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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Sulistyaningsih, Tria Purhadi Andari, Shofi Institut Teknologi Sepuluh Nopember Faculty of Science and Data Analytics Department of Statistics Surabaya Indonesia

ISBN: (纸本)9798350383010

Analytical challenges such as skewness and asymmetry often arise with continuous data, especially when values are strictly positive, rendering traditional models like the Normal distribution ineffective. Addressing these issues, this study introduces the Three-Parameter Log-Logistic Regression (LL3R) model, an advancement of the Three-Parameter Log-Logistic Distribution (LL3D). This research aims to apply the LL3R model to estimate parameters and test hypotheses, specifically analyzing hospitalization duration for Dengue Hemorrhagic Fever patients in 2022 at RSUD Haji, East Java Province. The response variable is the duration of hospitalization until recovery, with 10 predictor variables considered. The model's parameters are estimated using Conjugate Gradient (CG) and Berndt-Hall-Hall-Hausman (BHHH) technique for numerical iteration. Hypothesis testing employs the LRT test. Model effectiveness is gauged via the AICc. Results indicate the BHHH method outperforms CG with a lower AICc of 124,082 compared to CG's 3450,22 so that the best LL3R model is given by numerical iteration of BHHH method. Simultaneously, testing rejects the null hypothesis, and individual tests reveal significant effects of seven variables including body temperature, systolic and diastolic blood pressure, pulse frequency, respiratory rate, gender, and age on the duration of hospitalization until recovery of DHF patients at a 5% significance level. © 2024 IEEE.

关键词： Logistic regression

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Primordial magnetic non-Gaussianity with generic vacua and detection prospects in CMB spectral distortions

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Physical Review D 2024年第4期110卷 043511-043511页

作者： Arko Bhaumik Supratik Pal Physics and Applied Mathematics Unit Technology Innovation Hub on Data Science Big Data Analytics and Data Curation

Assuming a slow-roll inflationary model where conformal invariance of the Maxwell action is broken via a nonminimal kinetic coupling term, we investigate the non-Gaussian three-point cross-correlation function between the primordial curvature perturbation and the primordial magnetic field, under a fairly general choice of initial vacua for both the scalar and the gauge field sectors. Among the possible triangular configurations of the resulting cross-bispectrum, we find that the squeezed limit leads to local-type non-Gaussianity allowing a product form decomposition in terms of the scalar and magnetic power spectra, which is a generic result independent of any specific choice of the initial states. We subsequently explore its detection prospects in the cosmic microwave background (CMB) via correlations between prerecombination μ-type spectral distortions and temperature anisotropies, sourced by such a primordial cross-correlation. Our analysis with several proposed next-generation CMB missions forecasts a low value of the signal-to-noise ratio (SNR) for the μT spectrum if both the vacua are assumed to be pure Bunch-Davies. On the contrary, the SNR may be enhanced significantly for non-Bunch-Davies initial states for the magnetic sector within allowed bounds from current CMB data.

关键词： Cosmic microwave background Inflation Primordial magnetic fields

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Convolutional Neural Network for Sentiment Analysis of Indonesian Online News

Convolutional Neural Network for Sentiment Analysis of Indon...

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2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024

作者： Iswenda, Brilliant Ayang Irhamah Kuswanto, Heri Institut Teknologi Sepuluh Nopember Faculty of Science and Data Analytics Department of Statistics Surabaya Indonesia

ISBN: (纸本)9798331508579

The development of online news has made it the primary source of information for the public, replacing the previously dominant position of print media. In increasingly fierce competition, many media companies rely on page views to drive advertising revenue, making it one of their key performance indicators. By utilizing Convolutional Neural Networks (CNN), companies can extract sentiment from news headlines. The results of sentiment analysis predictions from online news headlines can be used to determine the proportion of popular news based on page views. This allows us to identify whether popular news (news with high page views) comes from negative or positive headlines. This information can be utilized by media outlets to create more engaging headlines to increase the number of popular news articles. By optimizing CNN parameters during training, the accuracy of the testing model can be enhanced. The results of this research show that sentiment analysis with Convolutional Neural Network shows the best accuracy result of 76.94% when using 100 filters and a filter size of 5. Positive news headlines are more likely to result in popular news compared to negative headlines © 2024 IEEE.

关键词： Convolutional neural networks

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Performance Analysis of Random Forest with Sampling for River Water Quality Classification 7

Performance Analysis of Random Forest with Sampling for Rive...

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7th International Conference on Informatics and Computational sciences, ICICoS 2024

作者： Fadhilah, Rahmi Kuswanto, Heri Prastyo, Dedy Dwi Institut Teknologi Sepuluh Nopember Faculty of Science and Data Analytics Department of Statistics Surabaya Indonesia

ISBN: (纸本)9798350375886

The availability of quality water is essential for the survival of organisms on Earth. However, monitoring and managing the environment surrounding the water source is crucial to ensure water is safe for consumption. While classification methods are commonly used to assess water quality, challenges arise when data is imbalanced between classes. data resampling techniques, including individual and combination, are often used to address this imbalance. This study aims to evaluate the performance of the Random Forest algorithm in classifying river water quality after resampling RACOG, RUS, and RACOG-RUS data, both with and without feature selection. data derived from the Provincial Environment Office was evaluated using various performance metrics. Our findings show that the Random Forest model, especially with individual RACOG resampling, exhibits the most promising performance, while the RUS model shows less than optimal performance. Interestingly, the combined RACOG-RUS approach effectively addresses the class imbalance problem. Moreover, feature selection contributes to improved performance in both the RUS and RACOG-RUS models, with the RACOG model showing consistent performance. Notably, the RACOG-RUS model consistently excels in prediction accuracy and stability. This research confirms the adoption of a combination approach to address river water quality classification data imbalance. © 2024 IEEE.

关键词： Classification Imbalance RACOG RACOG-RUS Random Forest RUS

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Two kinds of average approximation accuracy

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 481-490页

作者： Qingzhao Kong Wanting Wang Dongxiao Zhang Wenbin Zhang Department of Science Jimei UniversityXiamenChina Digital Fujian Big Data Modeling and Intelligent Computing Institute Jimei UniversityXiamenChina Department of Computer Science Michigan Technological UniversityHoughtonMichiganUSA

Rough set theory places great importance on approximation accuracy,which is used to gauge how well a rough set model describes a target ***,traditional approximation accuracy has limitations since it varies with changes in the target concept and cannot evaluate the overall descriptive ability of a rough set *** overcome this,two types of average approximation accuracy that objectively assess a rough set model’s ability to approximate all information granules is *** first is the relative average approximation accuracy,which is based on all sets in the universe and has several basic *** second is the absolute average approximation accuracy,which is based on undefinable sets and has yielded significant *** also explore the relationship between these two types of average approximation ***,the average approximation accuracy has practical applications in addressing missing attribute values in incomplete information tables.

关键词： rough sets rough set theory

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Online forecasting using neighbor-based incremental learning for electricity markets

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Neural Computing and Applications 2025年 1-18页

作者： Melgar-García, L. Gutiérrez-Avilés, D. Rubio-Escudero, C. Troncoso, A. Department of Artificial Intelligence Universidad Politécnica de Madrid Madrid28660 Spain Department of Computer Science University of Seville Avda. Reina Mercedes s/n Seville41012 Spain Data Science and Big Data Lab Pablo de Olavide University Seville41013 Spain

Electricity market forecasting is very useful for the different actors involved in the energy sector to plan both the supply chain and market operation. Nowadays, energy demand data are data coming from smart meters and have to be processed in real-time for more efficient demand management. In addition, electricity prices data can present changes over time such as new patterns and new trends. Therefore, real-time forecasting algorithms for both demand and prices have to adapt and adjust to online data in order to provide timely and accurate responses. This work presents a new algorithm for electricity demand and prices forecasting in real-time. The proposed algorithm generates a prediction model based on the k-nearest neighbors algorithm, which is incrementally updated in an online scenario considering both changes to existing patterns and adding new detected patterns to the model. Both time-frequency and error threshold based model updates have been evaluated. Results using energy demand from 2007 to 2016 and prices data for different time periods from the Spanish electricity market are reported and compared with other benchmark algorithms. © The Author(s) 2025.

关键词： Demand response

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Electricity Carbon Quota Trading Scheme based on Certificateless Signature and Blockchain

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Computer Modeling in Engineering & sciences 2024年第2期138卷 1695-1712页

作者： Xiaodong Yang Runze Diao Tao Liu Haoqi Wen Caifen Wang College of Computer Science and Engineering Northwest Normal UniversityLanzhou730070China Institute of China TelecomWanwei China TelecomWanwei Information Technology Co.Ltd.Lanzhou730030China College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Key Laboratory of Cryptography and Data Analytics Northwest Normal UniversityLanzhou730070China

The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading *** signatures are a new cryptographic technology that can address traditional cryptography’s general essential certificate requirements and avoid the problem of crucial escrowbased on identity ***,most certificateless signatures still suffer fromvarious security *** present a secure and efficient certificateless signing scheme by examining the security of existing certificateless signature *** ensure the integrity and verifiability of electricity carbon quota trading,we propose an electricity carbon quota trading scheme based on a certificateless signature and *** scheme utilizes certificateless signatures to ensure the validity and nonrepudiation of transactions and adopts blockchain technology to achieve immutability and traceability in electricity carbon quota *** addition,validating electricity carbon quota transactions does not require time-consuming bilinear pairing *** results of the analysis indicate that our scheme meets existential unforgeability under adaptive selective message attacks,offers conditional identity privacy protection,resists replay attacks,and demonstrates high computing and communication performance.

关键词： Electricity carbon trading certificateless signature blockchain forgery attack carbon quota

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A novel semantic segmentation approach based on U-Net,WU-Net,and U-Net++deep learning for predicting areas sensitive to pluvial flood at tropical area

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International Journal of Digital Earth 2023年第1期16卷 3661-3679页

作者： Laura Melgar-García Francisco Martínez-álvarez Dieu Tien Bui Alicia Troncoso Data Science&Big Data Lab Pablo de Olavide UniversitySevilleSpain GIS Group Department of Business and ITUniversity of South-Eastern NorwayGullbringvegen 36B?i Telemark3800Norway

Floods remain one of the most devastating weather-induced disastersworldwide, resulting in numerous fatalities each year and severelyimpacting socio-economic development and the ***, the ability to predict flood-prone areas in advance is crucialfor effective risk management. The objective of this research is to assessand compare three convolutional neural networks, U-Net, WU-Net, andU-Net++, for spatial prediction of pluvial flood with a case study at atropical area in the north of Vietnam. They are relative new convolutionalgorithms developed based on U-shaped architectures. For this task, ageospatial database with 796 historical flood locations and 12 floodindicators was prepared. For training the models, the binary crossentropywas employed as the loss function, while the Adaptive momentestimation (ADAM) algorithm was used for the optimization of themodel parameters, whereas, F1-score and classification accuracy (Acc)were used to assess the performance of the models. The results unequivocally highlight the high performance of the three models,achieving an impressive accuracy rate of 96.01%. The flood susceptibility maps derived from this research possess considerable utility for local authorities, providing valuable insights and informationto enhance decision-making processes and facilitate the implementation of effective risk management strategies.

关键词： Flash-flood assessment convolutional networks climate change GIS

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Enhancing wireless sensing via a target-mounted intelligent reflecting surface

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National science Review 2023年第8期10卷 13-14页

作者： Xiaodan Shao Rui Zhang School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

An intelligent reflecting surface(IRS),or its various equivalents such as an reconfigurable intelligent surface(RIS), is an emerging technology to control radio signal propagation in wireless systems. An IRS is a digitally controlled metasurface consisting of a large number of passive reflecting elements, which are connected to a smart controller to enable dynamic adjustments of the amplitude and/or phase of the incident signal on each element independently [1].

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HFANet: Hierarchical Feature-Enhanced Aggregation Network for Camouflaged Object Detection

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Journal of Shanghai Jiaotong University (science) 2025年 1-9页

作者： Zhang, Xinchao Zhu, Hengliang Mao, Guojun Department of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou350118 China

Camouflaged object detection (COD) aims to identify target objects in complex scenes with extremely high similarity to their surroundings, and has significant applications in military, medical, and other fields. This paper proposes a hierarchical feature-enhanced aggregation network (HFANet) for COD, aiming to address the situations that the target object is highly similar to the background. First, we adopt the pyramid vision Transformer model as the backbone for feature extraction. On top of it, the object-region amplification module and deep interaction guidance module are stacked to enhance the perception of camouflaged objects in complex scenes. Second, an enhanced receptive field module is designed to improve edge perception of camouflaged objects. At last, a multi-scale interactive fusion module is designed by cross-scale connection through adjacent layers, effectively improving the accuracy of COD. The proposed method is evaluated on three challenging datasets: CAMO, CHAMELEON, and COD10K. Evaluation results demonstrate superior performance compared to the state-of-the-art methods. © Shanghai Jiao Tong University 2024.

关键词： Object detection

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