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2024 IEEE Middle East Conference on Communications and Networking, MECOM 2024

作者： Kasgari, Abbas Akbarpour Chrysostomou, Chrysostomos Frederick University and Frederick Research Center Computer Engineering and Informatics Department of Electrical Engineering Nicosia Cyprus

ISBN: (纸本)9798350376715

The high demand for data rates in the fifth generation (5G) and beyond of wireless communication can be met by the Non-Orthogonal Multiple Access (NOMA) approach in the millimeter-wave (mmWave) frequency band. Joint power allocation and beamforming for 5 G and beyond mmWave-NOMA systems are essential, which can be achieved through optimization approaches. To this end, we have employed a Deep Reinforcement Learning (DRL) approach for policy generation, leading to an optimized sum-rate for users. Unlike existing methods, our approach is not susceptible to channel impulse response (CIR) estimation errors. The actor-critic framework is utilized to measure the immediate reward and provide new actions to maximize the overall Q-value of the network. The immediate reward is defined based on the summation of the rates of two users, considering the minimum guaranteed rate for each user and the total consumed power as constraints. The simulation results demonstrate the superiority of the proposed approach compared to state-of-the-art counterparts, including Time-Division Multiple Access (TDMA) and NLOS-NOMA optimization strategies, in terms of sum-rate of users, while considering both perfect and imperfect channel state information (CSI) at the receiver. This work can also be beneficial for communications in vehicular networks, particularly in applications involving Collective Perception Messages (CPMs) dissemination for autonomous driving. © 2024 IEEE.

关键词： Deep reinforcement learning

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Data Security, Privacy and Cyber Policy of Pakistan: A Closer Look 1

Data Security, Privacy and Cyber Policy of Pakistan: A Close...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Dhirani, Lubna Luxmi University of Limerick Department of Electronic and Computer Engineering Limerick Ireland

ISBN: (纸本)9798350373042

Living in an immersive, agile and a fully digital world where the scope, nature and ways of communication, connectivity, operations, business, finance, supply-chain, etc. have all changed and exposed the digital infrastructures and economies to a wider cyber threat landscape. Pakistan, being a developing nation, holds an impressive ranking at the global innovation index 2023, however with these advancements there have been increased security issues and cyber crimes affecting the unprivileged population of the Country. As per the United Nations Sustainable Development Goals 10 and 16, a strong emphasis to reduce inequalities and maintain peace, justice and strong institutions has been given. Unresolving problems affecting the under privileged may contribute to increased hate crime, bias, inequalities, conflicts and prejudice. This paper sheds light on the pressing issues related to lack of data and information security controls enabling misuse of personal identifiable information (i.e., name, health data, geotags, etc.) affecting the unprivileged population. Harvesting personal data is valuable to cyber criminals as it allows them to impersonate and incriminate others for the crimes they carried out. As reported in the print media, such cybercrimes have been on rise in Pakistan and thus this addresses an urgency to revise the national cybersecurity policy as per global cybersecurity norms. © 2024 IEEE.

关键词： Cybersecurity

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IoT Revolution: Exploring the Impact of loT Based Embedded System Technology on Healthcare, Agriculture and Business Startups in Smart Village 2

IoT Revolution: Exploring the Impact of loT Based Embedded S...

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2nd International Conference on Power, Electrical, electronics and Industrial Applications, PEEIACON 2024

作者： Gupta, Rittik Das Debnath, Sourav Gupta, Riva Das Chowdhury, Mrithika Department of Electrical and Electronic Engineering Dhaka Bangladesh Department of Electrical and Electronic Engineering Dhaka Bangladesh Department of Economics Chattogram Bangladesh Department of Computer Science and Engineering Chattogram Bangladesh

ISBN: (纸本)9798331517984

The paper illustrates the development of an integrated Internet of Things (IoT) gadget with smart agricultural and healthcare systems, as well as the possibility for their startup firms to build a smart village. The system comprises a carefully designed front end powered by Arduino and NodeMCU microcontrollers. It has a number of high-precision sensors, including the DS18B20 for body temperature, the Max30102 for heart rate and oxygen level, the FC-28 for soil moisture, the DHT-11 for temperature of the environment and maintaining humidity, the dust sensor for air quality detection, and calibrated MQ sensors for gas detection of CH4, H2S, and NH3. The back end simply interfaces with the Blynk application, ThingSpeak, and Google spreadsheet as data storage for detailed data visualization and real-time monitoring via smart devices. A rigorous sensor validation process that was carried out, indicating that this device's measurements align statistically with those of commercially available sensors, as validated by paired sample t-tests, Q-Q plot and descriptive data analysis in SPSS software. The prototypic data analysis system is also a key aspect of this research. Our proposed research not only supports the usefulness of our technology, but also gives a direction for future users to correctly evaluate their data. The integrated IoT device exhibits its utilization for smart healthcare and precision agriculture systems for the purpose of smart village. The combination of IoT along with startup enterprises is extending unique notions that could improve global food sustainability and individual well-being. This paper displays the potential for startups to develop innovative IoT, health, and agriculture solutions that will shape a future where technology improves lives and ensures food security. © 2024 IEEE.

关键词： Smart agriculture

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Stress Detection Using EEG Signals: A SVM-Based Classification and Feature Selection Approach 12

Stress Detection Using EEG Signals: A SVM-Based Classificati...

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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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Smart Victims Detection in Natural Disaster using Deep Learning 24

Smart Victims Detection in Natural Disaster using Deep Learn...

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24th International electronics Symposium, IES 2022

作者： Adamu Islam Mashuri, M. Hadi, Moch Zen Samsono Sudibyo, Rahardhita Widyatra Kristalina, Prima Pratiarso, Aries Department of Informatics and Computer Engineering Electronic Engineering Jawa Timur Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

Nowadays, artificial intelligence has been widely implemented in agriculture, animal husbandry, education, and many more. The most widely developed model at this time is the Deep Learning model. To get maximum results, the best Deep Learning architecture is needed. In this paper, we will compare methods for detecting victims of natural disasters based on the Deep Learning model. The models that we will compare are the You Only Look Once (YOLO) algorithm, namely YOLOv5, and Convolutional Neural Network (CNN) with the MobileNet model and the VGG-16 model. We train each algorithm through a natural disaster victim training data set and analyze performance to determine what model is the most optimal. To identify the types of victims of natural disasters. From our results, the MobileNet model is superior to other models with 98% accuracy. © 2022 IEEE.

关键词： Agriculture

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Feature analysis and selection for BGP anomaly detection 28

Feature analysis and selection for BGP anomaly detection

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28th Conference on Innovation in Clouds, Internet and Networks, ICIN 2025

作者： Hakimi, Rifqy Reed, Martin J. School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798331542399

Automated incidence reporting requires accurate and timely anomaly detection. This paper considers this in the context of the Border Gateway Protocol (BGP). BGP is crucial for Internet routing but is vulnerable to attacks due to a lack of widespread authentication. While rare, BGP disruptions can be highly detrimental to Internet performance and security. Detecting BGP anomalies helps network operators protect their networks and improve Internet reliability. In this work, we investigate and develop anomaly detection for BGP using Machine-Learning. We extract relevant features of BGP control plane messages from RIPE RIS and RouteViews public datasets. After extracting features, we employ various feature selection algorithms to extract the most relevant features and explore balancing the datasets. Finally, we explore detection latency, an important operating parameter for automated anomaly detection. The results show that BGP anomalies can be detected in the order of seven minutes when using real attack data and that the observation point for the BGP data has a significant effect on anomaly detection. © 2025 IEEE.

关键词： Adversarial machine learning

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Quasi-Static Measurement Performances of Flex-Sensor-Based and Inertial- Measurement -Unit-Based Sensory Gloves

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IEEE Sensors Journal 2024年第2期24卷 2162-2171页

作者： Calado, Alexandre Lin, Bor-Shing Lee, I-Jung Saggio, Giovanni University of Rome 'Tor Vergata ' Department of Electronic Engineering Rome00133 Italy National Taipei University Department of Computer Science and Information Engineering New Taipei City237 Taiwan

Sensory gloves convert hand postures and movements of fingers into electric signals. Different technologies can be adopted to achieve this conversion, and different approaches can be used to evaluate its effectiveness. In this study, we adopted two types of sensory gloves based on two types of sensors, namely, the resistive flex sensor (RFS) and the inertial measurement unit (IMU). We evaluated the conversion effectiveness in terms of repeatability, reproducibility, and reliability of quasi-static measurements. In particular, to consider the nonlinear characteristics of sensors, we propose an improvement of the usually adopted measurement test protocol used for assessing the performance of sensory gloves. According to our results, the two sensory gloves have similar reliability. However, the IMU-based glove provides better repeatability, and the RFS-based glove provides better reproducibility. Overall, the average range ± standard deviation and the intraclass correlation coefficient of the RFS-based glove were 5.66°± 2.22° and 0.73 ± 0.17, respectively, and those of the IMU-based glove were 7.80°± 2.47° and 0.76 ± 0.14, respectively. All in all, the novelty of this work concerns the comparison of two types of sensory gloves in terms of quasi-static measurement reproducibility and reliability and the improvement of an existing standard protocol for sensory glove assessment aimed at providing a more comprehensive analysis. © 2001-2012 IEEE.

关键词： Reliability analysis

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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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A 0.75 to 1.5 GHz Reconfigurable Diplexer With Independently Controllable Center Frequency and Insertion Phase

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IEEE Transactions on Circuits and Systems II: Express Briefs 2024年第4期71卷 1924-1928页

作者： Yi, Chengfei Chen, Xiong Chi, Pei-Ling Yang, Tao The School of Electronic Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China The Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu300 Taiwan

A novel reconfigurable diplexer with independently controllable center frequency and insertion phase is proposed in this brief. It simply consists of six coupled resonators and two non-resonating-nodes (NRNs). These six resonators are arranged in two triplet topologies and construct two typical third-order filters, forming the lower and upper channels in the diplexer, respectively. The NRNs are loaded at the receiver (RX) and transmitter (TX) ports. With the utilization of NRNs, the insertion phases of the upper and lower channels in the diplexer can be independently controlled. Optimization technique is used to obtain the coupling matrix of the diplexer, in which the NRN is considered to achieve a prespecified insertion phase. To validate the proposed idea, a microstrip prototype is fabricated and measured. Experiment results show that the center frequencies of lower and upper channels can be tuned from 0.75 GHz to 0.98 GHz, and from 1.23 GHz to 1.5 GHz, respectively. The insertion phases of both channels can be independently tuned from 0 to 120 degrees at each frequency tuning state. © 2023 IEEE.

关键词： Resonators

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An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

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computers, Materials & Continua 2025年第3期82卷 5057-5078页

作者： Isha Kiran Shahzad Ali Sajawal ur Rehman Khan Musaed Alhussein Sheraz Aslam Khursheed Aurangzeb Department of Computer Science University of Engineering and TechnologyLahore39161Pakistan Dipartimento di Farmacia e Biotecnologie Alma Mater Studiorum—Universita di BolognaBologna40126Italy Department of Information Sciences University of EducationLahore54770Pakistan Department of Computer Science National Textile UniversityKarachi CampusKarachi74900Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Electrical Engineering Computer Engineeringand InformaticsCyprus University of TechnologyLimassol3036Cyprus Department of Computer Science CTL EurocollegeLimassol3077Cyprus

Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he

关键词： Artificial Intelligence cardiovascular disease(CVD) explainability eXplainable AI(XAI) interpretability LIME machine learning(ML) SHAP

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