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A Federated Learning Anomaly Detection Approach for IoT Environments 2nd

A Federated Learning Anomaly Detection Approach for IoT Envi...

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2nd International Conference on Advances in Computing Research-ACR

作者： Suleiman, Basem Anaissi, Ali Yan, Wenbo Bello, Abubakar Zou, Sophie Tong, Ling Nga Meric Univ Sydney Sch Comp Sci Camperdown NSW Australia Univ Western Sydney Sch Social Sci Penrith NSW Australia

ISBN: (纸本)9783031569494;9783031569500

The fast-growing development of smart home environments and the popularity of IoT devices have increasingly raised security concerns about anomalous behaviour and events. Traditional methods employ machine learning (ML) models that require sharing data collected from many IoT devices from smart homes so anomalous behaviour can be learned and detected. Such approaches introduce many problems in terms of privacy of sensitive IoT data to be shared over the network, models performance overhead, and scalability. We propose a novel Federated Learning Anomaly Detection (FLAD) approach for IoT smart home environments that address these problems. Our FLAD approach maintains the privacy of IoT data and faster training and detection performance by training several local models, instead of sharing it with a global model, on data collected from local devices. The local models require sharing only the learning parameter values with a global model which aggregates these values and sent them back to the local models to update their learning accordingly. Our experimental analysis of our FLAD approach on real IoT smart devices demonstrated very high accuracy (reaching over 99%) which is very comparable with the accuracy of the non-FL approach. Furthermore, our FLAD approach maintains the highest level of IoT privacy and faster model training time as it retains the IoT data within its local models and reduces network communication overhead, which has the potential to scale to a very large number of IoT devices.

关键词： Federated Learning IoT Smart Home Anomaly Detection Deep Learning

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Cyberbullying Detection for Urdu Language Using Machine Learning 1

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2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Mustafa, Hamza Zafar, Kashif Natl Univ Comp & Emerging Sci Lahore Pakistan

ISBN: (数字)9783031628719

ISBN: (纸本)9783031628702;9783031628719

As social media users are rapidly growing, cyberbullying detection has become an increasingly essential topic for research. Cyberbullying is described as the sending or posting of text or photos designed to hurt or portray shame to another person or group of people using the Internet, cell phones, video game systems, or other technology. Cyberbullying is the use of social media to hurt or embarrass any other person. In the past, research in cyberbullying detection in English has been carried out, and cyberbullying detection involving the Urdu language has been ignored mostly because the Urdu language lacks resources. Urdu is a widely spoken language, especially in some parts of South Asia. It is the National Language of Pakistan. In this research, machine learning-based approaches are used for cyberbullying detection. (Dataset collection and source). The dataset is labeled by several different native speakers. The majority voting scheme is used for assigning a final label to a Tweet. Threemain feature extraction techniques used for the detection of cyberbullying are TF-IDF, BOW, and Glove, and different machine learning algorithms are implemented. After doing multiple experiments, it has been proven that the Extra Tree Classifier (ETC) with TFIDF outperformed other algorithms having a 79% accuracy score. However, the proposed approach performed better than the reported approaches that are based on machine learning for the Urdu language on sentiment analysis. Our experimental results also performed better on the sentiment dataset.

关键词： Cyberbullying Detection Urdu language Machine Learning

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Contribution to Characterizing Time-Dependent Handgrip Strength Profiles 21st

Contribution to Characterizing Time-Dependent Handgrip Stren...

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21st International Conference on Smart Technologies and Education (STE) on Smart Technologies for a Sustainable Future

作者： Cardoso, Alberto Urbano, Diana Restivo, Maria Teresa Univ Coimbra Dept Informat Engn CISUC LASI Coimbra Portugal Univ Porto Fac Engn FEUP LAETA INEGI Porto Portugal Univ Porto Fac Engn FEUP A3ES LAETA INEGI Porto Portugal

ISBN: (纸本)9783031619045;9783031619052

Themaximum value of HandGrip Strength (usually named by HGS) is an important biomarker that indicates several physical conditions such as diabetes, malnutrition, sarcopenia, frailty and general functional physical capacity. It is also relevant for evaluation purposes in the context of rehabilitation, in patients suffering from hand musculoskeletal or other pathologies. The HGS dynamometry is non-invasive, portable, easy to perform, fast and reliable, making it suitable for routine assessment in different health and sportsrelated areas. Besides the HGS value (maximum value), other parameters such as rate of force development and sustainability of force, obtained bymeasuring time-dependent HGS (HGS(t)), have also been used in different assessment contexts utilizing modern dynamometers such as the BodyGrip (nowon its commercialized version, Gripwise). A universal definition of the parameters characterizing HGS(t) curves has been proposed, and a study based on an artificial neural network model used those parameters to predict the frailty of elderlywomen. In thiswork, a newmethodology was used to investigate the characteristics of HGS(t). The main idea is to identify and explore a first or second-order continuous-time transfer function, which can be used to estimate the force profile, using the Mean Squared Error and the overall percentual fit as measures of the approximation quality. The authors consider the results to be promising for future exploitation of the model to investigate and extract the characteristics of the time-dependent HGS (HGS(t)).

关键词： Handgrip Handgrip Strength time profile Model Identification

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Hidden Knowledge Extraction for Association Rule Mining Technique Based on the Apriori and Frequent Pattern Growth Algorithms: A Case Study 9th

Hidden Knowledge Extraction for Association Rule Mining Tech...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Nsabimana, Thierry Niyonsaba, Therence Bimenyimana, Christian Ildegard Vlavonou, Ghislain Hounsou, Joel T. Niyonsaba, Gerard Univ Burundi Inst Stat Appl Nyamugerera Bujumbura Burundi Univ Grands Lacs St Michel Bujumbura Burundi Univ Abomey Calavi Inst Math & Sci Phys Dangbo Porto Novo Benin Ctr Porta Sion Ctr Rech Independant & Informatisat Syst Informat Bujumbura Burundi

ISBN: (纸本)9789819732883;9789819732890

Association rule mining technique is a branch of data mining area. It's purpose is to extract the hidden knowledge from databases D with transactions T. Due to the huge amount of data from multiple sources continuously being captured and stored in databases D, many industries, enterprises, to name just a few, are more interesting in exploring association rules from their transactional databases in order to increase their profits. In this paper, the simulation study is investigated based on the Apriori and Frequent Pattern Growth (FP-G) algorithms which are popular utilized in the discovery of the relevant hidden frequent items from transactional databases. This study focuses on the design and the implementation of the appropriate data structure using linked lists of array. The simulation of the algorithms is evaluated using a supermarket dataset named as retail market basket data so as to analyze the performance characteristics of the implemented algorithms, on a 8GB Random Access Memory (RAM) laptop computer. Furthermore, the investigation study uses the designed useful data structure and the features of the algorithms during the extraction of frequent itemsets on supermarket dataset, which are then used for generating the association rules, and performance costs estimated for different values of association rule length, minimum support, and time-consuming performance.

关键词： Data mining Apriori FP-Growth Frequent itemsets Association rule Support

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Geospatial and Temporal Modeling of Crime Rates Using Neural networks 13th

Geospatial and Temporal Modeling of Crime Rates Using Neural...

引用

13th Computer Science Online Conference

作者： Ndlovu, Simbarashe Meghanathan, Natarajan Jackson State Univ Dept Elect & Comp Engn & Comp Sci 1400 Lynch St Jackson MS 39217 USA

ISBN: (纸本)9783031702846;9783031702853

Crime forecasting has become an interesting concept of data science techniques like machine learning. In this research paper, we explore various architectures and algorithms of Neural Network (NN) models for predicting crime rates based on spatial and temporal crime data. The models are trained and tested on the dataset of crimes committed on a certain day of week, with geographic coordinates and generate a prediction or forecast of crimes to be committed across the region. The Long Short-Term Memory (LSTM) model of each of the algorithms to be explored achieves the highest accuracy. This research attempts to demonstrate the capability and reliability of NNs for crime forecasting based on the features already stated. This model could be of great value to law enforcement with resource allocation and strategic planning.

关键词： Neural networks Crime Forecasting Geospatial and Temporal Modeling Long Short-Term Memory Model Predictive Policing

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Quadratic Stabilizability of Switched Affine Descriptor systems 1

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9th International Congress on Information and Communication Technology (ICICT)

作者： Degla, Guy Dossou-Yovo, Marie-Louise Inst Math & Sci Phys Porto Novo Benin

ISBN: (数字)9789819733057

ISBN: (纸本)9789819733040;9789819733057

This paper deals with a generalization of our recent results according to which impulse free switched affine descriptor systems can be quadratically stable via state-dependent switching law. The systems considered in our previous work are those which encompass finitely many modes modelized by linear nonhomogeneous differential systems (not necessarily stable) with the same algebraic constraints and such that the linear and affine parts are both commuted with different switching laws. Under some regularity and impulse-freeness assumptions, we prove that if there exist moreover some Hurwitz convex combination of subsystems matrices flows and if some convex combination of affine vectors is zero, then we can arrange a state-dependent switching law for which the entire switched affine DAEs system is quadratically stable at the origin. But when no zero convex combination of affine vectors exists, we have established that the switched affine descriptor system can be stabilized quadratically around an equilibrium point lying in a convergence set defined by the convex combination of the matrices flows and the affine vectors of each mode. The established results have wide variety of applications in engineering such as electric networks stabilization and robotics to named only those there.

关键词： State-dependent switching law Hurwitz matrix flow Switched affine DAEs systems Quadratic stabilizability Convex combination

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What Is Your Dependency Level? A System to Evaluate Individual Dependency Levels Depending on the Social Context and Physical Activity 1

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Martin, Cristobal Ballesteros, Joaquin Cosano, Arturo Rondon, Luis Miguel Univ Malaga Malaga Spain Univ Malaga ITIS Software Malaga Spain Univ Malaga Dept Psicologia Social Trabajo Social Servicios Soci & Antropol Social Malaga Spain

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

Dependency, defined as the need for assistance in daily activities due to physical or cognitive limitations, is a crucial concern in healthcare and social services. Accurately appraising dependency levels is essential for allocating resources, planning care, and enhancing the quality of life for individuals needing support. This work details developing a system to assess and predict individuals' dependency levels through an optimized survey. Initially, a comprehensive scale with 26 items was created by social work experts and tested with 210 users with three different dependency levels recognized and 40 without any dependency. A machine learning technique has been used to find a reduced set of items that predict the dependency level accurately. The results classify the fourth classes with 91 % accuracy using only nine items. This system has been deployed and tested in a web application, so other systems or persons can use it to predict the individuals' dependency levels online.

关键词： Dependency assessment Machine learning Resource Allocation

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MojoVision: An Extended Conceptual Framework for Psoriasis Detection and Enhanced Treatment Suggestion 1

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Maghsoodi, Abtin Ijadi Quincey, Vicki Te Whatu Ora Hlth New Zealand Dept Intelligence & Insight Te Manawa Taki Reg Tauranga New Zealand Te Whatu Ora Hlth New Zealand Dept Rheumatol Te Manawa Taki Reg Tauranga New Zealand

ISBN: (数字)9783031700187

ISBN: (纸本)9783031700170;9783031700187

Psoriasis is a chronic immuno-dermatological condition affecting individuals of all ages and significantly impacting their quality of life. The proposed system enhances psoriasis management by integrating a hybrid deep learning technique into a clinical decision support framework, an advancement from a previously developed Computer-Aided Diagnosis (CAD) tool called Mojo Vision. This approach involves analyzing dermatological images to detect the presence of psoriasis and then categorizing it into one of seven distinct variants. Central to this system is the treatment suggestion to advise on the most effective treatment options based on the specific variant and severity of symptoms observed in patients. In this research, the enhancement to the decision support tool includes integrating Natural Language Processing (NLP) with Mojo Vision to create a multi-modal decision support tool. By incorporating NLP, the system gains a holistic understanding of each case, considering visual dermatological data and textual medical records, thus enabling more precise diagnoses and personalized treatment recommendations. This conceptual framework demonstrates high practicality in diagnostic and treatment advisory capacities, offering a significant advancement in the clinical management of psoriasis.

关键词： Computer-Aided Diagnosis Psoriasis Deep Learning Treatment Suggestion Natural Language Processing (NLP)

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Traffic State Prediction of Perturbed and Non-perturbed Traffic Scenarios 1

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Intelligent systems Conference (IntelliSys)

作者： Teng, Teck-Hou Jagadeesh, George Rosario Kunal, Thakkar Chung, Chong Chee ST Engn Ltd Data Analyt Strateg Technol Ctr DA STC Grp Technol Off Singapore Singapore Business Dev Ctr Mobility Rd Singapore Singapore

ISBN: (数字)9783031477218

ISBN: (纸本)9783031477201;9783031477218

Traffic state prediction is a regression problem of predicting traffic states such as travel speed, volume, occupancy and density. However, we observe that existing traffic state prediction models are often trained and tested on non-perturbed traffic scenarios. It lacks the robustness needed for handling perturbed traffic scenarios. This work addresses this gap in the stated problem statement using simulated data based on perturbed traffic scenarios. Through this work, we established the need to assess the performance of the regression models on the affected road segments rather than on the entire road network. In doing so, the performance of the regression models on the perturbed traffic scenarios has become apparent. In addition, we have confirmed that it helps to train the regression model using a mixture of traffic data from both types of traffic scenarios. For our experiments, we built a micro-simulation of a medium-sized traffic network based on the morning peak traffic scenario. We evaluated multiple regression models for multiple prediction horizons on the studied traffic scenarios. Our results based on the affected road segments reveal that Spatial-Temporal Graph Convolutional Network (ST-GCN) has the best mean absolute percentage error (MAPE) for all prediction horizons. Hence, it is established that ST-GCN is a more robust regression model for predicting traffic states under perturbed and non-perturbed traffic scenarios.

关键词： Traffic prediction Traffic perturbation Supervised learning Regression model

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Innovating in Nursing Education: A Game Prototype for Bridging the Gap in Family-Centered Care 1

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： de Oliveira, Jessica Ferreira Campos, Joana Martins, Teresa Silvia Fernandes, Carla Campos Ferreira, Marta Univ Porto Fac Engn Porto Portugal Nursing Sch Porto Porto Portugal CINTESIS RISE Nursing Sch Porto Porto Portugal Univ Porto INESC TEC Ctr Fac Engn Porto Portugal

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

In recent years, there has been an increasing trend towards innovative and interactive learning approaches. Serious games have emerged as a promising solution in health education, offering engaging and immersive learning experiences. This article presents the development steps of a mobile application to promote knowledge of nursing assessment and intervention in the family. A prototype was developed for Android devices using React Native technology and Firebase database, incorporating gamification elements. It was then evaluated by potential users. The results showed that the proposed solution successfully enhanced nurses' learning about family issues and dynamics, receiving positive feedback from users regarding its effectiveness and usability. By leveraging the power of mobile technology and gamification, this researchwork seeks to bridge an existing gap, contributing to the advancement of game-based educational approaches in the health field.

关键词： Gamification Family Nursing Serious Game Nurse Education

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