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• Review for "Hybrid Machine Learning Model for Forest Height Estimation from TanDEM-X and Landsat Data"
  crossref 2026-05-14T21:05:57Z 置信度 0.70
• Review of: "Implementing Machine Learning to predict the 10-year risk of Cardiovascular Disease"
  crossref Premaladha Jayaraman 2023-09-22T02:07:26Z 置信度 0.70
• Review for "Inverse design of skull osteoinductive implants with multi-level pore structure through machine learning"
  crossref 2024-08-27T17:03:15Z 置信度 0.70
• Review for "An improved custom convolutional neural network based hand sign recognition using machine learning algorithm"
  crossref 2024-03-20T17:19:55Z 置信度 0.70
• Peer Review #2 of "Classification model for accuracy and intrusion detection using machine learning approach (v0.2)"
  crossref 2021-04-12T02:32:23Z 置信度 0.70
• Review for "Machine Learning-Driven Multidimensional Tea Profiling from a Single SERS Spectrum: Toward Practical Application"
  crossref 2026-03-18T21:14:28Z 置信度 0.70
• Artificial Intelligence, Machine Learning, Automation, Robotics, Future of Work and Future of Humanity
  The exponential advancement in artificial intelligence (AI), machine learning, robotics, and automation are rapidly transforming industries and societies across the world. The way we work, the way we live, and the way we interact with others are expected to be…
  crossref Weiyu Wang, Keng Siau 2022-07-08T11:31:23Z 置信度 0.70
• Machine Learning Techniques For Effective Facilitation Of Teaching And Learning: A Narrative Review
  Traditional teaching learning has transformed significantly towards offering a learner an experience that to a greater extent mimics a human tutor; while in a computer-based or valued learning environment, Machine Learning (ML) techniques implemented as algori…
  crossref 2019-04-08T01:35:22Z 置信度 0.70
• A Machine Learning Approach to Identify Fake News
  crossref 2022-08-04T12:33:42Z 置信度 0.70
• Machine learning-based QoT estimation over optical wireless communication
  Abstract Fiber-optic communication and networks play an important role in communications technology. According to the last few decades, in this area, research development is growing rapidly. Machine learning (ML) algorithms for optical communications (OC) are …
  crossref Shakrajit Sahu, J. Christopher Clement 2023-01-03T04:49:37Z 置信度 0.70
• Understanding predictions of drug effectiveness using explainable Machine Learning models
  Abstract Purpose: The analysis of absorption, distribution, metabolism, and excretion (ADME) molecular properties is of relevance to drug design, as they directly influence the drug’s effectiveness at its target location. This study concerns their prediction, us…
  crossref Caroline König, Alfredo Vellido 2024-06-11T16:42:15Z 置信度 0.70
• Table 4: The table shows relevant review findings of conventional machine learning algorithms for different imaging modalities.
  crossref 2021-09-13T07:06:36Z 置信度 0.70
• Review for "Qualitative and quantitative analysis of pharmaceutical content by terahertz spectroscopy and machine learning algorithms"
  crossref 2026-07-27T21:34:47Z 置信度 0.70
• Performance Evaluation of Machine Learning Regression Models for Rainfall Prediction
  Abstract Accurate prediction of rainfall has always been the most demanding task involved in weather forecasting in view of significant variations in weather patterns. With the advent of machine learning algorithms, it is now possible to predict rainfall with …
  crossref Maaz Abdullah, saif said 2023-09-12T06:35:11Z 置信度 0.70
• Dog’s Emotion Extraction from Bark Using Machine Learning: A Review
  Understanding dogs’ emotional state is crucial for effective communication, animal welfare, and behavior analysis. This review paper explores the current methods and techniques for extracting emotions from dog bark. It discusses the significance of dog emotion…
  crossref Shovit Kumar, Dr. Raju Ranjan 2024-04-02T09:23:26Z 置信度 0.70
• Machine Learning and Artificial Intelligence Models for Pediatric Diabetic Ketoacidosis Prediction: A Systematic Review
  crossref Imran, Manal 2026-07-30T21:19:29Z 置信度 0.70
• Review for "A Machine Learning Approach to Wastewater Treatment: Gaussian Process Regression and Monte Carlo Analysis"
  crossref 2025-05-28T17:13:53Z 置信度 0.70
• Review of: "Tweeting AI: A Machine Learning Approach for Bird Species Detection and Classification"
  crossref Indranath Chatterjee 2023-07-27T02:06:29Z 置信度 0.70
• Review for "Beyond the static picture: a machine learning and molecular dynamics insight on singlet fission"
  crossref 2025-09-04T04:33:49Z 置信度 0.70
• Review of Applying machine learning methods to detect convection using GOES-16 ABI data
  crossref 2021-01-12T08:36:06Z 置信度 0.70
• Review for "Prediction of mechanical properties of Mg-rare earth alloys by machine learning"
  crossref 2022-10-13T17:02:30Z 置信度 0.70
• WITHDRAWN: Machine Learning Based Power Estimation for CMOS VLSI Circuits
  Abstract Nowdays, machine learning (ML) algorithms are receiving massive attention in most of the engineering application since it has capability in complex systems modelling using historical data. Estimation of power for CMOS VLSI circuit using various circui…
  crossref V. Govindaraj, B. Arunadevi 2021-07-21T17:50:09Z 置信度 0.70
• Review of: "Generative Artificial Intelligence Using Machine Learning on Wireless Ad Hoc Networks"
  crossref Zakwan Al-Arnaout 2024-08-13T12:18:46Z 置信度 0.70
• Review for "Insights on formation damage associated with hydraulic fracturing using image analysis and machine learning"
  crossref 2022-01-31T09:02:50Z 置信度 0.70
• Review of: "Tweeting AI: A Machine Learning Approach for Bird Species Detection and Classification"
  crossref Ganesh Yenurkar 2023-07-07T02:31:55Z 置信度 0.70
• Review of: "Implementing Machine Learning to predict the 10-year risk of Cardiovascular Disease"
  crossref Abdur Rasool 2023-09-09T06:35:27Z 置信度 0.70
• Comparative Analysis of Machine Learning Algorithms in Breast Cancer Classification
  Abstract Now days breast cancer has emerged as a diseases effecting women to suffer a life threating phase and eventually lead to death world wide. The prediction of breast cancer in woman at the initial stage can aggrandize recovery and chance of abidance con…
  crossref Satish Chaurasiya, Ranjit Rajak 2022-06-30T13:46:12Z 置信度 0.70
• Review for "Merging computational fluid dynamics and machine learning to reveal animal migration strategies"
  crossref 2021-04-06T17:01:37Z 置信度 0.70
• Review for "Hybrid Machine Learning Model for Forest Height Estimation from TanDEM-X and Landsat Data"
  crossref 2026-05-14T21:05:57Z 置信度 0.70
• Review for "DBMLFF: Linear scaling machine learning force fields via electron density decomposition for molecular electrolytes"
  crossref 2026-01-20T21:08:44Z 置信度 0.70
• Cost-Efficient Asset Allocation: Graph-Based Machine Learning for Dynamic Portfolio Rebalancing.
  Abstract This research introduces a novel approach to portfolio rebalancing by integrating Graph Neural Networks (GNNs) with Dijkstra's algorithm to optimize transaction costs in financial markets. GNNs are trained on historical stock data from major technolog…
  crossref Diego Vallarino 2025-02-03T07:42:41Z 置信度 0.70
• Machine learning-based identification of ferroptosis-related biomarkers in osteoarthritis
  Abstract Background Osteoarthritis (OA) is the most common joint disease and a major cause of chronic disability in elderly individuals. OA is characterized by degeneration of articular cartilage, structural changes in the subchondral bone structure, and forma…
  crossref Yingchao Jin, Hua Zhang 2023-09-01T17:32:46Z 置信度 0.70
• Machine Learning Approaches to Demand Forecasting in Data-scarce Environments: A Systematic Review
  Accurate demand forecasting is a cornerstone of effective supply chain management, inventory optimization, and operational planning. However, many real-world settings-including emerging markets, newly launched products, healthcare supply chains, and small-to-m…
  crossref Rishi Laddha 2026-07-29T13:35:14Z 置信度 0.70
• Review for "Machine learning guided tuning charge distribution by composition in MOFs for oxygen evolution reaction"
  crossref 2024-03-19T17:15:12Z 置信度 0.70
• Review for "Machine Learning-Based Classifier for Identifying Inpatients With Schizophrenia at High Risk of Suicide"
  crossref 2025-09-30T23:09:38Z 置信度 0.70
• Review for "Conformational Landscape of 2-Aminopurine-Substituted RNA Oligonucleotides from Machine-Learning-Driven Enhanced Sampling"
  crossref 2026-07-10T21:08:57Z 置信度 0.70
• Review for "Advances and innovations in machine learning-based spectral detection methods for trace organic pollutants"
  crossref 2025-12-09T21:03:21Z 置信度 0.70
• Review of: "Tweeting AI: A Machine Learning Approach for Bird Species Detection and Classification"
  crossref Anisha Rodrigues 2023-07-08T02:27:45Z 置信度 0.70
• Review for "DBMLFF: Linear scaling machine learning force fields via electron density decomposition for molecular electrolytes"
  crossref 2026-01-20T21:08:44Z 置信度 0.70
• Review for "Automated Structural Analysis of Small Angle Scattering Data from Common Nanoparticles via Machine Learning"
  crossref 2025-05-02T20:54:31Z 置信度 0.70
• Review for "A multi-omics machine learning classifier for outgrowth of cow’s milk allergy in children"
  crossref 2025-05-10T17:02:08Z 置信度 0.70
• Review of: "Generative Artificial Intelligence Using Machine Learning on Wireless Ad Hoc Networks"
  crossref Evilasio Costa Junior 2024-08-08T07:20:55Z 置信度 0.70
• Review for "Parsimonious machine learning for the global mapping of aboveground biomass potential"
  crossref 2025-03-20T18:20:01Z 置信度 0.70
• Review for "Beyond the static picture: a machine learning and molecular dynamics insight on singlet fission"
  crossref 2025-09-04T04:33:49Z 置信度 0.70
• Review of: "Machine Learning Methods in Algorithmic Trading: An Experimental Evaluation of Supervised Learning Techniques for Stock Price"
  crossref Elham Mohammed Thabit A. Alsaadi 2023-10-21T15:07:46Z 置信度 0.70
• Peer Review #2 of "A systematic review of literature on credit card cyber fraud detection using machine and deep learning (v0.1)"
  crossref 2023-04-22T02:30:20Z 置信度 0.70
• Peer Review Report For: Unintended consequences of machine learning in medicine? [version 1; peer review: 2 approved]
  crossref 2025-11-25T06:57:09Z 置信度 0.70
• Integrating Digital Forensics and Machine Learning for Retail Return Fraud Detection: A Comprehensive Review
  crossref Girish Kurkure, Dipita Dhande, Manisha Shirsath 2026-04-06T10:36:34Z 置信度 0.70
• Peer Review #3 of "A systematic review of literature on credit card cyber fraud detection using machine and deep learning (v0.2)"
  crossref 2023-04-22T02:30:27Z 置信度 0.70
• Prediction of Transition State Structures of General Chemical Reactions via Machine Learning
  Abstract The elucidation of transition state (TS) structures is ssential for understanding the mechanisms of chemical reactions and exploring reaction networks. Despite advances in computational approaches, TS searches remain still a challenging problem due to…
  crossref Sunghwan Choi 2022-09-29T17:36:08Z 置信度 0.70
• Review for "Machine Learning Model Predicts Postoperative Outcomes in Chronic Rhinosinusitis With Nasal Polyps"
  crossref 2024-08-08T17:08:06Z 置信度 0.70
• Review of: "Tweeting AI: A Machine Learning Approach for Bird Species Detection and Classification"
  crossref Monika Mathur 2023-08-14T03:29:12Z 置信度 0.70
• Review of: "Tweeting AI: A Machine Learning Approach for Bird Species Detection and Classification"
  crossref Omar Haddad 2023-07-17T06:14:38Z 置信度 0.70
• Review of: "Generative Artificial Intelligence Using Machine Learning on Wireless Ad Hoc Networks"
  crossref Shayma Wail Nourildean 2024-08-06T08:44:52Z 置信度 0.70
• Review for "Machine Learning-Based Classifier for Identifying Inpatients With Schizophrenia at High Risk of Suicide"
  crossref 2025-09-30T23:09:38Z 置信度 0.70
• Review of: "Tweeting AI: A Machine Learning Approach for Bird Species Detection and Classification"
  crossref Mukul Singh 2023-07-17T13:47:36Z 置信度 0.70
• Quantitative Analysis of Sorghum Starch With Machine Learning and Near-Infrared Spectroscopy
  Abstract A model based on NIR technology and machine learning algorithm is established for rapid quantification of sorghum starch. Using brewing sorghum as the primary raw material, and we collected near-infrared diffuse reflectance spectra using a Fourier tra…
  crossref Zongjun Li 2025-08-20T17:55:34Z 置信度 0.70
• Predicting Hydraulic Conductivity using Machine Learning techniques
  Abstract The hydraulic conductivity of water-saturated soils (Ks) is a critical parameter influencing water infiltration, runoff, and drainage. However, collecting this value is always costly and time-consuming in large scale measurements. This study employs s…
  crossref Thi-Thu-Ha Nguyen, Duc-Quang Vu 2024-06-28T17:38:43Z 置信度 0.70
• Review for "Creating a Real‐Time Capable Surrogate Model of a Wind Turbine Using Machine Learning"
  crossref 2025-10-16T21:15:52Z 置信度 0.70
• Machine Learning Algorithms for FCB Estimation in PPP-AR Technique
  Abstract Precise Point Positioning (PPP) technique have shown continuous improvement regarding positioning. The recent developments in PPP-AR (Ambiguity Resolution) have facilitated the resolution of integer ambiguity. Thereby, the observation period needed fo…
  crossref Furkan Karlitepe, Bahattin Erdogan 2024-02-19T18:24:50Z 置信度 0.70
• Machine Learning Based Indian raga Identification for Music Therapy
  Abstract The world has music. The inherent nature of music's language has a wide range of psychological effects on people. Numerous academics have looked into how music affects the mind. Medical science and technological developments are rediscovering music's …
  crossref KIRAN, Sunil Kumar D S 2022-08-15T00:41:01Z 置信度 0.70
• Review for "Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules"
  crossref 2023-12-06T16:13:25Z 置信度 0.70
• A Secure Approach to Detect Phishing Emails Based on Machine Learning and Deep Learning
  Abstract Detecting phishing emails remains a real challenge in cybersecurity, especially as attackers are constantly finding new ways to bypass traditional defence systems. This study provides an in-depth comparison between traditional machine learning algorit…
  crossref Mohamed Khayati, Driss Ait Omar, Mohamed Baslam 2025-08-07T08:24:36Z 置信度 0.70
• Machine Learning and Deep Learning Approaches for Early Alzheimer’s Detection in Patients with Subjective Cognitive Decline: A Systematic Literature Review
  crossref Zyad Taouil, Nourhène Ben Rabah, Bénédicte Grand 2025-02-28T12:43:20Z 置信度 0.70
• Mapping Almond Stem Water Potential using Machine Learning
  Abstract Almonds are a major crop in California which produces 80% of all the world’s almonds. Widespread drought and strict groundwater regulations pose significant challenges to growers. Irrigation regimes based on observed crop water status can help to opti…
  crossref Peter Savchik, Mallika Nocco, Isaya Kisekka 2023-03-30T22:15:28Z 置信度 0.70
• Toward a 'Standard Model' of Machine Learning
  crossref Zhiting Hu, Eric P. Xing 2022-10-27T16:03:52Z 置信度 0.70
• Review of: "Implementing Machine Learning to predict the 10-year risk of Cardiovascular Disease"
  crossref Santwana Sagnika 2023-11-19T13:08:35Z 置信度 0.70
• Review for "A machine learning approach to facilitate parasitic egg identification in a conspecific brood parasite"
  crossref 2025-11-27T22:47:54Z 置信度 0.70
• Review for "The Use of Artificial Intelligence and Deep Machine Learning in Oncologic Histopathology"
  crossref 2020-05-26T08:45:01Z 置信度 0.70
• Review for "Cylindrical microlasers: Emission research and machine learning-assisted analysis between ASE and lasing phenomena"
  crossref 2025-09-01T21:04:38Z 置信度 0.70
• EdgeBoost: Confidence boosting for resource constrained inference via selective offloading
  Deploying large Deep Neural Networks with state-of-the-art accuracy on edge devices is often impractical due to their limited resources. This paper introduces EdgeBoost, a selective input offloading system designed to overcome the challenges of limited computa…
  datacite Said, Naina, Landsiedel, Olaf 2025 置信度 0.66 EdgeAI | Inference offloading | Lightweight models | MCU | Model calibration | Temperature scaling | TinyMLComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial Intelligence
• TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy Preservation
  Natural Language Processing (NLP) operations, such as semantic sentiment analysis and text synthesis, often raise privacy concerns and demand significant on-device computational resources. Centralized learning (CL) on the edge provides an energy-efficient alte…
  datacite Radwan, Ahmed Y., Shehab, Mohammad, Alouini, Mohamed-Slim 2024 置信度 0.66 Machine Learning (cs.LG)Cryptography and Security (cs.CR)Information Theory (cs.IT)FOS: Computer and information sciencesFOS: Computer and information sciences
• Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications
  Neurosymbolic AI (NSAI) has recently emerged to mitigate limitations associated with deep learning (DL) models, e.g. quantifying their uncertainty or reason with explicit rules. Hence, TinyML hardware will need to support these symbolic models to bring NSAI to…
  datacite Leslin, Jelin, Trapp, Martin, Andraud, Martin 2025 置信度 0.66 Machine Learning (cs.LG)Performance (cs.PF)FOS: Computer and information sciencesFOS: Computer and information sciences
• Designing Climate-Conscious Edge AI Systems: A Computer Engineering Approach with Ultra-Low-Power Processors
  This paper outlines a practical framework for designing sustainable, off-grid AI systems using ultra-low-power microcontrollers and TinyML techniques. Grounded in Permacomputing principles, it explores how microcontrollers like the ESP32 can support local infe…
  datacite Keller, Stephane Jane 2025 置信度 0.66 PermacomputingSustainable AIEdge ComputingESP32Low-Power Systems
• Designing Climate-Conscious Edge AI Systems: A Computer Engineering Approach with Ultra-Low-Power Processors
  This paper explores how AI can be made more sustainable through ultra-low-power processors, edge computing, and minimalist design inspired by Permacomputing. Drawing on principles from Onur Mutlu’s architectural mindset, it highlights how TinyML and hardware l…
  datacite Keller, Stephane Jane 2025 置信度 0.66 Sustainability AIEdge ComputingUltra-Low-Power ProcessorsTinyMLPermacomputing
• dreaMLearning: Data Compression Assisted Machine Learning
  Despite rapid advancements, machine learning, particularly deep learning, is hindered by the need for large amounts of labeled data to learn meaningful patterns without overfitting and immense demands for computation and storage, which motivate research into a…
  datacite Zhao, Xiaobo, Hurst, Aaron, Karras, Panagiotis, Lucani, Daniel E. 2025 置信度 0.66 Machine Learning (cs.LG)Information Theory (cs.IT)Signal Processing (eess.SP)FOS: Computer and information sciencesFOS: Computer and information sciences
• Multi-Crop Disease Dataset
  This dataset presents a comprehensive collection of annotated images of diseased and healthy leaves across five important agricultural crops: Banana, Chilli, Radish, Groundnut, and Cauliflower. The dataset was created to support research in plant disease detec…
  datacite E, Prem Kumar 2025 置信度 0.66 Computer VisionImage ProcessingAgricultural EngineeringAgricultural HealthAgricultural Management
• Multi-Crop Disease Dataset
  This dataset presents a comprehensive collection of annotated images of diseased and healthy leaves across five important agricultural crops: Banana, Chilli, Radish, Groundnut, and Cauliflower. The dataset was created to support research in plant disease detec…
  datacite E, Prem Kumar 2025 置信度 0.66 Computer VisionImage ProcessingAgricultural EngineeringAgricultural HealthAgricultural Management
• Tiny Deep Ensemble: Uncertainty Estimation in Edge AI Accelerators via Ensembling Normalization Layers with Shared Weights
  The applications of artificial intelligence (AI) are rapidly evolving, and they are also commonly used in safety-critical domains, such as autonomous driving and medical diagnosis, where functional safety is paramount. In AI-driven systems, uncertainty estimat…
  datacite Ahmed, Soyed Tuhin, Hefenbrock, Michael, Tahoori, Mehdi B. 2025 置信度 0.66 Deep EnsembleBatchEnsembleTinyMLUncertainty EstimationMC-Dropout
• EtinyNet: Extremely Tiny Network for TinyML
  There are many AI applications in high-income countries because their implementation depends on expensive GPU cards (~2000$) and reliable power supply (~200W). To deploy AI in resource-poor settings on cheaper (~20$) and low-power devices (<1W), key modificati…
  datacite Association for Artificial Intelligence 2022, Gu, Lin, Lai, Rui, Li, Yishi 等 2022 置信度 0.66 Artificial Intelligence
• TinyML-Based Adaptive Pulse Shaping for Edge Intelligence in IoT/IIoT
  Edge intelligence in IoT and IIoT demands lightweight algorithms for data processing on resource-constrained devices. This paper introduces a novel adaptive pulse shape filter based on TinyML for PAPR and SER optimization on edge devices used in uplink IoT com…
  datacite Ali, Afan 2025 置信度 0.66 Signal Processing (eess.SP)FOS: Electrical engineering, electronic engineering, information engineeringFOS: Electrical engineering, electronic engineering, information engineering
• On-Sensor Convolutional Neural Networks with Early-Exits
  Tiny Machine Learning (TinyML) is a novel research field aiming at integrating Machine Learning (ML) within embedded devices with limited memory, computation, and energy. Recently, a new branch of TinyML has emerged, focusing on integrating ML directly into th…
  datacite Shalby, Hazem Hesham Yousef, De Vecchi, Arianna, Scandelli, Alice, Bartoli, Pietro 等 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Hardware Architecture (cs.AR)FOS: Computer and information sciencesFOS: Computer and information sciences
• Low-Cost Prototype for Bearing Failure Detection Using Tiny ML Through Vibration Analysis
  Authors: Andres Felipe Cotrino Herrera, Jesús Alfonso López Sotelo, Juan Carlos Blandón Andrade, Alonso Toro Lazo The following files are for a low-cost, open-source device designed to facilitate the learning of technologies like artificial intelligence in emb…
  datacite Cotrino, Andres 2024 置信度 0.66 Artificial IntelligenceTeachingMachine LearningVibration Analysis
• emlearn/emlearn-micropython: Release 0.1.0
  MicroPython integration for emlearn
  datacite Nordby, Jon 2023 置信度 0.66 TinyMLMachine LearningMicroPythonPythonEmbedded System
• LTO BATTERY USEFUL LIFE PREDICTION FOR ALWAYS ON EDGE AIoT BASED STRUCTURAL HEALTH MONITORING
  Although many high-sampling sensor systems tend to be power-hungry, critical monitoring applications require reliable battery-powered sensor nodes that can retrieve and compute data on the edge for years. With the advent of Tiny Machine Learning (TinyML), it i…
  datacite Arakistain, Ivan, Zamora, Diego, Garcia-Sanchez, David, Armijo, Alberto 2025 置信度 0.66
• Dendron: Enhancing Human Activity Recognition with On-Device TinyML Learning
  Human activity recognition (HAR) is a research field that employs Machine Learning (ML) techniques to identify user activities. Recent studies have prioritized the development of HAR solutions directly executed on wearable devices, enabling the on-device activ…
  datacite Shalby, Hazem Hesham Yousef, Roveri, Manuel 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training
  On-chip deep neural network (DNN) inference and training at the Extreme-Edge (TinyML) impose strict latency, throughput, accuracy, and flexibility requirements. Heterogeneous clusters are promising solutions to meet the challenge, combining the flexibility of …
  datacite Garofalo, Angelo, Tortorella, Yvan, Perotti, Matteo, Valente, Luca 等 2022 置信度 0.66 Heterogeneous clustertensor product engine (TPE)ultralow-power AI
• "Donate a Cry Corpus (Augmented)"
  "Quick classification of&nbsp;infant cries&nbsp;is vital to determine the reason for a baby's cry, especially in the first months of the baby's life. This study aims to develop a lightweight TinyML model for rapid classification of infant cries that can be use…
  datacite Gabor Veres 2025 置信度 0.66
• AI at the Edge: Exploring TinyML for Predictive Maintenance in Power Electronics on STM32 Microcontrollers
  This white paper presents a comprehensive exploration of deploying TinyML (Tiny Machine Learning) models on STM32 microcontrollers for predictive maintenance in power electronics systems such as Uninterruptible Power Supplies (UPS) and Battery Management Syste…
  datacite Aniket 2025 置信度 0.66 TinyMLSTM32Embedded AIPredictive MaintenancePower Electronics
• AI at the Edge: Exploring TinyML for Predictive Maintenance in Power Electronics on STM32 Microcontrollers
  This white paper presents a comprehensive exploration of deploying TinyML (Tiny Machine Learning) models on STM32 microcontrollers for predictive maintenance in power electronics systems such as Uninterruptible Power Supplies (UPS) and Battery Management Syste…
  datacite Aniket 2025 置信度 0.66 TinyMLSTM32Embedded AIPredictive MaintenancePower Electronics
• Edge AI and TinyML: Enabling Intelligent Systems at the Edge
  Artificial Intelligence has traditionally relied on centralized cloud infrastructures for data processing and model inference. However, this architecture poses challenges, including latency, privacy concerns, and network dependency. Edge AI addresses these iss…
  datacite Ghritlahare, Akhilesh 2025 置信度 0.66
• Edge AI and TinyML: Enabling Intelligent Systems at the Edge
  Artificial Intelligence has traditionally relied on centralized cloud infrastructures for data processing and model inference. However, this architecture poses challenges, including latency, privacy concerns, and network dependency. Edge AI addresses these iss…
  datacite Ghritlahare, Akhilesh 2025 置信度 0.66
• TinyML for Predictive Maintenance for Aircraft Ground Equipment; Navigating and Analyzing Internet-of-Things Security Risks
  Technical Project Abstract Aircraft Ground Equipment (AGE) is critical to the operations of the United States Air Force, but current maintenance processes are inefficient, leading to unexpected failures and increased costs. Tinker Air Force Base issued a Reque…
  datacite Glory Gurrola 2025 置信度 0.66 Internet of ThingsSecurity
• A Consciousness-Inspired Framework for Goal-Driven Autonomy in IoT and Software Agents
  This paper proposes a consciousness-inspired architectural framework for enabling goal-driven autonomy in Internet of Things (IoT) devices and software agents. Drawing from the biological distinction between the human brain and mind, the framework introduces a…
  datacite Aga, Ayaz 2025 置信度 0.66 Consciousness-inspired computingGoal-driven autonomyIoT agentsSelf-modeling systemsDigital autonomy
• A Consciousness-Inspired Framework for Goal-Driven Autonomy in IoT and Software Agents
  This paper proposes a consciousness-inspired architectural framework for enabling goal-driven autonomy in Internet of Things (IoT) devices and software agents. Drawing from the biological distinction between the human brain and mind, the framework introduces a…
  datacite Aga, Ayaz 2025 置信度 0.66 Consciousness-inspired computingGoal-driven autonomyIoT agentsSelf-modeling systemsDigital autonomy
• Kommunikations- und Aggregationsmethoden für das föderale Lernen
  Die Popularität und Fortschritte im maschinellen Lernen (ML) der letzten Jahrzehnte basieren maßgeblich auf den umfangreichen und diversen Datenmengen, die durch die Vernetzung unterschiedlicher Datenquellen über das Internet entstehen. Nach Angaben der Intern…
  datacite Wulfert, Lars 2024 置信度 0.66 Federated LearningTinyMLDecentralized Federated LearningEmbedded SystemsCommunication Efficient
• Edge Impulse model and the dataset for bird monitoring
  This research is about monitoring pest birds and scaring the such birds away. A tinyML model was trained and deployed on Arduino Nano 33 BLE Sense to monitor the pest birds. Whenever the model detects pest birds, a control is sent for trigger action to scare t…
  datacite Amenyedzi, Destiny Kwabla, Vodacek, Anthony, Kazeneza, Micheline, Mwaisekwa, Ipyana Issah 等 2024 置信度 0.66 Sustainable agricultural development
• Hardware/Software Co-Design of RISC-V Extensions for Accelerating Sparse DNNs on FPGAs
  The customizability of RISC-V makes it an attractive choice for accelerating deep neural networks (DNNs). It can be achieved through instruction set extensions and corresponding custom functional units. Yet, efficiently exploiting these opportunities requires …
  datacite Sabih, Muhammad, Karim, Abrarul, Wittmann, Jakob, Hannig, Frank 等 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Hardware Architecture (cs.AR)FOS: Computer and information sciencesFOS: Computer and information sciences
• Improving Health Monitoring Based on Smartwatches with Advanced Sensors and TinyML
  HWatch, a revolutionary smartwatch, addresses sensor constraints, cloud dependence,and high-power consumption in wearable health systems. It has three sensors: theMAX30100 for heart rate and SpO2, the MAX30205 for body temperature, and an IMU formotion trackin…
  datacite Chandini Mutta, Sk.Shameer, S. Meghana, N.S.R. Santosh 等 2025 置信度 0.66
• Improving Health Monitoring Based on Smartwatches with Advanced Sensors and TinyML
  HWatch, a revolutionary smartwatch, addresses sensor constraints, cloud dependence,and high-power consumption in wearable health systems. It has three sensors: theMAX30100 for heart rate and SpO2, the MAX30205 for body temperature, and an IMU formotion trackin…
  datacite Chandini Mutta, Sk.Shameer, S. Meghana, N.S.R. Santosh 等 2025 置信度 0.66