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1st International Conference of Intelligent Methods, systems and Applications, IMSA 2023

作者： Montasser, Reem Kadry Ismael, Osama El-Kholy, Moataz October University for Modern Sciences and Arts Faculty of Computer Science Software Engineering Department Giza Egypt Cairo University Faculty of Computers and Artificial Intelligence Information Systems Department Giza Egypt October University for Modern Sciences and Arts Faculty of Dentistry Oral Pathology Department Giza Egypt

ISBN: (纸本)9798350335569

Due to its simplicity of usage across a variety of applications, the K-Nearest Neighbor algorithm is usually utilized as a classification approach. The K-Nearest Neighbor algorithm's accuracy is greatly impacted by the occurrence of multidimensional and outlier data. This research uses a novel hybrid technique known as Particle Optimized Scored K-Nearest Neighbor (POSKNN) to build a model to predict the emergence of dental fluorosis. The proposed novel approach is implemented in two phases: the first phase helps in the resolution of multidimensional data by performing selection of feature using the Modified Particle Swarm Optimization algorithm, and the second phase helps in the resolution of the presence of outliers by making use of the results of the first phase and applying the newly proposed scored KNN technique to them. The suggested approach was tested using a real-world dataset acquired from three dentistry clinics in Egypt's El Baharia Oasis, which has 56 attributes, two classes, and 328 records. 10-fold cross validation was implemented to the model. The experimental evaluation was divided into two parts;evaluation for modified particle swarm optimization, evaluation for scored K nearest neighbor, and the hybrid particle optimized scored K nearest neighbor. The experiment's results demonstrated that the suggested approach outperformed the traditional KNN and the modified KNN. POSKNN achieved an accuracy of 94.5% when K value is 11. © 2023 IEEE.

关键词： Nearest neighbor search

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Intelligent Daycare: Enhancing Child Safety with IoT and Machine Learning Innovations

Intelligent Daycare: Enhancing Child Safety with IoT and Mac...

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International Conference on Communication and Electronics systems (ICCES)

作者： Amisha Prathyanga Pasindu Shyaminda Pasan Chamikara Sachin Lakshan Samantha Thelijjagoda Dharshana Kasthurirathna Department of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Child safety and well-being in daycare settings are important, thus it is vital that the persons in charge be attentive to them. This research tries to solve the problem by introducing a smart and detailed monitoring system that utilizes new technology to enhance safety and support caretakers. The system has various innovative features, such as child missing detection, behavior and facial expression analysis, cry analysis and categorization, and fall detection. Using high-resolution cameras and on-device Edge AI capabilities, it ensures real-time surveillance of children's activities while protecting privacy. The approach integrates behavior and facial expression analysis to assess emotional states, cry recognition to discover childcare needs, as well as a useful fall detection system that can tell the difference between falls and other movements. All these parts synergize to hand over the necessary information to the caregivers on time and in a way that they can act upon. The quantitative experiments in real-time childcare centers show high accuracy through all the components, attesting to the system's reliability and durability. This research has important significance in improving the safety and the frequency response in daycare centers because it gives caregivers automated tools to quickly address the needs of children. While promising, the system has drawbacks, such as potential biases in data models and a reliance on ideal environmental circumstances. Future work intends to improve flexibility across varied daycare situations, improve accuracy through larger datasets, and investigate ethical frameworks for wider application.

关键词： Privacy Ethics Accuracy Surveillance Machine learning Edge AI Real-time systems Safety Time factors Fall detection

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Audio-Assisted Smart Home Security Monitoring with Few Samples

Audio-Assisted Smart Home Security Monitoring with Few Sampl...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Haotian Chi Qi Ma Yuxuan Zhang Chenglong Fu Yuwei Wang Haijun Geng Xiaojiang Du School of Automation and Software Engineering Shanxi University Taiyuan China Department of Software and Information Systems University of North Carolina at Charlotte Charlotte NC USA Department of Electical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Smart home IoT devices have always been the target of various cyber attacks. By leveraging the smart home monitoring infrastructure, event-based anomaly detection is effective to detect anomalies that cause unfavorable working state of IoT devices. However, IoT events are proven to be vulnerable to event-targeted attacks which could be achieved by exploiting the vulnerabilities embedded in IoT devices, protocols and/or platforms. Thus, existing event-based anomaly detection is not robust in the case of unreliable input. To address this issue, our insight is that the embedded microphone components in many off-the-shelf home devices (e.g., smart doorbells, speakers, cameras, tablets, laptops, etc.) could be utilized to gather acoustic information to help increase the reliability and capability of smart home security monitoring systems. To verify this idea, we propose an audio-assisted framework IoTAudMon for detecting event-targeted attacks. Considering the heterogeneity and sparsity nature of smart homes IoT devices and events, we employ transfer learning to design a practical pipeline for extracting semantic information from audio, eliminating the requirement of human labeling and mitigating the cold start issue in existing solutions. Experiments on public datasets and real devices demonstrate the effectiveness of IoTAudMon.

关键词： Training Event detection Transfer learning Smart homes Acoustics Internet of Things Security Monitoring Anomaly detection Microphones

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Temporal Sensitivity Analysis of Internal Temperature Informed Charging Algorithms and Rapid Thermal Management System for E-mobility

Temporal Sensitivity Analysis of Internal Temperature Inform...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Akash Samanta Chandan Chetri Dominic Karnehm Antje Neve Sheldon Williamson Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada Institute of Intelligent Distributed Systems Bundeswehr University Munich Germany

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

The experimental studies presented in this paper reveal that existing thermal management systems (TMS) and temperature-informed charging algorithms exhibit a response time lag of at least 5.3 minutes due to their reliance on surface temperature measurements. The results indicate that changes in the internal thermal state of lithium-ion batteries (LIBs), induced by variations in charging currents, take an average of 2 minutes to manifest on the battery surface, particularly evident in cylindrical cells. Current thermal management systems for automotive battery packs solely rely on surface temperature measurements, neglecting the approximately 5.8°C temperature difference between the core and surface in TMS control. Consequently, changes in the battery's thermal state due to internal heat losses are not promptly detected by surface-mounted temperature sensors. This delayed response time accelerates battery degradation and increases the risk of thermal runaway events. In this study, temperature-informed fast charging algorithms, tested under various ambient conditions for LIBs, along with a comparative analysis, demonstrate that response time can be reduced by at least 2 minutes by considering internal temperature rather than relying solely on surface temperature measurements. Moreover, accounting for the temperature difference between the core and surface facilitates rapid TMS control and health-conscious fast charging, thereby mitigating the risk of thermal runaway events.

关键词： Temperature measurement Temperature sensors Heating systems Sensitivity analysis Surface charging Thermal management Time measurement Fast charging Time factors Thermal degradation

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Approaching Test Time Augmentation in the Context of Uncertainty Calibration for Deep Neural Networks

arXiv

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arXiv 2023年

作者： Conde, Pedro Barros, Tiago Lopes, Rui L. Premebida, Cristiano Nunes, Urbano J. The Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal Critical Software South Africa

With the rise of Deep Neural Networks, machine learning systems are nowadays ubiquitous in a number of real-world applications, which bears the need for highly reliable models. This requires a thorough look not only at the accuracy of such systems, but also at their predictive uncertainty. Hence, we propose a novel technique (with two different variations, named M-ATTA and V-ATTA) based on test time augmentation, to improve the uncertainty calibration of deep models for image classification. By leveraging an adaptive weighting system, M/V-ATTA improves uncertainty calibration without affecting the model’s accuracy. The performance of these techniques is evaluated by considering diverse metrics related to uncertainty calibration, demonstrating their robustness. Empirical results, obtained on CIFAR-10, CIFAR-100, Aerial Image Dataset, as well as in two different scenarios under distribution-shift, indicate that the proposed methods outperform several state-of-the-art post-hoc calibration techniques. Furthermore, the methods proposed also show improvements in terms of predictive entropy on out-of-distribution samples. Code for M/V-ATTA available at: https://***/pedrormconde/MV-ATTA Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

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Design, Modeling and Simulation of Cybercriminal Personality-Based Cyberattack Campaigns

Design, Modeling and Simulation of Cybercriminal Personality...

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Simulation Winter Conference

作者： Jeongkeun Shin Geoffrey B. Dobson L. Richard Carley Kathleen M. Carley Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Software and Societal Systems Department Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Cybersecurity challenges are inherently complex, characterized by both advanced technical elements and complex aspects of human cognition. Although extensive research has explored how victims' human factors affect their susceptibility to cyberattacks, the influence of cybercriminals' personalities on the pattern of cyberattack campaigns and the resulting damage to organizations has not received equivalent attention. To bridge this research gap through computer simulation, we introduce a cyberattack campaign designer that enables modelers to construct cyberattack campaigns. This tool allows modelers to define how the process or pattern of the cyberattack campaign can change based on the cybercriminal's personality and simulates how these personality differences influence the magnitude of cyberattack damage on the target organization. In this paper, through computer simulation, we demonstrate how cautious and reckless personalities result in variations in the cyberattack pattern and, consequently, affect the magnitude of cyberattack damage, despite identical cyberattack objectives and techniques used at each step.

关键词： Bridges Computational modeling computer simulation Organizations Human factors Cognition computer crime

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Collaboration And Versioning Framework – A Systematic Top-Down Approach

Collaboration And Versioning Framework – A Systematic Top-D...

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Conference on Model Driven engineering languages and systems Companion (MODELS-C)

作者： Jakob Pietron Alexander Raschke Joeri Exelmans Matthias Tichy Institute of Software Engineering and Programming Languages Ulm University Ulm Germany Department of Computer Science University of Antwerp – Flanders Make Antwerp Belgium

CONTEXT: Versioning allows users to efficiently create and evolve artifacts, not only but especially in the domain of model-driven engineering. Due to collaboration tools, the place and time where users jointly work at their artifacts do not play an important role anymore. Objective: We systematically elaborated a classification framework for collaboration and versioning tools. The intention is threefold: First, it should be possible to classify existing approaches. Second, deriving from user goals requirements to be met, and capabilities to be supported by a new tool. Third, given a set of capabilities, highlight which user goals can be achieved. Method: According to Kang and Lee, we systematically elaborated a problem space and a solution space and created mappings between both spaces and their sub-spaces. To demonstrate the applicability, we classified existing VCS like git, Google Docs, and several MDE-specific approaches. Results: The created feature diagram covers 238 features and contains about 87 constraints. Conclusion: The developed framework supports researchers and developers in classifying their tools, revealing new opportunities to improve their tools, and guiding the development of new tools.

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STEP UP: Systematically Motivating the Children with Low Psychological Maturity Level and Disabled Children using Gamification and Human computer Interaction 7

STEP UP: Systematically Motivating the Children with Low Psy...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Dharmarathne, R.S.C.K. Medagedara, K.A. Madhubashinee, N.B.W.N. Maitipe, P.T. Sriyaratna, Disni Abeywardena, Kavinga Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665421683

Children are the future of this world. Therefore, teaching them to have a better future is very important. Also, as the adults we have to motivate them to overcome the obstacles and challenges they face throughout their lifetime. When considering about the children, there are various types of children in our society. As examples there are children with special needs and there are children who are mentally and physically stable. Children with special needs require special attention than the other children. These kinds of children with special needs have various types of development disabilities. They are children with low psychological maturity level, autism, down syndrome, genetic disorders etc. We have proposed a system to motivate these children who are with low psychological maturity level named as 'STEP-UP'. This system is a combination of four individual modules that have the common goal to motivate these kinds of children which were implemented using gamification, Image processing, machine learning and Human computer Interaction. One individual module is focused on disabled children, and it will motivate those children using gamification. Another two modules are focusing on the children who are with low psychological maturity level. And that will motivate those kinds of children using gamification and HCI based technologies like Virtual Reality. The other module is a protocol to secure the data sent between the system and the database. The common goal of this overall STEP-UP system is to motivate the children with low psychological maturity level and disabled children. © 2022 IEEE.

关键词： Machine learning

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Ontological Model for Cohesive Smart Health Services Management

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computers, Materials & Continua 2023年第2期74卷 3679-3695页

作者： Muhammad Raza Naqvi Muhammad Waseem Iqbal Syed Khuram Shahzad M.Usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Le Laboratoire Génie de Production de l’École Nationale d’Ingénieurs de Tarbes(LGP-INP-ENIT) Universitéde ToulouseCEDEXTarbes65016France Department of Software Engineering Superior UniversityLahore53700Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Computer Science University of Management and TechnologySialkot51310Pakistan Department of Computer Science King Abdul-Aziz UniversityJeddah21577Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21577Saudi Arabia

Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of *** massive increase in the usage of health care Internet of things(IoT)applications has great technological evolvement in human *** are various smart health care services like remote patient monitoring,diagnostic,disease-specific remote treatments and *** applications are available in a split fashion and provide solutions for variant diseases,medical resources and remote service *** main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the *** ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare *** were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological *** proposed ontological model is derived from different classes,relationships,and constraints to integrate health care *** model is developed using Protégébased on each interrelated/correlated health care service having different *** querying SPARQL protocol and RDF query language(SPARQL)were used for knowledge *** Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology *** to other smart health care services integration systems,the proposed ontological model provides more cohesiveness.

关键词： Ontology internet of things smart health services integration

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Deep Learning-Based Operators for Evolutionary Algorithms

arXiv

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arXiv 2024年

作者： Shem-Tov, Eliad Sipper, Moshe Elyasaf, Achiya Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva8410501 Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel

We present two novel domain-independent genetic operators that harness the capabilities of deep learning: a crossover operator for genetic algorithms and a mutation operator for genetic programming. Deep Neural Crossover leverages the capabilities of deep reinforcement learning and an encoder-decoder architecture to select offspring genes. BERT mutation masks multiple gp-tree nodes and then tries to replace these masks with nodes that will most likely improve the individual's fitness. We show the efficacy of both operators through experimentation. © 2024, CC BY.

关键词： Genetic programming

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