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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Krupalija, Ehlimana Cogo, Emir Becirovic, Seila Prazina, Irfan Pozderac, Damir Besic, Ingmar University of Sarajevo Faculty of Electrical Engineering Department of Computer Science and Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350322910

Cause-effect graphs are a commonly used black-box testing method, and many different algorithms for converting system requirements to cause-effect graph specifications and deriving test case suites have been proposed. However, in order to test the efficiency of black-box testing algorithms on a variety of cause-effect graphs containing different numbers of nodes, logical relations and dependency constraints, a dataset containing a collection of cause-effect graph specifications created by authors of existing papers is necessary. This paper presents CEGSet, the first collection of existing cause-effect graph specifications. The dataset contains a total of 65 graphs collected from the available relevant literature. The specifications were created by using the ETF-RI-CEG graphical software tool and can be used by future authors of papers focusing on the cause-effect graphing technique. The collected graphs can be re-imported in the tool and used for the desired purposes. The collection also includes the specification of system requirements in the form of natural language from which the cause-effect graphs were derived where possible. This will encourage future work on automatizing the process of converting system requirements to cause-effect graph specifications. © 2023 IEEE.

关键词： Black-box testing

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Optimization of the test case minimization algorithm based on forward-propagation in cause-effect graphs 22

Optimization of the test case minimization algorithm based o...

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22nd International Symposium INFOTEH-JAHORINA, INFOTEH 2023

作者： Krupalija, Ehlimana Cogo, Emir Bećirović, Šeila Prazina, Irfan Pozderac, Damir Bešić, Ingmar University of Sarajevo Faculty of Electrical Engineering Department of Computer Science and Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9781665475464

Many different methods are used for generating blackbox test case suites. Test case minimization is used for reducing the feasible test case suite size in order to minimize the cost of testing while ensuring maximum fault detection. This paper presents an optimization of the existing test case minimization algorithm based on forward-propagation of the cause-effect graphing method. The algorithm performs test case prioritization based on test case strength, a newly introduced test case selection metric. The optimized version of the minimization algorithm was evaluated by using thirteen different examples from the available literature. In cases where the existing algorithm did not generate the minimum test case subsets, significant improvements of test effect coverage metric values were achieved. Test effect coverage metric values were not improved only in cases where maximum optimization was already achieved by using the existing algorithm. © 2023 IEEE.

关键词： Black-box testing

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IoT and AI-Driven Conceptual Model of Museum Ecosystem

IoT and AI-Driven Conceptual Model of Museum Ecosystem

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2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Puspasari, Shinta Siradjuddin, Indah Agustien Rachmansyah Fahtu Rahman, M. Ade Haversyalapa, Ditho Universitas Indo Global Mandiri Faculty of Computer Science Department of Informatics Palembang Indonesia Universitas Trunojoyo Faculty of Engineering Department of Informatics Madura Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Department of Computer System Palembang Indonesia

ISBN: (纸本)9798350381290

Museum ecosystems need digital transformation to provide value added to museums. This study discusses the digital museum ecosystem conceptual model based on artificial intelligence (AI) technology and the Internet of Things (IoT). The new conceptual model integrates three elements of the museum ecosystem, people, collections & infrastructure, and museum services collaborating and communicating to achieve outputs in supporting the achievement of museum organizational goals through IoT and AI-based digital technology. The conceptual model describes various forms of implementing IoT and AI in the museum ecosystem for museum management along with the value added or results generated for museum management. The development of this ecosystem conceptual model into a physical museum has an impact on optimizing museum performance by providing value added in the form of convenience and prosperity for users and various museum stakeholders, especially in Society 5.0. © 2023 IEEE.

关键词： Internet of things

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GAN-generated Synthetic Data and SVM-based Feature Selection for Improved Cardiovascular Disease Prediction

GAN-generated Synthetic Data and SVM-based Feature Selection...

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2023 Medical Technologies Congress, TIPTEKNO 2023

作者： Mandan, Amel Sulaiman Hazzaa, Nidhal Mohsin Yildiz, Oktay Informatics Institute Gazi University Computer Science Department Ankara Turkey Gazi University Faculty of Engineering Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350328967

Early diagnosis requires cardiovascular disease forecasting. Past patient interests can improve machine learning predictions. This study uses a Generative Adversarial Network (GAN) to augment data to address the issue of insufficient datasets. Our objective is to use the GAN and feature selection (FS) to tackle the issue of class imbalance. The study assesses the efficacy of three different machine learning (ML) algorithms in predicting coronary artery disease (CAD). The UCI repository provided the Z-Alizadeh sani dataset for our analysis. A GAN was used to balance an imbalanced dataset. We used GAN to augment data and synthesise data to improve the models. Support Vector Machine (SVM) as feature selection tool was used to extract more effective attributes. We trained and tested CAD prediction using machine learning algorithms using 5-fold cross validation. We compared the machine-learning algorithm's outcomes with and without GAN-based data enhancement and feature selection to evaluate its predicted accuracy. According to results GAN and feature selection improved accuracy, sensitivity, and specificity for all algorithms, suggesting that generated data and selected attributes can identify positive and negative cases. © 2023 IEEE.

关键词： Generative adversarial networks

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A Transfer Learning-Based Framework for Enhanced Classification of Perceived Mental Stress Using EEG Spectrograms

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IEEE Access 2025年 13卷 89266-89286页

作者： Khan, Sheharyar Noorani, Sadam Hussain Frnda, Jaroslav Rauf, Usman Arsalan, Aamir Saeed, Sanay Muhammad Umar University of Engineering and Technology Department of Computer Engineering Taxila47050 Pakistan HITEC University Department of Computer Science Taxila47050 Pakistan University of Žilina Faculty of Operation and Economics of Transport and Communications Department of Quantitative Methods and Economic Informatics Žilina01026 Slovakia Fatima Jinnah Women University Department of Software Engineering Rawalpindi46000 Pakistan

Stress is a significant health concern, impacting both physical and mental well-being. Prolonged exposure to stress can lead to numerous physical health issues, including cardiovascular diseases, and a weakened immune system. This study presents a novel methodology for classifying perceived mental stress using electroencephalography (EEG) signals. By utilizing the publicly available Leipzig Study for Mind-Body-Emotion Interactions dataset, we analyze EEG data collected from 53 participants over a 7-minute resting-state duration. Our approach involves transforming EEG signals into spectrograms using the Short-Time Fourier Transform (STFT), resulting in a time-frequency representation of the input signals. We employ transfer learning to fine-tune three pre-trained deep neural networks i.e., ResNet50, EfficientNetB0, and DenseNet121 for classifying stress into two and three levels. Our findings demonstrate that the ResNet50 model achieves superior classification accuracies of 95.80% and 86.02% for two and three-level stress classification, respectively, outperforming existing state-of-the-art methods. This study is the first to utilize STFT-generated spectrograms and transfer learning for perceived stress classification, highlighting the efficacy of deep learning techniques in quantifying perceived mental stress through non-invasive EEG recordings. Our results indicate that the proposed method can significantly enhance the accuracy of stress classification frameworks, offering potential improvements in mental health assessment and intervention strategies. © 2013 IEEE.

关键词： Noninvasive medical procedures

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The role of explainable Artificial Intelligence in high-stakes decision-making systems: a systematic review

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7827-7843页

作者： Sahoh, Bukhoree Choksuriwong, Anant School of Informatics Walailak University Tha Sala Nakhon Si Thammarat80160 Thailand Department of Computer Engineering Faculty of Engineering Prince of Songkla University Songkla Had Yai90112 Thailand

A high-stakes event is an extreme risk with a low probability of occurring, but severe consequences (e.g., life-threatening conditions or economic collapse). The accompanying lack of information is a source of high-stress pressure and anxiety for emergency medical services authorities. Deciding on the best proactive plan and action in this environment is a complicated process, which calls for intelligent agents to automatically produce knowledge in the manner of human-like intelligence. Research in high-stakes decision-making systems has increasingly focused on eXplainable Artificial Intelligence (XAI), but recent developments in prediction systems give little prominence to explanations based on human-like intelligence. This work investigates XAI based on cause-and-effect interpretations for supporting high-stakes decisions. We review recent applications in the first aid and medical emergency fields based on three perspectives: available data, desirable knowledge, and the use of intelligence. We identify the limitations of recent AI, and discuss the potential of XAI for dealing with such limitations. We propose an architecture for high-stakes decision-making driven by XAI, and highlight likely future trends and directions. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Bayesian networks

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PIT Performance Measurement using NFD Pipeline Parameters in Named Data Networking (NDN) 10

PIT Performance Measurement using NFD Pipeline Parameters in...

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10th International Conference on Wireless and Telematics, ICWT 2024

作者： Sucipto, Adi Ahdan, Syaiful Iskandar Syambas, Nana Rachmana Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science 35132 Indonesia

ISBN: (纸本)9798350377057

Although NDN is an entirely new architecture, its design principles are based on the success of the current Internet, they represent our knowledge of the advantages and disadvantages of the current Internet architecture, and they can be implemented gradually using the operations of the current Internet. The Pending Interest Table (PIT), the Forwarding Information Base (FIB), and the Content Store (CS) are the three data structures found inside the NDN router. Interest sent upstream to the content source is tracked by the Pending Interest Table (PIT), for data to be transmitted to requesters downstream. PIT is also used for measurement, loop detection, and the containment of recently satisfied interest. In this study, we did the simulation to measure and analyze the performance of PIT in NDN using NFD Pipeline Measurements. The NDNSim simulator was used in the research to observe PIT performance. The findings of the simulation and analysis indicated that in an NDN network, Satisfied Interest was influenced by the granularity of a prefix. At the number of interests 1000, the results showed a decrease in packets, including a decrease of around 21% in the Ininterest packet, and around 30% inSatisfiedInterest and InData. The effect of Prefix length also decreased in Ininterest ranging from 0.014-0.015%, while in inSatisfiedInterest and InData it ranged from 4.2-8.5%. The PIT's performance was influenced by the length of the prefix and the number of interest messages the customer sent. © 2024 IEEE.

关键词： Natural gas pipelines

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The Association Rule Methods and K-Means Clustering for Optimization Mapping of New Students Admission

The Association Rule Methods and K-Means Clustering for Opti...

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2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

作者： Harsono, Sugi Utami, Ema Yaqin, Ainul Universitas Amikom Yogyakarta Magister of Informatics Engineering Yogyakarta Indonesia Universitas Amikom Yogyakarta Computer Science Faculty Yogyakarta Indonesia

ISBN: (纸本)9798350350524

Educational service providers deliver information about student admission by installing banners, distributing brochures, and providing information directly to prospective new students annually. The target result can be outside expectations because there is no data analysis related to optimizing the support of the system. This study proposes the K-Means algorithm optimized with the Association Rules method to map the interests of prospective students in selecting the study program. The data collection method uses an explanatory research survey and correlational study with primary data from the questionnaires and documents for new students in the year 2022. The accuracy results of this study using the K-Means algorithm process to calculate the silhouette score, elbow methods, cross-combination experiments, and data sharing are 95.00%. The optimization accuracy with mapping relationships between variables using frequent itemset and Association Rules increased to 100%. The most significant factors influencing the student selection of study programs are religion 100%, transportation 85.71%, gender 83.33%, school status 80.95%, regency 73.81%, school origin 71.43%, the district of the school close to student life 66.67%, residence located at the parent's home 64.29%, place of birth 61.90%, and job future ambition 61.90%. © 2024 IEEE.

关键词： Association rules

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A Novel Inception Deep Learning model for Mobile AI Stroke Prediction system: AI Research and Industry Perspectives for Connected Health 6

A Novel Inception Deep Learning model for Mobile AI Stroke P...

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6th International Conference on Computing and informatics, ICCI 2024

作者： Elbagoury, Bassant M. Maskeliuans, Rytis Zaghow, Marwa Kamel, Nabil Faculty of Computer and Information Sciences Ain Shams University Computer Science and Artificial Intelligence Programs Egypt Faculty of Computer Science and Engineering King Salman International University KSIU Egypt Kaunas University of Technology Faculty of Informatics Department of Multimedia Engineering Lithuania Dell Technologies EMEA Egypt

ISBN: (纸本)9798350373875

Artificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep Learning algorithms and developing new models. The main objective is applying artificial Intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. Thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed. © 2024 IEEE.

关键词： Learning systems

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An Improved Structure for Academic Information Services through AI Chatbots

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Journal of engineering Science and Technology Review 2023年第5期16卷 164-173页

作者： Ula, Mutammimul Hardi, Richki Hipiny, Irwandi Department of Information Systems Faculty of Engineering Universitas Malikussaleh Aceh Indonesia Department of informatics Universitas Mulia Balikpapan Indonesia Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Sarawak Malaysia

Academic information service is the most critical factor that must be considered in a university. Service quality is an important indicator affecting all academics’ satisfaction and loyalty. Improvement of information services on campus has been a lot of research focusing on academics. Meanwhile, the software aspect has received less attention. Researchers have previously discussed information service frameworks but have not accommodated future needs. Software that must be considered can quickly respond to the user, one of which is a chatbot. Chatbots can be used to solve problems related to question-and-answer services, especially for academic institutions that do not have specific resources to handle this work. With chatbots, redundant user questions can be handled automatically. Chatbots are increasingly being offered as an alternative source of customer service. For a user to use a chatbot for this purpose, the user must trust the chatbot to provide the necessary support. To overcome this knowledge gap, a framework can integrate software, stakeholders, and academics in higher education. Therefore it is necessary to conduct new research to help answer future needs. © 2023 School of Science, IHU. All rights reserved.

关键词： Information services

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