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An ensemble framework of decision trees for class imbalance using partitioning

International Journal of Innovative Computing and applications 2024年第1期15卷 1-13页

作者： Kadappa, Vijayakumar Guggari, Shankru Veerabhaskar, Rajeshwari Devi Doddapoojari Department of Computer Applications B.M.S. College of Engineering Bengaluru560019 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India Department of Electronics and Communication Engineering Global Academy of Technology Bengaluru560098 India

Decision tree classifiers are widely used in machine learning and data mining due to their intuitiveness. However, they do not perform well for class-imbalanced data due to bias creation towards the majority class. Therefore, handling class-imbalanced data is an active research area in many applications of machine learning. Feature set partitioning paradigm is proven to be effective for classification by many researchers. In this paper, we extend the ideas of partitioning to propose a decision tree ensemble to overcome class-imbalance issues. The framework consists of balancing the data, partitioning the feature set, building local decision trees, then combining decisions. Some instances of the proposed framework (e.g., Ferrer diagram-based approach) are used to validate the performance of the framework. An empirical study is carried out using University of California Irvine (UCI), Knowledge Extraction Evolutionary Learning (KEEL), and other datasets. The performance of the instances of the proposed framework demonstrates improved performance compared to other benchmark machine learning approaches. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Adversarial machine learning

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AI-Driven Solutions for Predictive Maintenance in Smart Transportation Systems

AI-Driven Solutions for Predictive Maintenance in Smart Tran...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Hundekari, Sheela Mittal, Tanusha Dutt, Amit Bhavana, Munugapati Kumar, Rakesh Nijhawan, Ginni School of Computer Applications Pimpri Chinchwad University Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India

ISBN: (纸本)9798331512965

Predictive maintenance: A game changer in infrastructure and vehicle management, and able to cut maintenance expenses and downtime of smart transportation systems. This study explores ways in which smart transportation systems might use AI in order to plan maintenance schedules and forecast equipment breakdowns. Predictive algorithms, that can detect potential problems before raising to expensive repair or system breakdown, use real time sensor data from infrastructure and vehicles. Decision trees and neural networks are two of these models. By allowing AI driven solutions, transportation systems may transition from a reactive to a proactive maintenance practice which may support efficiency, safety, and reliability. Unsupervised learning methods are used to do anomaly detection for real time operations, and we examine the patterns in previous maintenance data with supervised learning approaches. The use of AI for predictive maintenance is also being studied for use in smart cities, where transportation networks are becoming more interconnected by IoT. The paper also considers challenges on data integration as well as the importance of scaling the algorithms and guaranteeing cybersecurity in these systems. Based on our research, smart transportation systems could benefit from AI to increase operational efficiency, reduce the cost of material and labor to maintain and repair equipment systems, and decrease the rate of disruptions. © 2025 IEEE.

关键词： Predictive maintenance

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Adaptive Treatment Planning via Multi-class Segmentation of GI Tract Tumours 6th

Adaptive Treatment Planning via Multi-class Segmentation of ...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Jain, Samyak Pal, Aarush Andrew, J. Maheshwari, Saatvik Department of Computer Science and Engineering and Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819774258

This study addresses gastrointestinal cancer radiation therapy challenges by implementing advanced deep learning techniques. We focus on automating manual segmentation tasks during treatment planning to enhance efficiency and patient tolerance. Our methodology uses a diverse 3D MRI scan dataset to employ image analysis, Convolutional Neural Networks (CNNs), and Image Segmentation. The primary architectural choices involve the U-Net and InceptionResNetv2 models, contributing to developing a predictive segmentation model. This substantial result underscores the effectiveness of our approach in achieving accurate segmentations in the clinical setting. The overarching objective of our project is to automate the segmentation of the stomach and intestines in MRI scans, thereby streamlining radiation therapy planning for improved efficiency and patient care. In our evaluations, the implemented model demonstrates a commendable dice coefficient of 0.7, as indicated by metrics such as dice loss. Anticipated outcomes encompass reduced treatment times, enhanced patient comfort, and optimised tumour radiation doses while minimising healthy tissue exposure. The successful implementation of this advanced model holds the potential to revolutionise current radiation therapy practices, significantly elevating cancer treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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An implementation of personalized therapy in Clinical Decision Support System using adaptive transformer and hybrid deep learning network

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Australian Journal of Electrical and Electronics engineering 2025年第1期22卷 105-120页

作者： Praveena Rachel Kamala, S. T, Saraswathi. Kaliraj, V. Nithya, A. Hema, R. Ramyadevi, R. Department of Information Technology Easwari Engineering college Tamil Nadu Chennai India Department of CSE S.A Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India Department of Computer Science and Applications SRM Institute of Science and Technology Tamil Nadu Chennai India

This paper suggests a new mechanism from deep learning concept for personalised therapy in Clinical Decision Support Systems (CDSS). Basically, the texts used for the observation are acquired from the standard data sources and then forwarded to the text preprocessing task. In the pre-processing phase, the punctuation and special character removal, stop word removal, and stemming process are applied to remove noise and help to eliminate the redundant information in order to improve the data quality. Further, the pre-processed text is applied to the Adaptive Transformer Net (ATN) for the feature extraction purpose, where the attributes in this task are optimally determined with the aid of the Adaptive Walrus Optimization Algorithm (AWOA). Finally, the resultant text is subjected to the Hybrid Deep Learning Network (HDLNet). The HDLNet model is implemented by integrating the ‘Residual Long Short-Term Memory (Residual LSTM) with Dilated Recurrent Neural Network (Dilated RNN)’. From the results, the sensitivity analysis performed in the implemented technique secured 3.7% more efficient than LSTM, 7.76% improved than MobileNet, 6.7% superior to residual LSTM, and 0.90% effective than dilated RNN in dataset 1. Throughout the validation, the conventional techniques are evaluated with the suggested personalised therapy in CDSS to prove its efficacy. ©, Engineers Australia.

关键词： Long short-term memory

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Efficient and Precise CNN based Machine Leaming Method for Microwave Medical Imaging in Cancer Detection 2

Efficient and Precise CNN based Machine Leaming Method for M...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Sunil, B. Sameer, S. Vaseekaran, K.V. Rishi, R. Department of Computer Applications SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai India

ISBN: (纸本)9798331504960

Cancer detection is critical for early diagnosis and treatment, and non-invasive imaging methods like microwave imaging have shown promise in this domain. This research presents a CNN-based machine learning model to improve the speed and accuracy of microwave medical imaging for cancer detection. Leveraging large datasets, advanced preprocessing techniques such as noise removal, normalization, and data augmentation were applied. The proposed model includes a multi-layer CNN architecture, optimized using stochastic gradient descent (SGD) and categorical cross-entropy loss functions. Evaluation metrics, including accuracy, sensitivity, and specificity, were calculated from a confusion matrix. The system achieved 95.6% accuracy, 94% sensitivity, and 96% specificity, outperforming traditional models both in terms of accuracy and computational efficiency. The results suggest that the proposed model is well-suited for real-time clinical applications. Future work will focus on expanding the dataset and integrating multimodal imaging to enhance diagnostic precision. © 2024 IEEE.

关键词： Stochastic models

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A Comparative Analysis of Image Processing and Deep Learning Techniques for License Plate Recognition in Difficult Environments 5

A Comparative Analysis of Image Processing and Deep Learning...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Tejaswi, S. Babu, Tina Tejaswi, K. Seshadripuram Degree College Department of Computer Science Mysuru India School of Engineering Department of Computer Science and Engineering Alliance University Bengaluru India JSS Science and Technology University Department of Computer Applications Mysuru India

ISBN: (纸本)9798331518097

License plate recognition is one of the challenging tasks and it belongs to ITS, due to backgrounds, variation of illumination, occlusion the recognition is became the challenging task. These challenges enabled a comparison between the conventional image enhancement and the deep learning scheme for license plate recognition. We analyze and compare multiple techniques that we consider significant, including, the Edge detection, Morphological operations and template matching and also deep learning models like CNNs and R-CNNs. In this framework of experiment scenarios, we scale out a comprehensive evaluation of the methods given different datasets and compare their accuracy, speed as well as stability in conditions that may be hostile. In the experiments, we see that Naive Bayes and SVM outperform in low-resource conditions, but deep learning methods are significantly more accurate and more flexible in accommodating complicated cases and trends, which makes deep learning methods the method of choice for contemporary applications. This work provides an understanding of the strengths and weaknesses of both approaches to serve as a guideline in choosing the most effective detection techniques in relation to certain environmental conditions. © 2024 IEEE.

关键词： Intelligent systems

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Sentiment Analysis on Covid-19 data Using BERT Model

Sentiment Analysis on Covid-19 data Using BERT Model

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： ArunKarthi, T. Shanthi, S. Nirmaladevi, K. Ashwinth, K. Dev, R.B. Rajkumar, N. Computer Science And Engineering Kongu Engineering College Erode India Psna College Of Engineering And Technology Department Of Computer Applications Tamilnadu Dindigul India

ISBN: (纸本)9798350371567

In a world where microblogging has become an integral part of modern life., the sudden outbreak of COVID-19 swiftly infiltrated public opinion on various social networking platforms. With individuals adhering to social distancing measures and self-isolation protocols to combat the coronavirus, expressing their fears and concerns on social media became their primary outlet. In order to obtain a thorough comprehension of the pandemic's effects on individuals' lives and the environments in which they live, we must recognize the critical function of social media as a repository of emotions. This paper delves into the realm of sentiment analysis, focusing on the opinions disseminated through Twitter. Our primary dataset comprises these tweets, which we meticulously processed and converted into tokens for analysis. We performed fine-tuning and added an additional classifier layer to BERT, a pre-trained model, in order to classify these feelings into three categories: positive, negative, and neutral. Subsequently, we scrutinized BERT's performance in carrying out this task. Impressively, the BERT model demonstrated an outstanding accuracy rate of 94 percent on our dataset, supplemented by commendable scores in various evaluation metrics. © 2024 IEEE.

关键词： Sentiment analysis

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Enhancing Road Safety: A Smart Accident Alert System for Rapid Emergency Response 18

Enhancing Road Safety: A Smart Accident Alert System for Rap...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Kishore, S. Nair, Rekha R. Babu, Tina Nipe Nitte University Department of Computer Applications Mangalore India Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9789380544519

Millions of lives are at risk annually due to traffic accidents, and the need for a proactive and efficient accident detection system is paramount. The traditional accident detection system faces some challenges such as limited scalability, connectivity, power consumption, and reliability of sensors. So proposed an accident detection system with Internet of Things (IoT) technologies to enhance road safety and emergency response mechanisms. The proposed system integrates advanced components such as Global Positioning System(GPS) modules, and Global System for Mobile Communication(GSM) modules to enable real-time monitoring, precise location tracking, and immediate communication with emergency services. The novelty of this work lies in the implementation of a cost-effective accident detection system that accurately provides alerts to the intended party. The system delivers alert messages containing longitude and latitude coordinates. Additionally, it initiates alerts in the form of phone calls, fostering timely communication with accident victims. This prompt interaction has the potential to save lives by facilitating on-demand treatment and immediate assistance to those in need. This methodology has the potential for future applications in industries such as Traffic Management and Analysis, Road Safety Enhancement, and Emergency Response Optimization. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Emergency Response Optimization Global Positioning System Global System for Mobile Communication Internet of Things ThingSpeak

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Road Extraction From Remote Sensing Images Using U-Net

Road Extraction From Remote Sensing Images Using U-Net

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Sai Kumar, D. Lokesh Jyothi, V. Esther Vardhani, Chennu Meghana, Bayarla Priya, Bobbili Hema Sharika, Alladi Computer Science And Engineering Pvp Siddhartha Engineering College Vijayawada India Vr Siddhartha Engineering College Department Of Computer Applications Vijayawada India

ISBN: (纸本)9798350371567

The enhancement of the transportation database through updated road networks is a critical aspect with a wide range of applications. Traditional methods rely on onsite measurements or human interpretation of remote sensing images for mapping purposes. However, these methods have significant drawbacks, including high expenses, lengthy processing times, and a substantial workforce requirement. To overcome this challenge, we propose a method leveraging the U-Net architecture for road extraction from multispectral satellite images. The satellite imagery used is sampled from the DigitalGlobe Images dataset, covering images captured over Thailand, Indonesia, and India, with a ground resolution of 50 cm/pixel. The U-Net model is trained on both original and augmented data, employing convolutional layers, max-pooling, and up-sampling to extract and reconstruct road masks. Performance evaluation is conducted using custom validation sets, measuring metrics such as IOU and loss. Results demonstrate the effectiveness of the proposed system, achieving high accuracy and validation scores. The extracted road networks contribute to enhancing transportation databases, offering valuable insights for urban planning and management. © 2024 IEEE.

关键词： Roads and streets

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