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Green Industrial Electronics and Sustainable Technologies (GIEST), IEEE International Conference on

作者： Divya Nimma Alok Singh Sengar Chamandeep Kaur Shamim Ahmad Khan S.D.Anushna I Infant Raj PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Dept. of Computer Science School of Sciences Noida International University Greater Noida Uttar Pradesh India Department of Computer Science Jazan University Jazan Saudi Arabia Glocal School of Science & Technology Glocal University U.P India Department of CSE(Data Science) Sri Indu College of Engineering and Technology Hyderabad India Department of CSE K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798331540142

ISBN: (纸本)9798331540159

Although there have been significant advances in computer vision recently, there are still difficulties in efficiently interpreting dynamic and complicated visual input. Through the integration of Gated Recurrent Units (GRUs) and Multi-Layer Perceptrons (MLPs) in advanced picture analysis, this study suggests a novel way to overcome these issues. Utilizing the advantages of both systems, the integrated model seeks to improve visual information comprehension and interpretation. Computer vision applications involving temporal context and semantic understanding are sometimes difficult for traditional approaches to handle. MLPs are good at collecting spatial features; on the other hand, GRUs are efficient at processing data sequentially. This research aims to develop and evaluate an integrated MLP-GRU model for advanced image analysis, combining the strengths of MLPs and GRUs. The model aims to achieve superior performance in tasks like object detection, image classification, and semantic segmentation. The model combines MLPs for spatial feature extraction and GRUs for capturing temporal dependencies within sequential data. The experimental results show significant improvements in accuracy of 98%, compared to traditional approaches. The model also demonstrates the ability to discern complex visual patterns and make accurate predictions. This study highlights the potential of hybrid neural network architectures in image analysis, highlighting the paradigm shift in computer vision and their ability to analyze diverse and dynamic visual data.

关键词： Computer vision Visualization Accuracy computational modeling Semantic segmentation Neural networks Semantics Computer architecture Object detection Predictive models

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Pelvic bone region segmentation (PBRS) from X-ray image using convolutional neural network (CNN)

Pelvic bone region segmentation (PBRS) from X-ray image usin...

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International Conference on Computer and Information Technology (ICCIT)

作者： Syed Alif Ul Alam Saadia Binte Alam Sourav Saha Mahmudul Haque Rashedur Rahman Syoji Kobashi Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Center for Computational & Data Sciences Independent University Bangladesh Dhaka Bangladesh Graduate School of Engineering University of Hyogo Shosha Himeji Hyogo Japan

Pelvic and hip fractures offer considerable public health risks with high morbidity and mortality rates. Because of the complicated bone structure of the pelvic bone region, detecting fractures is difficult. Though X-ray imaging is routinely utilised for detecting fractures, manual fracture diagnosis is prone to inaccuracies. This paper proposes the use of deep learning algorithms for automated segmentation of the pelvic bone region in X-ray images. In our work, we have investigated U-Net based pelvic area segmentation models with various convolutional neural network (CNN) backbones. The DenseNet121-based U-Net design emerged as the most optimal model, establishing a compromise between performance and computational efficiency. Although it had a modest loss in IoU and F1 scores when compared to InceptionNetV3, it had a remarkable 59.44% reduction in the number of parameters.

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Active Geospatial Search for Efficient Tenant Eviction Outreach

arXiv

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arXiv 2024年

作者： Sarkar, Anindya DiChristofano, Alex Das, Sanmay Fowler, Patrick J. Jacobs, Nathan Vorobeychik, Yevgeniy Washington University in St. Louis United States George Mason University United States Department of Computer Science and Engineering Division of Computational & Data Sciences Brown School Washington University in St. Louis United States

Tenant evictions threaten housing stability and are a major concern for many cities. An open question concerns whether data-driven methods enhance outreach programs that target at-risk tenants to mitigate their risk of eviction. We propose a novel active geospatial search (AGS) modeling framework for this problem. AGS integrates property-level information in a search policy that identifies a sequence of rental units to canvas to both determine their eviction risk and provide support if needed. We propose a hierarchical reinforcement learning approach to learn a search policy for AGS that scales to large urban areas containing thousands of parcels, balancing exploration and exploitation and accounting for travel costs and a budget constraint. Crucially, the search policy adapts online to newly discovered information about evictions. Evaluation using eviction data for a large urban area demonstrates that the proposed framework and algorithmic approach are considerably more effective at sequentially identifying eviction cases than baseline methods. © 2024, CC BY-NC-SA.

关键词： Budget control

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IoT-Enabled Precision Irrigation for Coconut Plantations: Enhancing Growth and Sustainability

IoT-Enabled Precision Irrigation for Coconut Plantations: En...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： M.Jaganmohan Reddy V. Ramu T. Grace Shalini R. Vincysahaba M. Devaki A. Athiraja Department of CSE (AIML) Malla Reddy Engineering College Secunderabad Telangana India Department of CSE(AI&ML) Kakatiya Institute of Technology and Science Hanamkonda Telangana India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Artificial Intelligence and Data Science St.Joseph college of engineering Chennai Tamil Nadu India Department of EEE Velammal college of engineering and technology Madurai Tamil Nadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

In agriculture, precision irrigation plays a pivotal role in optimizing crop growth and resource utilization. In this study, must proposean Internet of Things based irrigation remote monitoring system tailored for enhancing coconut plant growth. The system integrates sensor technology, Internet of Things connectivity, and data analytics to provide real-time monitoring and control of irrigation parameters such as soilmoisture, temperature, and humidity. Leveraging a network of sensors deployed in the coconut plantation, the system collects and analyzes environmental data to optimize irrigation schedules and ensure optimal growing conditions for coconut plants. Through a user-friendly interface accessible via web or mobile applications, farmers can remotely monitor the status of their plantation and adjust irrigation settings as ***, the system incorporates predictive analytics capabilities to anticipate irrigation needs based on historical data and weather forecasts, thereby enhancing water efficiency and crop productivity. Field trials demonstrate the effectiveness of this Internet of Things based irrigation system in improving coconut plant growth while reducing water consumption and operational costs. This findings underscore the potential of Internet of Things technologies in revolutionizing agricultural practices, facilitating sustainable crop management, and enhancing food security in coconut-producing regions.

关键词： Temperature sensors Productivity Irrigation Costs Crops Plantations Food security Internet of Things Sustainable development Predictive analytics

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"It Is Hard to Remove from My Eye": Design Makeup Residue Visualization System for Chinese Traditional Opera (Xiqu) Performers

arXiv

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arXiv 2024年

作者： Xiong, Zeyu Fu, Shihan Zhu, Yanying Zhu, Chenqing Ma, Xiaojuan Fan, Mingming Computational Media and Arts Thrust The Hong Kong University of Science and Technology Guangzhou China Data Science and Analysis Thrust The Hong Kong University of Science and Technology Guangzhou China Internet of Things Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Division of Integrative Systems and Design Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Chinese traditional opera (Xiqu) performers often experience skin problems due to the long-term use of heavy-metal-laden face paints. To explore the current skincare challenges encountered by Xiqu performers, we conducted an online survey (N=136) and semi-structured interviews (N=15) as a formative study. We found that incomplete makeup removal is the leading cause of human-induced skin problems, especially the difficulty in removing eye makeup. Therefore, we proposed EyeVis, a prototype that can visualize the residual eye makeup and record the time make-up was worn by Xiqu performers. We conducted a 7-day deployment study (N=12) to evaluate EyeVis. Results indicate that EyeVis helps to increase Xiqu performers' awareness about removing makeup, as well as boosting their confidence and security in skincare. Overall, this work also provides implications for studying the work of people who wear makeup on a daily basis, and helps to promote and preserve the intangible cultural heritage of practitioners. © 2024, CC BY-NC-ND.

关键词： Computer vision

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FINANCIAL VISION BASED REINFORCEMENT LEARNING TRADING STRATEGY

arXiv

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arXiv 2022年

作者： Tsai, Yun-Cheng Szu, Fu-Min Chen, Jun-Hao Chen, Samuel Yen-Chi Department of Data Science Soochow University China Department of Electrical Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan Computational Science Initiative Brookhaven National Laboratory

Recent advances in artificial intelligence (AI) for quantitative trading have led to its general superhuman performance in significant trading performance. However, the potential risk of AI trading is a "black box" decision. Some AI computing mechanisms are complex and challenging to understand. If we use AI without proper supervision, AI may lead to wrong choices and make huge losses. Hence, we need to ask about the AI "black box", including why did AI decide to do this or not? Why can people trust AI or not? How can people fix their mistakes? These problems also highlight the challenges that AI technology can explain in the trading field. © 2022, CC BY.

关键词： Deep learning

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Investigation of Automatic Part-of-Speech Tagging using CRF, HMM and LSTM on Misspelled and Edited Texts 5

Investigation of Automatic Part-of-Speech Tagging using CRF,...

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5th Artificial Intelligence and Cloud Computing Conference, AICCC 2022

作者： Aydinov, Farhad Huseynov, Igbal Sayadzada, Sofiya Rustamov, Samir Data Science University of Glasgow United Kingdom Computational Linguistics and Language Technologies Charles University Prague Czech Republic Department of Applied Mathematics and Computer Science Technical University of Denmark Copenhagen Denmark School of Information Technology and Engineering ADA University Baku Azerbaijan Institute of Control Systems BakuVarna Bulgaria Azerbaijan

ISBN: (纸本)9781450398749

Part-of-speech tagging is the process of assigning words in a given text to appropriate parts-of speech in order to reduce the disambiguation which may arise depending on the contextual usage of the words. In this paper, the problem of word sense disambiguation in Azerbaijani language is addressed by applying part of speech tagging on two varying data corpora, misspelled, and edited (clean) text using 3 different machine learning algorithms: Hidden Markov Model, Long Short-Term Memory, and Conditional Random Fields. The comparative analysis on the outcomes of the mentioned algorithms and their accuracy scores were analysed in the paper. The misspelled dataset for the experiments is provided by Unibank from their chatbot dialogues while the clean textual data was retrieved from the books and newspapers in Azerbaijani. The experiments showed that the Bidirectional LSTM has the highest accuracy scores for both edited (98.2%) and noisy (96.2%) data corpora. Suggested models can be used in the application of algorithms focuses on part of speech tags and syntactic structure of Azerbaijani language which is an agglutinative language belonging to Turkic languages family, thus enabling the research to be further investigated in other agglutinative languages with similar grammatical structure. © 2022 ACM.

关键词： Hidden Markov models

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Unbiased Parameter Estimation for Bayesian Inverse Problems

arXiv

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arXiv 2025年

作者： Chada, Neil K. Jasra, Ajay Maama, Mohamed Tempone, Raul Department of Mathematics City University of Hong Kong China School of Data Science The Chinese University of Hong Kong Shenzhen CN Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we consider the estimation of unknown parameters in Bayesian inverse problems. In most cases of practical interest, there are several barriers to performing such estimation, This includes a numerical approximation of a solution of a differential equation and, even if exact solutions are available, an analytical intractability of the marginal likelihood and its associated gradient, which is used for parameter estimation. The focus of this article is to deliver unbiased estimates of the unknown parameters, that is, stochastic estimators that, in expectation, are equal to the maximizer of the marginal likelihood, and possess no numerical approximation error. Based upon the ideas of [4] we develop a new approach for unbiased parameter estimation for Bayesian inverse problems. We prove unbiasedness and establish numerically that the associated estimation procedure is faster than the current state-of-the-art methodology for this problem. We demonstrate the performance of our methodology on a range of problems which include a PDE and ODE. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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Optimizing Forwarding Strategies in Sparsely Delay-Tolerant Networks with Short Contact Durations

Optimizing Forwarding Strategies in Sparsely Delay-Tolerant ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Yadesa Bedasa Nemera Leta Tesfaye Jule S. Sivakumar J Pavithra A. Sampathkumar Ravi Rastogi Department of Information Technology Dambi Dollo Univeristy Ethiopia Department of Physics College of Natural and Computational Science Dambi Dollo University Dambi Dollo Ethiopia Department of CSE Nehru Institute of Engineering and Technology Coimbatore India Department of Artificial intelligence and data science Dhanalakshmi Srinivasan College of Engineering Coimbatore India Department of Computer Science Dambi Dollo Univeristy Ethiopia Department of CSE Koneru Lakshmaiah Education Foundation Guntur AP India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Delay-tolerant networks (DTNs) are specific settings that experience either instability or the absence of a complete path between the source and destination nodes. The contact lifetime between pairs of network nodes is constrained by the mobility of nodes and the restricted bandwidth of peer- to-peer wireless transmission. In sparsely delay-tolerant networks, the re-establishment of contact between pairs of nodes is significantly delayed beyond the duration of the initial contact. Hence, the need for careful thought in designing a buffer control forwarding policy arises due to the limited contact period. Although forwarding algorithms have been designed extensively, much research has not been done on forwarding when there is a short contact time. When the message exchange between the pairs of network nodes is not completed during the contract period, they must wait until they are within communication range once more. To tackle the issue, we have suggested an innovative forwarding policy in buffer management that considers the duration of interaction. To assess the effectiveness of the proposed effort, we utilized the Opportunistic Network Environment (ONE) networking simulator. The suggested approach is combined with the MaxProp routing protocol buffer control forwarding policy and evaluated using two distinct metrics: bundle delivery rate and average delivery delay. In addition, we evaluated the performance of the suggested work by considering the number of nodes, buffer size, and simulation time. Across all the trials conducted in this thesis, the simulation results consistently demonstrate that our suggested method beats the existing forwarding strategy.

关键词： Wireless communication Measurement Simulation Receivers Programming Routing protocols Product delivery Peer-to-peer computing Delays Contracts

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Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation

arXiv

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arXiv 2024年

作者： Che, Fengdi Xiao, Chenjun Mei, Jincheng Dai, Bo Gummadi, Ramki Ramirez, Oscar A. Harris, Christopher K. Mahmood, A. Rupam Schuurmans, Dale Department of Computing Science University of Alberta United States School of Data Science The Chinese University of Hong Kong Shenzhen China Google DeepMind United Kingdom School of Computational Science and Engineering Georgia Tech United States Figure Uber United States CIFAR AI Chair Amii Canada

We prove that the combination of a target network and over-parameterized linear function approximation establishes a weaker convergence condition for bootstrapped value estimation in certain cases, even with off-policy data. Our condition is naturally satisfied for expected updates over the entire state-action space or learning with a batch of complete trajectories from episodic Markov decision processes. Notably, using only a target network or an over-parameterized model does not provide such a convergence guarantee. Additionally, we extend our results to learning with truncated trajectories, showing that convergence is achievable for all tasks with minor modifications, akin to value truncation for the final states in trajectories. Our primary result focuses on temporal difference estimation for prediction, providing high-probability value estimation error bounds and empirical analysis on Baird’s counterexample and a Four-room task. 1 Furthermore, we explore the control setting, demonstrating that similar convergence conditions apply to Q-learning. © 2024, CC BY-NC-ND.

关键词： Markov processes

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