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Estrus Detection System in Dairy Cows' using Machine learning and IoT 11

Estrus Detection System in Dairy Cows' using Machine Learnin...

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11th international conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Sasankar, Prachi Pande, Ms. Shruti Singh, Ms. Pranjali Wanjari, Ms. Ayushi Sadabai Raisoni Women's College Dept. of Computer Science MH Nagpur India

ISBN: (纸本)9798350348422

Dairy farming needs economic measures to monitor and detect Estrus conditions in cows. High detection rates is directly proportional to high-priced progesterone measurement devices. Lack of precision while detecting estrus in dairy cows are well-known factor for production losses. Visual detection is not the sole efficient method of estrus detection. A review is presented in this paper of various factors to be considered for finding out the perfect time to inseminate the cow Estrus detection devices, automated telemetered mass, expert systems, increasing efficiency, and detection accuracy are the prime focus of the paper. A novel approach using tail sensors and ear sensors with pressure data is used by the author. © 2023 IEEE.

关键词： Internet of things

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DEEP learning FROM CROWDSOURCED LABELS: COUPLED CROSS-ENTROPY MINIMIZATION, IDENTIFIABILITY, AND REGULARIZATION 11

DEEP LEARNING FROM CROWDSOURCED LABELS: COUPLED CROSS-ENTROP...

引用

11th international conference on learning Representations, ICLR 2023

作者： Ibrahim, Shahana Nguyen, Tri Fu, Xiao School of Electrical Engineering and Computer Science Oregon State University CorvallisOR97330 United States

Using noisy crowdsourced labels from multiple annotators, a deep learning-based end-to-end (E2E) system aims to learn the label correction mechanism and the neural classifier simultaneously. To this end, many E2E systems concatenate the neural classifier with multiple annotator-specific "label confusion" layers and co-train the two parts in a parameter-coupled manner. the formulated coupled cross-entropy minimization (CCEM)-type criteria are intuitive and work well in practice. Nonetheless, theoretical understanding of the CCEM criterion has been limited. the contribution of this work is twofold: First, performance guarantees of the CCEM criterion are presented. Our analysis reveals for the first time that the CCEM can indeed correctly identify the annotators' confusion characteristics and the desired "ground-truth" neural classifier under realistic conditions, e.g., when only incomplete annotator labeling and finite samples are available. Second, based on the insights learned from our analysis, two regularized variants of the CCEM are proposed. the regularization terms provably enhance the identifiability of the target model parameters in various more challenging cases. A series of synthetic and real data experiments are presented to showcase the effectiveness of our approach. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Deep learning

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Construction of an intelligent Mental Health Assessment System Based on Big data Algorithms 11th

Construction of an Intelligent Mental Health Assessment Syst...

引用

11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Men, Fangrui Digital Marketing Industry College Shandong Institute of Commerce and Technology Shandong Jinan250103 China

College students are in the stage of transitioning from teenagers to adults, and there are many problems that need to be adapted and adjusted in life, learning, and interacting with others. If these issues are not handled properly, it would lead to many mental health problems that affect normal learning and life. the paper use the data mining algorithm of Big data. through systematic intelligent evaluation, it can gain an overall understanding of the mental status of students. For students, it is effective to have a clear understanding of their mental health status, and to communicate with their home and school in a timely manner regarding their psychological problems. Early treatment of mental health can be more effective in completing rehabilitation;For universities, they can have a clear understanding of each student's psychological situation to avoid some difficult situations. At the same time, universities can timely intervene and treat students with psychological problems. this paper use data mining algorithms to build an intelligent evaluation system for mental health. through research, it is found that the accuracy of this method is 95% at the lowest and 97% at the highest. this method can comprehensively and objectively describe the changing characteristics of college students' mental health, and the results of mental health intelligence assessment are stable, providing valuable reference for their mental health management. © 2024 the Authors. Published by Elsevier B.V.

关键词： Electronic health record

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Hidden Figures: Architectural Challenges to Expose Parameters Lost in Code 15th

Hidden Figures: Architectural Challenges to Expose Parameter...

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15th ACMSIGCHI conference on engineering Interactive Computing Systems (EICS)

作者： Dix, Alan Swansea Univ Computat Foundry Swansea W Glam Wales Cardiff Metropolitan Univ Cardiff Wales

ISBN: (纸本)9783031592348;9783031592355

Many critical user interaction design decisions are made in the heat of detailed development. these include simple parameter choices or more complex weightings in intelligent algorithms. Many would be appropriate for expert design review, user-preference choices or optimisation by machine learning, but they are buried deep in the code. Although the developer may realise this potential, the location of the decision is far removed in the code from where user feedback occurs, data can be collected and machine learning could be applied. this position paper describes several case studies and uses them to frame an architectural challenge for tools and infrastructure to uncover these hidden variables to make them available for machine learning and user inspection.

关键词： intelligent interfaces machine learning user interface architecture

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Manual Prompt engineering is Not Dead: A Case Study on Large Language Models for Code Vulnerability Detection with DSPy 8

Manual Prompt Engineering is Not Dead: A Case Study on Large...

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8th international conference on data Science and Machine learning Applications, CDMA 2025

作者： Trad, Fouad Chehab, Ali Electrical and Computer Engineering American University of Beirut Beirut Lebanon

ISBN: (纸本)9798331539696

automated prompt engineering tools have recently emerged as a promising solution to simplify the traditional man-ual task of crafting prompts for large language models (LLMs). this study investigates whether such tools can fully replace manual prompt engineering for code vulnerability detection. We leverage the DSPy (Declarative Self-improving Python) frame-work, which uses modular 'signatures' rather than prompts to specify tasks. In DSPy, a signature defines the expected input-output behavior of a task and can optionally include a description to guide the model's objective. this signature is then auto-matically translated into an optimized prompt through DSPy's modules and optimizers. this study compares the performance of GPT-4o-mini on basic and detailed signatures to determine how DSPy optimizations affect model performance in each case. the basic signature prompts the model to classify code as vulnerable or not, while the detailed signature specifies particular vulnerabilities to identify. For each signature, we used DSPy's modules and optimizers to create three prompt configurations: zero-shot (baseline, where the model performs the task without examples), chain-of-thought (where the model shows step-by-step reasoning), and bootstrap few-shot (where the model is guided through a small set of examples). Results show that DSPy's automated optimizations improve performance for both signature types over the zero-shot baseline;however, detection performance increases significantly with detailed signatures. Specifically, the detailed signatures achieved higher F1 scores, with improvements of approximately 23 % for zero-shot, 13 % for chain-of-thought, and 11 % for bootstrap few-shot techniques. these findings indicate that while automated tools enhance prompt efficiency, optimal results are achieved by combining automated techniques with human-crafted signature details, underscoring the ongoing importance of manual refinement in specialized tasks. © 2025 IEEE.

关键词： Code Vulnerability Detection DSPy Large Language Models Prompt engineering

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Machine learning for the Analysis of Human Microbiome in Inflammatory Bowel Diseases: Literature Review 11th

Machine Learning for the Analysis of Human Microbiome in...

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10th international conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th international conference on Innovation and New Trends in Information Technology, INTIS 2023

作者： En Najih, Nouhaila Moussa, Pr. Ahmed Systems and Data Engineering Team ENSA University Abdelmalek Essaadi Tangier Morocco

ISBN: (纸本)9783031473654

the pathogenesis of inflammatory bowel disease is related to the imbalance of microbial community composition. A reduction in the diversity of the intestinal microbiota as well as specific taxonomic and functional shifts have been reported in inflammatory bowel diseases and may play a central role in the inflammatory process. the aim was to review recent developments in the structural and functional changes observed in the gastrointestinal microbiome in patients with inflammatory bowel diseases and to survey the use of machine learning to discover the microbiome in inflammatory bowel disease. We start by highlighting the relationship between gut microbiome disorder with inflammatory bowel disease including different studies that confirmed this association, then, we provide an overview of the use of Machine learning in analyzing the microbiome in inflammatory bowel diseases. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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CONFIDENCE-BASED FEATURE IMPUTATION FOR GRAPHS WIth PARTIALLY KNOWN FEATURES 11

CONFIDENCE-BASED FEATURE IMPUTATION FOR GRAPHS WITH PARTIALL...

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11th international conference on learning Representations, ICLR 2023

作者： Um, Daeho Park, Jiwoong Park, Seulki Choi, Jin Young Department of Electrical and Computer Engineering ASRI Seoul National University Korea Republic of

this paper investigates a missing feature imputation problem for graph learning *** methods have previously addressed learning tasks on graphs with missing ***, in cases of high rates of missing features, they were unable to avoid significant performance *** overcome this limitation, we introduce a novel concept of channel-wise confidence in a node feature, which is assigned to each imputed channel feature of a node for reflecting certainty of the *** then design pseudo-confidence using the channel-wise shortest path distance between a missing-feature node and its nearest known-feature node to replace unavailable true confidence in an actual learning *** on the pseudo-confidence, we propose a novel feature imputation scheme that performs channel-wise inter-node diffusion and node-wise inter-channel *** scheme can endure even at an exceedingly high missing rate (e.g., 99.5%) and it achieves state-of-the-art accuracy for both semi-supervised node classification and link prediction on various datasets containing a high rate of missing *** are available at https://***/daehoum1/pcfi. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Classification (of information)

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Predicting Depth Maps from Single RGB Images and Addressing Missing Information in Depth Estimation 11

Predicting Depth Maps from Single RGB Images and Addressing ...

引用

11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Chaar, Mohamad Mofeed Raiyn, Jamal Weidl, Galia Connected Urban Mobility Faculty of Engineering University of Applied Sciences Aschaffenburg Germany

ISBN: (纸本)9789897587450

Depth imaging is a crucial area in Autonomous Driving Systems (ADS), as it plays a key role in detecting and measuring objects in the vehicle’s surroundings. However, a significant challenge in this domain arises from missing information in Depth images, where certain points are not measurable due to gaps or inconsistencies in pixel data. Our research addresses two key tasks to overcome this challenge. First, we developed an algorithm using a multi-layered training approach to generate Depth images from a single RGB image. Second, we addressed the issue of missing information in Depth images by applying our algorithm to rectify these gaps, resulting in Depth images with complete and accurate data. We further tested our algorithm on the Cityscapes dataset and successfully resolved the missing information in its Depth images, demonstrating the effectiveness of our approach in real-world urban environments. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Image segmentation

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data AUGMENTATION ALONE CAN IMPROVE ADVERSARIAL TRAINING 11

DATA AUGMENTATION ALONE CAN IMPROVE ADVERSARIAL TRAINING

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11th international conference on learning Representations, ICLR 2023

作者： Li, Lin Spratling, Michael Department of Informatics King's College London 30 Aldwych LondonWC2B 4BG United Kingdom

Adversarial training suffers from the issue of robust overfitting, which seriously impairs its generalization performance. data augmentation, which is effective at preventing overfitting in standard training, has been observed by many previous works to be ineffective in mitigating overfitting in adversarial training. this work proves that, contrary to previous findings, data augmentation alone can significantly boost accuracy and robustness in adversarial training. We find that the hardness and the diversity of data augmentation are important factors in combating robust overfitting. In general, diversity can improve both accuracy and robustness, while hardness can boost robustness at the cost of accuracy within a certain limit and degrade them both over that limit. To mitigate robust overfitting, we first propose a new crop transformation, Cropshift, which has improved diversity compared to the conventional one (Padcrop). We then propose a new data augmentation scheme, based on Cropshift, with much improved diversity and well-balanced hardness. Empirically, our augmentation method achieves the state-of-the-art accuracy and robustness for data augmentations in adversarial training. Furthermore, when combined with weight averaging it matches, or even exceeds, the performance of the best contemporary regularization methods for alleviating robust overfitting. Code is available at: https://***/TreeLLi/DA-Alone-Improves-AT. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Hardness

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Exclusion Vis: A Visual Interactive System for Exclusion Analysis in Life Insurance Using Machine learning Techniques 11

Exclusion Vis: A Visual Interactive System for Exclusion Ana...

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11th IEEE international conference on Behavioural and Social Computing, BESC 2024

作者： Islam, Md Rafiqul thanh thao, Lam Nguyen Prome, Shanjita Akter Hossain Sakib, Md. Kowsar Sanin, Cesar Xu, Guandong Huang, Weidong Information Systems Australian Institute of Higher Education Sydney Australia College of Business & Management Uin University Viet Nam School of Computer Science Taylors University Malaysia Data Science Institute University of Technology Sydney Australia TD School University of Technology Sydney Australia

ISBN: (纸本)9798331531904

Exclusion analysis in life insurance stands out as a crucial task in minimizing customer risks. Numerous insur-ance companies have implemented artificial intelligence (AI) solutions to streamline processes and mitigate customer-related risks, specifically leveraging machine learning (ML) technolo-gies. While some studies have acknowledged its significance, there needs to be more to deliver comprehensive research on exclusion analysis utilizing advanced techniques. thus, this paper focuses on exclusion classification blending with visual interactive systems (Vis), aiming to comprehend data obtained from customer disclosure information. To tackle this challenge, 1) we initiate the investigation by employing various multi-label classifiers, 2) apply several ML techniques, utilizing data from a prominent Australian insurance company, where support vector classification (SVC) and random forest (RF) emerges as the superior performer, and 3) we introduce a visual interactive system called Exclusion Vis, meticulously crafted to facilitate in-depth exploration of the obtained results. the evaluation of Exclusion Vis further underscores its superiority compared to existing systems. © 2024 IEEE.

关键词： Sales

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