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6th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2024

作者： Hamzah Santosa, Paulus Insap Putra, Guntur Dharma Universitas Gadjah Mada Department of Electrical and Information Engineering Yogyakarta Indonesia

ISBN: (纸本)9798331533243

The present systematic literature review explores different methods of web prefetching and precaching to improve website performance, which examines relevant studies related to the decrease of overall latency and improvement of user experience through client-side enhancements. The present paper assesses conventional techniques in web prefetching, such as ML-based algorithms, AI-driven caching, and hybrid algorithms combined with data mining technology (DMT). The assessment focuses on key metrics such as load time, hit rate, and user satisfaction. The results indicate that AI, ML, and DMT, are effective techniques for prefetching and precaching, which indicates a notable enhancement in the overall performance. In addition, the survey highlights how these high-performing methods can be useful for both publicly accessible websites and internal websites accessed through an intranet, aiming to uphold user satisfaction and website performance. Future studies are anticipated to investigate how they can be used in private website supporting internal business processes to improve the quality of business operations. © 2024 IEEE.

关键词： predictive algorithm Systematic Literature Review User Satisfaction Web Caching Web Prefetching

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A neonatal sepsis prediction algorithm using electronic medical record data from Mbarara Regional Referral Hospital

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Intelligence-Based Medicine 2025年 11卷 100198-100198页

作者： Ezeobi Dennis, Peace Musiimenta, Angella Wasswa, William Kyoyagala, Stella Mbarara University of Science and Technology (MUST) Uganda Mbarara Regional Referral Hospital (MRRH) Uganda

Introduction: Neonatal sepsis is a global challenge that contributes significantly to neonatal morbidity and mortality. The current diagnostic methods depend on conventional culture methods, a procedure that takes time and leads to delays in making timely treatment decisions. This study proposes a machine learning algorithm utilizing electronic medical record (EMR) data from Mbarara Regional Referral Hospital (MRRH) to enhance early detection and treatment of neonatal sepsis. Methods: We performed a retrospective study on a dataset of neonates hospitalized for at least 48 h in the Neonatal Intensive Care Unit (NICU) at MRRH between October 2015 to September 2019 who received at least one sepsis evaluation. 482 records of neonates met the inclusion criteria and the dataset comprises 38 neonatal sepsis screening parameters. The study considered two outcomes for sepsis evaluations: culture-positive if a blood culture was positive, and clinically positive if cultures were negative but antibiotics were administered for at least 120 h. We implemented k-fold cross-validation with k set to 10 to guarantee robust training and testing of the models. Seven machine learning models were trained to classify inputs as sepsis positive or negative, and their performance was compared with physician diagnoses. Results: The results of this study show that the proposed algorithm, combining maternal risk factors, neonatal clinical signs, and laboratory tests (the algorithm demonstrated a sensitivity and specificity of at least 95 %) outperformed the physician diagnosis (Sensitivity = 89 %, Specificity = 11 %). SVM model with radial basis function, polynomial kernels, and DT model (with the highest AUROC values of 98 %) performed better than the other models. Conclusions: The study shows that the combination of maternal risk factors, neonatal clinical signs, and laboratory tests can help improve the prediction of neonatal sepsis. Further research is warranted to assess the potential perfo

关键词： Early onset sepsis (EOS) Electronic medical record (EMR) Neonatal sepsis prediction predictive algorithm Screening parameters Supervised machine learning

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Non – Invasive, smartphone image-based screening for sickle cell disease at the point-of-need

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Heliyon 2025年第2期11卷 e41830页

作者： Vital, Eudorah F. LiCalzi, Meredith Haak Mannino, Robert G. McGann, Patrick T. Lam, Wilbur A. Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta 30332 GA United States Sanguina Inc Atlanta 30092 GA United States Lifespan Comprehensive Sickle Cell Center Providence Rhode Island 02903 United States Alpert Medical School of Brown University Department of Pediatrics Providence Rhode Island 02903 United States Department of Pediatrics Division of Pediatric Hematology/Oncology Aflac Cancer Center and Blood Disorders Service of Children's Healthcare of Atlanta Emory University School of Medicine Atlanta 30322 GA United States Winship Cancer Institute of Emory University Atlanta 30322 GA United States Parker H. Petit Institute of Bioengineering and Biosciences Georgia Institute of Technology Atlanta 30332 GA United States Institute for Matter and Systems Georgia Institute of Technology Atlanta 30332 GA United States

Leveraging the increasing accessibility of smartphones in healthcare settings, we developed a smartphone app aimed at enhancing sickle cell disease (SCD) screening, particularly in resource-limited settings. Our application provides accurate and non-invasive SCD screening with instant results at the point-of-need. The app operates by analyzing patient fingernail images via a smartphone image to gauge anemia severity and by using targeted inquiries to identify SCD-related symptoms. These inputs collectively generate an SCD IMage and Patient profile-based Likelihood Estimation (SIMPLE) score, which estimates disease probability. The accuracy of the score depends on two inputs: the patient's anemia status and their health survey responses. We tested the app on 485 pediatric patients at Children's Healthcare of Atlanta (CHOA), achieving an overall sensitivity of 74 % and specificity of 76 % in screening for SCD among children aged 6 months to 21 years. Notably, the app demonstrated enhanced performance in the target demographic, with 100 % sensitivity and 75 % specificity for screening SCD in children aged 6 months to 5 years. This cost-effective and scalable app efficiently pinpoints and stratifies individuals, particularly those who missed early screening, for formal screening programs. © 2025 The Author(s)

关键词： Non-invasive Point-of-need predictive algorithm Screening Sickle cell disease Smartphone app

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Predictability of real temporal networks

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National Science Review 2020年第5期7卷 929-937页

作者： Disheng Tang Wenbo Du Louis Shekhtman Yijie Wang Shlomo Havlin Xianbin Cao Gang Yan School of Electronic and Information Engineering Beihang University Shanghai Institute of Intelligence Science and Technology Tongji University Network Science Institute Northeastern University Department of Physics Bar Ilan University CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation School of Physics Science and Engineering Tongji University

Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and *** evidence has shown that the temporal nature of links in many real-world networks is not ***, it is challenging to predict temporal link patterns while considering the entanglement between topological and temporal link patterns. Here, we propose an entropy-rate-based framework, based on combined topological–temporal regularities, for quantifying the predictability of any temporal network. We apply our framework on various model networks, demonstrating that it indeed captures the intrinsic topological–temporal regularities whereas previous methods considered only temporal aspects. We also apply our framework on 18 real networks of different types and determine their predictability. Interestingly,we find that, for most real temporal networks, despite the greater complexity of predictability brought by the increase in dimension, the combined topological–temporal predictability is higher than the temporal predictability. Our results demonstrate the necessity for incorporating both temporal and topological aspects of networks in order to improve predictions of dynamical processes.

关键词： temporal network predictability network entropy predictive algorithm

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Cooperative Vehicle Following based on predictive Control with Communication Delay Compensation 24

Cooperative Vehicle Following based on Predictive Control wi...

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24th International Conference on System Theory, Control and Computing (ICSTCC)

作者： Tiganasu, Alexandru Lazar, Corneliu Caruntu, Constantin Florin Gheorghe Asachi Tech Univ Iasi Dept Automat Control & Appl Informat Iasi Romania

ISBN: (纸本)9781728198095

In recent years, the problem of crowded public roads became more and more stringent. Some solutions for this problem may be represented by the individual advanced driver assistance systems (ADAS) that can make a vehicle more or less autonomous through a set of sensors, actuators and control algorithms. Depending on the level of automation, the driver needs to intervene or not in some traffic situations. Most of the ADAS systems treat two cases given by the movement direction of the vehicle: longitudinal or lateral. For the longitudinal direction the variable that must be controlled is the vehicle's velocity. In this paper, a cooperative vehicle following strategy is proposed based on predictive control. The movement of a leader and a follower together as a single entity is in focus for this case study. The leader is designed using a Cruise Control (CC) algorithm based on a generalized predictive controller (GPC). For the follower, a cooperative predictive adaptive controller (CPAC) with delay compensation is considered. Besides the feedback part, CPAC contains a feedforward element to counteract the negative effect of measurable disturbances that are represented by the velocities received from the leader. For this group of two vehicles implemented with the above-mentioned algorithms, a MATLAB/Simulink simulator was developed to analyze the obtained performance.

关键词： vehicle following cooperative control delay compensation predictive algorithm spacing/velocity adaptation inter-vehicle communications

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Friction Coefficient Analysis of Nano-crystalline TiO2-Added Alumina Ceramics

Friction Coefficient Analysis of Nano-crystalline TiO2-Added...

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International Conference on Thermal Engineering and Management Advances, ICTEMA 2020

作者： Haldar, Partha Bhattacharya, Tapas Kumar Modak, Nipu Department of Mechanical Engineering Government College of Engineering and Ceramic Technology Kolkata India Department of Ceramic Technology Government College of Engineering and Ceramic Technology Kolkata Kolkata India Department of Mechanical Engineering Jadavpur University Kolkata India

ISBN: (纸本)9789811623462

The friction coefficient of sintered alumina with nano-crystalline titania (up to 4 wt.%) is investigated in relation with sliding frequency and normal load by computational method. The proposed work studies the variation of friction coefficient with change in sliding frequency up to 60 Hz and normal load up to 1 kgf independently, and the data is used to develop an algorithm following the best fit topology. Among various curve fitting techniques like polynomial fit of degree two, exponential fit of degree one and two, smoothing spline and Weibull fit, with minimum root mean square error, cubic spline fit method is adopted to form smooth curves in MATLAB. The algorithm is validated to predict the addition level of titania in alumina for given friction coefficient and sliding frequency or normal load. The result lies within a maximum error of 4.43% for cubic spline fit. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coefficient of friction Cubic spline fit predictive algorithm RMSE TiO2-Alumina system

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A review on machine learning algorithms to predict daylighting inside buildings

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SOLAR ENERGY 2020年 202卷 249-275页

作者： Ayoub, Mohammed Arab Acad Sci Technol & Maritime Transport Architectural Engn & Environm Design Dept Alexandria Egypt

Steep increases in air temperatures and CO2 emissions have been associated with the global demand for energy. This is coupled with population growth and improved living standards that encourages the reliance on mechanical acclimatization. Lighting energy alone is responsible for a large portion of total energy consumption in office buildings;and the demand for artificial light is expected to grow in the next years. One of sustainable approaches to enhance energy-efficiency is to incorporate daylighting strategies, which entail the controlled use of daylight inside buildings. Daylight simulation is an active area of research that offers accurate estimations, yet requires a complex set of inputs. Even with today's computers, simulations are computationally expensive and time-consuming, hindering to acquire accelerated preliminary approximations in acceptable timeframes, especially for the iterative design alternatives. Alternatively, predictive models that build on machine learning algorithms have granted much interest from the building design community due to their ability to handle such complex non-linear problems, acting as proxies to heavy simulations. This research presents a review on the growing directions that exploit machine learning to rapidly predict daylighting performance inside buildings, putting a particular focus on scopes of prediction, used algorithms, data sources and sizes, besides evaluation metrics. This work should improve architects decision-making and increase the applicability to predict daylighting. Another implication is to point towards knowledge gaps and missing opportunities in the related research domain, revealing future trends that allow for such innovative approaches to be exploited more commonly in Architectural practice.

关键词： Daylighting Illuminance Prediction Estimation Machine learning predictive algorithm

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Optimal Operation of Microgrid Employing predictive Control 15

Optimal Operation of Microgrid Employing Predictive Control

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15th International Conference on Development and Application Systems (DAS)

作者： Butaru, Florinel Carstea, Ciprian Lascu, Cristian Ancuti, Mihaela-Codruta Univ Politehn Timisoara Elect Engn Dept Timisoara Romania

ISBN: (纸本)9781728168708

The purpose of the present paper is to build a predictive algorithm based on the output voltage of the LC filter in order to achieve the AC Microgrids operation in the maximum power point tracking of the captured solar energy. Methods of regulating the microgrids input voltage are conventionally achieved by linear control loops. This type of control presents limitations like high sensitivity to parameter variations and slow transient response. The operation of the microgrids at the maximum power point is analyzed. The inverter's the optimal power values are determined based on the simulated data. The simulations were carried out in the MATLAB/Simulink simulation environment. Based on the vector control, the voltage drops on the inverter transistors are calculated in advanced for the next step, based on the phase diagram. A predictive algorithm is designed to ensure a stable operation in the maximum power point area, MPP, (i.e. the optimal area from the energy point of view).

关键词： predictive algorithm solar power plant energy efficiency maximum power point variable solar power

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The Development and Validation of Simplified Machine Learning algorithms to Predict Prognosis of Hospitalized Patients With COVID-19: Multicenter, Retrospective Study

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JOURNAL OF MEDICAL INTERNET RESEARCH 2022年第1期24卷 e31549页

作者： He, Fang Page, John H. Weinberg, Kerry R. Mishra, Anirban Amgen Inc Ctr Observat Res 1120 Vet Blvd San Francisco CA 94080 USA Amgen Inc Digital Hlth & Innovat Thousand Oaks CA 91320 USA Amgen Inc Ctr Observat Res Thousand Oaks CA 91320 USA

Background: The current COVID-19 pandemic is unprecedented;under resource-constrained settings, predictive algorithms can help to stratify disease severity, alerting physicians of high-risk patients;however, there are only few risk scores derived from a substantially large electronic health record (EHR) data set, using simplified predictors as input. Objective: The objectives of this study were to develop and validate simplified machine learning algorithms that predict COVID-19 adverse outcomes;to evaluate the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and calibration of the algorithms;and to derive clinically meaningful thresholds. Methods: We performed machine learning model development and validation via a cohort study using multicenter, patient-level, longitudinal EHRs from the Optum COVID-19 database that provides anonymized, longitudinal EHR from across the United States. The models were developed based on clinical characteristics to predict 28-day in-hospital mortality, intensive care unit (ICU) admission, respiratory failure, and mechanical ventilator usages at inpatient setting. Data from patients who were admitted from February 1, 2020, to September 7, 2020, were randomly sampled into development, validation, and test data sets;data collected from September 7, 2020, to November 15, 2020, were reserved as the postdevelopment prospective test data set. Results: Of the 3.7 million patients in the analysis, 585,867 patients were diagnosed or tested positive for SARS-CoV-2, and 50,703 adult patients were hospitalized with COVID-19 between February 1 and November 15, 2020. Among the study cohort (n=50,703), there were 6204 deaths, 9564 ICU admissions, 6478 mechanically ventilated or EMCO patients, and 25,169 patients developed acute respiratory distress syndrome or respiratory failure within 28 days since hospital admission. The algorithms demonstrated high accuracy (AUC 0.89, 95% CI 0.89-0.89 on the test data set [n=

关键词： COVID-19 predictive algorithm prognostic model machine learning

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Predicting misdiagnosed adult-onset type 1 diabetes using machine learning

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DIABETES RESEARCH AND CLINICAL PRACTICE 2022年 191卷 110029-110029页

作者： Cheheltani, Rabee King, Nicholas Lee, Suyin North, Benjamin Kovarik, Danny -Molina, Carmella Evans Leavitt, Nadejda Dutta, Sanjoy IQVIA Real World Solut Predict Analyt Wayne PA USA JDRF New York NY USA Indiana Univ Sch Med Ctr Diabet & Metab Dis Indianapolis IN USA 200 Vesey St 28th Floor New York NY 10281 USA

Aims: It is now understood that almost half of newly diagnosed cases of type 1 diabetes are adult-onset. However, type 1 and type 2 diabetes are difficult to initially distinguish clinically in adults, potentially leading to inef-fective care. In this study a machine learning model was developed to identify type 1 diabetes patients mis-diagnosed as type 2 ***: In this retrospective study, a machine learning model was developed to identify misdiagnosed type 1 diabetes patients from a population of patients with a prior type 2 diabetes diagnosis. Using Ambulatory Elec-tronic Medical Records (AEMR), features capturing relevant information on age, demographics, risk factors, symptoms, treatments, procedures, vitals, or lab results were extracted from patients' medical ***: The model identified age, BMI/weight, therapy history, and HbA1c/blood glucose values among top predictors of misdiagnosis. Model precision at low levels of recall (10 %) was 17 %, compared to <1 % incidence rate of misdiagnosis at the time of the first type 2 diabetes encounter in ***: This algorithm shows potential for being translated into screening guidelines or a clinical decision support tool embedded directly in an EMR system to reduce misdiagnosis of adult-onset type 1 diabetes and implement effective care at the outset.

关键词： Adult -onset type 1 diabetes Machine learning Misdiagnosis Clinical decision support tool predictive algorithm AI

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