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machine learning-based development of cuproptosis-related lncRNA signature for prognosis and immunotherapy exploration in colon cancer 3

Machine learning-based development of cuproptosis-related ln...

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3rd international conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Liu, Yuchao Zhu, Ruiyu School of Biological and Chemical Engineering Zhejiang University of Science and Technology Hangzhou310023 China

ISBN: (纸本)9781510680449

This study aimed to construct a cuproptosis-related lncRNA signature through an integrated machine learningbased model to predict prognosis and response to immunotherapy in CC. Methods Raw RNA-seq data and corresponding clinicopathological information of patients with CC were retrieved from The Cancer Genome Atlas (TCGA) database. A gene-lncRNA co-expression analysis was applied to obtain cuproptosis-related lncRNAs. A prognostic risk model composed of cuproptosis-related lncRNAs was constructed using univariable Cox regression analysis and a machine learning-based integrative model. The clinicopathological samples were divided into high- and low-risk groups according to the median risk scores. Immunotherapy response was assessed across the groups using the Tumor Immune Dysfunction and Exclusion (TIDE) scores, while immune-related functions with single-sample gene set enrichment analysis (ssGSEA). Results The prognostic risk model was constructed based on 11 cuproptosis-related lncRNAs. The high-risk group exhibited higher immune-related function scores and a greater likelihood of immune evasion but a worse prognosis and immunotherapy outcomes. Conclusion The cuproptosis-related lncRNA signature could be utilized as an independent prognostic factor for CC to guide clinical treatment. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： machine learning

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Unsupervised Vehicle Re-Identification with Clustering and Appearance Style Exploration 24

Unsupervised Vehicle Re-Identification with Clustering and A...

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3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Taleb, Hamdan Zhu, Xiaobo Wang, Chao College of Electronic and Information Engineering Tongji University Shanghai China School of Informatics and Engineering Suzhou University Jiangsu Suzhou China

ISBN: (纸本)9798400718120

Vehicle re-identification (Re-ID) has emerged as a critical challenge in the fields of computer vision and intelligent transportation systems, driven by the need to accurately identify vehicles across multiple camera views in diverse urban environments. Traditional methods relying on appearance-based features such as color and resemblance often fall short due to the variability in vehicle appearances caused by factors like illumination changes, camera angles, and occlusions. To address these limitations, this paper proposes a novel approach that leverages unsupervised learning, integrating agglomerative clustering techniques and style transfer via the StarGAN2 model. By treating each camera as a unique style domain, the proposed method generates new training pictures with diverse styles, reducing camera-induced variability and enhancing cross-camera vehicle identification. The repeated learning framework further refines vehicle identities by clustering similar pictures, thereby improving the system’s ability to distinguish between vehicles in real-world scenarios. This approach not only minimizes the need for manual annotation but also enhances the robustness and adaptability of vehicle Re-ID systems, making it a valuable contribution to urban surveillance and intelligent transportation systems. © 2024 Copyright held by the owner/author(s).

关键词： Unsupervised learning

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An End-to-End Model for Chicken Detection in a Cluttered Environment 13

An End-to-End Model for Chicken Detection in a Cluttered Env...

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13th Iranian/3rd international machine Vision and Image Processing conference (MVIP)

作者： Chemme, Komeil Sadeghi Alitappeh, Reza Javanmard Univ Sci & Technol Mazandaran Elect & Comp Engn Dept Behshahr Mazandaran Iran

ISBN: (纸本)9798350350494;9798350350500

Animals in the livestock business need to be seen in real time and online in order for them to grow and develop. The growing global demand for meat and poultry, the development of the livestock and poultry business, and the expansion of their region have made it more difficult for humans to handle the task due to human mistake and the growing amount of work. But thanks to technological breakthroughs like artificial intelligence, learning models, and closed-circuit cameras, most tasks may now be left to robots, preventing human error. This system may be installed and connected worldwide, allowing it to count all animals at any one time and give us information on each animal's number and location. In this research, we created a model consisting of a segmentation module that readies input data for a detector model based on YOLOv8. In order to study chicken behavior, an integrated end-to-end approach is utilized to track and count the birds. The experimental results provide insight into the new proposal's accuracy performance compared to earlier methods, demonstrating an 8% improvement. The goal of this article is to teach a YOLOv8-based learning model that may be applied to the development of an automated, integrated system for counting and assessing animal welfare in the future.

关键词： Chicken detection Object detection Deep learning Segmentation

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Blockchain Technology and data Privacy: A Comprehensive Review and Future Perspective 3

Blockchain Technology and Data Privacy: A Comprehensive Revi...

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3rd IEEE international conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Ahirao, Purnima Shaikh, Bareerah Wahedna, Rahil Zubair K.J. Somaiya College of Engineering Department of Information Technology Mumbai India

ISBN: (纸本)9798350354348

Due to its decentralized and impervious to tampering nature, blockchain technology presents great opportunities for improving data privacy in a variety of contexts. This study offers a thorough analysis of the body of research on the subject of blockchain technology and data privacy. It explores privacy concerns in blockchain systems, clarifies privacy-preserving techniques, and examines their application in diverse contexts such as healthcare, IoT, crowdsensing, and federated learning. The paper highlights the challenges associated with blockchain-based privacy solutions, including data immutability, transparency, and scalability, and identifies emerging trends and opportunities for future research. This paper attempts to offer important insights into how blockchain technology can change the data privacy environment in our networked and data-driven society through a thorough analysis. Lastly, new developments and issues are addressed, including self-sovereign identification and integration with machine learning. Future research is encouraged to focus on issues like scalability, standardization, legal frameworks, and user education. © 2024 IEEE.

关键词： Federated learning

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Breast Cancer Diagnosis using machine learning: Challenges and Opportunities 3

Breast Cancer Diagnosis using Machine Learning: Challenges a...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Shobha, M. Kumar, N. Ashok Mohan Babu University School of Engineering Department of Ece Sree Sainath Nagar Andhra Pradesh Tirupati India

ISBN: (纸本)9798331509675

The purpose of this paper is to review breast cancer diagnosis methods. Cancer begins in cells, the basic building units of tissue. Sometimes the cell growth process fails, and new cells form when the body does not require them, while old or damaged cells do not die as they should. When this happens, a collection of cells frequently develops a mass of tissue known as a lump, growth, or tumor. One of the obstacles that current mammography image detection algorithms confront is the difficulty in analyzing dense tissues. This study provides a quick summary of various breast cancer diagnosis methods. This study also concludes that the features of the mammogram are the primary determinant of any method's accuracy. machine learning algorithms use these features as input for the predictions. Texture-Based Feature Extraction methods can be used to extract features from the mammogram images. To detect anomalies (tumors, masses, calcifications) and classify diseases (benign/malignant), we need a systematic approach that includes preprocessing, feature extraction, anomaly detection, and classification. In order to increase accuracy, machine learning algorithms need a lot of data. Obtaining sufficient data is one of the biggest obstacles to using machine learning algorithms. Therefore, an effective feature extraction method can be used to extract features from the mammography images before applying machine learning algorithms for the predictions. © 2025 IEEE.

关键词： Lung cancer

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A machine learning and Linear Regression Based Study of Momentum Changes and Impacts 3

A Machine Learning and Linear Regression Based Study of Mome...

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3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Tang, Hanxuan Wu, Fangfang Southwestern University of Finance and Economics Chengdu China

ISBN: (纸本)9798331519032

This paper explores the changes and effects of momentum in tennis matches, and introduces indicators such as "leverage", "turning point" and "leverage momentum" to construct a classification model. Three key models were developed: first, Logistic Regression, XGBoost and CatBoost models were used to analyze the effects of key features on momentum;second, hypothesis testing was used to verify the effects of momentum on match performance;and third, a turning point model was developed to reveal the key factors of momentum fluctuation moments. The results show the predictive accuracy of the model and a deep understanding of momentum changes, providing decision support for players and coaches. Through a data science approach, this study quantifies the impact of momentum, reveals its importance in the game, and provides new perspectives for controlling game momentum. © 2024 IEEE.

关键词： Logistic regression

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AgriSynth: machine learning - Driven Crop Intelligence, Fertilizer Rationalization and Disease Diagnostics 3

AgriSynth: Machine Learning - Driven Crop Intelligence, Fert...

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3rd international conference on Disruptive Technologies, ICDT 2025

作者： Gupta, Bhoomika Agarwal, Shivani Srivastava, Aditya Aaradhya Agrahari, Anant Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9798331519582

Agriculture is important to feeding the world's growing population, especially in the face of climate change. machine learning technology has the potential to greatly improve agriculture by helping farmers make more educated decisions. However, current systems have considerable limitations. Many fertilizer recommendations only address the key nutrients NPK: nitrogen, phosphorus, and potassium, fail to account for water availability, and rely on out-of-date historical data. This study would close these gaps by producing a machine learning system that can help farmers with crop recommendations, fertiliser guidance, and plant disease detection. The algorithm considers micronutrients and organic matter in addition to NPK when recommending fertiliser. It also incorporates water availability and real-time meteorological data to provide more precise crop suggestions, especially important for drought-prone areas. Deep learning methods analyze plant photos to detect diseases early on. Our findings indicate that by incorporating micro nutrients, water data, and real-time climate updates, the system can deliver more precise crop and fertilizer recommendations. The Random Forest and XGBoost models outperform, and the disease detection component helps to prevent crop losses by intervening early. The key takeaway from this study is that merging machine learning with large amounts of data can significantly improve farming techniques, raise crop yields, and provide farmers with reliable insights, thereby enhancing food security and sustainable agriculture. © 2025 IEEE.

关键词： Deep learning

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Unified Payment Interface Imposture and Scam Detection Using Deep learning And ANN 3

Unified Payment Interface Imposture and Scam Detection Using...

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3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Karthick, N. Leema Roselin, G. Tamilarasan, M. Kalaiselvi, K. Sudha, S. Jayanthi, J. IFET College of Engineering Department of Computer Science and Engineering Villupuram India

ISBN: (纸本)9798350391565

The Unified Payment Interface (UPI) has revolutionized digital payments in India, providing a convenient and secure way for individuals to transfer funds. However, the increasing popularity of UPI has also attracted malicious actors engaging in imposture and scams to defraud users. A novel approach is proposed to detect UPI imposture and scams using Deep learning and Artificial Neural Networks (ANN). This system leverages the power of deep learning techniques, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to analyze transaction data and user behavior patterns. A comprehensive dataset is collected and preprocessed, containing legitimate UPI transactions as well as historical data associated with known scams and imposture cases. By training the model on this dataset, it learns to identify suspicious patterns and anomalies in real-time transactions. © 2024 IEEE.

关键词： Convolutional neural networks

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Approach for Argumenting Safety on Basis of an Operational Design Domain 24

Approach for Argumenting Safety on Basis of an Operational D...

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IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Weiss, Gereon Zeller, Marc Schoenhaar, Hannes Drabek, Christian Kreutz, Andreas Fraunhofer Inst Cognit Syst IKS Munich Germany Siemens AG Munich Germany

ISBN: (纸本)9798400705915

The Operational Design Domain (ODD) is a representative model of the real world in which an Automated Driving System (ADS) is intended to operate. The definition of the ODD is a crucial part of the development process for such an artificial intelligence (AI)-enabled system. This is due to the fact that the ODD is the basis for several critical development activities, like defining system-level requirements, test & verification, and building a well-founded safety case for an AI-based ADS. Since an inadequately defined ODD poses a major safety concern for the entire development, an ODD must be defined completely and consistently during the development process. In this work, we present an approach for the ODD definition and maintenance during the development of safety-critical AI-based ADS functionalities and provide evidences to argue the sufficient completeness and consistency. We demonstrate the feasibility of our approach by an industrial use case of a fully automated system in the railway domain.

关键词： Operational Design Domain Autonomous System Safety Assurance AI machine learning Automated Train Operation

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Comparative Study of Hybrid machine learning Models for Indoor Localization Using SVM, GPR, and GCN with Augmented Techniques 3

Comparative Study of Hybrid Machine Learning Models for Indo...

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3rd international conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2024

作者： Markkandan, S. Reji, Sharon Deena Sairam, B. Shivani Narayan, L. Mridhula, S. School of Electronics Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350391244

Indoor positioning technology is vital for numerous applications, from intelligent building systems to emergency services. This research presents an innovative hybrid approach to indoor localization, combining Support Vector machines (SVM), Gaussian Process Regression (GPR), and Graph Convolutional Networks (GCNs). We enhance these models' effectiveness through advanced data augmentation methods, including the use of Generative Adversarial Networks (GANs). Our methodology is tested using the UJI Indoor Localization dataset, demonstrating notable enhancements in positioning precision. The GCN-augmented technique, in particular, yields superior performance metrics compared to other approaches. This study provides insights into the practical applicability and generalization capabilities of our hybrid model in real-world indoor environments. © 2024 IEEE.

关键词： Support vector machines

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