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The Queue’s Automated Creation of Doctor’s Calls by Patients in the Hospital with Visualization via the Mobile Application

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Journal of Mobile Multimedia 2023年第3期19卷 897-934页

作者： Zhuravska, Iryna Dvoretskyi, Mykhailo Kulakovska, Inessa Boiko, Anzhela Dvoretska, Svitlana Department of Computer Engineering Petro Mohyla Black Sea National University Mykolaiv Ukraine Software Engineering Department Petro Mohyla Black Sea National University Mykolaiv Ukraine Department of Intelligent Information Systems Petro Mohyla Black Sea National University Mykolaiv Ukraine

The spread of the COVID-19 virus is challenging society to provide medical care to a growing number of patients in the hospital. It is important to determine the patient from whom an urgent appeal was received earlier and to automate the creation of the calls’ queue. Determining the direction of the sound source, which is the patient’s voice, can be realized using the passive acoustic location method. In this case, it is necessary to place sound sensors in the wards with patients. In such a case, these sensors it is expedient to build in the lighting equipment executed in the shape of Platonic polyhedra. The microcontroller system, located inside such a spatial structure, ensures the transmission of sound to a server computer system. The above system alternately records the receipt of urgent appeals, analyzes the location of the sound source, and sends the relevant data to the doctor’s smartphone. The mobile application visualizes information about the location of the patients who need consultation or help. The proposed solution for intelligent analysis of voice appeals by inpatients may also be useful for post-stroke, post-infarction, and other bedridden patients who are unable to call medical staff otherwise than a voice. © 2023 River Publishers.

关键词： Automatic doctor calls’ queue determining the direction of the sound source microcontroller system mobile application Platonic polyhedra

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Extending ISO 15118-20 EV Charging: Preventing Downgrade Attacks and Enabling New Security Capabilities

Extending ISO 15118-20 EV Charging: Preventing Downgrade Att...

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Annual Conference on Privacy, Security and Trust, PST

作者： Ross Porter Morteza Biglari-Abhari Benjamin Tan Duleepa Thrimawithana Electrical Computer Systems & Software Engineering Department University of Auckland Auckland New Zealand Department of Electrical and Software Engineering University of Calgary Calgary Canada

ISBN: (数字)9798350367096

ISBN: (纸本)9798350367102

Previous works have identified that EV charging can be weaponised to attack the power grid. As a case study, we consider the newest charging protocol ISO 15118–20, which provides a high-level communication protocol for EV charging. We first highlight fundamental issues in ISO 15118–20 which prevent the development of security features within the existing standard: We show that an attacker can perform a downgrade attack on ISO 15118–20, and propose modifications to the standard to prevent this. We show how this can be used to enable the development of additional security features within the modified protocol. A proof of concept is developed to prove functionality, determine interoperability between various parties, verify that it meets the original standard's timing requirements, and does not impact charging speed nor noticeably affect the length of a charging session.

关键词： Privacy Protocols ISO Standards Weapons System performance Electric vehicle charging Timing Security Standards Testing

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Comparative Study of Static Analysis and Machine Learning Approaches for Detecting Android Banking Malware

Comparative Study of Static Analysis and Machine Learning Ap...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Alaa M. Ahmed Mogeeb A. Saeed Amr A. Hamood Abduljabar A. Alazab Kamel A. Ahmed Faculty of Engineering Computer Networks & Distributed Systems Al-saeed IT Taiz University Taiz Yemen Computer Networks & Distributed Systems department Al-saeed Faculty of Engineering & IT Taiz University Taiz Yemen

Android currently dominates the smartphone market, accounting for an impressive market share of over 70%. However, because of its widespread acceptance, mobile operating systems have become a prime target for bad actors looking to profit from them. Particularly Android has been subjected to an increasing barrage of malware assaults, including the infamous Android Banking Trojans. This study investigates the effectiveness of static analysis in locating Android banking malware in order to counter this threat. It does so by utilizing a wide range of features, including permissions, application programming interface (API) calls, opcodes, API packages, system commands, intents, strings, services, receivers, and activities. The study suggests using machine learning techniques to assess the detection of Android malware by utilizing various sets of classifiers in order to achieve this goal. The study also uses a feature selection approach to determine which features are most useful for telling malicious code apart from good code. 500 samples of malicious code and 500 samples of benign code make up the dataset that was used. The XGboost algorithm outperforms others in terms of accuracy, achieving an impressive accuracy value of 99.5% in malware detection after conducting a thorough comparison of various classifier sets. These results demonstrate the potential of static analysis and machine learning as useful tools in fending off the growing threats posed by Android malware.

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Prediction of Antimicrobial Peptides Using Deep Neural Networks

Prediction of Antimicrobial Peptides Using Deep Neural Netwo...

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16th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2023

作者： Gülsüm Söylemez, Ümmü Yousef, Malik Bakir-Gungor, Burcu Department of Software Engineering Faculty of Engineering Muş Alparslan University Muş Turkey Department of Information Systems Zefat Academic College Zefat13206 Israel Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri Turkey

Antimicrobial peptides (AMPs) are crucial elements of the innate immune system;and they are effective against bacteria that cause several diseases. These peptides are investigated as potential alternatives of antibiotics to treat infections. Since wet lab experiments are expensive and time-consuming, computational methods become crucial in this field. In this study, we suggest a computational technique for AMP prediction using deep neural networks (DNN). We trained a DNN classifier using physicochemical features that include a sequential model;and evaluated the model with 10-fold cross-validation on a benchmark dataset. We compared our method with other machine learning approaches and demonstrated that the method we developed generates higher performance (accuracy: 92%, precision: 92%, recall: 93%, f1: 93%, AUC: 98%). In our experiments, we have realized that there is a strong positive correlation between the ‘Normalized Hydrophobic Moment’ feature and ‘Angle Subtended by the Hydrophobic Residues’ feature;and strong negative correlations between ‘Normalized Hydrophobicity’ feature and ‘Disordered Conformation Propensity’ feature, and between ‘Amphilicity Index’ - ‘Disordered Conformation Propensity’ features. We believe that the approach we proposed could guide further experimental studies and could facilitate the prediction of other types of AMPs having anticancer, antivirus, antiparasitic activities. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Deep neural networks

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Heterogeneous Federated Learning System for Sparse Healthcare Time-Series Prediction

arXiv

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arXiv 2025年

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Distributed Systems and Informatic Devices Silesian University of Technology Poland

In this paper, we propose a heterogeneous federated learning (HFL) system for sparse time series prediction in healthcare, which is a decentralized federated learning algorithm with heterogeneous transfers. We design dense and sparse feature tensors to deal with the sparsity of data sources. Heterogeneous federated learning is developed to share asynchronous parts of networks and select appropriate models for knowledge transfer. Experimental results show that the proposed HFL achieves the lowest prediction error among all benchmark systems on eight out of ten prediction tasks, with MSE reduction of 94.8%, 48.3%, and 52.1% compared to the benchmark systems. These results demonstrate the effectiveness of HFL in transferring knowledge from heterogeneous domains, especially in the smaller target domain. Ablation studies then demonstrate the effectiveness of the designed mechanisms for heterogeneous domain selection and switching in predicting healthcare time series with privacy, model security, and heterogeneous knowledge transfer. © 2025, CC BY-NC-SA.

关键词： Federated learning

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IoT Based Drip Infusion Monitoring System

IoT Based Drip Infusion Monitoring System

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2022 IEEE Delhi Section Conference, DELCON 2022

作者： Maniktalia, Riddhi Tanwar, Satwik Billa, Ritvik Deepa, K. School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Software Systems Vellore India

ISBN: (纸本)9781665458832

During recent years, we have seen many technological advancements which help to take better care of patient's health and assure them fast and safe recovery. The most basic item necessary is competent patient care in hospitals, as well as proper management of fluid and electrolytes. In hospital almost all patients, mainly ICU patients, have to regulate the volume of fluids and electrolyte into the bloodstream 247 which is done using drip. These drips need regular monitoring or changing to maintain constant flow of fluids or to prevent any infection to patients. Though this system is simple, overcrowded hospitals and the risk of deficiency of nurses can put patients at health risk which can deteriorate patients' health more aggressively or in worse case it can lead to death. Almost in all hospitals, nurses or the hospital staff are responsible for monitoring the drip level. But unfortunately, because of their hectic schedule, the observer may forget to change the bottle at the appropriate time. During the pandemic, hospitals were overrun with patients, and nurses were unable to do manual regular checks on the drip conditions and drip level of every patient, even after working extra shifts. Many patients even died due to not being able to get proper care from nurses. The next step in providing more effective and easy healthcare is to automate such vital procedures. To overcome this critical situation, we are proposing an IoT (Internet of Things) based Drip Monitor System using Arduino UNO which eases the process of measuring and solves the issues of bubble formation in drips. © 2022 IEEE.

关键词： Health risks

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Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

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computer systems Science & engineering 2023年第8期46卷 2247-2262页

作者： N.Sharmili Saud Yonbawi Sultan Alahmari E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Computer Science and Engineering Department Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnamAndhra PradeshIndia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Facial expression recognition(FER)remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass *** techniques for this problem depend on hand-crafted features,namely,LBP,SIFT,and HOG,along with that a classifier trained on a database of videos or *** execute perform well on image datasets captured in a controlled condition;however not perform well in the more challenging dataset,which has partial faces and image ***,many studies presented an endwise structure for facial expression recognition by utilizing DL ***,this study develops an earthworm optimization with an improved SqueezeNet-based FER(EWOISN-FER)*** presented EWOISN-FER model primarily applies the contrast-limited adaptive histogram equalization(CLAHE)technique as a pre-processing *** addition,the improved SqueezeNet model is exploited to derive an optimal set of feature vectors,and the hyperparameter tuning process is performed by the stochastic gradient boosting(SGB)***,EWO with sparse autoencoder(SAE)is employed for the FER process,and the EWO algorithm appropriately chooses the SAE ***-ranging experimental analysis is carried out to examine the performance of the proposed *** experimental outcomes indicate the supremacy of the presented EWOISN-FER technique.

关键词： Facial expression recognition deep learning computer vision earthworm optimization hyperparameter optimization

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Grid Services Demonstration Using Service-Oriented DERMS

Grid Services Demonstration Using Service-Oriented DERMS

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Grid Edge Technologies Conference & Exposition (Grid Edge), IEEE PES

作者： Midrar Adham Sean J. Keene Tylor Slay Jaime T. Kolln Robert B. Bass Department of Electrical & Computer Engineering Portland State University Portland OR Distributed Systems Pacific Northwest National Laboratory Richland WA

ISBN: (数字)9798350352528

ISBN: (纸本)9798350352535

This work demonstrates four grid service use cases using a service-oriented DER Management System within an Energy Grid of Things network. Imposed by a set of rules referred to as the Energy Service Interface, the DER Management System uses IEEE 2030.5 as its primary communication protocol. To simulate the efforts of Grid Operators, Grid Service Providers, and consumers to provide grid services, a co-simulation environment that leverages the GridAPPS-D simulation platform was developed. The results show that a service-oriented DER Management System provides a means of achieving reliable and impactful grid services while protecting customers' privacy and ensuring their agency over their distributed Energy Resources.

关键词： Privacy Protocols Real-time systems distributed power generation Servers Reliability Information exchange

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Integrate Interpretable AI into Chest X ray Diagnosis through a Comparative Analysis of CNN Architectures with Grad CAM Visualizations

Integrate Interpretable AI into Chest X ray Diagnosis throug...

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Advances in Technology and Computing (ICATC), International Conference on

作者： A M Nadun Hirushan S P Kasthuri Arachchi Department of Computer Systems Engineering University of Kelaniya Sri Lanka Department of Software Engineering University of Kelaniya Sri Lanka

ISBN: (数字)9798331518219

ISBN: (纸本)9798331518226

Artificial Intelligence, as an aid to medical image interpretation has marked a significant advantage for improving diagnostic accuracy and aiding the clinical workflow. However, an issue that needs to be addressed is the "black box" nature of AI models, which makes them not very trusted or adoptable at a clinical level. This research focuses on the role of interpretable AI models with respect to being implementable within clinical workflows for influencing medical image interpretation. The study has trained and finalized the advanced deep learning architectures that includes VGG19, MobileNet as well InceptionResnetV2 to classify data of chest X-ray modalities using 13 distinct diseases labelled in multilabel classification problem. Several ensemble methods have been used to boost the prediction like Max Vote, HardVote, Simple Average and Weighted Average. The system evaluates accuracy, loss plots, Receiver Operating Characteristic (ROC) curves and Area Under the ROC Curve (AUC) scores. The results depicted the performance of VGG19 and MobileNet with 88.5% validation accuracies and their respective validation losses as 0.29 and 0.29. Attention layers and Grad-CAM will further ensure more explainability, thus instilling confidence in AI-driven decisions. This transparency is very key to the practical application of AI in healthcare and will lead to better patient outcomes, further optimizing clinical processes. This work potentially opens the door for wider AI deployment in clinical diagnostics by opening two major impediments: challenges in interpretability and transparency.

关键词： Training Accuracy Medical services computer architecture Receivers Picture archiving and communication systems Ensemble learning Artificial intelligence X-ray imaging Medical diagnostic imaging

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Day-ahead PV Output Power Forecasting Utilizing Boosting Recursive LightGBM-LSTM Framework

Day-ahead PV Output Power Forecasting Utilizing Boosting Rec...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Hokmabad, Hossein Nourollahi Husev, Oleksandr Vinnikov, Dmitri Belikov, Juri Petlenkov, Eduard Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia

ISBN: (纸本)9798350396782

Recently, global photovoltaic (PV) system installations have surged. Precise forecasting is vital for their grid integration and carbon emission cuts. However, due to fluctuating solar radiation, predicting PV output is difficult. Machine learning models, notably Long Short-Term Memory (LSTM) networks, offer a solution. This study presents a novel framework using a boosted recursive Light Gradient Boosting Machine (LightGBM)-LSTM network to forecast daily PV output at hourly intervals. Conducted in Northern Europe—a region with significant solar radiation variability—the models trained on meteorological and historical data showed a 12% improvement in RMSE, a 13% reduction in MAPE, and a 5% increase in the R2 score compared to standalone LSTM models. © 2023 IEEE.

关键词： boosting ensemble method LightGBM LSTM numerical weather prediction Solar power forecasting

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