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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Minu, M.S. Talapalli, Srikar Chevuri, Saahithi Medhasri, T.S. SRM Institute of Science and Technology Department of Computer Science Engineering Ramapuram Chennai India SRM Institute of Science and Technology Department of Computer Science Engineering with Specialization in Big Data Analytics Ramapuram Chennai India

ISBN: (纸本)9798331542344

The surging popularity of online movie databases has created a challenge for viewers: choosing a film from a massive library can be overwhelming. In this paper, it proposes to design a new hybrid movie recommendation powered by machine learning techniques as a solution to improve user experience. Among those techniques, two are combined into this paper: content-based filtering, using the real features of a movie such as genre and a director, and collaborative filtering, which uses the rating provided by users. This hybrid approach thus improves the intrinsic weaknesses that reside in both these methods individually. On the other hand, content-based filtering tends to reduce exploration to movies of the exact type one is doing well at, whereas, inversely, collaborative filtering settings often mean that a user will be exposed only to films within his or her already-recognized taste profile. It is expected that the proposed hybrid system will solve both problems by balancing this line between exploring familiar cinematic territory and making new recommendations based on both user preferences and the entire collective intelligence of the user base. The approach would aim to enrich user satisfaction by facilitating the selection of movies and providing a richer and more diversified movie-viewing experience. © 2024 IEEE.

关键词： Collaborative filtering

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On the Effect of Cross-Channel Normalization on Wide-Shallow Convolutional Networks 26

On the Effect of Cross-Channel Normalization on Wide-Shallow...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Alakkari, Salaheddin Mileo, Alessandra Department of Computer Science and Information Systems University of Limerick Ireland Insight Centre for Data Analytics Dublin City University Ireland

ISBN: (纸本)9781837242672

Cross-channel Normalization (CN) was first proposed in AlexNet paper as a biologically inspired normalization process mimicking the lateral inhibition phenomenon in biological neurons. However, the effect of such a normalization was not well explored in the literature due to the wide popularity of batch normalization. In this paper, we show that such a type of normalization can significantly enhance the network accuracy when applied to wide-shallow convolutional networks. Wide-shallow networks are more advantageous compared to deep CNNs since they significantly increase parallelism and reduce the sequential computation across layers. Our experiments show that using cross-channel normalization not only improves the accuracy of residual and standard convolutional networks on benchmark datasets but also significantly enhances the performance when considering limited and reduced representation examples. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Convolution

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Semantic grounding of LLMs using knowledge graphs for query reformulation in medical information retrieval

Semantic grounding of LLMs using knowledge graphs for query ...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Tommasel, Antonela Assent, Ira Isistan CONICET-UNCPBA Argentina Aarhus University Department of Computer Science Denmark Digit Aarhus University Centre for Digitalisation Big Data and Data Analytics Denmark

ISBN: (纸本)9798350362480

The widespread adoption of electronic health records has generated a vast amount of patient-related data, mostly presented in the form of unstructured text, which could be used for document retrieval. However, querying these texts in full could present challenges due to their unstructured and lengthy nature, as they may contain noise or irrelevant terms that can interfere with the retrieval process. Recently, large language models (LLMs) have revolutionized natural language processing tasks. However, despite their promising capabilities, their use in the medical domain has raised concerns due to their lack of understanding, hallucinations, and reliance on outdated knowledge. To address these concerns, we evaluate a Retrieval Augmented Generation (RAG) approach that integrates medical knowledge graphs with LLMs to support query refinement in medical document retrieval tasks. Our initial findings from experiments using two benchmark TREC datasets demonstrate that knowledge graphs can effectively ground LLMs in the medical domain. © 2024 IEEE.

关键词： Knowledge graph

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EGG-SynC: Exact GPU-parallelized Grid-based Clustering by Synchronization 26

EGG-SynC: Exact GPU-parallelized Grid-based Clustering by Sy...

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26th International Conference on Extending database Technology, EDBT 2023

作者： Jørgensen, Jakob Rødsgaard Assent, Ira Department of Computer Science Aarhus University Aarhus Denmark Department of Computer Science DIGIT Aarhus University Centre for Digitalisation Big Data and Data Analytics Aarhus University Aarhus Denmark

ISBN: (纸本)9783893180882

Clustering by synchronization (SynC) is a clustering method that is motivated by the natural phenomena of synchronization and is based on the Kuramoto model. The idea is to iteratively drag similar objects closer to each other until they have synchronized. SynC has been adapted to solve several well-known data mining tasks such as subspace clustering, hierarchical clustering, and streaming clustering. This shows that the SynC model is very versatile. Sadly, SynC has an O(T × n2 × d) complexity, which makes it impractical for larger datasets. E.g., Chen et al. [8] show runtimes of more than 10 hours for just n = 70, 000 data points, but improve this to just above one hour by using R-Trees in their method FSynC. Both are still impractical in real-life scenarios. Furthermore, SynC uses a termination criterion that brings no guarantees that the points have synchronized but instead just stops when most points are close to synchronizing. In this paper, our contributions are manifold. We propose a new termination criterion that guarantees that all points have synchronized. To achieve a much-needed reduction in runtime, we propose a strategy to summarize partitions of the data into a grid structure, a GPU-friendly grid structure to support this and neighborhood queries, and a GPU-parallelized algorithm for clustering by synchronization (EGG-SynC) that utilize these ideas. Furthermore, we provide an extensive evaluation against state-of-the-art showing 2 to 3 orders of magnitude speedup compared to SynC and FSynC. © 2023 ***. All rights reserved.

关键词： Synchronization

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Progressive Index Modulation Based CH Selection in ICIC Scheme for Multi-Hop WSN 11

Progressive Index Modulation Based CH Selection in ICIC Sche...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Monika, P. Vijayalakshmi, S. Sri Ramakrishna College of Arts and Science for Women Department of Computer Science Coimbatore India Sri Ramakrishna College of Arts and Science for Women Department of Data Analytics Coimbatore India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350357

Wireless Sensor network is the communication network in which the sensor nodes are located in various parts of the network and the base station in same network. The sensor nodes gather the data and communicate gathered information to the base station. The major issue in the transmission process of WSN is energy consumption. Some techniques and algorithms are suggested in many researches for reducing energy consumption to make the node to sustain in network for longer duration. This paper proposes the progressive index modulation ICIC strategy for cluster head selection and used indexing transmitting data. In intra cluster mode, cluster head is chosen based on probability factor and minimized energy utilized route is identified and indexing is initialized for that route in intra clustering scheme. The simulation is performed using different metrics like packet delivery rate, number of dead nodes and energy utilization. All the above metrics are evaluated in two different strategies like number of rounds and number of nodes. Proposed evaluation results provides better results when compared with ICIC strategy and it helps to utilize less energy and better data transmission. © 2024 IEEE.

关键词： Energy utilization

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Applying Competition-Based Learning to Stimulate Students' Practical and Competitive AI Ability in a Machine Learning Curriculum

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IEEE Transactions on Education 2024年第2期67卷 256-265页

作者： Chang, Hui-Tzu Lin, Chia-Yu National Yang Ming Chiao Tung University Center for Institutional Research and Data Analytics Hsinchu30010 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan City320317 Taiwan

Contribution: This study incorporates competition-based learning (CBL) into machine learning courses. By engaging students in innovative problem-solving challenges within information competitions, revealing that students' participation in online problem-solving competitions can improve their information technology, and showcase competitions can enhance their competition ability. Background: The CBL model seamlessly integrates project-based learning and competition, placing a strong emphasis on both collective learning and outcomes. This approach cultivates motivation among team members, driving them to enhance their learning and translate knowledge into practical experience. Research Questions: The objective is to examine the disparities in the development of theoretical knowledge, information technology, AI practical ability, and competition ability among students participating in online problem-solving competitions and showcase competitions, and discusses the potential moderating effect of competition type on the relationships between variables in the hypothetical model. Methodology: The study involved 74 students enrolled in machine learning course at a university. The students were given theoretical knowledge and information technology pretests and posttests in the 2nd and 17th weeks, respectively. In the 18th week, the students presented their projects using slideshows and were graded by judges while also submitting their final competition proposal and slides. Findings: Students in online problem-solving competitions can enhance their information technology, while those participating in showcase competitions can improve their competitive ability. Moreover, the competition type was found to moderate the relationships among theoretical knowledge, information technology, and AI model accuracy. The findings suggest that incorporating CBL into machine learning courses effectively cultivates students' AI practical and competitive abilities. © 1963-2012 IEEE.

关键词： Competition

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A Transmission and Transformation Fault Detection Algorithm Based on Improved YOLOv5

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computers, Materials & Continua 2023年第9期76卷 2997-3011页

作者： Xinliang Tang Xiaotong Ru Jingfang Su Gabriel Adonis School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Department of Computer Science and Information Systems Birkbeck Institute for Data AnalyticsLondonB100ABWC1E 7HXUK

On the transmission line,the invasion of foreign objects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the safe operation of power ***,a YOLOv5 target detection method based on a deep convolution neural network is *** this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the *** structure uses depth-wise separable convolution to reduce the amount of calculation and parameters;improve the detection *** the same time,to compensate for the detection accuracy,the Squeeze-and-Excitation Networks(SENet)attention model is fused into the algorithm framework and a new detection scale suitable for small targets is added to improve the significance of the fault target area in the *** pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data *** experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,*** the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.

关键词： Transmission line YOLOv5 multi-scale integration SENet

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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Towards Graph-Based Semi-Supervised Learning on Audio Embeddings for Label Classification 22

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

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

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

ISBN: (纸本)9798350345346

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. © 2023 IEEE.

关键词： Audio-based medical diagnostics AudioVec-Diagnosis Cough noises Disease classification Semi-supervised learning Wav2Vec2.0 model

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Multi-Agent RAG Chatbot Architecture for Decision Support in Net-Zero Emission Energy Systems 25

Multi-Agent RAG Chatbot Architecture for Decision Support in...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Gamage, Gihan Mills, Nishan De Silva, Daswin Manic, Milos Moraliyage, Harsha Jennings, Andrew Alahakoon, Damminda La Trobe University Centre For Data Analytics And Cognition Melbourne Australia Virginia Commonwealth University Department Of Computer Science Richmond United States

ISBN: (纸本)9798350340266

Modern energy platforms are increasingly leveraging Artificial Intelligence (AI) for effective decision-making and efficient operations. This has led to the development of expansive data spaces that comprise both structured and unstructured energy data in various modalities. Conversational agents with the most recent advancements in Large Language Models (LLM) are primed to facilitate the efficient retrieval of this diverse information for decision support. In this paper, we propose a multi-agent chatbot architecture for decision support in net-zero emissions energy systems, leveraging LLMs and Retrieval-Augmented Generation (RAG). This architecture consists of a Chatbot User Interface (UI), an advanced Natural Language Understanding (NLU) module for precise entity and intent recognition, a robust Chatbot Core with four specialized agents: Observer, Knowledge Retriever, Behavior Analyzer, and Visualizer and Response Construction Module. These components work together to address diverse decision support needs in energy environments, specifically for net zero carbon emissions initiatives that need to consider diverse parameters and large volumes of data. We showcase the chatbot's successful integration and evaluation for decision support in the net-zero emissions energy system of a large tertiary education institution. © 2024 IEEE.

关键词： User interfaces

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