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Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners:A Recommendation System

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Computers, Materials & Continua 2024年第4期79卷 351-372页

作者： Ameni ellouze Nesrine Kadri Alaa Alaerjan mohamed Ksantini Control and Energy Management Laboratory National Engineering SchoolPreparatory Institute for Engineering StudiesUniversity of SfaxSfax3029Tunisia Higher Institute of Computer Science and Communication Technologies University of SousseSousseBN19RHTunisia College of Computer and Information Sciences Jouf UniversityPO Box 2014SakakahSaudi Arabia

Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic *** and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or ***,smartphones and their associated sensing devices operate in distributed and unstable ***,collecting their data and extracting useful information is a significant *** this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical *** the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical *** system is based on the calculation of calories burned by each user during physical *** this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical *** identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional *** human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information *** Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal *** results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and *** overcome this constraint and improve performance(96%),we proposed to use the

关键词： Human physical activities smartphone sensors deep learning distributed monitoring recommendation system uncertainty healthy calories

Application of Remote Sensing and GIS in Mineral Alteration Mapping and Lineament Extraction Case of Oudiane elkharoub (Requibat Shield, Northern of Mauritania)

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Open Journal of Geology 2024年第9期14卷 823-854页

作者： Ould Mahmoud Hdeid Yousra Morsli mohamed Raji Zouhir Baroudi Malika Adjour Khaled Cheikh Nebagha Zein el Arby Vetah mohamed el Moktar Isselmou Brahim Vall Department of Geology Faculty of Sciences Ben Msik Hassan II University Casablanca Morocco Department of Geology Faculty of Sciences and Technology University of Nouakchott Al Asriya Nouakchott Mauritania National Agency for Geological Research and Mining Heritage Nouakchott Mauritania

The integration of remotely sensed data allowed the successful characterization of the mineral alteration zones of the Oudiane elkharoub area in the Northeastern part of Reguibat Shield using image transformation techniques. As both chemical and geochemical analyses showed significant Au, Ag, Cu, Pb, Mn, Cr, Ni, Th and Y anomalies, it’s very interesting to apply the remote sensing and GIS in mineral resources mapping. The remote sensing is a direct adjunct to the field, lithologic and structural mapping, and more recently, GIS has played an important role in the study of mineralization areas. The integration of several evidential maps highlighted the plausible areas with high concentrations of chlorite, epidote, kaolinite, calcite, alunite, hematite, illite and sulfur among other key mineral alterations that reflect the intensity of hydrothermal effects and the probable sites of ore bodies. The methodological approach integrates geological information acquired from Aster and Landsat 8 OLI/TIRS (Operational Land Imager/Thermal InfraRed Sensor) images and a multi-criteria GIS analysis. The superimposition of various lineament and hydrothermal alteration maps and the consideration of precious and base metal indicators allowed the zoning of sites likely to contain mineral concentrations. Remote sensing becomes an important tool for locating mineral deposits in its own right, when the primary and secondary processes of mineralization result in the formation of spectral anomalies. Reconnaissance lithological mapping is usually the first step of mineral resource mapping. This is complimented with structural mapping, as mineral deposits usually occur along or adjacent to geologic structures, and alteration mapping, as mineral deposits are commonly associated with hydrothermal alteration of the surrounding rocks. Ground truthing and laboratory studies including XRD analysis were utilized to verify the results.

关键词： Remote Sensing GIS ASTER Landsat 8 (OLI/TIRS) Structure Mineralization Alteration Mineral Lineament Reguibat Shield Oudiane elkharoub

Optimizing Grey Wolf Optimization: A Novel Agents’ Positions Updating Technique for Enhanced Efficiency and Performance

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Open Journal of Optimization 2024年第1期13卷 21-30页

作者： Mahmoud Khatab mohamed el-Gamel Ahmed I. Saleh Asmaa H. Rabie Atallah el-Shenawy Department of Mathematics and Engineering Physics Faculty of Engineering Mansoura University Mansoura Egypt Department of Computers and Control Faculty of Engineering Mansoura University Mansoura Egypt Department of Mathematics Faculty of Science New Mansoura University Mansoura Egypt

Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.

关键词： Grey Wolf Optimization (GWO) Metaheuristic Algorithm Optimization Problems Agents’ Positions Leader Wolves Optimal Fitness Values Optimization Challenges

Exploring impedance spectrum for lithium-ion batteries diagnosis and prognosis:A comprehensive review

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Journal of Energy Chemistry 2024年第8期95卷 464-483,I0010页

作者： Xinghao Du Jinhao Meng Yassine Amirat Fei Gao mohamed Benbouzid University of Brest UMR CNRS 6027 IRDL29238 BrestFrance School of electrical Engineering Xi'an Jiaotong UniversityXi'an 710049ShaanxiChina ISEN Yncréa Ouest L@bISEN29200 BrestFrance Universitéde Technolgie de Belfort-Montbéliard UMR CNRS 6174 FEMTO-ST90000 BelfortFrance

Lithium-ion batteries have extensive usage in various energy storage needs,owing to their notable benefits of high energy density and long *** monitoring of battery states and failure identification are indispensable for guaranteeing the secure and optimal functionality of the *** impedance spectrum has garnered growing interest due to its ability to provide a valuable understanding of material characteristics and electrochemical *** inspire further progress in the investigation and application of the battery impedance spectrum,this paper provides a comprehensive review of the determination and utilization of the impedance *** sources of impedance inaccuracies are systematically analyzed in terms of frequency response *** applicability of utilizing diverse impedance features for the diagnosis and prognosis of batteries is further ***,challenges and prospects for future research are discussed.

关键词： Lithium-ion battery Impedance spectrum Temperature monitoring Failure diagnosis Health prognosis

Correlation between DKK-1 Level and Bone Density Status in Children on Maintenance Haemodialysis

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Open Journal of Nephrology 2024年第1期14卷 48-61页

作者： Manal Abd el-Salam Marwa mohamed Anwar Rasha Mahmoud Gouda Naglaa Abou el Fattah Nadia Youssef Riad Department of Pediatrics Faculty of Medicine (For Girls) Al-Azhar University Cairo Egypt Department of Internal Medicine Faculty of Medicine (For Girls) Al-Azhar University Cairo Egypt Department of Clinical Pathology National Heart Institute Cairo Egypt

Background: Renal osteodystrophy (ROD) is a bone disorder resulting from chronic kidney disease (CKD) and related metabolic diseases. Dickkopf-related protein-1 (DKK-1) is critical in regulating bone biology. This study aimed to evaluate the serum DKK-1 level as a bone marker in children with CKD who undergo regular hemodialysis (HD). Subjects and Methods: This case-control study involved 40 children with CKD on HD and 40 healthy children as controls. The study measured serum DKK-1 levels and performed a dual-energy X-ray absorptiometry scan (DEXA) in line with routine laboratory investigations. Results: There was a significant increase in the serum level of DKK-1 in the patient group compared to the control group. The DKK-1 levels were 2540.65 (2215.4 - 2909.2) pg/ml and 1110.45 (885.45 - 1527.65) pg/ml, respectively, with a p-value of less than 0.001. In the hemodialysis group, 25 patients (62.5%) had low bone mineral density (BMD) with a Z-score of under -2.0. Eighteen of these patients had low BMD in both the neck of the femur and lumbar spines. Additionally, there was a significant increase in serum DKK-1 level in patients with low BMD (2567.35 (2303.8 - 3108.1) pg/ml) compared to patients with normal BMD (2454 (1859 - 2820) pg/ml) (p = 0.041). There was also a significant positive correlation between DKK1 level and phosphorus, alkaline phosphatase, and Parathormone serum levels. In conclusion, the study indicates a clear correlation between DKK-1 and BMD in children undergoing maintenance hemodialysis. DKK1 is a promising biomarker for CKD-MBD.

关键词： DKK-1 Children Haemodialysis Bone Density Status

A reinforcement learning approach for thermostat setpoint preference learning

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Building Simulation 2024年第1期17卷 131-146页

作者： Hussein elehwany mohamed Ouf Burak Gunay Nunzio Cotrufo Jean-Simon Venne Department of Environmental Engineering Carleton UniversityOttawaCanada Departmentof Building Civiland Environmental EngineeringConcordia UniversityMontrealCanada BrainBox Al Inc. MontrealCanada

Occupant-centric controls(OcC)is an indoor climate control approach whereby occupant feedback is used in the sequence of operation of building energy *** OcC has been used in a wide range of building applications,an OcC category that has received considerable research interest is learning occupants'thermal preferences through their thermostat interactions and adapting temperature setpoints *** recent studies used reinforcement learning(RL)as an agent for OcC to optimize energy use and occupant *** studies depended on predicted mean vote(PMV)models or constant comfort ranges to represent comfort,while only few of them used thermostat *** paper addresses this gap by introducing a new off-policy reinforcement learning(RL)algorithm that imitates the occupant behaviour by utilizing unsolicited occupant thermostat *** algorithm is tested with a number of synthetically generated occupant behaviour models implemented via the Python APl of *** simulation results indicate that the RL algorithm could rapidly learn preferences for all tested occupant behaviour scenarios with minimal exploration *** substantial energy savings were observed with most occupant scenarios,the impact on the energy savings varied depending on occupants'preferences and thermostat use behaviour stochasticity.

关键词： reinforcement learning preference learning occupant-centric controls smart thermostats off-policy learning

Development and validation of a novel questionnaire regarding vision screening among preschool teachers in Malaysia

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International Journal of Ophthalmology(English edition) 2024年第6期17卷 1102-1109页

作者： Shazrina Ariffin Saadah mohamed Akhir Sumithira Narayanasamy Faculty of Health Sciences Centre for Community Health Studies(ReaCH)Universiti Kebangsaan MalaysiaKuala Lumpur 50300Malaysia

AIM:To develop and evaluate the validity and reliability of a knowledge,attitude,and practice questionnaire related to vision screening(KAP-VST)among preschool teachers in ***:The questionnaire was developed through a literature review and discussions with *** and face validation were conducted by a panel of experts(n=10)and preschool teachers(n=10),respectively.A pilot study was conducted for construct validation(n=161)and test-retest reliability(n=60)of the newly developed ***:Based on the content and face validation,71 items were generated,and 68 items were selected after exploratory factor *** content validity index for items(I-CVI)score ranged from 0.8-1.0,and the content validity index for scale(S-CVI)/Ave was *** consistency was KR^(2)0=0.93 for knowledge,Cronbach’s alpha=0.758 for attitude,and Cronbach’s alpha=0.856 for ***:The KAP-VST is a valid and reliable instrument for assessing knowledge,attitude,and practice in relation to vision screening among preschool teachers in Malaysia.

关键词： validity reliability preschool teachers vision screening questionnaire

Numerical Modeling and Technico-Economic Analysis of a Hybrid Energy Production System for Self-Consumption: Case of Rural Area in the Comoros

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Journal of Power and Energy Engineering 2024年第5期12卷 24-59页

作者： Fahad Maoulida mohamed Aboudou Kassim Rabah Djedjig Ahmed Ihlal Mohammed el Ganaoui LERMAB Laboratory of Studies and Research on Wood Material IUT of Longwy University of Lorraine Cosnes-et-Romain Longwy France LEMA Energy and Applied Mechanics Laboratory Faculty of Sciences and Techniques University of Comoros Moroni Comoros IREMIS Institute University of Mayotte Mayotte France LMER Laboratory of Materials and Renewable Faculty of Sciences University Ibn Zohr BP Agadir Morocco

This study aims to provide electricity to a remote village in the Union of Comoros that has been affected by energy problems for over 40 years. The study uses a 50 kW diesel generator, a 10 kW wind turbine, 1500 kW photovoltaic solar panels, a converter, and storage batteries as the proposed sources. The main objective of this study is to conduct a detailed analysis and optimization of a hybrid diesel and renewable energy system to meet the electricity demand of a remote area village of 800 to 1500 inhabitants located in the north of Ngazidja Island in Comoros. The study uses the Hybrid Optimization Model for electric Renewable (HOMER) Pro to conduct simulations and optimize the analysis using meteorological data from Comoros. The results show that hybrid combination is more profitable in terms of margin on economic cost with a less expensive investment. With a diesel cost of $1/L, an average wind speed of 5.09 m/s and a solar irradiation value of 6.14 kWh/m2/day, the system works well with a proportion of renewable energy production of 99.44% with an emission quantity of 1311.407 kg/year. 99.2% of the production comes from renewable sources with an estimated energy surplus of 2,125,344 kWh/year with the cost of electricity (COE) estimated at $0.18/kWh, presenting a cost-effective alternative compared to current market rates. These results present better optimization of the used hybrid energy system, satisfying energy demand and reducing the environmental impact.

关键词： Hybrid System Rural Area electrification Comoros Techno-Economic Analysis PV-Wind-Diesel-Battery Meteorological Data HOMER Energy Pro

Particle Swarm Optimization-Based Hyperparameters Tuning of Machine Learning Models for Big COVID-19 Data Analysis

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Journal of Computer and Communications 2024年第3期12卷 160-183页

作者： Hend S. Salem mohamed A. Mead Ghada S. el-Taweel Department of Computer Sciences Suez Canal University Ismailia Egypt

Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. Machine learning (ML) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the Particle Swarm Optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random Forests, Decision Trees, Support Vector Machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results.

关键词： Big COVID-19 Data Machine Learning Hyperparameter Optimization Particle Swarm Optimization Computational Intelligence