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Enhancing Dry Eye Disease Detection Through the Application of an Ophthalmic Knowledge Graph 1

Enhancing Dry Eye Disease Detection Through the Application ...

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1st international conference on Intelligent Manufacturing and Cloud computing, ICIMCC 2024

作者： Cui, Jiazheng Lin, Zhiyuan Liu, Haoyang Liu, Yunxiao Wang, Mini Han Beijing Normal University Hong Kong Baptist University United International College Zhuhai China Digital Healthcare and Artificial Intelligence Association China Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai China Perspective Technology Group Zhuhai China Beijing Institute of Technology Zhuhai China Macao Polytechnic University China The Faculty of Medicine Chinese University of Hong Kong Hong Kong Zhuhai People's Hospital Zhuhai Clinical Medical College Jinan University Zhuhai China

ISBN: (纸本)9781643685779

Dry eye disease is a multifactorial condition that significantly impacts the quality of life for millions worldwide. Accurate diagnosis and treatment are often challenging due to the complex interplay of symptoms, risk factors, and underlying causes. This study presents an innovative approach to enhance dry eye detection through the development of an ophthalmic knowledge graph. By curating and organizing data from peer-reviewed literature, the knowledge graph captures key entities such as symptoms, treatments, and diagnostic methods, and systematically maps their relationships. The study employs natural language processing (NLP) and deep learning techniques to extract entities and relationships, which are then integrated into the knowledge graph using the Neo4j platform. This structured representation provides clinicians with an intuitive and comprehensive tool for improving diagnostic accuracy and developing personalized treatment strategies. The knowledge graph not only strengthens the connections between diverse clinical data but also facilitates the integration of AI-driven diagnostic methods. This research demonstrates the potential of knowledge graphs in advancing the field of ophthalmology, specifically in the diagnosis and management of dry eye disease. © 2025 The Authors.

关键词： Knowledge graph

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Standardized Post-Quantum Cryptography and Recent Developments in Quantum Computers

Standardized Post-Quantum Cryptography and Recent Developmen...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Shiney Chib Harshitha Raghavan Devarajan Swathi Chundru Padmaja Pulivarthy R. Augustian Isaac KrishnaMurthy Oku Department of Management Datta Meghe Institute of Management Studies Nagpur Maharashtra Department of Computer Science New York University Motivity labs INC Samsung Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai Tamil Nadu Department of Automation platform Development Tech Lead Infrastructure/Cloud Architecture AT&T Services Inc

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Traditional cryptographic frameworks are seriously threatened by the quick development of quantum computing, especially those that depend on public-key cryptosystems like RSA and ECC. Post-quantum cryptography (PQC) seeks to create cryptographic methods that are impervious to quantum computer assaults in order to overcome this difficulty. The National Institute of Standards and Technology (NIST), which has selected and standardised quantum-resistant techniques like CRYSTALS-Kyber for key encapsulation and CRYSTALS-Dilithium for digital signatures, has sparked recent advancements in PQC. At the same time, the need for reliable PQC solutions has increased due to developments in quantum computing, such as IBM's improvements in qubit stability and Google's “quantum supremacy” assertion. In order to protect data security in the post-quantum age, PQC regulations must be adopted globally, as highlighted by the intersection of these fields. Current developments in PQC standardisation and the changing state of quantum computing technology are highlighted in this paper.

关键词： Training Technological innovation Quantum computing Qubit Switches NIST Planning Internet Encryption Computer security

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advances in Crop Monitoring technologies: A Comprehensive Review

Advances in Crop Monitoring Technologies: A Comprehensive Re...

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Pervasive Computational technologies (ICPCT), international conference on

作者： Divya Rawat Shashank Awasthi Manas Kumar Mishra Ruby Faizan Uttaranchal School of Computing Science Uttaranchal University Dehradun India G.L. Bajaj Institute of Technology and Management Greater Noida India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Agricultural optimization and food security rely on effective plant monitoring. This essay probes the evolution and comparative analysis of modern technologies that are revolutionizing agricultural monitoring. In this paper, we show how artificial intelligence (AI), machine learning algorithms, and drones combined with the power of IoT sensors (smart agriculture) along rural data collection using remote sensing technologies will help to improve precision farm management by understanding the pros and cons in implementing these AI-driven new tools into smart devices capable of supporting higher crop yields and higher efficiency. Through a review of case studies and market trends, this article delves into how these technologies provide farmers with real-time data, predictive analytics, and actionable insights. Moral, monetary, and ecological reverberations of bonding these advances universally are likewise discussed. Ultimately, this comparative study to develop resilient and sustainable food production systems through the inclusion of modern technologies is an important opportunity for many difficult socioeconomic and climatic issues.

关键词： Smart agriculture Ethics Machine learning algorithms Reviews Tracking Artificial intelligence Socioeconomics Smart devices Monitoring Drones

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Assessing the Accuracy of Regression and Classification Models in Air Quality Prediction

Assessing the Accuracy of Regression and Classification Mode...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Harsh Kumar Vishwakarma Guru Prasad M S Vanshika Mittal Mudit Mittal PavanKumar Naik Chandrappa S Dept. of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India School of Computing and Information Technology REVA University Bangalore India Dept. of Computer Science and Engg. JAIN (DEEMED-TO-BE UNIVERSITY) Bengaluru India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

This research paper addresses the pressing issue of air quality monitoring and prediction using machine learning algorithms. With a dataset comprising pollutant levels from various states, we employed a comprehensive approach to data analysis, visualization, and preprocessing. The study aimed to predict the Air Quality Index related to key pollutants such as SO2, NO2, RSPM, and SPM. We utilized linear regression, decision tree regression, and random forest regression for prediction tasks and logistic regression, decision tree classification, random forest classification, and k-nearest neighbors for classification tasks. The proposed method was evaluated using different performance matrixes for regression and classification tasks, respectively. The results demonstrated the usefulness of the proposed models in predicting and classifying air quality levels. The proposed model provides a robust framework for air quality management and can be a valuable tool for environmental monitoring and policymaking.

关键词： Logistic regression Accuracy Machine learning algorithms Atmospheric modeling Predictive models Air quality Environmental monitoring Public healthcare Regression tree analysis Random forests

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Handling Non-stationarity with Distribution Shifts and data Dependency in Time Series Forecasting 25th

Handling Non-stationarity with Distribution Shifts and Data ...

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25th international conference on Parallel and Distributed computing, Applications and technologies, PDCAT 2024

作者： Hua, Qiang Wang, Xiao-Wen Zhang, Feng Dong, Chun-Ru College of Mathematics and Information Science Hebei University Baoding071002 China

ISBN: (纸本)9789819642069

Deep learning models have achieved substantial advancements in time series forecasting, leveraging their impressive capability to capture sequential dependencies. However, time series data often exhibits non-stationarity, where the data distribution undergoes rapid changes over time, posing significant challenges for accurate prediction. Some studies have proposed normalizing data within subsequences to eliminate non-stationarity and adopting a statistical forecasting module to model the evolution trend of the original time series’ statistical characteristics. Based on the predicted future statistical characteristics, denormalization is performed to restore stationarity, achieving accurate predictions of future distributions. This statistical forecasting approach has limitations in handling outliers in statistical features, resulting in insufficient model robustness. To address this issue, the Slice Adaptive Normalization module with Huber loss (H-SAN) is proposed, which effectively enhances the model’s robustness against outliers and improves prediction performance. Additionally, while this method employs local normalization, it utilizes long-sequence modeling for feature extraction, neglecting the inter-slice dependencies. Furthermore, this paper proposes a dual-token input strategy, which achieves simultaneous extraction and effective fusion of both global and local features based on the dual scales of input tokens. A network for non-stationary time series forecasting based on Distribution Shifts and data Dependency Network (DSDDNet) is constructed in this paper. Experimental results demonstrate that DSDDNet significantly outperforms existing benchmark models on standard datasets, validating its effectiveness and superiority. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Transforming High-Frequency Trading Predictions with CNN-LSTM Networks

Transforming High-Frequency Trading Predictions with CNN-LST...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： S. Baghavathi Priya Priyanga S Sri Surya Shobith Kamisetty Venkata Sai Vinay Neelapala Nithila G Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Electronics & Communication Engineering School of Electronics Engineering (SENSE) Vellore Institute of Technology Vellore India Department of Computer Science & Engineering School of Computer Science and Engineering (SCOPE) Vellore Institute of technology Vellore India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

High-Frequency Trading (HFT) has transformed financial markets by enabling rapid and automated trading. However, the prediction of critical market metrics, such as trading volumes, remains a challenge due to the noisy and complex nature of financial data. This paper presents a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model designed specifically for predicting trading volumes in the NIFTY50 index using high-resolution, minutelevel Yahoo Finance data. By combining CNN's spatial feature extraction capabilities with LSTM's temporal modeling strengths, the proposed approach effectively captures the intricate patterns and sequential dependencies inherent in high-frequency trading datasets. Comprehensive data preprocessing, including noise reduction and temporal segmentation, coupled with advanced cross-validation techniques, ensures robust performance across market conditions. Comprehensive evaluations using metrics such as Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2) demonstrate the model's efficacy in capturing intricate financial dynamics. This work underscores the potential of hybrid deep learning models in enhancing predictive analytics for high-frequency trading applications in emerging markets.

关键词： Fluctuations Noise reduction Finance Predictive models Feature extraction Convolutional neural networks Indexes Noise measurement Predictive analytics Long short term memory

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Extending the Progression Model of Alzheimer's Disease Combining KNN, LDA and SMOTE

Extending the Progression Model of Alzheimer's Disease Combi...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Tatireddy Subba Reddy Sowmya Gopisetti Cheviti Sanjay Kumar Gunnala Krishna Prasad Dommati Vaishnavi Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Alzheimer's disease (AD) is a challenging condition that significantly impacts quality of life and leads to cognitive impairment. As the most prevalent form of dementia, AD causes severe damage to nerve cells. Individuals affected by AD struggle with cognitive functions such as memory, thinking, and behaviour, which in turn disrupts their daily activities. Current methods for creating medical concept structures from various data sources— such as ICU and outpatient clinical records—often fall short in capturing the full complexity of disease progression. The primary issues include limitations in attention weight learning and restricted handling of data sources. To address these challenges, this study introduces a novel approach that combines three key techniques: Linear Discriminant Analysis (LDA) for reducing dimensionality, the (SMOTE) Synthetic Minority Over-sampling Technique for enhancing data through augmentation and addressing class imbalance, and K-Nearest Neighbours (KNN) for classification purposes. The proposed method was empirically tested and achieved a high accuracy rate of 98.6%, demonstrating its effectiveness in representing AD progression more comprehensively.

关键词： Accuracy Machine learning algorithms Computational modeling Soft sensors Neurons Machine learning Nearest neighbor methods data models Linear discriminant analysis Alzheimer's disease

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Prediction of Liver Diseases using Radiomics Features and Convolutional Neural Networks

Prediction of Liver Diseases using Radiomics Features and Co...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： A. Bathsheba Parimala R. S. Shanmugasundaram Department of Computer Science and Engineering Vinayaka Mission's Kirupananda Variyar Engineering College (A Constituent College of Vinayaka Mission's Research Foundation (Deemed to be University)) Salem Tamil Nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Liver disease is a serious health condition that affects millions of individuals worldwide. Radiomics and deep learning (DL) are two AI-based methods that are being actively researched in the field of liver imaging. The analysis of liver related problems is a tedious tasks and it requires models to classify and predict. Radiomics is an effective method for extracting an extensive amount of distinctive characteristics from medical images using data-characterization techniques. Radiomic features possess the ability to reveal tumoral patterns and traits that the human eye alone cannot detect. Convolutional Neural Networks (CNN) is well suited method to analyze visual data and it is most useful to diagnose the liver diseases with extracted radiomics features. This study suggests an extraction of radiomics features and classifies it using CNN for liver diseases. The combination of Radiomics and CNN improves the prediction performance.

关键词： Visualization Accuracy Liver diseases Computed tomography Medical services Predictive models Feature extraction Convolutional neural networks Medical diagnostic imaging Radiomics

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Predicting System Performance and Resource Exhaustion with Machine Learning: Tackling Class Imbalance

Predicting System Performance and Resource Exhaustion with M...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Shipra Swati Naween Kumar Ankit Dubey Vaibhav Saini Subham Sharma Ayushman Pranav SCSET Bennett University Greater Noida U.P. India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

This study introduces a robust machine learning pipeline for event classification, utilizing a large-scale dataset from eight Google Borg compute clusters (May 2019) available via Google Big- Query. The dataset includes detailed job submissions, scheduling decisions, and resource usage data, with enhancements such as 5-minute CPU usage histograms, shared resource reservations, and job- parent relationships critical for MapReduce task analysis. data preprocessing involves imputation for missing values, label encoding for categorical data, and SMOTE to address the class imbalance. Feature scaling using StandardScaler and dimensionality reduction via Principal Component Analysis (PCA) retained 95% of the variance. The dataset was divided into training and testing sets, preserving class distribution. Seven machine learning models were evaluated, spanning linear, non-linear, ensemble, and neural network approaches. The Support Vector Machine (SVM) achieved exceptional performance, with 98.0% accuracy, 97.5% cross-validation score, 98.2% precision, 97.8% recall, and 98.0% F1-score. This study highlights the evolution of workloads and resource usage patterns, contributing to developing scalable and efficient event classification frameworks in cloud computing.

关键词： Support vector machines Training Cloud computing Processor scheduling System performance Pipelines Neural networks Task analysis Principal component analysis Testing

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Role of Quantum Cryptography in Securing Communication Channels in Industry 4.0

Role of Quantum Cryptography in Securing Communication Chann...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Sonal Pramod Patil Vikram Nattamai Sankaran Kalpit Lamsoge Poovendran Alagarsundaram Eric Howard Prasanna Guduru G H Raisoni College of Engineering and Management Pune India Innovation Consultant Nagpur Maharashtra India Humetis Technologies Inc Kingston NJ USA Department of Physics and Astronomy Macquarie University Australia Department of Computer Science National Sanskrit University Tirupati Andhra Pradesh India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

In the context of Industry 4.0, which is characterised by networked systems, Internet of Things devices, and cyber-physical networks, quantum cryptography is essential to strengthening communication channels. In contrast to traditional cryptography techniques, quantum cryptography uses quantum physics concepts, notably quantum key distribution (QKD), to provide unmatched security. By taking advantage of the quantum property of superposition and measurement-induced disruption, QKD offers tamper-evident key exchanges, protecting data from efforts at eavesdropping. This guarantees that any communication interception causes system disruption and warns users of possible dangers. Quantum cryptography reduces the risks associated with traditional encryption techniques in Industry 4.0, as sensitive data moves across automated systems, smart factories, and decentralised supply chains. This is especially true as quantum computing advances. Industries can attain strong data integrity, confidentiality, and resistance against new cyberthreats by incorporating quantum-secured channels, opening the door for a dependable and safe digital transformation. This presentation highlights how quantum cryptography is revolutionising the security paradigms of Industry 4.0.

关键词： Costs Scalability Supply chains Communication channels Communications technology Fourth Industrial Revolution Communication networks Quantum cryptography Research and development Smart manufacturing

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