Computer Sciences Commons^™

Correcting Class Imbalance Through Synthetic Training Data And 3d Modeling For Carabid Pitfall Trap Sampling, Blair Mirka

Geography ETDs

Crowdsourced biodiversity data provide an accessible foundation for large-scale ecological monitoring, but class imbalance limits automated species identification, particularly for rare taxa. This research explores the use of synthetic training data generated from 3D models of carabid beetle museum specimens to improve detection and classification performance for underrepresented species in crowdsourced datasets. High-resolution 3D models were created to simulate variation in lighting, orientation, and background. These synthetic images were incorporated into convolutional neural network training datasets at varying synthetic-to-real ratios to assess their impact on classification accuracy. Models were evaluated using controlled pitfall-trap imagery to examine the influence of scene …

Modeling Synaptic Dysfunction As Neural Contagion: A Graph-Based Sedr Framework For Simulating Signal Spread, Michelle Marfo, Dr. Padmanabhan Seshaiyer, Alonso Ogueda-Oliva

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Modeling The Probability Of N Clonal Rosettes In A Bromeliaceae Genetic Individual, Erin N. Bodine, Layla K. Lammers

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Damslnet: Dual-Attention Multi-Scale Lightweight Network For Plant Disease Classification, Linfan Deng, Juan Qin, Kun Li, Jinhua Zhu, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Accurately identifying crop diseases plays a crucial role in advancing intelligent and modern agricultural production. Deep learning techniques have performed robust performance in classifying plant disease images. However, current studies face the challenge that many plant disease datasets are generated in controlled environments, leading to reduced model performance in real-world agricultural settings. This paper aims to provide a lightweight model that can accurately classify plant diseases in natural environments. Specifically, this paper investigates the Dual-Attention Multi-Scale Lightweight Network (DAMSLNet), which combines dual-attention-based multi-scale feature extraction and deep information fusion, to classify plant diseases. At the front end, the model employs …

Enzymes Of Calendula Officinalis L. As Affected By Foliar Application Of Nano-Nitrogen And Potassium Fertilizers, Under Water Stress Conditions, Aqeel Abdulabbas Alsudani, Qais Hussain Abbas Al-Semmak

Karbala International Journal of Modern Science

Water stress is a major environmental factor that limits the growth and productivity of Calendula officinalis L. To alleviate its negative effects, recent approaches have increasingly focused on nano-fertilizers that enhance plant antioxidant defenses. This study therefore aimed to evaluate the effect of foliar application of nano-nitrogen (0, 2, and 4 mL L⁻¹) and nano-potassium (0, 2, and 4 g L⁻¹) fertilizers under two irrigation regimes (100% and 50% of field capacity) on the activity of key antioxidant enzymes, including catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD). The results revealed that irrigation at 50% field capacity significantly increased CAT, …

Effect Of Au Nanoparticles Doping On The Structure, Surface Morphology And Optical Properties Of Znago Nanorods, Mohsin Talib Mohammed, Salah M. Saleh Al-Khazali, Bassam A. Salih, Adel H. Omran Alkhayatt

Karbala International Journal of Modern Science

In this work, nanoparticles (NPs) composed of noble elements (Au and Ag) were prepared using a chemical reduction method and then combined with a spray pyrolysis technique for synthesizing ZnAgO and Au-doped ZnAgO nanorods (NRs). The films were produced by adding 2 wt% of Ag NPs and 2, 4, and 6 wt% of Au NPs at 420°C. The results showed that the films have a polycrystalline ZnO (wurtzite) with a hexagonal structure. Also, all films had a preference orientation along the (002) plane. The crystal size increased with increasing Au doping from 64.4 to 65.33 nm. For ZnO doped with …

Securing The Digital Harvest: Cybersecurity As A Core Agribusiness Skill, Jody Herchenbach, George Grispos

Mountain Plains Business Conference

The digitization of agriculture, through IoT-enabled equipment, cloud platforms, and precision technologies, has improved efficiency and profitability while also introducing significant cybersecurity risks. These vulnerabilities can disrupt supply chains, compromise sensitive data, and undermine financial stability. Yet agribusiness degree programs often overlook cybersecurity education. This paper proposes integrating cybersecurity content on threat awareness, incident response, and data protection into agribusiness curricula. Embedding these elements equips graduates to manage both digital and financial risks, enhancing resilience and competitiveness. Such curricular innovation aligns technical and managerial training, preparing future agribusiness professionals to lead securely and sustainably in an increasingly connected industry.

Insect-Foundation: A Foundation Model And Large Multimodal Dataset For Vision-Language Insect Understanding, Thanh-Dat Truong, Hoang-Quan Nguyen, Xuan-Bac Nguyen, Ashley Dowling, Xin Li, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

Multimodal conversational generative AI has shown impressive capabilities in various vision and language understanding through learning massive text-image data. However, current conversational models still lack knowledge about visual insects since they are often trained on the general knowledge of vision-language data. Meanwhile, understanding insects is a fundamental problem in precision agriculture, helping to promote sustainable development in agriculture. Therefore, this paper proposes a novel multimodal conversational model, Insect-LLaVA, to promote visual understanding in insect-domain knowledge. In particular, we first introduce a new large-scale Multimodal Insect Dataset with Visual Insect Instruction Data that enables the capability of learning the multimodal foundation …

Synthesis Of Nitrogen-Enriched 3d Graphene Foam For Electrochemical Sensing Of Hydrogen Peroxide (H₂O₂), Bhargavi Dronavalli, Madhava Rao Vallabhaneni, Jatla Murali Prakash, Deepti Kolli, Dandamudi Srilaxmi

Karbala International Journal of Modern Science

In this work, A simple hydrothermal process was utilized to create 3D-foam-type Nitrogen-doped graphene (NDG). The synthesized material was characterized using a range of physicochemical characterization techniques. These techniques confirm that nitrogen is successfully incorporated into the carbon network, resulting in NDG. Cyclic voltammetry (CV) investigation demonstrated that NDG-modified glassy carbon electrode (NDG/GCE) displayed finite charge transfer properties against typical redox systems. NDG/GCE is identified as a simple, facile, and efficient electrocatalyst material for the estimation of hydrogen peroxide (H₂O₂). From CV analysis, it is revealed that NDG/GCE can produce a signal for electrochemical quantification of …

A Feature Engineering Technique For Enhancing The Generalization Of Machine Learning Models In Estimating Crop Evapotranspiration, Gaku Yokoyama, Sohta Harigai, Shigehiro Kubota, Koichi Nomura, Gregory R. Goldsmith, Daisuke Yasutake, Tomoyoshi Hirota, Masaharu Kitano

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Accurate and precise estimation of evapotranspiration (ET) is crucial for understanding the terrestrial carbon, water, and energy cycles. While process-based models of ET, such as the Penman–Monteith model offer robust generalization capabilities, they are limited by the need for detailed parameters (e.g., stomatal conductance,) that are challenging to measure continuously. On the other hand, machine learning models can estimate ET by capturing relationships between ET and environmental variables without experimentally measuring model parameters. However, machine learning models face the challenge of limited generalizability. This issue is particularly significant given the uncertainty introduced by changing climatic …

Synthesis And Characterization Of Fe-Zn Bimetallic Nanoparticles Via Two-Step Laser Ablation And Their Antibacterial Activity, Hudhaifa M. Mohammed, Sahar Naji Rashid

Karbala International Journal of Modern Science

In this study, Nd: YAG laser at a wavelength of (1064 nm), energies of (300 and 400 mJ), and a pulse repetition rate of (3 Hz) was used to synthesize metallic nanoparticles from iron and zinc individually using a one-step pulsed laser ablation approach, followed by the two-step synthesis of bimetallic nanoparticles. The physical properties of the synthesized nanoparticles were then investigated using UV-visible (UV-Vis), X-ray diffraction (XRD), field-emission scanning electron microscopy (FESEM), and energy-dispersive X-ray (EDX) techniques. The results of characterization of the obtained NPs confirmed the formation of core-shell nanocomposites, as evidenced by the increased absorbance intensity of …

Green Synthesis Of Copper And Silver Nanostructured Particles From Eremurus Plant Extract And Comparison Of Their Optical Properties And Antibacterial Activities, Doaa Ayad Kamil, Ali F. Al-Rawaf, Mohammed Yarub Hani, H.H. Obeed, Tabarek Falah Deindee, Mohammed Ridha Shaeed

Karbala International Journal of Modern Science

In the present investigation, silver (Ag) and copper (Cu) nanostructured particles were synthesized from Eremurus plant by means of an environmentally benign methodology in which plant extracts served as both reducing and stabilizing agents. The resultant nanostructured particles were subjected to characterization techniques, including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), field-emission scanning electron microscopy (FESEM), and energy-dispersive X-ray spectroscopy (EDXS). XRD analysis confirmed development of Ag nanostructured particles with face-centered cubic (fcc) structure and also Cu nanostructured particles with cubic form. Moreover, the average crystallite dimensions obtained for Ag and Cu were 18.3 nm and 65.7 nm, …

Fabrication And Characterization Of Epoxy Resin Co-Doped With 2,5-Diphenyloxazole And Cerium Fluoride Nanoparticles For Radiation Detection, Akapong Phunpueok, Jaruwan Seangrit, Sarawut Jaiyen, Krittiya Sreebunpeng, Wuttichai Chaiphaksa, Kewalee Nilgumhang, Voranuch Thongpool

Karbala International Journal of Modern Science

This paper presents the fabrication and characterization of a plastic scintillator prepared from epoxy resin doped with 2,5-diphenyloxazole (PPO) and cerium fluoride nanoparticles (CeF₃ NPs) for radiation detection. The CeF₃ NPs were prepared by a chemical process and examined by X-ray diffraction (XRD) and scanning electron microscopy (SEM); it was found that the prepared particles were true CeF₃ NPs with an average particle size of approximately 17 nm. The CeF₃ NPs were co-doped with PPO into epoxy resin and formed into a plastic scintillator of 3 cm in diameter and 2 cm in length. Analysis of …

Hybrid Path Planner For Centralized Multi-Robotic Long-Vehicle Based On Adaptive Dimensionality And Grey Wolf Algorithm, Noor Kadhim Ayoob, Ali Hadi Hasan

Karbala International Journal of Modern Science

The present robot planning methods pay no attention to the impact of the robot's size and the space it occupies in the environment on path planning. This paper presents a centralized hybrid method to plan optimal paths for multiple robotic long vehicles (RLVs) competing with each other to reach one common goal, taking into account the space that must be available to the RLV at each step to avoid narrow spaces that are too small to pass through. The environment analysis for each RLV is improved by assigning constant weight to the obstacles and calculating two new parameters: safety (SF) …

Influence Of Variable Climate On Mechanical Properties Of Composite Materials: An Experimental Study, Alexandra Tazhibaeva, Safaa M.R.H. Hussein, Mikhail Kuznetsov, Farid Shakirzyanov, Nikita Kharin, Igor Muravyev, Timur Agliullin, Gulshat Saleeva, Victor Mitryakin, Oleg Morozov, Oskar Sachenkov

Karbala International Journal of Modern Science

This study investigates the degradation of carbon fiber-reinforced plastic composites, fabricated via non-autoclave molding, under tropical climatic conditions across three regions over three years. Forty specimens (10 control, 30 exposed) with 2x2 twill weave and 0°/90° fiber orientation were subjected to tensile and compressive testing, microscopy, and X-ray computed tomography. Control specimens established baseline properties, while exposed specimens underwent three-year weathering. Statistical and principal component analyses revealed significant mechanical degradation, with tensile strength decreasing by up to 16.1% (p

Ibi-Dt: A Novel Approach Combining Individualized Bayesian Inference And Decision Tree For Identifying Cancer Drivers And Their Interactions, Md Asad Rahman, Gregory F. Cooper, Jinying Zhao, Xinghua Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Cancer is mainly caused by a relatively small portion of somatic genome alterations (SGAs), called cancer drivers. Despite success in identifying a good number of cancer drivers, many more remain to be discovered to explain various cancers. Moreover, limited tools are available to identify potential interactions among cancer drivers for a better understanding of oncogenesis. To tackle these challenges, we have developed a novel approach called individualized Bayesian inference using a decision tree (IBI-DT). IBI-DT recognizes the genetic heterogeneity among cancer patients, where different individuals or patient subgroups of distinct genomic makeup may have different drivers. IBI-DT works by constructing …

Optimizing Hip And Knee Assistance For Walking And Sit-To-Stand Transitions: An Intrinsic Muscle Mechanics Based Predictive Approach, Neethan Ratnakumar

Dissertations

As the global population ages, the demand for wearable assistive technologies continues to rise, driven by their potential to enhance mobility and independence in older adults. Effectively designed controllers for lower-limb exoskeletons to assist sit-to-stand (STS) and walking are crucial for delivering efficient, safe, and comfortable assistance during daily activities. Traditionally, controller optimization involves biomechanical modeling and user-specific customization. Musculoskeletal simulations play a central role in this process by providing insights into human-exoskeleton interaction dynamics, thereby informing and refining control strategies.

This work presents a simulation-driven approach for developing exoskeleton controllers for walking and STS using two distinct methods: optimal …

Design And Development Of A Standalone Digital Holographic Microscope Employing Phase-Driven Reconstruction And Classification For Biomedical Imaging And Optical Diagnostics, Charlotte Kyeremah

Graduate Doctoral Dissertations

Access to advanced biomedical imaging technologies remains a significant challenge in resource-limited settings, especially for early disease detection and monitoring of diseases such as malaria, HIV, and other blood-borne diseases. Although point-of-care (POC) devices have gained popularity in global health, many rely on antibody-based tests, lateral flow strips, or optical readouts that often lack quantitative capabilities, sensitivity to early infections, or versatility in different diagnostic targets. In addition, these systems are typically dependent on disposable reagents or manual interpretation, which limits their effectiveness in remote areas. Digital Holographic Microscopy (DHM) presents a promising alternative as a label-free imaging method capable …

Performance Analysis Of Computational Methods For Predicting Protein Function In Rare Diseases, Aichetou Mohamed Sidiya, Hanin Alzaher, Razan Almahdi, Tayeb Brahimi

Effat Undergraduate Research Journal

Protein function prediction is crucial for understanding the underlying mechanisms of rare diseases. With the increasing availability of computational methods including machine learning-based approaches, network-based methods, and sequence-based methods, predicting protein functions has become more accessible. However, it is not clear which of these methods performs better or how they compare to each other in terms of accuracy, efficiency, and scalability. In this study, we evaluate several computational methods for predicting protein functions in rare diseases using key performance indicators (KPIs). We analyze the strengths and weaknesses of each method and provide recommendations for researchers and clinicians interested in using …

Antifungal Peptides From Casein Milk Of Etawa Crossbreed (Capra Hircus) As Biopreservation Agent For Bread And Molecular Docking Studies, Dian Riana Ningsih, Winarto Haryadi, Rachma Wikandari, Tri Joko Raharjo

Karbala International Journal of Modern Science

Bread without peptide fraction treatment started to grow mold on the fourth day with a total of 24x10⁷ fungi colonies/ml. Meanwhile, the addition of peptide fractions GMC5, GMC6, GMC7, and GMC8 effectively preserved bread for up to 4 days, indicated by no mold growth. The best treatment was the GMC6 peptide fraction on day 8 which grew the least amount of fungus at 7x10⁷ spores/ml. Peptide interaction with the CaATPase receptor Aspergillus sp. was carried out using HADDOCK 2.4. The 3D CaATPase structure prediction resulted in a model with good structural quality. This was supported by residues in …

Crop Yield Prediction At Multiple Spatial Scales With Statistical Machine Learning, Vaibhav Charan, Pratishtha Poudel

Discovery Undergraduate Interdisciplinary Research Internship

Understanding and accurately predicting crop yield is becoming increasingly important today in the face of global food security challenges, and thus, the availability of standardized data and scalable models is the need of the hour. To support this, researchers have developed CY-Bench (Crop Yield Benchmark), a comprehensive dataset that helps forecast maize and wheat yields on a global scale. This research project primarily involved working with the CY-Bench dataset aiming to improve crop yield prediction through machine learning. Initially, papers explaining the CY-Bench dataset and other papers for agriculture modeling were studied and analyzed in detail. The research then progressed …

Advancing Fishery Dependent And Independent Habitat Assessments Using Automated Image Analysis: A Fisheries Management Agency Case Study, Scott Evans, Bronson Philippa, Carlo Mattone, Nick Konzewitsch, Renae Hovey, Marcus Sheaves, Gary A. Kendrick, Lynda M. Bellchambers

Fisheries Research Articles

Advances in artificial intelligence and machine learning have revolutionised data analysis, including in the field of marine and fisheries sciences. However, many fisheries agencies manage sensitive or proprietary data that cannot be shared externally, which can limit the adoption of externally hosted artificial intelligence platforms. In this study, we develop and evaluate two residual network-based automatic image annotation models to process fishery specific habitat data to support ecosystem-based fisheries management in the Exmouth Gulf Prawn Managed Fishery in Western Australia. Using an extensive dataset of 13,128 manually annotated benthic habitat images, we train a grid-based annotation model and an image-level …

Authentication And Message Integrity Verification For Emerging Wireless Networks, Ebuka Philip Oguchi

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation presents a comprehensive body of research on authentication and message integrity verification for emerging wireless networks, focusing on secret-free and physical layer security techniques across diverse, challenging, and unconventional environments.

It comprises four first-author contributions that span underground wireless systems, over-the-air (OTA) channels, vehicular communications, and nanoscale molecular networks.

The first contribution, Soil-Assisted Trust Establishment for Underground Wireless Networks (STUN), introduces a physical-layer trust bootstrapping protocol that achieves authentication and message integrity without pre-shared secrets. Leveraging underground-to-air propagation laws and trusted relay nodes, STUN resists active signal injection attacks and demonstrates security comparable to the unbalanced oil and …

Gene Regulatory Network Prediction Using Machine Learning, Deep Learning, And Hybrid Approaches, Sai Teja Mummadi, Md Khairul Islam, Victor Busov, Hairong Wei

Michigan Tech Publications

Construction of gene regulatory networks (GRNs) is essential for elucidating the regulatory mechanisms underlying metabolic pathways, biological processes, and complex traits. In this study, we developed and evaluated machine learning, deep learning, and hybrid approaches for constructing GRNs by integrating prior knowledge and large-scale transcriptomic data from Arabidopsis thaliana, poplar, and maize. Among these, hybrid models that combined convolutional neural networks and machine learning consistently outperformed traditional machine learning and statistical methods, achieving over 95% accuracy on the holdout test datasets. These models not only identified a greater number of known transcription factors regulating the lignin biosynthesis pathway but also …

Computational And In Vitro Investigation Of P. Crocatum Bioactive Compounds As Pancreatic Lipase Inhibitors, Gusnia Meilin Gholam, Dimas Andrianto, Dewi Anggraini Septaningsih, Mega Safithri

Karbala International Journal of Modern Science

Obesity, a prevalent metabolic disorder characterized by excessive fat accumulation, can severely affect overall health if left untreated. This study investigated the potential of a 70% ethanol extract from Piper crocatum (red betel) leaves as an in vitro inhibitor of pancreatic lipase (PL), supported by computational analyses to identify alternative compounds to orlistat. The phytochemical profile was characterized using LC-MS/MS, revealing alkaloids and terpenoids with contents of 1.1 ± 0.01 mg CE/g and 3.14 ± 0.3 mg UAE/g, respectively. The extract exhibited 49 ± 9.1% inhibition of PL activity. Molecular docking identified three promising compounds: calanolide A (10.43 kcal/mol), myricanone …

In Silico Prediction Of Cytotoxic T-Cell Epitopes From Helicobacter Pylori Virulence Factors Using An Immunoinformatics Approach, Demy Valerie Chacon, Kiana Alika Co, Daphne Noreen Enriquez, Aubrey Love Labarda, Reanne Eden Manongsong, Edward Kevin B. Bragais

Biology Faculty Publications

Background: Helicobacter pylori infects approximately half of the global population, leading to gastric and duodenal ulcers. Despite the availability of antibiotics, challenges such as patient reluctance, high treatment costs, and antibiotic resistance limit their effectiveness, making vaccination a promising alternative. This study used immunoinformatics to identify candidate epitopes for a multiepitope vaccine construct against H. pylori.

Material and methods: The protein variability server was utilized for conservation analysis. The epitopes were screened for antigenicity, allergenicity, toxicity, cross-reactivity, and population coverage. Selected epitopes were docked with their corresponding human leukocyte antigen (HLA) alleles, and thermodynamic quantities were determined. Five virulence …

Combination Of Irreversible Electroporation And Clostridium Novyi-Nt Bacterial Therapy For Colorectal Liver Metastasis, Zigeng Zhang, Guangbo Yu, Qiaoming Hou, Farideh Amirrad, Sha Webster, Surya M. Nauli, Jianhua Yu, Vahid Yaghmai, Aydin Eresen, Zhuoli Zhang

Pharmacy Faculty Articles and Research

Colorectal liver metastasis (CRLM) poses a significant challenge in oncology due to its high incidence and poor prognosis in unresectable cases. Current treatments, including surgical resection, systemic chemotherapy, and liver-directed therapies, often fail to effectively target hypoxic tumor regions, which are inherently more resistant to these interventions. This review examines the potential of a novel therapeutic strategy combining irreversible electroporation (IRE) ablation and Clostridium novyi-nontoxic (C. novyi-NT) bacterial therapy. IRE is a non-thermal tumor ablation technique that uses high-voltage electric pulses to create permanent nanopores in cell membranes, leading to cell death while preserving surrounding structures, and …

Antibacterial Potential Of Tapanuli Orangutan (Pongo Tapanuliensis) Food In Batang Toru Forest Against Escherichia Coli And Salmonella Typhi, Herna Febrianty Sianipar, Wahyu Widoretno, Luchman Hakim, Rezi Rahmi Amolia, Fatchiyah Fatchiyah

Karbala International Journal of Modern Science

Diarrhea is a common disease affecting orangutans, primarily caused by Escherichia coli and Salmonella typhi bacteria. To treat this disease, antibacterial food sources are essential as therapeutic agents for orangutans. The fruits consumed by Tapanuli orangutans include Campnosperma auriculatum, Agathis borneensis, Artocarpus heterophyllus, Castanopsis argantea, and Aglaia tomentosa. This study aims to examine the amino acid and phytochemical components with potential antibacterial properties in these five fruit species and their inhibitory effects on E. coli and S. typhi growth through cell lysis, observed using a Scanning Electron Microscope (SEM). The samples were tested for amino acids, phytochemicals, vitamin C content, …

Effects Of Plasma-Activated Water On Wheat: Germination And Seedling Development, Wafaa Abdulrazzaq Abdullah, Hadeel O. Ismael, Duaa A. Uamran, Hammad R. Humud

Karbala International Journal of Modern Science

The food sector must contend with issues such as pathogen resistance to some of the available chemical agents, environmental pollution, and climate change to provide healthy food for livestock and people. The application of atmospheric pressure plasma jets (APPJs) is one potential solution for such problems. Plasma is appropriate for effective surface decontamination regarding food products and seeds, surface decontamination, and achieving improved agricultural production yields. The impact of plasma-activated water (PAW) produced by plasma jet discharge (PJD) system on in vitro-cultivated wheat seeds is examined in this work. For this aim, a plasma jet system was constructed with a …

Predicting Sleep And Sleep Stage In Children Using Actigraphy And Heartrate Via A Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation, Robert Weaver Med, Phd, James White, Olivia Finnegan, Hongpeng Yang, Zifei Zhong, Keagan Kiely, Catherine Jones, Yan Tong, Srihari Nelakuditi, Rahul Ghosal, David E. Brown, Russell R. Pate Ph.D., Gregory J. Welk, Massimiliano De Zambotti, Yuan Wang, Sarah Burkart, Elizabeth L. Adams Phd, Bridget Armstrong, Michael Beets Med, Mph, Phd

Faculty Publications

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to predict wake (i.e., specificity, values typically < 70) and cannot predict sleep stages. Long short-term memory (LSTM) is a machine learning algorithm that may address these deficiencies. This study evaluated the agreement of LSTM sleep estimates from actigraphy and heartrate (HR) data with polysomnography (PSG). Children (N = 238, 5–12 years,52.8% male, 50% Black 31.9% White) participated in an overnight laboratory polysomnography. Participants were referred be-cause of suspected sleep disruptions. Children wore an ActiGraph GT9X accelerometer and two of three consumer wearables(i.e., Apple Watch Series 7, Fitbit Sense, Garmin Vivoactive 4) on their non-dominant wrist during the polysomnogram. LSTM estimated sleep versus wake and sleep stage (wake, not-REM, REM) using raw actigraphy and HR data for each 30-s epoch. Logistic regression and random forest were also estimated as a benchmark for performance with which to compare the LSTM results. A 10-fold cross-validation technique was employed, and confusion matrices were constructed. Sensitivity and specificity were calculated to assess the agreement between research-grade and consumer wearables with the criterion polysomnography. For sleep versus wake classification, LSTM outperformed logistic regression and random forest with accuracy ranging from 94.1to 95.1, sensitivity ranging from 94.9 to 95.9 across different devices, and specificity ranging from 84.5 to 89.6. The addition of HR improved the prediction of sleep stages but not binary sleep versus wake. LSTM is promising for predicting sleep and sleep staging from actigraphy data, and HR may improve sleep stage prediction.