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Projecting the criticality of COVID-19 transmission in India using GIS and machine learning methods

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Journal of Safety Science and Resilience 2021年第2期2卷 50-62页

作者： farhan mohammad khan Akshay Kumar Harish Puppala Gaurav Kumar Rajiv Gupta Department of Civil Engineering BITS PilaniPilaniRajasthanIndia BML Munjal University GurugramIndia

There is a new public health catastrophe forbidding the *** the advent and spread of 2019 novel coro-navirus(2019-nCoV).Learning from the experiences of various countries and the World Health Organization(WHO)guidelines,social distancing,use of sanitizers,thermal screening,quarantining,and provision of lock-down in the cities being the effective measure that can contain the spread of the *** complete lockdown helps in containing the spread,it generates complexity by breaking the economic activity ***,laborers,farmers,and workers may lose their daily *** to these detrimental effects,the government has to open the lockdown *** of the COVID-19 spread and analyzing when the cases would stop increasing helps in developing a *** attempt is made in this paper to predict the time after which the number of new cases stops rising,considering the strong implementation of lockdown conditions using three different techniques such as Decision Tree,Support Vector Machine,and Gaussian Process Regression algorithm are used to project the number of ***,the projections are used in identifying inflection points,which would help in planning the easing of lockdown in a few of the areas *** criticality in a region is evaluated using the criticality index(CI),which is proposed by authors in one of the past of research *** research work is made available in a dashboard to enable the decision-makers to combat the pandemic.

关键词： COVID-19 Machine learning Transmission Lockdown Gaussian process regression Support vector machine Decision tree

Traffic Priority-Aware Medical Data Dissemination Scheme for IoT Based WBASN Healthcare Applications

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Computers, Materials & Continua 2022年第6期71卷 4443-4456页

作者： Muhammad Anwar farhan Masud Rizwan Aslam Butt Sevia Mahdaliza Idrus mohammad Nazir Ahmad Mohd Yazid Bajuri Department of Computer Science NFC Institute of Engineering and Fertilizer ResearchFaisalabadPakistan Department of Statistics and Computer Science Faculty of Life Sciences Business ManagementUniversity of Veterinary and Animal SciencesLahorePakistan Department of Electronics Engineering NED University of Engineering and TechnologyKarachiPakistan School of Electrical Engineering Universiti Teknologi MalaysiaJohor BahruMalaysia Institute of IR4.0 Universiti Kebangsaan MalaysiaBangiSelangorMalaysia Department of Orthopedics and Traumatology Faculty of MedicineUniversiti Kebangsaan MalaysiaKuala LumpurMalaysia

Wireless Body Area Sensor Network(WBASN)is an automated system for remote health monitoring of *** under umbrella of Internet of Things(IoT)is comprised of small Biomedical Sensor Nodes(BSNs)that can communicate with each other without human *** BSNs can be placed on human body or inside the skin of the patients to regularly monitor their vital *** BSNs generate critical data as it is related to patient’s *** data traffic can be classified as Sensitive Data(SD)and Non-sensitive Data(ND)packets based on the value of vital *** data packets have different priority to *** ND packets may tolerate some delay or packet loss whereas,the SD packets required to be delivered on time with minimized packet loss otherwise it can be life threating to the *** this research,we propose a Traffic Priority-aware Medical Data Dissemination(TPMD2)scheme forWBASN to deliver the data packets according to their priority based on the sensitivity of the *** assessment of the proposed scheme is carried out in various *** simulation results of the TPMD2 scheme indicate a significant improvement in packets delivery,transmission delay and energy efficiency in comparison with the existing schemes.

关键词： WBANS wearable technology priority routing eHealth applications remote health monitoring

Big Model Strategy for Bridge Structural Health Monitoring Based on Data-Driven, Adaptive Method and Convolutional Neural Network (CNN) Group

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Structural Durability & Health Monitoring 2024年第6期18卷 763-783页

作者： Yadong Xu Weixing Hong mohammad Noori Wael A.Altabey Ahmed Silik Nabeel S.D.farhan Nanjing Highway Development Center Changzhou211106China Nanjing Zhixing Information Technology Co. Ltd.Nanjing210000China Department of Mechanical Engineering California Polytechnic State UniversitySan Luis ObispoCA 93405USA Department of Mechanical Engineering Faculty of EngineeringAlexandria UniversityAlexandria21544Egypt Department of Civil Engineering Nyala UniversityP.O.Box 155NyalaSudan School of Civil Engineering University of LeedsLeedsLS29JTUK

This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element *** ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge *** CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency *** integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge *** diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter *** approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure.

关键词： Structural Health Monitoring(SHM) bridges big model Convolutional Neural Network(CNN) Finite Element Method(FEM)

Highly efficient flexible perovskite solar cells with vacuum-assisted low-temperature annealed SnO2 electron transport layer

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Journal of Energy Chemistry 2022年第4期31卷 1-7页

作者： Xiaoguo Li Zejiao Shi Fatemeh Behrouznejad mohammad Hatamvand Xin Zhang Yaxin Wang Fengcai Liu Haoliang Wang Kai Liu Hongliang Dong farhan Mudasar Jiao Wang Anran Yu Yiqiang Zhan Center for Micro Nano Systems School of Information Science and Technology(SIST)Fudan UniversityShanghai 200433China Academy for Engineering&Technology Fudan UniversityShanghai 200433China Center for High Pressure Science and Technology Advanced Research PudongShanghai 201203China

The demand for lightweight, flexible, and high-performance portable power sources urgently requires high-efficiency and stable flexible solar cells. In the case of perovskite solar cells(PSCs), most of the common electron transport layer(ETL) needs to be annealed for improving the optoelectronic properties,while conventional flexible substrates could barely stand the high temperature. Herein, a vacuumassisted annealing SnO_(2) ETL at low temperature(100℃) is utilized in flexible PSCs and achieved high efficiency of 20.14%. Meanwhile, the open-circuit voltage(V_(oc)) increases from 1.07 V to 1.14 V. The flexible PSCs also show robust bending stability with 86.8% of the initial efficiency is retained after 1000 bending cycles at a bending radius of 5 mm. X-ray photoelectron spectroscopy(XPS), atomic force microscopy(AFM), and contact angle measurements show that the density of oxygen vacancies, the surface roughness of the SnO_(2) layer, and film hydrophobicity are significantly increased, respectively. These improvements could be due to the oxygen-deficient environment in a vacuum chamber, and the rapid evaporation of solvents. The proposed vacuum-assisted low-temperature annealing method not only improves the efficiency of flexible PSCs but is also compatible and promising in the large-scale commercialization of flexible PSCs.

关键词： Flexible perovskite solar cells Vacuum-assisted Electron transport layer Trap-assisted recombination

Augmenting Internet of Medical Things Security:Deep Ensemble Integration and Methodological Fusion

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Computer Modeling in Engineering & Sciences 2024年第12期141卷 2185-2223页

作者： Hamad Naeem Amjad Alsirhani Faeiz MAlserhani farhan Ullah Ondrej Krejcar Faculty of Informatics and Management Center for Basic and Applied ResearchUniversity of Hradec KraloveHradec Kralove50003Czech Republic Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Cybersecurity Center Prince Mohammad Bin Fahd UniversityAl JawharahKhobarDhahran34754Saudi Arabia

When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network *** protect IoMT devices and networks in healthcare and medical settings,our proposed model serves as a powerful tool for monitoring IoMT *** study presents a robust methodology for intrusion detection in Internet of Medical Things(IoMT)environments,integrating data augmentation,feature selection,and ensemble learning to effectively handle IoMT data *** rigorous preprocessing,including feature extraction,correlation removal,and Recursive Feature Elimi-nation(RFE),selected features are standardized and reshaped for deep learning *** using the BAT algorithm enhances dataset *** deep learning models,Transformer-based neural networks,self-attention Deep Convolutional Neural Networks(DCNNs),and Long Short-Term Memory(LSTM)networks,are trained to capture diverse data *** predictions form a meta-feature set for a subsequent meta-learner,which combines model *** classifiers validate meta-learner features for broad algorithm *** comprehensive method demonstrates high accuracy and robustness in IoMT intrusion *** were conducted using two datasets:the publicly available WUSTL-EHMS-2020 dataset,which contains two distinct categories,and the CICIoMT2024 dataset,encompassing sixteen *** results showcase the method’s exceptional performance,achieving optimal scores of 100%on the WUSTL-EHMS-2020 dataset and 99%on the CICIoMT2024.

关键词： Cyberattack ensemble learning feature selection intrusion detection smart cities machine learning BAT augmentation

Can Fuzzy Logic Predict Consumer Ethnocentric Tendencies？ An Empirical Analysis in Jordan

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Journal of Physical Science and Application 2011年第2期1卷 100-106页

作者： Saeb farhan AI Ganideh Galeb A. El Refae mohammad Aljanaideh Department of Marketing AI Zaytoonah University of Jordan Amman Jordan Dean of Economics and Business School AI Zaytoonah University of Jordan Amman Jordan Department of Mechatronic University of Jordan Amman Jordan

This paper applies fuzzy logic approach to examine consumer ethnocentrism level for Jordanians based on their socio-psychological variables namely, patriotism, nationalism and internationalism. To model the relationships between three inputs namely, patriotism, nationalism and internationalism and one output ethnocentrism MATLAB ANFIS has been used, utilizing a sample of 341 Jordanians live in Amman city. Results indicate that fuzzy logic model can predict correctly consumers ethnocentric tendencies knowing their patriotic, nationalistic and internationalist feelings. Fuzzy logic approach shows that high level of nationalism leads consumers to be ethnocentric.

关键词： Fuzzy logic consumer ethnocentrism patriotism.

Long-Term Observation of Black Carbon and Its Relationship with Aerosol Radiative Forcing over South Asia

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Journal of Meteorological Research 2024年第6期 1105-1121页

作者： Nimashi P.M.CHATHURANGIKA Md.Mozammel HAQUE Matthews NYASULU Rehana khan Md.farhan AHMED Mosammat J.Fardus JUI Rokaiya FALGUNI mohammad Lokman HOSSAIN Abul Kalam M.Lutfor RAHMAN Atmospheric Environment Center Joint Laboratory for International Cooperation on Climate and Environmental Change Ministry of Education Nanjing University of Information Science & Technology Key Laboratory of Meteorological Disaster Ministry of Education (KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD) Nanjing University of Information Science & Technology School of Ecology and Applied Meteorology Nanjing University of Information Science & Technology Department of Physics Higher Education Government of Khyber Pakhtunkhwa Peshawar Department of Chemistry Jagannath University

Black carbon(BC) is one of the major aerosol components with relatively high implications on climatic patterns through its radiative forcing(RF). South Asia has recently experienced an increased concentration of pollution;however, relatively fewer studies have been carried out on long-term assessment of BC and its implications. The present study analyzed the long-term concentration of BC in selected urban locations over South Asia using the Modern-Era Retrospective analysis for Research and Applications, version 2(MERRA-2). The study employed statistical analysis, including linear regression techniques, to assess the long-term concentration of BC. The results show that a rapid increase of BC is observed over most urban locations of South Asia with the predominance in winter and hence requires strict regional control measures to reduce the excess concentration of BC in the atmosphere. High concentration of BC in winter is attributed to anthropogenic activities and changes in meteorological conditions that enhance the accumulation of pollutants in the atmosphere. The relationship of BC with cloud top temperature and cloud effective radius demonstrates the direct and indirect effect of BC on cloud properties in this region. The RF results reveal that aerosol optical depth has positive aerosol RF in the atmosphere and negative RF at the top of the atmosphere(TOA) as well as at the bottom of the atmosphere(BOA). Negative RF at the TOA indicates less forcing efficiency due to fewer BC aerosols. On the other hand, averaging aerosol RF within the atmosphere reveals positive forcing, which suggests the efficiency force exerted by BC aerosols after absorbing solar radiation.

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