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2022 international conference on machine learning and data engineering, icmlde 2022

作者： Chaitanya, A. Shetty, Jayashree Chiplunkar, Priyamvada Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Food image recognition is one among the various propitious applications in the area of computer vision. An application with the ability to identify all kinds of food images along with its nutritious value will help people in maintaining a balanced diet. The proposed convolutional neural network (CNN) model can be used for the identification and classification of food images. A pre-trained Inception v3 CNN model is employed via transfer learning to galvanize the original custom-made CNN framework. With the aid of this pre-trained model, the learning process is boosted and is hence more efficient. data augmentation is performed on the training set as it improves the robustness of the model and as it also helps avoid overfitting. The predicted food label generated by the model is forwarded to the web crawlers for information retrieval. The web crawlers are deployed on the browser through automation for the retrieval of relevant information such as the food item's origin, nutritional details, its recipe, and even the nearby restaurants that serve the dish. The crawlers are built using Python Scrapy and the process of scraping data from the websites is automated through Selenium. The model achieved an accuracy of 97.00% for 20 classes. This model can be further enhanced in terms of scalability by building a deeper more advanced neural network, collecting more images per class for each of the respective food items and by fine-tuning the model hyperparameters. © 2022 Elsevier B.V.. All rights reserved.

关键词： Python

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Domain Adaptation Random Vector Functional Link for White Blood Cell Classification 23

Domain Adaptation Random Vector Functional Link for White Bl...

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23rd international conference on machine learning and Cybernetics, ICMLC 2024

作者： Wang, Qin Wang, Xi-Zhao Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798331528041

There's growing recognition of how machine learning can revolutionize the precision and swiftness of clinical diagnoses by improving the classification of white blood cells. However, a machine learning model specialized in white blood cell classification demands access to a large trove of well-annotated data. This poses a challenge, as in the medical field, annotating data comprehensively comes with a high cost in terms of both money and time. To circumvent this, domain adaptation (DA) techniques are employed to leverage similar cell images from related domains, thereby boosting the model's performance in white blood cell classification in specific contexts. Among the myriad DA approaches available for this application, including TCA-based and deep learning methods, all typically suffer from lengthy training durations. Addressing this challenge, we introduce a novel DA method inspired by the random vector functional link (RVFL) network, a type of neural network characterized by its random weights. Our proposed method, named Domain Adaptation Random Vector Functional Link (DA-RVFL), capitalizes on the efficiency of RVFL networks to enhance white blood cell classification. This innovative approach shows some insights for future research in the efficient and effective classification of white blood cells. © 2024 IEEE.

关键词： Deep learning

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A Volatile Organic Compounds Intelligent Monitoring and Warning Model Based on Deep learning Algorithm 2nd

A Volatile Organic Compounds Intelligent Monitoring and Warn...

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2nd international conference on Frontiers of Energy and Environmental engineering, CFEEE 2023

作者： Zhang, Honghui Yuan, Jun Dongguan University of Technology Dongguan China

ISBN: (纸本)9789819703715

Volatile Organic Compounds (VOCs) consist of diverse compounds that exhibit challenging predictability trends using conventional mechanism models. Within many industrial parks, VOCs present difficulties due to their multiple emission points and dispersed sources, making manual monitoring and data collection at each point labor and resource-intensive as well as highly inefficient. Consequently, the journey towards accomplishing large-scale and reliable VOCs monitoring and warning systems, as well as their effective implementation in industrial parks, remains a considerable endeavor. For the purpose of effectively predicting the changing trend of VOCs concentration in industrial parks to achieve the goal of intelligent early warning of possible pollution events, this study aims to develop corresponding machine learning methods in combination with data processing methods as well as cutting-edge machine learning methods including long short-term memory neural network (LSTM) to develop corresponding machine learning models and attain intelligent early warning of pollutants by predicting VOCs concentration and combining this with pollution factor warning standards. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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Tourist Trajectory Prediction Based on Improved LightGBM

Tourist Trajectory Prediction Based on Improved LightGBM

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2022 international conference on Statistics, data Science, and Computational Intelligence, CSDSCI 2022

作者： Zhao, Debin Hu, Zhengyuan Yang, Yinjian Institute of Big Data Kunshan Xuanlife Information Technology Co. Ltd Nanjing China

ISBN: (纸本)9781510661325

Considering the influences of the COVID-19 disease, systemic risks with respect to the tourism industry and the erratic preferences of the tourists have fiercely affected the performance of machine learning models for tourist trajectory prediction. This paper introduces a noise-reduced and Bayesian optimized light gradient boosting machine(LightGBM) to forecast the likelihood of visitors entering the consequent scenic attraction, accommodating to the variability of tourism attributes. The empirical evidence of tourism data in Luoyang City Hall from March 2020 to November 2021 illustrates that our practice surpasses the baseline LightGBM mechanism as well as a random search-based technique regarding prediction loss by 5.39% and 4.42% correspondingly. The proposed research demonstrates a promising stride in the improvement of intelligent tourism in the experimental area by enhancing tourist experiences and allocating tourism resources efficiently, which can also be smoothly applied to other scenic spots. © 2023 SPIE.

关键词： Forecasting

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Assessing the Quality of Computational Notebooks for a Frictionless Transition from Exploration to Production 22

Assessing the Quality of Computational Notebooks for a Frict...

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44th ACM/IEEE international conference on Software engineering - New Ideas and Emerging Results (ICSE-NIER)

作者： Quaranta, Luigi Univ Bari Bari Italy

ISBN: (纸本)9781665495981

The massive trend of integrating data-driven AI capabilities into traditional software systems is rising new intriguing challenges. One of such challenges is achieving a smooth transition from the explorative phase of machine learning projects - in which data scientists build prototypical models in the lab - to their production phase in which software engineers translate prototypes into production-ready AI components. To narrow down the gap between these two phases, tools and practices adopted by data scientists might be improved by incorporating consolidated software engineering solutions. In particular, computational notebooks have a prominent role in determining the quality of data science prototypes. In my research project, I address this challenge by studying the best practices for collaboration with computational notebooks and proposing proof-of-concept tools to foster guidelines compliance.

关键词： Software engineering Artificial Intelligence data Science machine learning computational notebooks static analysis tools linters

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Application of Convolutional Neural Networks for the Detection of Lightning in Weather Radar data 37

Application of Convolutional Neural Networks for the Detecti...

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37th international conference on Lightning Protection, ICLP 2024

作者： Schwalt, Lukas Schatz, Sebastian Obenaus, Friedrich Pack, Stephan Graz University of Technology Institute of High Voltage Engineering and System Performance Graz Austria

ISBN: (纸本)9783800764174

machine learning is increasingly being used in different fields of weather forecasting. Due to the difficulty of predicting lightning discharges, there are also approaches to use machine learning in lightning forecasting. As a subset of machine learning, Convolutional Neural Networks (CNN), are one of the methods used to automatically classify images. Thus, CNNs can be used to recognize patterns in weather radar data. The present study, shows a first approach of training five CNN models with weather radar data from Austro Control GmbH for Austria. The models are trained to decide whether lightning occurred in the respective radar image or not. The weather radar data of the years 2020, 2021 and 2022 were processed and merged from provided raw data. Lightning location system data from the Austrian Lightning Detection and Information System were used to categorize the weather radar data into two sub groups (Flash and No Flash). All models are pre-trained on the ImageNet dataset or used in the untrained state. The SqueezeNet model delivered the highest results in the pre-trained case and without feature extraction with a prediction accuracy of 89.84%. © 2024 IEEE, © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Convolutional neural networks

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Pay-as-you-Train: Efficient ways of Serverless Training 10

Pay-as-you-Train: Efficient ways of Serverless Training

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10th IEEE international conference on Cloud engineering (IC2E)

作者： Chahal, Dheeraj Mishra, Mayank Palepu, Surya Chaitanya Singh, Ravi Kumar Singhal, Rekha TCS Res Mumbai Maharashtra India

ISBN: (数字)9781665491150

ISBN: (纸本)9781665491150

Serverless (FaaS) architecture is emerging as a paradigm of choice for many application types, including event triggered, query processing, and machine learning (ML). The use of serverless platforms for ML inference is well known, but its applicability for model training is still under exploration. This paper presents an efficient "pay-as-you-train" methodology for training large deep learning models using serverless cloud services for compute and data management. Serverless compute (such as AWS Lambda) and serverless data management systems (such as AWS key-value store DynamoDB) impose restrictions on the computing time and size of the allowed data objects respectively. We present a novel approach for training deep learning models, which overcomes the limitations imposed by the underlying serverless platforms. We also present an analytical model to study the performance and cost involved in training using different data management services (such as AWS object storage S3, in-memory Memcached, and DynamoDB) as a communication channel with serverless platforms. Additionally, we compare the performance and cost of these services available on cloud. Our optimization techniques improve the performance and hence the cost of training by a factor of 1.2x to 5.5x with these services.

关键词： Serverless machine learning database performance

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A data-driven evaluation method for low-temperature performance of lithium-ion batteries

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ENERGY REPORTS 2023年 9卷 912-921页

作者： Xu, Helin Cheng, Lin Paizulamu, Daniyaer Zhang, Yongmi Zhu, Changyu Tsinghua Univ Key Lab Control & Simulat Power Syst & Generat Eq Beijing 100084 Peoples R China Huadian Inner Mongolia Energy Co LTD Hohhot 010010 Inner Mongolia Peoples R China

Energy storage system plays an important role in smoothing out the electricity supply from renewable energy and improving stability of the power system. At present, most energy storage systems are still battery energy storage systems (BESS). However, the time-varying temperature condition has a significant impact on discharge capacity of lithium-ion batteries. When lithium-ion battery operates in a low temperature environment, the discharge capacity of the battery decreases. Therefore, this paper develops a discharge capacity evaluation method for lithium-ion batteries at low temperature. Firstly, we analyze the battery discharge characteristics. On this basis, battery tests have been conducted and we proposed some health indicators. Finally input the measured data and health indicators into the machine learning model. The applicability and effectiveness of this method are analyzed through numerical results. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Lithium-ion battery Low temperature Capacity machine learning

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Prediction of Environment Temperature for Grain Warehouses Based on LightGBM and Transformer 12

Prediction of Environment Temperature for Grain Warehouses B...

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12th international conference on Advanced Cloud and Big data, CBD 2024

作者： Wang, Kun Li, Bingchan Mao, Bo College of Information Engineering Nanjing University of Finance and Economics Jiangsu Nanjing China College of Marine Engineering and Electrization Jiangsu Maritime Institute Jiangsu Nanjing China

ISBN: (纸本)9798331511074

With the increasing prominence of global climate change and food security issues, the monitoring and control of grain warehouse environments have become particularly important. The external environment temperature has a direct impact on the internal temperature of grain warehouses and the safety of stored grain. Therefore, accurately predicting the external environment temperature of grain warehouses is crucial for formulating reasonable warehouse management strategies. Existing research mainly focuses on traditional statistical methods and simple machine learning models, which have limited performance in handling complex time series data. This study employs advanced deep learning models and optimizes input features through feature engineering to improve the accuracy of temperature predictions. The study proposes a method to generate new feature variables through feature fusion and validates its effectiveness. By adding new time series feature variables to the dataset, the model's understanding of time series data is enhanced, effectively reducing experimental errors. A comparative analysis of the predictive performance of various models identifies the best model and feature combination. © 2024 IEEE.

关键词： Contrastive learning

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CLCT-GAN: Strong-Weak Contrastive learning for Reconstructing CT Images from Radiographs

CLCT-GAN: Strong-Weak Contrastive Learning for Reconstructin...

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international Joint conference on Neural Networks (IJCNN)

作者： Cheng, Shuangqin Hong, Yumao Chen, Qingliang Guo, Jinshun Li, Ming Zhang, Qiyi Jinan Univ Sch Informat Sci & Technol Guangzhou Peoples R China Guangdong Second Peoples Hosp Dept Intervent Vasc Med Guangzhou Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Generating CT images from radiographs has immense clinical potential, offering a novel approach to low-cost and low-radiation medical imaging. This method can significantly ease the workload of radiologists. Current machine and deep learning approaches mainly utilize fully supervised learning methods for CT image generation tasks. However, the feature representations learned in fully supervised settings are typically task-specific, dependent on large quantities of labeled data, and have limited generalizability. Addressing these challenges, we introduce a contrastive learning-based method for CT image generation, which includes two phases: pre-training and fine-tuning. The pre-training phase aims to learn feature representations from unlabeled radiographs. Specifically, we devised two simple yet efficient radiograph data augmentation methods, converting the original data into two related but different views. These are then input into an encoder module to learn discriminative feature representations. Labeled data is used to learn the medical image generation task during the fine-tuning phase. In evaluations across the LIDC-IDRI lung CT and IXI brain MRI datasets, our CLCT-GAN model exhibits not only outstanding performance in lung CT reconstructions but also showcases remarkable adaptability to brain MRI data from IXI, surpassing previous stateof-the-art models in these diverse medical imaging benchmarks.

关键词： Computerized Tomography Reconstruction radiographs Generative Adversarial Networks Contrastive learning Magnetic Resonance Image Reconstruction

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