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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Tasawong, Panuthep Limkonchotiwat, Peerat Manakul, Potsawee Udomcharoenchaikit, Can Chuangsuwanich, Ekapol Nutanong, Sarana School of Information Science and Technology VISTEC Thailand AI Singapore Singapore SCB 10X Thailand Department of Computer Engineering Chulalongkorn University Thailand

ISBN: (纸本)9798891761643

Entity disambiguation (ED) is crucial in natural language processing (NLP) for tasks such as question-answering and information extraction. A major challenge in ED is handling overshadowed entities-uncommon entities sharing mention surfaces with common entities. The current approach to enhance performance on these entities involves reasoning over facts in a knowledge base (KB), increasing computational overhead during inference. We argue that the ED performance on overshadowed entities can be enhanced during training by addressing shortcut learning, which does not add computational overhead at inference. We propose a simple yet effective debiasing technique to prevent models from shortcut learning during training. Experiments on a range of ED datasets show that our method achieves state-of-the-art performance without compromising inference speed. Our findings suggest a new research direction for improving entity disambiguation via shortcut learning mitigation. The code is available at https://***/panuthept/EfficientOvershadowedED. © 2024 Association for Computational Linguistics.

关键词： Contrastive Learning

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Autoencoder-LSTM Ensembles for Predictive Analysis of Financial Churn Patterns

Autoencoder-LSTM Ensembles for Predictive Analysis of Financ...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Raynukaazhakarsamy Rahul Pradhan Nadhema Ahmed Jaff G Venu Gopal Mariam Fadel Ali P. Nagaraj Department of Computer Applications Nehru Arts and Science College (Autonomous) Coimbatore India Department of Computer Engineering & Applications GLA University Mathura India Department of Business Administration College of Administrative and Financial Sciences Knowledge University Erbil Iraq Computer Science and Engineering (AI & ML) Geethanjali College of Engineering and Technology Telangana India Department of Radiology Techniques Uruk University Baghdad Iraq Department of AI & ML K. Ramakrishnan College of Engineering Trichy Tamilnadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

In the financial sector, customer churn is a significant problem that influences the organization’s revenue growth. Churn means that customers have left their current providers which is a negative factor for the providers or financial institutions; therefore, accurate churn prediction helps institutions to increase customer loyalty and satisfaction by improving the quality of service. In this paper, a new method of using Autoencoder and LSTM network for predicting financial churn patterns is introduced. The Autoencoder is used to reduce dimensions and for unsupervised learning acts as a pass through that allows the model to learn the latent patterns in customer behaviour and transactions. Since the LSTM has demonstrated its efficacy in analyzing temporal dependencies of a sequence in time series, it is used to predict churn by analyzing sequential customer activity. The ensemble approach combines the merits of the two models improving the predictive power and especially the stability in a situation of data scarcity and high noise levels. Experimental comparison with conventional predictive models such as decision trees and logistic regression reveal that the Autoencoder-LSTM is more accurate and sensitive than traditional models by comparison. In other words, the proposed model enhances the early identification of potential churners and such institutions can then take appropriate measures of containing it, and in the process, offer relevant retention solutions. The paper describes the benefits of utilizing deep learning models for churn prediction for the financial industry and provides information on how such methods might be used to improve customer retention. Possible future research avenues consist of improving the model interpretability and generalizing the approach to other industries with comparable churn prediction requirements. The accuracy obtained from the proposed method is higher at 99.7% compared to other methods.

关键词： Accuracy Computational modeling Autoencoders Time series analysis Predictive models Stability analysis Churn Predictive analytics Long short term memory Unsupervised learning

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Exploring Improved Asynchronous Federated Learning with Fresh Information in Contested Environment

Exploring Improved Asynchronous Federated Learning with Fres...

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IEEE International Conference on Information Reuse and Integration (IRI)

作者： Danda B. Rawat Dept of Electrical Engineering & Computer Science DoD Center of Excellence in AI/ML Howard University Washington DC USA

ISBN: (数字)9798350351187

ISBN: (纸本)9798350351194

To tackle weaknesses of traditional synchronous federated learning (FL), asynchronous FL has emerged as a solution. However, asynchronous FL also faces challenges, particularly in identifying optimal clients capable of contributing to a global model with minimal latency and loss with high accuracy, while prioritizing information freshness. This paper explores client selection strategies in asynchronous FL that takes account of computing and communication delays, freshness of the data/information being used in generating local models, and the loss while providing the best global model with high efficiency in the contested environment. Furthermore, we present formal mathematical analysis and performance evaluation using numerical results. Results show that the proposed improved asynchronous FL outperforms the other FL approaches.

关键词： Performance evaluation Accuracy Federated learning Computational modeling Data science Numerical models Delays Rough surfaces Faces

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Reactive Multi-agent Coordination using Auction-based Task Allocation and Behavior Trees

Reactive Multi-agent Coordination using Auction-based Task A...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Dahlquist, Niklas Lindqvist, Bjorn Saradagi, Akshit Nikolakopoulos, George Luleå University of Technology Robotics and Ai Group Department of Computer Science Electrical and Space Engineering LuleåSE-97187 Sweden

ISBN: (纸本)9798350335446

This article presents an architecture for multi-agent task allocation and task execution, through the unification of a market-inspired task-auctioning system with Behavior Trees for managing and executing lower level behaviors. We consider the scenario with multi-stage tasks, such as 'pick and place', whose arrival times are not known a priori. In such a scenario, a coordinating architecture is expected to be reactive to newly arrived tasks and the resulting rerouting of agents should be dependent on the stage of completion of their current multi-stage tasks. In the novel architecture proposed in this article, a central auctioning system gathers bids (cost-estimates for completing currently available tasks) from all agents, and solves a combinatorial problem to optimally assign tasks to agents. For every agent, it's participation in the auctioning system and execution of an assigned multi-stage task is managed using behavior trees, which switch among several well-defined behaviors in response to changing scenarios. The auctioning system is run at a fixed rate, allowing for newly added tasks to be incorporated into the auctioning system, which makes the solution reactive and allows for rerouting of some agents (subject to the states of the behavior trees). We demonstrate that the proposed architecture is especially well-suited for multistage tasks, where high costs are incurred when rerouting agents who have completed one or more stages of their current tasks. The proposed framework is experimentally validated in multiple scenarios in a lab environment. A video of a demonstration can be found at: https://***/ZdEkoOOlB2g. © 2023 IEEE.

关键词： Cost estimating

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GainS: Gradient Anomaly-aware Biomedical Instance Segmentation

GAInS: Gradient Anomaly-aware Biomedical Instance Segmentati...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Runsheng Jiang, Hao Zhou, Yanning Lin, Huangjing Wang, Liansheng Chen, Hao Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong Tencent Ai Lab Shenzhen China Imsight Ai Research Lab Shenzhen China Xiamen University Department of Computer Science Xiamen China Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Hong Kong Hong Kong Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

ISBN: (纸本)9798350386226

Instance segmentation plays a vital role in the morphological quantification of biomedical entities such as tissues and cells, enabling precise identification and delineation of different structures. Current methods often address the challenges of touching, overlapping or crossing instances through individual modeling, while neglecting the intrinsic interrelation between these conditions. In this work, we propose a Gradient Anomaly-aware Biomedical Instance Segmentation approach (GainS), which leverages instance gradient information to perceive local gradient anomaly regions, thus modeling the spatial relationship between instances and refining local region segmentation. Specifically, GainS is firstly built on a Gradient Anomaly Mapping Module (GAMM), which encodes the radial fields of instances through window sliding to obtain instance gradient anomaly maps. To efficiently refine boundaries and regions with gradient anomaly attention, we propose an Adaptive Local Refinement Module (ALRM) with a gradient anomaly-aware loss function. Extensive comparisons and ablation experiments in three biomedical scenarios demonstrate that our proposed GainS outperforms other state-of-the-art (SOTA) instance segmentation methods. The code is available at https://***/DeepGainS/GainS. © 2024 IEEE.

关键词： Adaptive Refinement Biomedical Instance Segmentation Gradient Anomaly Touching and Overlapping

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Skin Cancer Classification of Dermoscopic Images using Deep Learning 4

Skin Cancer Classification of Dermoscopic Images using Deep ...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Madhavi, G Bindu Sumithra, M. Devi, P Yamini Mahalaxmi, U.S.B.K. Saravanan, T. Shaker Reddy, Pundru Chandra Geethanjali College of Engineering and Technology Department of Cse -AI &ml Telangana Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science and Engineering School of Computing Tamil Nadu Chennai India Computer Science and Engineering Vignana Bharathi Institute of Technology Telangana Hyderabad India Electronics Communication Engineering Aditya University Andhra Pradesh Surampalem India P. S. R. Engineering College Department of Information Technology Tamil Nadu Sivakasi India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350364729

Pigmented skin lesions must be identified in order to detect hazardous skin-related disorders, especially cancer. To improve the correctness of skin cancer recognition, image detection strategies and classification abilities can be utilized. Based on the HAM10000 dataset, which contains 10015 photos, this research paper makes use of the dataset. An augmentation has been carried out on a subset of the dataset in the proposed work. Compared to an approach with &without data augmentation, is more likely to learn unique traits and attributes. To make the model more accurate, data augmentation is often used. However, until the model is strong, it will not be able to produce meaningful results when tested with the testing data. This work's model is robust since it makes use of the k-fold cross-validation technique. Machine learning methods and models from Convolutional Neural Networks have been evaluated for their classification accuracy. After comparing the suggested work's use of several machine learning methods, we found that Convolutional Neural Network produced the best results in terms of accuracy. We achieved a maximum accuracy of 95.53% using the CNN in the suggested setup. Early detection of seven types of skin illness is made easier by the suggested work, which may be authenticated and treated properly by medicinal professionals. © 2024 IEEE.

关键词： Convolutional neural networks

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Applying Convolutional Neural Networks for Disease Detection in Crop Images 7

Applying Convolutional Neural Networks for Disease Detection...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Dhasmana, Gulshan Al-Jawahry, Hassan M. Deepak, A. Aravind, K. Reddy, R. Akhilesh Yadav, Kanchan Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Tamilnadu Chennai India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India Gla University Department of Electrical Engineering Mathura India

ISBN: (纸本)9798350350067

Ensuring agricultural production and food security relies on the early and precise identification of crop diseases. It may be tedious, error-prone, and time-consuming to use traditional approaches to identify diseases. In this study, we investigate how Convolutional Neural Networks (CNNs) may be used to improve and automate the process of identifying diseases in crop photos. Our approach use deep learning to swiftly and reliably identify and classify a broad variety of agricultural diseases. We trained our CNN model on a diverse dataset that included images of crop leaves in various states of health. The dataset covers a broad variety of common illnesses and numerous kinds of crops, which guarantess that our model is resilient and can be applied to other situations. To avoid overfitting and boost the model's performance, we used sophisticated data augmentation methods. The CNN model surpasses traditional machine learning methods in terms of accuracy, as shown by a significant reduction in false positives and false negatives. The model's capacity to process and analyse images in real-time makes it beneficial in the field of disease management. This research emphasises the possibility of incorporating convolutional neural networks (CNNs) into precision agricultural methods, giving farmers a formidable instrument for early disease detection and prompt response to reduce crop losses. Improving the model's scalability for largescale agricultural applications, adding more disease kinds to the dataset, and increasing the dataset are all areas that will be the focus of future effort. © 2024 IEEE.

关键词： Convolutional neural networks

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Sentiment Analysis of Online Reviews in the Field of Tourism for Hyderabad Popular Places 2

Sentiment Analysis of Online Reviews in the Field of Tourism...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Rani, K. Pushpa Roja, G. Lalitha, E. Deepika, B. Dastagiriah, C. Arshad, Mohammad Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad Dundigal500043 India Department of Computer Science and Engineering Gnits Shaik Pet Hyderabad500104 India and Ai&ds Hyderabad India Anurag University Department of Cse Hyderabad500088 India Deccan College of Engineering and Technology Department of It TS Hyderabad500 001 India

ISBN: (纸本)9798350370188

Nowadays all most all users are using the internet for any information. The main objective of this paper is used to collecting the opinions from customers are useful for both travelers and consultants. Creating a new model of approach is profitable for business organizations and also customer satisfaction. Collecting the opinions, feelings of customer by creating online blogs, forum and website. In this model we developed a new algorithm regarding facilities such as accommodation, weather, number of places to visits, hotels. Based on this analysis the consultants improve their performance to get more profits. This algorithm extracting all features and efficiently classify the given training data by observing more hidden features in the given training data set. © 2024 IEEE.

关键词： Profitability

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Futuristic IoT based Smart Precision Agriculture: Brief Analysis

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Journal of Mobile Multimedia 2022年第3期18卷 935-956页

作者： Swamidason, Iwin Thanakumar Joseph Pandiyarajan, Shanthini Velswamy, Karunakaran Leela Jancy, P. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhrapradesh Vijayawada India Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Tamilnadu Coimbatore India Department of Computer Science and Engineering (AI & ML) and (Cybersecurity) Jain University Karnataka Bengaluru India Department of Information Technology Sri Sai Ram Institute of Technology Tamilnadu Chennai India

Agriculture is considered as the backbone of any nation across the globe. With the advent of modern technologies, smart tools and techniques are used in the agriculture/farming to build on the quantity as well as quality of the agriculture production to feed the basic necessity of the humans. Smart technology such as Internet of Things play a vital role in monitoring and analyzing various environmental parameters such as water level, humidity, soil moisture, air quality, UV level, rain etc. which are highly essential to ensure the fruitful yield of any nutritious crops. In this research article, precision agriculture concepts are investigated widely with the focus of improving the productivity level and also the effective utilization of resources with the minimal cost while compared with the conventional methodologies. © 2022 River Publishers.

关键词： Drones Internet of things (IoT) Irrigation Precision agriculture Sensors

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Advanced Deep Learning Approaches for Early Detection of Diabetic Retinopathy in Retinal Images

Advanced Deep Learning Approaches for Early Detection of Dia...

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Women in Computing (InCoWoCo), International Conference for

作者： D. Vetrithangam G. Pinki Azra Fathima Naresh Kumar Pegada V. Devi Aparajita Mukherjee Department of Computer Science and Engineering Chandigarh University Mohali India Department of CSE Methodist College of Engineering and Technology Hyderabad India Department of CSE(AI &ML) Vignana Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering(AI &ML) Keshav Memorial Engineering College Hyderabad India Department of CSE Hindusthan College of Engineering and Technology Coimbatore India Department of CSE University of Engineering and Management Kolkata India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Diabetic Retinopathy (DR) is one of the major causes of blindness worldwide and a complication of diabetes. Therefore, early identification and intervention are important to reduce the extent of vision loss and improve prognosis for patients. This paper presents an improved model for screening DR at its initial stage in retinal images using deep learning methods. The developed system employs convolutional neural networks (CNNs) to detect abnormalities in retinal images that indicate early signs of DR, thereby increasing the chances of screening in clinics. The proposed system utilizes large-scale Kaggle DR and Messidor datasets, with an optimized CNN for the analysis of retinal images. To address issues related to variations in image quality and acquisition methods, various data enhancement strategies are incorporated into the model. Additionally, an interpretability module based on Grad-CAM is included to explain the model's classifications and build trust with clinicians. Based on experimental results, it can be concluded that the proposed model demonstrates higher accuracy, sensitivity, and specificity compared to benchmark methods. This system shows potential in reducing the workload of ophthalmologists and improving early DR screening, particularly in resource-limited healthcare settings. Furthermore, it can be extended to other ophthalmic diseases, offering significant benefits to the broader healthcare system.

关键词： Deep learning Diabetic retinopathy Accuracy Computational modeling Blindness Sensitivity and specificity Retina Data models Convolutional neural networks Usability

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