White Paper on Industrial Applications of Computer Vision and Pattern Recognition

Garibotto, G.; Murrieri, Pierpaolo; Capra, Alessandro; Muro, Stefano De; Petillo, Ugo; Flammini, Francesco; Esposito, Mariangela; Pragliola, Concetta; Leo, Giuseppe Di; Lengu, Roald; Mazzino, Nadia; Paolillo, Alfredo; D’Urso, M.; Vertucci, Raffaele; Narducci, Fabio; Ricciardi, Stefano; Casanova, Andrea; Fenu, Gianni; Mizio, Marco De; Savastano, M.; Capua, Michele Di; Ferone, Alessio

doi:10.1007/978-3-642-41184-7_73

Cited by 18 publications

(3 citation statements)

References 4 publications

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“…AI is revolutionizing cybersecurity due to extensive analysis of data, faster reaction times, and effective customization of threat detection for limited records. Further, Artificial Intelligence has already-existing and synergistic applications for pattern recognition and computer vision to identify physical threats [12]. The authors of [15] have created a hybrid IoT generator, a framework for estimating cellular network performance.…”

Section: Related Workmentioning

confidence: 99%

Vulnerability Detection in Cyber-Physical System Using Machine Learning

C N S

2024

SCPE

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The cyber-physical system is a specific type of IoT communication environment that deals with communication through innovative healthcare (medical) devices. The traditional medical system has been partially replaced by this application, improving healthcare through efficiency, accessibility, and personalization. The intelligent healthcare industry utilizes wireless medical sensors to gather patient health information and send it to a distant server for diagnosis or treatment. The healthcare industry must increase electronic device accuracy, reliability, and productivity. Artificial intelligence (AI) has been applied in various industries, but cybersecurity for cyber-physical systems (CPS) is still a recent topic. This work presents a method for intelligent threat recognition based on machine learning (ML) that enables run-time risk assessment for better situational awareness in CPS security monitoring. Several machine learning techniques, including Nave Bayes (65.4\%), Support Vector Machine (64.1%), Decision Tree (89.6%), Random Forest (92.5%), and Ensemble crossover (EC) XG boost classifier (99.64), were used to classify the malicious activities on real-world testbeds. The outcomes demonstrate that the Ensemble crossover XG boost enabled the best classiﬁcation accuracy. When used in industrial reference applications, the model creates a safe environment where the patient is only made aware of risks when categorization optimism exceeds a specific limit, minimizing security managers' pressure and efficiently assisting their choices.

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Section: Related Workmentioning

confidence: 99%

Vulnerability Detection in Cyber-Physical System Using Machine Learning

C N S

2024

SCPE

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“…3 The e-Coaching Ecosystem: an Overview In this section, we provide an overview of the e-Coaching ecosystem by putting forward a typical interaction flow between a coach and a sportsman. We do not provide any technical details about the implementation of the software infrastructure [9,15], given that it is built entirely using a standard distributed software architecture with well-known cutting-edge technologies. The ecosystem is made up of three main actors: the business logic, a web application, and a mobile client available for both the Android and the iOS mobile platforms that records training statistics exploiting mainly devices' GPS sensor and external ECGs.…”

Section: Our Contributionmentioning

confidence: 99%

An e-coaching ecosystem: design and effectiveness analysis of the engagement of remote coaching on athletes

Boratto

Carta

Mulas

et al. 2017

Pers Ubiquit Comput

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Nowadays, the use of mobile applications and wearable technologies to support and encourage an active lifestyle has become widespread. Several studies put in evidence that the usage of these kinds of support has to be monitored by high-qualified figures, to favor a safe and a long-term adherence to training routines. In order to investigate the impact of these professionals, this work sets out to provide an overview and an evaluation of an e-Coaching ecosystem specifically designed for runners. The platform supports and guides people toward an active lifestyle by stimulating their motivation to exercise through the engagement provided by the interactions between users and human trainers. In this study we investigate the effectiveness of the support offered by the human trainers and the engagement of the users. The results show that the support of human qualified trainers is crucial. Users tend to be more engaged to train when their trainings are developed and remotely supervised by a human coach. This has resulted in more workout sessions performed with respect to users exercising by following standard or self-made routines without direct professional supervision. Our findings show that e-Coaching systems should develop their coaching protocols always taking into account the effectiveness of the support of qualified professionals over completely automated approaches.

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“…Many of the models presented so far (DP-3T [4] , the Exposure Notification framework by Apple/Google [5] ) use a centralized back-end system for the notification of contagion and contact events at risk. The main goal of the proposed system is to guarantee users’ privacy through a decentralized approach by exploiting blockchain technology, yielding transparent operation, consultation and prevention of wide data breaches which can impact a massive group of people [6] . In particular, the novelty proposed in this work consists in the use of a special type of token called Non Fungible Tokens (NFT) whose characteristics allows to uniquely represent a contagion that can be anonymously notified to tested person and also made public.…”

Section: Introductionmentioning

confidence: 99%