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12th Electrical Power, electronics, Communications, Controls and informatics Seminar, EECCIS 2024

作者： Sifaunnufus, Fi Imanur Bachtiar, Fitra Abdurrachman Prasetio, Barlian Henryranu Brawijaya University Faculty of Computer Science Department of Computer Engineering Malang Indonesia Brawijaya University Faculty of Computer Science Department of Informatics Engineering Malang Indonesia

ISBN: (纸本)9798350379914

Stress is a problem with various adverse effects, such as mental disorders and suicide. With these problems arising, a stress detection system is needed as an initial examination of the level of stress experienced. Generally, stress detection is done with a stress test at a hospital. However, medical examinations done in the hospital require high costs. Another alternative to examine stress levels is by using a biosignal electroencephalogram (EEG), which provides more objective and accurate results. In addition, there is a need to develop a portable device that can be used to detect stress. This study tries to detect stress levels using EEG Signals. The data used in this study is primary data that simulates several stress-level activities. In total, 55 subjects participated in this stress detection research. The received signal goes through preprocessing to clean it from noise and artifacts. The data will be extracted using Power Spectral Density (PSD) into power value for each signal frequency. The preprocessed input signals are then classified into indications of stress levels. This study uses Random Forest, SVM, and Decision Tree. The model is stored on a portable system with Raspberry Pi as the main component. EEG signals are taken using electrodes on Muse 2 and sent to the system. The classification model generates predicted stress levels and is displayed on a 16×2 LCD. SVM as the best model test result achieved a testing accuracy of 1, and an average computation time of 0.92. © 2024 IEEE.

关键词： Electroencephalography

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The Integration of Explainable AI Methods for the Classification of Medical Image Data 11

The Integration of Explainable AI Methods for the Classifica...

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11th International Conference on Electrical, electronic and Computing engineering, IcETRAN 2024

作者： Nikolić, Mina Janković, Dragan Stanimirović, Aleksandar Stoimenov, Leonid Faculty of Electronic Engineering Computer Science Department Niš Serbia

ISBN: (纸本)9798350386998

Deep convolutional neural network architectures have in recent years been widely used for enhancing various computer vision tasks, such as Image classification, Semantic Segmentation and Object detection. With great advancements in terms of quality of the obtained results, the path was paved for using these kinds of neural networks in the medical domain. But, when working with sensitive matters involving human lives, there is a need to consider the interpretability and explainability of these models and not just the typical evaluation metrics for the given task. To do such a thing, tools such as LIME and PyTorch Grad-CAM can be used, among many others. The integration of Explainable AI (XAI) methods proposed in this paper aims to enable the paradigm of XAI to be used in medical image classification tasks with the standardized MedMNIST dataset. By doing such an integration, a deeper analysis regarding the quality of the model can be enabled. In that way, instances that were misclassified can be visually examined and used to paint a clearer picture of the complete model's decision-making process. © 2024 IEEE.

关键词： Convolutional neural networks

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Improving the Stability of APUF to 100% without Extra Hardware Overhead for Enhancing the Performance of Security Authentication Protocols

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IEEE Internet of Things Journal 2025年第12期12卷 19818-19832页

作者： Zhou, Ziyu Wang, Pengjun Li, Gang Hu, Shuang Zhang, Yuejun The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The College of Electrical and Electronic Engineering Wenzhou University Wenzhou325000 China

With the increasing number of devices in the Internet of Things (IoT), security has become a necessary feature. Compared to traditional key encryption methods, IoT device authentication protocols based on strong Physically unclonable function (PUF) have the advantage of being difficult to leak and tamper with. In addition, the protocol can enhance the resistance of strong PUFs to machine learning (ML) attacks by encrypting private information. However, the quality of the authentication is strongly related to the stability of the generated responses. If a large cost is incurred to improve the stability of the strong PUF, it will consume the already scarce resources of the device. This paper proposes a challenge screening strategy to improve the response stability of a commonly used strong PUF, arbiter PUF (APUF). First, a precise modeling method of APUF is carried out using the logistic-regression ML algorithm. Subsequently, random noise is injected into the challenges or PUF mathematical model and then calculated to estimate whether a stable response can be produced. Finally, stable challenges are used for IoT device authentication, while the threshold of the protocol is increased in parallel. Experimental verification shows that the proposed strategy can increase the stability of APUF responses to 100% at different temperatures. Furthermore, it does not consume hardware overhead at the device end, which is of particular significance for applications in resource-constrained conditions. © 2014 IEEE.

关键词： Authentication Protocol

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A Strong PUF Based Security Protocol to Protect AI Model Parameters against Privacy Information Leakage

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IEEE Internet of Things Journal 2025年第12期12卷 20815-20827页

作者： Zhou, Ziyu Li, Gang Zhang, Yuejun Zheng, Ziyang Yuan, Tengfei Wang, Pengjun Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China Wenzhou University College of Electrical and Electronic Engineering Wenzhou325000 China

In the era of intelligent computing, with the aid of Internet of Things (IoT) technology, artificial intelligence (AI) chips can be embedded at the terminal, object, edge, and cloud levels, ultimately achieving the vision where there is computation, there is AI intelligence. This not only enhances the efficiency of production and daily life but also exponentially increases the risk of privacy information leakage within AI models. This paper leverages the characteristics of strong physical unclonable functions (PUFs), in which the inherent feature information is hidden in physical variations and difficult to steal, to design a security protocol based on strong PUFs that provides effective protection for AI model parameters in the IoT environment. The protocol treats AI model parameters as responses and selects challenges capable of generating these responses. Since the responses generated by the challenges can be considered as randomly generated, transmitting the challenges does not disclose the response information, thus avoiding the risk of parameter hacking. Additionally, the protocol utilizes machine learning (ML) modeling techniques and lightweight encryption technologies to reduce the storage costs for identity information and the computational overhead of encryption operations. Through a security analysis of the protocol, it demonstrates that even under ideal attack conditions, the proposed protocol can resist various attacks. By using formal verification with the ProVerif tool, it confirms the security of the protocol flow and the effective protection of private information. © 2014 IEEE.

关键词： Authentication Protocol

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Optimization of Cooperative RelayingMolecular Communications for Nanomedical Applications

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computer Modeling in engineering & sciences 2024年第2期138卷 1259-1275页

作者： Eman S.Attia Ashraf A.M.Khalaf Fathi E.Abd El-Samie Saied M.Abd El-atty Konstantinos A.Lizos Osama Alfarraj Heba M.El-Hoseny Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61111Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Informatics Faculty of Mathematics and Natural SciencesUniversity of Oslo(UiO)OsloNorway Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer science The Higher Future Institute for Specialized Technological StudiesEl ShoroukEgypt

Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)***,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is *** this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift *** fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)*** the previous work,the probability of bit error is expressed in a closed-form *** is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano *** simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.

关键词： Nanomedical system molecular communication cooperative relay optimization

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Modified SPICE-Compatible Model Integrating NBTI and Self-Heating Effects for VDMOS Transistors 11

Modified SPICE-Compatible Model Integrating NBTI and Self-He...

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11th International Conference on Electrical, electronic and Computing engineering, IcETRAN 2024

作者： Marjanović, Milos Veljković, Sandra Mitrović, Nikola Živanović, Emilija Gavrić, Aleksandar Danković, Danijel Faculty of Electronic Engineering Department of Microelectronics Niš Serbia Faculty of Electronic Engineering Department of Computer Science Niš Serbia

ISBN: (纸本)9798350386998

This paper describes a modified SPICE-compatible VDMOS transistor model that includes NBT and self-heating effects. A complete circuit diagram of the transistor is given, which includes the electrical part of the circuit that models the NBT effect and the equivalent thermal part of the circuit that models the self-heating effect. The parameters of the model were determined based on the technical documentation and the fitting method based on the performed experiments. Fresh transistors and NBT-stressed transistors, with and without a heatsink in the load-driving circuit, were considered. The results of experiments and simulations correspond to each other with a maximum deviation of up to 10%. The proposed model can be used in the design and analysis of transistor reliability parameters. © 2024 IEEE.

关键词： SPICE

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Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

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computers, Materials & Continua 2024年第6期79卷 3855-3875页

作者： Maie Aboghazalah Passent El-kafrawy Abdelmoty M.Ahmed Rasha Elnemr Belgacem Bouallegue Ayman El-sayed Math and Computer Science Department Faculty of ScienceMenoufia UniversityShebin El-komEgypt College of Engineering Computer Science DepartmentEffat UniversityJeddahKingdom of Saudi Arabia Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityShebin El-komEgypt Climate Change Information Center and Expert Systems Agriculture Research CenterGizaEgypt

Heart monitoring improves life ***(ECGs or EKGs)detect heart *** learning algorithms can create a few ECG diagnosis processing *** first method uses raw ECG and time-series *** second method classifies the ECG by patient *** third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer *** ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and *** using all three approaches have not been examined till *** researchers found that Machine Learning(ML)techniques can improve ECG *** study will compare popular machine learning techniques to evaluate ECG *** algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization *** plus prior knowledge has the highest accuracy(99%)of the four ML *** characteristics failed to identify signals without chaos *** 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

关键词： ECG extraction ECG leads time series prior knowledge and arrhythmia chaos theory QRS complex analysis machine learning ECG classification

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Comparison of Q&A Classification Using BiLSTM Based on Closed Domain Knowledge 12

Comparison of Q&A Classification Using BiLSTM Based on Close...

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12th Electrical Power, electronics, Communications, Controls and informatics Seminar, EECCIS 2024

作者： Gunawan, Alifi Lazuardi Bachtiar, Fitra Abdurrachman Setiawan, Budi Darma University Malang Faculty of Computer Science Brawijaya Informatics Engineering Department Indonesia

ISBN: (纸本)9798350379914

The faculty of computer science, Universitas Brawijaya (Filkom UB) is committed to providing quality services for the users especially internal and external stakeholders, one of which is through the HaloFilkom service. HaloFilkom services have limitations in terms of time. HaloFilkom services are not available 24 hours due to limited working hours. Questions asked by users are not answered directly. This weakness in the HaloFilkom system can be overcome by using a chatbot system. Chatbot is an interactive system that works with natural human language and can work 24 hours. Thus, the current study explores the basic chatbot model by classifying the Q&A in the closed domain knowledge. The dataset in this research is in the form of pairs of questions and answers regarding various topics at the Filkom UB. The knowledge is preprocessed using text preprocessing which includes case folding, tokenization, padding, and tensorization. One of the chatbot models is a generative model. Creating a generative chatbot model can be done using the Seq2Seq model mechanism which consists of an encoder and decoder. The model created consists of four different architectures, namely a model with an LSTM encoder without attention and with attention and a BiLSTM model encoder without attention and with attention. Hyperparameter tuning was conducted to obtain the best hyperparameter combination. The experiment results show the best hyperparameter combination obtained is hidden size 448, drop out rate 0.5, learning rate 0.001, batch size 64, and teacher force 0. The model with the best loss is obtained with a BiLSTM encoder architecture without an attention mechanism with a train loss of 0.120. The model with the highest BLEU Score was obtained by a model with a BiLSTM encoder architecture without an attention mechanism with a BLEU Score of 0.8587 on the training data. Testing using prompt testing obtained an average BLEU Score of 0.3745 on the BiLSTM encoder without an attention mechanism mo

关键词： Teaching

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Current Trends, Challenges and Techniques in XAI Field;A Tertiary Study of XAI Research 47

Current Trends, Challenges and Techniques in XAI Field;A Ter...

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47th ICT and electronics Convention, MIPRO 2024

作者： Brdnik, Saša Šumak, Boštjan University Of Maribor Institute Of Informatics Faculty Of Electrical Engineering And Computer Science Slovenia

ISBN: (纸本)9798350382495

This tertiary systematic literature review examines 29 systematic literature reviews and surveys in Explainable Artificial Intelligence (XAI) to uncover trends, limitations, and future directions. The study explores current explanation techniques, providing insights for researchers, practitioners, and policymakers interested in enhancing AI transparency. Notably, the increasing number of systematic literature reviews (SLRs) in XAI publications indicates a growing interest in the field. The review offers an annotated catalogue for human-computer interaction-focused XAI stakeholders, emphasising practitioner guidelines. Automated searches across ACM, IEEE, and science Direct databases identified SLRs published between 2019 and May 2023, covering diverse application domains and topics. While adhering to methodological guidelines, the SLRs often lack primary study quality assessments. The review highlights ongoing challenges and future opportunities related to XAI evaluation standardisation, its impact on users, and interdisciplinary research on ethics and GDPR aspects. The 29 SLRs, analysing over two thousand papers, include five directly relevant to practitioners. Additionally, references from the SLRs were analysed to compile a list of frequently cited papers, serving as recommended foundational readings for newcomers to the field. © 2024 IEEE.

关键词： Artificial intelligence

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Leveraging Outage probability in system limited by BX fading and Co-channel interference for ChatGPT-enabled QoS adaptation 31

Leveraging Outage probability in system limited by BX fading...

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31st Telecommunications Forum, TELFOR 2023

作者： Vujović, Vuk Milić, Dejan Petrović, Nenad Marković, Filip Suljović, Suad Stefanović, Radiša MB University Belgrade Computer Science and Informatics Information Technology Belgrade Serbia University of Niš Telecommunications Faculty of Electronic Engineering Niš Serbia University of Niš Computer Science and Informatics Faculty of Electronic Engineering Niš Serbia University of Priština Computer Science and Informatics Faculty of Technical Sciences Kosovska Mitrovica Serbia Computer Science and Informatics the Academy of Applied Technical Studies Belgrade Belgrade Serbia

ISBN: (纸本)9798350303131

In this paper, we performed an analysis of the outage probability (Pout) at the output of the receiver with selection combination (SC) with L branches of the wireless communication system in the presence of Beaulieu-Xie (BX) fading and BX co-channel interference (CCI). The mathematical form of the function for Pout as a normalized signal-to-interference ratio (SIR) was derived, based on which a graph was drawn for different values of fading and CCI parameters. The second part of the paper considers applying machine learning approach relying on ChatGPT with aim to enable fading type recognition based on Pout measurements, which can be further used in order to perform Quality of Service (QoS) adaptation by tuning receiver's parameters. © 2023 IEEE.

关键词： Quality of service

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