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Continual Cell Instance Segmentation of Microscopy Images

Continual Cell Instance Segmentation of Microscopy Images

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tzu-Ting Chuang Ting-Yun Wei Yu-Hsing Hsieh Chu-Song Chen Huei-Fang Yang Dept. Information Management National Sun Yat-sen University Taiwan Dept. Computer Science and Information Engineering National Taiwan University Taiwan

A continual cell instance segmenter aims to continually learn to segment new objects while preserving the ability to localize and distinguish old objects without access to previous data. Besides catastrophic forgetting, background shift, where the background class could contain objects in the old and unseen future classes, could occur. In addition, as acquiring annotations is label-intensive, cell images can be partially labeled. In this paper, we present iMRCNN, which extends Mask R-CNN with knowledge distillation and pseudo labeling, to address these challenges. To preserve the learned skills, the current student distills knowledge from the former teacher at output and feature levels. Furthermore, we employ a pseudo labeling scheme, where the teacher is utilized to identify objects with no labels provided, to deal with background shift and partially labeled data. Experiments on two microscopy image sets demonstrate the effectiveness of iMRCNN over other alternatives in various incremental learning scenarios.

关键词： Image segmentation Uncertainty Annotations Microscopy Measurement uncertainty Signal processing Acoustics

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Advancing Drug Discovery through a Deep Learning-Based Protein-Ligand Interaction Prediction Tool

Advancing Drug Discovery through a Deep Learning-Based Prote...

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

作者： Sudhir S Hunge Aman Rajendra Yadao Varsha Tekdas Shewate Prakash Namdeorao Lalsare Shiji Prasannan Kothuri Parashu Ramulu PG Dept. of Chemistry Chintamani College of Science Pombhurna Maharashtra Dept. Of Chemistry Shriram Arts And Science Junior College Ramtek Maharashtra Dept. of Chemistry Chintamani College of Science Pombhurna Maharashtra Dept. of Chemistry Shri Prabhu Ramchandra Junior College Nanda Tah Korpana Maharashtra Faculty of Management Studies Parul Institute of Management and Research Parul University Vadodara Gujarat Dept. of Computer Science and Engineering Indur Institute Of Engineering And Technology

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

In recent years, deep learning has emerged as a promising approach to advance drug discovery by accurately predicting interactions between proteins and small molecules, or ligands. The identification of these interactions is essential in developing drugs, as it informs us about a molecule's potential therapeutic efficacy. Traditional methods, like molecular docking, often struggle with accuracy and computational efficiency, limiting their application in high-throughput settings. In this paper, we propose a novel deep learning-based tool designed to predict protein-ligand interactions with enhanced precision and scalability. Our model leverages convolutional and graph neural networks to capture the complex, three-dimensional nature of these interactions. Using a large, curated dataset, we train and test the model to optimize binding affinity prediction, employing metrics such as mean squared error and accuracy to validate performance. The results show that our approach outperforms conventional techniques and recent machine learning models, demonstrating a significant improvement in predictive reliability. This tool has potential applications in screening vast compound libraries in early-stage drug discovery, reducing time and costs associated with experimental trials. By providing more accurate insights into protein-ligand affinities, our method represents a step forward in computational drug design, with implications for developing targeted therapies across various disease areas.

关键词： Proteins Drugs Deep learning Electric potential Accuracy Costs Computational modeling Predictive models Graph neural networks Drug discovery

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Partial Discharge Classification With 1D Convolutional Neural Network

Partial Discharge Classification With 1D Convolutional Neura...

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International Conference on Properties and Applications of Properties and Applications of Dielectric Materials

作者： Thepjit Cheypoca Wiboon Promphanich Aung Ye Thway Angelina Phimpissada Hankae Siwakorn Jeenmuang Norasage Pattanadech Instrumentation and Control Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Computer Engineering Dept. School of EngineeringKing Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Electrical Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Mechatronics Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350374605

ISBN: (纸本)9798350386165

This paper introduces a novel approach for classifying with the 1D Convolutional Neural Network model for partial discharge patterns, that consists of corona discharge, surface discharge and internal discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The Artificial Neural Network for the classification model was constructed. Moreover, 2×1D CNN feature extraction was utilized to reduce the curse of dimensionality in the dense layer of the proposed PD classification model. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed.

关键词： Partial discharges Partial discharge measurement Surface discharges Neurons Training data Discharges (electric) Data models Convolutional neural networks Integrated circuit modeling Testing

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Partial Discharge Classification with Transformer Neural Networks

Partial Discharge Classification with Transformer Neural Net...

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International Conference on Properties and Applications of Properties and Applications of Dielectric Materials

作者： Thepjit Cheypoca Wiboon Promphanich Aung Ye Thway Angelina Phimpissada Hankae Siwakorn Jeenmuang Norasage Pattanadech Instrumentation and Control Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Computer Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Electrical Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Mechatronics Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350374605

ISBN: (纸本)9798350386165

This paper introduces an approach with the Transformer Neural Networks model for partial discharge patterns classification, that consists of corona discharge, internal discharge and surface discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The phase value will be encoded into the charge array and Transformer Neural Network is constructed using Positional Embedding and Transformer Encoder Layer. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed

关键词： Partial discharges Phased arrays Partial discharge measurement Surface discharges Training data Pattern classification Transformers Discharges (electric) Biological neural networks Testing

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Enhancing Passive Index Investing: Augmenting Volatility Harvesting with Machine Learning Models for Higher Returns

Enhancing Passive Index Investing: Augmenting Volatility Har...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Vishvajeet Upadhyay Porinita Banerjee Shivaditya Upadhyay Suryadatta Research Center Suryadatta Institute of Management & Mass Communication Savitribai Phule Pune University Pune India Dept. of Financial Management Poona Institute of Management Sciences and Entrepreneurship Pune India Dept. of Computer Science and Engineering SRM IST Chennai and IIT Madras Chennai India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Active fund management has traditionally dominated investment strategies but has increasingly faced scrutiny due to its inability to consistently outperform benchmark indices, prompting a shift toward passive index investing. While passive strategies simplify investment by mirroring market returns, they inherently cap the potential for higher gains and expose investors to market volatility. This study explores how dynamic portfolio rebalancing, combined with predictive insights from CNN-GRU hybrid and transformer-based machine learning models, can enhance returns while preserving the benefits of index-based investing. By employing volatility harvesting and market timing strategies at various time scales, the proposed approach augments portfolio weights dynamically (by a small amount of 2.5%), favoring equity indices during bullish predictions and reducing exposure during bearish forecasts. Through this methodology, we demonstrate improved risk-return profiles, highlighting the untapped potential of augmented volatility harvesting as a cost-effective, AI-driven investment strategy.

关键词： Machine learning algorithms Computational modeling Machine learning Predictive models Transformers Timing Indexes Informatics Portfolios Investment

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Risk Assessment and Safety-as-a-Service Provisioning in IoV Networks: A Roadmap

Risk Assessment and Safety-as-a-Service Provisioning in IoV ...

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Global Conference for Advancement in Technology (GCAT)

作者： Patrali Pradhan Samiran Chattopadhyay Subrata Chowdhury Suchismita Rout Manoj Ranjan Mishra Sudhansu Shekhar Patra Dept. of Computer Science and Engineering Haldia Institute of Technology Haldia India Techno India University Kolkata India Dept. of Computer Science and Engineering Sreenivasa Institute of Tech. and Management Studies Chittoor Andhra Pradesh India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India School of Computer Applications KIIT Deemed to be University Bhubaneswar India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

This paper presents a framework for a Safety recommender system that offers Safety-as-a-Service (Safe-aaS) to on-road vehicles in IoT-based transportation systems. The proposed safety recommendation system integrates several phases, including preparing sensed data, self-supervised learning from the data, and predicting personalized safety plans. The customized safety plans, generated by analyzing on-road decision parameters such as dynamic road conditions, weather patterns, and driving assistance parameters, are suggested to registered users in real time through the Safe-aaS platform tailored to their specific *** a Safe-aaS enabled road transportation system, sensor nodes produce large volumes of data in a dynamic environment. Efficient analysis and storage of sensed data in edge devices poses a major challenge for service providers. Conversely, generating safety-related decisions with lower processing time ensures driver safety. The Safe-aaS-based recommender system provides safety-related decisions with low latency and processing delay. In the proposed recommendation system, a safety score is computed from the pre-processed sensed data across various parameters, followed by a user-specific similarity score and a corresponding risk score. We apply a fuzzy rule-based learning technique to predict optimized safety plans. To showcase the real-time scenario of our framework, we develop a user-friendly application interface at the primary stage. Extensive analysis shows that our system effectively detects on-road risks and adapts safety recommendations in dynamic weather and road conditions.

关键词： Roads Self-supervised learning Real-time systems Safety Risk management Vehicle dynamics Low latency communication Recommender systems Optimization Meteorology

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Integration of a multi-rate electron density profile observer in the plasma control system of TCV 49

Integration of a multi-rate electron density profile observe...

引用

49th EPS Conference on Plasma Physics, EPS 2023

作者： Pastore, F. Felici, F. Sauter, O. Bosman, T.O.S.J. Galperti, C. Vincent, B. Pau, A. Poels, Y. Ravensbergen, T. Vu, N.M.T. Lausanne Switzerland DIFFER - Dutch Institute for Fundamental Energy Research Energy Systems and Control Group Eindhoven Netherlands Eindhoven University of Technology Dept. of Mechanical Engineering Control Systems Technology Group Eindhoven Netherlands Eindhoven University of Technology Dept. of Mathematics and Computer Science Data Mining Group Eindhoven Netherlands ITER Organization Route de Vinon-sur-Verdon St. Paul Lez Durance13067 Cedex France

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A/B Testing and Audience Creation for Effective Digital Marketing: Evidences from Facebook Analytics

A/B Testing and Audience Creation for Effective Digital Mark...

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International Conference on Information Systems and computer Networks (ISCON)

作者： Nripendra Singh Anand Jaiswal Prakhar Singh Dilip Kr. Sharma Dept. of Management and Marketing PennWest University Clarion USA Dept. of Operations and SCM New Delhi Institute of Management Delhi India Computer Information Science Department PennWest University Clarion USA Dept. of Computer Engineering & Applications GLA University Mathura India

Digital marketers use a number of strategies, including custom audiences, lookalike audiences, ad creative, and A/B testing, to organize and enhance how they present advertisements to consumers. The purpose of this research is to show an effective method and process for running advertisements on Facebook more efficiently. The study has taken a case study of a company- Great Deal Tires and analyzed the Facebook analytics data and insights with A/B testing and audience creation approach to assess the digital marketing impact. The study has implemented three test strategies for designing and placing Facebook ads for Great Deal Tires in order to assess the marketing impact. In the first test, a custom audience was chosen, and an identical Facebook video ad’s impact was evaluated based on the duration of the ad. In the second test, A/B testing was used to test Facebook ads, and the results of the tests were compared across three different audience groups to determine how well the ads performed. The third test evaluated on the audience conversion in the terms of subscribing to the Page, in which three different ads were shown to three distinct audience groups in order to compare the effects of the various ads on the diverse audiences. The study’s findings provided important information about Facebook ad placement, its effects on various audience segments, and digital marketing strategies to increase audience outreach. The study is important in demonstrating that Facebook advertisements are an effective marketing medium and a digital marketer can achieve good outreach with a modest investment of funds.

关键词： Social networking (online) Companies Tires computer networks Testing Investment Information systems

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Machine Learning in Predictive Maintenance for Industrial Equipment

Machine Learning in Predictive Maintenance for Industrial Eq...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Saurabh Pratap Singh Rathore Anjali Gupta Akshat Parashar Dheeresh Upadhyay R.K. Deb Abhinav Gupta Dept. of Management ICAPSR New Delhi India Dept. of Pharmacy Madhav University Pindwara India Dept. of EEE Mangalayatan University Aligarh India Dept. of Mechanical Engineering Lingayas Vidyapeeth Faridabad India Dept. of Computer Science J.S. University Shikohabad India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The integration of machine learning into predictive maintenance strategies has revolutionized how industries manage equipment reliability & performance. This abstract examines the role of machine learning algorithms in predicting equipment failures & optimizing maintenance activities. By analyzing historical data, sensor information, & operational conditions, machine learning models can forecast potential failures, enabling preemptive maintenance actions that reduce unplanned down-times & extend equipment life. Techniques such as decision trees, neural networks, & clustering algorithms are explored for their effectiveness in detecting anomalies, predicting failures, & recommending maintenance schedules. This approach not only improves the efficiency of maintenance processes but also offers significant cost savings & safety enhancements. The research addresses practical challenges in deploying machine learning solutions, including data acquisition, model accuracy, & integration into existing maintenance systems. The abstract highlights the potential of these technologies to transform maintenance practices & drive future advancements in industrial equipment management.

关键词： Machine learning algorithms Automation Accuracy Machine learning Production Transforms Prediction algorithms Data models Maintenance Predictive maintenance

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LLM-Based Framework for Administrative Task Automation in Healthcare

LLM-Based Framework for Administrative Task Automation in He...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Senay A. Gebreab Khaled Salah Raja Jayaraman Muhammad Habib ur Rehman Samer Ellaham Dept. of Computer & Communication Engineering Khalifa University Abu Dhabi UAE Dept. of Management Science & Engineering Khalifa University Abu Dhabi UAE Dept. of Biomedical Engineering & Imaging Sciences King's College London London UK Improvement Dept. Heart & Vascular Institute & Continuous Cleveland Clinic Abu Dhabi UAE

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

Artificial Intelligence (AI) has been transformative in the healthcare sector, leading to enhanced precision in medical diagnosis, more effective treatment options, and a significant improvement in patient safety. However, computer-based administrative tasks, such as retrieval of medical and health records, patient registration, medical billing, filing and documentation, and appointment scheduling, still impose a heavy burden on healthcare professionals, causing a reduced quality of care and efficiency. In light of these challenges, this paper proposes a large language model (LLM)-based multi-agent framework designed to automate some of the administrative work in clinical settings. In our proposed solution, these LLM agents coordinate to parse instructions, breakdown tasks, and execute a sequence of actions in a workflow. They are equipped to not only execute documentation process at the database level but also operate directly on web-based electronic medical record (EMR) platforms. Moreover, the framework integrates data sources through a retrieval-augmented generation (RAG) system to allow streamlined interaction with patient information and medical records, mediated through an agent interface. The framework is designed with security in mind to defend against malicious prompts. We demonstrate the practicality of our solution by testing on various complex tasks that require the use of multiple tools and an EMR website. The result show the framework's effectiveness in handling diverse healthcare administrative tasks.

关键词： Processor scheduling Soft sensors Documentation Safety Security Medical diagnosis Task analysis

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