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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6842 条记录，以下是281-290 订阅

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Convergence of Deep learning and Edge Computing using Model Optimization 13

Convergence of Deep Learning and Edge Computing using Model ...

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13th Iranian/3rd international machine Vision and Image Processing conference (MVIP)

作者： Babaei, Peyman Islamic Azad Univ Comp Dept West Tehran Branch Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Edge systems are undergoing a groundbreaking computing evolution to support artificial intelligence, deep learning, and complex computational algorithms. Using cloud servers to perform deep learning model inference poses challenges such as response delays, increased communication costs, and data privacy concerns. Therefore, significant efforts have been made to push the processing of deep learning models to edge systems, which has led to the creation of edge intelligence as the intersection of learning and edge computing. learning models, especially deep convolutional neural networks, have made significant achievements in machine vision, which provide high accuracy and predictability by spending computing power and memory. If these models are optimized and deployed on edge systems, there will be a revolution in the applications of edge systems in real time. In this paper, by using optimization techniques such as quantization, weight pruning, and weight clustering, the possibility of deploying a typical convolutional neural network model on edge systems that have limited computing resources and memory is investigated. The results show that by using a collaborative algorithm, despite the slight decrease in the accuracy of the model, it is possible to achieve a small-sized model that can even be deployed on microcontrollers.

关键词： Model optimization Quantization Weight pruning Weight clustering Edge computing Deep learning

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Predicting the Weber Fractions for Stiffness Discrimination in Haptic Virtual Environments using machine learning 3

Predicting the Weber Fractions for Stiffness Discrimination ...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Karadogan, Ernur Cent Michigan Univ Sch Engn & Technol Robot & Hapt Lab Mt Pleasant MI 48859 USA

ISBN: (纸本)9798350372977;9798350372984

Weber fractions quantify an individual's sensitivity to changes in stimuli across modalities. These stimuli including but not limited to auditory, visual, and tactile modalities, measure an individual's sensitivity using Weber fractions. In haptic environments with tactile stimuli, changes in Weber fractions reflect an individual's ability to discern variations in touch, such as stiffness discrimination. Determining Weber fractions require laborious psychophysical experiments as they are repetitive, dependent on many factors such as the standard stimulus value tested and an individual's sensitivity in perceiving subtle differences in the presented stimuli. The goal of this study was to investigate the potential of using machine learning to predict the Weber fractions for an individual based on partial experimental data, which reduces the amount of resources required for both the participants (time and effort) and experimenters (time, effort, and associated cost). For this purpose, we conducted a psychophysical experiment using a stiffness discrimination task with computer-generated haptics objects. Thirty-eight participants completed the experiment by utilizing four different joints of their dominant arm across four separate sessions. At the end of the experiment, we computed the average Weber fraction for each individual across the four sessions and used machine learning to investigate which joints alone best predicted the average Weber fraction. Our findings indicate that data from only two sessions (involving the wrist and shoulder joints) were sufficient to predict the average Weber fraction across four sessions, which suggests considerable reduction in the experimentation efforts. Additional features such as anthropometric data and other performance parameters (proprioceptive acuity, reaction time, fingertip sensitivity etc.) can help increase the prediction power of the employed machine learning algorithms.

关键词： Haptics Virtual Reality Stiffness Discrimination Psychophysics machine learning

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Bayesian Neural Networks for Direct Short-Term Net Load Forecasting in Solar-Integrated Residential Buildings 3

Bayesian Neural Networks for Direct Short-Term Net Load Fore...

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3rd international conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Tziolis, Georgios Livera, Andreas Lopez-Lorente, Javier Herodotou, Panagiotis Makrides, George Georghiou, George E. University of Cyprus PV Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus DNV Arnhem Netherlands

ISBN: (纸本)9798350375923

Short-term net load forecasting (STNLF) is of utmost importance for the efficient operation and control of residential buildings, with integrated photovoltaic (PV) systems. The intermittent PV generation increases the uncertainty of STNLF and therefore the error. This study presents a machine learning model using Bayesian neural networks (BNNs) for direct STNLF in solar-integrated residential buildings. Historical data from PV-integrated households in Cyprus, with self-sufficiency between 88.59% and 294.12%, were used for assessing the performance of the BNN model. The proposed model was applied to data from 68 individual and aggregated households in Cyprus. The results indicated that the machine learning model exhibited normalized root mean square error of 7.20% for the aggregated data, rendering it suitable for STNLF in residential buildings. Finally, the model was compared to a naïve persistence model, achieving a skill score of 46.37% at the aggregated household level. © 2024 IEEE.

关键词： Adversarial machine learning

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Development of data Driven Model for Proton Exchange Membrane Fuel Cell Using machine learning Approaches 3

Development of Data Driven Model for Proton Exchange Membran...

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3rd IEEE international conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Dhaked, Dheeraj Kumar Kumar, Purushottam Ganguly, Sanjib Iit Guwahati Eee Department Assam Guwahati India

ISBN: (纸本)9798350313703

The world is moving towards a sustainable source of transportation and energy. Proton exchange membrane fuel cells (PEMFCs) are the major key for that. A multi-input data-based predictive model of PEM fuel cell is built using machine learning approach, such as, linear regression approach, support vector machine (SVM), decision tree (DT) method, random forest (RF) approach etc. Since fuel cell deterioration is very sensitive to operating circumstances, the transfer learning approach is used to accurately forecast fuel cell deterioration for varied activities, particularly in the early stages. The results illustrate the effectiveness and repeatability of the intended strategy. The created voltage prediction digital model is cross-validated using fuel cell real-time voltage collected during operation. This article's goal is to create an early-stage fuel cell voltage prediction digital model that can predict deterioration in real-time and is tolerant to various prediction patterns. The developed models are able to predict the voltage, and RF has the more accurate data model among all. © 2024 IEEE.

关键词： Support vector machines

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Distributed Classification by Divide and Conquer Approach 3

Distributed Classification by Divide and Conquer Approach

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Chen, Max Y. Wu, Qiang Illinois Math & Sci Acad 1500 Sullivan Rd Aurora IL 60506 USA Middle Tennessee State Univ Dept Math Sci Murfreesboro TN 37132 USA

ISBN: (纸本)9798350372977;9798350372984

In this paper, we investigate the efficacy of the divide and conquer approach for implementing distributed logistic regression and distributed support vector machine (SVM) algorithms for classification of large-scale datasets. This approach is designed to handle datasets that exceed the capacity of a single processor, necessitating the partitioning of data into multiple subsets. Logistic regression or SVM is then applied to each subset, yielding individual local classifiers. Subsequently, a global classifier is derived by aggregating these local classifiers to make the final decision. We propose three strategies for the aggregation stage: voting based on predicted labels, averaging of real-valued predictions, and averaging of posterior probabilities. Our analysis reveals that for distributed logistic regression, probability averaging is the most robust approach and is therefore recommended. Conversely, in the context of distributed SVM, probability averaging requires additional modeling but has a minimal impact on the performance. Therefore, functional averaging is recommended instead.

关键词： distributed machine learning logistic regression support vector machine divide and conquer

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Harnessing AI: Enhancing English Language Teaching through Innovative Tools 3

Harnessing AI: Enhancing English Language Teaching through I...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Adlin Jerusha, J. Rajakumari, R. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of English Tamil Nadu Chennai602105 India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

The use of Artificial Intelligence (AI) into education, namely English language instruction, offers significant possibilities for transformation. This research investigates a range of AI-powered tools and approaches that greatly improve learning results. This study conducts a thorough examination of the replies obtained from an online questionnaire poll to identify the primary domains in which AI applications demonstrate the highest level of effectiveness. These domains include customized learning environments, automated grading systems, and adaptive content delivery. An in-depth analysis is conducted to completely assess the efficacy of AI in enhancing language proficiency in areas such as grammar, vocabulary, pronunciation, and listening comprehension. In addition, the research examines the function of natural language processing (NLP) and machine learning in developing flexible learning modules that adjust to the unique learning speeds of individuals. The article also covers challenges related to data protection, ethical issues, and accessibility, offering a thorough examination of the practical consequences and constraints of AI in language instruction. This project intends to enhance the use of AI technologies in educational environments by highlighting their advantages and examining their limits. The goal is to improve language learning experiences by making them more interesting, effective, and inclusive. © 2024 IEEE.

关键词： Adversarial machine learning

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Classification of Student Mental Health Analysis using Logistic Regression and other classification techniques through machine learning Methods 3

Classification of Student Mental Health Analysis using Logis...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Pritam, Nishant Gill, Kanwarpartap Singh Kumar, Mukesh Rawat, Ruchira Banerjee, Deepak Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350381931

The examination of student mental health is a significant area of scholarly investigation that seeks to comprehend and address the psychological and emotional well-being of students within educational settings. The process involves the evaluation, anticipation, and provision of assistance for students' psychological well-being using various approaches, including as surveys, machine learning algorithms, and clinical assessments. The use of machine learning algorithms for the analysis of student mental health is a complex and consequential endeavour. This may provide insight on the identification of students who may be at risk and the appropriate timing for providing support. In this context, the use of machine learning methods, such as logistic regression and other classification algorithms, might potentially be advantageous. This research aims to identify some noteworthy concerns pertaining to the overall mental well-being of students. This paper explores the philosophical difficulties underlying these challenges and examines the answers provided by modern statistics and visualisation techniques. This study offers a variety of robust models, including as Random Forest, Decision Tree, SVM, and Logistic Regression, that possess several advantages and are well-suited for the analysis of categorical data. The research use a psycholinguistic data set to conduct a comprehensive comparison of different statistical methodologies. Upon conducting an evaluation of the classification models, including Linear Regression, Support Vector machine (SVM), Decision Tree, and Random Forest, it was seen that the Linear Regression Classification Technique exhibited the highest level of accuracy. Specifically, the Linear Regression model achieved a 65 percent accuracy rate across diverse optimisation parameters. © 2024 IEEE.

关键词： Students

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Intelligent Optimization Analysis of Automatic Pricing and Replenishment of Vegetable Products Based on Combinatorial machine learning Models 3

Intelligent Optimization Analysis of Automatic Pricing and R...

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3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Tang, Yuyang Xi'an University of Technology Xi'an China

ISBN: (纸本)9798350380989

Based on the idea of market demand and supply, this paper establishes a combined machine learning model by determining the indicators such as sales volume, demand, supply, etc., with the goal of maximizing the interests of the superstore, and adopts an intelligent optimization algorithm to realize the optimization analysis of automatic fixing and replenishment for vegetable commodities. First, Spearman correlation coefficient is used to quantitatively analyze the correlation between different categories to quantify the degree of correlation between different vegetable categories, and K-means cluster analysis is used to analyze the sales data of each category of vegetables, and different vegetables are divided into different clusters based on sales volume, so as to identify the vegetable items with similar sales volume. Then, XGBoost model and time series autoregressive model were used to construct a combined machine learning model, which realized the prediction of future price data based on historical price data and the prediction of future sales volume by combining the unit price and the price, and carried out the smoothing test and the seasonality test on the data to further optimize the predictive ability of the model. And it realized the prediction of future week's price, and the prediction of future week's price was realized based on the prediction results of the combined machine learning model. Based on the prediction results of the model, an automatic pricing strategy and replenishment strategy function with the goal of maximizing revenue is established, and genetic algorithm is applied to realize the solution of the model to obtain the corresponding optimal pricing. Finally, the objective function is shifted from the overall category to specific commodities, and the constraints adapted to it are set for specific time points, which are still solved by intelligent optimization algorithms and optimization analyses are carried out, and specific replenishment qua

关键词： genetic algorithm K-means cluster analysis spielman correlation coefficient time series autoregression XGBoost model

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Highresolution Remote Sensing Image Classification With Limited Training data 13

Highresolution Remote Sensing Image Classification With Limi...

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13th Iranian/3rd international machine Vision and Image Processing conference (MVIP)

作者： Ariaei, Mehdi Ghassemian, Hassan Imani, Maryam Tarbiat Modares Univ Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Accurate classification of land cover from aerial images is one of the research topics in remote sensing and is also in high demand in industry. However, obtaining labeled data for training different classifiers that heavily depend on supervision is still a challenging and resource-intensive task. Unsupervised methods have emerged as a powerful alternative to overcome the limitations associated with labeled data. Such methods have a high ability to discover hidden patterns and structures in multi-spectral images and have the possibility of classifying various types of land cover without relying on labeled samples. Our research primarily involved the analysis of World-View3 satellite imagery. Our strategy involved creating an advanced pipeline that extracted features using autoencoders. Through this approach, the multi-spectral images' key characteristics are efficiently extracted. Subsequently, we implement transfer learning to re-train the model with a limited number of labeled data. By applying transfer learning, our pipeline significantly enhances the capability of multispectral image processing, enabling a more comprehensive and accurate interpretation of satellite imagery data. Finally, we evaluate our results not only by providing a confusion matrix but also through a visual comparison between the class map and the RGB composition of the MSI image.

关键词： World-View3 Autoencoders Multi-Spectral images Deep learning Transfer learning Limited Training data

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Predicting COPD Severity Using machine learning and GOLD Criteria 3

Predicting COPD Severity Using Machine Learning and GOLD Cri...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Kinikar, Atharva Chandwani, Maanav Rane, Tushar Pune Institute of Computer Technology Dept. Information Technology Pune India Pune Institute of Computer Technology Dept. Computer Engineering Pune India

ISBN: (纸本)9798350381931

Breathing problems that get worse over time are a symptom of chronic obstructive pulmonary disease, or COPD. It is a catch-all phrase for a number of conditions including asthma, bronchitis, and emphysema. It is defined by a progressive constriction of the airways and an early aging of the lungs. Experts have attested to the fact that early identification helps lessen the symptoms of COPD. Thus, using GOLD criteria, patients' COPD severity can be predicted using contemporary methods like machine learning algorithms. The proposed prediction models predict severity of COPD of the patient on a scale of 1 to 4 as prescribed by the GOLD system. Our model was trained to utilize a derived feature set and machine learning classifiers, such as SVC, Random Forest Classifier, XGBoost classifier, Naive Bayes, and Hard Voting Ensemble, with the best possible parameter choice to achieve a commendable accuracy of 97.6%. The global COPD epidemic will be lessened with the use of this method and medical professionals' experience. © 2024 IEEE.

关键词： machine learning

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