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| 1 | More anxious than depressed:prevalence and correlates in a 15-nation study of anxiety disorders in people with type 2 diabetes mellitus显示文摘Background Anxiety disorder, one of the highly disabling, prevalent and common mental disorders, is known to be more prevalent in persons with type 2 diabetes mellitus (T2DM) than the general population, and the comorbid presence of anxiety disorders is known to have an impact on the diabetes outcome and the quality of life. However, the information on the type of anxiety disorder and its prevalence in persons with T2DM is limited. Aims To assess the prevalence and correlates of anxiety disorder in people with type 2 diabetes in different countries. Methods People aged 18-65 years with diabetes and treated in outpatient settings were recruited in 15 countries and underwent a psychiatric interview with the Mini-International Neuropsychiatric Interview. Demographic and medical record data were collected. Results A total of 3170 people with type 2 diabetes (56.2% women;with mean (SD) duration of diabetes 10.01 (7.0) years) participated. The overall prevalence of anxiety disorders in type 2 diabetic persons was 18%;however, 2.8% of the study population had more than one type of anxiety disorder. The most prevalent anxiety disorders were generalised anxiety disorder (8.1%) and panic disorder (5.1%). Female gender, presence of diabetic complications, longer duration of diabetes and poorer glycaemic control (HbA1c levels) were significantly associated with comorbid anxiety disorder. A higher prevalence of anxiety disorders was observed in Ukraine, Saudi Arabia and Argentina with a lower prevalence in Bangladesh and India. Conclusions Our international study shows that people with type 2 diabetes have a high prevalence of anxiety disorders, especially women, those with diabetic complications, those with a longer duration of diabetes and poorer glycaemic control. Early identification and appropriate timely care of psychiatric problems of people with type 2 diabetes is warranted. | Santosh K Chaturvedi Shayanth Manche Gowda Helal Uddin Ahmed Fahad D Alosaimi Nicola Andreone Alexey Bobrov Viola Bulgari Giuseppe Carra Gianluca Castelnuovo Giovanni de Girolamo Tomasz Gondek Nikola Jovanovic Thummala Kamala Andrzej Ki Nebojsa Lalic Dusica Lecic-Tosevski Fareed Minhas Victoria Mutiso David Ndetei Golam Rabbani Suntibenchakul Somruk Sathyanarayana Srikanta Rizwan Taj Umberto Valentini Olivera Vukovic Wolfgang Wolwer Larry Cimino Arie Nouwen Cathy Lloyd Norman Sartorius | 2019 | General Psychiatry2019，32，4： | 11 |
| 2 | An Intelligent Fine-Tuned Forecasting Technique for Covid-19 Prediction Using Neuralprophet Model显示文摘COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory disorders.It is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different countries in the year 2012 and 2002,respectively.Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty.The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution,and Random Forest Model.The analysis upon dataset has been performed considering the time duration from January 1st 2020 to16th July 2021.The model has been developed to obtain the forecast values till September 2021.This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country.In India,the cases are rapidly increasing day-by-day since mid of Feb 2021.The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave.To empower the prediction for future validation,the proposed model works effectively. | Savita Khurana Gaurav Sharma Neha Miglani Aman Singh Abdullah Alharbi Wael Alosaimi Hashem Alyami Nitin Goyal | 2022 | Computers, Materials & Continua2022，，4： | 2 |
| 3 | A Review of Sleep Disturbance in Hepatitis C显示文摘 | Sanjeev Sockalingam Susan E. Abbey Fahad Alosaimi Marta Novak | 2010 | Journal of Clinical Gastroenterology2010，，1： | 1 |
| 4 | Theoretical and Experimen-tal Investigation on the Application of Solar Water HeaterCoupled With Air Humidifier for Regeneration of LiquidDesiccant 显示文摘 | Alosaimy A S Hamed A M | 2011 | Energy2011，36，7： | 1 |
| 5 | A prospective study of maternal preference for indomethacin prophylaxis versus symptomatic treatment of a patent ductus arteriosus in preterm infants显示文摘 | AlFaleh K Alluwaimi E AlOsaimi A | 2015 | 15:472015，15，： | 1 |
| 6 | help-seeking behavior with depression and an among gastroenterological patients in Saudi Ara Association of xiety disorders bia显示文摘 | Alosaimi FD Al-Sultan O Alghamdi Q | 2014 | Saudi J Gastroenterol2014，20，4： | 1 |
| 7 | Theoretical and Experimental Investigation on the Application of Solar Water Heater Coupled with Air Humidifier for Regeneration of Liquid Desiccant显示文摘 | Alosaimy A S Hamed Ahmed M | 2011 | Energy2011，36，： | 1 |
| 8 | Integration of local and global geometrical cues for 3D face recognition显示文摘 | AlOsaimi F R Bennamoun M and Mian A | 2008 | Pattern Recognition2008，41，3： | 1 |
| 9 | Clinical review of treatment options for ma- jor depressive disorder in patients with coronary heart disease显示文摘 | Alosaimi FD Baker B | 2012 | Saudi Med J2012，33，11： | 1 |
| 10 | Clinical review of treat-ment options for major depressive disorder in patientswith coronary heart disease显示文摘 | ALOSAIMI F D BAKER B | 2012 | Saudi Med J2012，33，： | 1 |
| 11 | Impact of Tools and Techniques for Securing Consultancy Services显示文摘In a digital world moving at a breakneck speed,consultancy services have emerged as one of the prominent resources for seeking effective,sustainable and economically viable solutions to a given crisis.The present day consultancy services are aided by the use of multiple tools and techniques.However,ensuring the security of these tools and techniques is an important concern for the consultants because even a slight malfunction of any tool could alter the results drastically.Consultants usually tackle these functions after establishing the clients’needs and developing the appropriate strategy.Nevertheless,most of the consultants tend to focus more on the intended outcomes only and often ignore the security-specific issues.Our research study is an initiative to recommend the use of a hybrid computational technique based on fuzzy Analytical Hierarchy Process(AHP)and fuzzy Technique for Order Preference by Similarity to Ideal Solutions(TOPSIS)for prioritizing the tools and techniques that are used in consultancy services on the basis of their security features and efficacy.The empirical analysis conducted in this context shows that after implementing the assessment process,the rank of the tools and techniques obtained is:A7>A1>A4>A2>A3>A5>A6>A7,and General Electric McKinsey(GE-McKinsey)Nine-box Matrix(A7)obtained the highest rank.Thus,the outcomes show that this order of selection of the tools and techniques will give the most effective and secure services.The awareness about using the best tools and techniques in consultancy services is as important as selecting the most secure tool for solving a given problem.In this league,the results obtained in this study would be a conclusive and a reliable reference for the consultants. | Wael Alosaimi Abdullah Alharbi Hashem Alyami Masood Ahmad Abhishek Kumar Pandey Rajeev Kumar Raees Ahmad Khan | 2021 | Computer Systems Science & Engineering2021，37，6： | 0 |
| 12 | An Ensemble Approach to Identify Firearm Listing on Tor Hidden-Services显示文摘The ubiquitous nature of the internet has made it easier for criminals to carry out illegal activities online.The sale of illegal firearms and weaponry on dark web cryptomarkets is one such example of it.To aid the law enforcement agencies in curbing the illicit trade of firearms on cryptomarkets,this paper has proposed an automated technique employing ensemble machine learning models to detect the firearms listings on cryptomarkets.In this work,we have used partof-speech(PoS)tagged features in conjunction with n-gram models to construct the feature set for the ensemble model.We studied the effectiveness of the proposed features in the performance of the classification model and the relative change in the dimensionality of the feature set.The experiments and evaluations are performed on the data belonging to the three popular cryptomarkets on the Tor dark web from a publicly available dataset.The prediction of the classification model can be utilized to identify the key vendors in the ecosystem of the illegal trade of firearms.This information can then be used by law enforcement agencies to bust firearm trafficking on the dark web. | Hashem Alyami Mohd Faizan Wael Alosaimi Abdullah Alharbi Abhishek Kumar Pandey Md Tarique Jamal Ansari Alka Agrawal Raees Ahmad Khan | 2021 | Computer Systems Science & Engineering2021，38，8： | 0 |
| 13 | An Intelligent Forecasting Model for Disease Prediction Using Stack Ensembling Approach显示文摘This research work proposes a new stack-based generalization ensemble model to forecast the number of incidences of conjunctivitis disease.In addition to forecasting the occurrences of conjunctivitis incidences,the proposed model also improves performance by using the ensemble model.Weekly rate of acute Conjunctivitis per 1000 for Hong Kong is collected for the duration of the first week of January 2010 to the last week of December 2019.Pre-processing techniques such as imputation of missing values and logarithmic transformation are applied to pre-process the data sets.A stacked generalization ensemble model based on Auto-ARIMA(Autoregressive Integrated Moving Average),NNAR(Neural Network Autoregression),ETS(Exponential Smoothing),HW(Holt Winter)is proposed and applied on the dataset.Predictive analysis is conducted on the collected dataset of conjunctivitis disease,and further compared for different performance measures.The result shows that the RMSE(Root Mean Square Error),MAE(Mean Absolute Error),MAPE(Mean Absolute Percentage Error),ACF1(Auto Correlation Function)of the proposed ensemble is decreased significantly.Considering the RMSE,for instance,error values are reduced by 39.23%,9.13%,20.42%,and 17.13%in comparison to Auto-ARIMA,NAR,ETS,and HW model respectively.This research concludes that the accuracy of the forecasting of diseases can be significantly increased by applying the proposed stack generalization ensemble model as it minimizes the prediction error and hence provides better prediction trends as compared to Auto-ARIMA,NAR,ETS,and HW model applied discretely. | Shobhit Verma Nonita Sharma Aman Singh Abdullah Alharbi Wael Alosaimi Hashem Alyami Deepali Gupta Nitin Goyal | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 14 | Efficient Data Augmentation Techniques for Improved Classification in Limited Data Set of Oral Squamous Cell Carcinoma显示文摘Deep Learning(DL)techniques as a subfield of data science are getting overwhelming attention mainly because of their ability to understand the underlying pattern of data in making classifications.These techniques require a considerable amount of data to efficiently train the DL models.Generally,when the data size is larger,the DL models perform better.However,it is not possible to have a considerable amount of data in different domains such as healthcare.In healthcare,it is impossible to have a substantial amount of data to solve medical problems using Artificial Intelligence,mainly due to ethical issues and the privacy of patients.To solve this problem of small dataset,different techniques of data augmentation are used that can increase the size of the training set.However,these techniques only change the shape of the image and hence the classification model does not increase accuracy.Generative Adversarial Networks(GANs)are very powerful techniques to augment training data as new samples are created.This technique helps the classification models to increase their accuracy.In this paper,we have investigated augmentation techniques in healthcare image classification.The objective of this research paper is to develop a novel augmentation technique that can increase the size of the training set,to enable deep learning techniques to achieve higher accuracy.We have compared the performance of the image classifiers using the standard augmentation technique and GANs.Our results demonstrate that GANs increase the training data,and eventually,the classifier achieves an accuracy of 90%compared to standard data augmentation techniques,which achieve an accuracy of up to 70%.Other advanced CNN models are also tested and have demonstrated that more deep architectures can achieve more than 98%accuracy for making classification on Oral Squamous Cell Carcinoma. | Wael Alosaimi M.Irfan Uddin | 2022 | Computer Modeling in Engineering & Sciences2022，，6： | 0 |
| 15 | A Bio-Inspired Routing Optimization in UAV-enabled Internet of Everything显示文摘Internet of Everything(IoE)indicates a fantastic vision of the future,where everything is connected to the internet,providing intelligent services and facilitating decision making.IoE is the collection of static and moving objects able to coordinate and communicate with each other.The moving objects may consist of ground segments and ying segments.The speed of ying segment e.g.,Unmanned Ariel Vehicles(UAVs)may high as compared to ground segment objects.The topology changes occur very frequently due to high speed nature of objects in UAV-enabled IoE(Ue-IoE).The routing maintenance overhead may increase when scaling the Ue-IoE(number of objects increases).A single change in topology can force all the objects of the Ue-IoE to update their routing tables.Similarly,the frequent updating in routing table entries will result more energy dissipation and the lifetime of the Ue-IoE may decrease.The objects consume more energy on routing computations.To prevent the frequent updation of routing tables associated with each object,the computation of routes from source to destination may be limited to optimum number of objects in the Ue-IoE.In this article,we propose a routing scheme in which the responsibility of route computation(from neighbor objects to destination)is assigned to some IoE-objects in the Ue-IoE.The route computation objects(RCO)are selected on the basis of certain parameters like remaining energy and mobility.The RCO send the routing information of destination objects to their neighbors once they want to communicate with other objects.The proposed protocol is simulated and the results show that it outperform state-of-the-art protocols in terms of average energy consumption,messages overhead,throughput,delay etc. | Masood Ahmad Fasee Ullah Ishtiaq Wahid Atif Khan M.Irfan Uddin Abdullah Alharbi Wael Alosaimi | 2021 | Computers, Materials & Continua2021，，4： | 0 |
| 16 | Evaluating the Impacts of Security-Durability Characteristic:Data Science Perspective显示文摘Since the beginning of web applications,security has been a critical study area.There has been a lot of research done to figure out how to define and identify security goals or issues.However,high-security web apps have been found to be less durable in recent years;thus reducing their business continuity.High security features of a web application are worthless unless they provide effective services to the user and meet the standards of commercial viability.Hence,there is a necessity to link in the gap between durability and security of the web application.Indeed,security mechanisms must be used to enhance durability as well as the security of the web application.Although durability and security are not related directly,some of their factors influence each other indirectly.Characteristics play an important role in reducing the void between durability and security.In this respect,the present study identifies key characteristics of security and durability that affect each other indirectly and directly,including confidentiality,integrity availability,human trust and trustworthiness.The importance of all the attributes in terms of their weight is essential for their influence on the whole security during the development procedure of web application.To estimate the efficacy of present study,authors employed the Hesitant Fuzzy Analytic Hierarchy Process(H-Fuzzy AHP).The outcomes of our investigations and conclusions will be a useful reference for the web application developers in achieving a more secure and durable web application. | Abdullah Alharbi Masood Ahmad Wael Alosaimi Hashem Alyami Alka Agrawal Rajeev Kumar Abdul Wahid Raees Ahmad Khan | 2022 | Computer Systems Science & Engineering2022，41，5： | 0 |
| 17 | Quality of Service Aware Cluster Routing in Vehicular Ad Hoc Networks显示文摘In vehicular ad hoc networks(VANETs),the topology information(TI)is updated frequently due to vehicle mobility.These frequent changes in topology increase the topology maintenance overhead.To reduce the control message overhead,cluster-based routing schemes are proposed.In clusterbased routing schemes,the nodes are divided into different virtual groups,and each group(logical node)is considered a cluster.The topology changes are accommodated within each cluster,and broadcasting TI to the whole VANET is not required.The cluster head(CH)is responsible for managing the communication of a node with other nodes outside the cluster.However,transmitting real-time data via a CH may cause delays in VANETs.Such real-time data require quick service and should be routed through the shortest path when the quality of service(QoS)is required.This paper proposes a hybrid scheme which transmits time-critical data through the QoS shortest path and normal data through CHs.In this way,the real-time data are delivered efciently to the destination on time.Similarly,the routine data are transmitted through CHs to reduce the topology maintenance overhead.The work is validated through a series of simulations,and results show that the proposed scheme outperforms existing algorithms in terms of topology maintenance overhead,QoS and real-time and routine packet transmission. | Ishtiaq Wahid Fasee Ullah Masood Ahmad Atif Khan M.Irfan Uddin Abdullah Alharbi Wael Alosaimi | 2021 | Computers, Materials & Continua2021，，6： | 0 |
| 18 | MCBC-SMOTE:A Majority Clustering Model for Classification of Imbalanced Data显示文摘Datasets with the imbalanced class distribution are difficult to handle with the standard classification algorithms.In supervised learning,dealing with the problem of class imbalance is still considered to be a challenging research problem.Various machine learning techniques are designed to operate on balanced datasets;therefore,the state of the art,different undersampling,over-sampling and hybrid strategies have been proposed to deal with the problem of imbalanced datasets,but highly skewed datasets still pose the problem of generalization and noise generation during resampling.To overcome these problems,this paper proposes amajority clusteringmodel for classification of imbalanced datasets known as MCBC-SMOTE(Majority Clustering for balanced Classification-SMOTE).The model provides a method to convert the problem of binary classification into a multi-class problem.In the proposed algorithm,the number of clusters for themajority class is calculated using the elbow method and the minority class is over-sampled as an average of clustered majority classes to generate a symmetrical class distribution.The proposed technique is cost-effective,reduces the problem of noise generation and successfully disables the imbalances present in between and within classes.The results of the evaluations on diverse real datasets proved to provide better classification results as compared to state of the art existing methodologies based on several performance metrics. | Jyoti Arora Meena Tushir Keshav Sharma Lalit Mohan Aman Singh Abdullah Alharbi Wael Alosaimi | 2022 | Computers, Materials & Continua2022，，12： | 0 |
| 19 | Burden of routine orthopedic implant removal a single center retrospective study显示文摘BACKGROUND Open reduction and internal fixation represent prevalent orthopedic procedures,sparking ongoing discourse over whether to retain or remove asymptomatic implants.Achieving consensus on this matter is paramount for orthopedic surgeons.This study aims to quantify the impact of routine implant removal on patients and healthcare facilities.A retrospective analysis of implant removal cases from 2016 to 2022 at King Fahad Hospital of the University(KFHU)was conducted and subjected to statistical scrutiny.Among these cases,44%necessitated hospitalization exceeding one day,while 56%required only a single day.Adults exhibited a 55%need for extended hospital stays,contrasting with 22.8%among the pediatric cohort.The complication rate was 6%,with all patients experiencing at least one complication.Notably,34.1%required sick leave and 4.8%exceeded 14 d.General anesthesia was predominant(88%).Routine implant removal introduces unwarranted complications,particularly in adults,potentially prolonging hospitalization.This procedure strains hospital resources,tying up the operating room that could otherwise accommodate critical surgeries.Clearly defined institutional guidelines are imperative to regulate this practice.AIM To measure the burden of routine implant removal on the patients and hospital.METHODS This is a retrospective analysis study of 167 routine implant removal cases treated at KFHU,a tertiary hospital in Saudi Arabia.Data were collected in the orthopedic department at KFHU from February 2016 to August 2022,which includes routine asymptomatic implant removal cases across all age categories.Nonroutine indications such as infection,pain,implant failure,malunion,nonunion,restricted range of motion,and prominent hardware were excluded.Patients who had external fixators removed or joints replaced were also excluded.RESULTS Between February 2016 and August 2022,360 implants were retrieved;however,only 167 of those who met the inclusion criteria were included in this study.The remaining implants were rejected due to exclusion criteria.Among the cases,44%required more than one day in the hospital,whereas 56%required only one day.55%of adults required more than one day of hospitalization,while 22.8%of pediatric patients required more than one day of inpatient care.The complication rate was 6%,with each patient experiencing at least one complication.Sick leave was required in 34.1%of cases,with 4.8%requiring more than 14 d.The most common type of anesthesia used in the surgeries was general anesthesia(88%),and the mean(SD)surgery duration was 77.1(54.7)min.CONCLUSION Routine implant removal causes unnecessary complications,prolongs hospital stays,depletes resources and monopolizing operating rooms that could serve more critical procedures. | Ammar K AlOmran Nader Alosaimi Ahmed A Alshaikhi Omar M Bakhurji Khalid J Alzahrani Basil Ziyad Salloot Tamim Omar Alabduladhem Ahmed I AlMulhim Arwa Alumran | 2024 | World Journal of Orthopedics2024，15，2： | 0 |
| 20 | Automatic Surveillance of Pandemics Using Big Data and Text Mining显示文摘COVID-19 disease is spreading exponentially due to the rapid transmission of the virus between humans.Different countries have tried different solutions to control the spread of the disease,including lockdowns of countries or cities,quarantines,isolation,sanitization,and masks.Patients with symptoms of COVID-19 are tested using medical testing kits;these tests must be conducted by healthcare professionals.However,the testing process is expensive and time-consuming.There is no surveillance system that can be used as surveillance framework to identify regions of infected individuals and determine the rate of spread so that precautions can be taken.This paper introduces a novel technique based on deep learning(DL)that can be used as a surveillance system to identify infected individuals by analyzing tweets related to COVID-19.The system is used only for surveillance purposes to identify regions where the spread of COVID-19 is high;clinical tests should then be used to test and identify infected individuals.The system proposed here uses recurrent neural networks(RNN)and word-embedding techniques to analyze tweets and determine whether a tweet provides information about COVID-19 or refers to individuals who have been infected with the virus.The results demonstrate that RNN can conduct this analysis more accurately than other machine learning(ML)algorithms. | Abdullah Alharbi Wael Alosaimi MIrfan Uddin | 2021 | Computers, Materials & Continua2021，，7： | 0 |
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