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Papers by Francesco Benedetto

Research paper thumbnail of IMNMAGN: Integrative Multimodal Approach for Enhanced Detection of Neurodegenerative Diseases Using fusion of Multidomain Analysis with Graph Networks

Research paper thumbnail of Development of Wireless Smart Sleeve Using Pressure Sensitive Resistor

IEEE Access, Dec 31, 2022

Research paper thumbnail of Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP)

Research paper thumbnail of A signal processing methodology for assessing the performance of ASTM standard test methods for GPR systems

Signal Processing, Mar 1, 2017

Research paper thumbnail of Unauthorized Access Detection in Underlay Cognitive Satellite Networks

IEEE networking letters, Dec 1, 2021

Research paper thumbnail of Detecting CTC Attack in IoMT Communications using Deep Learning Approach

Advances in Science, Technology and Engineering Systems Journal

Research paper thumbnail of Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Research paper thumbnail of Special Issue “Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP)”

Research paper thumbnail of A signal processing methodology for assessing the performance of ASTM standard test methods for GPR systems

Research paper thumbnail of Automatic Aircraft Target Recognition by ISAR Image Processing based on Neural Classifier

International Journal of Advanced Computer Science and Applications, 2012

Research paper thumbnail of Two-Dwell Synchronization Techniques and Mimo Systems for Performance Improvements of 3G Mobile Communications

Int'l J. of Communications, Network and System Sciences, 2009

Research paper thumbnail of Global Communications Newsletter

IEEE Communications Magazine, 2013

Research paper thumbnail of Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP)

Research paper thumbnail of Securing Electric Vehicle Performance: Machine Learning-Driven Fault Detection and Classification

Research paper thumbnail of Enhancing Household Energy Consumption Predictions through Explainable AI Frameworks

IEEE access, 2024

Effective energy management is crucial for sustainability, carbon reduction, resource conservatio... more Effective energy management is crucial for sustainability, carbon reduction, resource conservation, and cost savings. However, conventional energy forecasting methods often lack accuracy, suggesting the need for advanced approaches. Artificial intelligence (AI) has emerged as a powerful tool for energy forecasting, but its lack of transparency and interpretability poses challenges for understanding its predictions. In response, Explainable AI (XAI) frameworks have been developed to enhance the transparency and interpretability of black-box AI models. Accordingly, this paper focuses on achieving accurate household energy consumption predictions by comparing prediction models based on several evaluation metrics, namely the Coefficient of Determination (R 2), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The best model is identified by comparison after making predictions on unseen data, after which the predictions are explained by leveraging two XAI frameworks: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). These explanations help identify crucial characteristics contributing to energy consumption predictions, including insights into feature importance. Our findings underscore the significance of current consumption patterns and lagged energy consumption values in estimating energy usage. This paper further demonstrates the role of XAI in developing consistent and reliable predictive models.

Research paper thumbnail of Rank Exponent Method Based Optimal Control of AGC for Two-Area Interconnected Power Systems

Research paper thumbnail of A Novel Constructive Unceasement Conditional Random Field and Dynamic Bayesian Network Model for Attack Prediction on Internet of Vehicle

IEEE access, 2024

Today's Internet of Vehicles (IoV) has soared by leveraging data gathered from transportation sys... more Today's Internet of Vehicles (IoV) has soared by leveraging data gathered from transportation systems, yet it grapples with security concerns stemming from network vulnerabilities, exposing it to cyber threats. This study proposes an innovative method to anticipate anomalies and exploit IoV services related to road traffic. Using the Unceasement Conditional Random Field Dynamic Bayesian Network Model (U-CRF-DDBN), this approach predicts the impact of network attacks, strategically managing vulnerable nodes and attackers. Through experimentation and comparisons with existing methods, our model demonstrates its effectiveness in mitigating IoV vulnerabilities. The U-CRF-DDBN strikes a superior balance, outperforming other approaches in intrusion detection for Internet of Vehicles systems. Evaluating its performance on the NSL-KDD dataset reveals a promising average Detection Rate of 93.512% and a low False Acceptance Rate of 0.125% for known attacks, highlighting its robustness. However, with unknown attacks, while the Detection Rate remains at 74.157%, there is an increased FAR of 16.47%, resulting in a slightly lower F1score of 0.822.

Research paper thumbnail of Enhancing Security of Host-based Intrusion Detection Systems for the Internet of Things

IEEE Access, Dec 31, 2023

The Internet of Things (IoT) infrastructure enables smart devices to learn, think, speak and perf... more The Internet of Things (IoT) infrastructure enables smart devices to learn, think, speak and perform. The facilities of the IoT devices can be enhanced to support an intelligent application through technologies like fog computing, smart networks, federated learning or explainable artificial intelligence infrastructures. In all these cases networking of IoT devices becomes inevitable. Where-ever there exists a network, a threat to the network infrastructure is also possible. The proposed work classifies various attacks on the hosts with the support of proven machine learning (ML) algorithms. This work performs the comparative analysis of all these classification parameters of the machine learning algorithms with the use of fuzzy-based recommendation systems. This work also lists out various incidents of intrusions on the IoT hosts in appropriate layers of the interface and proposes an efficient algorithm and framework to overcome the occurrences of the intrusions on the host side. In particular, we propose an effective security framework to deal with the intrusions that can deteriorate the host-based systems. The ranking of the algorithms is evaluated using fuzzy-based recommendation systems such as TOPSIS, VIKOR, MORA, WASPAS. The ensemble of machine learning algorithms such as Decision Tree, Lite Gradient Boost, Xtra Gradient Boost and Random Forest provide better values of accuracy (around 99%) with higher precision, recall and F1scores, thus proving their efficacy for intrusion detection in IoT networks.

Research paper thumbnail of An AI-Assisted Smart Healthcare System Using 5G Communication

IEEE Access, Dec 31, 2022

Technology's fast growth has profoundly impacted myriad areas, including healthcare. Implementing... more Technology's fast growth has profoundly impacted myriad areas, including healthcare. Implementing 5G networks offering high-speed and low-latency communication capabilities is one of the most promising technical developments. Parallel to this, artificial intelligence (AI) has become a robust data analysis and decision-making tool. This paper examines how 5G and AI are combined in the context of intelligent healthcare systems. T5G green communication systems must overcome several challenges to satisfy the need for more user capacity, faster network speeds, cheaper pricing, and less resource use. By applying 5G standards, data rates, and device dependability for Industry 4.0 applications may be significantly increased. Advanced security and decreased unauthenticated assaults from various platforms are also covered in the paper. An outline of prospective new technologies and security improvements was provided to safeguard 5G-based intelligent healthcare networks. This paper identifies several research issues and potential future directions for secure 5G-based smart healthcare. This article discusses Industry 4.0, 5G standards, and new research in future wireless communications to explore current research concerns related to 5G technology. A brand-new architecture is also suggested in the paper for Industry 4.0 and 5G-enabled intelligent healthcare systems. INDEX TERMS Artificial intelligence, healthcare system, Internet of Things,Network Simulator, Smart healthcare system, 5G communication system. I. INTRODUCTION T HE way we communicate, engage, and use technology is about to change because of the combination of Artificial Intelligence (AI) technology and the fifth-generation (5G) wireless networks. The power of super-fast, low-latency connectivity is combined with sophisticated algorithms and decision-making abilities when 5G and AI are used together. This introduction will address this convergence's advantages, uses, and difficulties while exploring the possibilities of 5G using artificial intelligence. The huge amounts of data produced by AI applications require an infrastructure that can handle them, and 5G networks deliver previously unheardof speeds, capacity, and dependability. 5G allows real-time communication and seamless connectivity thanks to its high data transfer rates and low latency. It provides the groundwork for a wide range of AI-powered services and applications. Several opportunities exist across numerous domains when AI is integrated with 5G networks. Healthcare is one industry where real-time AI analysis of massive amounts of medical data enables telemedicine, individualized healthcare, and remote patient monitoring. In the transportation industry, AI algorithms with 5G connectivity can improve the security and effectiveness of autonomous cars by allowing object detection, real-time decision-making, and vehicle-to-vehicle communication. Through intelligent systems and real-time data analysis, smart cities can use 5G and AI to optimize traffic management, increase energy efficiency, and improve urban planning. Predictive maintenance, robotics, and real

Research paper thumbnail of Los/Nlos Detection By The Normalized Rayleigh-Ness Test

Publication in the conference proceedings of EUSIPCO, Glasgow, Scotland, 2009

Research paper thumbnail of IMNMAGN: Integrative Multimodal Approach for Enhanced Detection of Neurodegenerative Diseases Using fusion of Multidomain Analysis with Graph Networks

Research paper thumbnail of Development of Wireless Smart Sleeve Using Pressure Sensitive Resistor

IEEE Access, Dec 31, 2022

Research paper thumbnail of Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP)

Research paper thumbnail of A signal processing methodology for assessing the performance of ASTM standard test methods for GPR systems

Signal Processing, Mar 1, 2017

Research paper thumbnail of Unauthorized Access Detection in Underlay Cognitive Satellite Networks

IEEE networking letters, Dec 1, 2021

Research paper thumbnail of Detecting CTC Attack in IoMT Communications using Deep Learning Approach

Advances in Science, Technology and Engineering Systems Journal

Research paper thumbnail of Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Research paper thumbnail of Special Issue “Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP)”

Research paper thumbnail of A signal processing methodology for assessing the performance of ASTM standard test methods for GPR systems

Research paper thumbnail of Automatic Aircraft Target Recognition by ISAR Image Processing based on Neural Classifier

International Journal of Advanced Computer Science and Applications, 2012

Research paper thumbnail of Two-Dwell Synchronization Techniques and Mimo Systems for Performance Improvements of 3G Mobile Communications

Int'l J. of Communications, Network and System Sciences, 2009

Research paper thumbnail of Global Communications Newsletter

IEEE Communications Magazine, 2013

Research paper thumbnail of Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP)

Research paper thumbnail of Securing Electric Vehicle Performance: Machine Learning-Driven Fault Detection and Classification

Research paper thumbnail of Enhancing Household Energy Consumption Predictions through Explainable AI Frameworks

IEEE access, 2024

Effective energy management is crucial for sustainability, carbon reduction, resource conservatio... more Effective energy management is crucial for sustainability, carbon reduction, resource conservation, and cost savings. However, conventional energy forecasting methods often lack accuracy, suggesting the need for advanced approaches. Artificial intelligence (AI) has emerged as a powerful tool for energy forecasting, but its lack of transparency and interpretability poses challenges for understanding its predictions. In response, Explainable AI (XAI) frameworks have been developed to enhance the transparency and interpretability of black-box AI models. Accordingly, this paper focuses on achieving accurate household energy consumption predictions by comparing prediction models based on several evaluation metrics, namely the Coefficient of Determination (R 2), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The best model is identified by comparison after making predictions on unseen data, after which the predictions are explained by leveraging two XAI frameworks: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). These explanations help identify crucial characteristics contributing to energy consumption predictions, including insights into feature importance. Our findings underscore the significance of current consumption patterns and lagged energy consumption values in estimating energy usage. This paper further demonstrates the role of XAI in developing consistent and reliable predictive models.

Research paper thumbnail of Rank Exponent Method Based Optimal Control of AGC for Two-Area Interconnected Power Systems

Research paper thumbnail of A Novel Constructive Unceasement Conditional Random Field and Dynamic Bayesian Network Model for Attack Prediction on Internet of Vehicle

IEEE access, 2024

Today's Internet of Vehicles (IoV) has soared by leveraging data gathered from transportation sys... more Today's Internet of Vehicles (IoV) has soared by leveraging data gathered from transportation systems, yet it grapples with security concerns stemming from network vulnerabilities, exposing it to cyber threats. This study proposes an innovative method to anticipate anomalies and exploit IoV services related to road traffic. Using the Unceasement Conditional Random Field Dynamic Bayesian Network Model (U-CRF-DDBN), this approach predicts the impact of network attacks, strategically managing vulnerable nodes and attackers. Through experimentation and comparisons with existing methods, our model demonstrates its effectiveness in mitigating IoV vulnerabilities. The U-CRF-DDBN strikes a superior balance, outperforming other approaches in intrusion detection for Internet of Vehicles systems. Evaluating its performance on the NSL-KDD dataset reveals a promising average Detection Rate of 93.512% and a low False Acceptance Rate of 0.125% for known attacks, highlighting its robustness. However, with unknown attacks, while the Detection Rate remains at 74.157%, there is an increased FAR of 16.47%, resulting in a slightly lower F1score of 0.822.

Research paper thumbnail of Enhancing Security of Host-based Intrusion Detection Systems for the Internet of Things

IEEE Access, Dec 31, 2023

The Internet of Things (IoT) infrastructure enables smart devices to learn, think, speak and perf... more The Internet of Things (IoT) infrastructure enables smart devices to learn, think, speak and perform. The facilities of the IoT devices can be enhanced to support an intelligent application through technologies like fog computing, smart networks, federated learning or explainable artificial intelligence infrastructures. In all these cases networking of IoT devices becomes inevitable. Where-ever there exists a network, a threat to the network infrastructure is also possible. The proposed work classifies various attacks on the hosts with the support of proven machine learning (ML) algorithms. This work performs the comparative analysis of all these classification parameters of the machine learning algorithms with the use of fuzzy-based recommendation systems. This work also lists out various incidents of intrusions on the IoT hosts in appropriate layers of the interface and proposes an efficient algorithm and framework to overcome the occurrences of the intrusions on the host side. In particular, we propose an effective security framework to deal with the intrusions that can deteriorate the host-based systems. The ranking of the algorithms is evaluated using fuzzy-based recommendation systems such as TOPSIS, VIKOR, MORA, WASPAS. The ensemble of machine learning algorithms such as Decision Tree, Lite Gradient Boost, Xtra Gradient Boost and Random Forest provide better values of accuracy (around 99%) with higher precision, recall and F1scores, thus proving their efficacy for intrusion detection in IoT networks.

Research paper thumbnail of An AI-Assisted Smart Healthcare System Using 5G Communication

IEEE Access, Dec 31, 2022

Technology's fast growth has profoundly impacted myriad areas, including healthcare. Implementing... more Technology's fast growth has profoundly impacted myriad areas, including healthcare. Implementing 5G networks offering high-speed and low-latency communication capabilities is one of the most promising technical developments. Parallel to this, artificial intelligence (AI) has become a robust data analysis and decision-making tool. This paper examines how 5G and AI are combined in the context of intelligent healthcare systems. T5G green communication systems must overcome several challenges to satisfy the need for more user capacity, faster network speeds, cheaper pricing, and less resource use. By applying 5G standards, data rates, and device dependability for Industry 4.0 applications may be significantly increased. Advanced security and decreased unauthenticated assaults from various platforms are also covered in the paper. An outline of prospective new technologies and security improvements was provided to safeguard 5G-based intelligent healthcare networks. This paper identifies several research issues and potential future directions for secure 5G-based smart healthcare. This article discusses Industry 4.0, 5G standards, and new research in future wireless communications to explore current research concerns related to 5G technology. A brand-new architecture is also suggested in the paper for Industry 4.0 and 5G-enabled intelligent healthcare systems. INDEX TERMS Artificial intelligence, healthcare system, Internet of Things,Network Simulator, Smart healthcare system, 5G communication system. I. INTRODUCTION T HE way we communicate, engage, and use technology is about to change because of the combination of Artificial Intelligence (AI) technology and the fifth-generation (5G) wireless networks. The power of super-fast, low-latency connectivity is combined with sophisticated algorithms and decision-making abilities when 5G and AI are used together. This introduction will address this convergence's advantages, uses, and difficulties while exploring the possibilities of 5G using artificial intelligence. The huge amounts of data produced by AI applications require an infrastructure that can handle them, and 5G networks deliver previously unheardof speeds, capacity, and dependability. 5G allows real-time communication and seamless connectivity thanks to its high data transfer rates and low latency. It provides the groundwork for a wide range of AI-powered services and applications. Several opportunities exist across numerous domains when AI is integrated with 5G networks. Healthcare is one industry where real-time AI analysis of massive amounts of medical data enables telemedicine, individualized healthcare, and remote patient monitoring. In the transportation industry, AI algorithms with 5G connectivity can improve the security and effectiveness of autonomous cars by allowing object detection, real-time decision-making, and vehicle-to-vehicle communication. Through intelligent systems and real-time data analysis, smart cities can use 5G and AI to optimize traffic management, increase energy efficiency, and improve urban planning. Predictive maintenance, robotics, and real

Research paper thumbnail of Los/Nlos Detection By The Normalized Rayleigh-Ness Test

Publication in the conference proceedings of EUSIPCO, Glasgow, Scotland, 2009