21,449 Results Found

This study presents the complete assembly and analysis of the mitochondrial genome (mitogenome) of Medicago lupulina L. var. vulgaris Koch, cultivar-population VIK32, line MlS-1, which forms an effective symbiosis not only with arbuscular mycorrhiza...

The availability of massive amounts of temporal data opens new perspectives of knowledge extraction and automated decision making for companies and practitioners. However, learning forecasting models from data requires a knowledgeable data science or...

The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks such as TinyML have created new opportunities for ML algorithms running withi...

The healthcare sector is undergoing a transformation with the integration of cutting-edge technologies such as machine learning (ML), the Internet-of-Things (IoT), and Cyber–Physical Systems (CPS). However, traditional ML systems often face cha...

Model-Based Systems Engineering (MBSE) is employing systems analysis, design, and development on models of these systems, bringing together different viewpoints, with a step-by-step increase of detail. As such, it replaces traditional document-centri...

Artificial Intelligence (AI) and Machine Learning (ML) have experienced rapid growth in both industry and academia. However, the current ML and AI models demand significant computing and processing power to achieve desired accuracy and results, often...

In this paper, we consider a generalized Mittag-Leffler (ML)-type function and establish several integral formulas involving Jacobi and related transforms. We also establish some of the composition of generalized fractional derivative formulas associ...

Tiny machine learning (TinyML) demands the development of edge solutions that are both low-latency and power-efficient. To achieve these on System-on-Chip (SoC) FPGAs, co-design methodologies, such as hls4ml, have emerged aiming to speed up the desig...

Tiny machine learning (tinyML) involves the application of ML algorithms on resource-constrained devices such as microcontrollers. It is possible to improve tinyML performance by using a meta-learning approach. In this work, we proposed lightweight b...

Ozone is one of the most important gases in the atmosphere as it plays different roles based on the levels it presents. The ozone layer in the stratosphere protects life on Earth by absorbing ultraviolet (UV) radiance while harming life at ground-lev...

In recent years, paradigms like production quality or zero-defect manufacturing have emerged, highlighting the need to improve quality and reduce waste in manufacturing systems. Although quality can be analyzed from various points of view during diff...

AutomationML (AML) can be seen as a partial knowledge-based solution for manufacturing and automation domains since it permits integrating different engineering data format, and also contains information about physical and logical structures of produ...

The adoption of Sensor Web Enablement (SWE) practices by sensor maintainers is hampered by the inherent complexity of the Sensor Model Language (SensorML), its high expressiveness, and the scarce availability of editing tools. To overcome these issue...

Lassa virus (LASV), a highly prevalent mammalian arenavirus endemic in West Africa, can cause Lassa fever (LF), which is responsible for thousands of deaths annually. LASV is transmitted to humans from naturally infected rodents. At present, there is...

Rotavirus (RV) is the main pathogen that causes severe diarrhea in infants and children under 5 years of age. No specific antiviral therapies or licensed anti-rotavirus drugs are available. It is crucial to develop effective and low-toxicity anti-rot...

Each country needs to monitor progress on their Sustainable Development Goals (SDGs) to develop strategies that meet the expectations of the United Nations. Data envelope analysis (DEA) can help identify best practices for SDGs by setting goals to co...

A prominent area of data analytics is “timeseries modeling” where it is possible to forecast future values for the same variable using previous data. Numerous usage examples, including the economy, the weather, stock prices, and the devel...

Automated Machine Learning (Auto-ML) has primarily been used to optimize network hyperparameters or post-processing parameters, while the most critical component for training a high-quality model, the dataset, is usually left untouched. In this paper...

Integrating artificial intelligence (AI) and machine learning (ML) into pharmaceutical manufacturing processes holds great promise for enhancing efficiency, product quality, and regulatory compliance. However, implementing good manufacturing practice...

To study the inhibitory effects on microphthalmia-associated transcription factor (MITF)-related biological aspects in malignant melanomas (MMs) in the presence or absence of the low-molecular MITF specific inhibitor ML329, cell viability, cellular m...

Construction companies are under pressure to enhance their site safety condition, being constantly challenged by rapid technological advancements, growing public concern, and fierce competition. To enhance construction site safety, literature investi...

In the era when product design must meet the needs of consumers, the products preferred by consumers are an important source of design creativity and design reference for product designers to design products. Therefore, how to effectively grasp the p...

This study addresses a notable gap in the climate change literature by examining the potential of artificial intelligence and machine learning (AI–ML) in urban climate change adaptation and sustainable development across major global continents. Whil...

Technical debt (TD) has emerged as a significant concern in the development of AI/ML applications, where rapid experimentation, evolving objectives, and complex data pipelines often introduce hidden quality and maintainability issues. Within this bro...

Model-Based Systems Engineering (MBSE) supports the system-level design of complex products effectively. Currently, system design and optimization for complex products are two distinct processes that must be executed using different software or platf...

Artificial intelligence has attracted much attention due to its learning capability to solve versatile problems. Using a convolutional neural network in machine learning (ML), we investigated the binary hypothesis test, which is a fundamental problem...

In neutrophils, intracellular Ca²⁺ levels are regulated by several transporters and pathways, namely SERCA [sarco(endo)plasmic reticulum Ca²⁺-ATPase], SOCE (store-operated calcium entry), and ROCE (receptor-operated calcium entry). However, the exact...

Some authors suggest that transitioning requirements engineering from the traditional statements in natural language with shall clauses to model-based requirements within a Model-Based Systems Engineering (MBSE) environment could improve communicatio...

Machine learning (ML) has emerged as a powerful tool in multiple sclerosis (MS) research, enabling more accurate diagnosis, prognosis prediction, and treatment optimization. However, the complexity of developing and deploying ML models poses challeng...

Ochratoxin A (OTA) is a naturally occurring mycotoxin mainly produced by certain species of Aspergillus and Penicillium and is a serious threat to human health and food safety. Previous studies showed that Brevundimonas naejangsanensis ML17 can compl...

In recent years, Model Based Systems Engineering (MBSE) has continued to develop as a standard for designing, managing, and maintaining increasingly complex systems. Unlike the document centric approach, MBSE puts the model at the heart of system des...

Globally, over 17 million people annually die from cardiovascular diseases, with heart disease being the leading cause of mortality in the United States. The ever-increasing volume of data related to heart disease opens up possibilities for employing...

Tunnel boring machines (TBMs) are essential for excavating metro tunnels, reducing disruptions to surrounding rock, and ensuring efficient progress. This study examines how machine learning (ML) models can predict key tunneling outcomes, focusing on...

Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person’s behavio...

The ability to assess tumor apoptotic response to therapy could provide a direct and prompt measure of therapeutic efficacy. ¹⁸F-labeled 2-(5-fluoro-pentyl)-2-methyl-malonic acid ([¹⁸F]ML-10) is proposed as a positron emission tomography (PET) apopto...

The Internet of Things is rapidly growing with the demand for low-power, long-range wireless communication technologies. Long Range Wide Area Network (LoRaWAN) is one such technology that has gained significant attention in recent years due to its ab...

Epilepsy is a neurological disease characterized by recurrent seizures caused by abnormal electrical activity in the brain. One of the methods used to diagnose epilepsy is through electroencephalogram (EEG) analysis. EEG is a non-invasive medical tes...

In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introdu...

Federated learning (FL) is a decentralized machine learning (ML) technique that learns from distributed data by moving the training process from a centralized server towards many clients rather than centralizing the client data, as is common with cla...

Cyprinid herpesvirus 3 (CyHV-3) can cause severe disease in koi and common carp (Cyprinus carpio). Currently, no effective treatment is available against CyHV-3 infection in koi. Both LSD1 and JMJD2 are histone demethylases (HD) and are critical for...

The construction industry remains one of the most hazardous sectors, with a high incidence of injuries and fatalities, making accurate accident prediction essential for improving safety performance. Although machine learning and deep learning approac...

The growing global energy demand and the pursuit of sustainability highlight the transformative potential of artificial intelligence (AI) and machine learning (ML) in energy systems. This thematic review explores their applications in energy generati...

Objectives: ABCB1-mediated multidrug resistance (MDR) compromises chemotherapy efficacy in colorectal cancer (CRC). Despite decades of research, no selective ABCB1 inhibitor has achieved clinical success. This study investigates ML210 as a novel ABCB...

TinyML/DL is a new subfield of ML that allows for the deployment of ML algorithms on low-power devices to process their own data. The lack of resources restricts the aforementioned devices to running only inference tasks (static TinyML), while traini...

Systems Theoretical Accident Model and Process (STAMP), which considers system safety as an emergent property of the system, is a more effective accident/loss causality model for modern complex systems. Based on STAMP, System Theoretical Process Anal...

Artificial intelligence (AI) and machine learning (ML) have revolutionized the way health organizations approach social media. The sheer volume of data generated through social media can be overwhelming, but AI and ML can help organizations effective...

Driver drowsiness is one of the main causes of traffic accidents today. In recent years, driver drowsiness detection has suffered from issues integrating deep learning (DL) with Internet-of-things (IoT) devices due to the limited resources of IoT dev...

This study proposes a health monitoring system for snoring detection utilizing Tiny Machine Learning (TinyML) models, specifically designed for resource-constrained wearable Internet of Things (IoT) devices. This research addresses significant constr...

Automated machine learning (AutoML) platforms are increasingly adopted in manufacturing to support data-driven decision-making. However, systematic and reproducible evaluations of their practical applicability remain limited. This study presents a co...

The advancement of wireless networking has significantly enhanced beamforming capabilities in Autonomous Unmanned Aerial Systems (AUAS). This paper presents a simple and efficient classical algorithm to route a collection of AUAS or drone swarms exte...