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Extended Forgery Detection Framework for COVID-19 Medical Data Using Convolutional Neural Network

Computers, Materials & Continua 2021年第9期68卷 3773-3787页

作者： Sajid Habib Gill Noor Ahmed Sheikh Samina Rajpar Zain ul Abidin N.Z.Jhanjhi Muneer Ahmad mirza abdur razzaq Sultan S.Alshamrani Yasir Malik Fehmi Jaafar Department of Computer Science Shah Abdul Latif UniversityKhairpur66020SindhPakistan National College of Business Administration&Economics Lahore54000Pakistan School of Computer Science and Engineering SCETaylor’s UniversityMalaysia Department of Information Systems Faculty of Computer Science&Information TechnologyUniversiti MalayaMalaysia Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Engineering&Computing SciencesNew York Institute of TechnologyVancouverCanada The Computer Research Institute of Montreal QuebecCanada

Medical data tampering has become one of the main challenges in the field of secure-aware medical data *** of normal patients’medical data to present them as COVID-19 patients is an illegitimate action that has been carried out in different ways ***,the integrity of these data can be *** detection is a method of detecting an anomaly in manipulated forged *** appropriate number of features are needed to identify an anomaly as either forged or non-forged data in order to find distortion or tampering in the original *** neural networks(CNNs)have contributed a major breakthrough in this type of *** has been much interest from both the clinicians and the AI community in the possibility of widespread usage of artificial neural networks for quick diagnosis using medical data for early COVID-19 patient *** purpose of this paper is to detect forgery in COVID-19 medical data by using CNN in the error level analysis(ELA)by verifying the noise pattern in the *** proposed improved ELA method is evaluated using a type of data splicing forgery and sigmoid and ReLU phenomenon *** proposed method is verified by manipulating COVID-19 data using different types of forgeries and then applying the proposed CNN model to the data to detect the data *** results show that the accuracy of the proposed CNN model on the test COVID-19 data is approximately 92%.

关键词： Data security data privacy medical-data forgery COVID-19 convolutional neural network machine learning deep learning

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Spatial and Temporal Analyses of Water Quality in the Dhrabi Watershed of Pakistan： Issues and Options

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Journal of Environmental Science and Engineering(A) 2012年第3期1卷 329-340页

作者： Muhammad Ashraf Theib Y. Oweis abdur razzaq Bashir Hussain Abdul Majid International Center for Agricultural Research in the Dry Areas （ICARDA） National Agricultural Research Centre lslamabad 44000 Pakistan International Center for Agricultural Research in the Dry Areas （ICARDA） Aleppo 15310 Syria Soil and Water Conservation Research Institute （SA WCRI） Chakwa148800 Pakistan

Monitoring water quality is important for maintaining a healthy watershed, but it is mostly ignored in watershed planning and management. In the Dhrabi watershed of Pakistan, the quality of surface water was monitored at 16 locations to assess suitability for irrigation over regular intervals during the period 2007-2010. Similarly, groundwater quality was monitored at 10 locations for drinking and irrigation purposes. There was high spatial and temporal variability in surface water quality. Electrical conductivity (EC) and residual sodium carbonate (RSC) either exceeded or fluctuated around permissible limits at most of the locations throughout the monitoring period. Therefore, the use of such water for irrigation needs special care, otherwise its prolonged use may pose soil salinity and sodicity problems. The trend of EC and RSC for groundwater was similar to that for surface water. Exchangeable Mg2+ exceeded permissible limits for most of the surface water and groundwater samples. In addition, microbial analysis of groundwater revealed that only two out of eight monitoring points during August 2009, none out of eight points during February 2010, and one out of nine points during June 2010 provided water fit for drinking. Soil samples were collected from the catchment areas of the major contributing streams and from the beds of the Kallar Kahar Lake and the Dhrabi Reservoir. The soil samples from the catchments showed high salinity and sodicity that may be the cause of high salinity and sodicity in the streams. The highest EC, sodium adsorption ratio (SAR) and exchangeable sodium percentage (ESP) in the bed samples from the Kallar Kahar Lake were about 43 dS/m, 56, and 45, respectively. These high values were due to the saline water brought into the lake with the runoff.

关键词：流域水质巴基斯坦时空分析土壤含盐量水库集水区选项水质监测环境质量监测

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Factors Affecting Treatment Interruption among TB Patients in Lagos Nigeria: Is There Any Need for Treatment Supporters?

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International Journal of Clinical Medicine 2016年第6期7卷 389-398页

作者： Olusola Adedeji Adejumo Olusoji James Daniel Andrew Folarin Otesanya Temitope Ashipa Esther Ngozi Adejumo Husseine A. abdur-razzaq Department of Community Health and Primary Health Care Lagos State University Teaching Hospital Ikeja Lagos Nigeria Department of Community Medicine and Primary Care Olabisi Onabanjo University Teaching Hospital Sagamu Ogun State Nigeria Medical Services Department Central Bank of Nigeria Lagos Nigeria Department of Community Medicine Babcock University Teaching Hospital Ilishan-Remo Ogun State Nigeria Department of Medical Laboratory Science Babcock University Ilisan-Remo Ogun State Nigeria Lagos State TB and Leprosy Control Programme Lagos State Ministry of Health Alausa Ikeja Lagos Nigeria

Background: This study assessed treatment interruption of tuberculosis (TB) patients managed by treatment supporters and health care workers and other predictors of treatment interruption. Methods: A descriptive cross-sectional study was conducted. Four hundred and seventy new smear positive TB patients above 14 years of age were consecutively recruited between October 1 and December 31 2012 from 34 (23 public and 11 private) directly observed treatment short course (DOTS) facilities that offered TB treatment and microscopy services. They were followed up till treatment was completed. Logistic regression was used to assess the predictors of treatment interruption. Results: A significantly higher proportion of smokers (58.6% vs 38.3%, p = 0.030), patients supervised by treatment supporters (44.4% vs 34.7%, p = 0.032), patients not counselled before initiation of treatment (55.6% vs 38.2%, p = 0.041), patients managed at private DOTS facilities (50% vs 36.3%, p = 0.010) and TB/HIV co-infected patients (54.2% vs 38.6%, p = 0.038) had treatment interruption. Predictors of treatment interruption were supervision by treatment supporters, smoking, lack of pre-treatment counselling and TB/HIV co-infection. Conclusion: A higher proportion of patients supervised by treatment supporters had treatment interruption than those supervised by health care workers. There may be a need to review the concept of treatment supervision by treatment supporters in Lagos state Nigeria.

关键词： Treatment Supporter Treatment Interruption Tuberculosis DOTS Nigeria

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