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arXiv 2025年

作者： Haputhanthri, Udith Storan, Liam Jiang, Yiqi Raheja, Tarun Shai, Adam Akengin, Orhun Miolane, Nina Schnitzer, Mark J. Dinc, Fatih Tanaka, Hidenori CNC Program Stanford University United States James H. Clark Center for Biomedical Engineering & Sciences Stanford University United States Kipo AI San Francisco United States Simplex Astera Institute EmeryvilleCA United States Geometric Intelligence Lab UC Santa Barbara Santa Barbara United States Howard Hughes Medical Institute United States Physics & Informatics Laboratories NTT Research Inc. SunnyvaleCA94085 United States Kavli Institute for Theoretical Physics UC Santa Barbara United States CBS-NTT Program in Physics of Intelligence Harvard University CambridgeMA94305 United States

Training recurrent neural networks (RNNs) is a high-dimensional process that requires updating numerous parameters. Therefore, it is often difficult to pinpoint the underlying learning mechanisms. To address this challenge, we propose to gain mechanistic insights into the phenomenon of abrupt learning by studying RNNs trained to perform diverse short-term memory tasks. In these tasks, RNN training begins with an initial search phase. Following a long period of plateau in accuracy, the values of the loss function suddenly drop, indicating abrupt learning. Analyzing the neural computation performed by these RNNs reveals geometric restructuring (GR) in their phase spaces prior to the drop. To promote these GR events, we introduce a temporal consistency regularization that accelerates (bioplausible) training, facilitates attractor formation, and enables efficient learning in strongly connected networks. Our findings offer testable predictions for neuroscientists and emphasize the need for goal-agnostic secondary mechanisms to facilitate learning in biological and artificial networks. © 2025, CC BY.

关键词： Phase space methods

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Software Effort Estimation with Use Case Points Using Ensemble Machine Learning Models

Software Effort Estimation with Use Case Points Using Ensemb...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Bhaskar Marapelli Anil Carie Sardar M. N. Islam College of Computing and Informatics Wolkite university Wolkite Ethiopia School of Computer Science and Engineering VIT-AP University Amaravathi India ISILC and Decision Sciences and Modelling Program Victoria University Melbourne Australia

ISBN: (纸本)9781665442329

Software development process includes estimating effort as a crucial task. The Use Case Point Analysis (UCPA) is a well know size metric that can be used to calculate effort. The software size is measured using use case diagrams in the UCPA method, using the calculated software size the effort required to complete the project is estimated. The traditional effort estimation with statistical methods is not accurate when compared to the real effort. In-accurate estimation of effort leads to problems with cost calculation and human resources calculation and it might lead to project failure. Machine learning techniques based on regression might help in estimating the effort with accuracy. In this study we proposed a method to identify best performing regression based machine learning model using two data sets Dataset1, Dataset2. Ensembles of different Machine learning methods is created and compared with individual methods and other ensemble methods to find an accurate estimation model. The results shows the individual model SVR and ensemble model GBR gives the best performance with a regression score above 98% with both data sets.

关键词： Support vector machines Statistical analysis Computational modeling Estimation Predictive models Boosting Software

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Multiclass Classification of Online Comments Based on Toxicity Scale 14th

Multiclass Classification of Online Comments Based on Toxici...

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14th International Conference on Computer Science and its Applications, CSA 2022 and the 16th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2022

作者： eun Kang, Jo Kim, Jisun su Do, Hyeon Oh, Sihyun hwa Lee, Si jee Jeung, Min seo Choi, Min Oh, Se Jong Doo, Ill Chul Department of Interdisciplinary Graduate Program of Linguistics and Informatics Yonsei University Graduate School Seoul Korea Republic of Department of Political Science and International Relations Hankuk University of Foreign Studies Seoul Korea Republic of Department of Japanese Language and Culture Soochow University Taipei Taiwan Department of English Linguistics & Language Technology Hankuk University of Foreign Studies Seoul Korea Republic of Department of Hungarian Hankuk University of Foreign Studies Yongin Korea Republic of Department of French Studies Hankuk University of Foreign Studies Yongin Korea Republic of Department of Information Communications Engineering Hankuk University of Foreign Studies Yongin Korea Republic of Artificial Intelligence Education Hankuk University of Foreign Studies Yongin Korea Republic of

ISBN: (纸本)9789819912513

Our research specifies the criteria for categorizing comments and supplements the limitations of the existing services that filter out only some of the direct malicious comments centered on specific vocabulary and phrases. In comparison to prior research detecting three sentiments as positive, negative, or neutral, we subdivided expressions into six categories. Our model applying classification downstream tasks to a pre-trained KcBERT model draws results reflecting the meaning and social context of the comments. An improved accuracy compared to previous studies shows a meaningful outcome. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records

arXiv

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arXiv 2023年

作者： Sivarajkumar, Sonish Huang, Yufei Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States

Objective: To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models. Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks. Results: FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines. Conclusion: FPM is a novel method to pre-train fair and unbiased patient representations from EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where bias and fairness are important. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Establishment and risk factor assessment of the abnormal body temperature probability prediction model (ABTP) for dairy cattle

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Scientific Reports 2024年第1期14卷 1-12页

作者： Lee, Tsair-Fwu Chiu, Chien-Liang Liu, Yen-Hsien Chang, Chu-Ho Shao, Jen-Chung Guo, Shih-Sian Liao, Yi-Lun Chen, Chia-Hui Tseng, Chin-Dar Chao, Pei-Ju Lee, Shen-Hao Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung 80778 Taiwan Medical Physics and Informatics Laboratory of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung 80778 Taiwan Department of Medical Imaging and Radiological Sciences Kaohsiung Medical University Kaohsiung 80708 Taiwan PhD Program in Biomedical Engineering Kaohsiung Medical University Kaohsiung 80708 Taiwan School of Dentistry College of Dental Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan Department of Radiation Oncology Linkou Chang Gung Memorial Hospital and Chang Gung University College of Medicine Linkou Taiwan

The study aims to develop an abnormal body temperature probability (ABTP) model for dairy cattle, utilizing environmental and physiological data. This model is designed to enhance the management of heat stress impacts, providing an early warning system for farm managers to improve dairy cattle welfare and farm productivity in response to climate change. The study employs the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to analyze environmental and physiological data from 320 dairy cattle, identifying key factors influencing body temperature anomalies. This method supports the development of various models, including the Lyman Kutcher-Burman (LKB), Logistic, Schultheiss, and Poisson models, which are evaluated for their ability to predict abnormal body temperatures in dairy cattle effectively. The study successfully validated multiple models to predict abnormal body temperatures in dairy cattle, with a focus on the temperature-humidity index (THI) as a critical determinant. These models, including LKB, Logistic, Schultheiss, and Poisson, demonstrated high accuracy, as measured by the AUC and other performance metrics such as the Brier score and Hosmer–Lemeshow (HL) test. The results highlight the robustness of the models in capturing the nuances of heat stress impacts on dairy cattle. The research develops innovative models for managing heat stress in dairy cattle, effectively enhancing detection and intervention strategies. By integrating advanced technologies and novel predictive models, the study offers effective measures for early detection and management of abnormal body temperatures, improving cattle welfare and farm productivity in changing climatic conditions. This approach highlights the importance of using multiple models to accurately predict and address heat stress in livestock, making significant contributions to enhancing farm management practices. © The Author(s) 2024.

关键词： Abnormal body temperature probability (ABTP) Dairy cattle Least absolute shrinkage and selection operator (LASSO) Temperature-humidity index (THI)

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A Clinical Decision Support System of Pressure Ulcers Tissue Classification 21

A Clinical Decision Support System of Pressure Ulcers Tissue...

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5th International Conference on Medical and Health informatics, ICMHI 2021

作者： Chang, Dun-Hao Chu, Po-Jui Li, Yi-Jhen Ning, Chun-Kai Chien, Ting-Ying Department of Information Management Yuan Ze University Taoyuan City Taiwan Department of Plastic and Aesthetic Surgery Far Eastern Memorial Hospital Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9781450389846

Pressure ulcers (PUs) are a common problem associated with great morbidity and cost. Because of the lack of professional personnel in the institutions or home care system, the diagnosis and treatment of PUs were sometimes delayed. We aim to develop an automatic tissue classification and severity evaluation system of PUs using AI technology. All PU images were collected from patients in the Far Eastern Memorial Hospital (FEMH) in recent 3 years and labeled according to different tissue types. The labeled images were used to train the tissue segmentation model with U-net convolutional neural network (CNN). The percentage of the different tissue can be calculated automatically and would be compared with the manually labeled one. The output of tissue classification was transformed to a clinical decision support system (CDSS) with a rule-base formula. Our study showed that proposed system can classify each tissue automatically and reach an accuracy about 93.6%. However, the CDSS's accuracy rate was only 64% for the referral suggestion and 62% for the care recommendation. Hence, the system should be modified with more data and information. © 2021 ACM.

关键词： Tissue

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Analysis A parallel combination between the Number Theorists aRe Us, Riverst Shamir Adleman and Triple Data Encryption Standard methods for measuring the speed of document security 2

Analysis A parallel combination between the Number Theorists...

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2nd International Conference on Mechanical, Electronics, Computer, and Industrial Technology, MECnIT 2018

作者： Hardiaman Hulu, Luken Candawan William Sipayung, David Hafis, Muhammad Christnatalis Informatics Engineering Study Program Deparment of Technology and Computer Science Universitas Prima Indonesia Medan Indonesia

Computer security is a very important thing in an information system. The strength of the NTRU algorithm is the difficulty of finding a short vector of a lattice (a discrete subgroup of a collection of vectors that includes the entire vector environment) of a random polynomial that has a large degree. The strength of the RSA algorithm lies in the level of difficulty in factoring non-prime numbers into the primary factor. As long as no efficient algorithm has been found to find the prime factors of large integers, the RSA algorithm is highly recommended for message encryption. And finally this is the TRIPEL DES algorithm designed using a 56-bit key, and that size is enough to run a secure encryption technique. This algorithm provides a simple solution that is running the DES Algorithm 3 times for data blocks. The results of our study of measuring speed on encryption and decryption using the same file are superior to the RSA algorithm compared to the NTRU and TRIPLE DES algorithms. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Security of data

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Traffic sign detection using histogram of oriented gradients and max margin object detection 2

Traffic sign detection using histogram of oriented gradients...

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2nd International Conference on Mechanical, Electronics, Computer, and Industrial Technology, MECnIT 2018

作者： Reinaldo Manurung, Natanael Simbolon, Juara Immanuel Christnatalis Informatics Engineering Study Program Deparment of Technology and Computer Science Universitas Prima Indonesia Medan Indonesia

Traffic signs are important markers in two-wheeled and four-wheeled vehicles. However, there is a change in direction or arrangement on the road that cannot be opened on a map which can cause incorrect information, which can cause traffic jams. In this journal the author uses a camera mounted on a car that provides a solution for drivers who issue problems that occur on the road that show directions or arrangements that are not directly updated using the HOG and MMOD methods. HOGs and MMODs are methods that can refute objects well, and move images and will be recognized immediately. The information received can be sent directly to an electronic map so that it can be accessed automatically by the driver's information and assistance and other information finds the right path, so that it can help the driver and can avoid traffic jams. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Traffic signs

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Design Technology of Geographical Information System of Rice Pandanwangi Cianjur Prediction Factors Products

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Journal of Physics: Conference Series 2021年第1期1764卷

作者： Ai Musrifah Andri Adikusuma Sutono Informatics Engineering Study Program Faculty of Engineering University of Suryakancana

Cianjur is one of the renowned rice producers, particularly with the presence of Pandanwangi rice that can be grown in the Cianjur area. Pandanwangi has become one of Cianjur City's icons, the rice supremacy created by Pandanwangi Cianjur's tenacity and distinctive scent (pandanus fragrance). The Interest of pandanwangi was very high, both within the town of Cianjur and outside the town of Cianjur, but this was inversely proportional to the desires of the groups of farmers who appeared to prefer to plant rice other than Pandanwangi, on the grounds that the planting of Pandanwangi had a long harvest and that the likelihood of crop failure was also very high. Problems with information flows that are not well integrated between farmers' groups, farmers' group organizations, and the Agriculture Department in adjusting land details, planting time, planting location, estimating total harvest and harvesting time. To solve this problem, a framework must be built that can integrate the information needed for Pandanwangi Cianjur Harvest Prediction with Pandanwangi farmers' regional analysis based on geographic information system technology, using Google Maps Service.

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Multivariate Information Theory Uncovers Synergistic Subsystems of the Human Cerebral Cortex

arXiv

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arXiv 2022年

作者： Varley, Thomas F. Pope, Maria Faskowitz, Joshua Sporns, Olaf Department of Psychological and Brain Sciences Indiana University BloomingtonIN47405 United States School of Informatics Computing & Engineering Indiana University BloomingtonIN47405 United States Program in Neuroscience Indiana University BloomingtonIN47405 United States Indiana University Network Science Institute Indiana University BloomingtonIN47405 United States

One of the most well-established tools for modeling the brain as a complex system is the functional connectivity network, which examines the correlations between pairs of interacting brain regions. While powerful, the network model is limited by the restriction that only pairwise dependencies are visible and potentially higher-order structures are missed. In this work, we explore how multivariate information theory can reveal higher-order, synergistic dependencies in the human brain. Using the O-information, a measure of whether the structure of a system is redundancy- or synergy-dominated, we show that synergistic subsystems are widespread in the human brain. We provide a mathematical analysis of the O-information to locate it within a larger taxonomy of multivariate complexity measures. We also show the O-information is related to a previously established measure, the Tononi-Sporns-Edelman complexity, and can be understood as an expected difference in integration between system scales. Highly synergistic subsystems typically sit between canonical functional networks, and may serve to integrate those networks. We then use simulated annealing to find maximally synergistic subsystems, finding that such systems typically comprise ≈10 brain regions, also recruited from multiple canonical brain systems. Though ubiquitous, highly synergistic subsystems are invisible when considering pairwise functional connectivity, suggesting that higher-order dependencies form a kind of "shadow structure" that has been unrecognized by established network-based analyses. We assert that higher-order interactions in the brain represent a vast and under-explored space that, made accessible with tools of multivariate information theory, may offer novel scientific insights. © 2022, CC BY-NC-ND.

关键词： Redundancy

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