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Optimization of Microservices Security 3

Optimization of Microservices Security

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Kalubowila, D.C. Athukorala, S.M. Tharaka, B.A.S. Samarasekara, H.W.Y.R. Samaratunge Arachchilage, Udara Srimath S. Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Microservices is a trending architecture, and due to its demanding features and behaviors, billions of business applications are developed based on it. Due to its remarkable ability to deploy and coordinate containerized microservices, Kubernetes deployments support the service mesh architectures, and that ensures secured inter-service communication. The Istio is the widely used service mesh tool at present. However, service-to-service communication happens in the present Istio architecture, and there is a probability of exchanging unauthorized and over-provisioned requests due to incorrect implementation. Currently, these requests are verified within the upstream microservice. Obtaining a response to an erroneous request may take considerable time latency. This research thereby aims to address a solution to reduce the latency of a response by implementing an external validation model. The proposed external validation model ensures that the required parameters are validated and actions are taken before requests reach the service level. External validation enables applications to save significant time and resources. © 2021 IEEE.

关键词： Mesh generation

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Supporting the Episodic Application of Clinical Guidelines over Significant Time Periods

Supporting the Episodic Application of Clinical Guidelines o...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Ben Shahar, Bruria Shahar, Yuval Jaffe, Shai Cohen, Odeya Shalom, Erez Selivanova, Maya Rimon, Ephraim Goldstein, Ayelet Department of Software and Information Systems Engineering Ben-Gurion University Israel Department of Nursing Faculty of Health Sciences Ben-Gurion University Israel Nursing Informatics lab Faculty of Health Sciences Ben-Gurion University Israel Herzfeld Geriatric Rehabilitation Medical Center Clalit Health Services Israel Department of Computer Science Hadassah Academic College Jerusalem Israel

ISBN: (纸本)9781643685335

Medical errors contribute significantly to morbidity and mortality, emphasizing the critical role of Clinical Guidelines (GLs) in patient care. Automating GL application can enhance GL adherence, improve patient outcomes, and reduce costs. However, several barriers exist to GL implementation and real-time automated support. Challenges include creating a formalized, machine-comprehensible GL representation, and an episodic decision-support system for sporadic treatment advice. This system must accommodate the non-continuous nature of care delivery, including partial actions or partially met treatment goals. We describe the design and implementation of an episodic GL-based clinical decision support system and its retrospective technical evaluation using patient records from a geriatric center. Initial evaluation scores of the e-Picard system were promising, with a mean 94% correctness and 90% completeness based on 50 random pressure ulcer patients. Errors were mainly due to knowledge specification, algorithmic issues, and missing data. Post-corrections, scores improved to 100% correctness and a mean 97% completeness, with missing data still affecting completeness. The results validate the system's capability to assess guideline adherence and provide quality recommendations. Despite initial limitations, we have demonstrated the feasibility of providing, through the e-Picard episodic algorithm, realistic medical decision-making support for noncontinuous, intermittent consultations. © 2024 The Authors.

关键词： Random errors

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Federated Non-Intrusive Load Monitoring for Smart Homes Utilizing Attention-Based Aggregation

Federated Non-Intrusive Load Monitoring for Smart Homes Util...

引用

IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Shamisa Kaspour Abdulsalam Yassine Department of Computer Science Lakehead University Thunder Bay Canada Department of Software Engineering Lakehead University Thunder Bay Canada

Nowadays, Non-Intrusive Load Monitoring (NILM) with Federated Learning (FL) framework has become a growing study towards providing a secure energy disaggregation system in smart homes. This study aims at deploying an attention-based aggregation (FedAtt) approach in FL to emphasize agents’ behavioral differences when consuming energy from various appliances. The goal of the proposed technique is to minimize the weighted distance between the parameters of the local model and the global model to better represent each local model’s characteristics. In this paper, we examine two different models for NILM: Short Sequence-to-Point (SS2P) and Variational Auto-Encoder (VAE). Our goal is to evaluate the effectiveness of FedAtt. The evaluation of the framework was carried out using the UK-DALE and REFIT datasets. The obtained results were then compared against centralized approaches of the models as well as FedAvg. Our findings show that FedAtt generates comparable results to the centralized model and FedAvg while improving the stability of FL at different values of added noise to local parameters.

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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities

2D Polynomial Regression Modeling for Predictive Analysis of...

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13th International Electrical engineering Congress, iEECON 2025

作者： Nantapanich, Nutyada Mahatthanajatuphat, Pisit Wongsin, Norakamon Lee, Wilaiporn Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Pathum Thani Thailand Yothinburana School Regular Program in Mathematics-Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331543952

The article introduces a two-dimensional polynomial regression model for the predictive analysis of glucose concentration in a fractal microwave sensor NP model, utilizing frequency and transmission coefficient differences to examine their correlation with glucose concentration levels. The sensor employed in this investigation is fabricated on an economical FR-4 substrate to minimize costs. The sensor structure is developed with the fractal structure reduction method established by the NP model to operate at a frequency of roughly 2.97 GHz. The CST Studio software is utilized to analyze the findings from the simulation of the suggested sensor structure. Furthermore, the proposed sensor is integrated into a test apparatus comprising a sensor platform and a liquid tube constructed from PPA material. The liquid under examination is a glucose and water mixture at varying concentrations. It was analyzed using a VNA instrument to evaluate its frequency response and the magnitude of the alteration in the transmission coefficient. The values are utilized to create a two-dimensional polynomial regression model. The experiments demonstrated that the proposed sensor model possesses an Adjusted R² value of 0.99499 and an average error of approximately 3.57% for glucose concentration analysis throughout the range of 5% to 30%, with sensitivities of 0.00879 dB/% for insertion loss and 0.092 MHz/% for frequency variation, facilitating accurate glucose concentration prediction analysis. © 2025 IEEE.

关键词： Polynomial regression

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IoT-enabled model for Digital Twin of Mental Stress (DTMS)

IoT-enabled model for Digital Twin of Mental Stress (DTMS)

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Ferdousi, Rahatara Hossain, M. Anwar El Saddik, Abdulmotaleb Canada King Saud University College of Computer Information Sciences Department of Software Engineering Saudi Arabia

ISBN: (纸本)9781665423908

Stress has become one of the mental health adversaries of the COVID-19 pandemic. Several stressors like fear of infection, lockdown, and social distancing are commonly accountable for the stress. The existing stress prediction systems are less compatible to handle diversly changing stressors during COVID-19. The traditional approaches often use incomplete features from limited sources (e.g., only wearable sensor or user device) and static prediction techniques. The Edge Artificial Intelligence (Edge AI) employs machine learning to make data from these sources usable for decision making. Therefore, In this study, we propose a Digital Twin of Mental Stress (DTMS) model that employs IoT-based multimodal sensing and machine learning for mental stress prediction. We obtained 98% accuracy for four widely used Machine Learning(ML) algorithms Naïve Bayes(NB), Random Forest(RF), Multilayer Perceptron(MLP), and Decision Tree (DT). The optimal Digital Twin Features (DTF) could reduce the classification time. © 2021 IEEE.

关键词： COVID-19

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Identifying Underrepresented Driving Scenarios in Training Datasets with SafeML Robustness Monitoring for Autonomous Driving Vehicles

Identifying Underrepresented Driving Scenarios in Training D...

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International Conference on System Reliability and Science (ICSRS)

作者： Bergler Matthias Tavakoli-Kolagari Ramin Lundqvist Kristina Department for Computer Science Nuremberg Institute of Technology Nuremberg Germany Dependable Software Engineering Mälardalens University Västerås Sweden

ISBN: (数字)9798350354508

ISBN: (纸本)9798350354515

This study investigates the evaluation of the representativeness of datasets in autonomous driving systems using statistical distance metrics. Eight different datasets, created in the Carla simulation environment, cover various driving conditions, including normal and impaired lighting scenarios, alternative routes, and challenging conditions such as power outages. The datasets were designed to represent similar routes under different conditions. Various distance metrics- Wasserstein, Kuiper, Anderson-Darling, Chernoff, DTS, and CVM-were applied to measure pairwise dataset distances. We anticipated that the dataset for a given route under ideal conditions would exhibit a large distance measure (of any of the listed distance measures) compared to the same route under impaired conditions (e.g., a power failure at the streetlights). However, we were particularly interested in whether a measurable jump at a (potential threshold) value could be recognized even with a smaller drop in dataset condition quality. The results of the study show that a normalization of these distance measures enables precise divergence comparisons and the determination of meaningful threshold values. This in turn means that normalized deviation measures can effectively identify deviations in real time, hence contributing to the development and monitoring of more reliable autonomous driving models.

关键词： Training Power measurement Lighting Time measurement Robustness Real-time systems Safety Power system reliability Autonomous vehicles Monitoring

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EEG Spectral Analysis for Inattention Detection in Academic Domain

EEG Spectral Analysis for Inattention Detection in Academic ...

引用

Frontiers in Education (FIE) Conference

作者： Sreekanth Gopi Nasrin Dehbozorgi Department of Computer Science Kennesaw State University Kennesaw GA Department of Software Engineering Kennesaw State University Kennesaw GA

The increasing rate of attention deficit among students, attributed to social media, has far-reaching consequences on their academic performance. Inattention or lack of attention is a state of absent-mindedness or not paying enough attention to the details. A rich body of literature suggests that it is highly associated with underachievement in the academic context. In particular, students who have been clinically diagnosed with Attention Deficit Hyperactivity Disorder(ADHD), a neuro-developmental disorder characterized by inattention, hyperactivity, and impulsivity symptoms are more at risk of under-performance and retention, with some leaving school without a terminal degree. In addition to the academic domain, inattention generally impacts the quality of life and future occupations. Research suggests that ADHD is also attributed to societal and unemployment excess costs as well as productivity loss and healthcare expenses which are estimated to be over $14K per adult in the United States (US). Although more than eight million adults were identified with ADHD in the US by 2018, not all inattention cases are associated with ADHD. Inattention could be caused by several other factors such as stress or anxiety and its early detection and timely intervention is critical, especially in the academic domain. There are existing studies that analyze brain signals by Electroencephalogram (EEG) scans to identify individuals who have ADHD. In this study, we developed a Machine learning (ML) pipeline model that is trained on both ADHD and inattention data to determine if a person is having an attention problem. In the first phase of developing the model, we trained a few different classifiers on a 19-channel public EEG dataset of 60 ADHD and 61 non-ADHD participants. Data analysis showed K-Nearest Neighbor (KNN) classifier outperformed other classifiers with an accuracy of 89%. While many of the existing papers focus on ADHD data, in this work we expand our model to analyze the

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Mobile Based Solution to Weight Loss Planning for Children (with Obesity) in Sri Lanka 3

Mobile Based Solution to Weight Loss Planning for Children (...

引用

3rd International Conference on Advancements in Computing, ICAC 2021

作者： Rajapakse, R.M.M.P.K. Mudalige, J.M.A.I. Perera, L.A.D.Y.S. Warakagoda, R.N.A.M.S.C.B. Siriwardana, Samanthi Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Obesity is a condition where there is excess fat in the body, and it is one of the world's most extreme and dangerous dietary diseases. Genetic factors, lack of physical activity, unhealthy eating patterns, or a combination of these factors are the most common causes of obesity. This is important because it influences every part of a child's life. More, in particular, this disorder leads to poor health and negative social standing with perceptions. Nowadays, children are paying keen interest in technology and related devices. Therefore, in this research, we are planning to give a mobile-based solution with a smart band that is used to monitor the child. In this solution, we are mainly focusing on Sri Lankan children with obesity who are aged between 5-10. In our solution, there are four main sections which are, monitoring child activities, recognizing the activities, and getting relevant data, then based on those data and previous activity completion levels, this solution will suggest activities for losing weight, provide specific diet plans for each child considering the health conditions and predict the probability of having main obesity-related diseases by using previously gathered data. © 2021 IEEE.

关键词： Deep learning

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An Improved Boolean Load Matrix-Based Frequent Pattern Mining

An Improved Boolean Load Matrix-Based Frequent Pattern Minin...

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3rd International Conference on Intelligent Computing and Optimization, ICO 2020

作者： Roy, Shaishab Akhtar, Mohammad Nasim Rahman, Mostafijur Department of Computer Science and Engineering Dhaka University of Engineering and Technology Gazipur Dhaka Bangladesh Department of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9783030681531

Frequent Pattern Mining (FPM) has been playing an essential role in data mining research. In literature, many algorithms have proposed to discover interesting association patterns. However, frequent pattern mining in a large-scale dataset is a complex task because of a prohibitively large number of the smaller pattern need to be generated first to identify the extended pattern. The complexity in terms of computational time is a big concern. In this paper, we have proposed an updated novel FPM algorithm that uses a boolean matrix and then decomposed the matrix vertically. Here we have reduced the computational time by reducing a few steps and added itemset if and only if satisfy the condition instead of pruning after added into the candidate itemset. Our proposed algorithm has reduced the computational time at least two times on the generation of the boolean matrix (i.e. Q), frequent-1 matrix (i.e., F1) and frequent-1 itemset (i.e., L1), which has improved the efficiency of the algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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Automated Area Based Explosive Trace Detection System

Automated Area Based Explosive Trace Detection System

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Monday F. Ohemu Ambrose A. Azeta Ibrahim A. Adeyanju Nuradeen Maidoki Department of Artiftial Intelligent ACETEL-National Open University of Nigria Nigeria Department of Software Engineering Namibia University of Science and Technology Namibia Department of Computer Engineering Federal University Oye-Ekiti Nigeria Natview Technology Abuja Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Explosive based attacks on people and sensitive places in the form of terrorism has become a global challenge that is making organizations such as airports, train station, security agencies and the government to do anything possible to secure the lives of people and some essential infrastructures. Several approaches have been adopted to prevent its occurrence that ranges from the use of animals, analytical means and sensor based approach to manual screaming by human personnel. With the development in the field of Artificial Intelligent, efforts are being made to deploy Sensor base system that leverage of machine learning technology to make the system highly accurate and sensitive in detection of explosive trace within an environment. This study presents the architecture of area base explosive trace detection utilizing deep learning model. The experiment shows the concept of area based explosive trace detection from trace properties of Carbon, Nitrogen, Oxygen and Hydrogen (CNOH). The deep leaning model recorded an accuracy of 98% on the explosive trace data. The system uses this information to detect the presence of explosion within an area.

关键词： Wireless sensor networks Accuracy Oxygen Terrorism Explosives Data models Real-time systems

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