Modern computing: Vision and challenges

Telematics and Informatics Reports, Год журнала: 2024, Номер 13, С. 100116 - 100116

Опубликована: Янв. 8, 2024

Язык: Английский

---

The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review

Neural Computing and Applications, Год журнала: 2024, Номер 36(11), С. 5757 - 5797

Опубликована: Янв. 13, 2024

Abstract Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance ML medical and bioinformatics owing to its accuracy, investigators discussed multiple solutions for developing function challenges using deep (DL) techniques. The importance DL Internet Things (IoT)-based bio- informatics lies ability analyze interpret large amounts complex diverse data real time, providing insights that can improve healthcare outcomes increase efficiency industry. Several applications IoT-based include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, drug discovery. review aims comprehensively evaluate synthesize existing body literature on applying intersection IoT with informatics. In this paper, we categorized most cutting-edge issues into five categories based technique utilized: convolutional neural network , recurrent generative adversarial multilayer perception hybrid methods. A systematic was applied study each one terms effective properties, like main idea, benefits, drawbacks, methods, simulation environment, datasets. After that, research approaches concerns emphasized. addition, several contributed implementation have been addressed, which are predicted motivate more studies develop progressively. According findings, articles evaluated features sensitivity, specificity, F -score, latency, adaptability, scalability.

Язык: Английский

---

Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service

Artificial Intelligence in Medicine, Год журнала: 2024, Номер 149, С. 102779 - 102779

Опубликована: Янв. 24, 2024

Язык: Английский

---

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Sustainability, Год журнала: 2023, Номер 15(16), С. 12406 - 12406

Опубликована: Авг. 15, 2023

With the swift pace of development artificial intelligence (AI) in diverse spheres, medical and healthcare fields are utilizing machine learning (ML) methodologies numerous inventive ways. ML techniques have outstripped formerly state-of-the-art practices, yielding faster more precise outcomes. Healthcare practitioners increasingly drawn to this technology their initiatives relating Internet Behavior (IoB). This area research scrutinizes rationales, approaches, timing human adoption, encompassing domains Things (IoT), behavioral science, edge analytics. The significance applications based on IoB stems from its ability analyze interpret copious amounts complex data instantly, providing innovative perspectives that can enhance outcomes boost efficiency IoB-based procedures thus aid diagnoses, treatment protocols, clinical decision making. As a result inadequacy thorough inquiry into employment ML-based approaches context using for applications, we conducted study subject matter, introducing novel taxonomy underscores need employ each method distinctively. objective mind, classified cutting-edge solutions challenges five categories, which convolutional neural networks (CNNs), recurrent (RNNs), deep (DNNs), multilayer perceptions (MLPs), hybrid methods. In order delve deeper, systematic literature review (SLR) examined critical factors, such as primary concept, benefits, drawbacks, simulation environment, datasets. Subsequently, highlighted pioneering studies issues. Moreover, several related implementation medicine been tackled, thereby gradually fostering further endeavors health studies. Our findings indicated Tensorflow was most commonly utilized setting, accounting 24% proposed by researchers. Additionally, accuracy deemed be crucial parameter majority papers.

Язык: Английский

---

Influences of wildfire on the forest ecosystem and climate change: A comprehensive study

Environmental Research, Год журнала: 2023, Номер 240, С. 117537 - 117537

Опубликована: Окт. 30, 2023

Язык: Английский

---

AI student success predictor: Enhancing personalized learning in campus management systems

Computers in Human Behavior, Год журнала: 2024, Номер 158, С. 108301 - 108301

Опубликована: Май 13, 2024

Язык: Английский

---

Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications

Journal of Sensor and Actuator Networks, Год журнала: 2025, Номер 14(1), С. 9 - 9

Опубликована: Янв. 22, 2025

Federated Learning (FL) has emerged as a pivotal approach for decentralized Machine (ML), addressing the unique demands of Internet Things (IoT) environments where data privacy, bandwidth constraints, and device heterogeneity are paramount. This survey provides comprehensive overview FL, focusing on its integration with IoT. We delve into motivations behind adopting FL IoT, underlying techniques that facilitate this integration, challenges posed by IoT environments, diverse range applications is making an impact. Finally, submission also outlines future research directions open issues, aiming to provide detailed roadmap advancing in settings.

Язык: Английский

---

Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction

PLoS ONE, Год журнала: 2025, Номер 20(1), С. e0310218 - e0310218

Опубликована: Янв. 24, 2025

Diabetes, a chronic condition affecting millions worldwide, necessitates early intervention to prevent severe complications. While accurately predicting diabetes onset or progression remains challenging due complex and imbalanced datasets, recent advancements in machine learning offer potential solutions. Traditional prediction models, often limited by default parameters, have been superseded more sophisticated approaches. Leveraging Bayesian optimization fine-tune XGBoost, researchers can harness the power of data analysis improve predictive accuracy. By identifying key factors influencing risk, personalized prevention strategies be developed, ultimately enhancing patient outcomes. Successful implementation requires meticulous management, stringent ethical considerations, seamless integration into healthcare systems. This study focused on optimizing hyperparameters an XGBoost ensemble model using optimization. Compared grid search (accuracy: 97.24%, F1-score: 95.72%, MCC: 81.02%), with achieved slightly improved performance 97.26%, MCC:81.18%). Although improvements observed this are modest, optimized represents promising step towards revolutionizing treatment. approach holds significant outcomes for individuals at risk developing diabetes.

Язык: Английский

---

BrainNet: Precision Brain Tumor Classification with Optimized EfficientNet Architecture

International Journal of Intelligent Systems, Год журнала: 2024, Номер 2024(1)

Опубликована: Янв. 1, 2024

Brain tumors significantly impact human health due to their complexity and the challenges in early detection treatment. Accurate diagnosis is crucial for effective intervention, but existing methods often suffer from limitations accuracy efficiency. To address these challenges, this study presents a novel deep learning (DL) approach utilizing EfficientNet family enhanced brain tumor classification detection. Leveraging comprehensive dataset of 3064 T1‐weighted CE MRI images, our methodology incorporates advanced preprocessing augmentation techniques optimize model performance. The experiments demonstrate that EfficientNetB(07) achieved 99.14%, 98.76%, 99.07%, 99.69%, 99.07% accuracy, respectively. pinnacle research EfficientNetB3 model, which demonstrated exceptional performance with an rate 99.69%. This surpasses many state‐of‐the‐art (SOTA) techniques, underscoring efficacy approach. precision high‐accuracy DL promises improve diagnostic reliability speed clinical settings, facilitating earlier more treatment strategies. Our findings suggest significant potential improving patient outcomes diagnosis.

Язык: Английский

---

ESHA-256_GBGO: a high-performance and optimized security framework for internet of medical thing

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 20, 2025

The increasing adoption of the Internet Medical Things (IoMT) has raised critical security challenges, necessitating robust encryption techniques to safeguard sensitive healthcare data. However, existing models often suffer from high computational overhead, inefficiency in handling large-scale IoMT data and vulnerability cyber threats. To address these this paper proposes a novel ESHA-256_GBGO framework, integrating Enhanced Secure Hash Algorithm-256 (ESHA-256) with Golden Butterfly Optimization (GBGO) algorithm for improved performance optimization. proposed approach enhances integrity, efficiency speed while ensuring minimal processing overhead. framework is implemented evaluated on real-world dataset measuring key indicators such as efficiency, time, throughput Experimental results demonstrate that model achieves 98.76% reduces overhead by 27.4% robustness compared conventional methods. These findings validate effectiveness securing networks making it scalable efficient solution real-time applications.

Язык: Английский

---