2023
Journal article
Open Access
Privacy by design in systems for assisted living, personalized care and well-being: a stakeholder analysis
Carboni A., Russo D., Moroni D., Barsocchi P.
The concept of privacy-by-design within a system for assisted living, personalized care and well-being is crucial to protect users from misuse of the data collected about their health. Especially if the information is collected through audio-video devices, the question is even more delicate due to the nature of this data. In fact, in addition to guaranteeing a high level of privacy, it is necessary to reassure end-users about the correct use of these streams. The evolution of data analysis techniques began to take In review on an important role and increasingly defined characteristics in recent years. In this article, with reference to European projects in the AHA/AAL domain, we will see a differentiation of the concept of privacy-by-design according to different dimensions (Technical, Contextual, Business) and to the Stakeholders involved. The analysis is intended to cover technical aspects, legislative and policies-related aspects also regarding the point of view of the municipalities and aspects related to the acceptance and, therefore, to the perception of the safety of these technologies by the final end-users.Source: Frontiers in digital health (2023). doi:10.3389/fdgth.2022.934609
DOI: 10.3389/fdgth.2022.934609
Project(s): PlatformUptake.eu
Metrics:
Carboni A., Russo D., Moroni D., Barsocchi P.
The concept of privacy-by-design within a system for assisted living, personalized care and well-being is crucial to protect users from misuse of the data collected about their health. Especially if the information is collected through audio-video devices, the question is even more delicate due to the nature of this data. In fact, in addition to guaranteeing a high level of privacy, it is necessary to reassure end-users about the correct use of these streams. The evolution of data analysis techniques began to take In review on an important role and increasingly defined characteristics in recent years. In this article, with reference to European projects in the AHA/AAL domain, we will see a differentiation of the concept of privacy-by-design according to different dimensions (Technical, Contextual, Business) and to the Stakeholders involved. The analysis is intended to cover technical aspects, legislative and policies-related aspects also regarding the point of view of the municipalities and aspects related to the acceptance and, therefore, to the perception of the safety of these technologies by the final end-users.Source: Frontiers in digital health (2023). doi:10.3389/fdgth.2022.934609
DOI: 10.3389/fdgth.2022.934609
Project(s): PlatformUptake.eu
Metrics:
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ISTI Repository 
| ISTI Repository | www.frontiersin.org | CNR ExploRA
2023
Conference article
Open Access
Evaluating the impact of anchors deployment for an AoA-based indoor localization system
Mavilia F., Barsocchi P., Furfari F., Girolami M.
Indoor localization techniques are rapidly moving toward the combination of multiple source of information. Among these, RSS, Time of Flight (ToF), Angle of Arrival (AoA) and of Departure (AoD) represent effective solutions for indoor environments. In this work, we propose an on-going activity investigating the performance of an indoor localization system based on the AoA-Bluetooth 5.1 specification, namely Direction Finding. We evaluate the effect of two anchor deployments and we test our localization algorithm by varying the orientation of the target according to four postures: North, West, South and East. From our study, we observe that anchor nodes deployed on the ceiling provide the best performance in terms of localization error. We conclude this work with a discussion of two further lines of investigation potentially increasing the performance of AoA-based indoor localization systems.Source: WONS 2023 - 18th Wireless On-Demand Network Systems and Services Conference, pp. 20–23, Madonna di Campiglio, Italy, 30/01/2023-01/02/2023
DOI: 10.23919/wons57325.2023.10061949
Metrics:
Mavilia F., Barsocchi P., Furfari F., Girolami M.
Indoor localization techniques are rapidly moving toward the combination of multiple source of information. Among these, RSS, Time of Flight (ToF), Angle of Arrival (AoA) and of Departure (AoD) represent effective solutions for indoor environments. In this work, we propose an on-going activity investigating the performance of an indoor localization system based on the AoA-Bluetooth 5.1 specification, namely Direction Finding. We evaluate the effect of two anchor deployments and we test our localization algorithm by varying the orientation of the target according to four postures: North, West, South and East. From our study, we observe that anchor nodes deployed on the ceiling provide the best performance in terms of localization error. We conclude this work with a discussion of two further lines of investigation potentially increasing the performance of AoA-based indoor localization systems.Source: WONS 2023 - 18th Wireless On-Demand Network Systems and Services Conference, pp. 20–23, Madonna di Campiglio, Italy, 30/01/2023-01/02/2023
DOI: 10.23919/wons57325.2023.10061949
Metrics:
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ISTI Repository | ieeexplore.ieee.org 
| CNR ExploRA
2023
Report
Unknown
THE D.3.2.1 - AA@THE User needs, technical requirements and specifications
Pratali L., Campana M. G., Delmastro F., Di Martino F., Pescosolido L., Barsocchi P., Broccia G., Ciancia V., Gennaro C., Girolami M., Lagani G., La Rosa D., Latella D., Magrini M., Manca M., Massink M., Mattioli A., Moroni D., Palumbo F., Paradisi P., Paternò F., Santoro C., Sebastiani L., Vairo C.
Deliverable D3.2.1 del progetto PNRR Ecosistemi ed innovazione - THESource: ISTI Project Report, THE, D3.2, 2023
Pratali L., Campana M. G., Delmastro F., Di Martino F., Pescosolido L., Barsocchi P., Broccia G., Ciancia V., Gennaro C., Girolami M., Lagani G., La Rosa D., Latella D., Magrini M., Manca M., Massink M., Mattioli A., Moroni D., Palumbo F., Paradisi P., Paternò F., Santoro C., Sebastiani L., Vairo C.
Deliverable D3.2.1 del progetto PNRR Ecosistemi ed innovazione - THESource: ISTI Project Report, THE, D3.2, 2023
See at: CNR ExploRA
2023
Journal article
Open Access
A Bluetooth 5.1 dataset based on angle of arrival and RSS for indoor localization
Girolami M., Furfari F., Barsocchi P., Mavilia F.
Several Radio-Frequency technologies have been explored to evaluate the efficacy of localization algorithms in indoor environments, including Received Signal Strength (RSS), Time of Flight (ToF), and Angle of Arrival (AoA). Among these, AoA technique has been gaining interest when adopted with the Bluetooth protocol. In this work, we describe a data collection measurement campaign of AoA and RSS values collected from Bluetooth 5.1 compliant tags and a set of anchor nodes deployed in the environment. We detail the adopted methodology to collect the dataset and we report all the technical details to reproduce the data collection process. The resulting dataset and the adopted software is publicly available to the community. To collect the dataset, we deploy four anchor nodes and four Bluetooth tags and we reproduce some representative scenarios for indoor localization: calibration, static, mobility, and proximity. Each scenario is annotated with an accurate ground truth (GT). We also assess the quality of the collected data. Specifically, we compute the Mean Absolute Error (MAE) between the AoA estimated by the anchors and the corresponding GT. Additionally, we investigate the packet loss metric which measures the percentage of Bluetooth beacons lost by the anchors.Source: IEEE access 11 (2023): 81763–81776. doi:10.1109/ACCESS.2023.3301126
DOI: 10.1109/access.2023.3301126
Metrics:
Girolami M., Furfari F., Barsocchi P., Mavilia F.
Several Radio-Frequency technologies have been explored to evaluate the efficacy of localization algorithms in indoor environments, including Received Signal Strength (RSS), Time of Flight (ToF), and Angle of Arrival (AoA). Among these, AoA technique has been gaining interest when adopted with the Bluetooth protocol. In this work, we describe a data collection measurement campaign of AoA and RSS values collected from Bluetooth 5.1 compliant tags and a set of anchor nodes deployed in the environment. We detail the adopted methodology to collect the dataset and we report all the technical details to reproduce the data collection process. The resulting dataset and the adopted software is publicly available to the community. To collect the dataset, we deploy four anchor nodes and four Bluetooth tags and we reproduce some representative scenarios for indoor localization: calibration, static, mobility, and proximity. Each scenario is annotated with an accurate ground truth (GT). We also assess the quality of the collected data. Specifically, we compute the Mean Absolute Error (MAE) between the AoA estimated by the anchors and the corresponding GT. Additionally, we investigate the packet loss metric which measures the percentage of Bluetooth beacons lost by the anchors.Source: IEEE access 11 (2023): 81763–81776. doi:10.1109/ACCESS.2023.3301126
DOI: 10.1109/access.2023.3301126
Metrics:
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ieeexplore.ieee.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Modelling the localization error of an AoA-based localization system
Furfari F., Barsocchi P., Girolami M., Mavilia F.
Indoor localization provides important context information to develop Intelligent Environments able to understand user situations, to react and adapt to changes in the surrounding environment. Bluetooth 5.1 Direction Finding (DF) is a recent specification based on angle of departure (AoD) and arrival (AoA) of radio signals and it is addressed to localize objects or people in indoor scenarios. In this work, we study the error propagation of an indoor localization system based on AoA technique and on multiple anchor receivers.Source: IE 2023 - 19th International Conference on Intelligent Environments, Island of Mauritius, 29-30/06/2023
DOI: 10.1109/ie57519.2023.10179094
Metrics:
Furfari F., Barsocchi P., Girolami M., Mavilia F.
Indoor localization provides important context information to develop Intelligent Environments able to understand user situations, to react and adapt to changes in the surrounding environment. Bluetooth 5.1 Direction Finding (DF) is a recent specification based on angle of departure (AoD) and arrival (AoA) of radio signals and it is addressed to localize objects or people in indoor scenarios. In this work, we study the error propagation of an indoor localization system based on AoA technique and on multiple anchor receivers.Source: IE 2023 - 19th International Conference on Intelligent Environments, Island of Mauritius, 29-30/06/2023
DOI: 10.1109/ie57519.2023.10179094
Metrics:
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ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2023
Conference article
Open Access
On the analysis of body orientation for indoor positioning with BLE 5.1 direction finding
Mavilia F., Barsocchi P., Furfari F., La Rosa D., Girolami M.
The last decade showed a clear technological trend toward the adoption of heterogeneous source of information, combined with data-fusion strategies to increase the performance of indoor localization systems. In this respect, the adoption of short-range network protocols such as WiFi and Bluetooth represent a common approach. We investigate, in this work, the use of Bluetooth 5.1 Direction Finding specification to test an indoor localization system solely based on the estimated Angle of Arrival (AoA) between an anchor and a receiver. We first detail our experimental data collection campaign and the adopted hardware. Then, we study not only the accuracy of the estimated angles on two reference planes but also the localization error introduced with the proposed algorithm by varying the body orientation of the target user, namely North, South, West, Est. Experimental results in a real-world indoor environment show an average localization error of 2.08m with only 1 anchor node and 5° of AoA' error for all 28 monitored locations. We also identify regions in which the AoA estimation rapidly decreases, giving rise to the possibility of identifying the boundaries of the adopted technology.Source: ICC 2023 - IEEE International Conference on Communications, pp. 204–209, Roma, Italy, 28/05-01/06/2023
DOI: 10.1109/icc45041.2023.10279029
Metrics:
Mavilia F., Barsocchi P., Furfari F., La Rosa D., Girolami M.
The last decade showed a clear technological trend toward the adoption of heterogeneous source of information, combined with data-fusion strategies to increase the performance of indoor localization systems. In this respect, the adoption of short-range network protocols such as WiFi and Bluetooth represent a common approach. We investigate, in this work, the use of Bluetooth 5.1 Direction Finding specification to test an indoor localization system solely based on the estimated Angle of Arrival (AoA) between an anchor and a receiver. We first detail our experimental data collection campaign and the adopted hardware. Then, we study not only the accuracy of the estimated angles on two reference planes but also the localization error introduced with the proposed algorithm by varying the body orientation of the target user, namely North, South, West, Est. Experimental results in a real-world indoor environment show an average localization error of 2.08m with only 1 anchor node and 5° of AoA' error for all 28 monitored locations. We also identify regions in which the AoA estimation rapidly decreases, giving rise to the possibility of identifying the boundaries of the adopted technology.Source: ICC 2023 - IEEE International Conference on Communications, pp. 204–209, Roma, Italy, 28/05-01/06/2023
DOI: 10.1109/icc45041.2023.10279029
Metrics:
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ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2023
Journal article
Open Access
Connectivity standards alliance matter: state of the art and opportunities
Belli D., Barsocchi P., Palumbo F.
Matter is an open-source, royalty-free connectivity standard developed by the Connectivity Standards Alliance (CSA-IoT). It aims to unify smart home devices and increase their compatibility across various ecosystems. Backed by major tech companies like Apple, Google, Amazon, and the Zigbee Alliance, Matter simplifies the development of IoT devices by providing a unified approach to connectivity. It offers a secure, reliable, and seamless way for devices to communicate and interact, regardless of the manufacturer. This paper aims to present the current state of adoption of the Matter specification by devices available on the market, and the certification process by the available software. It describes the main characteristics of Matter in its Specification 1.0 state, reviewing the features and functionalities of the Matter protocol, as well as the opportunities for its use and the challenges for its large-scale adoption in Matter-compliant IoT devices. We discuss the impact of Matter on IoT technologies and ecosystems, providing guidance for manufacturers and consumers. We analyze the emerging research challenges in its adoption and propose our recommendations on how to improve and extend this protocol for better use in the future.Source: Internet of Things 25 (2023). doi:10.1016/j.iot.2023.101005
DOI: 10.1016/j.iot.2023.101005
Metrics:
Belli D., Barsocchi P., Palumbo F.
Matter is an open-source, royalty-free connectivity standard developed by the Connectivity Standards Alliance (CSA-IoT). It aims to unify smart home devices and increase their compatibility across various ecosystems. Backed by major tech companies like Apple, Google, Amazon, and the Zigbee Alliance, Matter simplifies the development of IoT devices by providing a unified approach to connectivity. It offers a secure, reliable, and seamless way for devices to communicate and interact, regardless of the manufacturer. This paper aims to present the current state of adoption of the Matter specification by devices available on the market, and the certification process by the available software. It describes the main characteristics of Matter in its Specification 1.0 state, reviewing the features and functionalities of the Matter protocol, as well as the opportunities for its use and the challenges for its large-scale adoption in Matter-compliant IoT devices. We discuss the impact of Matter on IoT technologies and ecosystems, providing guidance for manufacturers and consumers. We analyze the emerging research challenges in its adoption and propose our recommendations on how to improve and extend this protocol for better use in the future.Source: Internet of Things 25 (2023). doi:10.1016/j.iot.2023.101005
DOI: 10.1016/j.iot.2023.101005
Metrics:
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ISTI Repository | www.sciencedirect.com | CNR ExploRA
2023
Journal article
Open Access
An ensemble of light gradient boosting machine and adaptive boosting for prediction of type-2 diabetes
Sai M. J., Chettri P., Panigrahi R., Garg A., Bhoi A. K., Barsocchi P.
Machine learning helps construct predictive models in clinical data analysis, predicting stock prices, picture recognition, fnancial modelling, disease prediction, and diagnostics. This paper proposes machine learning ensemble algorithms to forecast diabetes. The ensemble combines k-NN, Naive Bayes (Gaussian), Random Forest (RF), Adaboost, and a recently designed Light Gradient Boosting Machine. The proposed ensembles inherit detection ability of LightGBM to boost accuracy. Under fvefold cross-validation, the proposed ensemble models perform better than other recent models. The k-NN, Adaboost, and LightGBM jointly achieve 90.76% detection accuracy. The receiver operating curve analysis shows that k -NN, RF, and LightGBM successfully solve class imbalance issue of the underlying dataset.Source: International journal of computational intelligence systems (Online) 16 (2023). doi:10.1007/s44196-023-00184-y
DOI: 10.1007/s44196-023-00184-y
Metrics:
Sai M. J., Chettri P., Panigrahi R., Garg A., Bhoi A. K., Barsocchi P.
Machine learning helps construct predictive models in clinical data analysis, predicting stock prices, picture recognition, fnancial modelling, disease prediction, and diagnostics. This paper proposes machine learning ensemble algorithms to forecast diabetes. The ensemble combines k-NN, Naive Bayes (Gaussian), Random Forest (RF), Adaboost, and a recently designed Light Gradient Boosting Machine. The proposed ensembles inherit detection ability of LightGBM to boost accuracy. Under fvefold cross-validation, the proposed ensemble models perform better than other recent models. The k-NN, Adaboost, and LightGBM jointly achieve 90.76% detection accuracy. The receiver operating curve analysis shows that k -NN, RF, and LightGBM successfully solve class imbalance issue of the underlying dataset.Source: International journal of computational intelligence systems (Online) 16 (2023). doi:10.1007/s44196-023-00184-y
DOI: 10.1007/s44196-023-00184-y
Metrics:
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International Journal of Computational Intelligence Systems | link.springer.com | ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
A multi-level random forest model-based intrusion detection using fuzzy inference system for Internet of Things networks
Awotunde J. B., Ayo F. E., Panigrahi R., Garg A., Bhoi A. K., Barsocchi P.
Intrusion detection (ID) methods are security frameworks designed to safeguard network information systems. The strength of an intrusion detection method is dependent on the robustness of the feature selection method. This study developed a multilevel random forest algorithm for intrusion detection using a fuzzy inference system. The strengths of the flter and wrapper approaches are combined in this work to create a more advanced multi-level feature selection technique, which strengthens network security. The frst stage of the multi-level feature selection is the flter method using a correlation-based feature selection to select essential features based on the multi-collinearity in the data. The correlation-based feature selection used a genetic search method to choose the best features from the feature set. The genetic search algorithm assesses the merits of each attribute, which then delivers the characteristics with the highest ftness values for selection. A rule assessment has also been used to determine whether two feature subsets have the same ftness value, which ultimately returns the feature subset with the fewest features. The second stage is a wrapper method based on the sequential forward selection method to further select top features based on the accuracy of the baseline classifer. The selected top features serve as input into the random forest algorithm for detecting intrusions. Finally, fuzzy logic was used to classify intrusions as either normal, low, medium, or high to reduce misclassifcation. When the developed intrusion method was compared to other existing models using the same dataset, the results revealed a higher accuracy, precision, sensitivity, specifcity, and F1-score of 99.46%, 99.46%, 99.46%, 93.86%, and 99.46%, respectively. The classifcation of attacks using the fuzzy inference system also indicates that the developed method can correctly classify attacks with reduced misclassifcation. The use of a multi-level feature selection method to leverage the advantages of flter and wrapper feature selection methods and fuzzy logic for intrusion classifcation makes this study uniqueSource: International journal of computational intelligence systems (Online) 16 (2023). doi:10.1007/s44196-023-00205-w
DOI: 10.1007/s44196-023-00205-w
Metrics:
Awotunde J. B., Ayo F. E., Panigrahi R., Garg A., Bhoi A. K., Barsocchi P.
Intrusion detection (ID) methods are security frameworks designed to safeguard network information systems. The strength of an intrusion detection method is dependent on the robustness of the feature selection method. This study developed a multilevel random forest algorithm for intrusion detection using a fuzzy inference system. The strengths of the flter and wrapper approaches are combined in this work to create a more advanced multi-level feature selection technique, which strengthens network security. The frst stage of the multi-level feature selection is the flter method using a correlation-based feature selection to select essential features based on the multi-collinearity in the data. The correlation-based feature selection used a genetic search method to choose the best features from the feature set. The genetic search algorithm assesses the merits of each attribute, which then delivers the characteristics with the highest ftness values for selection. A rule assessment has also been used to determine whether two feature subsets have the same ftness value, which ultimately returns the feature subset with the fewest features. The second stage is a wrapper method based on the sequential forward selection method to further select top features based on the accuracy of the baseline classifer. The selected top features serve as input into the random forest algorithm for detecting intrusions. Finally, fuzzy logic was used to classify intrusions as either normal, low, medium, or high to reduce misclassifcation. When the developed intrusion method was compared to other existing models using the same dataset, the results revealed a higher accuracy, precision, sensitivity, specifcity, and F1-score of 99.46%, 99.46%, 99.46%, 93.86%, and 99.46%, respectively. The classifcation of attacks using the fuzzy inference system also indicates that the developed method can correctly classify attacks with reduced misclassifcation. The use of a multi-level feature selection method to leverage the advantages of flter and wrapper feature selection methods and fuzzy logic for intrusion classifcation makes this study uniqueSource: International journal of computational intelligence systems (Online) 16 (2023). doi:10.1007/s44196-023-00205-w
DOI: 10.1007/s44196-023-00205-w
Metrics:
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International Journal of Computational Intelligence Systems | link.springer.com | ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
Statistical analysis of design aspects of various YOLO-based Deep Learning models for object detection
Sirisha U., Phani Praveen S., Naga Srinivasu P., Barsocchi P., Bhoi A. K.
Object detection is a critical and complex problem in computer vision, and deep neural networks have significantly enhanced their performance in the last decade. There are two primary types of object detectors: two stage and one stage. Two-stage detectors use a complex architecture to select regions for detection, while one-stage detectors can detect all potential regions in a single shot. When evaluating the effectiveness of an object detector, both detection accuracy and inference speed are essential considerations. Two-stage detectors usually outperform one-stage detectors in terms of detection accuracy. However, YOLO and its predecessor architectures have substantially improved detection accuracy. In some scenarios, the speed at which YOLO detectors produce inferences is more critical than detection accuracy. This study explores the performance metrics, regression formulations, and single-stage object detectors for YOLO detectors. Additionally, it briefly discusses various YOLO variations, including their design, performance, and use cases.Source: International journal of computational intelligence systems (Online) 16 (2023). doi:10.1007/s44196-023-00302-w
DOI: 10.1007/s44196-023-00302-w
Metrics:
Sirisha U., Phani Praveen S., Naga Srinivasu P., Barsocchi P., Bhoi A. K.
Object detection is a critical and complex problem in computer vision, and deep neural networks have significantly enhanced their performance in the last decade. There are two primary types of object detectors: two stage and one stage. Two-stage detectors use a complex architecture to select regions for detection, while one-stage detectors can detect all potential regions in a single shot. When evaluating the effectiveness of an object detector, both detection accuracy and inference speed are essential considerations. Two-stage detectors usually outperform one-stage detectors in terms of detection accuracy. However, YOLO and its predecessor architectures have substantially improved detection accuracy. In some scenarios, the speed at which YOLO detectors produce inferences is more critical than detection accuracy. This study explores the performance metrics, regression formulations, and single-stage object detectors for YOLO detectors. Additionally, it briefly discusses various YOLO variations, including their design, performance, and use cases.Source: International journal of computational intelligence systems (Online) 16 (2023). doi:10.1007/s44196-023-00302-w
DOI: 10.1007/s44196-023-00302-w
Metrics:
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International Journal of Computational Intelligence Systems | link.springer.com | ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
An augmented modulated Deep Learning based intelligent predictive model for brain tumor detection using GAN ensemble
Sahoo S., Sushruta M., Baidyanath P., Bhoi A. K., Barsocchi P.
Brain tumor detection in the initial stage is becoming an intricate task for clinicians worldwide. The diagnosis of brain tumor patients is rigorous in the later stages, which is a serious concern. Although there are related pragmatic clinical tools and multiple models based on machine learning (ML) for the effective diagnosis of patients, these models still provide less accuracy and take immense time for patient screening during the diagnosis process. Hence, there is still a need to develop a more precise model for more accurate screening of patients to detect brain tumors in the beginning stages and aid clinicians in diagnosis, making the brain tumor assessment more reliable. In this research, a performance analysis of the impact of different generative adversarial networks (GAN) on the early detection of brain tumors is presented. Based on it, a novel hybrid enhanced predictive convolution neural network (CNN) model using a hybrid GAN ensemble is proposed. Brain tumor image data is augmented using a GAN ensemble, which is fed for classification using a hybrid modulated CNN technique. The outcome is generated through a soft voting approach where the final prediction is based on the GAN, which computes the highest value for different performance metrics. This analysis demonstrated that evaluation with a progressive-growing generative adversarial network (PGGAN) architecture produced the best result. In the analysis, PGGAN outperformed others, computing the accuracy, precision, recall, F1-score, and negative predictive value (NPV) to be 98.85, 98.45%, 97.2%, 98.11%, and 98.09%, respectively. Additionally, a very low latency of 3.4 s is determined with PGGAN. The PGGAN model enhanced the overall performance of the identification of brain cell tissues in real time. Therefore, it may be inferred to suggest that brain tumor detection in patients using PGGAN augmentation with the proposed modulated CNN technique generates the optimum performance using the soft voting approachSource: Sensors (Basel) 23 (2023). doi:10.3390/s23156930
DOI: 10.3390/s23156930
Metrics:
Sahoo S., Sushruta M., Baidyanath P., Bhoi A. K., Barsocchi P.
Brain tumor detection in the initial stage is becoming an intricate task for clinicians worldwide. The diagnosis of brain tumor patients is rigorous in the later stages, which is a serious concern. Although there are related pragmatic clinical tools and multiple models based on machine learning (ML) for the effective diagnosis of patients, these models still provide less accuracy and take immense time for patient screening during the diagnosis process. Hence, there is still a need to develop a more precise model for more accurate screening of patients to detect brain tumors in the beginning stages and aid clinicians in diagnosis, making the brain tumor assessment more reliable. In this research, a performance analysis of the impact of different generative adversarial networks (GAN) on the early detection of brain tumors is presented. Based on it, a novel hybrid enhanced predictive convolution neural network (CNN) model using a hybrid GAN ensemble is proposed. Brain tumor image data is augmented using a GAN ensemble, which is fed for classification using a hybrid modulated CNN technique. The outcome is generated through a soft voting approach where the final prediction is based on the GAN, which computes the highest value for different performance metrics. This analysis demonstrated that evaluation with a progressive-growing generative adversarial network (PGGAN) architecture produced the best result. In the analysis, PGGAN outperformed others, computing the accuracy, precision, recall, F1-score, and negative predictive value (NPV) to be 98.85, 98.45%, 97.2%, 98.11%, and 98.09%, respectively. Additionally, a very low latency of 3.4 s is determined with PGGAN. The PGGAN model enhanced the overall performance of the identification of brain cell tissues in real time. Therefore, it may be inferred to suggest that brain tumor detection in patients using PGGAN augmentation with the proposed modulated CNN technique generates the optimum performance using the soft voting approachSource: Sensors (Basel) 23 (2023). doi:10.3390/s23156930
DOI: 10.3390/s23156930
Metrics:
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Sensors | ISTI Repository | Sensors | www.mdpi.com | CNR ExploRA
2023
Journal article
Open Access
A unified metering system deployed for water and energy monitoring in smart city
Sushma N., Suresh H. N., Mohana Lakshmi J., Naga Srinivasu P., Bhoi A. K., Barsocchi P.
In the context of smart cities in India, accurate meter readings are crucial for managing household water and energy systems efficiently. However, traditional meter reading methods are costly and time-consuming due to the large number of users and the lack of daily usage analysis leading to customer dissatisfaction. The proposed solution to tackle this matter involves implementing an integrated wireless smart energy and water metering system that utilizes smart metering technology. This system can potentially revolutionize how utilities handle energy and water management. The integrated system is designed to replace the mechanical water meters and conventional digital energy meters, whose primary function is to accurately record meter readings for payment purposes, for automatic meter readings that do not require frequent trips to the location where the meters are installed. This article proposes a smart, integrated wireless metering system to revolutionize customer engagement and energy and water utility management. This technology enables the transmission of precise and secure data on water and energy consumption in real-time by employing Low Power Wide Area Networks (LPWAN) technology, known for its low power consumption, cost-effectiveness, long-range coverage, and efficient penetration. The system has a water flow sensor and PZEM-004T for real-time water and energy consumption readings. The interoperable features in the integrated water flow and energy meter are achieved through trial-and-error methods. The trials led to experimental findings that enabled successful communication between the energy and water flow meters and recorded accurate readings. The device provides the utility provider with real-time consumption statistics and the flexibility to turn on and off the system remotely. The system also helps the users by giving them real-time consumption data and preventing overloading situations. The device also notifies the utility company of the theft of electricity. The proposed system overcomes the gaps reported in the traditional systems and design challenges.Source: IEEE access 11 (2023): 80429–80447. doi:10.1109/ACCESS.2023.3299825
DOI: 10.1109/access.2023.3299825
Metrics:
Sushma N., Suresh H. N., Mohana Lakshmi J., Naga Srinivasu P., Bhoi A. K., Barsocchi P.
In the context of smart cities in India, accurate meter readings are crucial for managing household water and energy systems efficiently. However, traditional meter reading methods are costly and time-consuming due to the large number of users and the lack of daily usage analysis leading to customer dissatisfaction. The proposed solution to tackle this matter involves implementing an integrated wireless smart energy and water metering system that utilizes smart metering technology. This system can potentially revolutionize how utilities handle energy and water management. The integrated system is designed to replace the mechanical water meters and conventional digital energy meters, whose primary function is to accurately record meter readings for payment purposes, for automatic meter readings that do not require frequent trips to the location where the meters are installed. This article proposes a smart, integrated wireless metering system to revolutionize customer engagement and energy and water utility management. This technology enables the transmission of precise and secure data on water and energy consumption in real-time by employing Low Power Wide Area Networks (LPWAN) technology, known for its low power consumption, cost-effectiveness, long-range coverage, and efficient penetration. The system has a water flow sensor and PZEM-004T for real-time water and energy consumption readings. The interoperable features in the integrated water flow and energy meter are achieved through trial-and-error methods. The trials led to experimental findings that enabled successful communication between the energy and water flow meters and recorded accurate readings. The device provides the utility provider with real-time consumption statistics and the flexibility to turn on and off the system remotely. The system also helps the users by giving them real-time consumption data and preventing overloading situations. The device also notifies the utility company of the theft of electricity. The proposed system overcomes the gaps reported in the traditional systems and design challenges.Source: IEEE access 11 (2023): 80429–80447. doi:10.1109/ACCESS.2023.3299825
DOI: 10.1109/access.2023.3299825
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IEEE Access | ieeexplore.ieee.org | ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
A CrowdSensing-based approach for proximity detection in indoor museums with bluetooth tags
Girolami M., La Rosa D., Barsocchi P.
In this work, we investigate the performance of a proximity detection system for visitors in an indoor museum exploiting data collected from the crowd. More specifically, we propose a CrowdSensing-based technique for proximity detection. Users' smartphones can collect and upload RSS (Received Signal Strength) values of nearby Bluetooth tags to a backend server, together with some context-information. In turn, the collected data are elaborated with the goal of calibrating two proximity detection algorithms: a range-based and a learning-based algorithm. We embed the algorithms with R-app, a visiting museum application tested in the Monumental Cemetery's museum located in Piazza dei Miracoli, Pisa (IT). We detail in this work an experimental campaign to measure the performance improvements of the CrowdSensing approach with respect to state-of-the-art algorithms widely adopted in the field of proximity detection. Experimental results show a clear improvement of the performance when data from the crowd are exploited with the proposed architecture.Source: Ad hoc networks 154 (2023). doi:10.1016/j.adhoc.2023.103367
DOI: 10.1016/j.adhoc.2023.103367
Metrics:
Girolami M., La Rosa D., Barsocchi P.
In this work, we investigate the performance of a proximity detection system for visitors in an indoor museum exploiting data collected from the crowd. More specifically, we propose a CrowdSensing-based technique for proximity detection. Users' smartphones can collect and upload RSS (Received Signal Strength) values of nearby Bluetooth tags to a backend server, together with some context-information. In turn, the collected data are elaborated with the goal of calibrating two proximity detection algorithms: a range-based and a learning-based algorithm. We embed the algorithms with R-app, a visiting museum application tested in the Monumental Cemetery's museum located in Piazza dei Miracoli, Pisa (IT). We detail in this work an experimental campaign to measure the performance improvements of the CrowdSensing approach with respect to state-of-the-art algorithms widely adopted in the field of proximity detection. Experimental results show a clear improvement of the performance when data from the crowd are exploited with the proposed architecture.Source: Ad hoc networks 154 (2023). doi:10.1016/j.adhoc.2023.103367
DOI: 10.1016/j.adhoc.2023.103367
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Ad Hoc Networks | ISTI Repository | ISTI Repository | www.sciencedirect.com | CNR ExploRA
2023
Conference article
Open Access
Radio-frequency handoff strategies to seamlessly integrate indoor localization systems
Furfari F., Girolami M., Barsocchi P.
The widespread use of Location Based Services (LBS) drives the pervasive adoption of localization systems available anywhere. Environments equipped with multiple indoor localization systems (ILSs), require managing the transition from one ILS to another in order to continue localizing the user's device even when moving indoors or outdoor-to-indoor environments. In this paper, we focus on the handoff procedure, whose goal is enabling a device to trigger the transition between ILSs when specific conditions are verified. We describe the activation of handoff procedures by considering three types of ILS design and deployment, each with increasing complexity. Moreover, this work defines three handoff algorithms based on the proximity detection, and we test them in a realistic environment characterized by two contiguous ILSs.Source: IPIN 2023 - 13th International Conference on Indoor Positioning and Indoor Navigation, Nuremberg, Germany, 25-28/09/2023
DOI: 10.1109/ipin57070.2023.10332479
Metrics:
Furfari F., Girolami M., Barsocchi P.
The widespread use of Location Based Services (LBS) drives the pervasive adoption of localization systems available anywhere. Environments equipped with multiple indoor localization systems (ILSs), require managing the transition from one ILS to another in order to continue localizing the user's device even when moving indoors or outdoor-to-indoor environments. In this paper, we focus on the handoff procedure, whose goal is enabling a device to trigger the transition between ILSs when specific conditions are verified. We describe the activation of handoff procedures by considering three types of ILS design and deployment, each with increasing complexity. Moreover, this work defines three handoff algorithms based on the proximity detection, and we test them in a realistic environment characterized by two contiguous ILSs.Source: IPIN 2023 - 13th International Conference on Indoor Positioning and Indoor Navigation, Nuremberg, Germany, 25-28/09/2023
DOI: 10.1109/ipin57070.2023.10332479
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2023
Journal article
Open Access
An experimental evaluation based on direction finding specification for indoor localization and proximity detection
Girolami M., Mavilia F., Furfari F., Barsocchi P.
Radio-frequency technologies have been largely explored to deliver reliable indoor localization systems. However, at the current stage, none of the proposed technologies represent a de-facto standard. Although RSS-based (Received Signal Strength) techniques have been extensively studied, they suffer of a number of side-effects mainly caused by the complexity of radio propagation in indoor environments. A possible solution is designing systems exploiting multiple techniques, so that to compensate weaknesses of a specific source of information. Under this respect, Bluetooth represents an interesting technology, combining multiple techniques for indoor localization. In particular, the BT5.1 direction finding specification includes the possibility of estimating the angle between an emitting device and an antenna array. The Angle of Arrival (AoA) provides interesting features for the localization purpose, as it allows estimating the direction from which a signal is propagated. In this work, we detail our experimental setting based on a BT5.1-compliant kit to quantitatively measure the performance in three scenarios: static positioning, mobility and proximity detection. Scenarios provide a robust benchmark allowing us to identify and discuss features of AoA values also in comparison with respect to traditional RSS-based approaches.Source: IEEE journal of indoor and seamless positioning and navigation 2 (2023): 36–50. doi:10.1109/JISPIN.2023.3345268
DOI: 10.1109/jispin.2023.3345268
Metrics:
Girolami M., Mavilia F., Furfari F., Barsocchi P.
Radio-frequency technologies have been largely explored to deliver reliable indoor localization systems. However, at the current stage, none of the proposed technologies represent a de-facto standard. Although RSS-based (Received Signal Strength) techniques have been extensively studied, they suffer of a number of side-effects mainly caused by the complexity of radio propagation in indoor environments. A possible solution is designing systems exploiting multiple techniques, so that to compensate weaknesses of a specific source of information. Under this respect, Bluetooth represents an interesting technology, combining multiple techniques for indoor localization. In particular, the BT5.1 direction finding specification includes the possibility of estimating the angle between an emitting device and an antenna array. The Angle of Arrival (AoA) provides interesting features for the localization purpose, as it allows estimating the direction from which a signal is propagated. In this work, we detail our experimental setting based on a BT5.1-compliant kit to quantitatively measure the performance in three scenarios: static positioning, mobility and proximity detection. Scenarios provide a robust benchmark allowing us to identify and discuss features of AoA values also in comparison with respect to traditional RSS-based approaches.Source: IEEE journal of indoor and seamless positioning and navigation 2 (2023): 36–50. doi:10.1109/JISPIN.2023.3345268
DOI: 10.1109/jispin.2023.3345268
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ieeexplore.ieee.org | ISTI Repository | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Let's talk about k-NN for indoor positioning: myths and facts in RF-based fingerprinting
Torres-Sospedra J., Pendão C., Silva I., Meneses F., Quezada-Gaibor D., Montoliu R., Crivello A., Barsocchi P., Pérez-Navarro A., Moreira A.
Microsoft proposed RADAR in 2000, the first indoor positioning system based on Wi-Fi fingerprinting. Since then, the indoor research community has worked not only to improve the base estimator but also on finding an optimal RSS data representation. The long-term objective is to find a positioning system that minimises the mean positioning error. Despite the relevant advances in the last 23 years, a disruptive solution has not been reached yet. The evaluation with non-open datasets and comparisons with non-optimized baselines make the analysis of the current status of fingerprinting for indoor positioning difficult. In addition, the lack of implementation details or data used for evaluation in several works make results reproducibility impossible. This paper focuses on providing a comprehensive analysis of fingerprinting with k-NN and settling the basement for replicability and reproducibility in further works, targeting to bring relevant information about k-NN when it is used as a baseline comparison of advanced fingerprint-based methods.Source: IPIN 2023 - 13th International Conference on Indoor Positioning and Indoor Navigation, Nuremberg, Germany, 25-28/09/2023
DOI: 10.1109/ipin57070.2023.10332535
Metrics:
Torres-Sospedra J., Pendão C., Silva I., Meneses F., Quezada-Gaibor D., Montoliu R., Crivello A., Barsocchi P., Pérez-Navarro A., Moreira A.
Microsoft proposed RADAR in 2000, the first indoor positioning system based on Wi-Fi fingerprinting. Since then, the indoor research community has worked not only to improve the base estimator but also on finding an optimal RSS data representation. The long-term objective is to find a positioning system that minimises the mean positioning error. Despite the relevant advances in the last 23 years, a disruptive solution has not been reached yet. The evaluation with non-open datasets and comparisons with non-optimized baselines make the analysis of the current status of fingerprinting for indoor positioning difficult. In addition, the lack of implementation details or data used for evaluation in several works make results reproducibility impossible. This paper focuses on providing a comprehensive analysis of fingerprinting with k-NN and settling the basement for replicability and reproducibility in further works, targeting to bring relevant information about k-NN when it is used as a baseline comparison of advanced fingerprint-based methods.Source: IPIN 2023 - 13th International Conference on Indoor Positioning and Indoor Navigation, Nuremberg, Germany, 25-28/09/2023
DOI: 10.1109/ipin57070.2023.10332535
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ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2022
Conference article
Open Access
Trends in smartphone-based indoor localisation
Potortì F., Crivello A., Palumbo F., Girolami M., Barsocchi P.
Indoor localisation is a thriving field, whose progresses are mainly led by innovations in sensor technology, both hardware and software. With a focus on smartphone-based personal navigation, we examine the evolution of sensing technologies in eleven leading applications. In order to select applications we choose among independently-tested prototypes, as opposed to simulation or laboratory-only experiments. To this end, we look at the best performers in the smartphone-based Tracks of IPIN competitions. This selection is particularly severe and significant, as this competition Track is performed live, without an opportunity for competitors to instrument or prepare the site or to know the path in advance and with only two attempts allowed, of which the best result is taken. An independent actor holds in hand the smartphone running the competing system, and results are downloaded from the phone immediately after the competition path is completed, without any post-processing. We show how sensing technologies have evolved from 2014 to 2019 and show a trend towards improving accuracy performance. Last, we provide insight in the role that sensors and algorithms play in the evolution of smartphone-based indoor localisation solutions.Source: IPIN 2021 - International conference on Indoor Positioning and Indoor Navigation, Lloret de Mar, 29/11/2021-02/12/2021
DOI: 10.1109/ipin51156.2021.9662530
Metrics:
Potortì F., Crivello A., Palumbo F., Girolami M., Barsocchi P.
Indoor localisation is a thriving field, whose progresses are mainly led by innovations in sensor technology, both hardware and software. With a focus on smartphone-based personal navigation, we examine the evolution of sensing technologies in eleven leading applications. In order to select applications we choose among independently-tested prototypes, as opposed to simulation or laboratory-only experiments. To this end, we look at the best performers in the smartphone-based Tracks of IPIN competitions. This selection is particularly severe and significant, as this competition Track is performed live, without an opportunity for competitors to instrument or prepare the site or to know the path in advance and with only two attempts allowed, of which the best result is taken. An independent actor holds in hand the smartphone running the competing system, and results are downloaded from the phone immediately after the competition path is completed, without any post-processing. We show how sensing technologies have evolved from 2014 to 2019 and show a trend towards improving accuracy performance. Last, we provide insight in the role that sensors and algorithms play in the evolution of smartphone-based indoor localisation solutions.Source: IPIN 2021 - International conference on Indoor Positioning and Indoor Navigation, Lloret de Mar, 29/11/2021-02/12/2021
DOI: 10.1109/ipin51156.2021.9662530
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ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2022
Journal article
Open Access
Intrusion detection in cyber-physical environment using hybrid naïve Bayes-decision table and multi-objective evolutionary feature selection
Panigrahi R., Borah S., Pramanik M., Bhoi A. K., Barsocchi P., Nayak S. R., Alnumay W.
Researchers are motivated to build effective Intrusion Detection Systems because of the implications of malicious actions in computing, communication, and cyber-physical systems (IDSs). In order to develop signature-based intrusion detection techniques that are suitable for use in cyber-physical environments, state-of-the-art supervised learning algorithms are devised. The main contribution of this research is the introduction of a signature-based intrusion detection model that is based on a hybrid Decision Table and Naive Bayes technique. In addition, the contribution of the suggested method is evaluated by comparing it to the existing literature in the field. In the preprocessing stage, Multi-Objective Evolutionary Feature Selection (MOEFS) feature selection has been used to select only five attack features from the recent CICIDS017 dataset. Keeping in view the class imbalance nature of CICIDS2017 dataset, adequate attack samples has been selected with more weightage to the attack classes having a smaller number of instances in the dataset. A hybrid of Decision Table and Naive Bayes models were combined to train and detect intrusions. Detection of botnets, port scans, Denial of Service (DoS)/Distributed Denial of Service (DDoS) attacks, such as Golden-Eye, Hulk, Slow httptest, slowloris, Heartbleed, Brute Force attacks, such as Patator (FTP), Patator (SSH), and Web attacks such as Infiltration, Web Brute Force, SQL Injection, and XSS, are all successfully detected by the proposed hybrid detection model. The proposed approach shows an accuracy of 96.8% using five features of CICIDS2017, which is higher than the accuracy of methods discussed in the literatures.Source: Computer communications 188 (2022): 133–144. doi:10.1016/j.comcom.2022.03.009
DOI: 10.1016/j.comcom.2022.03.009
Metrics:
Panigrahi R., Borah S., Pramanik M., Bhoi A. K., Barsocchi P., Nayak S. R., Alnumay W.
Researchers are motivated to build effective Intrusion Detection Systems because of the implications of malicious actions in computing, communication, and cyber-physical systems (IDSs). In order to develop signature-based intrusion detection techniques that are suitable for use in cyber-physical environments, state-of-the-art supervised learning algorithms are devised. The main contribution of this research is the introduction of a signature-based intrusion detection model that is based on a hybrid Decision Table and Naive Bayes technique. In addition, the contribution of the suggested method is evaluated by comparing it to the existing literature in the field. In the preprocessing stage, Multi-Objective Evolutionary Feature Selection (MOEFS) feature selection has been used to select only five attack features from the recent CICIDS017 dataset. Keeping in view the class imbalance nature of CICIDS2017 dataset, adequate attack samples has been selected with more weightage to the attack classes having a smaller number of instances in the dataset. A hybrid of Decision Table and Naive Bayes models were combined to train and detect intrusions. Detection of botnets, port scans, Denial of Service (DoS)/Distributed Denial of Service (DDoS) attacks, such as Golden-Eye, Hulk, Slow httptest, slowloris, Heartbleed, Brute Force attacks, such as Patator (FTP), Patator (SSH), and Web attacks such as Infiltration, Web Brute Force, SQL Injection, and XSS, are all successfully detected by the proposed hybrid detection model. The proposed approach shows an accuracy of 96.8% using five features of CICIDS2017, which is higher than the accuracy of methods discussed in the literatures.Source: Computer communications 188 (2022): 133–144. doi:10.1016/j.comcom.2022.03.009
DOI: 10.1016/j.comcom.2022.03.009
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ISTI Repository | www.sciencedirect.com | CNR ExploRA
2022
Journal article
Open Access
The ForEx++ based decision tree ensemble approach for robust detection of Parkinson's disease
Pramanik M., Pradhan R., Nandy P., Bhoi A. K., Barsocchi P.
The progressive reduction of dopaminergic neurons in the human brain, especially at the substantia nigra is one of the principal causes of Parkinson's Disease (PD). Voice alteration is one of the earliest symptoms found in PD patients. Therefore, the impaired PD subjects' acoustic voice signal plays a crucial role in detecting the presence of Parkinson's. This manuscript presents four distinct decision tree ensemble methods of PD detection on a trailblazing ForEx++ rule-based framework. The Systematically Developed Forest (SysFor) and a Penalizing Attributes Decision Forest (ForestPA) ensemble approaches has been used for PD detection. The proposed detection schemes efficiently identify positive subjects using primary voice signal features, viz., baseline, vocal fold, and time-frequency. A novel feature selection scheme termed Feature Ranking to Feature Selection (FRFS) has also been proposed to combine filter and wrapper strategies. The proposed FRFS scheme encompasses Gel's normality test to rank and selects outstanding features from baseline, time-frequency, and vocal fold feature groups. The SysFor and ForestPA decision forests underneath the ForEx++ rule-based framework on both FRFS feature ranking and subset selection represents Parkinson's detection approaches, which expedite a better overall impact on segregating PD from control subjects. It has been observed that the ForestPA decision forest in the ForEx++ framework on FRFS ranked features proved to be a robust Parkinson's detection scheme. The proposed models deliver the highest accuracy of 94.12% and a lowest mean absolute error of 0.25, resulting in an Area Under Curve (AUC) value of 0.97.Source: Journal of ambient intelligence & humanized computing (Print) (2022). doi:10.1007/s12652-022-03719-x
DOI: 10.1007/s12652-022-03719-x
Metrics:
Pramanik M., Pradhan R., Nandy P., Bhoi A. K., Barsocchi P.
The progressive reduction of dopaminergic neurons in the human brain, especially at the substantia nigra is one of the principal causes of Parkinson's Disease (PD). Voice alteration is one of the earliest symptoms found in PD patients. Therefore, the impaired PD subjects' acoustic voice signal plays a crucial role in detecting the presence of Parkinson's. This manuscript presents four distinct decision tree ensemble methods of PD detection on a trailblazing ForEx++ rule-based framework. The Systematically Developed Forest (SysFor) and a Penalizing Attributes Decision Forest (ForestPA) ensemble approaches has been used for PD detection. The proposed detection schemes efficiently identify positive subjects using primary voice signal features, viz., baseline, vocal fold, and time-frequency. A novel feature selection scheme termed Feature Ranking to Feature Selection (FRFS) has also been proposed to combine filter and wrapper strategies. The proposed FRFS scheme encompasses Gel's normality test to rank and selects outstanding features from baseline, time-frequency, and vocal fold feature groups. The SysFor and ForestPA decision forests underneath the ForEx++ rule-based framework on both FRFS feature ranking and subset selection represents Parkinson's detection approaches, which expedite a better overall impact on segregating PD from control subjects. It has been observed that the ForestPA decision forest in the ForEx++ framework on FRFS ranked features proved to be a robust Parkinson's detection scheme. The proposed models deliver the highest accuracy of 94.12% and a lowest mean absolute error of 0.25, resulting in an Area Under Curve (AUC) value of 0.97.Source: Journal of ambient intelligence & humanized computing (Print) (2022). doi:10.1007/s12652-022-03719-x
DOI: 10.1007/s12652-022-03719-x
Metrics:
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link.springer.com | ISTI Repository | CNR ExploRA
2022
Contribution to book
Open Access
Cognitive Internet of Things (IoT) and computational intelligence for mental well-being
Thapa S., Ghimire A., Adhikari S., Bhoi A. K., Barsocchi P.
In the current world of competition and constant struggle, taking care of mental well-being is of the upmost importance. With hundreds of millions of people suffering from mental disorders like depression, Alzheimer disease, schizophrenia, etc. each year, intelligent systems are needed that can diagnose, track, and manage the mental well-being of individuals. Computers have been widely used in various clinical applications such as clinical text analysis and medical image analysis. Computers have found uses in various medical domains such as cancer diagnosis, tumor analysis, etc. Computational intelligence has been used in building smart predictive models that can be of huge significance for diseases that require early clinical intervention. Depression, Alzheimer disease, etc. are mental disorders that worsen with time when not treated appropriately. Thus, there should be systems to identify mental disorders at an early stage. In this chapter, various methods in which mental well-being can be taken care of using computational intelligence are discussed.Source: Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data, edited by Bhoi A.K., de Albuquerque V.H.C., Srinivasu P.N., Marques G., pp. 59–77, 2022
DOI: 10.1016/b978-0-323-85751-2.00004-9
Metrics:
Thapa S., Ghimire A., Adhikari S., Bhoi A. K., Barsocchi P.
In the current world of competition and constant struggle, taking care of mental well-being is of the upmost importance. With hundreds of millions of people suffering from mental disorders like depression, Alzheimer disease, schizophrenia, etc. each year, intelligent systems are needed that can diagnose, track, and manage the mental well-being of individuals. Computers have been widely used in various clinical applications such as clinical text analysis and medical image analysis. Computers have found uses in various medical domains such as cancer diagnosis, tumor analysis, etc. Computational intelligence has been used in building smart predictive models that can be of huge significance for diseases that require early clinical intervention. Depression, Alzheimer disease, etc. are mental disorders that worsen with time when not treated appropriately. Thus, there should be systems to identify mental disorders at an early stage. In this chapter, various methods in which mental well-being can be taken care of using computational intelligence are discussed.Source: Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data, edited by Bhoi A.K., de Albuquerque V.H.C., Srinivasu P.N., Marques G., pp. 59–77, 2022
DOI: 10.1016/b978-0-323-85751-2.00004-9
Metrics:
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ISTI Repository | www.sciencedirect.com | CNR ExploRA