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Aspect category and sentiment polarity detection using a permutation language-based transformer model

Procedia Computer Science 2025年 258卷 4179-4189页

作者： Ashok Kumar Jayaraman Tina Ester Trueman Gayathri Ananthakrishnan Abirami Murugappan Erik Cambria Manikandan G Satanik Mitra HCLTech Engineering and R&D Services Chennai Tamil Nadu India Department of Computer Science University of the People California USA School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Information Science and Technology Anna University Chennai India College of Computing and Data Science Nanyang Technological University Nanyang Singapore School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Management BITS Pilani India

Aspect-based sentiment analysis (ABSA) performs fine-grained analysis on text to determine a specific aspect category and a sentiment polarity. recently, machine learning models have played a key role in ABSA tasks. In particular, transformer-based pre-trained models have achieved promising results in natural language processing tasks. Therefore, we propose a permutation based XLNet fine-tuning model for aspect category detection and sentiment polarity detection. Our model learns bidirectional contexts via positional encoding and factorization order. We evaluate the proposed permutation language model on three ABSA datasets, namely, SentiHood, SemEval 2015, and SemEval 2016. Specifically, we studied the ABSA tasks in a constrained system with a multi-class environment. Our result indicates that the proposed permutation language model achieves a better result.

关键词： Aspect-based sentiment analysis permutation language model deep learning transformers multiclass classification

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An improved meteorological variables-based aerosol optical depth estimation method by combining a physical mechanism model with a two-stage model

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Chemosphere 2024年 363卷 142820页

作者： Li, Fuxing Shi, Xiaoli Wang, Shiyao Wang, Zhen de Leeuw, Gerrit Li, Zhengqiang Li, Li Wang, Wei Zhang, Ying Zhang, Luo State Environmental Protection Key Laboratory of Satellite Remote Sensing Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Geographical Sciences Hebei Normal University Hebei Key Laboratory of Environmental Change and Ecological Construction Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change Shijiazhuang050024 China R&D Satellite Observations 3730AE De Bilt Netherlands

A two-stage model integrating a spatiotemporal linear mixed effect (STLME) and a geographic weight regression (GWr) model is proposed to improve the meteorological variables-based aerosol optical depth (AOd) retrieval method (Elterman retrieval model-ErM). The proposed model is referred to as the STG-ErM model. The STG-ErM model is applied over the Beijing-Tianjin-Hebei (BTH) region in China for the years 2019 and 2020. The results show that data coverage increased by 39.0% in 2019 and 40.5% in 2020. Cross-validation of the retrieval results versus multi-angle implementation of atmospheric correction (MAIAC) AOd shows the substantial improvement of the STG-ErM model over earlier meteorological models for AOd estimation, with a determination coefficient (r2) of daily AOd of 0.86, root mean squared prediction error (rMSE) and the relative prediction error (rPE) of 0.10 and 36.14% in 2019 and r2 of 0.86, rMSE of 0.12 and rPE of 37.86% in 2020. The fused annual mean AOd indicates strong spatial variation with high value in south plain and low value in northwestern mountainous areas of the BTH region. The overall spatial seasonal mean AOd ranges from 0.441 to 0.586, demonstrating strongly seasonal variation. The coverage of STG-ErM retrieved AOd, as determined in this exercise by leaving out part of the meteorological data, affects the accuracy of fused AOd. The coverage of the meteorological data has smaller impact on the fused AOd in the districts with low annual mean AOd of less than 0.35 than that in the districts with high annual mean AOd of greater than 0.6. If available, continuous daily meteorological data with high spatiotemporal resolution can improve the model performance and the accuracy of fused AOd. The STG-ErM model may serve as a valuable approach to provide data to fill gaps in satellite-retrieved AOd products. © 2024 Elsevier Ltd

关键词： Optical properties

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Mind reasoning Manners: Enhancing Type Perception for Generalized Zero-shot Logical reasoning over Text

arXiv

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arXiv 2023年

作者： Xu, Fangzhi Liu, Jun Lin, Qika Zhao, Tianzhe Zhang, Jian Zhang, Lingling School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D National Engineering lab for Big Data Analytics Xi’an China

Logical reasoning task involves diverse types of complex reasoning over text, based on the form of multiple-choice question answering. Given the context, question and a set of options as the input, previous methods achieve superior performances on the full-data setting. However, the current benchmark dataset has the ideal assumption that the reasoning type distribution on the train split is close to the test split, which is inconsistent with many real application scenarios. To address it, there remain two problems to be studied: (1) How is the zero-shot capability of the models (train on seen types and test on unseen types)? (2) How to enhance the perception of reasoning types for the models? For problem 1, we propose a new benchmark for generalized zero-shot logical reasoning, named ZsLr. It includes six splits based on the three type sampling strategies. For problem 2, a type-aware model TaCo is proposed. It utilizes both the heuristic input reconstruction and the contrastive learning to improve the type perception in the global representation. Extensive experiments on both the zero-shot and full-data settings prove the superiority of TaCo over the state-of-the-art methods. Also, we experiment and verify the generalization capability of TaCo on other logical reasoning dataset. © 2023, CC BY.

关键词： Benchmarking

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Transfer Function Based Feature Selection Using Harris Hawk Optimization for Biomedical Applications

Transfer Function Based Feature Selection Using Harris Hawk ...

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Smart Electronics and Communication Systems (ISENSE), International Conference on

作者： S. Jayachitra A. Prasanth Prasannakiruba G S Srichandana Abbineni Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Tamilnadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Computer Technology Bannari Amman Institute of Technology Sathyamangalam Department of ET-CSE(Data Science) CVR College of Engineering Hyderabad

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

Feature Selection plays key role to truncate the high dimensionality in the datasets through eradicating the unwanted and noisy attributes to improve the performance of classification. The Meta-heuristic technique is one of the most optimistic search techniques to travel the feature space to locate finest feature subset. The Harris Hawk Optimization (HHO) excited from hunting approach of hawk in nature. The cost effectiveness and simplicity formulates the algorithm prominent and superior in solving optimization problems. The binary version of the optimization is accountable for delineating continuous searching space to discrete feature space. Here, 6 transfer functions with S-Shaped and V-Shaped families were interposed and analyzed. Twelve benchmark datasets from UCI repository has been introduced to examine the transfer functions and choose the best feature pertaining to mitigate local minima and high speed of convergence. To analyze the efficacy of transfer function, the proposed method was compared over exiting algorithms and the proposed results attain higher classification accuracy in many of the datasets.

关键词： Costs Communication systems Metaheuristics Transfer functions Benchmark testing Feature extraction Space exploration Classification algorithms Noise measurement Convergence

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Next-Gen Home Automation: Sensor-Based Connectivity and Appliance Control 1st

Next-Gen Home Automation: Sensor-Based Connectivity and Appl...

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1st International research Conference on Computing Technologies for Sustainable development, IrCCTSd 2024

作者： Nithiyaraj, E. Emerson Srinivass, S. Mukilan, K. Muthukumar, M. Nanmaran, r. Srimathi, S. Department of Artificial Intelligence and Data Science Mepco Schlenk Engineering College Sivakasi India Department of ECE Ramco Institute of Technology Rajapalayam India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Biotechnology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9783031823824

Internet of Things (IoT) integrated smart home automation systems use sensor networks and communication modules to extend home energy efficiency, security, and convenience. IoT transforms houses into intelligent environment that are remotely monitored and controlled from anywhere using communication technologies. In this paper, a NodeMCU microcontroller and a variety of sensors, such as a PIr for motion detection, a gas sensor for monitoring hazardous gases, a flame sensor for fire detection, a dHT11 for temperature and humidity sensing, as well as smart locks, lights and fans for environmental control, are used to develop and implement a smart home system. With a user-friendly smartphone interface, the suggested system enables residents to monitor and operate their home appliances remotely in real- time, improving security, comfort, and energy efficiency. This article intends to further the development of smart home technology by providing a thorough overview of system design, sensor integration, and functionality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart homes

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G-Bot: revealing Government Programs with Smarted Assistance

G-Bot: Revealing Government Programs with Smarted Assistance

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recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), International Conference on

作者： A. ramathilagam Kavitha Gunasekaran Elamparithi Pandian M. Satheesh Kumar d. david Neels Ponkumar r. Saravanakumar Department of Computer Science and Engineering P.S.R. Engineering College Sivakasi Tamil Nadu India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

residents have had trouble getting accurate state program information for years. G-bot's innovative technology employs NLP to link customers to state-level policies and activities to bridge this knowledge gap. G-bot's core NLP module is taught to deliver reliable and trustworthy information to help people make healthy and progressing decisions. G-bot provides real-time, reliable data to promote public awareness, transparency, and involvement in government processes. Users may explore the enormous state program and regulatory information using G-bot's simple interface. G-bot's Flask integration helps users locate vital information. G-bot enhances user-centric apps beyond information retrieval. AI-powered language processing ushers in a new era, letting individuals shape their communities. G-bot uses NEr to identify government entities. This tool simplifies identifying organizations, programs, laws, agencies, and people, improving search results and customizing insights for better decision-making.

关键词： Accuracy Shape Government Natural languages decision making Market research Information retrieval real-time systems Microelectronics reliability

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Learning Vision-and-Language Navigation from YouTube Videos

Learning Vision-and-Language Navigation from YouTube Videos

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International Conference on Computer Vision (ICCV)

作者： Kunyang Lin Peihao Chen diwei Huang Thomas H. Li Mingkui Tan Chuang Gan South China University of Technology Information Technology R&D Innovation Center of Peking University Peking University Shenzhen Graduate School Key Laboratory of Big Data and Intelligent Robot Ministry of Education UMass Amherst MIT-IBM Watson AI Lab

Vision-and-language navigation (VLN) requires an embodied agent to navigate in realistic 3d environments using natural language instructions. Existing VLN methods suffer from training on small-scale environments or unreasonable path-instruction datasets, limiting the generalization to unseen environments. There are massive house tour videos on YouTube, providing abundant real navigation experiences and layout information. However, these videos have not been explored for VLN before. In this paper, we propose to learn an agent from these videos by creating a large-scale dataset which comprises reasonable path-instruction pairs from house tour videos and pre-training the agent on it. To achieve this, we have to tackle the challenges of automatically constructing path-instruction pairs and exploiting real layout knowledge from raw and unlabeled videos. To address these, we first leverage an entropy-based method to construct the nodes of a path trajectory. Then, we propose an action-aware generator for generating instructions from unlabeled trajectories. Last, we devise a trajectory judgment pretext task to encourage the agent to mine the layout knowledge. Experimental results show that our method achieves state-of-the-art performance on two popular benchmarks (r2r and rEVErIE). Code is available at https://***/JeremyLinky/YouTube-VLN

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Annotation Guided Generative Adversarial Networks for Image Augmentation of Age-related Macular degeneration

Annotation Guided Generative Adversarial Networks for Image ...

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Annual Conference of Industrial Electronics Society

作者： raja Chandrasekaran Saketh reddy Balpunuri Isuru Senadheera Vishnu Sai Nallagatla Lokesh Akula Praveen VelTech Rangarajan Dr Sagunthala R&D Institute of Science & Technology Chennai India Centre for Data Analytics and Cognition La Trobe Business School La Trobe University Melbourne Australia Department of Ophthalmology ACSR Government Medical College Nellore Andhra Pradesh India

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Age-related macular degeneration (AMd) is a common eye disease that affects millions of people worldwide, leading to central vision loss. It mostly occurs in people over 50 years old. As per the literature, AMd diagnosis requires high-quality and diverse fundus images, which are often scarce and expensive to obtain. We propose a novel generative adversarial network (GAN) to address this challenge. It can synthesize realistic and clinically relevant images with AMd anomalies (drusen and Abnormalities of Choroidal Neovascular Membrane(CNVM)), enhancing dataset diversity and aiding automated diagnosis. Our GAN architecture is supplemented with (manual annotation guided) spatial attention maps to emphasize on anomalies guided through manually generated label maps. Further we propose 2 Siamese like discriminators, for individual anomalies which emphasize the Generator module to generate AMd anomaly (drusen and abnormalities of CNVM) specific images. The proposed GAN leverages various loss functions, including contrastive loss and anomaly loss in the Siamese like discriminator, and the adversarial loss in the combined model. The anomaly loss inspired by (retinal detail Loss) measures the prominence of AMd signatures in the generated images. Our approach successfully generates synthetic images with specific AMd anomalies, aiding early diagnosis. The proposed GAN extended excellent dataset augmentation capabilities with 3% and 4% increase in diagnostic accuracy of drusen and abnormalities of CNVM, respectively.

关键词： Macular degeneration Productivity Accuracy Annotations Prevention and mitigation Manuals Generative adversarial networks retina Loss measurement Safety

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Enhancement of IoT based Flood detection and Prevention using Arduino UNO with WiFi Module 9

Enhancement of IoT based Flood Detection and Prevention usin...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Charaan, r. M. dilip Shobana, J. Krishnamoorthy, P. Princy, B. Anni Abinaya, r.J. Murugesan, K. Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India SRM Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu India Karpagam Academy of Higher Education Department of Electronics and Communication Engineering Tamil Nadu India Loyola Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798350397376

The overflow of water from a lake or river usually causes flooding. Sometimes, a dam breach might result in the unexpected release of vast quantities of water. Some of the water seeps into the ground, flooding the region. In a station, rivers are involving the riverbanks. Along with a lack of goods and houses, businesses, and offices, street infrastructure floods contain bacteria, sewage from waste sites, and chemical spills, which later cause a number of diseases. The rate of change in river stage in real time, which can assist signal the gravity and immediacy of this hazard, is a crucial piece of information for flood predictions. Understanding the type of storm that produced the moisture, including its duration, strength, and actual extent, is important for determining the possible severity of the flood. In this system, an Arduino Uno is connected to four separate sensors: a humidity sensor, a flow sensor, a float sensor, and an ultrasonic sensor. The float sensor detects when the water is full. With the aid of IOT, these sensor combinations are utilised to predict floods, alert the appropriate authorities, and sound an immediate alarm in adjacent communities to rapidly relay information about potential floods. These sensors provide data via the IOT's WiFi module. When flooding conditions are detected, the system warns the nearby villages and places and estimates how long it would take for help to reach at a particular location. The technology also determines when it might be considered a flood and gives them a window of time to leave in case it does. © 2023 IEEE.

关键词： Floods

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Cutting-Edge technology: Exploring the Experimental Possibilities of Smart Virtual Wardrobe design Using Internet of Things

Cutting-Edge Technology: Exploring the Experimental Possibil...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： B. Arunsundar P Anusha K. Mekala devi M Manimaraboopathy r. ratheesh Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of ECE R.M.K. Engineering College Kavaraipettai Chennai Department of ECE S.A. Engineering College Chennai Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science Technology Chennai Department of ECE Agni College of Technology Chennai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Modern technological developments have mostly centered on three areas: communication, the workplace, and entertainment. Choosing an outfit is a daily struggle for everyone. The issue isn’t a lack of clothing; rather, it’s the inability to distinguish between those that you use frequently and those that have been in storage for quite some time. In addition to keeping tabs on your garments, a smart wardrobe can also provide outfit recommendations based on your calendar’s events. In order to assist you in deciding which garments to sell or donate, the system creates statistical data based on the frequency of wear. An intelligent wardrobe system for garment management, identification, and recommendation is suggested in this study, along with a virtual try-on system based on image processing. To test how well the suggested system works, it is cross-validated with the traditional model known as Virtual Wardrobe (VW). This system is dubbed Smart IoT Virtual Wardrobe (SIoTVW). In order to recognize and categorize the garments in the closet, the system makes use of machine learning algorithms and neural network based approaches. In order to provide the user with outfit recommendations that are suitable for various events and climates, the smart system also remembers their daily routine and clothing preferences. In addition, the system generates virtual fit-on dress models using image processing and neural network based approaches, allowing users to virtually try on various garments and view how they appear before deciding what to wear. In order to keep the clothes in pristine condition, the suggested system uses Internet of Things (IoT) sensors and application programming interfaces to track the wardrobe’s status, which includes illumination, humidity, and temperature. The system’s study reveals that it is capable of accurately detecting garments, offering clothing recommendations to users, and simulating virtual try-on dress models, which makes it an attractive option for c

关键词： Temperature sensors Schedules Image processing Clothing Neural networks Sensor systems and applications real-time systems

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