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共 20255 条记录，以下是321-330 订阅

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Sentiment Analysis of User Reviews on Bert-Based E-Commerce Platforms 3

Sentiment Analysis of User Reviews on Bert-Based E-Commerce ...

引用

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

作者： Huiyang, Yin Jin, Hou Hongwen, Li Lanxin, Liu Hao, Zhou Sichuan University of Science & Engineering Yibin China

ISBN: (纸本)9798350380989

The task of e-commerce platform user review sentiment analysis is to collect user reviews on various e-commerce platforms to make a dataset, and then manually annotate them with labels to perform sentiment analysis using deep learning methods. In deep learning-based sentiment analysis, embedding generation of text and feature extraction are the core parts, and this paper chooses to study these two parts and proposes a deep learning model based on bert-bigru-textcnn. © 2024 IEEE.

关键词： bert bigru deep learning emotional analysis user review

来源：评论

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Air Pollution Forecasting using Machine learning with Temporal Fusion Transformer and Graph Neural Networks 3

Air Pollution Forecasting using Machine Learning with Tempor...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Rath, Subhashree Maneesha, P. Dept.of.Computer Science and Engineering New Horizon College Of Engineering Autonomous College Affiliated to VTU Bengaluru560103 India

ISBN: (纸本)9798331527549

Air pollution is a significant obstacle to achieving urban sustainability and public health, particularly in rapidly developing regions. Accurate air quality prediction is essential for proactive pollution management and policy formulation. In this study, we employ sophisticated machine learning frameworks, such as Temporal Fusion Transformer (TFT) and Graph Neural Networks (GNNs), to predict air quality levels. TFT is designed to capture temporal dependencies, making it ideal for time-series forecasting, while GNNs model spatial relationships, enabling the analysis of pollutant dispersion across monitoring stations .A synthetic dataset comprising 50 rows was generated, simulating pollutant concentrations (PM2.5, PM10, CO, NO2, SO2) and meteorological variables (temperature, humidity, wind speed). The models were assessed using criteria like R2, RMSE, and MAE. TFT achieved an R2 of 0.97 and an RMSE of 4.2 µg/m3, significantly outperforming traditional models like Random Forest and XGBoost. GNN provided spatial insights, identifying pollutant hotspots and their dispersion patterns. This research highlights the superior accuracy and scalability of TFT and GNN for air quality prediction. The results underline their potential for real-time monitoring, smart city integration, and proactive environmental management, offering a robust solution for addressing urban air pollution challenges. © 2025 IEEE.

关键词： Graph neural networks

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The Application of intelligent database System in Material Support 24

The Application of Intelligent Database System in Material S...

引用

3rd international conference on Frontiers of Artificial Intelligence and Machine learning (FAIML)

作者： Zhai, Chenggong Xu, Hongsi Lv, Huifang Zhou, Liyong Wan, Fei Zhang, Pinghua Army Logist Acad Dept Quartermaster Procurement Chongqing Peoples R China Army Logist Acad Dept Logist Command Chongqing Peoples R China Army Logist Acad Mil Logist Dept Chongqing Peoples R China

ISBN: (纸本)9798400709777

Material support is a crucial part of the daily operations of many organizations and enterprises. With the continuous development of information technology, the application of intelligent database systems in the field of material support has become an important tool for improving efficiency, reducing costs, and providing better decision support. It can be used for material inventory management, real-time monitoring of inventory levels, and automatic triggering of ordering or replenishment processes. In addition, the intelligent database system can also predict future material needs, optimize the supply chain, assist in emergency response and material allocation, and conduct quality control and tracking. These characteristics can improve the efficiency, accuracy, and reliability of material management, ensure timely supply and quality safety of materials, and meet people's basic living needs and emergency rescue needs

关键词： intelligent database Material support data analysis and prediction Supply chain optimization

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Integrated Testing System for Substation Relay Protection Devices Based on Cloud Date Interaction 3

Integrated Testing System for Substation Relay Protection De...

引用

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

作者： Gao, Bingwei Zhao, Zhanglei Ye, Chao Mei, Hong Liu, Xi State Grid Wenzhou Power Supply Company Wenzhou China

ISBN: (纸本)9798350380989

With the expansion of the power grid, the number of secondary equipment in intelligent substations has significantly increased. We should put forward higher requirements for the quality and speed of equipment testing and maintenance. In response to the lack of integrated testing methods for cross bay equipment in intelligent substations, and the existence of missed, over checked, and blind inspections in the maintenance for protective devices, we have built an integrated testing system for secondary equipment in substations based on cloud data interaction, and adopted an intelligent substation integrated testing method based on cloud data interaction to achieve integrated testing of cross bay equipment in intelligent substations. © 2024 IEEE.

关键词： Cloud data interaction integrated testing relay protection

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An Robust Machine learning Methods for Sentiments Analysis of Amazon Product in E-Commerce 3

An Robust Machine Learning Methods for Sentiments Analysis o...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Kumar Mannepalli, Praveen Khatri, Parul Patel, Meenakshi Khan, Sana Chandigarh University Department of Computer Science and Engineering Mohali India Oriental Institute of Science & Technology Bhopal India Bhopal India

ISBN: (纸本)9798331528201

The rise in popularity of online platforms like social media, search engine optimisation, and email has propelled a field of Sentiment Analysis, which seeks to decipher an underlying meaning of a text, to the forefront of global discourse. In order to do sentiment analysis on Amazon product evaluations, this article presents a comparison examination of several machine learning algorithms. SVM, DT, RFC, CNN, and LSTM models will be the primary targets of their investigation. Research using sentiment analysis involves customers who are aware of how a product makes them feel. The data collection utilised is a Kaggle of product reviews from Amazon. It turns out that the LSTM ML algorithm is the most accurate of all the ones that were tried. The results show the LSTM model shows a higher accuracy of 97%, in comparison to other models. Research like this improves both the decision-making process and the online buying experience by shedding light on customer opinion. © 2024 IEEE.

关键词： Adversarial machine learning

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An Efficient Deep learning based Waste Management System for Sustainable Environment 3

An Efficient Deep Learning based Waste Management System for...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Saghana, K. Saranya, P. Mahesh Reddy, A. Keerthy Rai, V. Ramasubramanian, B. Sudhakaran, P. Department of ECE Sri Venkatesa Perumal College of Engineering and Technology Andra Pradesh Puttur India Department of ECE Sri Venkateswara College of Engineering and Technology Andra Pradesh Puttur India Department of ECE Siddharth Institute of Engineering and Technology Andra Pradesh Puttur India Department of ECE SRM TRP Engineering College Tamil nadu Tiruchirappalli India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Efficient Management of solid and liquid waste is crucial for maintaining environmental sustainability and public health. This work presents a novel approach for the detection and segregation of solid and liquid waste using deep learning techniques. By leveraging Convolutional Neural Networks (CNNs), a system is designed which is capable of automatically classifying waste into solid or liquid categories based on real-time image data from waste disposal units. The system also integrates Internet of Things (IoT) devices to transmit the classified waste data to a remote central collection centre for optimized disposal processes. The model was trained on a diverse dataset of waste images and achieved high accuracy in distinguishing between solid and liquid waste. Once classified, the waste information is communicated via a secure wireless network to the central collection centre, which can coordinate timely collection and disposal efforts. This approach not only automates the waste management process but also reduces manual errors and delays in waste segregation and disposal. This work also discusses the potential for scalability and implementation in smart city infrastructures. Compared to the state of art method existing in the literature, this proposed method achieves an accuracy of 98.31 %, Precision of 97.93%, recall of 98.17% with a F1- score of 0.98. This system demonstrates the effectiveness of deep learning in waste classification and proposes a scalable solution for enhancing waste management practices, ultimately contributing to a more sustainable environment. © 2025 IEEE.

关键词： Convolutional neural networks

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Hybrid AI-Electronic Systems for Real-Time Edge Processing in IoT Networks 3

Hybrid AI-Electronic Systems for Real-Time Edge Processing i...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Sweety Prasanna Kiruba, G. Esakki Madura, E. Kumar, Bura Vijay Kavitha, M. Reddy, T. Srinivas Athiraja, A. Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Sathyamangalam India School of Computer Science and Artificial Intelligence Sr University Warangal India Kathir College of Engineering Department of Artificial Intelligence and Data Science Neelambur Coimbatore India Malla Reddy Engineering College Department of Ece Main Campus Maisammaguda Telangana Medchal India Hyderabad India

ISBN: (纸本)9798331515911

Real-time data processing is facing many issues as a result of the Internet of Things' rapid expansion, especially at the network's edge where latency, bandwidth, and compute resources are limited. The creation of hybrid AI-electronic systems for real-time edge processing is investigated as a solution to these problems. To improve the effectiveness and precision of data processing in Internet of Things contexts, the research combines machine learning algorithms with sophisticated electronic circuits. The techniques include creating a brand-new hybrid framework that blends edge computing and AI models and focuses for low latency and low power consumption. When compared to conventional edge processing systems, extensive simulations and real-world research show a 25% reduction in processing latency and a 15% gain in data categorization accuracy. According to the findings, the suggested hybrid AI-electronic system can successfully handle the requirements of contemporary IoT networks and offer a scalable real-time edge processing solution. This strategy has potential for use in smart city applications, industrial automation, and other fields in the future. © 2025 IEEE.

关键词： Adversarial machine learning

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Enhanced Pneumonia Detection in Chest X-rays through a Hybrid DL-ML Ensemble Model 3

Enhanced Pneumonia Detection in Chest X-rays through a Hybri...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Harshitha, Rasuri Sree, Mudu Navya Kiran Kumar, G. Anila, M. China Ramu, S. Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Gandipet Telangana Hyderabad India

ISBN: (纸本)9798331527549

Pneumonia is a respiratory infection and has occurred in more than 450 million people every year, causing about 2.5 million deaths, especially among infants, the aged, and people with compromised immune systems. It inflames the lungs by filling air sacs with fluid or pus, causing coughing, fever, and labored breathing. It is very essential to diagnose at the right time because pneumonia may be confused with pulmonary tuberculosis and seasonal infections, thus delaying the treatment and raising the risk of death. Currently, diagnostic methods rely on conventional image analysis models such as InceptionResNetV2, Xception, VGG16, ResNet50, and DenseNet121 in addition to transfer learning and standalone algorithms. However, these approaches lack the accuracy required for the detection of pneumonia. This study proposes a hybrid model combining deep learning, especially CNNs, with machine learning classifiers. It enhances specificity and sensitivity in diagnosing pneumonia from CXR images for earlier and more accurate detection. It could lead to a major decrease in mortality associated with pneumonia based on prompt effective treatment. Feature extraction from the CNN is married with machine learning classification to maximize the diagnostic power. This approach may provide an excellent framework from which future augmentations can focus on severity stratification and also more general types of thoracic diseases. © 2025 IEEE.

关键词： Diagnosis

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Neural Networks in Human Activity Recognition: Trends, Challenges, and Future Directions 3

Neural Networks in Human Activity Recognition: Trends, Chall...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Valli, C. Amutha, R. Department of EEE Sri Venkateswaraa college of Technology Sriperumbudur India Department of ECE Sri Sivasubramaniya Nadar College of Engineering Kalavakkam India

ISBN: (纸本)9798331527549

Human Action Analysis is a procedure in tracking individual gestures, motion and actions which finds applications, including healthcare, surveillance, security and human-computer communication. With the proliferation of sensors and deep learning techniques, HAR has marked remarkable development in the past few years. This review paper gives a brief survey of the current studies in Activity Analysis, focusing on deep learning-based approaches. The various sensors employed in HAR, including accelerometers, gyroscopes, and vision-based sensors were used in acquiring signals. The main objective is to design an appropriate model for recognizing/classifying human actions effectively to support elderly and differently abled people. The evolution of deep learning architectures used in detection such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks can be utilized for action recognition. The characterization of these algorithms is evaluated using various metric indices including precision, accuracy, recall, F1-score, and mean average precision. The challenges and limitations of existing HAR systems, including data purity, imbalance nature of class, and positioning of sensor were also presented. This review aims to provide a clear picture in Action Recognition, highlighting the Pros and cons of different approaches. By identifying the prospects and obstacles, this review seeks to inspire future research in behaviour analysis, ultimately leading to the development of more accurate, efficient, and robust systems. © 2025 IEEE.

关键词： Convolutional neural networks

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intelligent Identification Method for Intermittent Electricity Theft Behavior of Users Based on Multitask learning 3

Intelligent Identification Method for Intermittent Electrici...

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3rd international conference on Energy engineering and Power Systems, EEPS 2023

作者： Gao, Sheng State Grid Beijing Tongzhou Electric Power Supply Company Beijing101000 China

ISBN: (纸本)9798350313857

In order to improve the detection rate and AUC value of electricity theft behavior recognition results, and reduce the false detection rate, a user intermittent electricity theft behavior intelligent recognition method based on multi task learning is proposed. By collecting and analyzing user electricity consumption data, identify the characteristics of electricity theft behavior, obtain electricity theft feature data, and use factor analysis to dimensionally regulate the electricity theft feature data. Based on the results of data processing, train a multi task learning model to gain a more comprehensive understanding of users' electricity consumption and identify intermittent electricity theft behavior. The experimental results show that the detection rate and AUC value of the proposed method are higher, and the false detection rate is significantly reduced, indicating that the recognition effect of this method is good. © 2023 IEEE.

关键词： Factor analysis

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