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34th ACM Web Conference, WWW 2025

作者： Li, Yishuo Zhang, Qi Lu, Wenpeng Peng, Xueping Zhang, Weiyu Si, Jiasheng Gong, Yongshun Hu, Liang Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Department of Computer Science and Technology Tongji University Shanghai China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia School of Software Shandong University Jinan China

ISBN: (纸本)9798400712746

Medication recommendation aims to suggest personalized drug combinations to patients based on their longitudinal medical histories stored in electronic health record (EHR) datasets. Patients’ Dynamic Treatment Regimes (DTRs) determine how patients’ drug combinations change along with the evolution of disease treatment. DTRs are effective for comprehending disease-treatment dynamics and for recommending a timely and personalized combination of medications for patients. However, existing medication recommender systems (MRSs) overlook the multiple treatment pathways generated by the intervention of DTRs and can only recommend a single treatment paradigm, ignoring the fact that patients may be at different treatment stages and thus require different treatment regime. Such disregard leads to a significant limitation in recommending personalized medication combinations tailored to different treatment stages, yielding greatly compromised accuracy and applicability of MRSs. Moreover, existing methods often overlook the time interval information over patients’ successive visits, which is critical to indicate patients’ treatment evolution. To address these significant gaps, we propose a Time-aware Medication Recommendation Framework via Intervention of Dynamic Treatment Regimes, called MR-DTR. To explicitly illustrate the intervention processes of DTRs on similar patients, we employ a co-guided graph to connect various patient sequences. In addition, to fully utilize the time interval information, we design a time-aware guidance mechanism dedicated to the co-guided graph to efficiently learn medication representation using the patient’s guidance information. We also introduce relative time intervals in the encoder to act as positional information. Extensive experiments on two real-world datasets demonstrate that MR-DTR surpasses state-of-the-art models in terms of recommendation performance. © 2025 Copyright held by the owner/author(s).

关键词： Electronic health record

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Modeling and Evaluating Stock Market and Commodity Price Fluctuation Using Time Series Sequence Learning

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SN computer science 2025年第5期6卷 1-18页

作者： Veningston, K. Ronalda, M. Shaikh, Tawseef Ayoub Gazi, Farhaan Bhagat, Sanket Department of Computer Science and Engineering National Institute of Technology Srinagar Jammu and Kashmir Srinagar 190006 India

Algorithmic trading using neural network models has gained significant attention in predicting stock and commodity price fluctuations and understanding their dynamics. Deep learning techniques have shown significant results in predicting stock market prices in recent years. Modelling stock market price dynamics using sequence learning techniques leverages the historical trends and dependencies in price data to predict future price movements. This paper presents a comprehensive analysis of Autoregressive Integrated Moving Average (ARIMA), standard neural network, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) for forecasting stock movements in various industry stocks. The main objective is to assess the performance of each model in predicting stock price fluctuations. Each model undergoes systematic validation and evaluation based on key performance indicators such as accuracy, normalised root mean squared error (NRMSE), and Weighted Mean Absolute Percentage Error (WMAPE). The forecast made by each model is compared to the actual stock prices to measure the effectiveness of the forecasting methods. This paper presents a time series sequence learning approach for modelling and evaluating stock market price fluctuation. The proposed approach uses an LSTM network to learn the sequential patterns in stock price data. The LSTM network is trained on a historical dataset of stock prices, and it is then used to predict future stock prices. The proposed approach is evaluated on a dataset of stock prices from seven different industry domains, including Investment, Infrastructure, Information technology, Pharma, Fast Moving Consumer Goods and agri-business (FMCG), Vehicles, and Banking, which enables a comprehensive evaluation of various models. The results show that the proposed LSTM approach can achieve notable accuracy in predicting future stock prices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Lt

关键词： Algorithmic trading Sequence learning Share price dynamics Stock market price forecast Time-series analysis

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Environmental water quality prediction based on COOT-CSO-LSTM deep learning

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Environmental science and Pollution Research 2024年第42期31卷 54525-54533页

作者： Rajagopal, Sankarasubbu Ganesh, Sundaram Sankar Karthick, Alagar Sampradeepraj, Thangavel Department of Information Technology National Engineering College Tamilnadu Kovilpatti628503 India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu Thiruvallur601206 India Renewable Energy Lab Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu Virudhunagar626126 India

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model’s performance. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai River, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird’s reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alternate framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Macroinvertebrates

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Hybrid Recommender System for E-Learning Platforms 14

Hybrid Recommender System for E-Learning Platforms

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14th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Sujithra, M. Rajarajeshwari, K. Dhamodaran, B. Department of computing – Data Science Coimbatore Institute of Technology Coimbatore India

Internet use has steadily increased over the past 25 years. Numerous online services are improving and simplifying life for people. E-learning is one of these services, which makes the learning process simpler. In this work, a recommendation system will be presented that will assist online course providers in automating course suggestion. By finding patterns in courses based on some attributes like field of interest, skill set, educational qualification, course content, etc. of an individual and courses, this recommendation system is built using hybrid machine learning recommendation techniques, i.e., combination of Collaborative (item based) and content-based recommendation and KNN classifiers. Lastly, the recommendations are pooled from the two blocks of recommended courses (which were discovered using a collaborative technique), similar courses (which is found using content-based finding). © Grenze Scientific Society, 2023.

关键词： Collaborative filtering

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Smart Agriculture: Advanced Rice Disease Detection Using YOLOv10 for Enhanced Crop Health Management 3

Smart Agriculture: Advanced Rice Disease Detection Using YOL...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Surya, V. Santhi, S. Department of Computer Science and Engineering KIT – Kalaignarkarunanidhi Institute of technology Coimbatore India KIT – Kalaignarkarunanidhi Institute of technology Coimbatore India

ISBN: (纸本)9798331532420

Rice, an essential crop for global food security, faces significant threats from diseases that impact both yields and economic stability. While traditional methods like SVM, KNN, and CNNs have contributed to disease detection, their limitations in real-time application and scalability hinder their effectiveness in practical field scenarios. This research highlights YOLOv10 as a groundbreaking deep learning model capable of detecting rice leaf diseases with remarkable speed and accuracy. Its advanced design overcomes the shortcomings of earlier techniques, facilitating real-time diagnosis and classification of plant diseases. By ensuring timely interventions and minimizing crop damage, YOLOv10 supports sustainable farming practices and reduces agricultural losses. This innovative solution represents a significant step forward in modern, efficient crop management. © 2024 IEEE.

关键词： Smart agriculture

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A Various Approaches of Machine Learning Algorithms for Kidney Disease Prediction

A Various Approaches of Machine Learning Algorithms for Kidn...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Gomathi, G. Sumathy, V. Sivakamasundari, P. Deepa, R. Srm Institute of Science and Technology Department of Computing Technologies TN Kattankulathur603203 India St. Joseph's Institute of Technology Department of Information Technology TN Chennai India

ISBN: (纸本)9798331543617

Chronic kidney disease is a serious health issue that affects people around the world. Proper diagnosis and treatment must be initiated as soon as feasible. The primary purpose of this research is to detect the existence or absence of CKD in the human body by analyzing a number of features obtained from a few medical tests. This research presents an ML-CKDP model that aims to improve dataset preprocessing for CKD classification and provide a web-based application for prediction. The proposed model includes a full data pretreatment protocol for transforming categorical variables to numerical values, imputing missing data, and normalizing using Min-Max scaling. To improve the datasets, a variety of techniques are used, including correlation, chi-squared, variance threshold, recursive feature elimination, sequential forward selection, lasso regression, and ridge regression. To predict CKD, the model uses four classifiers: K-Neighbors (KNC), Random Forest Classifiers (RFC), XgBoost (XgB), and Gausian Naive Bayes (GNB). The models' effectiveness is evaluated by testing their accuracy, examining the confusion matrix, and computing the Precision, Recall, F1 score, and support. In contrast to other algorithms performing on the same dataset, K-Neighbors classifiers (KNC) achieve an 89% accuracy rate, Random Forest Classifiers (RFC) and Gausian Naive Bayes (GNB) produce an 87% accuracy rate, and XgBoost (XgB) achieve a 99% accuracy rate to predict CKDs. The XgBoost classifier has the highest performance indicators. This study concludes that current advances in machine learning, as well as the use of predictive modeling. © 2024 IEEE.

关键词： Random forests

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Dual Variational Generation Framework for Enhanced Cross-Modal Face Recognition 15

Dual Variational Generation Framework for Enhanced Cross-Mod...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Evany Anne, M. Brindha, M. Sivakumaran, N. National Institute of Technology Computer Science and Engineering Tiruchirappalli India National Institute of Technology Instrumentation and Control Engineering Tiruchirappalli India

ISBN: (纸本)9798350370249

One of the major challenges in face recognition is accurately identifying faces under varying lighting conditions, leading to a problem known as Heterogeneous Face Recognition (HFR). The objective is to integrate the capabilities of the modified Dual Variational Generator (DVG-Face) framework with traditional recognition methods, such as Local Binary Pattern Histogram (LBPH) and the HaarCascade framework, to provide a robust solution to the challenges of heterogeneous face recognition. This involves developing and implementing a model that successfully handles the complexity of HFR tasks utilizing synthetic images generated from Near-Infrared (NIR) and Visible (VIS) modalities. To guarantee identity consistency in the produced paired heterogeneous images, pairwise identity preserving loss is also used. The integration of Haar Cascades, the LBPH algorithm, and the DVG approach, along with person reidentification using the SSRL model, establishes a reliable framework for facial recognition in real-world security applications. © 2024 IEEE.

关键词： Face recognition

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Enhancing Cyclone Center Identification in Radar Images through Deep Learning and Match Recognition 15

Enhancing Cyclone Center Identification in Radar Images thro...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shanmugam, Thirumurugan Chandrashekar, Chirag Subburaj, Maheswari Sivaraman, Arun Kumar Sultana, Ajmery Thangavel, Senthil Kumar University of Technology and Applied Sciences College of Computing and Information Sciences Suhar Oman Vellore Institute of Technology School of Computer Science and Engineering Chennai India Photon Interactive Inc Digital Engineering Solution Center ON Canada Algoma University Brampton School of Computer Science and Technology ON Canada Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone's center is vital for predicting its intensity and trajectory. However, challenges persist in automatically locating the center due to the diverse nature of cyclone morphology and structure. To address this, the deep convolutional network's capability is leveraged to capture various structural features in images by proposing two-step approach for cyclone center localization. Initially, a pre-trained EfficientDet model is employed using transfer learning to obtain weights. Subsequently, these weights, along with the data, are utilized in a deep learning model to provide precise coordinates of the cyclone center in the respective image. The effectiveness of existing deep learning and machine learning models show that the cyclone prediction systems have an accuracy ranging from 86% to 92% or better, and cyclone eye detection accuracy surpassing 87%. Experiment outcomes indicate that the proposed methodology outperforms conventional methods and existing works, showcasing its potential for enhancing cyclone monitoring and forecasting. © 2024 IEEE.

关键词： Cyclone detection deep learning efficient det and YOLO model transfer learning

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Homomorphic Encryption Library, Framework, Toolkit and Accelerator: A Review

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SN computer science 2024年第1期5卷 24页

作者： Dhiman, Shalini Mahato, Ganesh Kumar Chakraborty, Swarnendu Kumar Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Jote 791113 India

Homomorphic encryption ensures secure computation on encrypted data without the need for decryption beforehand. It enables the secure offloading of computations to untrusted servers. This paper provides a comprehensive description of multiple methods for conducting secure computations, along with the appropriate approaches and tools needed for successful implementation. Various strategies are outlined for selecting a suitable homomorphic encryption (HE) library, assisting developers and researchers in determining the most suitable library for their projects. To begin with, the paper presents a comparison of homomorphic encryption libraries based on different parameters. Additionally, it outlines the steps involved in choosing the right framework for effective implementation, comparing them based on various parameters. The Framework discussed in the paper supports a range of homomorphic encryption libraries and provides detailed information about them. Furthermore, the paper surveys three different homomorphic encryption accelerators and compares them to determine which one would maximize bootstrapping throughput when implemented. Lastly, the Fully Homomorphic Encryption-IBM toolkit is discussed. This toolkit supports the development of resources, primarily involved in the flow of messages. The paper concludes that secure computation is achievable by implementing the appropriate tools, considering their performance and implementation limitations. Moreover, the selection of the appropriate library, framework, and accelerator depends on the specific demands and requirements of the implementation chosen by the developer. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Evervault encryption engine HEaaN Homomorphic encryption IBM toolkit MS SEAL Pallisade

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FERMixNet: An Occlusion Robust Facial Expression Recognition Model With Facial Mixing Augmentation and Mid-Level Representation Learning

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IEEE Transactions on Affective computing 2025年第2期16卷 639-654页

作者： Huang, Yansong Peng, Junjie Zhang, Wenqiang Zhao, Tong Chen, Gan Tan, Shuhua Yi, Fen Wang, Lu Shanghai University School of Computer Engineering and Science Shanghai200444 China Shanghai University School of Computer Engineering and Science Shanghai Institute for Advanced Communication and Data Science Shanghai200444 China Fudan University Academy for Engineering and Technology School of Computer Science and Technology Shanghai200437 China YTO Express Company Ltd. National Logistics Engineering Laboratory Shanghai201708 China

Facial expressions can provide a better understanding of people’s mental status and attitudes towards specific things. However, facial occlusion in real world is an unfavorable phenomenon that greatly affects the performance of facial expression recognition models. Recent works addressing the occlusion problem have primarily relied on attention mechanisms or occlusion discarding methods that focus on non-occluded regions of the face. However, these methods have not achieved a good balance between occlusion robustness and model efficiency. In this paper, we propose a simple and efficient model, called FERMixNet, for occluded facial expression recognition. The model incorporates a novel facial mixing augmentation strategy (FERMix) that generates new training samples by simulating real-world facial occlusion and preserving high expression-related semantic information. By co-training the original and newly generated samples, the model’s occlusion robustness is improved without increasing its complexity during inference. Additionally, to further enhance the model’s occlusion robustness, we include mid-level representation learning in the network to learn the discriminative non-occluded local features of the samples with low computational cost. Extensive experiments on four public facial occlusion datasets: Occlusion-RAF-DB, Occlusion-FERPlus and FED-RO show that the proposed model achieves state-of-the-art results which demonstrates the good robustness of our method for occluded facial expression recognition. Meanwhile, the proposed model also achieves state-of-the-art results on the in-the-wild facial expression datasets RAF-DB, AffectNet-8, and AffectNet-7. It proves that the proposed model has good application prospects in real world. © 2010-2012 IEEE.

关键词： Face recognition

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