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Edge AI on Constrained IoT Devices: Quantization Strategies for Model Optimization

Edge AI on Constrained IoT Devices: Quantization Strategies ...

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Intelligent systems Conference

作者： Shabir, Muhammad Yasir Torta, Gianluca Damiani, Ferruccio Univ Torino Dept Comp Sci Cso Svizzera 185 I-10149 Turin Italy Univ Kotli AJ&K Kotli Pakistan

ISBN: (纸本)9783031664274;9783031664281

In the field of deep learning (DL), the deployment of complex Neural networks (NN) models on memory-constrained devices presents a significant challenge. TinyML focuses on optimizing DL models for such environments, where computational and storage resources are limited. The main key aspect of this optimization involves reducing the size of the models without compromising their performance too much. We have investigated the efficacy of various quantization techniques in optimizing DL models for deployment on memory-constrained devices. To understand the challenges of memory requirements of standard deep learning models, we conducted comprehensive literature reviews and identified quantization methods as a potent approach for model size reduction. Our study targets popular NN architectures such as ResNetV1 and V2, MobileNetV1 and V2, and introduces a custom-designed model, examining their suitability to TinyML constraints. We have analyzed CIFAR10 and MNIST datasets to assess the impact of four distinct quantization techniques on model size and accuracy. These techniques include Dynamic Range Quantization, Full Integer Quantization, Float16 Quantization, and Integer 16x8 Quantization. Our aim is to contribute valuable insights into model optimization for efficient deployment in resourcelimited environments.

关键词： Quantization Optimization Deep learning Memory constrained devices Machine learning AI

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How Variables Graphs May Help to Correct Erroneous MAS Specifications

How Variables Graphs May Help to Correct Erroneous MAS Speci...

引用

Intelligent systems Conference (IntelliSys)

作者： Mermet, Bruno Simon, Gaele CNRS GREYC UMR 6072 Caen France Univ Le Havre Caen France

ISBN: (纸本)9783031477171;9783031477188

This paper is situated in the context of multi-agent systems validation using theorem proving techniques. This document presents a preliminary case study, which is a part of a broader investigation aiming to explore whether such techniques could aid developers in detecting and characterizing errors in MAS specifications. Indeed, regardless of the verification system used (model checking or theorem proving), understanding the reason of a failure in order to correct the specification is most of the time rather difficult. In this article, we propose a method that may help in this task. This method relies on the variables (and their dependencies) that appear in proof obligations generated by GDT4MAS, a specification and verification method dedicated to Multi-Agent systems. Graphs generated thanks to dependencies between variables occurring in an unproved theorem may indeed help to identify certain types of mistakes, giving a way to correct the specification.

关键词： Multi-agent systems Proof failure Debugging

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Prognosis of Dengue Incidences in India Using LSTM with Multivariate Data 4th

Prognosis of Dengue Incidences in India Using LSTM with Mult...

引用

4th Congress on Intelligent systems (CIS)

作者： Sharma, Shruti Gupta, Yogesh Kumar Banasthali Vidhyapith Dept Comp Sci Jaipur Rajasthan India SVKMs NMIMS Indore STME Indore India

ISBN: (纸本)9789819990399;9789819990405

Monitoring climatic change and its impacts unique to the disease is required for predicting the expansion of dengue. One of the infectious diseases transmitted by vectors with the fastest rate of transmission is dengue. In this study, we have proposed a forecasting model for the prediction of dengue cases by considering the climatic variables for all the states of India for last 12 years. For our study, we have accumulated data with factors like temperature (minimum and maximum), relative humidity, mean wind speed, dengue cases and deaths. For the prediction, the different models ANN, RNN, LSTM, Stacked LSTM, BiLSTM for forecasting time series prediction are implemented and then compared. During our study, it is found that humidity and maximum temperature have a strong correlation with the number of dengue cases. BiLSTM model outperformed all other deep learning model with an accuracy of 98.91%. This study will assist the healthcare practitioners to take informed decisions beforehand.

关键词： Machine learning Forecasting Dengue disease Epidemic disease outbreak Time series

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Gamification in Higher Education Assessment Through Kahoot 1

引用

11st World Conference on Information systems and Technologies (WorldCIST)

作者： Salazar-Vallejo, Geovanna Rivera-Rogel, Diana Univ Tecn Particular Loja Fac Social Sci Educ & Humanities CP 11-01-608 Loja Ecuador

ISBN: (数字)9783031456459

ISBN: (纸本)9783031456442;9783031456459

The approach of gamified assessments has significant implications in student learning and encourages educators to develop dynamic learning environments with permanent feedback, establishing a formative context that, through dynamic techniques, evaluates the content taught and measures the progress of learning. Responding to this background, the present research determines through a quantitative methodology, the potential of Kahoot in the university evaluation of Clinical Psychology students of the Universidad Tecnica Particular de Loja (UTPL) and establishes a comparison between their average bimonthly evaluation score on the platform and the bimonthly written exam developed in the classroom. It is concluded that students improve their learning with the use of the Kahoot platform, presenting high levels of success in the topics raised throughout the academic course, which demonstrates its feasibility to reinforce the academic results obtained and process knowledge during professional training. In addition, it was established that the use of Kahoot is feasible in preparing students for a final written evaluation.

关键词： Education Gamification Evaluation

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Novel Hybrid Methods for Journal Article Summarization Combining Graph Method and Rough Set TFIDF Method with Pegasus Model 1

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International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Kurian, K. Sheena Mathew, Sheena Cochin Univ Sci & Technol Sch Engn Kochi Kerala India

ISBN: (数字)9789819984794

ISBN: (纸本)9789819984787;9789819984794

Journal article summarization shortens an article, including all the relevant and significant topics. As the number of journal articles published every year is increasing rapidly, the significance of research on journal article summarization also increases. This work generates a summary of journal articles from the ScisummNet dataset using extractive, abstractive and novel hybrid methods. The summary generated by these methods is then analyzed using word overlap and semantic similarity scores to find the best summary. The precision, recall and f-measure scores of rouge-1, rouge-2, rouge-L and rouge-Lsum scores are analyzed to find the word overlap score and precision, recall and f-measure of the BERT score to find the semantic similarity score. The summary generated by the novel hybrid methods combining the extractive methods like the graph with the sum of weights, the graph with bushy path and the rough set TFIDF methods with the abstractive Pegasus fine-tuned model gives the best rouge and BERT scores among the other hybrid methods.

关键词： Abstractive Fine-tuning Graph methods Rough set Pre-trained models Summarization

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Using Clustering Ensembles and Heuristic Search to Estimate the Number of Clusters in Datasets

Using Clustering Ensembles and Heuristic Search to Estimate ...

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Intelligent systems Conference (IntelliSys)

作者： Odebode, Afees Adegoke Arzoky, Mahir Tucker, Allan Mann, Ashley Maramazi, Faisal Swift, Stephen Brunel Univ London England

ISBN: (纸本)9783031477140;9783031477157

This paper presents a heuristic search-based approach for estimating the number of clusters within a dataset based on an ensemble of clustering methods. We combine a number of clustering results into near-optimal subsets using two distinct approaches. Firstly, a Gray code-based implementation evaluates the quality of all possible subsets;the quality and consistency were excellent, but the search was exhaustive, leading to exponential run-time as the volume and dimension of the dataset increased. For this reason, a Random Mutation Hill Climbing-based alternative is introduced, which evaluates the subsets in small increments, mimicking the Gray code implementation, with a minimum of ninety-two per cent accuracy (mean 96%) and a significant gain in speed (linear as opposed to exponential run-time). Our algorithms are tested on real-world and benchmark datasets, and their performance is compared to other state-of-the-art estimators with promising results. Additionally, a heuristic is presented to guide when to use the exhaustive or heuristic search.

关键词： Random mutation hill climbing Ensembles Convergence point Estimating the number of clusters

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Heart Disease Detection Using Deep Learning: A Survey 2nd

Heart Disease Detection Using Deep Learning: A Survey

引用

2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Shakir, Sahar Obied, Ali Univ Al Qadisiyah Coll Comp Sci & Informat Technol Dept Comp Sci Al Diwaniyah Iraq

ISBN: (纸本)9783031628801;9783031628818

Heart disease is the leading cause of death worldwide and one of the key problems confronting today's globe. A recent study by the World Health Organization found 17.9 million people die each year from heart-related disorders. In medicine and healthcare, one of the most often used diagnostic instruments is the echocardiogram. Recent developments in machine learning and deep learning applications show that it is possible to identify cardiac illness early on utilizing echocardiogram and patient data. This paper uses a literature review technique to identify the challenges associated with heart disease prediction to provide a more thorough evaluation of the information that is currently available. For this investigation, the kind of heart illness, algorithms, datasets, and methods have all been considered.

关键词： Heart Disease Echocardiogram Deep Learning Transfer Learning

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Application of Mean-Variance Cloning Technique to Investigate the Comparative Performance Analysis of Classical Classifiers on Imbalance and Balanced Data 1

引用

Intelligent systems Conference (IntelliSys)

作者： Okwonu, Friday Zinzendoff Ahad, Nor Aishah Apanapudor, Joshua Sarduana Arunaye, Festus Irimisose Sharipov, Olimjon Shukurovich Delta State Univ Dept Math Fac Sci PMB 1 Abraka Nigeria Univ Utara Malaysia Sch Quantitat Sci Coll Arts & Sci Sintok 06010 Kedah Malaysia Natl Univ Uzbekistan Sch Math Dept Stat Tashkent Uzbekistan

ISBN: (数字)9783031477157

ISBN: (纸本)9783031477140;9783031477157

Group imbalance data often occur in high dimensional practical classification problems where the number of attributes exceeds the number of instances. In this case, the researcher is faced with dual problems such as (i) biasedness towards the majority group over the minority group and (ii) dimensionality or singularity of the covariance matrix. In such situations, the classical classifiers dependent on the sample mean and covariance matrix are impracticable for classifications. This study focused on the effects of group imbalance data on n > p classification problems. First, we develop a procedure that could transform the minority group into the majority group before the classifiers are applied. This study aims to determine whether there are observable effects on the classifiers' performance for imbalanced and balanced data sets. We also investigated whether the over and under-sampling influence the computational time of the classifiers. The results revealed that the Fisher linear classification method (FLCM) performed comparably for imbalanced and balanced data and outperformed the nearest mean classifier (NMC) and the independent classification rule (ICR). The study demonstrated that the MVCT effects on the classifiers are data-dependent. Therefore, the investigation showed that sample size balancing irrespective of the data dimension does not have a strong impact on the classifier's performance. This analysis concludes that for the n > p classification problem, the FLCM classifier has comparable performance on the imbalanced and balanced data, similar results were observed for the NMC and ICR classifiers. The over and under-sampling of the data set has insignificant effects on the computational time of the classifiers.

关键词： Imbalance Balanced Sample size Over-sampling Under-sampling Classifiers

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Estimating Alighting Stops and Transfers from AFC Data: The Case Study of Porto 11th

Estimating Alighting Stops and Transfers from AFC Data: The ...

引用

11st World Conference on Information systems and Technologies (WorldCIST)

作者： Hora, Joana Marta, Campos Ferreira Camanho, Ana Galvao, Teresa Univ Porto Fac Engn R Dr Roberto Frias P-4200465 Porto Portugal INESC TEC R Dr Roberto Frias P-4200465 Porto Portugal

ISBN: (纸本)9783031456503;9783031456510

This study estimates alighting stops and transfers from entry-only Automatic Fare Collection (AFC) data. The methodology adopted includes two main steps: an implementation of the Trip Chaining Method (TCM) to estimate the alighting stops from AFC records and the subsequent application of criteria for the identification of transfers. For each pair of consecutive AFC records on the same smart card, a transfer is identified considering a threshold for the walking distance, a threshold for the time required to perform an activity, and the validation of different boarding routes. This methodology was applied to the case study of Porto, Portugal, considering all trips performed by a set of 19999 smart cards over one year. The results of this methodology allied with visualization techniques allowed to study Origin-Destination (OD) patterns by type of day, seasonally, and by user frequency, each analyzed at the stop level and at the geographic area level.

关键词： Origin-Destination Trip Chaining Method Public Transportation Automatic Fare Collection

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Automatic Generation of a Large Multiple-Choice Question-Answer Corpus

Automatic Generation of a Large Multiple-Choice Question-Ans...

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Intelligent systems Conference

作者： Kauchak, David Song, Vivien Mishra, Prashant Leroy, Gondy Harber, Phil Rains, Stephen Hamre, John Morgenstein, Nick Pomona Coll Claremont CA 91711 USA Univ Arizona Tucson AZ 85721 USA

ISBN: (纸本)9783031664274;9783031664281

Large corpora with fine-grained metrics for difficulty and understandability are a critical resource for developing algorithms and tools to create more informative content. We introduce a new approach for automatically generating a large corpus of health-related content with associated multiple-choice questions using Google's related questions and ChatGPT, including two new algorithms for generating potential wrong answers. We compare both the question quality as well as the suggested wrong answers using automatedmetrics and user studies. Overall, we find both algorithms generate reasonable questions that are complementary. Google questions use more accessible language and are easier to answer while ChatGPT questions appear easier, but are more difficult to answer and have better coverage over the entire text. For wrong answer generation, we find ChatGPT produces higher quality wrong answers that are more likely to be good distractors and are more closely related to the text content than our corpus-based approaches. We recommend both questions as options for studies with wrong answers generated by ChatGPT.

关键词： Corpus generation Text difficulty Large language model applications

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