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Article: Food traceability as driver for the competitiveness in Italian food service companies

Tessitore, Sara / Iraldo, Fabio / Apicella, Andrea / Tarabella, Angela

Journal of foodservice business research. 2022 Jan. 02, v. 25, no. 1

2022

Abstract: Consumers, companies and institutions have discussed the importance of food traceability in the EU for some time. The research objective is to compare perceptions of the role, importance and main “components” of food traceability in the food service ... ...

Abstract	Consumers, companies and institutions have discussed the importance of food traceability in the EU for some time. The research objective is to compare perceptions of the role, importance and main “components” of food traceability in the food service field (in food service points of sale: restaurants, hotels, café, bars, catering etc.) between Italian consumers and Italian Hotel, Restaurants and Catering companies. The comparative analysis identified whether consumers and companies have the same concept of food traceability and its relevance in the hospitality field. A survey was implemented to collect feedback on seven items that can be considered as main part of food traceability. A Student t-test was used to identify the statistically significant differences in answers between consumers and the food service companies (mainly Hotels, Restaurants and Catering), and to assess the relevance of food traceability in their perceptions. An ordered logit regression was implemented to assess the determined variables on the companies and consumers behaviors. The research demonstrated that food service companies often have a biased perception of consumers’ opinions, beliefs and expectations regarding food traceability, compared with what consumers really think and want. The most original aspect of the study is the comparison of the perceptions identifying similarities and differences in the two samples.
Keywords	hospitality industry ; research ; surveys ; t-test ; traceability
Language	English
Dates of publication	2022-0102
Size	p. 57-84.
Publishing place	Routledge
Document type	Article
ZDB-ID	2094789-6
ISSN	1537-8039 ; 1537-8020
ISSN (online)	1537-8039
ISSN	1537-8020
DOI	10.1080/15378020.2021.1918536
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Hidden Classification Layers

Apicella, Andrea / Isgrò, Francesco / Prevete, Roberto

a study on Data Hidden Representations with a Higher Degree of Linear Separability between the Classes

2023

Abstract: In the context of classification problems, Deep Learning (DL) approaches represent state of art. Many DL approaches are based on variations of standard multi-layer feed-forward neural networks. These are also referred to as deep networks. The basic idea ... ...

Abstract	In the context of classification problems, Deep Learning (DL) approaches represent state of art. Many DL approaches are based on variations of standard multi-layer feed-forward neural networks. These are also referred to as deep networks. The basic idea is that each hidden neural layer accomplishes a data transformation which is expected to make the data representation "somewhat more linearly separable" than the previous one to obtain a final data representation which is as linearly separable as possible. However, determining the appropriate neural network parameters that can perform these transformations is a critical problem. In this paper, we investigate the impact on deep network classifier performances of a training approach favouring solutions where data representations at the hidden layers have a higher degree of linear separability between the classes with respect to standard methods. To this aim, we propose a neural network architecture which induces an error function involving the outputs of all the network layers. Although similar approaches have already been partially discussed in the past literature, here we propose a new architecture with a novel error function and an extensive experimental analysis. This experimental analysis was made in the context of image classification tasks considering four widely used datasets. The results show that our approach improves the accuracy on the test set in all the considered cases. Comment: Paper submitted for peer-review
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2023-06-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: High-wearable EEG-based distraction detection in motor rehabilitation.

Apicella, Andrea / Arpaia, Pasquale / Frosolone, Mirco / Moccaldi, Nicola

Scientific reports

2021 Volume 11, Issue 1, Page(s) 5297

Abstract: A method for EEG-based distraction detection during motor-rehabilitation tasks is proposed. A wireless cap guarantees very high wearability with dry electrodes and a low number of channels. Experimental validation is performed on a dataset from 17 ... ...

Abstract	A method for EEG-based distraction detection during motor-rehabilitation tasks is proposed. A wireless cap guarantees very high wearability with dry electrodes and a low number of channels. Experimental validation is performed on a dataset from 17 volunteers. Different feature extractions from spatial, temporal, and frequency domain and classification strategies were evaluated. The performances of five supervised classifiers in discriminating between attention on pure movement and with distractors were compared. A k-Nearest Neighbors classifier achieved an accuracy of 92.8 ± 1.6%. In this last case, the feature extraction is based on a custom 12 pass-band Filter-Bank (FB) and the Common Spatial Pattern (CSP) algorithm. In particular, the mean Recall of classification (percentage of true positive in distraction detection) is higher than 92% and allows the therapist or an automated system to know when to stimulate the patient's attention for enhancing the therapy effectiveness.
MeSH term(s)	Adult ; Attention/physiology ; Brain-Computer Interfaces ; Data Accuracy ; Electrodes ; Electroencephalography/instrumentation ; Female ; Healthy Volunteers ; Humans ; Imagination/physiology ; Male ; Motor Activity/physiology ; Neurological Rehabilitation/instrumentation ; Neurological Rehabilitation/methods ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Wearable Electronic Devices ; Wireless Technology/instrumentation ; Young Adult
Language	English
Publishing date	2021-03-05
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Validation Study
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-84447-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: EEG-based detection of emotional valence towards a reproducible measurement of emotions.

Apicella, Andrea / Arpaia, Pasquale / Mastrati, Giovanna / Moccaldi, Nicola

Scientific reports

2021 Volume 11, Issue 1, Page(s) 21615

Abstract: A methodological contribution to a reproducible Measurement of Emotions for an EEG-based system is proposed. Emotional Valence detection is the suggested use case. Valence detection occurs along the interval scale theorized by the Circumplex Model of ... ...

Abstract	A methodological contribution to a reproducible Measurement of Emotions for an EEG-based system is proposed. Emotional Valence detection is the suggested use case. Valence detection occurs along the interval scale theorized by the Circumplex Model of emotions. The binary choice, positive valence vs negative valence, represents a first step towards the adoption of a metric scale with a finer resolution. EEG signals were acquired through a 8-channel dry electrode cap. An implicit-more controlled EEG paradigm was employed to elicit emotional valence through the passive view of standardized visual stimuli (i.e., Oasis dataset) in 25 volunteers without depressive disorders. Results from the Self Assessment Manikin questionnaire confirmed the compatibility of the experimental sample with that of Oasis. Two different strategies for feature extraction were compared: (i) based on a-priory knowledge (i.e., Hemispheric Asymmetry Theories), and (ii) automated (i.e., a pipeline of a custom 12-band Filter Bank and Common Spatial Pattern). An average within-subject accuracy of 96.1 %, was obtained by a shallow Artificial Neural Network, while k-Nearest Neighbors allowed to obtain a cross-subject accuracy equal to 80.2%.
MeSH term(s)	Adult ; Algorithms ; Electroencephalography/methods ; Emotions/physiology ; Female ; Humans ; Male ; Models, Psychological ; Neural Networks, Computer ; Reproducibility of Results ; Signal Processing, Computer-Assisted
Language	English
Publishing date	2021-11-03
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-00812-7
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Article ; Online: Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI.

Annuzzi, Giovanni / Apicella, Andrea / Arpaia, Pasquale / Bozzetto, Lutgarda / Criscuolo, Sabatina / De Benedetto, Egidio / Pesola, Marisa / Prevete, Roberto

IEEE journal of biomedical and health informatics

2024 Volume 28, Issue 5, Page(s) 3123–3133

Abstract: Type 1 diabetes mellitus (T1DM) is characterized by insulin deficiency and blood sugar control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates basal insulin delivery and glucose monitoring. However, APs are unable ... ...

Abstract	Type 1 diabetes mellitus (T1DM) is characterized by insulin deficiency and blood sugar control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates basal insulin delivery and glucose monitoring. However, APs are unable to manage postprandial glucose response (PGR) due to limited knowledge of its determinants, requiring additional information for accurate bolus delivery, such as estimated carbohydrate intake. This study aims to quantify the influence of various meal-related factors on predicting postprandial blood glucose levels (BGLs) at different time intervals (15 min, 60 min, and 120 min) after meals by using deep neural network (DNN) models. The prediction models incorporate preprandial blood glucose values, insulin dosage, and various meal-related nutritional factors such as intake of energy, carbohydrates, proteins, lipids, fatty acids, fibers, glycemic index, and glycemic load as input variables. The impact of input features was assessed by exploiting eXplainable Artificial Intelligence (XAI) methodologies, specifically SHapley Additive exPlanations (SHAP), which provide insights into each feature's contribution to the model predictions. By leveraging XAI methodologies, this study aims to enhance the interpretability and transparency of BGL prediction models and validate clinical literature hypotheses. The findings can aid in the development of decision-support tools for individuals with T1DM, facilitating PGR management and reducing the risks of adverse events. The improved understanding of PGR determinants may lead to advancements in AP technology and improve the overall quality of life for T1DM patients.
MeSH term(s)	Humans ; Diabetes Mellitus, Type 1/blood ; Diabetes Mellitus, Type 1/drug therapy ; Blood Glucose/analysis ; Neural Networks, Computer ; Artificial Intelligence ; Adult ; Male ; Female ; Blood Glucose Self-Monitoring/methods ; Forecasting
Chemical Substances	Blood Glucose
Language	English
Publishing date	2024-05-06
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2023.3348334
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Article ; Online: A survey on modern trainable activation functions.

Apicella, Andrea / Donnarumma, Francesco / Isgrò, Francesco / Prevete, Roberto

Neural networks : the official journal of the International Neural Network Society

2021 Volume 138, Page(s) 14–32

Abstract: In neural networks literature, there is a strong interest in identifying and defining activation functions which can improve neural network performance. In recent years there has been a renovated interest in the scientific community in investigating ... ...

Abstract	In neural networks literature, there is a strong interest in identifying and defining activation functions which can improve neural network performance. In recent years there has been a renovated interest in the scientific community in investigating activation functions which can be trained during the learning process, usually referred to as trainable, learnable or adaptable activation functions. They appear to lead to better network performance. Diverse and heterogeneous models of trainable activation function have been proposed in the literature. In this paper, we present a survey of these models. Starting from a discussion on the use of the term "activation function" in literature, we propose a taxonomy of trainable activation functions, highlight common and distinctive proprieties of recent and past models, and discuss main advantages and limitations of this type of approach. We show that many of the proposed approaches are equivalent to adding neuron layers which use fixed (non-trainable) activation functions and some simple local rule that constrains the corresponding weight layers.
MeSH term(s)	Machine Learning/classification ; Machine Learning/standards
Language	English
Publishing date	2021-02-09
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2021.01.026
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Book ; Online: On The Effects Of Data Normalisation For Domain Adaptation On EEG Data

Apicella, Andrea / Isgrò, Francesco / Pollastro, Andrea / Prevete, Roberto

2022

Abstract: In the Machine Learning (ML) literature, a well-known problem is the Dataset Shift problem where, differently from the ML standard hypothesis, the data in the training and test sets can follow different probability distributions, leading ML systems ... ...

Abstract	In the Machine Learning (ML) literature, a well-known problem is the Dataset Shift problem where, differently from the ML standard hypothesis, the data in the training and test sets can follow different probability distributions, leading ML systems toward poor generalisation performances. This problem is intensely felt in the Brain-Computer Interface (BCI) context, where bio-signals as Electroencephalographic (EEG) are often used. In fact, EEG signals are highly non-stationary both over time and between different subjects. To overcome this problem, several proposed solutions are based on recent transfer learning approaches such as Domain Adaption (DA). In several cases, however, the actual causes of the improvements remain ambiguous. This paper focuses on the impact of data normalisation, or standardisation strategies applied together with DA methods. In particular, using \textit{SEED}, \textit{DEAP}, and \textit{BCI Competition IV 2a} EEG datasets, we experimentally evaluated the impact of different normalization strategies applied with and without several well-known DA methods, comparing the obtained performances. It results that the choice of the normalisation strategy plays a key role on the classifier performances in DA scenarios, and interestingly, in several cases, the use of only an appropriate normalisation schema outperforms the DA technique. Comment: Published in its final version on Engineering Applications of Artificial Intelligence (EAAI) https://doi.org/10.1016/j.engappai.2023.106205
Keywords	Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Signal Processing
Subject code	006
Publishing date	2022-10-03
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: EEG-based measurement system for monitoring student engagement in learning 4.0.

Apicella, Andrea / Arpaia, Pasquale / Frosolone, Mirco / Improta, Giovanni / Moccaldi, Nicola / Pollastro, Andrea

Scientific reports

2022 Volume 12, Issue 1, Page(s) 5857

Abstract: A wearable system for the personalized EEG-based detection of engagement in learning 4.0 is proposed. In particular, the effectiveness of the proposed solution is assessed by means of the classification accuracy in predicting engagement. The system can ... ...

Abstract	A wearable system for the personalized EEG-based detection of engagement in learning 4.0 is proposed. In particular, the effectiveness of the proposed solution is assessed by means of the classification accuracy in predicting engagement. The system can be used to make an automated teaching platform adaptable to the user, by managing eventual drops in the cognitive and emotional engagement. The effectiveness of the learning process mainly depends on the engagement level of the learner. In case of distraction, lack of interest or superficial participation, the teaching strategy could be personalized by an automatic modulation of contents and communication strategies. The system is validated by an experimental case study on twenty-one students. The experimental task was to learn how a specific human-machine interface works. Both the cognitive and motor skills of participants were involved. De facto standard stimuli, namely (1) cognitive task (Continuous Performance Test), (2) music background (Music Emotion Recognition-MER database), and (3) social feedback (Hermans and De Houwer database), were employed to guarantee a metrologically founded reference. In within-subject approach, the proposed signal processing pipeline (Filter bank, Common Spatial Pattern, and Support Vector Machine), reaches almost 77% average accuracy, in detecting both cognitive and emotional engagement.
MeSH term(s)	Electroencephalography ; Emotions ; Humans ; Signal Processing, Computer-Assisted ; Students ; Support Vector Machine
Language	English
Publishing date	2022-04-07
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-09578-y
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Book ; Online: Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems

Apicella, Andrea / Giugliano, Salvatore / Isgrò, Francesco / Prevete, Roberto

2021

Abstract: A central issue addressed by the rapidly growing research area of eXplainable Artificial Intelligence (XAI) is to provide methods to give explanations for the behaviours of Machine Learning (ML) non-interpretable models after the training. Recently, it ... ...

Abstract	A central issue addressed by the rapidly growing research area of eXplainable Artificial Intelligence (XAI) is to provide methods to give explanations for the behaviours of Machine Learning (ML) non-interpretable models after the training. Recently, it is becoming more and more evident that new directions to create better explanations should take into account what a good explanation is to a human user. This paper suggests taking advantage of developing an XAI framework that allows producing multiple explanations for the response of image a classification system in terms of potentially different middle-level input features. To this end, we propose an XAI framework able to construct explanations in terms of input features extracted by auto-encoders. We start from the hypothesis that some autoencoders, relying on standard data representation approaches, could extract more salient and understandable input properties, which we call here \textit{Middle-Level input Features} (MLFs), for a user with respect to raw low-level features. Furthermore, extracting different types of MLFs through different type of autoencoders, different types of explanations for the same ML system behaviour can be returned. We experimentally tested our method on two different image datasets and using three different types of MLFs. The results are encouraging. Although our novel approach was tested in the context of image classification, it can potentially be used on other data types to the extent that auto-encoders to extract humanly understandable representations can be applied. Comment: published on Knowledge-Based Systems
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2021-06-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Strategies to exploit XAI to improve classification systems

Apicella, Andrea / Di Lorenzo, Luca / Isgrò, Francesco / Pollastro, Andrea / Prevete, Roberto

2023

Abstract: Explainable Artificial Intelligence (XAI) aims to provide insights into the decision-making process of AI models, allowing users to understand their results beyond their decisions. A significant goal of XAI is to improve the performance of AI models by ... ...

Abstract	Explainable Artificial Intelligence (XAI) aims to provide insights into the decision-making process of AI models, allowing users to understand their results beyond their decisions. A significant goal of XAI is to improve the performance of AI models by providing explanations for their decision-making processes. However, most XAI literature focuses on how to explain an AI system, while less attention has been given to how XAI methods can be exploited to improve an AI system. In this work, a set of well-known XAI methods typically used with Machine Learning (ML) classification tasks are investigated to verify if they can be exploited, not just to provide explanations but also to improve the performance of the model itself. To this aim, two strategies to use the explanation to improve a classification system are reported and empirically evaluated on three datasets: Fashion-MNIST, CIFAR10, and STL10. Results suggest that explanations built by Integrated Gradients highlight input features that can be effectively used to improve classification performance. Comment: This work has been accepted to be presented to The 1st World Conference on eXplainable Artificial Intelligence (xAI 2023), July 26-28, 2023 - Lisboa, Portugal
Keywords	Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2023-06-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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