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1.
Zhongguo Yi Liao Qi Xie Za Zhi ; 48(3): 319-322, 2024 May 30.
Artigo em Chinês | MEDLINE | ID: mdl-38863101

RESUMO

Objective: Strengthen the legal, compliant, and rational use of medical equipment and further guide the rationalization of medical behaviors. Methods: By utilizing the Internet of Things (IoT) and image analysis technology, collect real-time operation data of the equipment, establish a real-time running database for medical equipment, and cooperate with the 12 key links of the "whole life" of the equipment and the 8+6 management system framework to implement lean management of the efficiency, benefit, and effectiveness of medical equipment usage. Results: It realizes the improvement of the quality and efficiency of medical equipment, cost reduction and cost control, and provides data support for scientific decision-making. Conclusion: This study innovates the management model for the entire life cycle of medical equipment, providing a scientific approach to the management of hospital equipment.


Assuntos
Equipamentos e Provisões Hospitalares , Internet das Coisas , Equipamentos e Provisões , Administração de Materiais no Hospital , Controle de Custos
2.
PLoS One ; 19(6): e0301078, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38900762

RESUMO

Wireless communications have lately experienced substantial exploitation because they provide a lot of flexibility for data delivery. It provides connection and mobility by using air as a medium. Wireless sensor networks (WSN) are now the most popular wireless technologies. They need a communication infrastructure that is both energy and computationally efficient, which is made feasible by developing the best communication protocol algorithms. The internet of things (IoT) paradigm is anticipated to be heavily reliant on a networking architecture that is currently in development and dubbed software-defined WSN. Energy-efficient routing design is a key objective for WSNs. Cluster routing is one of the most commonly used routing techniques for extending network life. This research proposes a novel approach for increasing the energy effectiveness and longevity of software-defined WSNs. The major goal is to reduce the energy consumption of the cluster routing protocol using the firefly algorithm and high-efficiency entropy. According to the findings of the simulation, the suggested method outperforms existing algorithms in terms of system performance under various operating conditions. The number of alive nodes determined by the proposed algorithm is about 42.06% higher than Distributed Energy-Efficient Clustering with firefly algorithm (DEEC-FA) and 13.95% higher than Improved Firefly Clustering IFCEER and 12.05% higher than another referenced algorithm.


Assuntos
Algoritmos , Redes de Comunicação de Computadores , Software , Tecnologia sem Fio , Tecnologia sem Fio/instrumentação , Internet das Coisas
3.
Sensors (Basel) ; 24(11)2024 May 24.
Artigo em Inglês | MEDLINE | ID: mdl-38894166

RESUMO

The healthcare industry went through reformation by integrating the Internet of Medical Things (IoMT) to enable data harnessing by transmission mediums from different devices, about patients to healthcare staff devices, for further analysis through cloud-based servers for proper diagnosis of patients, yielding efficient and accurate results. However, IoMT technology is accompanied by a set of drawbacks in terms of security risks and vulnerabilities, such as violating and exposing patients' sensitive and confidential data. Further, the network traffic data is prone to interception attacks caused by a wireless type of communication and alteration of data, which could cause unwanted outcomes. The advocated scheme provides insight into a robust Intrusion Detection System (IDS) for IoMT networks. It leverages a honeypot to divert attackers away from critical systems, reducing the attack surface. Additionally, the IDS employs an ensemble method combining Logistic Regression and K-Nearest Neighbor algorithms. This approach harnesses the strengths of both algorithms to improve attack detection accuracy and robustness. This work analyzes the impact, performance, accuracy, and precision outcomes of the used model on two IoMT-related datasets which contain multiple attack types such as Man-In-The-Middle (MITM), Data Injection, and Distributed Denial of Services (DDOS). The yielded results showed that the proposed ensemble method was effective in detecting intrusion attempts and classifying them as attacks or normal network traffic, with a high accuracy of 92.5% for the first dataset and 99.54% for the second dataset and a precision of 96.74% for the first dataset and 99.228% for the second dataset.


Assuntos
Algoritmos , Segurança Computacional , Atenção à Saúde , Internet das Coisas , Humanos , Tecnologia sem Fio , Computação em Nuvem , Confidencialidade
4.
Sensors (Basel) ; 24(11)2024 May 26.
Artigo em Inglês | MEDLINE | ID: mdl-38894222

RESUMO

The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity.


Assuntos
Fontes de Energia Elétrica , Internet das Coisas , Humanos , Análise de Regressão , Monitorização Fisiológica/métodos , Algoritmos
5.
Sensors (Basel) ; 24(11)2024 May 26.
Artigo em Inglês | MEDLINE | ID: mdl-38894225

RESUMO

The Internet of Things (IoT) is a growing network of interconnected devices used in transportation, finance, public services, healthcare, smart cities, surveillance, and agriculture. IoT devices are increasingly integrated into mobile assets like trains, cars, and airplanes. Among the IoT components, wearable sensors are expected to reach three billion by 2050, becoming more common in smart environments like buildings, campuses, and healthcare facilities. A notable IoT application is the smart campus for educational purposes. Timely notifications are essential in critical scenarios. IoT devices gather and relay important information in real time to individuals with special needs via mobile applications and connected devices, aiding health-monitoring and decision-making. Ensuring IoT connectivity with end users requires long-range communication, low power consumption, and cost-effectiveness. The LPWAN is a promising technology for meeting these needs, offering a low cost, long range, and minimal power use. Despite their potential, mobile IoT and LPWANs in healthcare, especially for emergency response systems, have not received adequate research attention. Our study evaluated an LPWAN-based emergency response system for visually impaired individuals on the Hazara University campus in Mansehra, Pakistan. Experiments showed that the LPWAN technology is reliable, with 98% reliability, and suitable for implementing emergency response systems in smart campus environments.


Assuntos
Internet das Coisas , Humanos , Aplicativos Móveis , Tecnologia sem Fio
6.
Sensors (Basel) ; 24(11)2024 Jun 04.
Artigo em Inglês | MEDLINE | ID: mdl-38894422

RESUMO

The growth of IoT healthcare is aimed at providing efficient services to patients by utilizing data from local hospitals. However, privacy concerns can impede data sharing among third parties. Federated learning offers a solution by enabling the training of neural networks while maintaining the privacy of the data. To integrate federated learning into IoT healthcare, hospitals must be part of the network to jointly train a global central model on the server. Local hospitals can train the global model using their patient datasets and send the trained localized models to the server. These localized models are then aggregated to enhance the global model training process. The aggregation of local models dramatically influences the performance of global training, mainly due to the heterogeneous nature of patient data. Existing solutions to address this issue are iterative, slow, and susceptible to convergence. We propose two novel approaches that form groups efficiently and assign the aggregation weightage considering essential parameters vital for global training. Specifically, our method utilizes an autoencoder to extract features and learn the divergence between the latent representations of patient data to form groups, facilitating more efficient handling of heterogeneity. Additionally, we propose another novel aggregation process that utilizes several factors, including extracted features of patient data, to maximize performance further. Our proposed approaches for group formation and aggregation weighting outperform existing conventional methods. Notably, significant results are obtained, one of which shows that our proposed method achieves 20.8% higher accuracy and 7% lower loss reduction compared to the conventional methods.


Assuntos
Internet das Coisas , Redes Neurais de Computação , Humanos , Atenção à Saúde , Algoritmos , Aprendizado de Máquina
7.
Sensors (Basel) ; 24(11)2024 Jun 06.
Artigo em Inglês | MEDLINE | ID: mdl-38894471

RESUMO

The integration of cutting-edge technologies such as the Internet of Things (IoT), robotics, and machine learning (ML) has the potential to significantly enhance the productivity and profitability of traditional fish farming. Farmers using traditional fish farming methods incur enormous economic costs owing to labor-intensive schedule monitoring and care, illnesses, and sudden fish deaths. Another ongoing issue is automated fish species recommendation based on water quality. On the one hand, the effective monitoring of abrupt changes in water quality may minimize the daily operating costs and boost fish productivity, while an accurate automatic fish recommender may aid the farmer in selecting profitable fish species for farming. In this paper, we present AquaBot, an IoT-based system that can automatically collect, monitor, and evaluate the water quality and recommend appropriate fish to farm depending on the values of various water quality indicators. A mobile robot has been designed to collect parameter values such as the pH, temperature, and turbidity from all around the pond. To facilitate monitoring, we have developed web and mobile interfaces. For the analysis and recommendation of suitable fish based on water quality, we have trained and tested several ML algorithms, such as the proposed custom ensemble model, random forest (RF), support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), bagging, boosting, and stacking, on a real-time pond water dataset. The dataset has been preprocessed with feature scaling and dataset balancing. We have evaluated the algorithms based on several performance metrics. In our experiment, our proposed ensemble model has delivered the best result, with 94% accuracy, 94% precision, 94% recall, a 94% F1-score, 93% MCC, and the best AUC score for multi-class classification. Finally, we have deployed the best-performing model in a web interface to provide cultivators with recommendations for suitable fish farming. Our proposed system is projected to not only boost production and save money but also reduce the time and intensity of the producer's manual labor.


Assuntos
Aprendizado de Máquina , Lagoas , Qualidade da Água , Animais , Peixes , Algoritmos , Monitoramento Ambiental/métodos , Máquina de Vetores de Suporte , Aquicultura/métodos , Internet das Coisas , Pesqueiros
8.
Sensors (Basel) ; 24(11)2024 Jun 06.
Artigo em Inglês | MEDLINE | ID: mdl-38894478

RESUMO

Identification of different species of animals has become an important issue in biology and ecology. Ornithology has made alliances with other disciplines in order to establish a set of methods that play an important role in the birds' protection and the evaluation of the environmental quality of different ecosystems. In this case, the use of machine learning and deep learning techniques has produced big progress in birdsong identification. To make an approach from AI-IoT, we have used different approaches based on image feature comparison (through CNNs trained with Imagenet weights, such as EfficientNet or MobileNet) using the feature spectrogram for the birdsong, but also the use of the deep CNN (DCNN) has shown good performance for birdsong classification for reduction of the model size. A 5G IoT-based system for raw audio gathering has been developed, and different CNNs have been tested for bird identification from audio recordings. This comparison shows that Imagenet-weighted CNN shows a relatively high performance for most species, achieving 75% accuracy. However, this network contains a large number of parameters, leading to a less energy efficient inference. We have designed two DCNNs to reduce the amount of parameters, to keep the accuracy at a certain level, and to allow their integration into a small board computer (SBC) or a microcontroller unit (MCU).


Assuntos
Aves , Redes Neurais de Computação , Vocalização Animal , Animais , Aves/fisiologia , Aves/classificação , Vocalização Animal/fisiologia , Aprendizado de Máquina , Internet das Coisas , Inteligência Artificial , Aprendizado Profundo , Algoritmos
9.
PLoS One ; 19(6): e0305415, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38889129

RESUMO

How can a smart home system control a connected device to be in a desired state? Recent developments in the Internet of Things (IoT) technology enable people to control various devices with the smart home system rather than physical contact. Furthermore, smart home systems cooperate with voice assistants such as Bixby or Alexa allowing users to control their devices through voice. In this process, a user's query clarifies the target state of the device rather than the actions to perform. Thus, the smart home system needs to plan a sequence of actions to fulfill the user's needs. However, it is challenging to perform action planning because it needs to handle a large-scale state transition graph of a real-world device, and the complex dependence relationships between capabilities. In this work, we propose SmartAid (Smart Home Action Planning in awareness of Dependency), an action planning method for smart home systems. To represent the state transition graph, SmartAid learns models that represent the prerequisite conditions and operations of actions. Then, SmartAid generates an action plan considering the dependencies between capabilities and actions. Extensive experiments demonstrate that SmartAid successfully represents a real-world device based on a state transition log and generates an accurate action sequence for a given query.


Assuntos
Internet das Coisas , Humanos , Algoritmos
10.
BMC Med Inform Decis Mak ; 24(1): 153, 2024 Jun 04.
Artigo em Inglês | MEDLINE | ID: mdl-38831390

RESUMO

BACKGROUND: The increased application of Internet of Things (IoT) in healthcare, has fueled concerns regarding the security and privacy of patient data. Lightweight Cryptography (LWC) algorithms can be seen as a potential solution to address this concern. Due to the high variation of LWC, the primary objective of this study was to identify a suitable yet effective algorithm for securing sensitive patient information on IoT devices. METHODS: This study evaluates the performance of eight LWC algorithms-AES, PRESENT, MSEA, LEA, XTEA, SIMON, PRINCE, and RECTANGLE-using machine learning models. Experiments were conducted on a Raspberry Pi 3 microcontroller using 16 KB to 2048 KB files. Machine learning models were trained and tested for each LWC algorithm and their performance was evaluated based using precision, recall, F1-score, and accuracy metrics. RESULTS: The study analyzed the encryption/decryption execution time, energy consumption, memory usage, and throughput of eight LWC algorithms. The RECTANGLE algorithm was identified as the most suitable and efficient LWC algorithm for IoT in healthcare due to its speed, efficiency, simplicity, and flexibility. CONCLUSIONS: This research addresses security and privacy concerns in IoT healthcare and identifies key performance factors of LWC algorithms utilizing the SLR research methodology. Furthermore, the study provides insights into the optimal choice of LWC algorithm for enhancing privacy and security in IoT healthcare environments.


Assuntos
Segurança Computacional , Internet das Coisas , Aprendizado de Máquina , Humanos , Segurança Computacional/normas , Algoritmos , Confidencialidade/normas
11.
PLoS One ; 19(6): e0304067, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38833448

RESUMO

Edge computing is a scalable, modern, and distributed computing architecture that brings computational workloads closer to smart gateways or Edge devices. This computing model delivers IoT (Internet of Things) computations and processes the IoT requests from the Edge of the network. In a diverse and independent environment like Fog-Edge, resource management is a critical issue. Hence, scheduling is a vital process to enhance efficiency and allocation of resources properly to the tasks. The manuscript proposes an Artificial Neural Network (ANN) inspired Antlion algorithm for task orchestration Edge environments. Its aim is to enhance resource utilization and reduce energy consumption. Comparative analysis with different algorithms shows that the proposed algorithm balances the load on the Edge layer, which results in lower load on the cloud, improves power consumption, CPU utilization, network utilization, and reduces average waiting time for requests. The proposed model is tested for healthcare application in Edge computing environment. The evaluation shows that the proposed algorithm outperforms existing fuzzy logic algorithms. The performance of the ANN inspired Antlion based orchestration approach is evaluated using performance metrics, power consumption, CPU utilization, network utilization, and average waiting time for requests respectively. It outperforms the existing fuzzy logic, round robin algorithm. The proposed technique achieves an average cloud energy consumption improvement of 95.94%, and average Edge energy consumption improvement of 16.79%, 19.85% in average CPU utilization in Edge computing environment, 10.64% in average CPU utilization in cloud environment, and 23.33% in average network utilization, and the average waiting time decreases by 96% compared to fuzzy logic and 1.4% compared to round-robin respectively.


Assuntos
Algoritmos , Redes Neurais de Computação , Lógica Fuzzy , Internet das Coisas , Computação em Nuvem
12.
Biosensors (Basel) ; 14(5)2024 Apr 25.
Artigo em Inglês | MEDLINE | ID: mdl-38785688

RESUMO

Electrochemical biosensors include a recognition component and an electronic transducer, which detect the body fluids with a high degree of accuracy. More importantly, they generate timely readings of the related physiological parameters, and they are suitable for integration into portable, wearable and implantable devices that are significant relative to point-of-care diagnostics scenarios. As an example, the personal glucose meter fundamentally improves the management of diabetes in the comfort of the patients' homes. This review paper analyzes the principles of electrochemical biosensing and the structural features of electrochemical biosensors relative to the implementation of health monitoring and disease diagnostics strategies. The analysis particularly considers the integration of the biosensors into wearable, portable, and implantable systems. The fundamental aim of this paper is to present and critically evaluate the identified significant developments in the scope of electrochemical biosensing for preventive and customized point-of-care diagnostic devices. The paper also approaches the most important engineering challenges that should be addressed in order to improve the sensing accuracy, and enable multiplexing and one-step processes, which mediate the integration of electrochemical biosensing devices into digital healthcare scenarios.


Assuntos
Técnicas Biossensoriais , Dispositivos Eletrônicos Vestíveis , Humanos , Técnicas Eletroquímicas , Sistemas Automatizados de Assistência Junto ao Leito , Internet das Coisas
13.
Environ Monit Assess ; 196(6): 582, 2024 May 28.
Artigo em Inglês | MEDLINE | ID: mdl-38806872

RESUMO

IoT is a game-changer across all fields, including chemistry. Embracing sustainable practices and green chemistry, the miniaturization and automation of systems, and their integration into IoT is key to achieving these principles, as a rising trend with momentum. Particularly, IoT and analytical chemistry are linked in the rapid exchange of analytical data for environmental, industrial, healthcare, and educational applications. Meanwhile, cooperation with other fields of science is evident, and there is a prompt and subjective analysis of information related to analytical systems and methodologies. This paper will review the concepts, requirements, and architecture of IoT and its role in the miniaturization and automation of analytical tools using electronic modules and sensors. The aim is to explore the standards and perspectives of IoT and its interaction with different aspects of analytical chemistry. Additionally, it aimed to explain the basics and applications of IoT for chemists, and its relevance to different subfields of analytical chemistry, particularly in the field of environmental chemical surveillance. The article also covers updating IoT devices and creating DIY-based degradation devices to enhance the educational aspect of chemistry and reduce barriers to lab facilities and equipment. Lastly, it will explore how IoT is really important and how it's going to significantly impact analytical chemistry.


Assuntos
Monitoramento Ambiental , Internet das Coisas , Miniaturização , Monitoramento Ambiental/métodos
14.
Comput Biol Chem ; 111: 108110, 2024 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-38815500

RESUMO

The recent advances in artificial intelligence modern approaches can play vital roles in the Internet of Medical Things (IoMT). Automatic diagnosis is one of the most important topics in the IoMT, including cancer diagnosis. Breast cancer is one of the top causes of death among women. Accurate diagnosis and early detection of breast cancer can improve the survival rate of patients. Deep learning models have demonstrated outstanding potential in accurately detecting and diagnosing breast cancer. This paper proposes a novel technology for breast cancer detection using CrossViT as the deep learning model and an enhanced version of the Growth Optimizer algorithm (MGO) as the feature selection method. CrossVit is a hybrid deep learning model that combines the strengths of both convolutional neural networks (CNNs) and transformers. The MGO is a meta-heuristic algorithm that selects the most relevant features from a large pool of features to enhance the performance of the model. The developed approach was evaluated on three publicly available breast cancer datasets and achieved competitive performance compared to other state-of-the-art methods. The results show that the combination of CrossViT and the MGO can effectively identify the most informative features for breast cancer detection, potentially assisting clinicians in making accurate diagnoses and improving patient outcomes. The MGO algorithm improves accuracy by approximately 1.59% on INbreast, 5.00% on MIAS, and 0.79% on MiniDDSM compared to other methods on each respective dataset. The developed approach can also be utilized to improve the Quality of Service (QoS) in the healthcare system as a deployable IoT-based intelligent solution or a decision-making assistance service, enhancing the efficiency and precision of the diagnosis.


Assuntos
Algoritmos , Neoplasias da Mama , Humanos , Neoplasias da Mama/diagnóstico , Feminino , Aprendizado Profundo , Redes Neurais de Computação , Internet das Coisas
15.
Environ Sci Pollut Res Int ; 31(27): 39372-39387, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38819512

RESUMO

Accurate air pollution prediction is vital for residents' well-being. This research introduces a secure air quality monitoring system using neural networks and blockchain for robust analysis, precise predictions, and early pollution detection. Blockchain guarantees data integrity, security, and transparency. Goals include real-time air quality data, secure blockchain recording, and enhanced safety through informed decisions. The research integrates blockchain and IoT for short- and long-term air quality monitoring, utilizing an optimized neural network. IoT sensors collect PM2.5, PM10, CO, NO2, and SO2, processed through noise removal and normalization, with feature extraction using N-tuple contrastive learning. Predictions utilize Graph attention-based deep Residual shrinkage Network and Bidirectional long short Term Memory (GRNBTM) categorized into five levels. An adaptive bowerbird algorithm optimizes parameters, reducing computational complexity. Blockchain integration ensures secure, tamper-proof data storage with a lightweight consensus-based algorithm. The GRNBTM model's air quality monitoring performance is extensively simulated and analyzed at 30-min, 2-h, 1-day, and 1-month intervals, demonstrating superior performance over existing techniques.


Assuntos
Poluição do Ar , Monitoramento Ambiental , Redes Neurais de Computação , Monitoramento Ambiental/métodos , Blockchain , Algoritmos , Poluentes Atmosféricos/análise , Internet das Coisas
16.
Stud Health Technol Inform ; 314: 108-112, 2024 May 23.
Artigo em Inglês | MEDLINE | ID: mdl-38785013

RESUMO

The growing integration of Internet of Things (IoT) technology within the healthcare sector has revolutionized healthcare delivery, enabling advanced personalized care and precise treatments. However, this raises significant challenges, demanding robust, intelligible, and effective monitoring mechanisms. We propose an interpretable machine-learning approach to the trustworthy and effective detection of behavioral anomalies within the realm of medical IoT. The discovered anomalies serve as indicators of potential system failures and security threats. Essentially, the detection of anomalies is accomplished by learning a classifier from the operational data generated by smart devices. The learning problem is dealt with in predictive association modeling, whose expressiveness and intelligibility enforce trustworthiness to offer a comprehensive, fully interpretable, and effective monitoring solution for the medical IoT ecosystem. Preliminary results show the effectiveness of our approach.


Assuntos
Internet das Coisas , Aprendizado de Máquina , Medicina de Precisão , Humanos , Segurança Computacional
17.
Transl Vis Sci Technol ; 13(5): 18, 2024 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-38776108

RESUMO

Purpose: We aimed to design, develop, and evaluate an internet of things-enabled patch (IoT patch) for real-time remote monitoring of adherence (or patch wear time) during patch treatment in child participants in clinical trials. This study provides healthcare providers with a tool for objective, real-time, and remote assessment of adherence and for making required adjustments to treatment plans. Methods: The IoT patch had two temperature microsensors and a wireless chip. One sensor was placed closer to the skin than the other, resulting in a temperature difference depending on whether the patch was worn. When the patch was worn, it measured temperatures every 30 seconds and transmitted temperature data to a cloud server via a mobile application every 15 seconds. The patch was evaluated via 2 experiments with 30 healthy adults and 40 children with amblyopia. Results: Excellent monitoring accuracy was observed in both adults (mean delay of recorded time data, 0.4 minutes) and children (mean, 0.5 minutes). The difference between manually recorded and objectively recorded patch wear times showed good agreement in both groups. Experiment 1 showed accurate monitoring over a wide range of temperatures (from 0 to 30°C). Experiment 2 showed no significant differences in wearability (ease-of-use and comfort scores) between the IoT and conventional patches. Conclusions: The IoT patch offers an accurate, real-time, and remote system to monitor adherence to patch treatment. The patch is comfortable and easy to use. The utilization of an IoT patch may increase adherence to patch treatment based on accurate monitoring. Translational Relevance: Results show that the IoT patch can enable real-time adherence monitoring in clinical trials, improving treatment precision, and patient compliance to enhance outcomes.


Assuntos
Internet das Coisas , Tecnologia sem Fio , Humanos , Feminino , Masculino , Adulto , Criança , Tecnologia sem Fio/instrumentação , Cooperação do Paciente , Desenho de Equipamento/métodos , Pré-Escolar , Adulto Jovem , Dispositivos Eletrônicos Vestíveis , Tecnologia de Sensoriamento Remoto/instrumentação , Tecnologia de Sensoriamento Remoto/métodos
18.
Sci Rep ; 14(1): 10280, 2024 05 04.
Artigo em Inglês | MEDLINE | ID: mdl-38704423

RESUMO

In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients' medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier's error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL's effectiveness and efficiency in identifying diseases is evaluated and compared.


Assuntos
Inteligência Artificial , Internet das Coisas , Humanos , Prognóstico , Aprendizado Profundo , Doença Crônica , Algoritmos
19.
Sci Rep ; 14(1): 10412, 2024 05 06.
Artigo em Inglês | MEDLINE | ID: mdl-38710744

RESUMO

The proposed work contains three major contribution, such as smart data collection, optimized training algorithm and integrating Bayesian approach with split learning to make privacy of the patent data. By integrating consumer electronics device such as wearable devices, and the Internet of Things (IoT) taking THz image, perform EM algorithm as training, used newly proposed slit learning method the technology promises enhanced imaging depth and improved tissue contrast, thereby enabling early and accurate disease detection the breast cancer disease. In our hybrid algorithm, the breast cancer model achieves an accuracy of 97.5 percent over 100 epochs, surpassing the less accurate old models which required a higher number of epochs, such as 165.


Assuntos
Algoritmos , Neoplasias da Mama , Dispositivos Eletrônicos Vestíveis , Humanos , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/diagnóstico , Internet das Coisas , Feminino , Imagem Terahertz/métodos , Teorema de Bayes , Diagnóstico por Imagem/métodos , Processamento de Imagem Assistida por Computador/métodos , Aprendizado de Máquina
20.
PLoS One ; 19(5): e0302196, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38820435

RESUMO

Web applications are important for various online businesses and operations because of their platform stability and low operation cost. The increasing usage of Internet-of-Things (IoT) devices within a network has contributed to the rise of network intrusion issues due to malicious Uniform Resource Locators (URLs). Generally, malicious URLs are initiated to promote scams, attacks, and frauds which can lead to high-risk intrusion. Several methods have been developed to detect malicious URLs in previous works. There has been a good amount of work done to detect malicious URLs using various methods such as random forest, regression, LightGBM, and more as reported in the literature. However, most of the previous works focused on the binary classification of malicious URLs and are tested on limited URL datasets. Nevertheless, the detection of malicious URLs remains a challenging task that remains open to research. Hence, this work proposed a stacking-based ensemble classifier to perform multi-class classification of malicious URLs on larger URL datasets to justify the robustness of the proposed method. This study focuses on obtaining lexical features directly from the URL to identify malicious websites. Then, the proposed stacking-based ensemble classifier is developed by integrating Random Forest, XGBoost, LightGBM, and CatBoost. In addition, hyperparameter tuning was performed using the Randomized Search method to optimize the proposed classifier. The proposed stacking-based ensemble classifier aims to take advantage of the performance of each machine learning model and aggregate the output to improve prediction accuracy. The classification accuracies of the machine learning model when applied individually are 93.6%, 95.2%, 95.7% and 94.8% for random forest, XGBoost, LightGBM, and CatBoost respectively. The proposed stacking-based ensemble classifier has shown significant results in classifying four classes of malicious URLs (phishing, malware, defacement, and benign) with an average accuracy of 96.8% when benchmarked with previous works.


Assuntos
Aprendizado de Máquina , Segurança Computacional , Internet das Coisas , Algoritmos
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