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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Yin, Wen Liu, Cencen Xu, Yi Wahla, Ahmad Yiting, Huang Zheng, Dezhang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Laboratory of Intelligent Collaborative Computing Chengdu611731 China Bashu Middle School Chongqing400013 China

ISBN: (纸本)9782493814104

Although there have been some studies uses prompt learning for the Aspect-based Sentiment Analysis(ABSA) tasks, their methods of prompt-tuning are simple and crude. Compared with vanilla fine-tuning methods, prompt learning intuitively bridges the objective form gap between pre-training and fine-tuning. Concretely, simply constructing prompt related to aspect words fails to fully exploit the potential of Pre-trained Language Models, and conducting more robust and professional prompt engineering for downstream tasks is a challenging problem that needs to be solved urgently. Therefore, in this paper, we propose a novel syntax-aware enhanced prompt method (SynPrompt), which sufficiently mines the key syntactic information related to aspect words from the syntactic dependency tree. Additionally, to effectively harness the domain-specific knowledge embedded within PLMs for the ABSA tasks, we constructed two adaptive prompt frameworks to enhance the perception ability of the above method. After conducting extensive experiments on three benchmark datasets, we have found that our method consistently achieves favorable results. These findings not only demonstrate the effectiveness and rationality of our proposed methods but also provide a powerful alternative to traditional prompt-tuning. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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Enhancing Efficiency in 6G Radio Resource Allocation Using Machine Learning Algorithms 5th

Enhancing Efficiency in 6G Radio Resource Allocation Using M...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Kamalraj, R. Nautiyal, Kshitij Taranum, Ayesha Savita Karnataka Bangalore India Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Using machine gaining knowledge of algorithms with 6G radio resource allocation can assist to enhance network performance by using automating the selection making technique on a cease-to-stop foundation. This paper proposes a more suitable implementation of 6G radio aid allocation the usage of device getting to know algorithms to expand a framework for optimized useful resource utilization. The consequences of the experiments conducted in a real-world testbed validate the effectiveness of the proposed structure in achieving the desired goals consisting of improved community performance and decreased strength consumption. Moreover, the proposed improved implementation might be relevant in a huge range of other resource allocation eventualities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Resource allocation

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A Model for the Analytical Performance of Data Lake in Stock Market Analysis with Databricks Delta Lake

A Model for the Analytical Performance of Data Lake in Stock...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Kamalakkannan, S. Yasmin, A. Arunkumar, R. Kavitha, P. School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Information Technology Chennai Pallavaram India Jbas College for Women Department of Computer Science Chennai India University of South Wales Faculty of Computing Engineering and Science Treforest/Newport Campus United Kingdom School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Computer Applications Chennai Pallavaram India

ISBN: (纸本)9798350300857

Stock market investments are highly rewarding but also high in risk. Modern investors use variety of tools to take informed investment decisions. In the current era of digital world, financial service industry has generated huge volume and immense verities of data with extreme speed. Due to the rapid growth in data collection and the heterogeneous nature and complexity of the data, there is a need for Big Data analytical solution that would be able to deal with the stock market data. Large volumes of unstructured, heterogeneous raw data can be stored in a massively scalable manner using data lakes, which are the ideal solution to the big data storage conundrum. The ability of a data lake to preserve data in its original format while processing it at runtime using a schema on-read technique is its key feature. The challenge faced in the data lake is performing analytics which is a significant tool to calculate and analyze the stock market. The proposed architecture of Azure Databricks DeltaLake (ADDL) with Azure DataLake Storage Generation 2 (ADLSG2) is used for analytical processes like Fibonacci retracement for better stock analysis, which aid in forecasting the market price for better investment. As a result, the research focus is to produce a storage having read as well as write capabilities by taking into consideration the Extract-Load-Transform (ELT) operation on the datasource. In this experimental databricks implementation, runtime is performed using open source of Apache Spark API and a highly improved execution engine, which results in a significant performance improvement when comparing to the standard source of Apache Spark available on the ADLS platform. Additionally, the Fibonacci retracement level calculation is achieved with the analytics and forecasting of test close price with various ML and DL techniques such as KNN, LSTM are compared with original price of the test data for better prediction of forecast close price. © 2023 IEEE.

关键词： Big data

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Enhancing Cyclone Center Identification in Radar Images through Deep Learning and Match Recognition 15

Enhancing Cyclone Center Identification in Radar Images thro...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shanmugam, Thirumurugan Chandrashekar, Chirag Subburaj, Maheswari Sivaraman, Arun Kumar Sultana, Ajmery Thangavel, Senthil Kumar University of Technology and Applied Sciences College of Computing and Information Sciences Suhar Oman Vellore Institute of Technology School of Computer Science and Engineering Chennai India Photon Interactive Inc Digital Engineering Solution Center ON Canada Algoma University Brampton School of Computer Science and Technology ON Canada Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone's center is vital for predicting its intensity and trajectory. However, challenges persist in automatically locating the center due to the diverse nature of cyclone morphology and structure. To address this, the deep convolutional network's capability is leveraged to capture various structural features in images by proposing two-step approach for cyclone center localization. Initially, a pre-trained EfficientDet model is employed using transfer learning to obtain weights. Subsequently, these weights, along with the data, are utilized in a deep learning model to provide precise coordinates of the cyclone center in the respective image. The effectiveness of existing deep learning and machine learning models show that the cyclone prediction systems have an accuracy ranging from 86% to 92% or better, and cyclone eye detection accuracy surpassing 87%. Experiment outcomes indicate that the proposed methodology outperforms conventional methods and existing works, showcasing its potential for enhancing cyclone monitoring and forecasting. © 2024 IEEE.

关键词： Cyclone detection deep learning efficient det and YOLO model transfer learning

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Enhancing Earthquake Prediction through Hybrid Deep Learning Approach 2

Enhancing Earthquake Prediction through Hybrid Deep Learning...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kandan, M. Sarkar, Swagata Sharma, Meenakshi Rajeswari, V. Poonkuzhali, P. Srimathi, S. SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur India Sri Sairam Engineering College Artificial Intelligence and Data Science Chennai India Global Group of Institute Department of CSE Amritsar India Panimalar Engineering College Information Technology India R.M.D Engineering College Kavaraipettai India Saveetha School of Engineering SIMATS Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350317558

Earthquakes, one of the greatest natural catastrophes, kill people and harm the economy. Heavy earthquakes and tsunamis may kill thousands and cost millions. Globally, predicting major earthquakes is important. Because earthquake prediction is difficult, researchers employ machine learning to improve forecasts. Many countries have early earthquake warning systems to save lives. Temporal rather than geographical characteristics dominated previous research. A similarity between earthquake and non-seismic signals makes real-time earthquake detection with cellphones or IoT devices problematic. This system may change entity interactions using ML and an IoT network. EEWS metrics are tracked using IoT. However, ML can assess these actions and determine smart city risk reduction and catastrophe management strategies. A deep learning approach for continuous earthquake forecasting utilising past seismic events is proposed. This research predicts earthquakes by position and depth using machine learning and deep learning. ConvLSTM, a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), extracts important waveform elements to make the classifier reliable in earthquake parameters. © 2024 IEEE.

关键词： Convolutional Neural Network Deep Learning Earthquake LSTM Machine Learning

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Forecasting of Groundwater Quality Utilizing an Efficient K-Means with Principal Component Analysis Approach 2

Forecasting of Groundwater Quality Utilizing an Efficient K-...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Adnan, Myasar Mundher Manjula, B.M. Mishra, Antim Dev Ganti, Swathi Bala Dhandayuthapani, V. The Islamic University of Babylon Babylon Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering School of Engineering and Technology Sushant College Gurgaon India Department of Nano Technology Andhra University Visakhapatnam India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Oman

ISBN: (纸本)9798331504960

Groundwater stands as a pivotal freshwater resource in numerous regions worldwide, but the burgeoning population and diverse economic activities have adversely impacted its quality. However, the K-means algorithm is sensitive to the initial placement of centroids, where outliers can significantly influence the position of centroids resulting in suboptimal clusters. To address this, it is recommended to remove or clip outliers before applying K-means scales well to large datasets making it suitable for handling substantial amounts of data. In this proposed method, attributes are first calculated using Principal Component Analysis (PCA) determines the weight of each attribute based on eigenvalues. The PCA, by determining attribute weights and Rapid Centroid Estimation (RCE), by establishing the initial cluster centre, contribute to the overall effectiveness of the method. The classification step utilizes a Linear Support Vector Machine (SVM), suitable for linear hyper line data when the dataset can be classified into two classes using a single straight line. The results obtained showcase the superiority of the proposed method, achieving a Root Mean Squared Error (RMSE) of 1.23, R-squared (R2) of 0.90, Mean Absolute Deviation (MAD) of 0.28 and E of 0.97 on the Indian groundwater dataset. These outcomes affirm better detection and classification performance compared to other existing methods, such as Random Forest (RF), Decision Tree (DT) and K-Nearest Neighbour (KNN). © 2024 IEEE.

关键词： Mean square error

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Event-triggered Anti-disturbance Anti-Saturation Tracking Control for Systems with Nonlinearity:Application to Helicopter Elevation Angle Model 41

Event-triggered Anti-disturbance Anti-Saturation Tracking Co...

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第41届中国控制会议

作者： Jiangtong Wang Yang Yi Yunkai Wu Weixing Zheng College of Information Engineering Yangzhou University School of Electronics and Information Jiangsu University of Science and Technology School of Computing Engineering and MathematicsUniversity of Western Sydney

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This article concerns a disturbance observer-based(DOB) event-triggered anti-disturbance tracking control strategy for CE150 Helicopter *** the same time,actuator saturation is introduced compulsively,which is expressed by convex *** the one hand,it is to verify the performance of the system in the saturated state,and on the other hand,the actuator can be avoided from being damaged by excessive ***,the purpose of introducing the event-triggered mechanism is to save communication resources and the function of DOB proportional-integral(PI) controller is to suppress the disturbance and track the trajectory of the reference ***,the feasible gain can be obtained by utilizing the convex optimization and linear matrix inequation(LMI) ***,simulation is provided to demonstrate the feasibility and validity of the proposed control method.

关键词： Disturbance-observer-based control(BOBC) disturbance rejection convex optimization event-triggered control tracking control

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Revolutionizing Aquaculture Practices: IoT and Deep Learning-based Remote Monitoring for Sustainable Fish Farming 5

Revolutionizing Aquaculture Practices: IoT and Deep Learning...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Chelladurai, S. Prince Shiny, G.Susan Jeevitha, S.V. Swapna, P. Pavithra, R. Arulraj, S. Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Chennai Kattankulathur India Sethu institute of Technology Department of information technology Kariapatti Virudhunagar Tamil Nadu Pulloor626115 India Bannari Amman Institute of Technology Department of information technology Erode India Velammal college of engineering and technology Department of Cse Madurai India Rathinam Technical Campus Department of Ece Coimbatore India

ISBN: (纸本)9798331504403

Aquaculture plays a vital role in meeting the growing demand for seafood while addressing sustainability concerns in fisheries. To optimize fish growth and ensure efficient resource utilization an innovative IoT and Deep Learning-based remote monitoring system is proposed tailored for aquaculture operations. Our system integrates sensor technology, IOT connectivity, and Deep Learning algorithms to provide real-time monitoring and control of key environmental parameters such as water temperature, pH levels, oxygen concentration, and feeding behavior. By leveraging a network of sensors deployed in aquaculture facilities, our system collects and analyzes environmental data to optimize aquaculture practices and ensure optimal conditions for fish growth. Through a user-friendly interface accessible via web or mobile applications, aqua-culturists can remotely monitor the status of their facilities and adjust operational parameters as needed. Additionally, our system utilizes Deep Learning algorithms to analyze historical data and predict optimal feeding schedules, thereby enhancing feed efficiency and fish growth rates. Field trials demonstrate the effectiveness of our IOT and Deep Learning-based monitoring system in improving fish health and productivity while reducing environmental impacts and operational costs. Our findings highlight the potential of IoT and Deep Learning technologies in revolutionizing aquaculture practices, promoting sustainability, and enhancing food security in fisheries © 2024 IEEE.

关键词： Fish

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Multi-attribute wearable pressure sensor based on multilayered modulation with high constant sensitivity over a wide range

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Nano Research 2023年第5期16卷 7583-7592页

作者： Ning Li Song Gao Yang Li Jianwen Liu Wenhao Song Guozhen Shen School of Information Science and Engineering University of JinanJinan 250022China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China School of Aeronautical and Astronautical Engineering Purdue University West LafayetteIN 47907USA

Flexible pressure sensors capable of monitoring diverse physiological signals and body movements have garnered tremendous attention in wearable electronic ***,high constant sensitivity over a wide pressure range combined with breathability,biocompatibility,biodegradability is pivotal for manufacturing of reliable pressure sensors in practical sensing *** this work,inspired by the multilayered structure of skin epidermis,we propose and demonstrate a multi-attribute wearable piezoresistive pressure sensor consisting of multilayered gradient conductive poly(ε-caprolactone)nanofiber membranes *** response to externally applied pressure,a layer-by-layer current path is activated inside the multilayered membranes composites,leading to the most salient sensing performance of high constant sensitivity of 33.955 kPa^(−1) within the pressure range of 0–80 *** proposed pressure sensor also exhibits a fast response–relaxation time,a low detection limit,excellent stability,which can be successfully used to measure human physiological ***,an integrated sensor array system that can locate objects’positions is constructed and applied to simulate sitting posture *** results indicate that the proposed pressure sensor holds great potential in health monitoring and wearable electronic devices.

关键词： wearable pressure sensors multilayer structure high constant sensitivity multi-attribute physiological signals monitoring

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Smart Drive Safe: Harnessing CNN for Enhanced Traffic Sign Recognition and Voice Alerts 5

Smart Drive Safe: Harnessing CNN for Enhanced Traffic Sign R...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Priya, D. Divya Chinnasamy, P. Ayyasamy, Ramesh Kumar Kiran, Ajmeera Jalil, Norazira Binti A. Sangodiah, Anbuselvan Mlr Institue of Technology Department of Computer Science Engineering Hyderabad India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Srivilliputtur India Faculty of Information and Communication Technology Perak Kampar Malaysia Quest International University Faculty of Computing and Engineering Perak Ipoh Malaysia

ISBN: (纸本)9798350379945

Traffic signs secure their safety on the roads and the protection of others around them;everyone must be aware of traffic signs and other aspects of road safety. Traffic sign detection is a highly relevant computer vision problem that forms the basis of numerous applications in sectors like the automotive industry. Between classes, traffic signs differ widely in terms of colour, shape, and the use of pictograms or text. The project's classification of traffic signs in an image into various categories is based on a deep neural network model. IoT devices that capture traffic signs and notify users of them are used to build a model. © 2024 IEEE.

关键词： Automotive industry

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