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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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GRADIENT-GUIDED IMPORTANCE SAMPLING FOR learning BINARY ENERGY-BASED MODELS 11

GRADIENT-GUIDED IMPORTANCE SAMPLING FOR LEARNING BINARY ENER...

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11th international conference on learning Representations, ICLR 2023

作者： Liu, Meng Liu, Haoran Ji, Shuiwang Department of Computer Science & Engineering Texas A&M University College StationTX77843 United States

learning energy-based models (EBMs) is known to be difficult especially on discrete data where gradient-based learning strategies cannot be applied directly. Although ratio matching is a sound method to learn discrete EBMs, it suffers from expensive computation and excessive memory requirements, thereby resulting in difficulties in learning EBMs on high-dimensional data. Motivated by these limitations, in this study, we propose ratio matching with gradient-guided importance sampling (RMwGGIS). Particularly, we use the gradient of the energy function w.r.t. the discrete data space to approximately construct the provably optimal proposal distribution, which is subsequently used by importance sampling to efficiently estimate the original ratio matching objective. We perform experiments on density modeling over synthetic discrete data, graph generation, and training Ising models to evaluate our proposed method. the experimental results demonstrate that our method can significantly alleviate the limitations of ratio matching, perform more effectively in practice, and scale to high-dimensional problems. Our implementation is available at https://***/divelab/RMwGGIS. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Importance sampling

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Smart Recruitment System 15

Smart Recruitment System

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Dugyala, Rakshitha Gaddam, Vinay Kumar Eroju, Harika Dantuluri, Mohana Varma Madhubabu, C.H. Bv Raju Institute Of Technology Dept. of Computer Science Engineering Medak Telangana Narsapur India

ISBN: (纸本)9798350370249

this project proposes the development of an advanced job applicants assessment and recruitment system that harnesses the power of artificial intelligence (AI) to streamline and enhance the hiring process. the system integrates cuttingedge AI algorithms, including natural language processing (NLP) and machine learning, to automate and optimize key stages of recruitment. the system begins with an AI-driven resume screening process that analyzes candidate resumes, extracting relevant information and evaluating qualifications against predefined criteria. Additionally, NLP algorithms are employed to comprehend the context and nuances of the resumes, enabling a more accurate assessment of candidates' skills and experiences. Furthermore, the system incorporates machine learning models to dynamically adapt and learn from past recruitment data, continually improving its ability to identify suitable candidates. this iterative learning process ensures a more effective and efficient selection of candidates over time. the intelligent-based system also facilitates automated interview scheduling, taking into account both candidate availability and interviewer preferences. By automating this process, the system optimizes the coordination of interviews, reducing the time and effort required from human resources personnel. Collaboration with HR professionals and data scientists is crucial for refining the system's algorithms and ensuring alignment with industry standards and ethical considerations. the proposed solution aims to revolutionize the traditional recruitment process by providing a robust, intelligent, and data-hy7driven approach to talent acquisition. © 2024 IEEE.

关键词： Artificial intelligence(AI) data-driven approach Ethical considerations HR collaboration intelligent recruitment Iterative learning Job applicants assessment Machine learning Natural language processing (NLP) Resume screening Talent acquisition

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Multi-Level Deep Reinforcement learning-based Edge Caching Strategies in Vehicular Networks 11

Multi-Level Deep Reinforcement Learning-based Edge Caching S...

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11th international Symposium on Telecommunication, IST 2024

作者： Soleymani, Taban Yazdani, Nasser Shariatpanahi, Seyed Pooya University of Tehran Electrical and Computer Engineering Department Tehran Iran

ISBN: (纸本)9798350356250

Recent advances in internet of vehicles (IoV) have highlighted the importance of vehicular networks in intelligent transportation systems (ITS). the rapid growth in the number of connected sensors and users requires a better networking scheme. However, the dynamic nature of actual networks poses significant challenges. In addition, content delivery should occur within a specific time frame, known as the delivery deadline, and conventional quality of service (QoS) metrics must be met. In this paper, we implemented cooperative edge caching strategies aimed at reducing the weighted average of energy consumption and content access delay in a vehicular networks while considering the data delivery deadline. By utilizing an abstract layer, along with federated deep reinforcement learning (DRL) and hierarchical DRL, we enhanced the efficiency of the caching scheme. this approach mitigates the challenges posed by the non-stationary nature of file popularity distribution and user behaviors. Our results show that the use of DRL agents in federated and hierarchical structures allows us to develop an effective caching strategy without requiring prior knowledge, such as file popularity distribution or user request patterns while improving cache hit rates, reducing data transmission power and access delay in IoV networks. © 2024 IEEE.

关键词： Deep reinforcement learning

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Integrating Deep learning and Ensemble Methods for Robust Tomato Disease Detection: A Hybrid CNN-RF Model Analysis 11

Integrating Deep Learning and Ensemble Methods for Robust To...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Chaudhary, Preeti Verma, Aditya Kukreja, Vinay Sharma, Rishabh Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science and Engineering Uttrakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350350357

the timely and accurate diagnosis of plant diseases is what determines the efficiency of farming and food security in the world. In the situation of tomato cultivation, diseases like tomato spot, wilt, blight, and rot are the ones that cause a lot of damage to the yield and quality. this paper presents a new hybrid model that combines convolutional neural networks (CNN) and random forest (RF) for the effective and accurate classification of tomato diseases. the model uses the CNN deep learning capabilities for feature extraction from photos of the tomato leaves and then utilizes RF robust classification for differentiating between four major disease categories and healthy ones. Our approach includes data collection and preprocessing, design and training of model architecture, and performance evaluation through various metrics. the hybrid model has been rigorously assessed on a dataset containing images of diseased and healthy tomato leaves, all in all yielding an impressive overall accuracy of 97.03%. this framework not only expresses the utility of this model as a trustworthy instrument for agricultural disease management, but it also clearly surpasses other models in the field. the results of the study emphasize the advantage of CNN and RF integration, which confirm higher performance in terms of disease classification accuracy, precision, recall, and F1 score. the success of the hybrid model leads to an exploration of the potential role of artificial intelligence for farmers in agriculture, as it demonstrates a promising way to tackle the existing challenges of plant disease detection and classification. By its high accuracy and stable performance, this model is an important part of creating smart and automated agricultural diagnostic systems, which directly leads to enhanced disease management, increased crop yields, and sustainable agriculture. © 2024 IEEE.

关键词： Convolutional neural networks

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Fault Detection On Heat Pump Operational data Using Machine learning Algorithms 11

Fault Detection On Heat Pump Operational Data Using Machine ...

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11th international conference on Internet of things: Systems, Management and Security, IOTSMS 2024

作者： Rahman, Md Mahbubur Malekian, Reza Akerstroem, Vilhelm Malmö University Department of Computer Science and Media Technology Malmö20506 Sweden Malmö University Internet of Things and People Research Centre Malmö20506 Sweden Engineering Software Robert Bosch GmbH Lund Sweden

ISBN: (纸本)9798350366501

Heat pumps, being complex systems, are susceptible to various malfunctions. By harnessing contemporary IoT technologies, these devices continuously transmit data which enables monitoring, maintenance, and efficiency. this study focuses on identifying compressor short duration cycles as faults through supervised machine learning algorithms such as XGBoost, Random Forest, SVM, and k-NN. data preprocessing and labeling were conducted using extensive logged data from heat pump systems, addressing issues like high dimensionality, data sparsity, and temporal dependencies. the methodology included feature engineering, interpolation of missing data, and downsampling for compressor short duration cycles. Supervised machine learning models were applied to classify these short duration cycles. Among the models, XGBoost achieved the highest accuracy and F1-scores, effectively distinguishing between normal and fault conditions. the findings highlight the potential of machine learning to enhance predictive maintenance and operational efficiency in heat pumps. © 2024 IEEE.

关键词： Predictive maintenance

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Auto-encoders for Detection and Counting of Live/Dead Cells 11

Auto-encoders for Detection and Counting of Live/Dead Cells

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11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Melouk, Omar Klingner, Anke Elmasry, Ramez M. Salem, Mohammed A.-M. German University in Cairo Media Engineering and Technology Cairo Egypt German University in Cairo Media Engineering and Technology Department of Physics Cairo Egypt

ISBN: (纸本)9798350322101

Counting cells and detecting their viability is a task of utmost importance in the medical field, but this task has only 2 ways, namely manual and automatic cell counting. Manual cell counting is reliant on the human counting the cells and classifying them as viable or non-viable and therefore is prone to human error is monotonous and time-consuming and relies on the experience of the counter. Automatic cell counting is accurate, but not all labs are equipped with an automatic counter, and that is because of its high price. One could leverage the power of machine learning to create a machine that could count cells and classify them effectively and without the high cost of automatic cell counters. the methodology contains the methods of acquiring a dataset, how to create binary images for this dataset, and experimentation with 3 different machine learning models, namely VGG-16 Unet, ResUnet, and Attention ResUnet. the models predict maps of the following accuracy values: VGG-16 U-net scored 82%, ResUnet scored 86%, and Attention ResUnet 82%, with training images equal to 402 images, which is considered very good compared to other models that usually use data sets with double or triple the images used in this paper. © 2023 IEEE.

关键词： Cells

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Loan Default Prediction Using Machine learning 11

Loan Default Prediction Using Machine Learning

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Robinson, Natasha Sindhwani, Nidhi Aiit Amity University Noida India

ISBN: (纸本)9798350350357

the project titled 'Loan Default Prediction Using Machine learning' has been developed with the aim of enhancing the evaluation of credit risk in financial institutions. Traditional models for credit scoring often encounter difficulties in capturing the intricate financial behaviours, thus necessitating the utilization of advanced machine learning techniques. By making use of a comprehensive dataset that incorporates information about borrowers as well as historical financial data, this project employs algorithms such as Random Forest and Gradient Boosting. through the preprocessing of data and the application of feature engineering methods, the dataset is optimized, and the performance of the models is thoroughly evaluated using metrics such as accuracy and precision. the selected models are further refined through the process of hyperparameter tuning, ensuring that their predictive capabilities are optimized. the success of this project lies in its provision of a reliable tool to financial institutions, enabling them to accurately predict the risks associated with loan defaults. Once validated, the final model seamlessly integrates into the existing loan processing systems, thereby empowering lenders to make more well-informed decisions. By contributing to the advancement of credit risk assessment methodologies through machine learning, this project is poised to bring about a revolutionary change in the way financial institutions manage their loan portfolios, offering improved accuracy and efficiency in the identification of potential loan defaults. © 2024 IEEE.

关键词： Forecasting

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Blueprint of Tomorrow: Contrasting Off-Line and On-Line Drawing Tasks for Alzheimer's Disease Screening 25th

Blueprint of Tomorrow: Contrasting Off-Line and On-Line Draw...

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25th international conference on intelligent data engineering and automated learning

作者： Hosseini-Kivanani, Nina Salobrar-Garcia, Elena Elvira-Hurtado, Lorena Salas, Mario Schommer, Christoph Leiva, Luis A. Univ Luxembourg Esch Sur Alzette Luxembourg Ramon Castroviejo Inst Ophthalmol Res Madrid Spain Univ Complutense Madrid Madrid Spain Hosp Clin San Carlos Geriatr Serv Memory Unit Madrid Spain

ISBN: (纸本)9783031777301;9783031777318

Alzheimer's disease (AD) is the leading cause of dementia. Although there is currently no cure for AD, early detection of cognitive decline can help clinicians mitigate its impact. Recently, Machine learning (ML) approaches have been developed to automatically analyze handwriting and hand-drawing tasks to support the early diagnosis of AD. In this paper, we study pentagon and clock drawing tests using both off-line (scanned image pixels) and on-line (discrete point sequences) data as input to several ML models (i.e., DensNet, ResNet, EfficientNet, RNN, LSTM, and GRU). Our study is the first to determine the most effective modality (on-line vs. off-line) and drawing tasks to distinguish healthy controls from AD patients (binary classification) as well as two stages of AD severity (multi-class classification). Our results suggest that, contrary to other domains, the off-line modality outperforms the on-line one, sometimes by a large margin: 90% vs. 60% accuracy in binary classification and 53% vs. 82% accuracy in multi-class classification. this suggests that, for drawing tasks and small-scale datasets, image-based representations may be more effective in predicting AD than those relying on more complex data representations.

关键词： Alzheimer disease Off-line handwriting On-line handwriting Deep learning data augmentation Classification

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Modeling Messaging Metadata to Identify Digital Disagreements among Non-incarcerated Adolescents in the Juvenile Justice System 11

Modeling Messaging Metadata to Identify Digital Disagreement...

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11th international conference on Affective Computing and intelligent Interaction (ACIIW)

作者： Pandey, Harshit Rizzo, Christie Collibee, Charlene Sathyanarayana, Aarti Northeastern Univ Khoury Coll Comp Sci Boston MA 02115 USA Northeastern Univ Bouve Coll Hlth Sci Boston MA 02115 USA Brown Univ Warren Alpert Med Sch Providence RI 02912 USA

ISBN: (纸本)9798350327434

the psychological wellbeing of adolescents, including their relationships with their parents and friends, is highly affected by romantic relationship conflict. At-risk youth - who are involved with the juvenile justice system and are especially vulnerable to aggression, defiance, and behavioral problems experience relationship conflict at rates that exceed those of youth in the community. the pervasive usage of mobile technology by adolescents provides unprecedented data on their social interactions, communication patterns, and romantic relationship conflicts;as well as an opportunity to develop timely interventions to improve their wellbeing. this study investigates the potential of utilizing text messaging metadata from conversations with parental figures, friends, and other social connections for scalable early identification of moments of disagreement in romantic relationships in non-incarcerated, justice-involved youth. We utilize changepoint detection on text message metadata across various relationships, including the number of messages, length of messages, and response time, to identify changes in communication patterns across hours, days, and weeks, to estimate the day of a romantic disagreement. When comparing our predicted disagreement times to the current standard of intervention delivery, at random, we found statistically significant improvements in predicting disagreements with romantic partners. Our findings demonstrate that messaging metadata can effectively identify moments of disagreement in adolescents, and can be used for the delivery of just-in-time interventions for high-risk adolescents.

关键词： human-computer interaction ubiquitous computing law and social sciences machine learning mobile data change point detection juvenile delinquents

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UNI-MOL: A UNIVERSAL 3D MOLECULAR REPRESENTATION learning FRAMEWORK 11

UNI-MOL: A UNIVERSAL 3D MOLECULAR REPRESENTATION LEARNING FR...

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11th international conference on learning Representations, ICLR 2023

作者： Zhou, Gengmo Gao, Zhifeng Ding, Qiankun Zheng, Hang Xu, Hongteng Wei, Zhewei Zhang, Linfeng Ke, Guolin Renmin University of China China DP Technology AI for Science Institute Beijing China

Molecular representation learning (MRL) has gained tremendous attention due to its critical role in learning from limited supervised data for applications like drug design. In most MRL methods, molecules are treated as 1D sequential tokens or 2D topology graphs, limiting their ability to incorporate 3D information for downstream tasks and, in particular, making it almost impossible for 3D geometry prediction/generation. In this paper, we propose a universal 3D MRL framework, called Uni-Mol, that significantly enlarges the representation ability and application scope of MRL schemes. Uni-Mol contains two pretrained models with the same SE(3) Transformer architecture: a molecular model pretrained by 209M molecular conformations;a pocket model pretrained by 3M candidate protein pocket data. Besides, Uni-Mol contains several finetuning strategies to apply the pretrained models to various downstream tasks. By properly incorporating 3D information, Uni-Mol outperforms SOTA in 14/15 molecular property prediction tasks. Moreover, Uni-Mol achieves superior performance in 3D spatial tasks, including protein-ligand binding pose prediction, molecular conformation generation, etc. the code, model, and data are made publicly available at https://***/dptech-corp/Uni-Mol. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Proteins

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