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Personalization of wellness recommendations using contextual interpretation
Afzal, Muhammad,Ali, Syed Imran,Ali, Rahman,Hussain, Maqbool,Ali, Taqdir,Khan, Wajahat Ali,Amin, Muhammad Bilal,Kang, Byeong Ho,Lee, Sungyoung Elsevier 2018 expert systems with applications Vol.96 No.-
<P><B>Abstract</B></P> <P>A huge array of personalized healthcare and wellness systems are introduced into the portfolio of digital health and quantified-self movement in recent years. These systems share common capabilities including self-tracking/monitoring and self-quantifications, based on the raw sensory data. These capabilities provide solid ground for the users to be more aware of their health; however, such measures are inefficient for changing the unhealthy habits of the users. In order to induce healthy habits in the users, a system must be capable of generating context-aware personalized recommendations. The main obstacle in this regard is the contextual interpretation of recommendations based on user's current context and contextual preferences. To resolve these issues, we propose a methodology of cross-context interpretation of recommendations (CCIR) for personalized health and wellness services. The CCIR method adds additional capabilities to the traditional reasoning methods and builds advanced form of the reasoning with the incorporation of contextual factors in the process of interpretations of the recommendations. With CCIR, the self-quantification systems can be enhanced to generate personalized recommendations in addition to tracking, quantifying, and monitoring user activities. In order to validate the proposed CCIR methodology, a set of 40 contextual scenarios and corresponding recommendations are presented for the evaluation collected from 40 different end users and 10 domain experts. Using chi-square test evaluation, the results demonstrated acceptable “goodness of fit” indices for the system developed on proposed CCIR methodology with respect to the end users’ opinion. Also from the statistical observation, it is found that there exists a higher level agreement towards the system between the participants of both end users and experts.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A method for cross-context interpretations of health and wellness recommendations. </LI> <LI> A mechanism of refining generalized recommendations to personalized recommendations. </LI> <LI> The contextual interpretations are made for increasing the user acceptability of a system. </LI> </UL> </P>
Comparative analysis of switched inductor-based quasi-Z-source inverters
Afzal, Raheel,Tang, Yu,Song, Yinghao The Korean Institute of Power Electronics 2022 JOURNAL OF POWER ELECTRONICS Vol.22 No.10
A switched-inductor quasi-Z-source inverter (SI-qZSI) exhibits a higher gain than a quasi-Z-source inverter (qZSI) while keeping continuous input current. Like renewable power generation systems that result in low input voltage, SI-qZSI exhibits boosting ability that may not be adequate in some cases. A voltage-lifting unit can be formed by alternating one of the diodes in the switch inductor unit. A high step-up topology, such as qZSI with voltage-lifting unit (qZSI-VL), can be derived. The boosting factor of qZSI-VL can be enhanced further by retaining all the merits of the SI-qZSI. The overall conduction loss can be reduced under the same output and input voltage for the qZSI-VL, thereby enhancing efciency. In this study, the analysis of the qZSI-VL and its operating principle are explained. The characteristic comparison of qZSI-VL with the SI-qZSI and qZSI is discussed in detail. Finally, the prototypes of qZSI-VL and SI-qZSI are designed in the lab. Simulation and experiments are performed to verify the analysis.
Afzal, M.,Zahid, Saleem Asian Australasian Association of Animal Productio 2004 Animal Bioscience Vol.17 No.7
Effects of addition of a mycotoxin detoxifier in poultry feed were studied in broilers. Aflatoxins were present in the poultry feed as 28 ppb (normal feed), 78 ppb (contaminated feed) and 170 ppb (highly contaminated feed). The mycotoxin detoxifier was used in 3 concentrations i.e. 1, 3 and 5 kg/ton of feed. Aflatoxins reduced the body weight in broiler chicken and treatment of contaminated feed with low level of detoxifier improved the body weight equivalent to that of normal feed. Higher level of detoxifier proved better than lower level addition in alleviating the effects of highly contaminated feed. Addition of detoxifier also resulted in improvement of FCR to the level of normal feed. Antibody levels against Newcastle disease virus on day 28 of age were significantly lower in chicken fed on contaminated feed. Addition of detoxifier in feed improved the antibody levels in chicken. Mortality was highest in groups given contaminated feed throughout the study period of 7 weeks. Significant mortality was also observed in groups given highly contaminated feed for 2 weeks. Mortality in chicken given detoxifier added contaminated feed was lowest and similar to the group given normal feed. The study shows that mycotoxin detoxifier containing oxyquinol, dichloro-thymol and micronized yeast can effectively neutralize the ill-effects of aflatoxins in poultry feed.
Comprehensible knowledge model creation for cancer treatment decision making
Afzal, Muhammad,Hussain, Maqbool,Ali Khan, Wajahat,Ali, Taqdir,Lee, Sungyoung,Huh, Eui-Nam,Farooq Ahmad, Hafiz,Jamshed, Arif,Iqbal, Hassan,Irfan, Muhammad,Abbas Hydari, Manzar Elsevier 2017 Computers in biology and medicine Vol.82 No.-
<P><B>Abstract</B></P> <P> <I>Background</I>: A wealth of clinical data exists in clinical documents in the form of electronic health records (EHRs). This data can be used for developing knowledge-based recommendation systems that can assist clinicians in clinical decision making and education. One of the big hurdles in developing such systems is the lack of automated mechanisms for knowledge acquisition to enable and educate clinicians in informed decision making. <I>Materials and Methods</I>: An automated knowledge acquisition methodology with a comprehensible knowledge model for cancer treatment (CKM-CT) is proposed. With the CKM-CT, clinical data are acquired automatically from documents. Quality of data is ensured by correcting errors and transforming various formats into a standard data format. Data preprocessing involves dimensionality reduction and missing value imputation. Predictive algorithm selection is performed on the basis of the ranking score of the weighted sum model. The knowledge builder prepares knowledge for knowledge-based services: clinical decisions and education support. <I>Results</I>: Data is acquired from 13,788 head and neck cancer (HNC) documents for 3447 patients, including 1526 patients of the oral cavity site. In the data quality task, 160 staging values are corrected. In the preprocessing task, 20 attributes and 106 records are eliminated from the dataset. The Classification and Regression Trees (CRT) algorithm is selected and provides 69.0% classification accuracy in predicting HNC treatment plans, consisting of 11 decision paths that yield 11 decision rules. <I>Conclusion</I>: Our proposed methodology, CKM-CT, is helpful to find hidden knowledge in clinical documents. In CKM-CT, the prediction models are developed to assist and educate clinicians for informed decision making. The proposed methodology is generalizable to apply to data of other domains such as breast cancer with a similar objective to assist clinicians in decision making and education.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Automated methods for data acquisition from clinical documents and preprocessing. </LI> <LI> Data quality assessment and standardization of language for improved data accuracy. </LI> <LI> Machine learning algorithm selection on the basis of weighted sum model's ranking score. </LI> <LI> The development of a decision tree-based knowledge model for treatment recommendations. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
A Sampling Plan for the Selection of Supplier using Process Yield Index based on Linear Profiles
Afzal, Kalsoom,Aslam, Muhammad,Jun, Chi-Hyuck,Ahmad, Liaquat Korean Institute of Industrial Engineers 2017 Industrial Engineeering & Management Systems Vol.16 No.2
In this article, a multiple dependent state repetitive (MDSR) sampling plan is developed to choose a more capable process by comparing two suppliers' processes. The proposed MDSR plan is based on the ratio of two process yield indices based on the estimated linear profiles. The linear profile for both processes is estimated by a regression model with a single explanatory variable having the specified number of levels. The operating characteristic function of the proposed plan will be derived. The plan parameters related to the decision rule as well as the sample size are determined by minimizing the average sample number while satisfying the producer's and the consumer's risks.