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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Tanjina Ahmed Tuly Tanjid Ahammed Shafin Md. Zahid Hasan Md. Hasan Imam Bijoy Mohammad Shamsul Arefin Mohammad Shorif Uddin Department of Computer Science and Engineering Health Informatics Research Lab Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh Department of Computer Science and Engineering Jahangirnagar University Savar Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Diabetes mellitus is a chronic condition characterized by insufficient insulin production and the inability to effectively utilize the insulin hormone, leading to elevated blood glucose levels. As diabetes is incurable and persists throughout a patient's lifetime, effective management requires early identification of risk factors to ensure continuous and feasible treatment. This study proposes a unique decision support system based on the Mamdani Fuzzy Inference System (MFIS) to predict diabetes risk. This study utilizes a publicly available diabetes dataset containing 442 data that includes nine key health metrics such as BMI, blood glucose, cholesterol levels, and blood pressure to predict the risk of diabetes. The dataset aims to support early diagnosis and better diabetes management by providing insights into individual risk factors. This resource contributes to the development of more accurate decision-support systems in healthcare. We identify key variables and establish a membership function to assess individual risk. The Fuzzy Inference System (FIS) is well-suited for handling complex and uncertain conditions, making it an effective tool for this application. The advantages of the MFIS include its ability to incorporate expert knowledge through linguistic variables and fuzzy rules. This system demonstrates the practical benefits of MFIS in developing intelligent decision-making tools, particularly in the domain of diabetes risk prediction.

关键词： Fuzzy logic Decision support systems Accuracy Uncertainty Predictive models Blood pressure Diabetes Glucose Insulin Blood

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Understanding User Attention and Engagement in Digital Marketing: An Eye-Tracking Analysis of Native Advertisements 24

Understanding User Attention and Engagement in Digital Marke...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Theodora Kremantala Stefanos Balaskas Maria Rigou Computer Engineering and Informatics Department University of Patras Patras Greece Department of Management Science and Technology University of Patras Patras Greece

ISBN: (纸本)9798400713170

This study investigates the effectiveness of various types of native ads in terms of capturing users' attention and engagement using eye-tracking data to analyze user interactions. To this end, 47 participants compared four types of native ads with two traditional banner ads embedded within a website designed specifically for the needs of the study. Results confirm that native ads, and particularly content integrated ones like recommendation widgets and native ads in-feed, attract higher levels of attention compared to traditional banner ads. These ads maintain high deception rates, with users mostly failing to recognize them, a fact that raises questions about users' trust and annoyance. The variation that creates the highest click-through rate is the promoted listing, while typical banner advertisements attracted limited engagement suffering from banner blindness. This study highlights the delicate balance between effective ad placement and ethical transparency by advertisers, emphasizing the need for clear labelling to maintain user trust. These findings present useful insights for marketers and interface designers, with guidance on creating effective yet usable ad designs in digital spaces.

关键词： dark design patterns

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Enhancing Text Anonymisation: A Study on CRF, LSTM, and ELMo for Advanced Entity Recognition

Enhancing Text Anonymisation: A Study on CRF, LSTM, and ELMo...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Siniosoglou, Ilias Asimopoulos, Dimitris Argyriou, Vasileios Lagkas, Thomas Lytos, Anastasios Moscholios, Ioannis D. Goudos, Sotirios K. Sarigiannidis, Panagiotis University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece MetaMind Innovations P.C. R and D Department Kozani Greece Kingston University Department of Networks and Digital Media Kingston upon Thames United Kingdom International Hellenic University Department of Computer Science Greece Sidroco Holdings Ltd. Nicosia Cyprus University of Peloponnese Department of Informatics and Telecommunications Department Tripoli Greece Aristotle University of Thessaloniki Physics Department Thessaloniki Greece

ISBN: (纸本)9798350318845

It is essential to create effective strategies for safeguarding private data while preserving the value of textual data in the face of rising privacy concerns in the digital era. In this research, we give a thorough investigation of Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), and Embeddings from Language Models (ELMo)-based text anonymisation methods [1]. The strengths of CRF, LSTM, and ELMo are combined in the proposed approach to overcome the difficulty of text anonymisation. LSTM is a recurrent neural network architecture that can model long-term dependencies, CRF is a probabilistic graphical model that captures the dependencies among the sequence of words, and ELMo offers contextual word representations utilizing deep bidirectional language models. These models are leveraged in order to identify and subsequently obfuscate sensitive information in the frame of textual anonymisation. Experimental results show that when compared to conventional methods, all three methods - CRF, LSTM, and ELMo - achieve considerable improvements in text anonymisation. © 2024 IEEE.

关键词： Long short-term memory

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Comprehensive Analysis and Remediation of Insecure Direct Object References (IDOR) Vulnerabilities in Android APIs 1

Comprehensive Analysis and Remediation of Insecure Direct Ob...

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1st IEEE International Conference on Cryptography, informatics, and Cybersecurity, ICoCICs 2023

作者： Yulianto, Semi Abdullah, Roni Reza Soewito, Benfano Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia National Institute of Science and Technology Faculty of Informatics Engineering Jakarta12630 Indonesia Bina Nusantara University Binus Graduate Program. Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339437

The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API security, protect user data, and bolster application resilience. © 2023 IEEE.

关键词： Sensitive data

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Predicting Heat Stroke Risk: A Clinical Decision Support System using Fuzzy Association Mining Approach

Predicting Heat Stroke Risk: A Clinical Decision Support Sys...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Abdullah Al Noman Rinvi Jaman Riti Md. Zahid Hasan Md. Hasan Imam Bijoy Mohammad Shorif Uddin Mohammad Shamsul Arefin Department of Computer Science and Engineering Health Informatics Research Lab Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Savar Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Heat stroke is a serious medical condition that requires immediate treatment and is exacerbated by intense heat and climate change. This paper introduces a Clinical Decision Support System (CDSS) for heat stroke risk assessment based on Fuzzy Association Rule Mining. The system evaluates key attributes extracted from the Stanford Bioengineering Senior Capstone Project dataset, including daily water intake, cardiovascular history, heat index, environmental and rectal temperatures, blood pressure, pulse rate, humidity, age, and sex. These attributes help identify detailed patterns associated with heat stroke risk. Using fuzzy logic, the CDSS addresses the inherent vagueness of medical information through a set of "if- then" rules designed for healthcare practitioners. The system is tested with historical data, demonstrating its effectiveness in recognizing critical parameters to provide personalized, timely attention to potential heat stroke cases. It aims to reduce heat- related illnesses and fatalities by enabling rapid data-driven decision-making in healthcare. Despite certain limitations, this study highlights the necessity of intelligent systems for proactive health management.

关键词： Decision support systems Climate change Fuzzy systems Risk management Predictive models Medical services Medical conditions

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RadioModRec-l: A Large Scale Radio Frequency Dataset for Automatic Modulation Recognition Research 4

RadioModRec-l: A Large Scale Radio Frequency Dataset for Aut...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Adetiba, Emmanuel Olasina, Jamiu R. Obiyemi, Obiseye Covenant University Covenant Applied Informatics and Communication Africa Center of Excellence Dept. of Electrical and Information Engineering Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban4001 South Africa Federal Polytechnic Ilaro Dept. of Computer Engineering Ilaro Nigeria Durban University of Technology Space Science Centre Dept. of Electrical Power Engineering Durban South Africa

ISBN: (纸本)9798350353266

Access to well-curated large datasets remains a significant bottleneck in AI-based research within wireless communication. Rapid advancements in neighbouring fields, such as computer vision and natural language processing, are largely due to the availability of extensive open-access datasets. However, similar progress has not been observed in wireless communication. To address this gap, we curated a comprehensive dataset for fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK. Our dataset considers Rayleigh and Rician channel models under Additive White Gaussian Noise (A WGN) with SNRs ranging from -20dB to + 20dB in 5dB increments. The data samples were converted to constellation signal images and carefully pre-processed. Named RadioModRec, this dataset provides a valuable resource for researchers to train and evaluate AI models. It is freely accessible on Kaggle, promoting further innovation in the wireless communication domain. © 2024 IEEE.

关键词： Signal to noise ratio

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Peer to Peer Federated Learning: Towards Decentralized Machine Learning on Edge Devices 7

Peer to Peer Federated Learning: Towards Decentralized Machi...

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7th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Karras, Aristeidis Karras, Christos Giotopoulos, Konstantinos C. Tsolis, Dimitrios Oikonomou, Konstantinos Sioutas, Spyros University of Patras Decentralized Systems Computing Group Computer Engineering and Informatics Department Patras Greece University of Patras Department of Management Science and Technology Patras Greece Ionian University Department of Informatics Corfu Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

Federated Learning (FL) is an emerging technique that assures user privacy and data integrity in distributed machine learning environments. To perform so, chunks of data are trained across edge devices and a high performance cluster server maintains a local copy without exchanging it with other parties. In this work, we investigate a FL scenario in a real-world case study using 5, 10 and 20 Raspberry Pi devices acting as clients. Under this setup, we employ the widely known FedAvg algorithm which trains each client for several local epochs and then the weight of each model is aggregated. Moreover we perform experiments across imbalanced and noisy data so as to explore scalability and robustness based on real-world datasets were noise is present and we also propose two innovative algorithms where the FL scenario is considered as a peer-to-peer formulation. Ultimately, to ensure that each device is not oversampled a client-balancing Dirichlet sampling algorithm with probabilistic guarantees is proposed. © 2022 IEEE.

关键词： computer aided instruction

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Dynamics of cold random hyperbolic graphs with link persistence

arXiv

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arXiv 2022年

作者： Zambirinis, Sofoclis Hartle, Harrison Papadopoulos, Fragkiskos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol3036 Cyprus Network Science Institute Northeastern University BostonMA02115 United States

We consider and analyze a dynamic model of random hyperbolic graphs with link persistence. In the model, both connections and disconnections can be propagated from the current to the next snapshot with probability ω ∈ [0, 1). Otherwise, with probability 1−ω, connections are reestablished according to the random hyperbolic graphs model. We show that while the persistence probability ω affects the averages of the contact and intercontact distributions, it does not affect the tails of these distributions, which decay as power laws with exponents that do not depend on ω. We also consider examples of real temporal networks, and we show that the considered model can adequately reproduce several of their dynamical properties. Our results advance our understanding of the realistic modeling of temporal networks and of the effects of link persistence on temporal network properties. Copyright © 2022, The Authors. All rights reserved.

关键词： Probability distributions

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Comparing Ensemble Learning Techniques on Data Transmission Reduction for IoT Systems 1st

Comparing Ensemble Learning Techniques on Data Transmission...

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1st International Conference on Advances in Computing Research, ACR’23

作者： Salah, Ahmad Bekhit, Mahmoud Alkalbani, Asma M. Mohamed, Mohamed A. Lestari, Nur I. Fathalla, Ahmed Computer Science Department Faculty of Computers and Informatics Zagazig University Zagazig Sharkia Egypt Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Ad Dhahirah516 Oman Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia Kent Institute Australia SydneyNSW Australia 2P Perfect Presentation Riyadh Riyadh Saudi Arabia Computer Science Department Faculty of Computers and Informatics Suez Canal University Ismailia Egypt

ISBN: (纸本)9783031337420

The Internet of Things (IoT) systems include a massive number of connected devices. The communication between these devices requires a huge amount of communication. Thus, it is very crucial for an IoT system to reduce communication volumes by minimizing the amount of data transmission. There are several approaches to achieving the goal of data transmission reduction, such as data compression and dual prediction. Dual prediction schemes received more attention in comparison to data compression. The mainstream techniques for proposing dual prediction schemes in the literature can be classified into two main groups, namely, filter-based and deep learning-based methods. The filter-based methods, such as the 1-D Kalman filter, are lightweight in terms of running time and the model’s memory requirements. On the other hand, deep learning-based methods require more memory space and training time, but deep learning models are more accurate as predictive models in comparison to filter-based methods in dual prediction schemes. There are very limited efforts to utilize machine learning methods in dual prediction schemes as a compromise between the aforementioned mainstream techniques. In this work, we extended one of these limited efforts, which utilizes boosting ensemble learning as a machine learning predictive method in a proposed dual prediction scheme. The current work proposes exploring the performance gap between the three main approaches to ensemble learning, namely, boosting, stacking, and bagging. The three proposed ensemble learning models are evaluated on a real dataset and compared against state-of-the-art methods. The obtained results show that among the ensemble learning models, boosting and bagging models are better than the stacking model, but the three proposed ensemble learning models outperformed the state-of-the-art methods of comparison. For instance, the average numbers of mispredictions for all of the experiments were 580, 551, and 1,106 for boosting, ba

关键词： Forecasting

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Angularly Stable, Single-Layer Absorptive Metasurface for X-Band Applications

Angularly Stable, Single-Layer Absorptive Metasurface for X-...

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Microwave, Antennas & Circuits (ICMAC), International Conference on

作者： Muhammad Danial Shafi Ahsaan Gul Hassan Harun Ildeş Inam Ullah Nosherwan Shoaib Symeon Nikolaou School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Electrical and Electronics Engineering Hacettepe University Ankara Turkey Frederick Research Center Nicosia Cyprus Electrical Engineering Department Frederick University Nicosia Cyprus

ISBN: (数字)9798331518424

ISBN: (纸本)9798331518431

This work presents the design of a single-layer absorptive metasurface capable of achieving broadband absorption across the X -band over a wide range of incidence angles. The design introduces novelty through its unit cell configuration, angular stability, enhanced fractional bandwidth, and absorption without the use of an air gap. Both measured and simulated results are in strong agreement, underscoring the potential of the proposed metasurface for radar cross-section (RCS) reduction applications. Overall, the absorption ratio (AR) exceeds 80% for incidence angles up to 50°, highlighting its effectiveness for practical applications.

关键词： Electric potential Radar cross-sections Absorption Circuits Bandwidth Metasurfaces Stability analysis Broadband communication Circuit stability Electromagnetics

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