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Automatic Smart Crawling on Twitter for Weather Information in Indonesia

Procedia Computer science 2023年 227卷 795-804页

作者： Kartika Purwandari Reza Bayu Perdana Join W.C. Sigalingging Reza Rahutomo Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta 10720 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Although Twitter is a popular platform for social interaction analysis and text data mining, it faces challenges with geolocation automation. To address this problem, the researchers propose the utilization of a Support Vector Machine (SVM) model to develop an automated Twitter crawling system. The system aims to collect data related to weather in Indonesia by employing Twin, a Python-based Twitter scraping software. To overcome null geolocations, the study incorporates aliases created based on the common practice of Indonesian users mentioning the country's location in tweets. The results demonstrate that the SVM model, combined with automated smart crawling, achieves an 85% accuracy rate.

关键词： Social media Twitter SVM smart crawling weather

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Autonomous self-evolving research on biomedical data: the DREAM paradigm

arXiv

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arXiv 2024年

作者： Deng, Luojia Wu, Yijie Ren, Yongyong Lu, Hui Department of Bioinformatics and Biostatistics School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China SJTU-Yale Joint Center for Biostatistics and Data Science Technical Center for Digital Medicine National Center for Translational Medicine Shanghai Jiao Tong University Shanghai China

In contemporary biomedical research, the efficiency of data-driven approaches is hindered by large data volumes, tool selection complexity, and human resource limitations, necessitating the development of fully autonomous research systems to meet complex analytical needs. Such a system should include the ability to autonomously generate research questions, write analytical code, configure the computational environment, judge and interpret the results, and iteratively generate more in-depth questions or solutions, all without human intervention. In this study, we developed DREAM, the first biomedical data-dRiven self-Evolving Autonomous systeM, which can independently conduct scientific research without any human involvement. Utilizing a clinical dataset and two omics datasets, DREAM demonstrated its ability to raise and deepen scientific questions, with difficulty scores for clinical data questions surpassing top published articles by 5.7% and outperforming GPT-4 and bioinformatics graduate students by 58.6% and 56.0%, respectively. Overall, DREAM has a success rate of 80% in autonomous clinical data mining. Certainly, human can participate in different steps of DREAM to achieve more personalized goals. After evolution, 10% of the questions exceeded the average scores of top published article questions on originality and complexity. In the autonomous environment configuration of the eight bioinformatics workflows, DREAM exhibited an 88% success rate, whereas GPT-4 failed to configure any workflows. Using the clinical dataset as an example, DREAM was over 10,000 times more efficient than the average scientist with a single computer core, and capable of revealing new discoveries. As a self-evolving autonomous research system, DREAM provides an efficient and reliable solution for future biomedical research. In the future, this research paradigm may also have a revolutionary impact on other data-driven scientific research fields. Copyright © 2024, The Authors. All right

关键词： Clinical research

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A Privacy-Preserving Framework for Collaborative Machine Learning with Kernel Methods

A Privacy-Preserving Framework for Collaborative Machine Lea...

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IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Anika Hannemann Ali Burak Ünal Arjhun Swaminathan Erik Buchmann Mete Akgün Dept. of Computer Science Leipzig University Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig Germany Medical Data Privacy and Privacy-preserving Machine Learning (MDPPML) University of Tübingen Institute for Bioinformatics and Medical Informatics (IBMI) University of Tübingen Germany

It is challenging to implement Kernel methods, if the data sources are distributed and cannot be joined at a trusted third party for privacy reasons. It is even more challenging, if the use case rules out privacy-preserving approaches that introduce noise or entail significant computational overhead. An example for such a use case is machine learning on clinical data. To realize exact and efficient privacy preserving computation of kernel methods, we propose FLAKE, a Framework for Learning with Anonymized KErnels on horizontally distributed data. With our method, the data sources mask their data so that a Gram matrix can be computed without compromising privacy or utility. The Gram matrix allows to calculate many kernel matrices, which can be used to train kernel-based machine learning algorithms such as Support Vector Machines. We prove that our framework prevents an adversary from learning the input data or the number of input features under a semi-honest threat model. The conducted experiments on clinical, genomic, and image data provide confirmation that our approach is applicable across a wide range of settings. Additionally, our method outperforms comparable approaches in both computational efficiency and accuracy. Thus, FLAKE is a lightweight, applicable approach suitable for various use cases.

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Determining Variables Associated with Annual Oil Palm Yield: An Explainable Gradient Boosting Approach

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Procedia Computer science 2023年 227卷 262-271页

作者： Gregorius Natanael Elwirehardja Teddy Suparyanto Miftakhurrokhmat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia 55282 Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta Indonesia 55584 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous studies showed that meteorological variables, including rainfalls and wind speed, are associated with oil palm yields. In this research, the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive exPlanations (SHAP) were deployed for analyzing the importance of 15 agrometeorological variables in predicting oil palm yield. The best model attained 1.911 RMSE and 0.855 R2. By analyzing the weights and gains of the XGBoost model along with the SHAP values, it was found that the yield in the previous year, the age and number of plants, the area of peat lands, and meteorological parameters such as rainfall rates and the number of rainy days in the previous three years were considered important. The previous year's yield in particular possesses the greatest weight and gain values according to the model, and the highest SHAP value among all input variables. However, the meteorological data used in this research are only limited to rainfall rates and the number of rainy days. In the future, more diverse variables can be analyzed.

关键词： Oil Palm Yield Machine Learning XGBoost XAI SHAP

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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Single-cell omics: experimental workflow, data analyses and applications

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science China(Life sciences) 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi Department of Clinical Laboratory the Affiliated Wuhu Hospital of East China Normal University(The Second People’s Hospital of Wuhu City)Wuhu 241000China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration(Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 201210China Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Beijing Institutes of Life Science Chinese Academy of SciencesBeijing 100101China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology(CQB)Peking UniversityBeijing 100871China Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women’s HospitalZhejiang University School of MedicineHangzhou 310006China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal U

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular *** exploration of how the genomes orchestrate the formation and maintenance of each cell,and control the cellular phenotypes of various organismsis,is both captivating and *** the inception of the first single-cell RNA technology,technologies related to single-cell sequencing have experienced rapid advancements in recent *** technologies have expanded horizontally to include single-cell genome,epigenome,proteome,and metabolome,while vertically,they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR ***-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases,including ***,we comprehensively summarize recent advances in single-cell omics technologies,with a specific focus on the methodology *** overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

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Development of artificial intelligence for variable rate application based oil palm fertilization recommendation system 6

Development of artificial intelligence for variable rate app...

引用

6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Firmansyah, Erick Pardamean, Bens Ginting, Candra Mawandha, Hangger Gahara Pratama Putra, Dian Suparyanto, Teddy Institute of Agriculture STIPER Faculty of Agriculture Yogyakarta55282 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Institute of Agriculture STIPER Faculty of Agriculture Yogyakarta55282 Indonesia

ISBN: (纸本)9781665449373

The need for fertilization knowledge that meets good cultivation principles is the background for the need for a platform that can help provide advice on fertilization implementation. The conventional method through leaf and soil analysis to determine the dose of fertilization has many obstacles in its implementation. The availability of experts to provide advice is also not available at all times. One way to transfer knowledge to non-experts is to use Artificial Intelligence (AI) in the form of a dynamic expert system. The purpose of this research is to create a dynamic expert system that can provide advice and apply fertilization quickly, cheaply, accurately, and available at all times. In building this AI system, a knowledge base, working memory, inference engine, and interface are needed. The implementation of dynamic expert system development consists of four stages, namely (1) literature study, (2) laboratory analysis, (3) construction of an inference engine, and (4) interface creation. Based on the literature study that has been carried out, it is known that there are three domains of knowledge of oil palm fertilization, namely the domain of soil, plants and climate. Each of these knowledge domains consists of attributes, sub-attributes, and facts of knowledge which are then arranged in the form of mathematical rules. The relationship between the three knowledge domains is used as the basis for making fertilization rules. The result of this research is an application with Artificial Intelligence to provide information on nutritional needs according to plant age, population, production, land area and location. The application provides recommendations for the type, frequency, amount of fertilizer, and time of application. © 2021 IEEE.

关键词： Inference engines

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SRECG: ECG Signal Super-Resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification

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IEEE Transactions on Consumer Electronics 2023年第3期69卷 250-260页

作者： Chen, Tsai-Min Tsai, Yuan-Hong Tseng, Huan-Hsin Liu, Kai-Chun Chen, Jhih-Yu Huang, Chih-Han Li, Guo-Yuan Shen, Chun-Yen Tsao, Yu National Taiwan University and Academia Sinica Graduate Program of Data Science Taipei106319 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei115 Taiwan Taiwan Artificial Intelligence Academy Foundation Program of Taiwan AI Academy New Taipei22065 Taiwan Artificial Intelligence Foundation Technology Development Center New Taipei24158 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei10617 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei115 Taiwan National Taiwan University Department of Mathematics Taipei10617 Taiwan

A combination of cloud-based deep learning (DL) algorithms with portable/wearable (P/W) devices has been developed as a smart heath care system to support automatic cardiac arrhythmias (CAs) classification using electrocardiography (ECG). However, long-term and continuous ECG monitoring is challenging because of limitations of batteries and transmission bandwidth of P/W devices while incorporated with consumer electronics (CE). A feasible approach to address this challenge is to decrease sampling rates. However, low sampling rates lead to low-resolution signals that hinder the CAs classification performance. In this study, we propose a DL-based ECG signal super-resolution framework (called SRECG) to enhance low-resolution ECG signals by jointly considering the accuracies when applied to the DL-based high-resolution multiclass classifier (HMC) of CAs. In our experiments, we downsampled the ECG signals from the CPSC2018 dataset and evaluated their HMC accuracies with and without the SRECG. Experimental results show that SRECG can well improve the HMC accuracies as compared to traditional interpolation methods. Moreover, approximately half of the CAs classification accuracies of HMC were maintained within the enhanced ECG signals by SRECG. The promising results confirm that SRECG can be suitably used to enhance low-resolution ECG signals from P/W devices with CE to improve their cloud-based HMC performances. © 1975-2011 IEEE.

关键词： Electrocardiography

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Design,methodology,and baseline of whole city‑million scale children and adolescents myopia survey(CAMS)in Wenzhou,China

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Eye and Vision 2021年第1期8卷 303-316页

作者： Liangde Xu Youyuan Zhuang Guosi Zhang Yunlong Ma Jian Yuan Changseng Tu MiaoMiao Li Wencan Wang Yaru Zhang Xiaoyan Lu Jing Li Xinting Liu Zhengbo Xue Meng Zhou Jie Sun Jinhua Bao Ming Li Fan Lu Hong Wang Jianzhong Su Jia Qu School of Ophthalmology and Optometry and Eye Hospital Wenzhou Medical UniversityWenzhou 325027China State Key Laboratory of Ophthalmology Optometry and Visual ScienceWenzhou 325027China Institute of Biomedical Big Data Wenzhou Medical UniversityWenzhou 325027China National Clinical Research Center for Ocular Disease Wenzhou 325027China College of Bioinformatics Science and Technology Harbin Medical UniversityHarbin 150081People’s Republic of China

Background:Myopia is the most common visual impairment in children and adolescents *** study described an economical and effective population-based screening pipeline and performed the project of a million scale children and adolescents myopia survey(CAMS),which will shed light on the further study of myopia from the level of epidemiology and precision ***:We developed a novel population-based screening pattern,an intelligent screening process and internetbased information transmission and analysis system to carry out the survey consisting of school children in Wenzhou,*** examination items include unaided distance visual acuity,presenting distance visual acuity,and noncycloplegic *** and high myopia were defined as spherical equivalent(SE)≤−1.00 diopters(D)and SE≤−6.00 D,***,the reports of the vision checking were automatically sent to parents and the related *** CAMS project will be done two to four times annually with the support of the *** online eyesight status information management system(OESIMS)was developed to construct comprehensive and efficient electronic vision health records(EVHRs)for myopia information inquiry,risk pre-warning,and further ***:The CAMS completed the first-round of screening within 30 days for 99.41%of Wenzhou students from districts and counties,in June 2019.A total of 1,060,925 participants were eligible for CAMS and 1,054,251(99.37%participation rate)were selected through data quality control,which comprised 1305 schools,and 580,609,251,050 and 170,967 elementary,middle,and high school *** mean age of participants was 12.21±3.32 years(6-20 years),the female-to-male ratio was *** prevalence of myopia in elementary,middle,and high school students was 38.16%,77.52%,and 84.00%,respectively,and the high myopia incidence was 0.95%,6.90%,and 12.98%.Conclusions:The CAMS standardized myopia screening model involves automating large-scal

关键词： Baseline Vision screening Myopia prevention and control Population-based

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Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression

arXiv

引用

arXiv 2023年

作者： Baek, Youngsoo Berchuck, Samuel I. Mukherjee, Sayan Department of Statistical Science Duke University DurhamNC27705 United States Department of Biostatistics & Bioinformatics Duke University DurhamNC27705 United States Center for Scalable Data Analysis and Artificial Intelligence Universität Leipzig Leipzig04105 Germany Deparments of Mathematics Computer Science Biostatistics & Bioinformatics Duke University NC United States Max Planck Institute for Mathematics in the Sciences Leipzig Germany

In this paper, we compare and contrast the behavior of the posterior predictive distribution to the risk of the maximum a posteriori (MAP) estimator for the random features regression model in the overparameterized regime. We will focus on the variance of the posterior predictive distribution (Bayesian model average) and compare its asymptotics to that of the risk of the MAP estimator. In the regime where the model dimensions grow faster than any constant multiple of the number of samples, asymptotic agreement between these two quantities is governed by the phase transition in the signal-to-noise ratio. They also asymptotically agree with each other when the number of samples grows faster than any constant multiple of model dimensions. Numerical simulations illustrate finer distributional properties of the two quantities for finite dimensions. We conjecture they have Gaussian fluctuations and exhibit similar properties as found by previous authors in a Gaussian sequence model, which is of independent theoretical interest. © 2023, CC BY.

关键词： Signal to noise ratio

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