.188%. Therefore, the difference in performance between these two algorithms is not statistically significant. Conclusion: The study's findings indicate that the Novel Multilayer Perceptron model outperforms in the analysis of fraud transaction detection performance. ©2024 IEEE.

关键词： Authentication

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Algorithmic Approaches, Practical Implementations and Future Research Directions in Machine Learning 1

Algorithmic Approaches, Practical Implementations and Future...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Vaishali Sharma, Deepank Kumar Punia, Sanjeev Galgotias University School of Computing Science and Engineering Greater Noida India G L Bajaj Computer Science and Engineering Department Greater Noida India

ISBN: (纸本)9798350356816

Machine learning has become a disruptive force that is advancing technology and changing industries. With an emphasis on algorithmic techniques, real-world applications, and important future research avenues, this study examines the rapidly changing field of machine learning. It explores the fundamentals of supervised, unsupervised, and semi-supervised learning algorithms and highlights how they are used in a variety of fields, including autonomous systems, healthcare, and finance. The report highlights the potential of several important future research directions, such as explainable AI, robust and privacy-preserving learning, quantum machine learning, and multimodal AI, to overcome present constraints and open up hitherto unheard-of possibilities. These multidisciplinary research avenues emphasize the value of interdisciplinary cooperation in addressing difficult problems and guaranteeing the creation of morally sound and significant AI systems. In order to help academics and practitioners who want to progress the subject, this review attempts to give a thorough grasp of the present situation and potential future direction of machine learning. © 2024 IEEE.

关键词： Self-supervised learning

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Optimizing Traffic Sign Detection using Neural Networks based CNN Classification 3

Optimizing Traffic Sign Detection using Neural Networks base...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Jenifer, A. Mary Balamanigandan, R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Tamil Nadu Chennai602105 India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331509675

To improve road safety, robust traffic sign detection and classification systems are needed with increasing complexity of the urban environments. To introduce a comprehensive solution by employing advanced image processing techniques as well as convolutional neural networks (CNNs) for recognizing traffic signs. The image acquisition part begins by collecting input images in various conditions to keep model robust. After the acquisition step a series of preprocessing steps are noise reduction, histogram equalization, image resizing to standardize input images for further analysis. These preprocessing techniques make it easier to filter and segment images. The Color and shape features to segment out potential traffic sign candidates with accurate segmentation and extract ROIs as a standalone task. After defining ROIs, the three state-of-the-art CNN architectures are Alex Net, Efficient Net and U Net is applied for classification tasks. To evaluate each architecture for which usability it can accurately classify different types of traffic sign, such as stop signs, pedestrian crossings or speed limits. Finally, Efficient Net will give good accuracy and computational efficiency other than Alex Net and U Net. Significant improvement in detection rates and classification performance are obtained from the integration of image processing methods with deep learning techniques. The proposed system is able to work not only in controlled environments, but also is very resilient to challenges, such as varying lighting and occlusions. The results suggest that this approach could be adaptively deployed in intelligent transportation systems for safer driving conditions and more aware road users. © 2025 IEEE.

关键词： Noise abatement

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Semantic-Forward Relaying for 6G: Performance Boosts with ResNet-18 and GoogLeNet Plus 13

Semantic-Forward Relaying for 6G: Performance Boosts with Re...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Erkantarci, Betül Çoban, Mert Korkut Bozoǧlu, Abdulkadir Kose, Abdulkadir Abdullah Gül University Department of Computer Engineering Kayseri Turkey Abdullah Gül University Graduate School of Engineering and Science Kayseri Turkey

ISBN: (纸本)9798350368086

This paper investigates the integration of advanced deep learning architectures, namely ResNet-18, GoogleNet and enhanced GoogleNet (GoogleNet Plus), into the Semantic-Forward (SF) relaying framework for cooperative communications in 6G networks. The SF relaying framework enhances transmission efficiency and robustness by leveraging semantic information at relay nodes. We analyze and compare the performance of these deep learning models in terms of validation accuracy, semantic accuracy, and Euclidean distance (ED) metrics on the CIFAR-10 dataset. Results indicate that ResNet-18 achieves the highest performance due to its residual learning architecture. GoogleNet Plus, incorporating Automatic Mixed Precision (AMP) training and the Adam optimizer, demonstrates improved stability and efficiency compared to the original GoogleNet. The results highlights the potential of deep learning models to enhance semantic processing capabilities in SF relaying, contributing to the development of more efficient, resilient, and adaptive cooperative communication systems in 6G networks. © 2024 IEEE.

关键词： Cooperative communication

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Wildfire Detection from Sentinel Imagery Using Convolutional Neural Network (CNN) 8th

Wildfire Detection from Sentinel Imagery Using Convolutional...

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8th International Conference on Smart City Applications, SCA 2023

作者： Abujayyab, Sohaib K. M. Karas, Ismail R. Hashempour, Javad Emircan, E. Orçun, K. Ahmet, G. Department of Fire Safety Engineering The International College of Engineering and Management Muscat112 Oman Department of Computer Engineering Karabuk University Demir Celik Campus Karabuk78050 Turkey School of Engineering University of Central Lancashire Preston United Kingdom

ISBN: (纸本)9783031543753

Wildfires are a significant threat to the environment and human life, and early detection is crucial for effective wildfire management. The aim of this research work is to develop a deep learning model using CNN method for detecting wildfire using satellite imagery. The CNN model development process involves splitting the dataset into training and testing sets, pre-processing the data using ImageDataGenerator function, building a deep learning model using Sequential function, compiling the model using Adam optimizer, categorical cross-entropy loss function, and accuracy metric, and training the model using the fit generator function. The CNN model architecture includes Conv2D, MaxPooling2D, and Dense layers. The dataset was collected from Sentinel-2 L1C, including 159 fire images and 149 non-fire images from different places in the Mediterranean region of Turkey. The CNN model was developed using the Keras library and trained for 200 epochs using the Adam optimizer. The model achieved an accuracy of 92.5% and a loss of 0.22 on the test set, outperforming existing methods for wildfire detection. The research work contributes to the field of wildfire science and management by providing a deep learning CNN detection model that can accurately predict wildfire behavior using satellite imagery. The outcomes of the research work can enable more effective and efficient wildfire mitigation efforts, improving the safety and well-being of communities affected by wildfires. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Neural network models

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A Stacking Ensemble Approached Mesothelioma Diagnosis Prediction 2

A Stacking Ensemble Approached Mesothelioma Diagnosis Predic...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Gupta, Surbhi Grover, Sarthak Department of Computer Science and Engineering Model Institute of Engineering and Technology J&k Jammu India Oakridge International School Karnataka Bangalore India

ISBN: (纸本)9798331504106

Pleural Mesothelioma is a cancer that forms in tissue covering the lungs and chest wall. It is most frequently caused by prior asbestos exposure. Millions of people suffer grave repercussions because their conditions are discovered too late. The study advocates the supremacy of machine learning strategies for enhancing the early and precise prediction of mesothelioma diagnosis. This study has used multiple classification models on the Mesothelioma dataset, available in the public domain. The study handles the class imbalance problem using SMOTE, and feature selection uses a Genetic Algorithm. The machine learning strategies used in the current study are Decision Trees, Random Forests, and Support Vector Machines. Further, this study has proposed ensemble models, namely weighted voting classifier and stacking ensemble classifier. All the machine and ensemble learning models have been evaluated using benchmark parameters like accuracy, sensitivity, specificity, F1-Score, MCC score, and AUC-ROC Curves. The experimental results depict that ensemble models performed better in comparison to single classifiers. Also, the best prediction accuracy (95.5%) was achieved using the proposed Stacking Ensemble Model. This model proposed in the study can be utilized in clinics as a decision support system. ©2024 IEEE.

关键词： Random forests

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Emergency Vehicle Detection Using Deep ConvNet2D and computer Vision 2

Emergency Vehicle Detection Using Deep ConvNet2D and Compute...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Poongavanam, Elumalaivasan Prathik, A. Munirathinam, T. Kayalvizhi, V. Rajesh, R. Mahavishnu, C. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Shreenivasa Engineering College Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350328202

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements Deep ConvNet2D (Convolutional Network 2D) and computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses Deep ConvNet2D Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases. © 2024 IEEE.

关键词： Real time systems

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