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Artificial Neural Network Modeling for Predicting Thermal Conductivity of EG/Water-Based CNC Nanofluid for Engine Cooling Using Different Activation Functions

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Frontiers in Heat and Mass Transfer 2024年第2期22卷 537-556页

作者： Munirul Hasan Mustafizur Rahman Mohammad Saiful Islam Wong Hung Chan Yasser M.Alginahi Muhammad Nomani Kabir Suraya Abu Bakar Devarajan Ramasamy Faculty of Computing Universiti Malaysia Pahang Al-Sultan AbdullahPekanPahang26600Malaysia Faculty of Mechanical and Automotive Engineering Technology Universiti Malaysia Pahang Al-Sultan AbdullahPekanPahang26600Malaysia Department of Management and Information Technology St.Francis College179 Livingston StBrooklynNY11201USA Dell Technologies Data Analyticsand AutomationBukit TengahBukit Mertajam14000Malaysia Department of Computer Science Adrian CollegeAdrianMIUSA Department of Computer Science&Engineering United International University BangladeshUnited CityMadani AvenueBaddaDhaka1212Bangladesh

A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature *** most radiators that are used to cool an engine,water serves as a cooling *** performance of a radiator in terms of heat transmission is significantly influenced by the incorporation of nanoparticles into the cooling *** and uniformity of nanoparticle distribution are the two major factors for the practical use of *** shape and size of nanoparticles also have a great impact on the performance of heat *** researchers are investigating the impact of nanoparticles on heat *** study aims to develop an artificial neural network(ANN)model for predicting the thermal conductivity of an ethylene glycol(EG)/waterbased crystalline nanocellulose(CNC)nanofluid for cooling internal combustion *** implementation of an artificial neural network considering different activation functions in the hidden layer is made to find the bestmodel for the cooling of an engine using the *** of the model with different activation functions in artificial neural networks are analyzed for different nanofluid concentrations and *** artificial neural networks,Levenberg–Marquardt is an optimization approach used with activation functions,including Tansig and Logsig functions in the training *** findings of each training,testing,and validation phase are presented to demonstrate the network that provides the highest level of *** best result was obtained with Tansig,which has a correlation of 0.99903 and an error of 3.7959×10^(–8).It has also been noticed that the Logsig function can also be a good model due to its correlation of 0.99890 and an error of 4.9218×10^(–8).Thus ourANNwith Tansig and Logsig functions demonstrates a high correlation between the actual output and the predicted output.

关键词： Artificial neural network activation function thermal conductivity nanocellulose

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Causal Reasoning in Large Language Models using Causal Graph Retrieval Augmented Generation

Causal Reasoning in Large Language Models using Causal Graph...

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Conference on Human System Interactions, HSI

作者： Chamod Samarajeewa Daswin De Silva Evgeny Osipov Damminda Alahakoon Milos Manic Centre for Data Analytics and Cognition La Trobe University Australia Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Department of Computer Science Virginia Commonwealth University Richmond USA

ISBN: (数字)9798350362916

ISBN: (纸本)9798350362923

Large Language Models (LLMs) are leading the Generative Artificial Intelligence transformation in natural language understanding. Beyond language understanding, LLMs have demonstrated capabilities in reasoning tasks, including commonsense, logical, and mathematical reasoning. However, their proficiency in causal understanding has been limited due to the complex nature of causal reasoning. Several recent studies have discussed the role of external causal models for improved causal understanding. Building on the success of Retrieval-Augmented Generation (RAG) for factual reasoning in LLMs, this paper introduces a novel approach that utilizes Causal Graphs as external sources for establishing causal relationships between complex vectors. This method is empirically evaluated using two benchmark datasets across the metrics of Context Relevance, Answer Relevance, and Grounding, in its ability to retrieve relevant context with causal alignment. The retrieval effectiveness is further compared with traditional RAG methods that are based on semantic proximity.

关键词： Measurement Limiting Large language models Semantics Benchmark testing Cognition Vectors

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Parm: Efficient Training of Large Sparsely-Activated Models with Dedicated Schedules

Parm: Efficient Training of Large Sparsely-Activated Models ...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xinglin Pan Wenxiang Lin Shaohuai Shi Xiaowen Chu Weinong Sun Bo Li Data Science and Analytics Thrust The Hong Kong University of Science and Technology (Guangzhou) Department of Computer Science Hong Kong Baptist University School of Computer Science and Technology Harbin Institute of Technology Shenzhen Department of Computer Science and Engineering The Hong Kong University of Science and Technology

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Sparsely-activated Mixture-of-Expert (MoE) layers have found practical applications in enlarging the model size of large-scale foundation models, with only a sub-linear increase in computation demands. Despite the wide adoption of hybrid parallel paradigms like model parallelism, expert parallelism, and expert-sharding parallelism (i.e., MP+EP+ESP) to support MoE model training on GPU clusters, the training efficiency is hindered by communication costs introduced by these parallel paradigms. To address this limitation, we propose Parm, a system that accelerates MP+EP+ESP training by designing two dedicated schedules for placing communication tasks. The proposed schedules eliminate redundant computations and communications and enable overlaps between intra-node and inter-node communications, ultimately reducing the overall training time. As the two schedules are not mutually exclusive, we provide comprehensive theoretical analyses and derive an automatic and accurate solution to determine which schedule should be applied in different scenarios. Experimental results on an 8-GPU server and a 32-GPU cluster demonstrate that Parm outperforms the state-of-the-art MoE training system, DeepSpeed-MoE, achieving 1.13× to 5.77× speedup on 1296 manually configured MoE layers and approximately 3× improvement on two real-world MoE models based on BERT and GPT-2.

关键词： Training Schedules Costs Processor scheduling Computational modeling Prototypes Graphics processing units

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FEDERATED INVERSE PROBABILITY TREATMENT WEIGHTING FOR INDIVIDUAL TREATMENT EFFECT ESTIMATION

arXiv

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arXiv 2025年

作者： Yin, Changchang Chen, Hong-You Chao, Wei-Lun Zhang, Ping Department of Computer Science and Engineering The Ohio State University ColumbusOH United States Department of Computer Science and Engineering Department of Biomedical Informatics Translational Data Analytics Institute The Ohio State University ColumbusOH United States

Individual treatment effect (ITE) estimation is to evaluate the causal effects of treatment strategies on some important outcomes, which is a crucial problem in healthcare. Most existing ITE estimation methods are designed for centralized settings. However, in real-world clinical scenarios, the raw data are usually not shareable among hospitals due to the potential privacy and security risks, which makes the methods not applicable. In this work, we study the ITE estimation task in a federated setting, which allows us to harness the decentralized data from multiple hospitals. Due to the unavoidable confounding bias in the collected data, a model directly learned from it would be inaccurate. One well-known solution is Inverse Probability Treatment Weighting (IPTW), which uses the conditional probability of treatment given the covariates to re-weight each training example. Applying IPTW in a federated setting, however, is non-trivial. We found that even with a well-estimated conditional probability, the local model training step using each hospital’s data alone would still suffer from confounding bias. To address this, we propose FED-IPTW, a novel algorithm to extend IPTW into a federated setting that enforces both global (over all the data) and local (within each hospital) decorrelation between covariates and treatments. We validated our approach on the task of comparing the treatment effects of mechanical ventilation on improving survival probability for patients with breadth difficulties in the intensive care unit (ICU). We conducted experiments on both synthetic and real-world eICU datasets and the results show that FED-IPTW outperform state-of-the-art methods on all the metrics on factual prediction and ITE estimation tasks, paving the way for personalized treatment strategy design in mechanical ventilation usage. © 2025, CC BY-NC-SA.

关键词： Inverse problems

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Mining fuzzy local periodic activity pattern for Smart home applications

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Knowledge-Based Systems 2024年 293卷

作者： Iqbal, Mohammad Prayitno, Adila Sekarrati Dwi Pao, Hsing-Kuo Mukhlash, Imam Department of Mathematics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Sukolilo East Java Surabaya60111 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Perceiving resident movements from ubiquitous sensor devices can aid health and safety management in smart home environment. One can find resident movement patterns to get notification about what should be aware of to prepare for possible accidents. The goal of this work is to propose a method to search for periodic residents’ activity patterns given smart home applications. Specifically, the proposed method is aimed to capture fuzzy local periodic activity patterns (FLPAP) from the smart home data. A FLPAP is an activity pattern that periodically occurs for a particular duration in fuzzy time intervals. We construct an algorithm to help to find the FLPAP called Fuzzy Local Periodic Activity Pattern Miner (FLoPMiner). On the side, a parameter called k-scale period is well-chosen to reorganize the data to make it suitable for the need of periodic activity pattern mining. The proposed method was demonstrated for its effectiveness on a public smart home dataset. After all, we successfully retrieved periodic resident activity patterns, which can be applied to self-health management systems, especially when focusing on a precise scale period. Overall, the proposed method shows descent pattern representation against the state-of-the-art methods of the same category. © 2024 Elsevier B.V.

关键词： Automation

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Towards Graph-Based Semi-Supervised Learning on Audio Embeddings for Label Classification

Towards Graph-Based Semi-Supervised Learning on Audio Embedd...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Rishabh Chandaliya Mohan Timilsina John Breslin Martin Serrano Department of Computer Science University of Galway Ireland Insight SFI Research Centre for Data Analytics Galway Ireland

The increasing importance of audio-based healthcare diagnostics, particularly in chronic respiratory problems, has prompted the development of novel approaches for disease classification using the full spectrum analysis of cough sounds. This paper presents an innovative study exploring the potential of employing supervised and semi-supervised learning methodologies for disease categorization based on cough audio samples. Specifically, this study focuses on scenarios characterized by a shortage of annotated data related to chronic diseases. The objectives of our study involved the utilization of standard machine learning algorithms for direct classification based on embeddings, as well as the integration of Graph Neural Networks (GNNs) on the KNN graph. Preliminary results indicate that GNN models consistently outperformed traditional classifiers. For instance, with just 1 % of the data, GAT and GCN achieved AUC PR values of 0.84 and 0.87, respectively, surpassing all traditional methods. The superiority mentioned above was sustained even when the data fraction was augmented to 3% and 5%. The usefulness of graph neural networks (GNNs) was further supported by comprehensive performance measures, wherein the graph convolutional network (GCN) exhibited exceptional preci-sion and PR (precision-recall). In summary, the AudioVecDiagnosis framework presents a promising opportunity for further investigation in audio-based healthcare diagnostics. It provides an optimum approach for situations with a limited availability of labeled data.

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Prediction of Compressive Strength of Green Concrete by Artificial Neural Network 1st

Prediction of Compressive Strength of Green Concrete by Arti...

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1st International Conference on Advanced Communication and Intelligent Systems, ICACIS 2022

作者： Verma, Manvendra Upreti, Kamal Dadhich, Priyanka Ghosh, Soumi Khatri, Vishal Singh, Prashant Department of Civil Engineering GLA University Uttar Pradesh Mathura India Department of Computer Science and Engineering Akhilesh Das Gupta Institute of Technology and Management Delhi India Department of Big Data Analytics Asia Pacific Institute of Management Delhi India Department of Information Technology Maharaja Agrasen Institute of Technology Delhi India Department of Computer Science and Engineering Bhagwan Parshuram Institute of Technology Delhi India Department of Computer Science and Engineering Sunder Deep Engineering College Ghaziabad India

ISBN: (纸本)9783031250873

The concrete business has just come out with a brand-new product called geopolymer concrete. Because of its resilience in harsh environments and its high strength, it has the potential to revolutionise the whole construction industry. It is an excellent substitute for the traditional kind of concrete. It outperforms traditional concrete in terms of sustainability, ecology, durability, and economic viability. In this day and age, the use of machine learning strategies is not just the future of the research and development industry, but of all industries. These methods provide predictions about the outcomes by analysing the facts from the past. The process of determining the outcomes or worth of anything in the construction sector is very challenging, time consuming, and arduous. Without having to conduct destructive experiments or take samples, the strength of a mix design may now be more accurately predicted thanks to these procedures. By using deep learning and the random forest method, the purpose of this research is to predict the compressive strength of flyash-based geopolymer concrete. This will be accomplished by comparing the two algorithms with varying degrees of error and coefficient correlation. It has been shown via the use of simulated data that the random forest algorithm is the method that is most suited for making predictions about compressive strength. After the developing a model, the various errors were found for accuracy. The mean absolute error, root mean square error, relative absolute error, and root relative squared error are 3.46%, 5.94%, 64.32%, and 82.97%, respectively for the deep learning predicted compressive strength. The errors provide the proof of model accuracy to predict the compressive strength on the basis of ingredients proportions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Compressive strength

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Multimodal Supported Chatbot Framework for People with Aphasia

Multimodal Supported Chatbot Framework for People with Aphas...

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Moratuwa Engineering Research Conference (MERCon)

作者： R.L.A. Sanduni Ayesha L.B. Hasini M. Vijayarathna N.P.A. Vithanage Uthayasanker Thayasivam Damminda Alahakoon Achini Adikari Nuwan Pallewela Department of Computer Science and Engineering University of Moratuwa Katubedda Sri Lanka Research Centre for Data Analytics and Cognition La Trobe University Australia

ISBN: (数字)9798331529048

ISBN: (纸本)9798331529055

Chatbots have become a trending topic with emerging platforms like ChatGPT, Gemini, and Copilot, for conversation assistance. Current chatbots mainly focus on the general public assuming a natural flow of conversation. However, there is a need for a chatbot that supports people with various communication disabilities. This research fills this gap by offering a novel technique for a chatbot that assists people with Aphasia, a condition characterised by difficulties with language. We propose a multi-modal chatbot that is customised and designed to assist users with communication disabilities in navigating a website. Unlike typical chatbots, which rely on one form of communication, our architecture combines multiple modalities to improve comprehension and promote effective communication for people with Aphasia. We focus on gathering multimodal inputs by recognising and combining user intents from diverse sources. The use of Txtai, an all-in-one embeddings database for semantic search improves our chatbot’s capacity to process various inputs efficiently. We leverage specialised models like Whisper for audio transcription and MediaPipe Gesture Recognizer for gesture detection to enhance user interactions. Additionally, Rasa Core integration improves conversational experiences for users. We propose that this new approach will make communication more accessible and inclusive for individuals with Aphasia.

关键词： Navigation Semantic search Intent recognition Diversity reception Oral communication Gesture recognition Machine learning Aphasia Chatbots User experience

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Comparative Analysis of Spatial Human Mobility Parameters in 15 Most Populous U.S. Metropolitan Statistical Areas

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IAENG International Journal of Applied Mathematics 2024年第6期54卷 997-1012页

作者： Matloub, Zaid Kostanic, Ivica Senior Data Engineer at Better World Analytics MelbourneFL United States Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL United States

This research paper presents the results of two studies investigating human mobility patterns in the 15 largest Metropolitan Statistical Areas (MSAs) in the United States. It studied 14 daily mobility parameters aggregated at the MSA level, derived from four primary mobility parameters: Number of Visited Locations (N_LOC), Number of Unique Visited Locations (N_ULOC), Radius of Gyration (R_GYR), and Distance Traveled (D_TRAV) over a 30-day period. The first study was conducted on data from two large MSAs, one coastal and one inland (Boston and Atlanta, respectively). The aim was to examine associations between daily values of mobility parameters aggregated at the MSA level and identify those carrying similar or identical information. Results of factor analysis showed that these could be adequately described by two independent factors, pointing to one or two of the mobility parameters as sufficient to represent the whole set in analyses based on associations. These could either be D_TRAV, as it had high loadings on both factors, or N_LOC and R_GYR due to their high loadings on the two extracted factors. The second study was conducted on daily mobility datasets from the 15 MSAs. The aim was to compare daily mobility patterns of these MSAs and group them based on their mobility pattern similarities. Factor analysis of the aggregated mean daily distances (D_TRAV) across different MSAs over the studied period classified them into two distinct groups: one predominantly composed of inland MSAs and the other primarily of coastal MSAs. Strong weekly cycle trends emerged in these groups. Specifically, individuals from the inland MSA group tended to travel the furthest on Fridays and the least on Sundays, whereas those from the coastal MSA group traveled the most on Saturdays and the least on Mondays. This weekly pattern was robust, with 7-day lag autocorrelations of mean daily parameter values ranging between 0.81 to 0.99, excluding the mean daily N_LOC. These findings offer a

关键词： Big data

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Minimum Separator Reconfiguration 18

Minimum Separator Reconfiguration

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18th International Symposium on Parameterized and Exact Computation, IPEC 2023

作者： Gomes, Guilherme C. M. Legrand-Duchesne, Clément Mahmoud, Reem Mouawad, Amer E. Okamoto, Yoshio Dos Santos, Vinicius F. Van Der Zanden, Tom C. Department of Computer Science Federal University of Minas Gerais Belo Horizonte Brazil LaBRI CNRS Université de Bordeaux France Virginia Commonwealth University RichmondVA United States Department of Computer Science American University of Beirut Beirut Lebanon Graduate School of Informatics and Engineering The University of Electro-Communications Chofu Japan Department of Data Analytics and Digitalisation Maastricht University Netherlands

ISBN: (纸本)9783959773058

We study the problem of reconfiguring one minimum s-t-separator A into another minimum s-tseparator B in some n-vertex graph G containing two non-adjacent vertices s and t. We consider several variants of the problem as we focus on both the token sliding and token jumping models. Our first contribution is a polynomial-time algorithm that computes (if one exists) a minimum-length sequence of slides transforming A into B. We additionally establish that the existence of a sequence of jumps (which need not be of minimum length) can be decided in polynomial time (by an algorithm that also outputs a witnessing sequence when one exists). In contrast, and somewhat surprisingly, we show that deciding if a sequence of at most jumps can transform A into B is an NP-complete problem. To complement this negative result, we investigate the parameterized complexity of what we believe to be the two most natural parameterized counterparts of the latter problem;in particular, we study the problem of computing a minimum-length sequence of jumps when parameterized by the size k of the minimum s-t-separators and when parameterized by the number of jumps. For the first parameterization, we show that the problem is fixed-parameter tractable, but does not admit a polynomial kernel unless NP ⊆ coNP/poly. We complete the picture by designing a kernel with O(2) vertices and edges for the length of the sequence as a parameter. © 2023 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Separators

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