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Offline Content-based Recommendation System for Wikimedia Commons Contents

Procedia Computer Science 2025年 257卷 485-494页

作者： Ahidjo Maitaigahasse Jean Louis Kedieng Ebongue Fendji Marcellin Atemkeng Department Mathematics and Computer Science Faculty of Science University of Ngaoundere 454 Ngaoundere Cameroon Centre for Research Experimentation and Production School of Chemical Engineering and Mineral Industries University of Ngaoundere 454 Ngaoundere Cameroon Stellenbosch Institute for Advanced Study (STIAS) Wallenberg Research Centre at Stellenbosch University Stellenbosch South Africa Department of Robotics and Industrial Informatics School of Chemical Engineering and Mineral Industries University of Ngaoundere 454 Ngaoundere Cameroon Department of Mathematics Rhodes University P.O. Box 6140 Grahamstown South Africa

Recommendation systems are essential for enhancing digital experiences, but their reliance on internet connectivity limits accessibility in regions with limited or no access. This paper presents an offline content-based recommendation system designed to operate without internet dependency by leveraging precomputed Term Frequency-Inverse Document Frequency (TF-IDF) vectors and cosine similarity. To optimize offline performance, we store a locally computed TF-IDF matrix, allowing efficient retrieval of relevant media items through matrix multiplication instead of real-time computation. The system is evaluated using twenty simulated personas representing diverse user interests, demonstrating its ability to generate personalized relevant recommendations. By eliminating the need for online data access, our system makes educational content from Wikimedia Commons accessible in remote areas, schools, and offline learning environments. These findings highlight the potential of offline recommendation systems in bridging the digital divide and providing equitable access to personalized learning resources.

关键词： Content-based recommendation system Wikimedia commons TF-IDF Cosine Similarity

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Connectivity of single neurons classifies cell subtypes in mouse brains

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Nature Methods 2025年第4期22卷 861-873页

作者： Liu, Lijuan Yun, Zhixi Manubens-Gil, Linus Chen, Hanbo Xiong, Feng Dong, Hongwei Zeng, Hongkui Hawrylycz, Michael Ascoli, Giorgio A. Peng, Hanchuan New Cornerstone Science Laboratory SEU-ALLEN Joint Center Institute for Brain and Intelligence Southeast University Nanjing China School of Biological Science & Medical Engineering Southeast University Nanjing China Tencent AI Lab Bellevue WA United States UCLA Brain Research and Artificial Intelligence Nexus Department of Neurobiology David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States Allen Institute for Brain Science Seattle WA United States Center for Neural Informatics Bioengineering Department and Neuroscience Program Krasnow Institute for Advanced Study George Mason University Fairfax VA United States Shanghai Academy of Natural Sciences Fudan University Shanghai China

Classification of single neurons at a brain-wide scale is a way to characterize the structural and functional organization of brains. Here we acquired and standardized a large morphology database of 20,158 mouse neurons and generated a potential connectivity map of single neurons based on their dendritic and axonal arbors. With such an anatomy–morphology–connectivity mapping, we defined neuron connectivity subtypes for neurons in 31 brain regions. We found that cell types defined by connectivity show distinct separation from each other. Within this context, we were able to characterize the diversity in secondary motor cortical neurons, and subtype connectivity patterns in thalamocortical pathways. Our findings underscore the importance of connectivity in characterizing the modularity of brain anatomy at the single-cell level. These results highlight that connectivity subtypes supplement conventionally recognized transcriptomic cell types, electrophysiological cell types and morphological cell types as factors to classify cell classes and their identities. © The Author(s) 2025.

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Offline Content-based Recommendation System for Wikimedia Commons Contents 16

Offline Content-based Recommendation System for Wikimedia Co...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Maitaigahasse, Ahidjo Ebongue Fendji, Jean Louis Kedieng Atemkeng, Marcellin Department Mathematics and Computer Science Faculty of Science University of Ngaoundere Ngaoundere 454 Cameroon Centre for Research Experimentation and Production School of Chemical Engineering and Mineral Industries University of Ngaoundere Ngaoundere 454 Cameroon Stellenbosch Institute for Advanced Study (STIAS) Wallenberg Research Centre Stellenbosch University Stellenbosch South Africa Department of Robotics and Industrial Informatics School of Chemical Engineering and Mineral Industries University of Ngaoundere Ngaoundere 454 Cameroon Department of Mathematics Rhodes University P.O. Box 6140 Grahamstown South Africa

关键词： Content-based recommendation system Cosine Similarity TF-IDF Wikimedia commons

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Method for modelling the number of HIV and AIDS cases using least square spline Biresponse nonparametric negative binomial regression

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MethodsX 2025年 14卷 103336页

作者： Ramadan, Arip Chamidah, Nur Budiantara, I Nyoman Lestari, Budi Aydin, Dursun Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Surabaya 60231 Indonesia Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Statistics Department Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences The University of Jember Jember 68121 Indonesia Department of Statistics Faculty of Science Muğla Sıtkı Koçman University Muğla 48000 Turkey Research Scholar at Department of Mathematics University of Wisconsin Oshkosh Algoma Blvd Oshkosh 54901 WI United States

We propose a new statistical modeling method to analyze functional relationship between predictor variables and correlated response variables, and apply it for modeling the number of HIV and AIDS cases influenced by early-age marriages and contraceptive users. The proposed method is LS-Spline BNNBR that is a Biresponse Nonparametric Negative Binomial Regression (BNNBR) based on least squares spline (LS-Spline). To validate the proposed method, we compare it with classical method, namely Ordinary Least Square Biresponse Parametric Negative Binomial Regression (OLS-BPNBR). The results show that the LS-Spline BNNBR method provided deviance value of 7.542334 which is smaller than that of OLS-BPNBR method, namely 7.993935. Thus, the proposed method has better prediction fit than the classical method. • This study discusses new statistical modeling method for the number of HIV and AIDS cases influenced by early-age marriages and contraceptive users where the response variables are correlated with each other. • The model estimation results show that the number of HIV and AIDS cases in Indonesia will decrease if the early-age marriages percentage increases and the contraceptive users percentage decreases. • The results are useful to provide a scientific basis for making policies to control the number of HIV and AIDS cases in Indonesia. © 2025 The Authors

关键词： AIDS BLSSNNBR model Contraceptive user Early-age marriage HIV Least square spline

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The Anticancer and Apoptotic Activity of the Endophytic Fungus Phomopsis Sp. Isolated from Soursop Leaves in Mcf-7 Breast Cancer Cells: In Vitro and Computational in Silico Studies

SSRN

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SSRN 2025年

作者： Dwicesaria, Maheswari Alfira Priosoeryanto, Bambang Pontjo Hasan, Akhmad Endang Zainal Irsal, Riyan Alifbi Putera Gholam, Gusnia Meilin Mahendra, Fachrur Rizal Suminto, Syaefudin Indonesia Department of Biochemistry Faculty of Mathematics and Natural Sciences IPB University Dramaga Campus Bogor16680 Indonesia School of Veterinary Medicine and Biomedicine IPB University Bogor16680 Indonesia Faculty of Engineering Kyushu University Nishi-ku Fukuoka819-0395 Japan Tropical Biopharmaca Research Center IPB University Bogor16128 Indonesia Bioinformatics Study Program Faculty of Mathematics and Natural Sciences IPB University Bogor16680 Indonesia

This study investigated the anticancer potential of the endophytic fungus Phomopsis sp., extracted from soursop leaves. The metabolite profile of Phomopsis sp. was analyzed and its anticancer and apoptotic activities were assessed in both human breast cancer cell (MCF-7) and normal Vero cell lines. Phomopsis sp. were cultivated in yeast malt broth, extracted with ethyl acetate, and separated using column chromatography. The brine shrimp lethality test gave ten fractions (F1–F10), with F1 demonstrating the highest cytotoxicity (LC50 = 2.33 µg/mL). The obtained fractions were evaluated for antiproliferative effects, revealing an IC50 of 5.65 µg/mL for MCF-7 cells and 5.21 µg/mL for Vero cells, indicating effective anti-proliferation through apoptosis, as confirmed by acridine orange-ethidium bromide staining. Liquid Chromatography-tandem Mass Spectrometry identified 38 active compounds in F1, including known anticancer agents such as cytosporone C, kynurenic acid, maraniol, (-)-caryophyllene oxide, and chalcone. Computational in silico analyses utilizing PASS online, molecular docking, ADMET/pharmacokinetic studies, density functional theory (DFT) analysis, and molecular dynamics (MD) simulations further validated chalcone as a promising anticancer candidate. Chalcone exhibited favorable ADMET/pharmacokinetic properties and achieved the highest docking score -8.392 kcal/mol among the compounds tested. MD simulations over 300 ns demonstrated that chalcone outperformed tamoxifen, with binding energy calculations revealing a value of -227.069 kJ/mol compared to -45.598 kJ/mol for tamoxifen. These findings highlight the therapeutic potential of Phomopsis sp. and its bioactive compounds, particularly chalcones, as candidates for effective anti-breast cancer therapies. © 2025, The Authors. All rights reserved.

关键词： Column chromatography

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Methodology for optimizing electrical grounding grids in stratified soils using advanced calculation techniques and evolutionary algorithms

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Swarm and Evolutionary Computation 2025年 95卷

作者： Silva, Carlos Leandro Borges da Pires, Thyago Gumeratto Silva Filho, Antonio Marcelino Bulhões, Junio Santos Belo, Orlando M. Oliveira Rodrigues, Clóves Gonçalves Coimbra, Antonio Paulo Calixto, Wesley Pacheco Electrical Mechanical & Computer Engineering School Federal University of Goias Goiania Goias PC 74.605-220 Brazil Institute of Systems and Robotics Coimbra University CoimbraPC 3030-290 Portugal Department of Informatics School of Engineering University of Minho BragaPC 4710-057 Portugal Electrical Power Systems Study Group Federal Institute of Tocantins Palmas TocantinsPC 77.021-090 Brazil Graduate Program in Production and Systems Engineering Pontifical Catholic University of Goias Goiania Goias PC 74605-010 Brazil Mato Grosso Brazil Technology Research and Development Center Federal Institute of Goias Goiania Goias PC 74.055-110 Brazil

This paper presents a practical methodology for optimizing the geometry of electrical grounding grids at industrial frequencies of 50Hz and 60Hz, integrating advanced calculation techniques and evolutionary algorithms to improve the safety and operational performance of electrical grounding systems. The proposed approach is particularly beneficial for industrial automation and control systems, where effective grounding is necessary to maintain system reliability and prevent downtime. This methodology employs mathematical modeling and computational tools to optimize grid parameters, ensuring compliance with safety standards while reducing operational costs, thus contributing to the overall efficiency of automated systems in industrial environments. The study reports a reduction of up to 66% in the number of vertical rods and 40% in horizontal conductors compared to traditional methods. These results indicate that the proposed methodology can significantly reduce material usage and costs while maintaining electrical safety in accordance with regulatory standards, making it applicable to a wide range of industrial settings, including substations and automated facilities. © 2025 The Authors

关键词： Electric grounding

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Grapenet_Lv1 for the Prediction of Optimum Hand-Picking Time of Nagano Purple Grapes

SSRN

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SSRN 2025年

作者： Islam, Md. Parvez Shinagawa, K. Hatou, K. Abdurrakhman, Arief Dian, K. Department of Biomechanical Systems Faculty of Agriculture Ehime University 3-5-7 Tarumi Matsuyama790-8566 Japan United Graduate School of Agriculture Ehime University 3-5-7 Tarumi Matsuyama790-8566 Japan Department of Instrumentation Engineering Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Business Mathematics Program Study School of Applied STEM Universitas Prasetiya Mulya BSD Campus BSD City Kav. Edutown I.1 Tangerang15339 Indonesia Indonesia

We investigated the optimum hand-picking time of Nagano Purple, a rare Japanese table grape variety. The color sensitivity between pure red–purple–black and pure purple–black makes it difficult for farmers to harvest these fruits at the right time while maintaining high sweetness level. We manually extracted 13 color parameters, namely, X, Y, Z, x, y, L*, a*, b*, L, a, b, C*, and h, from 355 grape skin samples using a colorimeter, which is a time-consuming and expensive process. In addition, we developed a 15-layer shallow long short-term memory (LSTM) and bidirectional long short-term memory (BLSTM) neural network, namely, GrapeNet_Lv1, with a learnable parameter of 1.39E+06. This network is compact and requires low computational cost for training. Furthermore, it has the ability to prevent the problems of vanishing gradient and overfitting. The combination of all the 13 aforementioned parameters yielded promising results in predicting color and sweetness levels, with test accuracies of 0.83 and 0.76, respectively. In the prediction of color level, our proposed network GrapeNet_Lv1 achieves a high validation accuracy of 0.92, F1 score of 0.88, recall of 0.87, low validation loss of 0.39, and RMSE of 0.31. However, in the prediction of sweetness level, GrapeNet_Lv1 achieves a relatively low validation accuracy of 0.79, F1 score of 0.70, recall of 0.69, high validation loss of 0.72, and RMSE of 0.33. Further research can help improve the accuracy. GrapeNet_Lv1 can be applied to other crops, where, predicting the whose optimal hand-picking time is difficult to predict based on color recognition. Keywords: Neural network;LSTM-BLSTM;Nagano Purple, color, sweetness © 2025, The Authors. All rights reserved.

关键词： Long short-term memory

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License plate Recognition for moving vehicles case : at night and under rain condition 2

License plate Recognition for moving vehicles case : at nigh...

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2nd International Conference on informatics and Computing, ICIC 2017

作者： Ismail, Muh Department of Informatics Electrical Engineering Study Program Hasanuddin University Makassar Indonesia

ISBN: (纸本)9781538629840

Research for the detection and identification of existing license plate focus on static image under different weather conditions. This research aims to detect and recognize the license plate on moving vehicle at night and under rain condition. The method used to detect license plate is Cascade Classifier with Local Binary Pattern (LBP) as a descriptor, top-hat transform for image enhancement, and recognizing character of license plate detected is using Optical Character Recognition (OCR) and Template Matching (TM). The testing process using 9 videos data with duration 30 seconds. The testing result that accuracy rates LBP OCR reach 77.42% increase 32.26% than without top-hat transform and LBP TM 64.52% increase 45.16% than without top-hat transform. © 2017 IEEE.

关键词： Local binary pattern

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Innovation and utilization of coffee skin waste (Coffea robusta L) as a superior compost for the growth of pepper cuttings (Piper nigrum L.)

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AIP Conference Proceedings 2025年第1期3186卷

作者： Awalul Fatiqin Dewi Lestari R. A. Hoetary Tirta Amalia Riri Novita Sunarti Ike Apriani Anita Restu Puji Raharjeng Rr Mini Sari Wulan Thathit Suprayogi Yahya Febrianto Sudarman Rahman Shesanthi Citrariana Rokiy Alfanaar Mu’afa Purwa Arsana Yithro Serang Wansadaj Jaroenram Biology Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Biology Study Program Faculty of Science and Technology Universitas Islam Negeri Raden Fatah Palembang Palembang South Sumatra Indonesia Education Biology Study Program Faculty of Teacher Training and Education Universitas Islam Negeri Raden Fatah Palembang Palembang South Sumatra Indonesia Physics Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Pharmacy Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Chemistry Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Mathematics Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Department of applied chemistry Chaoyang University of Technology Taichung City Taiwan National center for genetic engineering and biotechnology (BIOTEC) Pathum Thani Thailand

This study aimed to determine the potential of coffee skin waste (Coffea robusta L) as a superior compost innovation for the growth of pepper-cutting seedlings (Piper nigrum L). This study used an experimental method with a completely randomized design using five treatments and five repeats, variations in the dose of coffee skin waste used P0 (control), P1 (90 g), P2 (135 g), P3 (180 g), and P4 (225 g). The shoot height, number of leaves, number of roots, and length of roots were measured. The results showed that coffee skin waste compost significantly affected shoot height, especially at P1 (90 g), resulting in an average growth height of 7.24 cm. In addition, the highest number of leaves was found at a concentration of P1 (90 g), with an average of two leaves. Although there was no significant effect on the number of roots, the concentration of P1 (90 g) yielded the best results, with an average of 33 root blades, and the best root length was obtained at the concentration of P1 (90 g), with an average of 44.6 cm. Therefore, coffee skin waste significantly influenced shoot height and the number of leaves on pepper-cut seedlings (Piper nigrum L.) in the P1 treatment (90 g).

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Empirical mode decomposition of near-infrared spectroscopy signals for predicting oil content in palm fruits

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Information Processing in Agriculture 2023年第3期10卷 289-300页

作者： Inna Novianty Ringga Gilang Baskoro Muhammad Iqbal Nurulhaq Muhammad Achirul Nanda Computer Engineering Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Informatics Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Technology and Plantation Production Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Department of Agricultural and Biosystem Engineering Faculty of Agro-Industrial TechnologyUniversitas PadjadjaranJatinangor 45363Indonesia

Oil content estimation in palm fruits is a precious property that significantly impacts oil palm production,starting from the upstream and *** content can be used to monitor the progress of the oil palm fresh fruit bunch(FFB)and be applied to identify product *** on the near-infrared(NIR)signals,this study proposes an empirical mode decomposition(EMD)technique to decompose signals and predict the oil content of palm ***,350 palm fruits with Tenera varieties(Elaeis guineensis ***),at various ages of maturity,were harvested from the Cikabayan Oil Palm Plantation(IPB University,Indonesia).Second,each sample was sent directly to the laboratory for NIR signal measurements and oil content ***,the EMD analysis and arti-ficial neural network(ANN)were employed to correlate the NIR signals and oil ***,a robust EMD-ANN model is generated by optimizing the lowest possible *** on performance evaluation,the proposed technique can predict oil content with a coefficient of determination(R2)of 0.933±0.015 and a root mean squared error(RMSE)of 1.446±*** results demonstrate that the model has a good predictive capacity and has the potential to predict the oil content of palm fruits directly,without neither solvents nor reagents,which makes it environmentally ***,the proposed technique has a promising potential to be applied in the oil palm *** like this will lead to the effective and efficient management of oil palm production.

关键词： Artificial neural network Empirical mode decomposition Oil palm Oil content prediction

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