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Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

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SN computer science 2024年第5期5卷 564页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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CNN-Based Multi-Output and Multi-Task Regression for Supershape Reconstruction from 3D Point Clouds

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第5期49卷 37-48页

作者： Remmach, Hassnae Razak, Siti Fatimah Abdul Ullah, Arif Yogarayan, Sumendra Sayeed, Md Shohel Mrhari, Amine Computer Systems Engineering Laboratory Cadi Ayyad University Marrakesh40000 Morocco Faculty of Information Science and Technology Multimedia University Ayer Keroh75450 Malaysia Research in Computer Science Laboratory Faculty of Sciences Ibn Tofail University Kenitra Morocco

In a number of industries, including computer graphics, robotics, and medical imaging, three-dimensional reconstruction is essential. In this research, a CNN-based Multi-output and Multi-Task Regressor with deep learning capabilities is proposed for three-dimensional object reconstruction from 3D point cloud. Our approach is grounded in the original Point Net architecture, which addresses the difficulties associated with convolution when applied to point clouds. Firstly, this paper is modified using a Multi-Output Regressor to accurately recreate Super forms from 3D point clouds. Using this method, we first extract features from the 3D point cloud using Point Net. After that, a Multi-Output Regressor receives these data and uses them to anticipate the Super shape parameters needed to reconstruct the shape. Taking in the data, the Multi-Output Regressor retrieved characteristics from Point Net and simultaneously predicts several outcomes. Second, a Multi-Task Regressor is used to modify the Point Net. The network gains from the capacity to transfer knowledge from one task to another, improving the model's overall performance. The model would forecast the ten parameters needed to create the shape in the case of rebuilding Super shapes. The test findings were better than expected;they are intriguing in terms of prediction accuracy and cost, and they update the result by 80%, which is a good accomplishment for the study. © 2025 Slovene Society Informatika. All rights reserved.

关键词： 3D reconstruction

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Computing Connected Resolvability of Graphs Using Binary Enhanced Harris Hawks Optimization

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Intelligent Automation & Soft Computing 2023年第5期36卷 2349-2361页

作者： Basma Mohamed Linda Mohaisen Mohamed Amin Mathematics and Computer Science Department Faculty of ScienceMenoufia UniversityShebin Elkom32511Egypt Faculty of Computer and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

In this paper,we consider the NP-hard problem ofﬁnding the minimum connected resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identiﬁed by its vector of distances to the ver-tices in B.A resolving set B of G is connected if the subgraph B induced by B is a nontrivial connected subgraph of *** cardinality of the minimal resolving set is the metric dimension of G and the cardinality of minimum connected resolving set is the connected metric dimension of *** problem is solved heuristically by a binary version of an enhanced Harris Hawk Optimization(BEHHO)*** is theﬁrst attempt to determine the connected resolving set *** combines classical HHO with opposition-based learning,chaotic local search and is equipped with an S-shaped transfer function to convert the contin-uous variable into a binary *** hawks of BEHHO are binary encoded and are used to represent which one of the vertices of a graph belongs to the connected resolving *** feasibility is enforced by repairing hawks such that an addi-tional node selected from V\B is added to B up to obtain the connected resolving *** proposed BEHHO algorithm is compared to binary Harris Hawk Optimi-zation(BHHO),binary opposition-based learning Harris Hawk Optimization(BOHHO),binary chaotic local search Harris Hawk Optimization(BCHHO)*** results conﬁrm the superiority of the BEHHO for determining connected metric dimension.

关键词： Connected resolving set binary optimization harris hawks algorithm

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Recent Trends in Compact Planar Antennas at 5G Sub-6 GHz and mm Wave Frequency Bands for Automotive Wireless Applications: A Review

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Progress In Electromagnetics Research C 2024年 143卷 169-180页

作者： Kumar, Ashish Aljaidi, Mohammad Singh, Manpreet Alshammari, Mohammed Alsuwaylimi, Amjad A. Alenezi, Sami M. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13110 Jordan Department of Computer Sciences Faculty of Computing and Information Technology Northern Border University Rafha91911 Saudi Arabia Department of Information Technology College of Computing and Information Technorlogy Northern Border University Arar91431 Saudi Arabia

5G wireless communication offers higher channel capacity, high data rate, sufficient bandwidth, enhanced coverage, and reliable link as compared to the previous generation mobile networks. Also, 5G becomes more relevant with the recent launches of low earth orbit satellites by various ventures like starlink, amazon, one web, etc. As these satellites have heights up to the range from 300 km to 1000 km, the free space path loss decreases drastically which in turn improves the signal reliability and efficient communication at dead spots. With these advancements, antenna researchers have the freedom to design compact, easy to manufacture, with adequate gain user equipment terminal antennas which can be easily integrated in the modern passenger car body. This paper will focus on the recent development in the design considerations of the compact antenna design at sub-6 GHz (n78 frequency band) and mm wave (n278 frequency band) according to 3gpp standards. This manuscript also discusses various design specifications like selection of material for antennas, design complexity, feeding methods, fabrication and measurement challenges and performance parameters which include reflection coefficient, gain, polarization, axial ratio, cross polarization discrimination, radiation efficiency, radiation pattern shape, etc. The proposed antenna design considerations will facilitate the possible integration into the various parts of the car body according to the recent vehicular applications for uninterrupted communication. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Radiation efficiency

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Optimization of Transmission Distance in Symmetric Wireless Power Transfer Topology 47

Optimization of Transmission Distance in Symmetric Wireless ...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Vinko, Davor Srnović, Mario Bilandžija, Domagoj Filipović, Luka Faculty Of Electrical Engineering Computer Science And Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350382495

Inductive wireless power transfer (WPT) system uses alternating magnetic field to transmit power from the transmitter to the receiver. To confine the magnetic field, WPT coils are realized with high permeability substrate material that ensures shielding. Aside from shielding, high permeability substrate also increases the coil inductance. When transmitter and receiver coils are placed in close proximity, high permeability substrate of transmitter coil increases the inductance of the receiver coil, and vice versa. This additional inductance increase is a function of the substrate material type and the distance between the coils. To achieve high efficiency and low losses of the WPT system, a capacitor network is added to both transmitter and receiver coil. In a common serial-serial (S-S) WPT system topology, the capacitor networks are asymmetric, i.e. the capacitor network of the transmitter and the receiver are not identical. The efficiency of asymmetric S-S topology is significantly impaired due to variable coil inductance. To attain high efficiency in such operating conditions, this paper proposes symmetric S-S WPT topology with emphasis on transmission distance optimization. © 2024 IEEE.

关键词： Inductive power transmission

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Comparison of Nine Deep Regressors in Continuous Blood Pressure Estimation Using Single-Channel Photoplethysmograms under the PulseDB

Comparison of Nine Deep Regressors in Continuous Blood Press...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Yamamoto, Takumi Kanoga, Suguru Sugiura, Yuta Tokyo Japan Keio University Faculty of Science and Technology Department of Information and Computer Science Kanagawa Japan

ISBN: (纸本)9798331531614

Blood pressure (BP) is a vital parameter in medical treatment and diagnosis, and as a non-invasive method to measure BP, some deep learning models have been proposed to estimate BP from photoplethysmograms (PPGs). However, the datasets and the method of dividing them into training and testing subsets are not uniform, making it difficult to compare them fairly. In this study, we compared the performance of nine deep learning models for estimating systolic and diastolic BP using PPGs. We used PulseDB, which has "calibration-based subset"and "calibration-free subset"as test subsets. The calibration-based subset has the same subject's data in the training subset, and the calibration-free subset does not have the same subject's data in the training subset. The results showed that ST-ResNet performed the best, and it is important to evaluate the models using both calibration-based and calibration-free subsets, and to prevent the overfitting by using the weight decay. © 2025 IEEE.

关键词： Blood pressure

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Robustified principal component analysis for feature selection in EEG signal classification

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2023年第5期7卷 46-54页

作者： Martin, R. Faculty of Computer Science & Information Technology Jazan University Saudi Arabia

Feature engineering is an important step in data analysis, especially for machine learning applications. A wide range of feature selection methods are being used in Electroencephalography (EEG) signal processing applications. Principal Component Analysis (PCA) is considered an ideal method for feature selection whenever high dimensional data is obtained, especially in signal processing applications. Following an examination of various EEG signal processing frameworks, PCA emerged as the winner in the battle to reduce dimensionality. Despite its widespread use, it has been found to be ineffective for EEG signal processing problems like epileptic seizure detection due to the nonlinear nature of the signal properties. Traditional methods for solving PCA are insufficient in this case, so suggest a novel technique. In this paper, PCA is explored with an EEG classification model. The proposed work demonstrates how PCA is robustified for an EEG signal processing scenario by applying kernel functions. Statistical features are extracted from EEG data after preprocessing by the Desecrate Wavelet Transform (DWT). Initially, the classical PCA algorithm is applied for feature selection by reducing the dimensionality. Later, the algorithm is robustified by applying a Gaussian kernel in a nonlinear, high-dimensional feature space. In an EEG classification of epileptic seizure detection, the adoption of robustified PCA outperforms conventional PCA in terms of accuracy © 2023, International Journal of Systematic Innovation. All rights reserved

关键词： Dimensionality Reduction Electroencephalography (EEG) Feature Engineering PCA Signal Processing

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A Conceptual Model e-Collaboration for Rural Tourism - Combining Soft System Methodology and UML 7

A Conceptual Model e-Collaboration for Rural Tourism - Combi...

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7th information Systems International Conference, ISICO 2023

作者： Purwaningsih, Mardiana Purwandari, Betty Hidayanto, Achmad Nizar Faculty of Computer Science Universitas Indonesia Indonesia Faculty of Information Technology Perbanas Institute Indonesia

Through the IT utilization, stakeholders in rural tourism can engage in e-collaboration to develop tourism products and services together. While existing literature has not extensively addressed the e-collaboration model specifically tailored for rural tourism, this research sheds light on the emerging role of IT and its value within this context. To effectively model the intricate and less-structured environment of e-collaboration in rural tourism, the SSM is deemed the most suitable approach. Nonetheless, one limitation of SSM is the absence of explicit references during the implementation stage, requiring an additional methodological approach to realize the e-collaboration conceptual model within a system. To overcome this limitation, the present study applies both SSM and UML an e-collaboration system for rural tourism. The findings of this research can serve as a valuable reference for the development of an e-collaboration tool. © 2023 The Authors. Published by Elsevier B.V.

关键词： information services

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