Research

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33 pages, 1164 KiB

Open AccessArticle

Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

by Styliani I. Kampezidou, Archana Tikayat Ray, Anirudh Prabhakara Bhat, Olivia J. Pinon Fischer and Dimitri N. Mavris

Eng 2024, 5(1), 384-416; https://doi.org/10.3390/eng5010021 - 29 Feb 2024

Cited by 1 | Viewed by 952

Abstract

This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e., feature engineering and model selection), principles (i.e., bias–variance decomposition, model [...] Read more.

This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e., feature engineering and model selection), principles (i.e., bias–variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, and output interpretability), models (i.e., neural networks and regression models), methods (i.e., cross-validation and data augmentation), metrics (i.e., Mean Squared Error and F1-score) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community regarding complete and rigorous machine learning workflows and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are non-AI experts. This paper may be of significant value to academic researchers developing and prototyping machine learning workflows for their own research or as customer-tailored solutions for government and industry partners. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Engineering Improvements)

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19 pages, 8228 KiB

Open AccessArticle

Pose Detection and Recurrent Neural Networks for Monitoring Littering Violations

by Nyayu Latifah Husni, Okta Felia, Abdurrahman, Ade Silvia Handayani, Rosi Pasarella, Akhmad Bastari, Marlina Sylvia, Wahyu Rahmaniar, Seyed Amin Hosseini Seno and Wahyu Caesarendra

Eng 2023, 4(4), 2722-2740; https://doi.org/10.3390/eng4040155 - 30 Oct 2023

Viewed by 968

Abstract

Infrastructure development requires various considerations to maintain its continuity. Some public facilities cannot survive due to human indifference and irresponsible actions. Unfortunately, the government has to spend a lot of money, effort, and time to repair the damage. One of the destructive behaviors [...] Read more.

Infrastructure development requires various considerations to maintain its continuity. Some public facilities cannot survive due to human indifference and irresponsible actions. Unfortunately, the government has to spend a lot of money, effort, and time to repair the damage. One of the destructive behaviors that can have an impact on infrastructure and environmental problems is littering. Therefore, this paper proposes a device as an alternative for catching littering rule violators. The proposed device can be used to monitor littering and provide warnings to help officers responsible for capturing the violators. In this innovation, the data obtained by the camera are sent to a mini-PC. The device will send warning information to a mobile phone when someone litters. Then, a speaker will turn on and issue a sound warning: “Do not litter”. The device uses pose detection and a recurrent neural network (RNN) to recognize a person’s activity. All activities can be monitored in a more distant place using IoT technology. In addition, this tool can also monitor environmental conditions and replace city guards to monitor the area. Thus, the municipality can save money and time. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Engineering Improvements)

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11 pages, 228 KiB

Open AccessFeature PaperArticle

Finding the Age and Education Level of Bulgarian-Speaking Internet Users Using Keystroke Dynamics

by Denitsa Grunova and Ioannis Tsimperidis

Eng 2023, 4(4), 2711-2721; https://doi.org/10.3390/eng4040154 - 25 Oct 2023

Viewed by 868

Abstract

The rapid development of information and communication technologies and the widespread use of the Internet has made it imperative to implement advanced user authentication methods based on the analysis of behavioural biometric data. In contrast to traditional authentication techniques, such as the simple [...] Read more.

The rapid development of information and communication technologies and the widespread use of the Internet has made it imperative to implement advanced user authentication methods based on the analysis of behavioural biometric data. In contrast to traditional authentication techniques, such as the simple use of passwords, these new methods face the challenge of authenticating users at more complex levels, even after the initial verification. This is particularly important as it helps to address risks such as the possibility of forgery and the disclosure of personal information to unauthorised individuals. In this study, the use of keystroke dynamics has been chosen as a biometric, which is the way a user uses the keyboard. Specifically, a number of Bulgarian-speaking users have been recorded during their daily keyboard use, and then a system has been implemented which, with the help of machine learning models, recognises certain acquired or intrinsic characteristics in order to reveal part of their identity. The results show that users can be categorised using keystroke dynamics, in terms of the age group they belong to and in terms of their educational level, with high accuracy rates, which is a strong indication for the creation of applications to enhance user security and facilitate their use of Internet services. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Engineering Improvements)

11 pages, 1525 KiB

Open AccessArticle

A Basic Study for Predicting Dysphagia in Panoramic X-ray Images Using Artificial Intelligence (AI) Part 2: Analysis of the Position of the Hyoid Bone on Panoramic Radiographs

by Yukiko Matsuda, Emi Ito, Migiwa Kuroda, Kazuyuki Araki, Wataru Nakada and Yoshihiko Hayakawa

Eng 2023, 4(4), 2542-2552; https://doi.org/10.3390/eng4040145 - 10 Oct 2023

Viewed by 1026

Abstract

Background: Oral frailty is associated with systemic frailty. The vertical position of the hyoid bone is important when considering the risk of dysphagia. However, dentists usually do not focus on this position. Purpose: To create an AI model for detection of the position [...] Read more.

Background: Oral frailty is associated with systemic frailty. The vertical position of the hyoid bone is important when considering the risk of dysphagia. However, dentists usually do not focus on this position. Purpose: To create an AI model for detection of the position of the vertical hyoid bone. Methods: In this study, 1830 hyoid bone images from 915 panoramic radiographs were used for AI learning. The position of the hyoid bone was classified into six types (Types 0, 1, 2, 3, 4, and 5) based on the same criteria as in our previous study. Plan 1 learned all types. In Plan 2, the five types other than Type 0 were learned. To reduce the number of groupings, three classes were formed using combinations of two types in each class. Plan 3 was used for learning all three classes, and Plan 4 was used for learning the two classes other than Class A (Types 0 and 1). Precision, recall, f-values, accuracy, and areas under the precision–recall curves (PR-AUCs) were calculated and comparatively evaluated. Results: Plan 4 showed the highest accuracy and PR-AUC values, of 0.93 and 0.97, respectively. Conclusions: By reducing the number of classes and not learning cases in which the anatomical structure was partially invisible, the vertical hyoid bone was correctly detected. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Engineering Improvements)

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17 pages, 862 KiB

Open AccessArticle

WB Score: A Novel Methodology for Visual Classifier Selection in Increasingly Noisy Datasets

by Wagner S. Billa, Rogério G. Negri and Leonardo B. L. Santos

Eng 2023, 4(4), 2497-2513; https://doi.org/10.3390/eng4040142 - 25 Sep 2023

Viewed by 715

Abstract

This article addresses the challenges of selecting robust classifiers with increasing noise levels in real-world scenarios. We propose the WB Score methodology, which enables the identification of reliable classifiers for deployment in noisy environments. The methodology addresses four significant challenges that are commonly [...] Read more.

This article addresses the challenges of selecting robust classifiers with increasing noise levels in real-world scenarios. We propose the WB Score methodology, which enables the identification of reliable classifiers for deployment in noisy environments. The methodology addresses four significant challenges that are commonly encountered: (i) Ensuring classifiers possess robustness to noise; (ii) Overcoming the difficulty of obtaining representative data that captures real-world noise; (iii) Addressing the complexity of detecting noise, making it challenging to differentiate it from natural variations in the data; and (iv) Meeting the requirement for classifiers capable of efficiently handling noise, allowing prompt responses for decision-making. WB Score provides a comprehensive approach for classifier assessment and selection to address these challenges. We analyze five classic datasets and one customized flooding dataset in São Paulo. The results demonstrate the practical effect of using the WB Score methodology is the enhanced ability to select robust classifiers for datasets in noisy real-world scenarios. Compared with similar techniques, the improvement centers around providing a visual and intuitive output, enhancing the understanding of classifier resilience against noise, and streamlining the decision-making process. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Engineering Improvements)

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25 pages, 8416 KiB

Open AccessArticle

Machine Learning-Based Real-Time Prediction of Formation Lithology and Tops Using Drilling Parameters with a Web App Integration

by Houdaifa Khalifa, Olusegun Stanley Tomomewo, Uchenna Frank Ndulue and Badr Eddine Berrehal

Eng 2023, 4(3), 2443-2467; https://doi.org/10.3390/eng4030139 - 21 Sep 2023

Cited by 1 | Viewed by 1394

Abstract

The accurate prediction of underground formation lithology class and tops is a critical challenge in the oil industry. This paper presents a machine-learning (ML) approach to predict lithology from drilling data, offering real-time litho-facies identification. The ML model, applied via the web app [...] Read more.

The accurate prediction of underground formation lithology class and tops is a critical challenge in the oil industry. This paper presents a machine-learning (ML) approach to predict lithology from drilling data, offering real-time litho-facies identification. The ML model, applied via the web app “GeoVision”, achieves remarkable performance during its training phase with a mean accuracy of 95% and a precision of 98%. The model successfully predicts claystone, marl, and sandstone classes with high precision scores. Testing on new data yields an overall accuracy of 95%, providing valuable insights and setting a benchmark for future efforts. To address the limitations of current methodologies, such as time lags and lack of real-time data, we utilize drilling data as a unique endeavor to predict lithology. Our approach integrates nine drilling parameters, going beyond the narrow focus on the rate of penetration (ROP) often seen in previous research. The model was trained and evaluated using the open Volve field dataset, and careful data preprocessing was performed to reduce features, balance the sample distribution, and ensure an unbiased dataset. The innovative methodology demonstrates exceptional performance and offers substantial advantages for real-time geosteering. The accessibility of our models is enhanced through the user-friendly web app “GeoVision”, enabling effective utilization by drilling engineers and marking a significant advancement in the field. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Engineering Improvements)

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16 pages, 332 KiB

Open AccessArticle

Artificial Intelligence and Industry 4.0? Validation of Challenges Considering the Context of an Emerging Economy Country Using Cronbach’s Alpha and the Lawshe Method

by Paulliny Araújo Moreira, Reimison Moreira Fernandes, Lucas Veiga Avila, Leonardo dos Santos Lourenço Bastos and Vitor William Batista Martins

Eng 2023, 4(3), 2336-2351; https://doi.org/10.3390/eng4030133 - 12 Sep 2023

Cited by 1 | Viewed by 2197

Abstract

Background: Artificial Intelligence has been an area of great interest and investment in the industrial sector, offering numerous possibilities to enhance efficiency and accuracy in production processes. In this regard, this study aimed to identify the adoption challenges of Artificial Intelligence and determine [...] Read more.

Background: Artificial Intelligence has been an area of great interest and investment in the industrial sector, offering numerous possibilities to enhance efficiency and accuracy in production processes. In this regard, this study aimed to identify the adoption challenges of Artificial Intelligence and determine which of these challenges apply to the industrial context of an emerging economy, considering the aspects of Industry 4.0. Methods: To achieve this objective, a literature review was conducted, and a survey was carried out among professionals in the industrial field operating within the Brazilian context. The collected data were analyzed using a quantitative approach through Cronbach’s alpha and the Lawshe method. Results: The results indicate that to enhance the adoption of Artificial Intelligence in the industrial context of an emerging economy, taking into account the needs of Industry 4.0, it is important to prioritize overcoming challenges such as “Lack of clarity in return on investment,” “Organizational culture,” “Acceptance of AI by workers,” “Quantity and quality of data,” and “Data protection”. Conclusions: Therefore, based on the achieved results, it can be concluded that they contribute to the development of strategies and practical actions aimed at successfully driving the adoption of Artificial Intelligence in the industrial sector of developing countries, aligning with the principles and needs of Industry 4.0. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Engineering Improvements)

20 pages, 15973 KiB

Open AccessFeature PaperArticle

Assessment of Leaf Area and Biomass through AI-Enabled Deployment

by Dmitrii Shadrin, Alexander Menshchikov, Artem Nikitin, George Ovchinnikov, Vera Volohina, Sergey Nesteruk, Mariia Pukalchik, Maxim Fedorov and Andrey Somov

Eng 2023, 4(3), 2055-2074; https://doi.org/10.3390/eng4030116 - 25 Jul 2023

Viewed by 1194

Abstract

Leaf area and biomass are important morphological parameters for in situ plant monitoring since a leaf is vital for perceiving and capturing the environmental light as well as represents the overall plant development. The traditional approach for leaf area and biomass measurements is [...] Read more.

Leaf area and biomass are important morphological parameters for in situ plant monitoring since a leaf is vital for perceiving and capturing the environmental light as well as represents the overall plant development. The traditional approach for leaf area and biomass measurements is destructive requiring manual labor and may cause damages for the plants. In this work, we report on the AI-based approach for assessing and predicting the leaf area and plant biomass. The proposed approach is able to estimate and predict the overall plants biomass at the early stage of growth in a non-destructive way. For this reason we equip an industrial greenhouse for cucumbers growing with the commercial off-the-shelf environmental sensors and video cameras. The data from sensors are used to monitor the environmental conditions in the greenhouse while the top-down images are used for training Fully Convolutional Neural Networks (FCNN). The FCNN performs the segmentation task for leaf area calculation resulting in 82% accuracy. Application of trained FCNNs to the sequences of camera images allowed the reconstruction of per-plant leaf area and their growth-dynamics. Then we established the dependency between the average leaf area and biomass using the direct measurements of the biomass. This in turn allowed for reconstruction and prediction of the dynamics of biomass growth in the greenhouse using the image data with 10% average relative error for the 12 days prediction horizon. The actual deployment showed the high potential of the proposed data-driven approaches for plant growth dynamics assessment and prediction. Moreover, it closes the gap towards constructing fully closed autonomous greenhouses for harvests and plants biological safety. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Engineering Improvements)

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10 pages, 1690 KiB

Open AccessArticle

A Neural Network-Based Hydrological Model for Very High-Resolution Forecasting Using Weather Radar Data

by Leonardo B. L. Santos, Cintia P. Freitas, Luiz Bacelar, Jaqueline A. J. P. Soares, Michael M. Diniz, Glauston R. T. Lima and Stephan Stephany

Eng 2023, 4(3), 1787-1796; https://doi.org/10.3390/eng4030101 - 24 Jun 2023

Cited by 4 | Viewed by 1226

Abstract

Many hydro-meteorological disasters in small and steep watersheds develop quickly and significantly impact human lives and infrastructures. High-resolution rainfall data and machine learning methods have been used as modeling frameworks to predict those events, such as flash floods. However, a critical question remains: [...] Read more.

Many hydro-meteorological disasters in small and steep watersheds develop quickly and significantly impact human lives and infrastructures. High-resolution rainfall data and machine learning methods have been used as modeling frameworks to predict those events, such as flash floods. However, a critical question remains: How long must the rainfall input data be for an empirical-based hydrological forecast? The present article employed an artificial neural network (ANN)hydrological model to address this issue to predict river levels and investigate its dependency on antecedent rainfall conditions. The tests were performed using observed water level data and high-resolution weather radar rainfall estimation over a small watershed in the mountainous region of Rio de Janeiro, Brazil. As a result, the forecast water level time series only archived a successful performance (i.e., Nash–Sutcliffe model efficiency coefficient (NSE) > 0.6) when data inputs considered at least 2 h of accumulated rainfall, suggesting a strong physical association to the watershed time of concentration. Under extended periods of accumulated rainfall (>12 h), the framework reached considerably higher performance levels (i.e., NSE > 0.85), which may be related to the ability of the ANN to capture the subsurface response as well as past soil moisture states in the watershed. Additionally, we investigated the model’s robustness, considering different seeds for random number generating, and spacial applicability, looking at maps of weights. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Engineering Improvements)

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16 pages, 23615 KiB

Open AccessArticle

Real-Time Detection of Bud Degeneration in Oil Palms Using an Unmanned Aerial Vehicle

by Alexis Vázquez-Ramírez, Dante Mújica-Vargas, Antonio Luna-Álvarez, Manuel Matuz-Cruz and José de Jesus Rubio

Eng 2023, 4(2), 1581-1596; https://doi.org/10.3390/eng4020090 - 31 May 2023

Viewed by 904

Abstract

This paper presents a novel methodology for the early detection of oil palm bud degeneration based on computer vision. The proposed system uses the YOLO algorithm to detect diseased plants within the bud by analyzing images captured by a drone within the crop. [...] Read more.

This paper presents a novel methodology for the early detection of oil palm bud degeneration based on computer vision. The proposed system uses the YOLO algorithm to detect diseased plants within the bud by analyzing images captured by a drone within the crop. Our system uses a drone equipped with a Jetson Nano embedded system to obtain complete images of crops with a 75% reduction in time and with 40% more accuracy compared to the traditional method. As a result, our system achieves a precision of 92% and a recall of 96%, indicating a high detection rate and a low false-positive rate. In real-time detection, the system is able to effectively detect diseased plants by monitoring an entire hectare of crops in 25 min. The system is also able to detect diseased plants other than those it was trained on with 43% precision. These results suggest that our methodology provides an effective and reliable means of early detection of bud degeneration in oil palm crops, which can prevent the spread of pests and improve crop production. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Engineering Improvements)

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17 pages, 2892 KiB

Open AccessArticle

Neuroevolution Application to Collaborative and Heuristics-Based Connected and Autonomous Vehicle Cohort Simulation at Uncontrolled Intersection

by Frederic Jacquelin, Jungyun Bae, Bo Chen and Darrell Robinette

Eng 2023, 4(2), 1320-1336; https://doi.org/10.3390/eng4020077 - 01 May 2023

Viewed by 1350

Abstract

Artificial intelligence is gaining tremendous attractiveness and showing great success in solving various problems, such as simplifying optimal control derivation. This work focuses on the application of Neuroevolution to the control of Connected and Autonomous Vehicle (CAV) cohorts operating at uncontrolled intersections. The [...] Read more.

Artificial intelligence is gaining tremendous attractiveness and showing great success in solving various problems, such as simplifying optimal control derivation. This work focuses on the application of Neuroevolution to the control of Connected and Autonomous Vehicle (CAV) cohorts operating at uncontrolled intersections. The proposed method implementation’s simplicity, thanks to the inclusion of heuristics and effective real-time performance are demonstrated. The resulting architecture achieves nearly ideal operating conditions in keeping the average speeds close to the speed limit. It achieves twice as high mean speed throughput as a controlled intersection, hence enabling lower travel time and mitigating energy inefficiencies from stop-and-go vehicle dynamics. Low deviation from the road speed limit is hence continuously sustained for cohorts of at most 50 m long. This limitation can be mitigated with additional lanes that the cohorts can split into. The concept also allows the testing and implementation of fast-turning lanes by simply replicating and reconnecting the control architecture at each new road crossing, enabling high scalability for complex road network analysis. The controller is also successfully validated within a high-fidelity vehicle dynamic environment, showing its potential for driverless vehicle control in addition to offering a new traffic control simulation model for future autonomous operation studies. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Engineering Improvements)

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15 pages, 1328 KiB

Open AccessFeature PaperArticle

Ensemble Machine Learning Techniques for Accurate and Efficient Detection of Botnet Attacks in Connected Computers

by Stephen Afrifa, Vijayakumar Varadarajan, Peter Appiahene, Tao Zhang and Emmanuel Adjei Domfeh

Eng 2023, 4(1), 650-664; https://doi.org/10.3390/eng4010039 - 16 Feb 2023

Cited by 9 | Viewed by 2258

Abstract

The transmission of information, ideas, and thoughts requires communication, which is a crucial component of human contact. The utilization of Internet of Things (IoT) devices is a result of the advent of enormous volumes of messages delivered over the internet. The IoT botnet [...] Read more.

The transmission of information, ideas, and thoughts requires communication, which is a crucial component of human contact. The utilization of Internet of Things (IoT) devices is a result of the advent of enormous volumes of messages delivered over the internet. The IoT botnet assault, which attempts to perform genuine, lucrative, and effective cybercrimes, is one of the most critical IoT dangers. To identify and prevent botnet assaults on connected computers, this study uses both quantitative and qualitative approaches. This study employs three basic machine learning (ML) techniques—random forest (RF), decision tree (DT), and generalized linear model (GLM)—and a stacking ensemble model to detect botnets in computer network traffic. The results reveled that random forest attained the best performance with a coefficient of determination (R²) of 0.9977, followed by decision tree with an R² of 0.9882, while GLM was the worst among the basic machine learning models with an R² of 0.9522. Almost all ML models achieved satisfactory performance, with an R² above 0.93. Overall, the stacking ensemble model obtained the best performance, with a root mean square error (RMSE) of 0.0084 m, a mean absolute error (MAE) of 0.0641 m, and an R² of 0.9997. Regarding the stacking ensemble model as compared with the single machine learning models, the R² of the stacking ensemble machine learning increased by 0.2% compared to the RF, 1.15% compared to the DT, and 3.75% compared to the GLM, while RMSE decreased by approximately 0.15% compared to the GLM, DT, and RF single machine learning techniques. Furthermore, this paper suggests best practices for preventing botnet attacks. Businesses should make major investments to combat botnets. This work contributes to knowledge by presenting a novel method for detecting botnet assaults using an artificial-intelligence-powered solution with real-time behavioral analysis. This study can assist companies, organizations, and government bodies in making informed decisions for a safer network that will increase productivity. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Engineering Improvements)

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20 pages, 1385 KiB

Open AccessFeature PaperArticle

SECDFAN: A Cyber Threat Intelligence System for Discussion Forums Utilization

by Georgios Sakellariou, Panagiotis Fouliras and Ioannis Mavridis

Eng 2023, 4(1), 615-634; https://doi.org/10.3390/eng4010037 - 15 Feb 2023

Cited by 1 | Viewed by 2113

Abstract

Cyber Threat intelligence (CTI) systems offer new capabilities in the arsenal of information security experts, who can explore new sources of data that were partially exploited during the past decades. This paper deals with the exploitation of discussion forums as a source of [...] Read more.

Cyber Threat intelligence (CTI) systems offer new capabilities in the arsenal of information security experts, who can explore new sources of data that were partially exploited during the past decades. This paper deals with the exploitation of discussion forums as a source of raw data for a cyber threat intelligence process. Specifically, it analyzes the discussion forums’ characteristics and investigates their relationship with CTI. It proposes a semantic schema for the representation of data collected from discussion forums. Then, it applies a systematic methodology to design the reference architecture of the SECDFAN system, which handles the creation of CTI products following a comprehensive approach from the source selection to CTI product sharing and security experts’ collaboration. The final product of this work is the SECDFAN reference architecture. The contribution of this paper is the development of a CTI reference architecture of a system that, by design, handles all CTI-related issues for creating CTI products by analyzing the content of discussion forums. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Engineering Improvements)

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Review

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27 pages, 2312 KiB

Open AccessReview

RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities

by R. M. M. R. Rathnayake, Madduma Wellalage Pasan Maduranga, Valmik Tilwari and Maheshi B. Dissanayake

Eng 2023, 4(2), 1468-1494; https://doi.org/10.3390/eng4020085 - 28 May 2023

Cited by 7 | Viewed by 4314

Abstract

The rapid expansion of the Internet of Things (IoT) and Machine Learning (ML) has significantly increased the demand for Location-Based Services (LBS) in today’s world. Among these services, indoor positioning and navigation have emerged as crucial components, driving the growth of indoor localization [...] Read more.

The rapid expansion of the Internet of Things (IoT) and Machine Learning (ML) has significantly increased the demand for Location-Based Services (LBS) in today’s world. Among these services, indoor positioning and navigation have emerged as crucial components, driving the growth of indoor localization systems. However, using GPS in indoor environments is impractical, leading to a surge in interest in Received Signal Strength Indicator (RSSI) and machine learning-based algorithms for in-building localization and navigation in recent years. This paper aims to provide a comprehensive review of the technologies, applications, and future research directions of ML-based indoor localization for smart cities. Additionally, it examines the potential of ML algorithms in improving localization accuracy and performance in indoor environments. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Engineering Improvements)

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