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34th Medical Informatics Europe Conference, MIE 2024

作者： Chatzimina, Maria E. Papadaki, Helen A. Pontikoglou, Charalampos Tsiknakis, Manolis Dept. of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece Institute of Computer Science FORTH Heraklion Greece Dept. of Hematology School of Medicine University of Crete Heraklion Greece

ISBN: (纸本)9781643685335

Enabling patients to actively document their health information significantly improves understanding of how therapies work, disease progression, and overall life quality affects for those living with chronic disorders such as hematologic malignancies. Advancements in artificial intelligence, particularly in areas such as natural language processing and speech recognition, have resulted in the development of interactive tools tailored for healthcare. This paper introduces an innovative conversational agent tailored to the Greek language. The design and deployment of this tool, which incorporates sentiment analysis, aims at gathering detailed family histories and symptom data from individuals diagnosed with hematologic malignancies. Furthermore, we discuss the preliminary findings from a feasibility study assessing the tool's effectiveness. Initial feedback on the user experience suggests a positive reception towards the agent's usability, highlighting its potential to enhance patient engagement in a clinical setting. © 2024 The Authors.

关键词： Chatbots

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Advancements in Animal Tracking: Assessing Deep Learning Algorithms 15

Advancements in Animal Tracking: Assessing Deep Learning Alg...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Akter, Tahmina Majumder, Avishek Mahmud, Tanjim Habib, Israt Binteh Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh International Islamic University Chittagong Dept. of Computer Science and Engineering Chittagong Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

In the rapidly advancing field of computer vision, object detection has become crucial for various applications, including animal tracking, face detection, and surveillance systems. This study investigates the efficacy of contemporary object detection methodologies by evaluating the performance of the You Only Look Once (YOLO) models and TensorFlow Model Zoo architectures for animal tracking. YOLO models, known for their ability to process entire images in real-time and predict bounding boxes and class probabilities simultaneously, offer significant advantages over traditional methods such as Convolutional Neural Networks (CNNs) and Fast R-CNNs. This paper compares the performance of YOLOv5 and YOLOv7, alongside TensorFlow-based models like Faster R-CNN ResNetv152 and SSD ResNet101, using a dataset of animal images. Our findings reveal that YOLOv5 outperforms other models with a mean average precision (mAP) of 9 7.5%, demonstrating superior accuracy and efficiency in object detection tasks. YOLOv7 also shows strong performance with an mAP of 96.7%, while TensorFlow Model Zoo's Faster R-CNN and SSD models lag behind with mAPs of 81.9% and 81.6%, respectively. The results highlight the significant advancements in deep learning and object detection algorithms, particularly the advantages of YOLO's architecture in handling complex detection tasks in real-world scenarios. © 2024 IEEE.

关键词： Convolutional neural networks

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Rainfall Forecasting Using Hybrid Learning Method

Rainfall Forecasting Using Hybrid Learning Method

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2024 International of Seminar on Application for Technology of Information and Communication, iSemantic 2024

作者： Hawari, Ihtifazhuddin Isnanto, R. Rizal Dwi Nurhayati, Oky Diponegoro University Dept. of Electrical Engineering Semarang Indonesia Diponegoro University Dept. of Computer Engineering Semarang Indonesia

ISBN: (纸本)9798350366648

Rainfall is one of the most important meteorological variables. Rainfall has a multiplicity of effects on agriculture, ecosystems and urbanization. The amount of rainfall cannot currently be determined due to climate change. For this reason, in order to mitigate the effects that arise from heavy rainfall, it is crucial to forecast the amount of rainfall. Currently, statistical techniques and machine learning are two of the methods has been used to forecast rainfall. These methods have been successfully to forecast rainfall. However, it still has low accuracy and need more time to forecast because the rainfall influence parameters are time-series and non-linear. This study will employ a hybrid learning technique to resolve the issue. The method used is a combination of two algorithms, namely long short-term memory (LSTM) and particle swarm optimization (PSO), and the prediction system will be evaluated using RMSE and MAE. Time-series and non-linear rainfall data will be overcome by the LSTM algorithm, and uncertain hyperparameter settings in the LSTM method will be overcome by the PSO algorithm. The ideal LSTM algorithm employing PSO optimization yields the following hyperparameter value results: 143 neurons, learning rate of 0.0000840, and 122 epochs in 50 iterations. With the lowest RMSE and MAE values of 18.557095 and 8.972361, respectively, the prediction model's optimal hyperparameter values can forecast the next five days based on the data used. © 2024 IEEE.

关键词： forecast hybrid learning LSTM PSO rainfall

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SMART HOME ENVIRONMENT MODELLED WITH A MULTI-AGENT SYSTEM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: electrical engineering and computer science 2023年第1期85卷 365-376页

作者： Rasras, Mohammad Marin, Iuliana Radu, Șerban Dept. of Engineering in Foreign Languages University POLITEHNICA of Bucharest Romania Dept. of Computer Science University POLITEHNICA of Bucharest Romania

A smart home can be considered a place of residence that enables the management of appliances and systems to help with day-to-day life by automated technology. In the current paper is described a prototype that simulates a context-aware environment, developed in a designed smart home. The smart home environment has been simulated using three agents and five locations in a house. The context-aware agents behave based on predefined rules designed for daily activities. Our proposal aims to reduce operational cost of running devices. In the future, monitors of health aspects belonging to home residents will sustain their healthy life daily. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Multi agent systems

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A Single-Weight Neural Network for MIMO Signal Detection: Algorithm and Implementation 10

A Single-Weight Neural Network for MIMO Signal Detection: Al...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Ho, Yi-Cheng Yen, Mao-Hsu Lu, Hoang-Yang National Taiwan Ocean University Dept. of Computer Science and Engineering Keelung Taiwan National Taiwan Ocean University Dept. of Electrical and Engineering Keelung Taiwan

ISBN: (纸本)9798331532895

Existing deep learning-based MIMO detectors often face substantial memory demands due to the need to store neural network weights, posing a significant challenge to practical implementation. To address this issue, we propose a single-weight neural network, namely SW-Net. By reducing each layer's weight vector to a scalar, the required hardware storage is significantly reduced. Simulation results demonstrate that hardware requirements can be reduced while maintaining bit error rate (BER) performance. Additionally, we present the corresponding VLSI hardware architecture, implemented using TSMC's 90-nm CMOS technology, operating at 707 MHz. This design requires 1109 kGEs and achieves a throughput of 707 Mbps. © 2024 IEEE.

关键词： Deep neural networks

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Lightweight Federated Learning based White Space Detector for Cognitive Radios

Lightweight Federated Learning based White Space Detector fo...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Galib, Md Mehedi Hassan Younis, Mohamed University of Maryland Baltimore County Dept. of Computer Science and Electrical and Engineering BaltimoreMD21250 United States

ISBN: (纸本)9798350351255

In cognitive communication, detecting white space is critical for preventing interference with primary user's transmissions. Rather than modifying the radio transceivers, several machine learning (ML)-based detection techniques have been proposed, where a model is trained offline and then employed to infer white spaces in real-time. Despite their viability for spectrum sensing, these techniques are computationally complex, especially when deep neural network (DNN) models are pursued. Moreover, training a centralized model requires transmitting massive data to the fusion center (FC), which ultimately results in congestion on the transmission channel rather than flagging opportunities for cognitive transmissions. This paper opts to fill the technical gap by proposing: (1) a federated learning model that enables effective design of spectrum monitors in a distributed manner, and (2) a spiking neural network (SNN)-based lightweight white space detector that solves the issue of computationally expensive DNN models and is suitable for resource-constrained devices. The SNN-based federated learning (SFL) model employs secondary users to train their respective SNN models using local data (spatial locality) and sends the gradient of SNN to FC. FC combines the individual SNN models and sends the aggregated model back to each edge node. Validation using live LTE data has demonstrated the effectiveness of SFL with a detection accuracy of 91.16%. © 2024 IEEE.

关键词： Radio transceivers

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Leveraging Information and Communication Technology (ICT) To Combat Corruption: A Systematic Review

Leveraging Information and Communication Technology (ICT) To...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Oluwambo Tolulope, Olowe Ariyo Adebiyi, Ayodele Akindeji, Kayode Landmark University Dept. of Computer Science Omuaran Nigeria Durban University of Technology Dept. of Electrical Power Engineering Durban South Africa

ISBN: (纸本)9798350358155

Corruption, a global phenomenon disproportionately affecting developing nations, undermines governance and economic development. This systematic literature review examines the role of Information and Communications Technology (ICT) in mitigating corruption. Despite conflicting evidence, ICT interventions, including transactional and transparency reforms, offer promising avenues for combating corrupt practices. The review highlights the diverse methodologies employed, with a significant focus on AI-related approaches. Collaboration among researchers, policymakers, and stakeholders is essential for developing comprehensive strategies to address corruption effectively, particularly leveraging ICT advancements. © 2024 IEEE.

关键词： Artificial intelligence

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Exploring Machine Learning Techniques for Accurate Heart Disease Detection: A Comprehensive Study

Exploring Machine Learning Techniques for Accurate Heart Dis...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Singh, Pardeep Chaudhary, Vipin Kumar Bhatia, Arvind Kumar Sharma, Gaurav Kumbhawat, Amit Singh, Suchita Lovely Professional University Dept. of Computer Science Engineering Punjab Phagwara India Buddha Institute of Technology Dept. of Computer Science Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350385199

Globally, heart disease poses a significant challenge to public health. Early identification is crucial for effectively managing and treating heart conditions. Machine learning has shown promising results in predicting medical problems for the past few years. In this study, A system was developed that is capable of detecting cardiovascular diseases using machine learning methods. The project consists of Data loading, Exploratory data analysis, preprocessing, training, evaluation and storage. A dataset was introduced that contained an array of characteristics related to the cardiovascular health status (such as age, gender, cholesterol level.). However, training measured the accuracy of 3 algorithms: K Nearest Neighbours (KNN), Decision Tree, and Random Forest in identifying heart ailment. The article also returns an ensemble graph that considers the forecasts of both these models together, saving the best GNB model for future use. Results showed that the machine learning methods are proficient in heart problem identification, as in this hybrid model, with a 97.32% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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Systematic Common Information Model (CIM) Profile Management for Advanced Distribution Management System

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Transactions of the Korean Institute of electrical Engineers 2024年第1期73卷 1-9页

作者： Kim, Hyun Joong Oh, Jae Young Yoon, Yong Tae Jung, Wonwook Shim, Jaeseong Shon, Jin-Man Dept. of Electrical Engineering Hoseo University Korea Republic of Dept. of Energy Engineering Korea Institute of Energy Technology Korea Republic of Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of Korea Electric Power Corporation Research Institute Rep. of Korea Korea Republic of

Common Information Model (CIM) is an essential technique for ensuring interoperability in Energy Management Systems (EMS) by defining data profiles for information exchange. Given the changing system environment, CIM undergoes periodic revisions, necessitating updates to the profiles used in EMS. However, comparing and interpreting CIM as defined in the Unified Modeling Language (UML) can be time-consuming and may lead to human errors. In this context, this study proposes systematic approaches for managing data profiles using an automatic tracking function for CIM and a structured process for profile management. The effectiveness of this approach is demonstrated by applying it to profiles defining the key functions of Korea's advanced distribution management system (ADMS), such as operational limits, renewable energy sources, and power transformers. This research can serve as a guideline for addressing practical issues in securing interoperability in ADMS. © 2024 Korean Institute of electrical Engineers. All rights reserved.

关键词： Interoperability

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Multi-Agent H-Learning Based Cooperative Spectrum Sensing for Cognitive Radio Networks 32

Multi-Agent H-Learning Based Cooperative Spectrum Sensing fo...

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32nd International Conference on electrical engineering, ICEE 2024

作者： Fard, E. Karimpour Nouri, M. Behroozi, H. Sobhi-Givi, S. Sharif University of Technology Dept. of Electrical Engineering Tehran Iran University of Mohaghegh Ardabili Dept. of Electrical and Computer Engineering Ardabil Iran

ISBN: (纸本)9798350376340

Fast and accurate methods for spectrum sensing (SS) are the key elements in cognitive radio networks (CRNs) that achieve high SS. This paper proposes a reinforcement learning (RL) scheme for secondary users (SUs) in a massive multiple-input multiple-output (MIMO) CRN to determine occupation and emptiness of scanned wideband channels. In the proposed H-learning method, the clustered neighboring SUs collect data to check the status of channels that improve reliability of measured sensing. Then, they share information about different frequency channels with other clusters and the status of channel occupation is determined for all SUs. This model-based learning method increases finding free channels and enhances spectral efficiency (SE) in presence of large probability of missing detection. Furthermore, each SU learns the presence pattern of primary user (PU) and gather a dynamic scan priority list to decrease scan overhead and access delays. Simulation results demonstrate the multi-agent reinforcement modified H-learning (MH-learning) method can achieve better performance in terms of PU usage percent, number of attempts, call block rate, and probability of detection in comparison to H-learning and some state-of-art learning methods. © 2024 IEEE.

关键词： Reinforcement learning

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