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optimization of the online collaboration platform: an event queue management algorithm based on debounce and throttle techniques 4

Optimization of the online collaboration platform: an event ...

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4th International conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Xue, Tianbao Lan, Quanxiang School of Mathematics and Computer Science Panzhihua University Sichuan Panzhihua China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Against the backdrop of accelerating digital transformation, online collaboration platforms have become indispensable tools for remote work and learning. However, with the increase in the number of users, the problem of high concurrency event processing has become increasingly prominent. Therefore, this paper proposes an event queue management algorithm (EQA-DT) based on debouncing and throttling techniques to optimize the efficiency of event processing in high concurrency scenarios. The EQA-DT algorithm uses a debounce technique to control the frequency of event capture, ensuring that only the last event triggered by the user is recorded within a short period of time. It also uses a throttling technique to periodically push events in the queue to the server, limiting the frequency of events being pushed to the server. We compared the EQA-DT algorithm with traditional event processing methods in an experimental setting. The results showed that in high concurrency scenarios, the EQA-DT algorithm is superior to traditional event processing methods in terms of reducing server pressure, reducing the length of the event queue, and improving system response speed. This algorithm provides an effective solution for improving the performance of online collaboration platforms and lays the foundation for future research in this area. © 2025 SPIE.

关键词： Concurrency control

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Evaluation of Teacher Performance using HybPSOFA - A Hybrid Approach

Evaluation of Teacher Performance using HybPSOFA - A Hybrid ...

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Visual Analytics and Data visualization (ICVADV), International conference on

作者： Sanskriti Agarwal Pitchika P N G Phani Kumar Junali Jasmine Jena Suchismita Das Manas Ranjan Nayak Mahendra Kumar Gourisaria School of Computer Engineering KIIT Deemed to be University Bhubaneshwar Odisha India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Evaluating teacher performance is essential for enhancing educational quality, but accurately estimating the performance is very challenging due to the complexity and subjectivity of the assessment criteria. It is a good real-time problem where optimization algorithms have utmost application. This paper investigates the use of metaheuristic algorithms, such as Particle Swarm optimization (PSO), the Firefly Algorithm (FA) and proposed HybPSO-FA approach, to estimate the teacher performance. By combining the strengths of both PSO and Firefly algorithms, the hybrid technique aims to optimize performance estimation while minimizing computational complexity. The results of the proposed method indicate that the hybrid algorithm significantly outperforms the standalone methods in terms of achieving the optimal value for teacher performance evaluation and convergence speed. This study highlights the potential of using hybrid optimization techniques for performance evaluation in academics having various criteria, making them more reliable and efficient for use in educational institutions.

关键词： Performance evaluation Visual analytics Metaheuristics Estimation Data visualization Real-time systems Reliability Particle swarm optimization Computational complexity Convergence

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Hybrid Recommender Systems: Bridging Accuracy Gaps with Quantitative Evidence

Hybrid Recommender Systems: Bridging Accuracy Gaps with Quan...

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Visual Analytics and Data visualization (ICVADV), International conference on

作者： Diya Akshita Dhivakaran Vishali Muralidharan Balaraman Sundarambal N Kirubakaran Department of Computer Science and Business Systems Chennai Institute of Technology Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Recommender systems have become a staple in the digital ecosystems such as in personalizing contents, products, and services, driving user engagement. The present study discusses the hybrid recommender system, a paradigm based on the combination of the strengths of multiple recommender systems techniques, to overcome some of the handicaps of individual techniques, such as collaborative filtering, content-oriented filtering, and knowledge-driven systems. We look at the different methodologies and address challenges in the direction of scalability, sparsity, and cold-start problems. This paper also offers a comparative insight of how hybrid systems improve user engagement compared to the traditional recommender system techniques. Finally, this study address the challenges faced by the hybrid systems and the future scope of research.

关键词： Ethics Technological innovation Privacy Accuracy Reviews Visual analytics Scalability Hybrid power systems Recommender systems optimization

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Leveraging Machine Learning techniques for Accurate Heart Disease Prediction Through Data Insights and Model optimization

Leveraging Machine Learning Techniques for Accurate Heart Di...

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Disruptive Technologies (ICDT), International conference on

作者： Mandeep Kaur Sandeep Chouhan School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

This research paper aims to develop a machine learning-based framework for heart disease prediction using critical cardiovascular health indicators. Based on a strong dataset, the study uses classification algorithms such as logistic regression, k-nearest neighbours, and random forest to model the complex relationships between clinical and demographic factors. Advanced data preprocessing and visualization techniques are included in the methodology to identify trends and correlations that may not be otherwise seen, thereby allowing a better understanding of the underlying data. The framework focuses on feature selection and hyperparameter optimisation to improve the performance of the model, thus making the prediction reliable. Various cross-validation and randomised search strategies are used for finetuning models to tackle issues with overfitting or class-imbalance distributions in data. Data visualization of features, including scatter plots or bar charts, offer actionable interpretation into how diseases manifest among the population because of variables such as age and levels of cholesterol and heart rates. With the integration of machine learning with a comprehensive analysis of data, the research will help in the early detection and prevention of heart disease. The approach proposed here can offer scalable and adaptive frameworks for clinical applications within personalized healthcare and data-driven solutions. Further directions in this area would be to increase the dataset size and real-time monitoring to be more precise in predictive and practical applicability in decision-making processes. framework for clinical applications. Future work should include increasing the dataset size and incorporating real-time monitoring to make predictions more accurate and with practical use in medical decision-making.

关键词： Heart Analytical models Accuracy Data visualization Predictive models Data models Real-time systems optimization Random forests Diseases

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Optimizing Social Media Management and Content Marketing: A Strategic Approach to Meeting Organic Digital Demands

Optimizing Social Media Management and Content Marketing: A ...

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Visual Analytics and Data visualization (ICVADV), International conference on

作者： Sachin Swami Ranjana Mishra Neha Dixit Preeti Pandey IES institute of Technology and management IES University Bhopal MP India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In the rapidly evolving digital age, social media platforms have become indispensable tools for businesses seeking to connect with diverse audiences. This study investigates the optimization of social media management and content marketing through data-driven strategies to enhance organic growth and audience interaction. Key methodologies include platform analysis, audience segmentation, predictive analytics, and multi-channel content repurposing. Results demonstrate significant improvements across various performance metrics. For instance, TikTok and Instagram achieved the highest interaction rates of 18.2% and 14.3%, respectively. Predictive analytics enhanced interaction rates from 8.9% to 12.4% and click-through rates by 50%. Multi-channel repurposing strategies improved total reach scores from 5.0 to 8.0 and audience retention rates from 62.0% to 74.5%. The overall content strategy yielded an accuracy rate of 91.5% in aligning content with audience preferences, demonstrating the efficacy of advanced analytical techniques. These findings highlight the importance of leveraging data and technology to craft targeted and efficient content strategies, ensuring sustained growth and interaction in the competitive digital landscape.

关键词： Video on demand Social networking (online) Visual analytics Data visualization Information age Web sites Multimedia communication Predictive analytics optimization Business

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A Survey of 3D Gaussian Splatting: optimization techniques, Applications, and AI-Driven Advancements

A Survey of 3D Gaussian Splatting: Optimization Techniques, ...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International conference

作者： Santosh Reddy P Abhiram H K S Archish Department of Computer Science Engineering B.N.M. Institute of Technology Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

3D Gaussian Splatting (3DGS) has emerged as a transformative technique for real-time, high-quality 3D rendering. This paper surveys the advancements in 3DGS, focusing on optimization strategies, applications across diverse domains, and AI-driven innovations that enhance its utility. optimization techniques, such as Mini-Splatting and sparsification frameworks, address challenges like computational overhead and memory inefficiency while maintaining visual fidelity. AI has further expanded the capabilities of 3DGS through frameworks like SceneTeller, ART3D and GaussianEditor, enabling text-to-3D generation, object-level manipulation, and artistic scene creation with multi-view consistency. Applications of 3DGS range from autonomous driving and robotics to virtual and augmented reality, highlighting its adaptability and efficiency. This survey underscores the importance of continued research to refine 3DGS’s scalability and applicability, solidifying its role at the intersection of computer graphics and artificial intelligence.

关键词： Surveys Technological innovation visualization Three-dimensional displays Scalability Rendering (computer graphics) Real-time systems Artificial intelligence optimization Robots

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A Hybrid B-Tree and Enhanced Merkle Hash Tree Approach for Secure Cloud Data Management

A Hybrid B-Tree and Enhanced Merkle Hash Tree Approach for S...

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Visual Analytics and Data visualization (ICVADV), International conference on

作者： Thiruneelakandan. S Sherin Eliyas Sathish Kumar M Department Of Computer Application Hindustan Institute of Technology and Science Chennai Tamilnadu India Department of computer applications Hindustan institute of technology and science Chennai Tamilnadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Data security, integrity verification, and efficient retrieval issues are much more pronounced with large datasets in cloud storage systems. The angle of this paper is the proposal of hybrid architecture of AES, EMHT, B-tree indexing, and POR techniques to complete the optimization of cloud operations. The way the system was evaluated against the traditional approaches was based on several key metrics such as encryption time, integrity verification time, update time, and memory usage for datasets up to the 15GB limit. The experimental results show that the proposed system has 6.50% less verification time and 38.44% better update time when compared to existing methods without compromising the effectiveness of encryption. These indicate that the proposed approach is cost-effective, secure, and scalable in a large-scale cloud storage environment. The contribution of this paper is a practical solution for future enhanced data management and security in modern cloud systems.

关键词： Measurement Cloud computing Visual analytics Scalability Memory Encryption Reliability Proposals optimization Testing

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Exploring Advanced Optimized Machine Learning Frameworks for Enhanced Analysis and Predictive Modeling of Drilling Well Production Performance

Exploring Advanced Optimized Machine Learning Frameworks for...

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International conference on Communication Systems and Network Technologies (CSNT)

作者： Md Ahammad Sharif K. Siva Kumar Venkata Ramana Avula Mohammed Huzaifa Sonu Kumar Emandi Krishna Sai Department of Petroleum Engineering Godavari Institute of Engineering and Technology (A) Rajahmundry Andhra Pradesh

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

The oil and gas industry is increasingly depending on advanced data analytics to maximize production and reduce costs. Perhaps the greatest difficult issue in this industry is precisely estimating the performance of drilling wells to improve decision-making processes. This paper provides ML techniques for predicting drilling well production using ensemble learning. To develop an accurate prediction system, the framework includes Exploratory Data Analysis (EDA), data preprocessing, visualization, optimization, and model selection. In this work, combining RF with XGBoost aims to improve predictive performance through utilizing the capabilities of both algorithms. Firefly optimization is utilized to tune hyperparameters in the hybrid model-building process. This hybrid model is specifically intended for error prediction in firefly optimization scenarios. The approach is developed in Python software and exhibits the real-world application of advanced ML techniques in industrial conditions. The proposed hybrid RF-XGBoost model exceeds in terms of error predicting for drilling well production, with an RMSE of 0.01, MAE of 0.01, and MSE of 0.10. The results indicate the approach is effectively accurate in predicting errors in complex data settings, providing insights that have a significant effect on operational decisions in the field of well data management.

关键词： Drilling Accuracy Data analysis Production Predictive models Gas industry Software Petroleum industry optimization Python

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Real-Time 3D visualization of Radiation Measurements from a Multi-Modal Directional System in GPS Denied Environments

Real-Time 3D Visualization of Radiation Measurements from a ...

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IEEE Annual Computing and Communication Workshop and conference (CCWC)

作者： Nathan Kassai Paul Oh Department of Mechanical Engineering University of Nevada Las Vegas. Las Vegas NV USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

This project presents the development of a realtime 3D visualization system for radiation measurements in GPS-denied environments. The system combines radiation detection with lidar-generated point cloud mapping, enabling operators to visualize radiation sources relative to their surroundings. Housed in various system configurations; a lightweight, portable backpack, and a roller-bag, the system allows for easy navigation and radiation mapping in challenging environments. Each system is equipped with an NVIDIA Jetson AGX Orin for data processing, and a Livox MID-360 lidar for 3D environmental scanning. The system supports both single and multi-user configurations, enabling collaborative radiation mapping efforts in complex scenarios. The data from each user is aggregated into a unified map, color-coded to indicate radiation levels. This comprehensive, real-time approach enhances situational awareness, particularly in environments where traditional radiation detection systems struggle. The system's lightweight visualization tools allow users to access radiation and environmental data remotely, either via handheld devices or web-based platforms like ROSBoard and Vizanti. The development focuses on improving both portability and real-time data visualization, making it suitable for large-scale environmental monitoring, public safety, and nuclear disaster response applications. Future improvements include enhanced data visualization techniques and further optimizations for handheld devices.

关键词： Point cloud compression Three-dimensional displays Laser radar Navigation Radiation detectors Data visualization Real-time systems Public security Systems support optimization

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Machine Learning optimization techniques for Accurate Melanoma Diagnosis

Machine Learning Optimization Techniques for Accurate Melano...

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Automation and Computation (AUTOCOM), International conference on

作者： Jaspreet Sidhu Jason Marandi Ashu Katyal Nagaraj Patil Kalyan Acharjya Veera Nagaiah Maddikayala Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Mechanical Engineering JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Applications (DCA) Presidency College Bengaluru India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Melanoma is the deadliest form of skin cancer, claiming more than 7,000 lives each year in the United States; roughly 67, where there were almost over196 new cases URITY CASES ANNUALLY IN THE US. Because of its visual similarity to non-melanomas, it is more important than ever that melanoma be discovered early for effective treatment. As per the research, machine learning (ML) techniques have gained high popularity for automated image analysis and classification during melanoma diagnosis, and they have assisted dermatologists in diagnosing efficiently. At the same time, the performance of ML models relies on how optimization techniques are implemented in training. ML optimization tools buck as gradient descent and poor initialization may result in black-box solutions that could lead to misdiagnosis. New approaches with deep learning and reinforcement have led to dramatic improvements in the performance of machine-learning models over the last several years. Historically, deep learning algorithms have proven highly accurate in classifying melanoma images because they can automatically learn complex features from the data. In addition, they tried to optimize the feature selected and make your ML model interpretable by using the reinforcement learning method, which searches for an optimal solution through a trial-and-error process. Conclusion optimization of machine learning techniques is essential for providing accurate melanoma diagnosis. Recent years of the digital revolution, stupefied by advanced arguments like Deep Learning and Reinforcement learning models, have contributed to high accuracy in Machine learning models a dream come true for dermatologists with timely detection & cement of this deadly disease.

关键词： Deep learning visualization Accuracy Refining Melanoma Machine learning Reinforcement learning Feature extraction optimization Tuning

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