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Artificial Intelligence, Computational Electronics and Communication System (AICECS), International Conference on

作者： N K Shiva Chandra Rajath Anirudhan Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

ISBN: (数字)9798350391244

ISBN: (纸本)9798350391251

The project “AI-Based Aircraft Recognition System” aims to develop an advanced system for automatically recognizing and identifying aircraft using AI techniques. The increasing role of artificial intelligence (AI) in various sectors, including aviation, has driven the need for more efficient and accurate recognition systems. Aircraft recognition has attracted attention due to its potential uses in areas like military operations, air traffic control, and aviation surveillance. This project proposes the use of advanced AI algorithms to address gaps in current aircraft recognition systems by enhancing accuracy, performance, and real-time application.

关键词： Accuracy Military computing Surveillance Aerospace electronics Traffic control Military aircraft Real-time systems Air traffic control Artificial intelligence Aircraft

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Boosting-Based Machine Learning Approaches for Diabetes Prediction using Indian Demographic and Health Survey data 3

Boosting-Based Machine Learning Approaches for Diabetes Pred...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Kallur, Shanta Yaligar, Suman Thotad, Puneeth N. Kle Institute of Technology Affiliated to Visvesvaraya Technological University Dept. of Computer Science & Engineering Hubballi580027 India Kle Institute of Technology Affiliated to Visvesvaraya Technological University Dept. of Master of Computer Applications Hubballi580027 India

ISBN: (纸本)9798350364569

Traditional diagnostic techniques are time-consuming and prone to error. Performance is added however time and cost are reduced by using computer-aided diagnostic methods. This study aims to calculate how healthy boosting-based machine learning classifiers perform in automatically identifying diabetes in the Indian context. For this experiment, Indian demographic and health survey 2021 data on women aged 19 to 79 are considered. This data includes clinical, anthropometric, and biochemical components. 12,103 of the 706,504 responders who tested positive for diabetic testing were considered. The predictive exploration-based boosting machine learning models are adopted, such as adaptive boosting, categorical boosting, extreme gradient boosting, gradient boosting, and light gradient boosting models. Kernel Principal Component Analysis is employed for feature extraction. The Extreme Gradient Boosting model demonstrated strong performance on the diabetes dataset both before and after applying Kernel PCA. The metrics including accuracy, F1-score, precision, and recall for the XGBoost model showed improvements: accuracy increased from 81% to 83%, F1-score from 81% to 82%, precision from 81% to 88%, and recall from 81% to 76%.In contrast, Adaptive Boosting performed poorly, while Categorical Boosting, Gradient Boosting, and Light Gradient Boosting models achieved moderate performance levels. Overall, the model attained optimal performance across metrics such as accuracy, precision, Area Under the Curve (AUC), and recall. © 2024 IEEE.

关键词： Adaptive boosting

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A Modified U-Net for Semantic Segmentation of Liver and Liver Tumors From CT Scans 1st

A Modified U-Net for Semantic Segmentation of Liver and Li...

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1st International Conference on Computation of Artificial Intelligence and Machine Learning, ICCAIML 2024

作者： Rashmi, R. Girisha, S. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031714832

Medical image analysis algorithms have been quite popular for automating the segmentation of the liver and liver tumours in recent years. A system like this would also lessen radiologists’ workload and subjective interpretations. Deep learning models are commonly used in this field due to their performance improvement over conventional methods of medical image processing. However, deep learning models’ capacity for class imbalance adaptation is rarely studied, particularly in the instance of segmenting liver tumours given their tiny size. Therefore, in the current study, a straightforward Convolutional Neural Network (CNN) model based on the well-known U-Net architecture is suggested in order to capture more contextual information and solve the class imbalance issue. Additionally, a combination of the loss function is intended to guarantee precise segmentation of liver tumours. A detailed qualitative and quantitative analysis is carried out on LiTs dataset to validate the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer Aided Diagnostic Systems CT scan Deep Learning Liver Tumor Semantic Segmentation

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Enhancing Histopathological Image Analysis: A Study on Effect of Color Normalization and Activation Functions 1st

Enhancing Histopathological Image Analysis: A Study on Effe...

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1st International Conference on Computation of Artificial Intelligence and Machine Learning, ICCAIML 2024

作者： Sudhamsh, G.V.S. Rashmi, R. Girisha, S. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031714832

Histopathological images have significant potential for disease diagnosis and prognosis, but their inherent color variations can impede accurate analysis and reliable model performance. Color variations in histopathological images can indeed be a major challenge for accurate diagnosis and reliable model performance. This study investigates the critical role of color normalization in mitigating these inconsistencies, improving feature extraction, the model’s generalizability, and reducing staining bias. More this study examines the impact of activation functions, essential components in deep learning architectures for histopathological image analysis. Selecting the optimal function is crucial for optimizing the model performance. By emphasizing the importance of color normalization and careful activation function selection, this study lays the groundwork for more robust and reliable analysis of histopathological images, ultimately leading to improved disease diagnosis. In the present work, different segmentation architectures are analyzed with and without color normalization and with different activation functions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Integrating IoT in Transportation for Smart City Development for Secured data Processing 4

Integrating IoT in Transportation for Smart City Development...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Priya, R. Lakshmi, R. Anandha Sudha, C. Pal, Souvik Ali, Ghassan Ahmed Alajmani, Samah H. Vels Institute of Science Technology and Advanced Studies Dept. of Computer Applications Chennai India Anna Adarsh College for Women Dept. of Computer Applications Chennai India SSKV College of Arts and Science for Women Dept. of Computer Applications Kanchipuram India Saveetha College of Liberal Arts & Sciences Saveetha Institute of Medical & Technical Sciences Chennai India Universiti Islam Sultan Sharif Ali Faculty of Islamic Technology Brunei College of Computer & IT Taif University Department of Information Technology Saudi Arabia

ISBN: (纸本)9798350353266

In this study, we propose the architecture and implementation of an IoT and machine learning powered Integrated Transport Management (ITM-SCD)for Smart City Development to mitigate the issues described above. The design takes all potential transportation system difficulties into account by utilizing many scenarios. The model integrates the various city systems into a single, cohesive whole, with an emphasis on secured data. People's movements can be monitored that smart transportation, healthcare, and entertainment can be provided. We discuss the challenges of transit security and smart city applications, as well as possible solutions based on AI and ML. Then, for smart health, transportation, and energy, some solutions to these privacy and security issues are offered. © 2024 IEEE.

关键词： Traffic congestion

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Spice Vision: Deep Learning Enhanced Spice Classification System 3

Spice Vision: Deep Learning Enhanced Spice Classification Sy...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Pujari, Laxmi Belavatgi, Meenaz Sajjan, Mahantesh M. Kamatar, Vishwanath Surasura, Pradeep Ammanagi, Nausheen K.L.E. Institute of Technology Dept. of Master of Computer Applications Hubballi580027 India K.L.E Institute of Technolog Dept. of Computer Science and Engineering Hubballi580027 India

ISBN: (纸本)9798350364569

Spices are admired for their distinctive flavors, fragrances, and health advantages. They are an essential part of industrial uses, medical procedures, and culinary customs. The automatic classification of 15 different varieties of Indian spices which are essential to both Indian food and traditional medicine is the main subject of this study. To guarantee quality and consistency, these spices underwent a thorough data preparation procedure after being painstakingly gathered. Then, Convolutional Neural Network (CNN) models were trained and tested using the preprocessed dataset. As a family of deep learning algorithms, CNNs are particularly good at classifying images because they extract hierarchical characteristics directly from the input. The study highlights the possibility for automated systems to precisely detect and classify distinct spices based on their visual properties and investigates the efficacy of various CNN architectures for spice classification. Gathering and preprocessing photos of the spices was part of the model creation procedure. Based on how well these models classified the test images, their performance was assessed. The accompanying picture illustrates the findings, which demonstrate the different degrees of accuracy attained by each model. © 2024 IEEE.

关键词： Convolutional neural networks

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Robust Sensor Networks: Applying AI for Fault Identification in Challenging Operational Contexts

Robust Sensor Networks: Applying AI for Fault Identification...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Manjunatha A S Venkatramana Bhat P Ramakrishna M Matti Nidhi Prabhu Dept. of Computer and Communication Engineering NITTE (Deemed to be University) NMAM Institute of Technology Nitte Mangaluru Karnataka Dept. of Computer Science and Engineering NITTE (Deemed to be University) NMAM Institute of Technology Nitte Mangaluru Karnataka Dept. of Data Science and Computer Applications Manipal Academy of Higher Education Manipal Institute of Technology Manipal India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Wireless Sensor Networks (WSNs) are highly implemented in harsh and dynamic environments. In such configurations, faults and anomalies easily occur, causing unwanted disruptions in data accuracy and network performance. In the context of reliability and stability in WSN operations, effective fault detection techniques are vital. This research work discusses how several machine learning models may be applied, including Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors in order to detect fault in WSNs. The performance evaluation was performed based on major criteria, which are accuracy, precision, recall, and Fl score. This, therefore, makes these approaches to machine learning effective in identifying faults efficiently, hence offering a well-balanced solution that is scalable and robust for integrity and operational effectiveness of WSN s.

关键词： Fault diagnosis Wireless sensor networks Logistic regression Accuracy Fault detection Nearest neighbor methods Robustness Decision trees Random forests Testing

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An Efficient Model for Early Detection of Maize Leaf Disease using Deep Learning Approaches 4

An Efficient Model for Early Detection of Maize Leaf Disease...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Kallur, Shanta Yaligar, Suman Thotad, Puneeth N. K.L.E.Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India K.L.E.Institute of Technology Dept. of Master of Computer Applications Hubballi India

ISBN: (纸本)9798350354218

Maize plays a fundamental role as a staple food for millions worldwide. However, the significant threat posed by various diseases to maize cultivation has led to substantial yield losses on a global scale. Early and accurate detection of these diseases is essential for prompt intervention and effective treatment strategies. In this regard, the development of advanced detection methods employing deep learning and image processing has gained attention, offering the potential to revolutionize disease identification and management practices. Accordingly, this research introduces a Deep Convolutional Neural Network model combined with Transfer Learning techniques, reducing the training time for the AI model. Furthermore, data Augmentation practices are employed to improve accuracy by addressing dataset variability. The projected model achieves an impressive accuracy of 98.6%, crucial for mitigating current crop wastage resulting from undetected diseases. Enhancing accuracy further is essential for improving current crop yield conditions impacted by undetected diseases. Moreover, a GUI-based application model is developed for early detection of maize diseases, aiding farmers in their fields. This accuracy can be further increased by incorporating additional parameters. Traditional image recognition and Deep Learning approaches are progressively making their way into the field of plant disease recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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Flood extent mapping in SAR images using semi-supervised approach

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Results in Engineering 2025年 26卷

作者： S, Girisha G, Savitha P, Sughosh Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Civil Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Floods pose a significant threat to both human populations and critical infrastructure. They are caused by excessive precipitation, snowmelt, or infrastructure failures. Precise mapping of flood levels is essential for directing emergency response, allocating resources as efficiently as possible, and estimating the degree of damage. Accurate semantic segmentation of flood-zones from SAR images is necessary for disaster management as it reduces human error and facilitates fast decision-making. However, the complexity of SAR images and the scarcity of annotated datasets make it difficult to develop efficient computer vision algorithms for this use. The paper employs consistency regularization and pseudo-label generation to overcome these issues and presents a novel hybrid semi-supervised semantic segmentation method. In particular, the study presents a student-teacher paradigm in which robust feature learning is ensured by training the teacher model through consistency regularization and self-supervised learning. An attention module-based tailored student model is developed to precisely identify flood-zones. Auxiliary decoders are also added throughout the training phase to reinforce consistency regularization and help the model capture extensive hierarchical characteristics. The suggested method was assessed with publicly accessible datasets, and the outcomes show notable gains in both qualitative and quantitative measures. The findings highlight the method's effectiveness in improving flood-zone segmentation accuracy, leading to more efficient disaster management. © 2025 The Authors

关键词： Semantic Segmentation

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Facial Expression Recognition Using Multi-Block Deep CNN 5

Facial Expression Recognition Using Multi-Block Deep CNN

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Priyadarshini, V. Srinivasulu Reddy, U. Reddy Chirra, Venkata Rami Mrudula, M. Suneetha, M. National Institute of Technology Dept. of Computer Science & Engineering Andhra Pradesh India Machine Learning & Data Analytics Lab National Institute of Technology Dept. of Computer Applications Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Malla Reddy College of Engineering Department of CSE Telangana India Independent Researcher Amaravati India

ISBN: (纸本)9798331528539

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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