Data Science Commons^™

Gene Expression Changes In Human Cerebral Arteries Following Hemoglobin Exposure: Implications For Vascular Responses In Sah, Devin W Mcbride, Kumar T Peeyush, Spiros L Blackburn

Faculty, Staff and Student Publications

Subarachnoid hemorrhage (SAH), characterized by the presence of hemoglobin (Hb) in the subarachnoid space, significantly impacts cerebral vessels, leading to various pathological outcomes. The toxicity of cell-free Hb released from erythrocytes and its metabolites after SAH causes vasoconstriction and neuronal damage, and correlates with delayed ischemic neurological deficits (DIND). While animal models have provided substantial and invaluable data in the research of aneurysmal SAH, the specific effects of subarachnoid blood on cerebral arteries remain greatly understudied. Here, we describe the changes in the genetic profile of human cerebral arteries exposed to free Hb for 48 h. We performed an ex …

A Bayesian Deep Segmentation Framework For Glioblastoma Tumor Segmentation Using Follow-Up Mris, Juan C Rodriguez Quintero, Octavio Arevalo, Kangyi Zhao, Jay-Jiguang Zhu, Roy F Riascos, Mahboubeh Madadi, Xiaoqian Jiang, Shayan Shams

Faculty, Staff and Student Publications

Background: Glioblastoma (GBM) is the most common malignant brain tumor with an abysmal prognosis. Since complete tumor cell removal is impossible due to the infiltrative nature of GBM, accurate measurement is paramount for GBM assessment. Preoperative magnetic resonance images (MRIs) are crucial for initial diagnosis and surgical planning, while follow-up MRIs are vital for evaluating treatment response. The structural changes in the brain caused by surgical and therapeutic measures create significant differences between preoperative and follow-up MRIs. In clinical research, advanced deep learning models trained on preoperative MRIs are often applied to assess follow-up scans, but their effectiveness in this …

Integrating Data Management Plans Into The Unified Architecture Framework Standards Views, Cansu Yalim, Holly A. H. Handley

Engineering Management & Systems Engineering Faculty Publications

System Architecting translates an operational concept into a model of the system to be realized. There is a need for a Data Management Plan (DMP) to be included in the overall system engineering process with the advent of Digital Engineering. Data longevity, accessibility, and integrity can all be improved throughout the system's lifecycle by a well-defined DMP. System engineers use an architecture framework to arrange the system data into several sets of viewpoints. Incorporating a DMP at this point specifies the procedures for gathering, storing, retrieving, and maintaining data to ensure that all interested parties have access to current, correct …

Sampling Balanced High-Quality Data To Train An Automatic Mesh Generator, Jie Pan, Jingwei Huang, Gengdong Cheng, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

In real-world scenarios, high-quality data are often scarce and imbalanced, yet it is essential for the optimal performance of data-driven algorithmic models. Data synthesis methods are commonly used to address this issue; however, they typically rely heavily on the original dataset, which limits their ability to significantly improve performance. This article presents a quality function-based method for directly generating high-quality data and applies it to a mesh generation algorithm to demonstrate its efficiency and effectiveness. The proposed approach samples input-output pairs of the algorithm based on their feature spaces, selects high-quality samples using a defined quality function that evaluates the …

In Search Of The Rational Voter In The 2020 Presidential Election: Understanding The Impact Of Voter Costs And Benefits On Turnout, Norou Diawara, Tiffany Henley, Samuel L. Brown, Md Iqbal Hossain

Mathematics & Statistics Faculty Publications

The ability to vote is one of the most valuable rights and privileges afforded by the Constitution of the United States to its citizens. For many, voting is not just a civic duty; it is also a choice. Voting is crucial to our democracy, and any changes to it may affect the efficiency of the democratic process. The bigger question is whether voters behave rationally by engaging in a cost-benefit calculus in deciding whether or not to vote. Using data science, this paper will examine the probability of voting and investigate its impact via cost and benefit among other variables …

Deepsecure: A Novel Deep Learning Model For Effective Detection Of Attacks On Big Data In Internet Of Urban Things, Laiba Sabir, Nadeem Javaid, Mariam Akbar, Nabil Alrajeh, Safdar Hussain Bouk, Abdulaziz Aldegheishem

School of Cybersecurity Faculty Publications

The Internet of Urban Things (IoUTs) regularly generates large amounts of data, making it a focus of cyberthreats such as denial-of-service attacks and malware bot networks. Traditional intrusion detection systems struggle to detect intricate attack patterns, handle class imbalance, capture temporal dependencies, and exhibit transparency. To address these limitations, we introduce a novel deep machine learning model, DeepSecure, a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction and Deep Neural Networks for attack classification. DBN is used for feature selection through unsupervised learning to extract hierarchical representations in the IoUTs network data. We assess the random …

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

Energy-Based Deep Incomplete Multi-View Clustering, Ziyu Wang, Yiming Du, Rui Ning, Lusi Li

Computer Science Faculty Publications

Incomplete multi-view clustering (IMVC) deals with real-world scenarios where certain views are partially missing, posing significant challenges to effective clustering. Most existing IMVC approaches face a trade-off: imputation-free methods suffer from information bias and imbalance, while full-imputation methods risk introducing and propagating noise. To overcome these limitations, we propose Energy-Based Deep Incomplete Multi-View Clustering (Energy-DIMC), a novel selective-imputation framework that leverages energy-based models (EBMs) to guide reliable imputations and robust clustering. EBMs assess data compatibility by assigning lower energy to more coherent structures, effectively modeling complex inter-view and inter-sample dependencies. Inspired by EBMs, Energy-DIMC integrates four key components: 1) a …

Privshap: A Finer-Granularity Network Linearization Method For Private Inference, Xiangrui Xu, Zhenzhen Wang, Rui Ning, Chunsheng Xiu, Hongyi Wu

Computer Science Faculty Publications

Private inference applies cryptographic techniques like homomorphic encryption, garble circuit and secret sharing to keep both sides privacy in a client-server setting during inference. It is often hindered by the high communication overheads, especially at non-linear activation layers such as ReLU. Hence ReLU pruning has been widely recognized as an efficient way to accelerate private inference. Existing approaches to ReLU pruning typically rely on coarse hypothesis, which assume an inverse correlation between the importance of ReLU and linear layers or shallow activation layers have less importance for universal models, to assign the budgets according to the layer while preserving the …

Icu-Length Of Stay Prediction On Electronic Health Records Using Graph Neural Networks And Homogeneous Similarity Graphs, Ahmad F. Al Musawi, Pratip Rana, Sibtanu Raha, Joshua Braunstein, William C. Sleeman Iv, Rishabh Kapoor, Preetam Ghosh

Computer Science Faculty Publications

Predicting the length of stay (LoS) is important for hospital administration, as it helps allocate proper resources, such as bed management and hospital staffing. Patients' Electronic Health Records (EHRs) contain highly relevant data for LoS prediction; however, their integration and effective use in predictive modeling for accurately estimating LoS remain challenging. To address this, we propose a homogeneous Graph Neural Network (GNN)-based framework for predicting LoS. This method employs a comprehensive data fusion strategy based on the hospital Visit-based Similarity Graph (VSG), which integrates diverse multi-modal clinical features into a coherent, homogeneous graph representation. Next, this VSG is fed into …

Applying The Matching Law To Major League Baseball (Mlb), Christopher Watkins, Vincent Berardi

Psychology Faculty Articles and Research

The application of the generalized matching equation (GME) has been detailed in a variety of sports, including football, basketball, and others. However, only a limited number of studies have focused on Major League Baseball (MLB), and they typically have examined ≤ 5 players and/or focused on a single behavior. This paper increases the generalizability of such work by using newly available, state-of-the-art data from thousands of players to explore the GME in several scenarios within three aspects of a baseball game - defense, pitching and batting. We found that the GME accurately summarized response allocation in most scenarios, with r …

Advancing Multivariate Time Series Similarity Assessment: An Integrated Computational Approach, Franck B.N. Tonle, Henri E.Z. Tonnang, Milliam M.Z. Ndadji, Maurice Tchoupe Tchendji, Armand Nzeukou, Kennedy Senagi, Saliou Niassy

All Peer-Reviewed Publications

Data mining, particularly multivariate time series data analysis, is crucial in extracting insights from complex systems and supporting informed decision-making across diverse domains. However, assessing the similarity of multivariate time series data presents several challenges, including dealing with large datasets, addressing temporal misalignments, and necessitating efficient and comprehensive analytical frameworks. A novel integrated computational approach, Multivariate Time series Alignment and Similarity Assessment (MTASA) is proposed to address these challenges. MTASA is built upon a hybrid methodology designed to optimise time series alignment, complemented by a multiprocessing engine that enhances the utilisation of computational resources. This integrated approach comprises four key …

Evaluating Multimodal Ai Systems: A Comparative Analysis Of Large Languagel Model-Based Models For Text, Image, And Video Generation, Azeezat O. Akinola

College of Graduate Studies: Theses & Dissertations

In the era of rapid technological advancement, efficient content generation, application development, and data management are crucial for meeting the demands of dynamic digital environments. This thesis uses state-of-the-art models to explore three core areas: AI-driven video content creation, text-to-image-to-text consistency, and automatic text summarization. The first study investigates the potential of AI-powered text-to-video generation to democratize video production and enhance storytelling. By comparing the performance of three models—ModelScope, Text2Video (Zero), and Motion Consistency—this study assessed the quality of generated videos using CLIP scores. It evaluated statistical significance through t-tests and homogeneity tests. Results indicate that ModelScope outperformed the others, …

Data Injustice In Global Justice, Asaf Lubin, Cherry Tang

Articles by Maurer Faculty

In May 2020, the United Nations Secretary-General unveiled a sweeping “Data Strategy for Action by Everyone, Everywhere,” seeking to unlock the UN’s “full data potential.” The International Criminal Court’s Office of the Prosecutor followed suit, declaring in 2023 its intent to acquire advanced cyber forensic tools so as to hold the “widest range of digital evidence globally.” Across international institutions, data-driven governance has become the norm, with humanitarian agencies and tribunals transforming into “data hubs and information clearinghouses.” This Article critiques the unfettered datafication of global justice by international courts and organizations. These entities have aggressively expanded their data-driven operations …

Biotime 2.0: Expanding And Improving A Database Of Biodiversity Time Series, Maria Dornelas, Laura H. Antão, Amanda E. Bates, Viviana Brambilla, Jonathan M. Chase, Cher F. Y. Chow, Ada Fontrodona-Eslava, Anne E. Magurran, Inês S. Martins, Faye Moyes, Alban Sagouis, Samuel Adu-Acheampong, Daniel Acquah-Lamptey, Dušan Adam, Penelope A. Ajani, Aitor Albaina, Pablo Almaraz, Jeongseop An, Roger Sigismund Anderson, Madelaine Jean Robertson Anderson, Alexsander Z. Antunes, Ivan Arismendi, Linda Armbrecht, Pedro Aros-Mardones, Sreejith Kalpuzha Ashtamoorthy, Narayanan Ayyappan, Gal Badihi, Joseph J. Bailey, Andrew H. Baird, Mark Edward Baird, Sreekumar Vadakkethil Balakrishnan, José António L. Barão-Nóbrega, Adi Barash, Miguel Barbosa, Jos Barlow, Claus Bässler, Matthieu Beaumont, Natalie Beenaerts, Tiago Octavio Begot, Wallace Beiroz, Ricardo Beldade, David M. Bell, Alecia Bellgrove, Jonathan Belmaker, Lisandro Benedetti-Cecchi, Cassandra E. Benkwitt, Pamela Medina-Van Berkum, Brandon T. Bestelmeyer, Matthew C. Betts, Maxwell Kelvin Billah, Anne D. Bjorkman, Magdalena Błażewicz, Christopher P. Bloch, Shane A. Blowes, Antonio Bode, Juliano A. Bogoni, Thomas Bolger, Timothy C. Bonebrake, Erik Bonsdorff, Roberta Bottarin, Luke N. Brokensha, Rob W. Brooker, Andrew J. Brooks, Helge Bruelheide, Thiago Almeida Bueno, Claire Laguionie, Mariana Lopes Campagnoli, James Cant, Erica Pellegrini Caramaschi, Alexandre Caron, Tadhg Carroll, Tancredi Caruso, Juan Carvajal-Quintero, Giuseppe Castaldelli, Edward Castañeda-Moya, Pedro V. Castilho, Sonia Zanini Cechin, Shahar Chaikin, Uchangi Manjunatha Chandrashekara, Tory J. Chase, Chaolun Allen Chen, Jorge José Cherem, Sei-Woong Choi, Erica M. Christensen, Alexander V. Christianini, Jackson Wing Four Chu, Peter Coad, Carl Van Colen, Lise Comte, Elisabeth J. Cooper, J. Hans C. Cornelissen, Eddy Cosson, Unai Cotano, Luc Crevecoeur, Shannan Kyle Crow, Graeme S. Cumming, Vanessa S. Daga, Gabriella Damasceno, Gergana N. Daskalova, Claire H. Davies, Robert A. Davis, Frank P. Day, Sussy De-La-Zerda, Amy Elizabeth Deacon, Indradatta De Castro-Arrazola, Steven Degraer, Kharran Deonarinesingh, Juan C. Diaz-Ricaurte, Christopher R. Dickman, Tara Dirilgen, Ciaran John Dolan, J. Emmett Duffy, Timothy E. Dunn, Giselda Durigan, Ciara Dwyer, Steven Earl, Dor Edelist, Graham John Edgar, Sally Edmonson, Ashley K. Elgin, Kari Elsa Ellingsen, Sarah C. Elmendorf, Ruth S. Eriksen, S. K. Morgan Ernest, Ruben Escribano, Paula Cabral Eterovick, Brian S. Evans, Jason D. Everett, Vesela Evtimova, Dan A. Exton, Andrew J. Fairbairn, Filipe Moreli Fantacini, Fabiano Turini Farah, Fábio Zanella Farneda, Mario E. Favila, Philippe Fernandez-Fournier, Braulio Fernández-Zapata, Diogo F. Ferreira, Carola Ferronato, Christopher R. Du Feu, Alessandra Fidelis, David A. Fifield, Vilmar Picinatto Filho, Walter Mesquita Filho, Robert N. L. Fitt, Carlos A. H. Flechtmann, William R. Fraser, Donna L. Fraser, Lídia Freixas, John Fryxell, Garrett J. Fundakowski, Scott Stanley Gabara, Elise Gallois, Mariana Garcia Criado, Emili García-Berthou, Joaquim Garrabou, Andrew R. Gates, Roberto Cazzola Gatti, Anna Gavioli, Tal Gavriel, Benoit Gendreau-Berthiaume, Xingli Giam, Carina Gjerdrum, Michael Glemnitz, Jasmin Annica Godbold, Daniel Gómez-Gras, Rodrigo Barbosa Gonçalves, Andy Goold, Richard R. Gordon, Menachem Goren, Fernando Vilas Boas Goulart, William G. Gould, Meagan M. Graboski, Nicholas A. J. Graham, Maurício Eduardo Graipel, Laura J. Grange, Aaron C. Greenville, Gary D. Grossman, Valeria A. Guinder, Peter Haase, Gary N. Haskins, Kris Havstad, Luise Hermanutz, Michael Julian Hames Hickford, Pamela Hidalgo, Pedro Higuchi, Andrew S. Hoey, Gert Van Hoey, Annika Hofgaard, Kristen T. Holeck, Robert D. Hollister, Richard T. Holmes, Mia Odell Hoogenboom, Joaquín Hortal, Tammy Horton, Chih-Hao Hsieh, Christine L. Huffard, Ida-Maria Huikkonen, Allen H. Hurlbert, Julian Hynes, Pascal Irz, Natalia Macedo Ivanauskas, Akemi Iwayama, Darren K. James, Ute Jandt, Anna M. Jażdżewska, Merlijn Jocque, Sophie T. Johnston, Samuel E. I. Jones, Faith A. M. Jones, Julia A. Jones, Edite Jucevica, Ugis Kagainis, Maiko Kagami, Jungwon Kang, Xuejia Ke, Erin Colleen Keeley, Rebecca Kinnear, Kari Klanderud, Uwe Klinck, Roel Van Klink, Stefan Klotz, Carolien Kockaert, Halvor Knutsen, Matti Koivula, Alessandra Kortz, Peter Kriegel, Chao-Yang Kuo, David J. Kushner, Rosina Kyerematen, Raphaël Lagarde, Lesley T. Lancaster, Ori Frid Landau, Wouter Van Landuyt, Eric R. Larson, Mai Lazarus, Cheol Min Lee, Jonathan S. Lefcheck, Jonas J. Lembrechts, Renato A. Ferreira De Lima, Romullo Guimarães Lima, Nathália G. S. Lima, Cristina Linares, Sandra C. Lindstrom, Francisco Lloret, John David Lloyd, Cleonice Maria Cardoso Lobato, David M. Lodge, Peter Richard Long, Celeste López-Abbate, Adrià López-Baucells, Julio Louzada, Maite Louzao, Antonella Lugliè, Micheli Ribeiro Luiz, S. Ellen Macdonald, Joshua S. Madin, André Lincoln Barroso Magalhães, Rajindra Mahabir, David Maphisa, Thomas Edward Martin, Marcio Martins, Patrick T. Martone, Silvia Matesanz, Shin-Ichiro S. Matsuzaki, Thomas J. Matthews, Iain Mccombe Matthews, Connie J. Maxwell, Kent P. Mcfarland, Brian J. Mcgill, Diane Marie Mcknight, Michael J. Mcwilliam, Jason Meador, Henning Meesenburg, Kristin Meier, Viesturs Melecis, Peter L. Meserve, Christoph F. J. Meyer, Anders Michelsen, Natali Olivia Roman Miiller, Marco Milardi, Nataliya Milchakova, Robert J. Miller, Jonathan Millett, Tom Moens, Luciano F. A. Montag, Jon Moore, Jörg Müller, Akhil Murali, Shauna Ann Murray, Isla H. Myers-Smith, Randall W. Myster, Masahiro Nakamura, Sasi Nayar, Francis Neat, James A. Nelson, Michael Paul Nelson, Boris P. Nikolov, Rym Nouioua, Collins Ayine Nsor, Michael O' Connor, Edward Adzesiwor Obodai, Amy Marie Offland, Romà Ogaya, Hisako Ogura, Thomas A. Okey, Julian D. Olden, Luiz Gustavo Rodrigues Oliveira-Santos, Jeffrey C. Oliver, Esben Moland Olsen, Vladimir G. Onipchenko, Daniel Oro, Dais Ozolins, Krzysztof Pabis, Bachisio Mario Padedda, Facundo X. Palacio, Alain Paquette, Sinta Trilestari Pardede, David M. Patersib, Sarah Pausina, Raphaël Pélissier, Steven C. Pennings, Josep Penuelas, Felipe Walter Pereira, Nivaldo Peroni, Sergio Picó, Francesca Pilotto, Hudson Tercio Pinheiro, Oscar Pizarro, Roberto Pizzolotto, Francesco Pomati, Paulo Santos Pompeu, Dominique Ponton, Eric Post, Nicolas Poulet, Juha Pöyry, Steven J. Presley, Herbert H. T. Prins, Pieter Provoost, Kathleen L. Prudic, Vignesh Punjayil, Petr Pyšek, Pascal Querner, Juan Pablo Quimbayo, Indar W. Ramnarine, Daniel C. Reed, Peter Bernard Reich, Suzanne M. Remillard, Cerren Richards, Anthony James Richardson, Itai Van Rijin, Victor H. Rivera-Monroy, Christian Rixen, Kevin Peter Robinson, Ricardo Rocha, Ricardo R. Rodrigues, Cassy Rodrigues, Bjørn De Roos, Denise De C. De Rossa-Feres, Loreta Rosselli, Peter Charles Rothlisberg, Ana Rubio, Lars G. Rudstam, Catalina S. Ruz, Nancy B. Rybicki, Gunther Van Ryckegem, Andrew L. Rypel, Jon P. Sadler, Victor Satoru Saito, Sofia Sal, Renato Portela Salomão, Nathan J. Sanders, Flavio A. M. Santos, Tiago Gomes Dos Santos, Swapan Kumar Sarker, Sara E. Scanga, Marcus Schaub, Jochen Schmidt, Inger Kappel Schmidt, Robert L. Schooley, Alfred Schultz, Alberto Scotti, Amanda Serpell-Stevens, Filipe C. Serrano, Elizabeth H. Shadwick, Matthew Shaft, Thomas W. Sherry, Erika Mayumi Shimabukuro, Jacek Siciński, Caya Sievers, Fernando Rodrigues Da Silva, Ana Carolina Da Silva, Juliana M. Silveira, Tadeu Siqueira, Arunkumar Kavidapadinjattathil Sivadasan, Prasad Theruvil Parambil Sivan, Agnija Skuja, Amalia L. Slaughter, Jasper A. Slingsby, Joseph R. Smith, Bruno Eleres Soares, Martin Solan, Flaviana Maluf Souza, Gabriel B. G. Souza, Joshua L. Sprague, Ulrich Stachow, J. John Stadt, Christopher D. Stallings, Radoslav Hristov Stanchev, Emily H. Stanley, Brian M. Starzomski, Jose Mauro Sterza, Maarten Stevens, F. Gary Stiles, Stefan Stoll, Rick D. Stuart-Smith, Yzel Rondon Súarez, Laura Super, Sarah R. Supp, Tapio Sutela, Iain M. Suthers, Anna Suuronen, Kerrie M. Swadling, Daniel K. Szydlowski, Hisatomo Taki, Sara Jeanne Snell Taylor, Pablo A. Tedesco, Nils Teichert, Akira Terui, Gary P. Thiede, Anne Thimonier, Oliver Thomas, Peter Allan Thompson, Simon Thorn, Jeremy S. Tiemann, Luís Felipe Toledo, Anne Tolvanen, Maria Teresa Zugliani Toniato, Ignasi Torre, Marcos Adriano Tortato, Kumiko Totsu, Andrew Trant, Robert R. Twilley, Hirokazu Urabe, Pierre Valade, Nelson Valdivia, Martha Isabel Vallejo, Thomas J. Valone, Jan Vanaverbeke, Tiago Silveira Vasconcelos, Teppo Vehanen, Fábio Venturoli, Hans M. Verheye, Hendrik Jannes Wietse Vermeulen, Arne Verstraeten, Marcelo Vianna, Rui Vieira, João Paulo Santos Vieira-Alencar, Marc Vilella, Jean Ricardo Simões Vitule, Lien Van Vu, Robert B. Waide, Paige S. Warren, Joseph Paul Wayman, Sara L. Webb, Benjamin Weigel, Ellen A. R. Welti, Fritha West, Fulgor Westermann, Matthew A. Whalen, Ethan P. White, Claire E. Widdicombe, Richard Williams, Mark Williamson, Michael R. Willig, Sonja Wipf, Eric J. Woehler, Alje Woldering, Kerry D. Woods, Wu-Bing Xu, Ruthy Yahel, Zeren Yang, Kyle J. A. Zawada, Camila Zornosa-Torres, Assaf Zvuloni

Biological Sciences Faculty Publications

Motivation: Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database.

Main Types of Variables Included: The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised …

Spatial Analysis And Machine Learning Integration For Nutritional Status Mapping Using Ann And Random Forest Models, Desi Anis Anggraini, Fachrul Kurniawan, Fresy Nugroho, Meidya Koeshardianto, Mohammad Iqbal Bachtiar

Knowledge Engineering and Data Science

Nutritional problems among children under five remain a major public health challenge. This research seeks to create a spatially oriented system for evaluating and mapping nutritional status utilizing Artificial Neural Network (ANN) and Random Forest (RF) algorithms. Data obtained from the Sumenep District Health Office included age, weight, height, and gender variables. Both models were trained using a 70:30 data ratio and evaluated with accuracy, precision, recall, and F1-score metrics. The ANN model achieved an accuracy of 95.8%, while the RF model reached 97.7%. Classification results were visualized through a Geographic Information System (GIS) to illustrate spatial distribution and identify …

Time Series Forecasting With Lstm: An Extensive Content Analysis, Andri Pranolo, Xiaofeng Zhou, Yingchi Mao

Knowledge Engineering and Data Science

This paper presents a comprehensive bibliometric and content review of the trend, architecture, and application of long short-term memory (LSTM) models for time series forecasting. The study aims to provide insights into the overall statistics and distribution of papers focused on LSTM for forecasting. Additionally, the research questions address the most highly cited papers based on LSTM approaches in forecasting, the most productive journals in this field, and identifying trends, gaps, summary tasks and their performance, datasets availability, and future research directions for LSTM in forecasting. This paper is a comprehensive review of LSTM for forecasting from 2017 to 2023 …

From Data To Insight: A Machine Learning Approach In Classifying Dairy Cow Productivity Level And Identifying Important Influencing Variables, Fatkhurokhman Fauzi, Achmad Fauzan, Rhendy K P Widiyanto, Khairil Anwar Notodiputro, Bagus Sartono

Knowledge Engineering and Data Science

Identifying influential predictor variables is crucial for enhancing model interpretability in supervised classification. This study applies Permutation Variable Importance (PVI), a model-agnostic approach, to evaluate variable relevance after model fitting. Using data from the 2024 Indonesia Dairy Cow Productivity Survey, this research investigates five classification techniques: (1) Support Vector Machine (SVM), (2) Neural Network (NN), (3) k-Nearest Neighbors (kNN), (4) Naïve Bayes Classifier (NB), and (5) Logistic Regression (LR), to identify which method(s) yield the best performance based on evaluation metrics such as accuracy, sensitivity, and specificity. PVI is employed to identify the most influential predictor variables within the best-performing …

A Hybrid Soft Voting And Stacking-Based Meta-Learning Approach For Sentiment Analysis Of Bangkalan Batik, Moh. Imron Wahyudi, Lailil Muflikhah, Rizal Setya Perdana

Knowledge Engineering and Data Science

Sentiment analysis is an important field in Natural Language Processing (NLP) that focuses on processing consumer opinions to gain useful insights. The information generated from sentiment analysis can be used as a basis for business decision-making, service quality evaluation, and the formulation of more effective marketing strategies. In the local context, Bangkalan Batik, as one of Madura's distinctive cultural products, has high economic value and cultural identity. However, consumer reviews available online, for example through Google Maps, are still rarely utilized optimally by MSMEs as a source of strategic information. Therefore, this study was conducted to develop a sentiment classification …

Mapping Of Product Sales Potential Based On Brands In The East Kalimantan Region Using Hybrid Analytical Framework, Achmad F O Gaffar Mr, Mulyanto Mulyanto Mr, Arief Bw Putra Mr, Muhammad Taufiq Sumadi Mr, Emmilya Umma Aziza Gaffar

Knowledge Engineering and Data Science

In geographically dispersed markets, operational costs should be reflected in sales planning to support accurate performance evaluation. However, such considerations are often neglected in practice. This study proposes a hybrid analytical framework to map brand-based product sales potential, with and without operational cost consideration, using historical sales data from PT Karya Inti Total Anugerah (PT KITA) in East Kalimantan. The framework integrates spatial, statistical, and machine learning techniques. Principal Component Analysis (PCA) is used to reduce the dimensionality of variables related to travel distance, total sales, and units sold, where travel distance represents the primary contributor to operational costs. K-Means …