Characterisation of physiological responses to odours in autism spectrum disorders: a preliminary study

Abnormal sensory perception is among the earliest symptoms of autism spectrum disorders (ASD). Despite mixed findings, olfactory perception seems to be altered in ASD. There is also evidence that automatic responses to odours can serve as biomarkers of ASD. However, this potential use of odour-based biomarkers for ASD is still underexplored. In this study, we aimed to investigate whether physiological responses to social and non-social odours, measured with electrocardiography (ECG) and facial electromyography (EMG), can be used to characterise and predict ASD in adults. For that, we extracted 32 signal features from a previously collected database of 11 adults with ASD and 48 adults with typical development (TD). Firstly, non-parametric tests were performed, showing significant differences between the ASD and the TD groups in 10 features. Secondly, a k-nearest-neighbour classifier with a leave-one-out strategy was employed, obtaining an F1-score of 67%. Although caution is needed due to the small sample size, this study provides preliminary evidence supporting the use of physiological responses to social and non-social odours as a potential diagnostic tool for ASD in adults.This work is also funded by national funds, European Regional Development Fund, FSE through COMPETE2020, through FCT, in the scope of the framework contract foreseen in the numbers 4, 5, and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19.publishe

Robust human locomotion and localization activity recognition over multisensory

Human activity recognition (HAR) plays a pivotal role in various domains, including healthcare, sports, robotics, and security. With the growing popularity of wearable devices, particularly Inertial Measurement Units (IMUs) and Ambient sensors, researchers and engineers have sought to take advantage of these advances to accurately and efficiently detect and classify human activities. This research paper presents an advanced methodology for human activity and localization recognition, utilizing smartphone IMU, Ambient, GPS, and Audio sensor data from two public benchmark datasets: the Opportunity dataset and the Extrasensory dataset. The Opportunity dataset was collected from 12 subjects participating in a range of daily activities, and it captures data from various body-worn and object-associated sensors. The Extrasensory dataset features data from 60 participants, including thousands of data samples from smartphone and smartwatch sensors, labeled with a wide array of human activities. Our study incorporates novel feature extraction techniques for signal, GPS, and audio sensor data. Specifically, for localization, GPS, audio, and IMU sensors are utilized, while IMU and Ambient sensors are employed for locomotion activity recognition. To achieve accurate activity classification, state-of-the-art deep learning techniques, such as convolutional neural networks (CNN) and long short-term memory (LSTM), have been explored. For indoor/outdoor activities, CNNs are applied, while LSTMs are utilized for locomotion activity recognition. The proposed system has been evaluated using the k-fold cross-validation method, achieving accuracy rates of 97% and 89% for locomotion activity over the Opportunity and Extrasensory datasets, respectively, and 96% for indoor/outdoor activity over the Extrasensory dataset. These results highlight the efficiency of our methodology in accurately detecting various human activities, showing its potential for real-world applications. Moreover, the research paper introduces a hybrid system that combines machine learning and deep learning features, enhancing activity recognition performance by leveraging the strengths of both approaches

A Cluster-Based Energy-Efficient Secure Optimal Path-Routing Protocol for Wireless Body-Area Sensor Networks

Recently, research into Wireless Body-Area Sensor Networks (WBASN) or Wireless Body-Area Networks (WBAN) has gained much importance in medical applications, and now plays a significant role in patient monitoring. Among the various operations, routing is still recognized as a resource-intensive activity. As a result, designing an energy-efficient routing system for WBAN is critical. The existing routing algorithms focus more on energy efficiency than security. However, security attacks will lead to more energy consumption, which will reduce overall network performance. To handle the issues of reliability, energy efficiency, and security in WBAN, a new cluster-based secure routing protocol called the Secure Optimal Path-Routing (SOPR) protocol has been proposed in this paper. This proposed algorithm provides security by identifying and avoiding black-hole attacks on one side, and by sending data packets in encrypted form on the other side to strengthen communication security in WBANs. The main advantages of implementing the proposed protocol include improved overall network performance by increasing the packet-delivery ratio and reducing attack-detection overheads, detection time, energy consumption, and delay

An optimized stacking ensemble model for phishing websites detection

Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectivel

Entropy in Image Analysis III

Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future

Cybersecurity and the Digital Health: An Investigation on the State of the Art and the Position of the Actors

Cybercrime is increasingly exposing the health domain to growing risk. The push towards a strong connection of citizens to health services, through digitalization, has undisputed advantages. Digital health allows remote care, the use of medical devices with a high mechatronic and IT content with strong automation, and a large interconnection of hospital networks with an increasingly effective exchange of data. However, all this requires a great cybersecurity commitment—a commitment that must start with scholars in research and then reach the stakeholders. New devices and technological solutions are increasingly breaking into healthcare, and are able to change the processes of interaction in the health domain. This requires cybersecurity to become a vital part of patient safety through changes in human behaviour, technology, and processes, as part of a complete solution. All professionals involved in cybersecurity in the health domain were invited to contribute with their experiences. This book contains contributions from various experts and different fields. Aspects of cybersecurity in healthcare relating to technological advance and emerging risks were addressed. The new boundaries of this field and the impact of COVID-19 on some sectors, such as mhealth, have also been addressed. We dedicate the book to all those with different roles involved in cybersecurity in the health domain

Energy efficient Routing Protocols for Underwater Acoustic Wireless Sensor Network

Technological advancement regarding oceanic world discovery and monitoring has led to autonomous communication, which results in the emergence of the Internet of underwater things (IoUT). Underwater acoustic wireless sensor networks have become one of the most recently researched within the IoUT. An underwater acoustic wireless sensor network consists of sensor nodes, autonomous vehicles, and remotely operated vehicles which are normally deployed to carry out a collaborative task within an underwater region. Underwater acoustic wireless sensor networks have become one of the most recently researched area which supports long transmission range. However, acoustic signals experience deformation due to factors which consist of noise, propagation delay, and low bandwidth. Sensor nodes are battery dependent which mean they are difficult to recharge or replace once deployed. Routing protocols play important role in the communication process between these sensor nodes. As a result, this research aims to develop an energy efficient routing protocol that can bring about optimal policies for energy consumption in the process of data aggregation and transmission. The developed routing protocol focused on sparse and dense network architectures by examining the popular ad-hoc routing protocol action on demand distance vector routing protocol (AODV) for sparse networks and low energy adaptive clustering hierarchy (LEACH) for dense network. For a sparse architecture this research identifies current energy and overhead challenges facing AODV which in turn modifies the protocol by creating a new energy aware and overhead friendly routing protocol called action on demand distance vector sparse underwater acoustic routing protocol (AODV-SUARP) for underwater communication. AODV-SUARP introduces the mechanism of route stability function (RSF) by colour mode to select the most energy efficient route to forwards packets. For dense architecture this research identifies the energy challenge facing the conventional LEACH routing protocol which in turn leads to its modification by creating a new energy aware routing protocol called low energy adaptive clustering hierarchy dense underwater acoustic routing protocol (LEACH-DUARP). Furthermore, for the optimal selection of eligible cluster head in a subsequent round LEACH-DUARP introduces a concept called the stability function value (SFV). The developed routing protocols (AODV-SUARP and LEACH-DUARP) were implemented in NS-3 and validated using mathematical modelling. Results obtained indicated both AODV-SUARP and LEACH-DUARP achieves a considerable result compared to other routing protocols in terms of residual energy, packet delivery ratio, and number of dead nodes

Biosensors for Diagnosis and Monitoring

Biosensor technologies have received a great amount of interest in recent decades, and this has especially been the case in recent years due to the health alert caused by the COVID-19 pandemic. The sensor platform market has grown in recent decades, and the COVID-19 outbreak has led to an increase in the demand for home diagnostics and point-of-care systems. With the evolution of biosensor technology towards portable platforms with a lower cost on-site analysis and a rapid selective and sensitive response, a larger market has opened up for this technology. The evolution of biosensor systems has the opportunity to change classic analysis towards real-time and in situ detection systems, with platforms such as point-of-care and wearables as well as implantable sensors to decentralize chemical and biological analysis, thus reducing industrial and medical costs. This book is dedicated to all the research related to biosensor technologies. Reviews, perspective articles, and research articles in different biosensing areas such as wearable sensors, point-of-care platforms, and pathogen detection for biomedical applications as well as environmental monitoring will introduce the reader to these relevant topics. This book is aimed at scientists and professionals working in the field of biosensors and also provides essential knowledge for students who want to enter the field