IF 2.6

Algorithms Pub Date : 2025-12-01 Epub Date: 2025-12-02 DOI: 10.3390/a18120764

Marie Amale Huynh, Aaron Kline, Saimourya Surabhi, Kaitlyn Dunlap, Onur Cezmi Mutlu, Mohammadmahdi Honarmand, Parnian Azizian, Peter Washington, Dennis P Wall

{"title":"Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder.","authors":"Marie Amale Huynh, Aaron Kline, Saimourya Surabhi, Kaitlyn Dunlap, Onur Cezmi Mutlu, Mohammadmahdi Honarmand, Parnian Azizian, Peter Washington, Dennis P Wall","doi":"10.3390/a18120764","DOIUrl":"10.3390/a18120764","url":null,"abstract":"Early detection of Autism Spectrum Disorder (ASD), a neurodevelopmental condition characterized by social communication challenges, is essential for timely intervention. Naturalistic home videos collected via mobile applications offer scalable opportunities for digital diagnostics. We leveraged GuessWhat, a mobile game designed to engage parents and children, which has generated over 3000 structured videos from 382 children. From this collection, we curated a final analytic sample of 688 feature-rich videos centered on a single dyad, enabling more consistent modeling. We developed a two-step pipeline: (1) filtering to isolate high-quality videos, and (2) feature engineering to extract interpretable behavioral signals. Unimodal LSTM-based models trained on eye gaze, head position, and facial expression achieved test AUCs of 86% (95% CI: 0.79-0.92), 78% (95% CI: 0.69-0.86), and 67% (95% CI: 0.55-0.78), respectively. Late-stage fusion of unimodal outputs significantly improved predictive performance, yielding a test AUC of 90% (95% CI: 0.84-0.95). Our findings demonstrate the complementary value of distinct behavioral channels and support the feasibility of using mobile-captured videos for detecting clinically relevant signals. While further work is needed to improve generalizability and inclusivity, this study highlights the promise of real-time, scalable autism phenotyping for early interventions.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"18 12","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13313078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148350361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodal LLM vs. Human-Measured Features for AI Predictions of Autism in Home Videos. 多模态LLM与人工测量特征对家庭视频中自闭症的人工智能预测。

IF 2.6

Algorithms Pub Date : 2025-11-01 Epub Date: 2025-10-29 DOI: 10.3390/a18110687

Parnian Azizian, Mohammadmahdi Honarmand, Aditi Jaiswal, Aaron Kline, Kaitlyn Dunlap, Peter Washington, Dennis P Wall

{"title":"Multimodal LLM vs. Human-Measured Features for AI Predictions of Autism in Home Videos.","authors":"Parnian Azizian, Mohammadmahdi Honarmand, Aditi Jaiswal, Aaron Kline, Kaitlyn Dunlap, Peter Washington, Dennis P Wall","doi":"10.3390/a18110687","DOIUrl":"10.3390/a18110687","url":null,"abstract":"Autism diagnosis remains a critical healthcare challenge, with current assessments contributing to average diagnostic ages of 5 and extending to 8 in underserved populations. With the FDA approval of CanvasDx in 2021, the paradigm of human-in-the-loop AI diagnostics entered the pediatric market as the first medical device for clinically precise autism diagnosis at scale, while fully automated deep learning approaches have remained underdeveloped. However, the importance of early autism detection, ideally before 3 years of age, underscores the value of developing even more automated AI approaches, due to their potentials for scale, reach, and privacy. We present the first systematic evaluation of multimodal LLMs as direct replacements for human annotation in AI-based autism detection. Evaluating seven Gemini model variants (1.5-2.5 series) on 50 YouTube videos shows clear generational progression: version 1.5 models achieve 72-80% accuracy, version 2.0 models reach 80%, and version 2.5 models attain 85-90%, with the best model (2.5 Pro) achieving 89.6% classification accuracy using validated autism detection AI models (LR5)-comparable to the 88% clinical baseline and approaching crowdworker performance of 92-98%. The 24% improvement across two generations suggests the gap is closing. LLMs demonstrate high within-model consistency versus moderate human agreement, with distinct assessment strategies: LLMs focus on language/behavioral markers, crowdworkers prioritize social-emotional engagement, clinicians balance both. While LLMs have yet to match the highest-performing subset of human annotators in their ability to extract behavioral features that are useful for human-in-the-loop AI diagnosis, their rapid improvement and advantages in consistency, scalability, cost, and privacy position them as potentially viable alternatives for aiding diagnostic processes in the future.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"18 11","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13313062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148350345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inferring the Timing of Antiretroviral Therapy by Zero-Inflated Random Change Point Models Using Longitudinal Data Subject to Left-Censoring. 利用纵向数据左截的零膨胀随机变点模型推断抗逆转录病毒治疗的时机。

IF 2.6

Algorithms Pub Date : 2025-06-01 Epub Date: 2025-06-05 DOI: 10.3390/a18060346

Hongbin Zhang, McKaylee Robertson, Sarah L Braunstein, David B Hanna, Uriel R Felsen, Levi Waldron, Denis Nash

引用次数: 0

Synthesizing Explainability Across Multiple ML Models for Structured Data. 结构化数据跨多个ML模型的可解释性综合。

IF 2.6

Algorithms Pub Date : 2025-06-01 Epub Date: 2025-06-18 DOI: 10.3390/a18060368

Emir Veledar, Lili Zhou, Omar Veledar, Hannah Gardener, Carolina M Gutierrez, Jose G Romano, Tatjana Rundek

{"title":"Synthesizing Explainability Across Multiple ML Models for Structured Data.","authors":"Emir Veledar, Lili Zhou, Omar Veledar, Hannah Gardener, Carolina M Gutierrez, Jose G Romano, Tatjana Rundek","doi":"10.3390/a18060368","DOIUrl":"10.3390/a18060368","url":null,"abstract":"Explainable Machine Learning (XML) in high-stakes domains demands reproducible methods to aggregate feature importance across multiple models applied to the same structured dataset. We propose the Weighted Importance Score and Frequency Count (WISFC) framework, which combines importance magnitude and consistency by aggregating ranked outputs from diverse explainers. WISFC assigns a weighted score to each feature based on its rank and frequency across model-explainer pairs, providing a robust ensemble feature-importance ranking. Unlike simple consensus voting or ranking heuristics that are insufficient for capturing complex relationships among different explainer outputs, WISFC offers a more principled approach to reconciling and aggregating this information. By aggregating many \"weak signals\" from brute-force modeling runs, WISFC can surface a stronger consensus on which variables matter most. The framework is designed to be reproducible and generalizable, capable of taking important outputs from any set of machine-learning models and producing an aggregated ranking highlighting consistently important features. This approach acknowledges that any single model is a simplification of complex, multidimensional phenomena; using multiple diverse models, each optimized from a different perspective, WISFC systematically captures different facets of the problem space to create a more structured and comprehensive view. As a consequence, this study offers a useful strategy for researchers and practitioners who seek innovative ways of exploring complex systems, not by discovering entirely new variables but by introducing a novel mindset for systematically combining multiple modeling perspectives.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"18 6","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12885564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146155614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Finding Multiple Optimal Solutions to an Integer Linear Program by Random Perturbations of Its Objective Function. 用目标函数的随机扰动求整数线性规划的多个最优解。

IF 1.8

Algorithms Pub Date : 2025-03-01 Epub Date: 2025-03-04 DOI: 10.3390/a18030140

Noah Schulhof, Pattara Sukprasert, Eytan Ruppin, Samir Khuller, Alejandro A Schäffer

{"title":"Finding Multiple Optimal Solutions to an Integer Linear Program by Random Perturbations of Its Objective Function.","authors":"Noah Schulhof, Pattara Sukprasert, Eytan Ruppin, Samir Khuller, Alejandro A Schäffer","doi":"10.3390/a18030140","DOIUrl":"10.3390/a18030140","url":null,"abstract":"Integer linear programs (ILPs) and mixed integer programs (MIPs) often have multiple distinct optimal solutions, yet the widely used Gurobi optimization solver returns certain solutions at disproportionately high frequencies. This behavior is disadvantageous, as, in fields such as biomedicine, the identification and analysis of distinct optima yields valuable domain-specific insights that inform future research directions. In the present work, we introduce MORSE (Multiple Optima via Random Sampling and careful choice of the parameter Epsilon), a randomized, parallelizable algorithm to efficiently generate multiple optima for ILPs. MORSE maps multiplicative perturbations to the coefficients in an instance's objective function, generating a modified instance that retains an optimum of the original problem. We formalize and prove the above claim in some practical conditions. Furthermore, we prove that for 0/1 selection problems, MORSE finds each distinct optimum with equal probability. We evaluate MORSE using two measures; the number of distinct optima found in <math><mi>r</mi></math> independent runs, and the diversity of the list (with repetitions) of solutions by average pairwise Hamming distance and Shannon entropy. Using these metrics, we provide empirical results demonstrating that MORSE outperforms the Gurobi method and unweighted variations of the MORSE method on a set of 20 Mixed Integer Programming Library (MIPLIB) instances and on a combinatorial optimization problem in cancer genomics.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"18 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143794351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anomaly Detection in High-Dimensional Time Series Data with Scaled Bregman Divergence. 基于比例Bregman散度的高维时间序列数据异常检测。

IF 2.6

Algorithms Pub Date : 2025-02-01 Epub Date: 2025-01-24 DOI: 10.3390/a18020062

Yunge Wang, Lingling Zhang, Tong Si, Graham Bishop, Haijun Gong

{"title":"Anomaly Detection in High-Dimensional Time Series Data with Scaled Bregman Divergence.","authors":"Yunge Wang, Lingling Zhang, Tong Si, Graham Bishop, Haijun Gong","doi":"10.3390/a18020062","DOIUrl":"10.3390/a18020062","url":null,"abstract":"The purpose of anomaly detection is to identify special data points or patterns that significantly deviate from the expected or typical behavior of the majority of the data, and it has a wide range of applications across various domains. Most existing statistical and machine learning-based anomaly detection algorithms face challenges when applied to high-dimensional data. For instance, the unconstrained least-squares importance fitting (uLSIF) method, a state-of-the-art anomaly detection approach, encounters the unboundedness problem under certain conditions. In this study, we propose a scaled Bregman divergence-based anomaly detection algorithm using both least absolute deviation and least-squares loss for parameter learning. This new algorithm effectively addresses the unboundedness problem, making it particularly suitable for high-dimensional data. The proposed technique was evaluated on both synthetic and real-world high-dimensional time series datasets, demonstrating its effectiveness in detecting anomalies. Its performance was also compared to other density ratio estimation-based anomaly detection methods.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"18 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11790285/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries 地图导航：空间查询中的欧氏距离与路网距离

IF 2.3

Algorithms Pub Date : 2024-01-10 DOI: 10.3390/a17010029

Pornrawee Tatit, Kiki Adhinugraha, David Taniar

{"title":"Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries","authors":"Pornrawee Tatit, Kiki Adhinugraha, David Taniar","doi":"10.3390/a17010029","DOIUrl":"https://doi.org/10.3390/a17010029","url":null,"abstract":"Using spatial data in mobile applications has grown significantly, thereby empowering users to explore locations, navigate unfamiliar areas, find transportation routes, employ geomarketing strategies, and model environmental factors. Spatial databases are pivotal in efficiently storing, retrieving, and manipulating spatial data to fulfill users’ needs. Two fundamental spatial query types, k-nearest neighbors (kNN) and range search, enable users to access specific points of interest (POIs) based on their location, which are measured by actual road distance. However, retrieving the nearest POIs using actual road distance can be computationally intensive due to the need to find the shortest distance. Using straight-line measurements could expedite the process but might compromise accuracy. Consequently, this study aims to evaluate the accuracy of the Euclidean distance method in POIs retrieval by comparing it with the road network distance method. The primary focus is determining whether the trade-off between computational time and accuracy is justified, thus employing the Open Source Routing Machine (OSRM) for distance extraction. The assessment encompasses diverse scenarios and analyses factors influencing the accuracy of the Euclidean distance method. The methodology employs a quantitative approach, thereby categorizing query points based on density and analyzing them using kNN and range query methods. Accuracy in the Euclidean distance method is evaluated against the road network distance method. The results demonstrate peak accuracy for kNN queries at k=1, thus exceeding 85% across classes but declining as k increases. Range queries show varied accuracy based on POI density, with higher-density classes exhibiting earlier accuracy increases. Notably, datasets with fewer POIs exhibit unexpectedly higher accuracy, thereby providing valuable insights into spatial query processing.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"81 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139440524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model Checking 基于排序点识别聚类结构的规范挖掘聚类算法和模型检查

IF 2.3

Algorithms Pub Date : 2024-01-10 DOI: 10.3390/a17010028

Y. Fan, Meng Wang

{"title":"Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model Checking","authors":"Y. Fan, Meng Wang","doi":"10.3390/a17010028","DOIUrl":"https://doi.org/10.3390/a17010028","url":null,"abstract":"Software specifications are of great importance to improve the quality of software. To automatically mine specifications from software systems, some specification mining approaches based on finite-state automatons have been proposed. However, these approaches are inaccurate when dealing with large-scale systems. In order to improve the accuracy of mined specifications, we propose a specification mining approach based on the ordering points to identify the clustering structure clustering algorithm and model checking. In the approach, the neural network model is first used to produce the feature values of states in the traces of the program. Then, according to the feature values, finite-state automatons are generated based on the ordering points to identify the clustering structure clustering algorithm. Further, the finite-state automaton with the highest F-measure is selected. To improve the quality of the finite-state automatons, we refine it based on model checking. The proposed approach was implemented in a tool named MCLSM and experiments, including 13 target classes, were conducted to evaluate its effectiveness. The experimental results show that the average F-measure of finite-state automatons generated by our method reaches 92.19%, which is higher than most related tools.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"3 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personalized Advertising in E-Commerce: Using Clickstream Data to Target High-Value Customers 电子商务中的个性化广告：利用点击流数据锁定高价值客户

IF 2.3

Algorithms Pub Date : 2024-01-10 DOI: 10.3390/a17010027

V. Sakalauskas, D. Kriksciuniene

{"title":"Personalized Advertising in E-Commerce: Using Clickstream Data to Target High-Value Customers","authors":"V. Sakalauskas, D. Kriksciuniene","doi":"10.3390/a17010027","DOIUrl":"https://doi.org/10.3390/a17010027","url":null,"abstract":"The growing popularity of e-commerce has prompted researchers to take a greater interest in deeper understanding online shopping behavior, consumer interest patterns, and the effectiveness of advertising campaigns. This paper presents a fresh approach for targeting high-value e-shop clients by utilizing clickstream data. We propose the new algorithm to measure customer engagement and recognizing high-value customers. Clickstream data is employed in the algorithm to compute a Customer Merit (CM) index that measures the customer’s level of engagement and anticipates their purchase intent. The CM index is evaluated dynamically by the algorithm, examining the customer’s activity level, efficiency in selecting items, and time spent in browsing. It combines tracking customers browsing and purchasing behaviors with other relevant factors: time spent on the website and frequency of visits to e-shops. This strategy proves highly beneficial for e-commerce enterprises, enabling them to pinpoint potential buyers and design targeted advertising campaigns exclusively for high-value customers of e-shops. It allows not only boosts e-shop sales but also minimizes advertising expenses effectively. The proposed method was tested on actual clickstream data from two e-commerce websites and showed that the personalized advertising campaign outperformed the non-personalized campaign in terms of click-through and conversion rate. In general, the findings suggest, that personalized advertising scenarios can be a useful tool for boosting e-commerce sales and reduce advertising cost. By utilizing clickstream data and adopting a targeted approach, e-commerce businesses can attract and retain high-value customers, leading to higher revenue and profitability.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"91 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139440304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Sparrow Search-Exponential Distribution Optimization with Differential Evolution for Parameter Prediction of Solar Photovoltaic Models 用于太阳能光伏模型参数预测的混合麻雀搜索-指数分布优化与差分进化算法

IF 2.3

Algorithms Pub Date : 2024-01-09 DOI: 10.3390/a17010026

Amr A. Abd El-Mageed, A. Al-Hamadi, Samy Bakheet, Asmaa H. Abd El-Rahiem

{"title":"Hybrid Sparrow Search-Exponential Distribution Optimization with Differential Evolution for Parameter Prediction of Solar Photovoltaic Models","authors":"Amr A. Abd El-Mageed, A. Al-Hamadi, Samy Bakheet, Asmaa H. Abd El-Rahiem","doi":"10.3390/a17010026","DOIUrl":"https://doi.org/10.3390/a17010026","url":null,"abstract":"It is difficult to determine unknown solar cell and photovoltaic (PV) module parameters owing to the nonlinearity of the characteristic current–voltage (I-V) curve. Despite this, precise parameter estimation is necessary due to the substantial effect parameters have on the efficacy of the PV system with respect to current and energy results. The problem’s characteristics make the handling of algorithms susceptible to local optima and resource-intensive processing. To effectively extract PV model parameter values, an improved hybrid Sparrow Search Algorithm (SSA) with Exponential Distribution Optimization (EDO) based on the Differential Evolution (DE) technique and the bound-constraint modification procedure, called ISSAEDO, is presented in this article. The hybrid strategy utilizes EDO to improve global exploration and SSA to effectively explore the solution space, while DE facilitates local search to improve parameter estimations. The proposed method is compared to standard optimization methods using solar PV system data to demonstrate its effectiveness and speed in obtaining PV model parameters such as the single diode model (SDM) and the double diode model (DDM). The results indicate that the hybrid technique is a viable instrument for enhancing solar PV system design and performance analysis because it can predict PV model parameters accurately.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"44 20","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139442474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Algorithms最新文献