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Multi-model-based interactive authoring environment for creating shareable medical knowledge
Ali, T.,Hussain, M.,Ali Khan, W.,Afzal, M.,Hussain, J.,Ali, R.,Hassan, W.,Jamshed, A.,Kang, B.H.,Lee, S. Elsevier Science Publishers 2017 Computer methods and programs in biomedicine Vol.150 No.-
Objective: Technologically integrated healthcare environments can be realized if physicians are encouraged to use smart systems for the creation and sharing of knowledge used in clinical decision support systems (CDSS). While CDSSs are heading toward smart environments, they lack support for abstraction of technology-oriented knowledge from physicians. Therefore, abstraction in the form of a user-friendly and flexible authoring environment is required in order for physicians to create shareable and interoperable knowledge for CDSS workflows. Our proposed system provides a user-friendly authoring environment to create Arden Syntax MLM (Medical Logic Module) as shareable knowledge rules for intelligent decision-making by CDSS. Methods and materials: Existing systems are not physician friendly and lack interoperability and shareability of knowledge. In this paper, we proposed Intelligent-Knowledge Authoring Tool (I-KAT), a knowledge authoring environment that overcomes the above mentioned limitations. Shareability is achieved by creating a knowledge base from MLMs using Arden Syntax. Interoperability is enhanced using standard data models and terminologies. However, creation of shareable and interoperable knowledge using Arden Syntax without abstraction increases complexity, which ultimately makes it difficult for physicians to use the authoring environment. Therefore, physician friendliness is provided by abstraction at the application layer to reduce complexity. This abstraction is regulated by mappings created between legacy system concepts, which are modeled as domain clinical model (DCM) and decision support standards such as virtual medical record (vMR) and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT). We represent these mappings with a semantic reconciliation model (SRM). Results: The objective of the study is the creation of shareable and interoperable knowledge using a user-friendly and flexible I-KAT. Therefore we evaluated our system using completeness and user satisfaction criteria, which we assessed through the system- and user-centric evaluation processes. For system-centric evaluation, we compared the implementation of clinical information modelling system requirements in our proposed system and in existing systems. The results suggested that 82.05% of the requirements were fully supported, 7.69% were partially supported, and 10.25% were not supported by our system. In the existing systems, 35.89% of requirements were fully supported, 28.20% were partially supported, and 35.89% were not supported. For user-centric evaluation, the assessment criterion was 'ease of use'. Our proposed system showed 15 times better results with respect to MLM creation time than the existing systems. Moreover, on average, the participants made only one error in MLM creation using our proposed system, but 13 errors per MLM using the existing systems. Conclusion: We provide a user-friendly authoring environment for creation of shareable and interoperable knowledge for CDSS to overcome knowledge acquisition complexity. The authoring environment uses state-of-the-art decision support-related clinical standards with increased ease of use.
The Analysis of the Activity Patterns of Dog with Wearable Sensors Using Machine Learning
Ali Hussain,Sikandar Ali,Hee-Cheol Kim(김희철) 한국정보통신학회 2021 한국정보통신학회 종합학술대회 논문집 Vol.25 No.1
The Activity patterns of animal species are difficult to access and the behavior of freely moving individuals can not be assessed by direct observation. As it has become large challenge to understand the activity pattern of animals such as dogs, and cats etc. One approach for monitoring these behaviors is the continuous collection of data by human observers. Therefore, in this study we assess the activity patterns of dog using the wearable sensors data such as accelerometer and gyroscope. A wearable, sensor -based system is suitable for such ends, and it will be able to monitor the dogs in real-time. The basic purpose of this study was to develop a system that can detect the activities based on the accelerometer and gyroscope signals. Therefore, we purpose a method which is based on the data collected from 10 dogs, including different nine breeds of different sizes and ages, and both genders. We applied six different state-of-the-art classifiers such as Random forests (RF), Support vector machine (SVM), Gradient boosting machine (GBM), XGBoost, k-nearest neighbors (KNN), and Decision tree classifier, respectively. The Random Forest showed a good classification result. We achieved an accuracy 86.73% while the detecting the activity.
Channel Clustering and QoS Level Identification Scheme for Multi-Channel Cognitive Radio Networks
Ali, Amjad,Yaqoob, Ibrar,Ahmed, Ejaz,Imran, Muhammad,Kwak, Kyung Sup,Ahmad, Adnan,Hussain, Syed Asad,Ali, Zulfiqar IEEE 2018 IEEE communications magazine Vol.56 No.4
<P>The increasing popularity of wireless services and devices necessitates high bandwidth requirements; however, spectrum resources are not only limited but also heavily underutilized. Multiple license channels that support the same levels of QoS are desirable to resolve the problems posed by the scarcity and inefficient use of spectrum resources in multi-channel cognitive radio networks (MCRNs). One reason is that multimedia services and applications have distinct, stringent QoS requirements. However, due to a lack of coordination between primary and secondary users, identifying the QoS levels supported over available licensed channels has proven to be problematic and has yet to be attempted. This article presents a novel Bayesian non-parametric channel clustering scheme, which identifies the QoS levels supported over available license channels. The proposed scheme employs the infinite Gaussian mixture model and collapsed Gibbs sampler to identify the QoS levels from the feature space of the bit rate, packet delivery ratio, and packet delay variation of licensed channels. Moreover, the real measurements of wireless data traces and comparisons with baseline clustering schemes are used to evaluate the performance of the proposed scheme.</P>
Impedance Spectroscopy of Lead-free Bi0.5(Na0.78K0.22)0.5TiO3-(Na0.5K0.5)NbO3 Piezoelectric Ceramics
Ali Hussain,이재신,김진수,Aman Ullah,김일원,안창원 한국물리학회 2010 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.57 No.41
A lead-free piezoelectric 0.97Bi0.5(Na0.78K0.22)0.5TiO3-0.03(Na0.5K0.5)NbO3 (BNKTN-3) polycrystalline ceramic was synthesized by using a solid-state reaction method. X-ray diffraction (XRD)indicated the formation of a BNKTN-3 single phase having tetragonal symmetry. The temperature dependences of the dielectric properties of BNKTN-3 were investigated in the temperature range of 30 - 550 ℃ at various frequencies (0.01 kHz - 1 MHz), and its electrical properties were investigated by using complex impedance spectroscopy. Analyzing the complex impedance relaxation by using a Cole-Cole plot, we found the frequency dependence of the impedance relaxations to be one impedance arc in the complex impedance plot. The responses of electrical conduction are discussed in relation to grain effect upon equivalent circuit model.
Recommendations Service for Chronic Disease Patient in Multimodel Sensors Home Environment
Hussain, Maqbool,Ali, Taqdir,Khan, Wajahat Ali,Afzal, Muhammad,Lee, Sungyoung,Latif, Khalid Mary Ann Liebert 2015 TELEMEDICINE JOURNAL AND E HEALTH Vol.21 No.3
<P>With advanced technologies in hand, there exist potential applications and services built around monitoring activities of daily living (ADL) of elderly people at nursing homes. Most of the elderly people in these facilities are suffering from different chronic diseases such as dementia. Existing technologies are mainly focusing on non-medication interventions and monitoring of ADL for addressing loss of autonomy or well-being. Monitoring and managing ADL related to cognitive behaviors for non-medication intervention are very effective in improving dementia patients' conditions. However, cognitive functions of patients can be improved if appropriate recommendations of medications are delivered at a particular time. Previously we developed the Secured Wireless Sensor Network Integrated Cloud Computing for Ubiquitous-Life Care (SC(3)). SC(3) services were limited to monitoring ADL of elderly people with Alzheimer's disease and providing non-medication recommendations to the patient. In this article, we propose a system called the Smart Clinical Decision Support System (CDSS) as an integral part of the SC(3) platform. Using the Smart CDSS, patients are provided with access to medication recommendations of expert physicians. Physicians are provided with an interface to create clinical knowledge for medication recommendations and to observe the patient's condition. The clinical knowledge created by physicians as the knowledge base of the Smart CDSS produces recommendations to the caregiver for medications based on each patient's symptoms.</P>