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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

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Design and Development of Soil Monitoring System for Precision Farming On Small-Scale Outdoor Farm 3

Design and Development of Soil Monitoring System for Precisi...

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3rd international conference on Smart Cities, Automation and intelligent Computing Systems, ICON-SONICS 2023

作者： Saputra, Dezdyta Poszarevac Nugraha, M.B. Tampubolon, Marojahan Arhan, Kahfi Sabillah Universitas Multimedia Nusantara Electrical Engineering Tangerang Indonesia

ISBN: (纸本)9781509062805

The agricultural sector is of paramount importance in enhancing the national economy and serving as a primary food source. Given the limited availability of arable land, there is a pressing need to enhance agricultural productivity through precision agriculture to provide affordable food source. Precision agriculture aims to optimize the timing, quantity, and location of farming practices. Nevertheless, the full potential of agricultural technology innovations is yet to be harnessed by farmers, resulting in suboptimal utilization of these technologies. To address this issue, there is a growing imperative to monitor plant health and crop production in real-time using sensors and agricultural technology. Therefore, a tool has been developed to enable comprehensive plant health measurements, encompassing parameters such as ambient temperature, soil moisture, air humidity, light, CO2 levels, and the levels of essential nutrients, namely Nitrogen, Phosphorus, and Potassium (NPK). This data is seamlessly processed by the Node-RED server, which presents the sensor parameter information through visually appealing graphs and icons. This intuitive data presentation empowers users to gain a profound understanding of the information and conduct in-depth analyses of plant condition and nutritional aspects. The tool showcased in this product exhibits an impressive success rate of 100% in delivering on these objectives. © 2023 IEEE.

关键词： Agricultural technology

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Hindi Hit Songs Prediction Using Machine learning Algorithms 3

Hindi Hit Songs Prediction Using Machine Learning Algorithms

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3rd IEEE international conference on intelligent Technologies, CONIT 2023

作者： Beniwal, Rohit Batra, Satvik Gupta, Shivank Shekhar, Shashi Delhi Technological University Department of CSE Delhi India

ISBN: (纸本)9798350338607

The music industry has grown from independent composers releasing their self-published work to an entire extensive industry wherein an enormous amount of money is at stake depending on whether or not a particular song will be a hit on the charts. As a result, the music production industry has become more digitized and data-centric in order to try and find a mechanism for predicting a hit song. Extensive research work has been done to predict the salability of a particular song being put out by composers. However, the current research has been limited to Western English language songs only. Due to the lack of datasets and preliminary research on regional language songs, hit prediction for regional language songs has not been given any attention in the existing state of the art. Therefore, in this research paper, we predict the profitability and salability of a Hindi language song by predicting whether or not the song will be a hit or in other words whether or not the song will be listed on top music charts. High-level features of a song have been incorporated into our models as parameters that will affect the profitability of the song in question. To demonstrate this, we created a new dataset of Hindi songs. Moreover, we compared the results of various algorithms such as Random Forrest, Gaussian Naive Bayes, Multi-layer Perceptron (MLP), AdaBoost, and Logistic Regression optimized using SGD. Our results show that MLP achieved the best outcomes with a precision of 0.9429, recall of 0.8875, and F1 score of 0.8849. © 2023 IEEE.

关键词： Forecasting

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Unscrambling Representation and Recognizing Gaits in an intelligent Attendance System 4

Unscrambling Representation and Recognizing Gaits in an Inte...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Prathik, K. Girija, J. Bangalore Institute of Technology Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350300888

Traditional approaches to managing attendance have been entirely transformed by the advent of automated and intelligent solutions due to the development of innovative attendance systems. This research introduces a revolutionary intelligent attendance system that uses unscrambled representation and gait identification using an Inception v3 model based on transfer learning to precisely identify people based on their gait patterns and record attendance. By employing transfer learning, the proposed method improves the pre-trained weights of the Inception v3 model using the outdoor gait dataset, enabling it to capture and analyze the distinctive features of human gait accurately. By separating and storing many components that go into a person's gait pattern, incorporating disentangled representation improves the system's accuracy and produces more accurate identification results. The intelligent attendance system has many benefits compared to conventional attendance management strategies. The proposed model streamlines the attendance procedures and reduces administrative expenses by removing the dependency on traditional techniques like ID cards, PINs, or biometrics. The proposed system is non-intrusive in operation, protecting user privacy and convenience. MATLAB is used for programming, and OpenCV is used to process and analyze the video and image data from the database. The system completes comprehensive testing on an outdoor gait dataset with a remarkable accuracy of 94%, proving its reliability and usefulness in practical situations. © 2023 IEEE.

关键词： MATLAB

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Federating Unlabeled Samples: A Semi-supervised Collaborative Framework for Whole Slide Image Analysis 23rd

Federating Unlabeled Samples: A Semi-supervised Collaborativ...

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23rd international conference on intelligent data engineering and automated learning (IDEAL)

作者： Launet, Laetitia del Amor, Rocio Colomer, Adrian Mosquera-Zamudio, Andres Moscardo, Anais Monteagudo, Carlos Zhao, Zhiming Naranjo, Valery Univ Politecn Valencia I3B Valencia Spain Univ Valencia Hosp Clin Univ Valencia Dept Pathol Valencia Spain Univ Amsterdam Multiscale Networked Syst Amsterdam Netherlands

ISBN: (纸本)9783031217524;9783031217531

Over the last decades, deep learning-based algorithms have witnessed tremendous progress in the medical field to assist pathologists in clinical decisions and reduce their workload. For these models to reach their full potential, access to large and diverse datasets is essential, but collaborations between hospitals are highly limited by privacy-related regulations. At the same time, medical institutions do not always have specialized pathologists to diagnose biopsies and label local data. To address these limitations, federated learning gained traction to enable multi-institution model training without sharing sensitive patient data. However, this technique is still in its infancy when it comes to digital pathology applications, and does not consider institutions with unlabeled data in federations. In this paper, we introduce a novel semi-supervised federated learning approach that promotes multi-institutional training of deep learning models while integrating unlabeled collaborating data sources into the federated setup. The experimental results show a better performance for models trained under a federated setting with both labeled and unlabeled data. Optimally, this framework will also bring the promise of assisting clinical decisions in hospitals that do not have specialized pathologists.

关键词： Federated learning Self-training Whole slide images Multiple instance learning

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Enhancing Demand Forecasting Accuracy Through Market Trend Analysis: Leveraging NLP Algorithms for data-Driven Insights 3

Enhancing Demand Forecasting Accuracy Through Market Trend A...

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3rd IEEE international conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023

作者： Gandhi, Mohammad Asif I. Yadav, Sameer Pol, Naveen Sivakumar, G. Chandrakala, P. Krishna, P.Vamsi Channel Paradise Chs Ltd. Maharashtra India University of Allahabad Department of Commerce and Business Administration Uttar Pradesh Prayagraj India Isbr Business School Bangalore India Sengunthar Engineering College Department of Cse Tamil Nadu Erode India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering College Chennai India School of Management and Commerce Malla Reddy University Hyderabad India

ISBN: (纸本)9798350358742

In this study, we investigate how Natural Language Processing (NLP) approaches have revolutionized demand forecasting in the stock market. We show a constant gain in prediction accuracy when using NLP-derived features in forecasting models, such as attitudes, subjects, and entities. Our research shows that models driven by natural language processing are more accurate overall. Case studies highlight how NLP insights have been put to use in the real world to improve stock demand forecasts. These learnings, gleaned through textual data analysis and real-time market emotions, provide investors the ability to make better judgments. The next steps for this study will be to improve natural language processing techniques for more accuracy, to broaden data sources to incorporate alternative data streams, and to investigate cutting-edge machine learning approaches. These developments have the potential to significantly improve stock market forecasting, giving investors more accurate resources for navigating the financial markets. © 2023 IEEE.

关键词： Sentiment analysis

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A joint feature updating training method based on time-series prediction 3

A joint feature updating training method based on time-serie...

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2022 3rd international conference on Computer Information and Big data Applications, CIBDA 2022

作者： Wang, Jing Shu, Chang College of Information Engineering Wuchang Institute of Technology Wuhan China School of Civil Engineering Wuhan University Wuhan China

ISBN: (纸本)9783800758760

In order to improve the performance and accuracy of single prediction model in multi category products sales forecasting, a joint feature updating training method of multi category sales forecasting based on ensemble learning is proposed in this paper. This training method which using Stacking strategy combined with Catboost, Decision Tree, XGB Regression can learn more data points than the single model. The model divides data sets into a category part and a numerical part. A label encoder or other category encoders could be applied to process the category part, whereas time-series feature methods could be applied to process the numerical part. After that, an iterative predicting method is involved in making long-term predictions. The method includes a feature updater that helps generate the feature for the next predicting step. The Retail data Analytics data set from Kaggle was used to test the efficiency of the proposed model. The results shows that the proposed model had high efficiency and predicting accuracy. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Efficiency

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Swarm learning based Decentralized and Confidential Transmission Scheme for Distribution PV Systems 3

Swarm Learning based Decentralized and Confidential Transmis...

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3rd international conference on New Energy and Power engineering, ICNEPE 2023

作者： Li, Zhihai Zhou, Yuze Li, Wei Deng, Boya Tang, Dongjun Yang, Yan Peng, Jianyu Hu, Jiawei State Grid Electric Power Research Institute Co. Ltd Nanjing China Nanjing Nari Information & Communication Technology Co. Ltd Nanjing China State Grid Jiangsu Electric Power Co. Ltd. Changzhou Power Supply Branch Changzhou China

ISBN: (纸本)9798350393224

As a renewable and sustainable energy source, distributed photovoltaics (PV) offer a practical solution to reduce carbon emissions and energy costs and increase energy independence for users. For photovoltaic (PV) operators, data transmission is crucial for monitoring power generation, performance analysis, fault detection and remote control of PV systems where communication efficiency and data privacy remain the primary concerns. However, photovoltaic devices from different operators or companies expect to exchange information or use each other's historical experience without the need for a central server while maintaining data privacy and communication efficiency. In order to solve this problem, we propose an original framework based on swarm learning (SL) to achieve a decentralized peer-to-peer transmission approach without relying on a central server. This framework also incorporates a credibility-weighted algorithm for enhancing the fairness of PV device participation. This is added to improve the fairness of photovoltaic device participation. The experimental results illustrate that the scheme proposed in this paper can improve the system's reliability effectively while maintaining privacy. © 2023 IEEE.

关键词： credibility-weighted distributed PV privacy-preserving transmission swarm learning

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Hardware circuit design of intelligent fast charger control system 3

Hardware circuit design of intelligent fast charger control ...

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2024 3rd international conference on Electronics and Integrated Circuit Technology, EICT 2024

作者： Tang, Lili Wang, Yubo Fang, Tianyu College of Computer and Information Engineering Zhixing College of Hubei University Wuhan430011 China

This design is an intelligent fast charger system based on the STC89C52 MCU. The system uses the battery management chip MAX1811, which can control the intelligent charging of lithium batteries. Users can adjust the power mode through pre-set buttons, including fast and slow charging modes. The charging system will detect battery parameters and then charge the battery. During the charging process, the system uses the MAX157 chip to detect battery parameters and check for any abnormalities. A portion of the battery data detected by the chip will be sent to the LCD screen LCD1602 through the MCU. Users can understand the battery situation in real time. After charging is completed, the system will remind users promptly through LED alarm lights and buzzers, and turn on power-off protection to ensure battery safety. The system uses a USB connector for power supply. Charging the lithium battery through the output interface will reset the system every time the power is turned on, requiring the user to reset the battery charging system. © Published under licence by IOP Publishing Ltd.

关键词： Lithium batteries

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From data Pre-processing to Hate Speech Detection: An Interdisciplinary Study on Women-targeted Online Abuse 3

From Data Pre-processing to Hate Speech Detection: An Interd...

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3rd IEEE international conference on intelligent Technologies, CONIT 2023

作者： Rathod, Rutuja G. Barve, Yashoda Saini, Jatinderkumar R. Rathod, Sourav Surayadatta College of Management Information & Research Technology Pune India Pune India AI&ML Deparment Pune India

ISBN: (纸本)9798350338607

The term 'misogyny' conveys hatred and disrespect for women. It is a type of sexism that stems from the idea that women are less valuable than men and can take many forms, such as verbal abuse, sexual harassment, and physical violence. One area where misogyny is particularly prevalent is in online spaces. Women who speak out and share their experiences online often face vicious attacks, including violence, rape, and death threats. This phenomenon, often referred to as 'online harassment,' can have devastating effects on women's mental health and well-being. Despite the growing recognition of this issue, there is still a lack of understanding about the nature and extent of hate speech toward women on social media. In this paper, the authors aim to address this gap by analyzing a dataset of tweets on Twitter containing hate speech toward women. The authors introduced the Measuring Hate Speech corpus, a dataset used while studying hate speech towards women. From this dataset, the authors extract some of them to build the model. And authors proposed Machine learning algorithms like Logistic Regression with 0.92% accuracy. And to look further to fill the gap, the authors developed Support Vector Machine (SVM) algorithm, Topic Modeling technique to extract the topics from the corpus. This study contributes to a better understanding of hate speech on social media platforms as Twitter informs strategies for resisting this pervasive problem. © 2023 IEEE.

关键词： Support vector machines

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Lung Cancer Detection by Using CNN Architecture Models 1

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3rd international conference on Artificial-Business Analytics, Quantum and Machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Takale, Dattatray G. Mahalle, Parishit N. Sakhare, Sachin R. Gawali, Piyush P. Deshmukh, Gopal Khan, Vajid Banchhor, Chitrakant B. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of AI & DS Vishwakarma Institute of Information Technology SPPU Pune India Department of Computer Engineering K. J. College of Engineering and Management Research SPPU Pune India

ISBN: (数字)9789819725083

ISBN: (纸本)9789819725076

Cancer disorders are caused when certain body cells enlarge uncontrollably and spread throughout the body. Lung cancerous lesion tumors can be detected by radiologists with the help of medical imaging techniques like Computed Tomography (CT) scan images, by using chest X-rays, or by using MRI. We examine various medical image-based datasets, their availability, and the number of cases they contain. Due to the abundance of pictures, a CT scan-based strategy is employed. The vastness of the collection made it difficult for radiologists to diagnose quickly and correctly. Among the data that was supplied, there is a great deal of computer tomography (CT) scan pictures. Chest radiographs are the source of diagnostic mistakes in around ninety percent of instances involving lung cancer. In chest radiographs, it might be difficult for radiologists to differentiate a lung lesion from bones, pulmonary arteries, mediastinal structures, and other complicated anatomical features. This can be a challenge for diagnostic purposes. In order to tackle this issue, distinct predictive analytics and intelligent retrieval techniques are employed, like traditional Computer Aided Diagnosis (CAD), Convolutional Neural Network (CNN), Dens-Net, Agile CNN, and LeNet. These deep structured learning-based methods mostly used complex neural networks, which caused vanishing gradient problems. To solve such a problem, we are using the ResNet50 method approach to get better result accuracy. CT scan images from (LIDC/IDRI) are being used for the dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computerized tomography

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