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19 pages, 4218 KiB

Open AccessArticle

Accurate Medical Vial Identification Through Mixed Reality: A HoloLens 2 Implementation

by Bahar Uddin Mahmud, Guan Yue Hong, Afsana Sharmin, Zachary D. Asher and John D. Hoyle, Jr.

Electronics 2024, 13(22), 4420; https://doi.org/10.3390/electronics13224420 - 11 Nov 2024

Viewed by 1480

Abstract

The accurate identification of medicine vials is crucial for emergency medical services, especially for vials that resemble one another but have different labels, volumes, and concentrations. This study introduces a method to detect vials in real-time using mixed reality technology through Microsoft HoloLens [...] Read more.

The accurate identification of medicine vials is crucial for emergency medical services, especially for vials that resemble one another but have different labels, volumes, and concentrations. This study introduces a method to detect vials in real-time using mixed reality technology through Microsoft HoloLens 2. The system is also equipped with an SQL server to manage barcode and vial information. We conducted a comparative analysis of the barcode detection capabilities of the HoloLens 2 camera and an external scanner. The HoloLens 2 effectively identified larger barcodes when they were 20–25 cm away in normal lighting conditions. However, it faced difficulties in detecting smaller barcodes that were consistently detected by the external scanner. The frame rate investigation revealed performance fluctuations: an average of 10.54 frames per second (fps) under standard lighting conditions, decreasing to 10.10 fps in low light and further reducing to 10.05 fps when faced with high barcode density. Resolution tests demonstrated that a screen resolution of 1920 × 1080 yielded the best level of accuracy, with a precision rate of 98%. On the other hand, a resolution of 1280 × 720 achieved a good balance between accuracy 93% and speed. The HoloLens 2 demonstrates satisfactory performance under ideal circumstances; however, enhancements in detecting algorithms and camera resolution are required to accommodate diverse surroundings. This approach seeks to help paramedics make quick and accurate decisions during critical situations and tackle common obstacles such as reliance on networks and human mistakes. Our new approach of a hybrid method that integrates an external Bluetooth scanner with the MR device gives optimal results compared to the scanner-only approach. Full article

(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)

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26 pages, 555 KiB

Open AccessArticle

Cracking the Core: Hardware Vulnerabilities in Android Devices Unveiled

by Antonio Muñoz

Electronics 2024, 13(21), 4269; https://doi.org/10.3390/electronics13214269 - 31 Oct 2024

Cited by 2 | Viewed by 2775

Abstract

As Android devices become more prevalent, their security risks extend beyond software vulnerabilities to include critical hardware weaknesses. This paper provides a comprehensive and systematic review of hardware-related vulnerabilities in Android systems, which can bypass even the most sophisticated software defenses. We compile [...] Read more.

As Android devices become more prevalent, their security risks extend beyond software vulnerabilities to include critical hardware weaknesses. This paper provides a comprehensive and systematic review of hardware-related vulnerabilities in Android systems, which can bypass even the most sophisticated software defenses. We compile and analyze an extensive range of reported vulnerabilities, introducing a novel categorization framework to facilitate a deeper understanding of these risks, classified by affected hardware components, vulnerability type, and the potential impact on system security. The paper addresses key areas such as memory management flaws, side-channel attacks, insecure system-on-chip (SoC) resource allocation, and cryptographic vulnerabilities. In addition, it examines feasible countermeasures, including hardware-backed encryption, secure boot mechanisms, and trusted execution environments (TEEs), to mitigate the risks posed by these hardware threats. By contextualizing hardware vulnerabilities within the broader security architecture of Android devices, this review emphasizes the importance of hardware security in ensuring system integrity and resilience. The findings serve as a valuable resource for both researchers and security professionals, offering insights into the development of more robust defenses against the emerging hardware-based threats faced by Android devices. Full article

(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)

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13 pages, 5394 KiB

Open AccessArticle

A Novel Electromagnetic Sensing Generative Adversarial Network for Uniaxial Objects

by Chien-Ching Chiu, Po-Hsiang Chen, Hao Jiang and Bo-Yu Shi

Electronics 2024, 13(20), 4027; https://doi.org/10.3390/electronics13204027 - 13 Oct 2024

Viewed by 835

Abstract

Electromagnetic imaging achieves enhanced resolution by leveraging the advanced sensing and data analysis capabilities of Internet of Things (IoT) systems. This paper introduces a novel learning approach for generative adversarial networks (GANs) to tackle significant challenges in electromagnetic sensing. The proposed method involves [...] Read more.

Electromagnetic imaging achieves enhanced resolution by leveraging the advanced sensing and data analysis capabilities of Internet of Things (IoT) systems. This paper introduces a novel learning approach for generative adversarial networks (GANs) to tackle significant challenges in electromagnetic sensing. The proposed method involves deploying additional transmitters and receivers to irradiate TM (transverse magnetic) and TE (transverse electric) polarization waves around uniaxial objects to capture the scattered field in free space. Subsequently, scattered field generative adversarial networks (SF-GANs) are utilized to simulate and learn the characteristics of Maxwell’s equations. Numerical simulations and experimental results demonstrate the superior performance of the SF-GANs compared to backpropagation generative adversarial networks (BP-GANs). Furthermore, it is worth noting that our method is capable of reconstructing high-dielectric-constant objects. Full article

(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)

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20 pages, 9914 KiB

Open AccessArticle

A Comparative Study of Machine Learning Models for Predicting Meteorological Data in Agricultural Applications

by Jelena Šuljug, Josip Spišić, Krešimir Grgić and Drago Žagar

Electronics 2024, 13(16), 3284; https://doi.org/10.3390/electronics13163284 - 19 Aug 2024

Cited by 4 | Viewed by 2411

Abstract

This study aims to address the challenges of climate change, which has led to extreme temperature events and reduced rainfall, using Internet of Things (IoT) technologies. Specifically, we monitored the effects of drought on maize crops in the Republic of Croatia. Our research [...] Read more.

This study aims to address the challenges of climate change, which has led to extreme temperature events and reduced rainfall, using Internet of Things (IoT) technologies. Specifically, we monitored the effects of drought on maize crops in the Republic of Croatia. Our research involved analyzing an extensive dataset of 139,965 points of weather data collected during the summer of 2022 in different areas with 18 commercial sensor nodes using the Long-Range Wide Area Network (LoRaWAN) protocol. The measured parameters include temperature, humidity, solar irradiation, and air pressure. Newly developed maize-specific predictive models were created, taking into account the impact of urbanization on the agrometeorological parameters. We also categorized the data into urban, suburban, and rural segments to fill gaps in the existing literature. Our approach involved using 19 different regression models to analyze the data, resulting in four regional models per parameter and four general models that apply to all areas. This comprehensive analysis allowed us to select the most effective models for each area, improving the accuracy of our predictions of agrometeorological parameters and helping to optimize maize yields as weather patterns change. Our research contributes to the integration of machine learning and AI into the Internet of Things for agriculture and provides innovative solutions for predictive analytics in crop production. By focusing on solar irradiation in addition to traditional weather parameters and accounting for geographical differences, our models provide a tool to address the pressing issue of agricultural sustainability in the face of impending climate change. In addition, our results have practical implications for resource management and efficiency improvement in the agricultural sector. Full article

(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)

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15 pages, 2391 KiB

Open AccessArticle

OccTr: A Two-Stage BEV Fusion Network for Temporal Object Detection

by Qifang Fu, Xinyi Yu and Linlin Ou

Electronics 2024, 13(13), 2611; https://doi.org/10.3390/electronics13132611 - 3 Jul 2024

Viewed by 1641

Abstract

Temporal fusion approaches are critical for 3D visual perception tasks in IOV (Internet of Vehicles), but they often rely on intermediate representations without fully utilizing position information from the previous frame’s detection results, which cannot compensate for the lack of depth information in [...] Read more.

Temporal fusion approaches are critical for 3D visual perception tasks in IOV (Internet of Vehicles), but they often rely on intermediate representations without fully utilizing position information from the previous frame’s detection results, which cannot compensate for the lack of depth information in visual data. In this work, we propose a novel framework called OccTr (Occupancy Transformer) that combines two temporal cues, intermediate representation and back-end representation, via occupancy map to enhance temporal fusion in object detection task. OccTr leverages attention mechanisms to perform both intermediate and back-end temporal fusion by incorporating intermediate BEV (bird’s-eye view) features and back-end prediction results of the detector. Our two-stage framework includes occupancy map generation and cross-attention feature fusion. In stage one, the prediction results are converted into occupancy grid map format to generate back-end representation. In stage two, the high-resolution occupancy maps are fused with BEV features using cross-attention layers. This fused temporal cue provides a strong prior for the temporal detection process. Experimental results demonstrate the effectiveness of our method in improving detection performance, achieving an NDS (nuScenes Detection Score) metric score of 37.35% on the nuScenes test set, which is 1.94 points higher than the baseline. Full article

(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)

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16 pages, 4255 KiB

Open AccessArticle

Evaluating Visual Dependence in Postural Stability Using Smartphone and Stroboscopic Glasses

by Brent A. Harper, Michael Shiraishi and Rahul Soangra

Electronics 2024, 13(11), 2166; https://doi.org/10.3390/electronics13112166 - 2 Jun 2024

Viewed by 1359

Abstract

This study explores the efficacy of integrating stroboscopic glasses with smartphone-based applications to evaluate postural control, offering a cost-effective alternative to traditional forceplate technology. Athletes, particularly those with visual and visuo-oculomotor enhancements due to sports, often suffer from injuries that necessitate reliance on [...] Read more.

This study explores the efficacy of integrating stroboscopic glasses with smartphone-based applications to evaluate postural control, offering a cost-effective alternative to traditional forceplate technology. Athletes, particularly those with visual and visuo-oculomotor enhancements due to sports, often suffer from injuries that necessitate reliance on visual inputs for balance—conditions that can be simulated and studied using visual perturbation methods such as stroboscopic glasses. These glasses intermittently occlude vision, mimicking visual impairments that are crucial in assessing dependency on visual information for postural stability. Participants performed these tasks under three visual conditions: full vision, partial vision occlusion via stroboscopic glasses, and no vision (eyes closed), on foam surfaces to induce postural instability. The use of a smartphone app to measure postural sway was validated against traditional force plate measurements, providing a comparative analysis of both tools under varied sensory conditions. We investigated postural parameters like anterior–posterior and medial–lateral sway ranges, root mean square values, 95% confidence ellipse area, and sway velocity and median dominant sway frequency from both the smartphone and the force plates. Our findings indicate that force plates exhibit high sensitivity to various visual conditions, as evidenced by significant differences observed in certain postural parameters, which were not detected by smartphone-based measurements. Overall, our findings indicate that smartphones show promise as a cost-effective alternative to force plate measurements for routine monitoring of postural control in sports, although they may not achieve the same level of accuracy as force plates. The integration of stroboscopic glasses further refined the assessment by effectively simulating visual impairments, thereby allowing precise evaluation of an individual’s ability to maintain balance under visually perturbed conditions. Full article

(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)

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26 pages, 2948 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Real-Time AI-Driven Fall Detection Method for Occupational Health and Safety

by Anastasiya Danilenka, Piotr Sowiński, Kajetan Rachwał, Karolina Bogacka, Anna Dąbrowska, Monika Kobus, Krzysztof Baszczyński, Małgorzata Okrasa, Witold Olczak, Piotr Dymarski, Ignacio Lacalle, Maria Ganzha and Marcin Paprzycki

Electronics 2023, 12(20), 4257; https://doi.org/10.3390/electronics12204257 - 14 Oct 2023

Cited by 8 | Viewed by 4322

Abstract

Fall accidents in industrial and construction environments require an immediate reaction, to provide first aid. Shortening the time between the fall and the relevant personnel being notified can significantly improve the safety and health of workers. Therefore, in this work, an IoT system [...] Read more.

Fall accidents in industrial and construction environments require an immediate reaction, to provide first aid. Shortening the time between the fall and the relevant personnel being notified can significantly improve the safety and health of workers. Therefore, in this work, an IoT system for real-time fall detection is proposed, using the ASSIST-IoT reference architecture. Empowered with a machine learning model, the system can detect fall accidents and swiftly notify the occupational health and safety manager. To train the model, a novel multimodal fall detection dataset was collected from ten human participants and an anthropomorphic dummy, covering multiple types of fall, including falls from a height. The dataset includes absolute location and acceleration measurements from several IoT devices. Furthermore, a lightweight long short-term memory model is proposed for fall detection, capable of operating in an IoT environment with limited network bandwidth and hardware resources. The accuracy and F1-score of the model on the collected dataset were shown to exceed 0.95 and 0.9, respectively. The collected multimodal dataset was published under an open license, to facilitate future research on fall detection methods in occupational health and safety. Full article

(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)

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13 pages, 2718 KiB

Open AccessArticle

Predicting DDoS Attacks Using Machine Learning Algorithms in Building Management Systems

by İsa Avcı and Murat Koca

Electronics 2023, 12(19), 4142; https://doi.org/10.3390/electronics12194142 - 5 Oct 2023

Cited by 10 | Viewed by 5205

Abstract

The rapid growth of the Internet of Things (IoT) in smart buildings necessitates the continuous evaluation of potential threats and their implications. Conventional methods are increasingly inadequate in measuring risk and mitigating associated hazards, necessitating the development of innovative approaches. Cybersecurity systems for [...] Read more.

The rapid growth of the Internet of Things (IoT) in smart buildings necessitates the continuous evaluation of potential threats and their implications. Conventional methods are increasingly inadequate in measuring risk and mitigating associated hazards, necessitating the development of innovative approaches. Cybersecurity systems for IoT are critical not only in Building Management System (BMS) applications but also in various aspects of daily life. Distributed Denial of Service (DDoS) attacks targeting core BMS software, particularly those launched by botnets, pose significant risks to assets and safety. In this paper, we propose a novel algorithm that combines the power of the Slime Mould Optimization Algorithm (SMOA) for feature selection with an Artificial Neural Network (ANN) predictor and the Support Vector Machine (SVM) algorithm. Our enhanced algorithm achieves an outstanding accuracy of 97.44% in estimating DDoS attack risk factors in the context of BMS. Additionally, it showcases a remarkable 99.19% accuracy in predicting DDoS attacks, effectively preventing system disruptions, and managing cyber threats. To further validate our work, we perform a comparative analysis using the K-Nearest Neighbor Classifier (KNN), which yields an accuracy rate of 96.46%. Our model is trained on the Canadian Institute for Cybersecurity (CIC) IoT Dataset 2022, enabling behavioral analysis and vulnerability testing on diverse IoT devices utilizing various protocols, such as IEEE 802.11, Zigbee-based, and Z-Wave. Full article

(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)

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15 pages, 4913 KiB

Open AccessArticle

Evaluation of the Improved Extreme Learning Machine for Machine Failure Multiclass Classification

by Nico Surantha and Isabella D. Gozali

Electronics 2023, 12(16), 3501; https://doi.org/10.3390/electronics12163501 - 18 Aug 2023

Cited by 9 | Viewed by 1824

Abstract

The recent advancements in sensor, big data, and artificial intelligence (AI) have introduced digital transformation in the manufacturing industry. Machine maintenance has been one of the central subjects in digital transformation in the manufacturing industry. Predictive maintenance is the latest maintenance strategy that [...] Read more.

The recent advancements in sensor, big data, and artificial intelligence (AI) have introduced digital transformation in the manufacturing industry. Machine maintenance has been one of the central subjects in digital transformation in the manufacturing industry. Predictive maintenance is the latest maintenance strategy that relies on data and artificial intelligence techniques to predict machine failure and remaining life assessment. However, the imbalanced nature of machine data can result in inaccurate machine failure predictions. This research will use techniques and algorithms centered on Extreme Learning Machine (ELM) and their development to find a suitable algorithm to overcome imbalanced machine datasets. The dataset used in this research is Microsoft Azure for Predictive Maintenance, which has significantly imbalanced failure classes. Four improved ELM methods are evaluated in this paper, i.e., extreme machine learning with under-sampling/over-sampling, weighted-ELM, and weighted-ELM with radial basis function (RBF) kernel and particle swarm optimization (PSO). Our simulation results show that the combination of ELM with under-sampling gained the highest performance result, in which the average F1-score reached 0.9541 for binary classification and 0.9555 for multiclass classification. Full article

(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)

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17 pages, 1853 KiB

Open AccessArticle

A Power Load Forecasting Method Based on Intelligent Data Analysis

by He Liu, Xuanrui Xiong, Biao Yang, Zhanwei Cheng, Kai Shao and Amr Tolba

Electronics 2023, 12(16), 3441; https://doi.org/10.3390/electronics12163441 - 14 Aug 2023

Cited by 9 | Viewed by 2252

Abstract

Abnormal electricity consumption behavior not only affects the safety of power supply but also damages the infrastructure of the power system, posing a threat to the secure and stable operation of the grid. Predicting future electricity consumption plays a crucial role in resource [...] Read more.

Abnormal electricity consumption behavior not only affects the safety of power supply but also damages the infrastructure of the power system, posing a threat to the secure and stable operation of the grid. Predicting future electricity consumption plays a crucial role in resource management in the energy sector. Analyzing historical electricity consumption data is essential for improving the energy service capabilities of end-users. To forecast user energy consumption, this paper proposes a method that combines adaptive noise-assisted complete ensemble empirical mode decomposition (CEEMDAN) with long short-term memory (LSTM) networks. Firstly, considering the challenge of directly applying prediction models to non-stationary and nonlinear user electricity consumption data, the adaptive noise-assisted complete ensemble empirical mode decomposition algorithm is used to decompose the signal into trend components, periodic components, and random components. Then, based on the CEEMDAN decomposition, an LSTM prediction sub-model is constructed to forecast the overall electricity consumption by using an overlaying approach. Finally, through multiple comparative experiments, the effectiveness of the CEEMDAN-LSTM method is demonstrated, showing its ability to explore hidden temporal relationships and achieve smaller prediction errors. Full article

(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)

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21 pages, 3732 KiB

Open AccessArticle

On Dynamic Node Cooperation Strategy Design for Energy Efficiency in Hierarchical Federated Learning

by Zhuo Li, Sailan Zou and Xin Chen

Electronics 2023, 12(11), 2362; https://doi.org/10.3390/electronics12112362 - 23 May 2023

Viewed by 1423

Abstract

In Hierarchical Federated Learning (HFL), opportunistic communication provides opportunities for node cooperation. In this work, we optimize the node cooperation strategy using opportunistic communization with the objective to minimize energy cost under the delay constraint. We design an online node cooperation strategy (OSRN) [...] Read more.

In Hierarchical Federated Learning (HFL), opportunistic communication provides opportunities for node cooperation. In this work, we optimize the node cooperation strategy using opportunistic communization with the objective to minimize energy cost under the delay constraint. We design an online node cooperation strategy (OSRN) based on the optimal stopping theory. Through theoretical analysis, we prove the NP-hardness of the problem investigated and the competition ratio that can be achieved by OSRN. We conduct thorough simulation experiments and find that the proposed algorithm outperforms the random selection algorithm SNNR with 22.04% reduction in energy cost. It is also observed that the energy cost can be reduced by 20.20% and 13.54%, respectively, compared with the existing methods CFL and THF. Full article

(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)

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Review

Jump to: Research

36 pages, 706 KiB

Open AccessReview

Artificial Intelligence Tools for the Agriculture Value Chain: Status and Prospects

by Fotis Assimakopoulos, Costas Vassilakis, Dionisis Margaris, Konstantinos Kotis and Dimitris Spiliotopoulos

Electronics 2024, 13(22), 4362; https://doi.org/10.3390/electronics13224362 - 7 Nov 2024

Cited by 8 | Viewed by 11363

Abstract

This article explores the transformative potential of artificial intelligence (AI) tools across the agricultural value chain, highlighting their applications, benefits, challenges, and future prospects. With global food demand projected to increase by 70% by 2050, AI technologies—including machine learning, big data analytics, and [...] Read more.

This article explores the transformative potential of artificial intelligence (AI) tools across the agricultural value chain, highlighting their applications, benefits, challenges, and future prospects. With global food demand projected to increase by 70% by 2050, AI technologies—including machine learning, big data analytics, and the Internet of things (IoT)—offer critical solutions for enhancing agricultural productivity, sustainability, and resource efficiency. The study provides a comprehensive review of AI applications at multiple stages of the agricultural value chain, including land use planning, crop selection, resource management, disease detection, yield prediction, and market integration. It also discusses the significant challenges to AI adoption, such as data accessibility, technological infrastructure, and the need for specialized skills. By examining case studies and empirical evidence, the article demonstrates how AI-driven solutions can optimize decision-making and operational efficiency in agriculture. The findings underscore AI’s pivotal role in addressing global agricultural challenges, with implications for farmers, agribusinesses, policymakers, and researchers. This article aims to advance the evolving research and discussions on sustainable agriculture, contributing insights that promote the adoption of AI technologies and influence the future of farming. Full article

(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)

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23 pages, 530 KiB

Open AccessReview

Machine Learning-Based Intrusion Detection Methods in IoT Systems: A Comprehensive Review

by Brunel Rolack Kikissagbe and Meddi Adda

Electronics 2024, 13(18), 3601; https://doi.org/10.3390/electronics13183601 - 11 Sep 2024

Cited by 6 | Viewed by 10515

Abstract

The rise of the Internet of Things (IoT) has transformed our daily lives by connecting objects to the Internet, thereby creating interactive, automated environments. However, this rapid expansion raises major security concerns, particularly regarding intrusion detection. Traditional intrusion detection systems (IDSs) are often [...] Read more.

The rise of the Internet of Things (IoT) has transformed our daily lives by connecting objects to the Internet, thereby creating interactive, automated environments. However, this rapid expansion raises major security concerns, particularly regarding intrusion detection. Traditional intrusion detection systems (IDSs) are often ill-suited to the dynamic and varied networks characteristic of the IoT. Machine learning is emerging as a promising solution to these challenges, offering the intelligence and flexibility needed to counter complex and evolving threats. This comprehensive review explores different machine learning approaches for intrusion detection in IoT systems, covering supervised, unsupervised, and deep learning methods, as well as hybrid models. It assesses their effectiveness, limitations, and practical applications, highlighting the potential of machine learning to enhance the security of IoT systems. In addition, the study examines current industry issues and trends, highlighting the importance of ongoing research to keep pace with the rapidly evolving IoT security ecosystem. Full article

(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)

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41 pages, 1342 KiB

Open AccessReview

Internet of Underwater Things: A Survey on Simulation Tools and 5G-Based Underwater Networks

by Lewis Nkenyereye, Lionel Nkenyereye and Bruce Ndibanje

Electronics 2024, 13(3), 474; https://doi.org/10.3390/electronics13030474 - 23 Jan 2024

Cited by 15 | Viewed by 6479

Abstract

The term “Internet of Underwater Things (IoUT)” refers to a network of intelligent interconnected underwater devices designed to monitor various underwater activities. The IoUT allows for a network of autonomous underwater vehicles (AUVs) to communicate with each other, sense their surroundings, collect data, [...] Read more.

The term “Internet of Underwater Things (IoUT)” refers to a network of intelligent interconnected underwater devices designed to monitor various underwater activities. The IoUT allows for a network of autonomous underwater vehicles (AUVs) to communicate with each other, sense their surroundings, collect data, and transmit them to control centers on the surface at typical Internet speeds. These data serve as a valuable resource for various tasks, including conducting crash surveys, discovering shipwrecks, detecting early signs of tsunamis, monitoring animal health, obtaining real-time aquatic information, and conducting archaeological expeditions. This paper introduces an additional set of alternative simulation tools for underwater networks. We categorize these tools into open-source and licensed simulator options and recommend that students consider using open-source simulators for monitoring underwater networks. There has not been widespread deployment or extensive research on underwater 5G-based networks. However, simulation tools provide some general insights into the challenges and potential issues associated with evaluating such networks, based on the characteristics of underwater communication and 5G, by surveying 5G-based underwater networks and 5G key aspects addressed by the research community in underwater network systems. Through an extensive review of the literature, we discuss the architecture of both Internet of Underwater application-assisted AUVs and Internet of Underwater Things communications in the 5G-based system. Full article

(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)

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30 pages, 1817 KiB

Open AccessReview

Tracing the Influence of Large Language Models across the Most Impactful Scientific Works

by Dana-Mihaela Petroșanu, Alexandru Pîrjan and Alexandru Tăbușcă

Electronics 2023, 12(24), 4957; https://doi.org/10.3390/electronics12244957 - 10 Dec 2023

Cited by 13 | Viewed by 14016

Abstract

In recent years, large language models (LLMs) have come into view as one of the most transformative developments in the technical domain, influencing diverse sectors ranging from natural language processing (NLP) to creative arts. Their rise signifies an unprecedented convergence of computational prowess, [...] Read more.

In recent years, large language models (LLMs) have come into view as one of the most transformative developments in the technical domain, influencing diverse sectors ranging from natural language processing (NLP) to creative arts. Their rise signifies an unprecedented convergence of computational prowess, sophisticated algorithms, and expansive datasets, pushing the boundaries of what was once thought to be achievable. Such a profound impact mandates a thorough exploration of the LLMs’ evolutionary trajectory. Consequently, this article conducts a literature review of the most impactful scientific works, using the reliable Web of Science (WoS) indexing database as a data source in order to attain a thorough and quality-assured analysis. This review identifies relevant patterns, provides research insights, traces technological growth, and anticipates potential future directions. Beyond mapping the known, this study aims to highlight uncharted areas within the LLM landscape, thereby catalyzing future research endeavors. The ultimate goal is to enhance collective understanding, encourage collaboration, and guide subsequent innovations in harnessing the potential of LLMs for societal and technological advancement. Full article

(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)

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25 pages, 5570 KiB

Open AccessReview

A Comprehensive Review on Multiple Instance Learning

by Samman Fatima, Sikandar Ali and Hee-Cheol Kim

Electronics 2023, 12(20), 4323; https://doi.org/10.3390/electronics12204323 - 18 Oct 2023

Cited by 18 | Viewed by 10050

Abstract

Multiple-instance learning has become popular over recent years due to its use in some special scenarios. It is basically a type of weakly supervised learning where the learning dataset contains bags of instances instead of a single feature vector. Each bag is associated [...] Read more.

Multiple-instance learning has become popular over recent years due to its use in some special scenarios. It is basically a type of weakly supervised learning where the learning dataset contains bags of instances instead of a single feature vector. Each bag is associated with a single label. This type of learning is flexible and a natural fit for multiple real-world problems. MIL has been employed to deal with a number of challenges, including object detection and identification tasks, content-based image retrieval, and computer-aided diagnosis. Medical image analysis and drug activity prediction have been the main uses of MIL in biomedical research. Many Algorithms based on MIL have been put forth over the years. In this paper, we will discuss MIL, the background of MIL and its application in multiple domains, some MIL-based methods, challenges, and lastly, the conclusions and prospects. Full article

(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)

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