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In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as severe network security threats. For conventional network security tools it is extremely difficult to distinguish between the higher traffic volume of a DDoS attack and large number of legitimate users accessing a targeted network service or a resource. Although these attacks have been widely studied, there are few works which collect and analyse truly representative characteristics of DDoS traffic. The current research mostly focuses on DDoS detection and mitigation with predefined DDoS data-sets which are often hard to generalise for various network services and legitimate users’ traffic patterns. In order to deal with considerably large DDoS traffic flow in a Software Defined Networking (SDN), in this work we proposed a fast and an effective entropy-based DDoS detection. We deployed generalised entropy calculation by combining Shannon and Renyi entropy to identify distributed features of DDoS traffic—it also helped SDN controller to effectively deal with heavy malicious traffic. To lower down the network traffic overhead, we collected data-plane traffic with signature-based Snort detection. We then analysed the collected traffic for entropy-based features to improve the detection accuracy of deep learning models: Stacked Auto Encoder (SAE) and Convolutional Neural Network (CNN). This work also investigated the trade-off between SAE and CNN classifiers by using accuracy and false-positive results. Quantitative results demonstrated SAE achieved relatively higher detection accuracy of 94% with only 6% of false-positive alerts, whereas the CNN classifier achieved an average accuracy of 93%.
Raja Majid Ali Ujjan; Zeeshan Pervez; Keshav Dahal; Wajahat Ali Khan; Asad Masood Khattak; Bashir Hayat. Entropy Based Features Distribution for Anti-DDoS Model in SDN. Sustainability 2021, 13, 1522 .
AMA StyleRaja Majid Ali Ujjan, Zeeshan Pervez, Keshav Dahal, Wajahat Ali Khan, Asad Masood Khattak, Bashir Hayat. Entropy Based Features Distribution for Anti-DDoS Model in SDN. Sustainability. 2021; 13 (3):1522.
Chicago/Turabian StyleRaja Majid Ali Ujjan; Zeeshan Pervez; Keshav Dahal; Wajahat Ali Khan; Asad Masood Khattak; Bashir Hayat. 2021. "Entropy Based Features Distribution for Anti-DDoS Model in SDN." Sustainability 13, no. 3: 1522.
Mining social network data and developing user profile from unstructured and informal data are a challenging task. The proposed research builds user profile using Twitter data which is later helpful to provide the user with personalized recommendations. Publicly available tweets are fetched and classified and sentiments expressed in tweets are extracted and normalized. This research uses domain-specific seed list to classify tweets. Semantic and syntactic analysis on tweets is performed to minimize information loss during the process of tweets classification. After precise classification and sentiment analysis, the system builds user interest-based profile by analyzing user’s post on Twitter to know about user interests. The proposed system was tested on a dataset of almost 1 million tweets and was able to classify up to 96% tweets accurately.
Asad Masood Khattak; Rabia Batool; Fahad Ahmed Satti; Jamil Hussain; Wajahat Ali Khan; Adil Mehmood Khan; Bashir Hayat. Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation. Complexity 2020, 2020, 1 -11.
AMA StyleAsad Masood Khattak, Rabia Batool, Fahad Ahmed Satti, Jamil Hussain, Wajahat Ali Khan, Adil Mehmood Khan, Bashir Hayat. Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation. Complexity. 2020; 2020 ():1-11.
Chicago/Turabian StyleAsad Masood Khattak; Rabia Batool; Fahad Ahmed Satti; Jamil Hussain; Wajahat Ali Khan; Adil Mehmood Khan; Bashir Hayat. 2020. "Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation." Complexity 2020, no. : 1-11.
Fahad Ahmed Satti; Taqdir Ali; Jamil Hussain; Wajahat Ali Khan; Asad Masood Khattak; Sungyoung Lee. Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability. 2020, 1 -36.
AMA StyleFahad Ahmed Satti, Taqdir Ali, Jamil Hussain, Wajahat Ali Khan, Asad Masood Khattak, Sungyoung Lee. Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability. . 2020; ():1-36.
Chicago/Turabian StyleFahad Ahmed Satti; Taqdir Ali; Jamil Hussain; Wajahat Ali Khan; Asad Masood Khattak; Sungyoung Lee. 2020. "Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability." , no. : 1-36.
The lack of Interoperable healthcare data presents a major challenge, towards achieving ubiquitous health care. The plethora of diverse medical standards, rather than common standards, is widening the gap of interoperability. While many organizations are working towards a standardized solution, there is a need for an alternate strategy, which can intelligently mediate amongst a variety of medical systems, not complying with any mainstream healthcare standards while utilizing the benefits of several standard merging initiates, to eventually create digital health personas. The existence and efficiency of such a platform is dependent upon the underlying storage and processing engine, which can acquire, manage and retrieve the relevant medical data. In this paper, we present the Ubiquitous Health Profile (UHPr), a multi-dimensional data storage solution in a semi-structured data curation engine, which provides foundational support for archiving heterogeneous medical data and achieving partial data interoperability in the healthcare domain. Additionally, we present the evaluation results of this proposed platform in terms of its timeliness, accuracy, and scalability. Our results indicate that the UHPr is able to retrieve an error free comprehensive medical profile of a single patient, from a set of slightly over 116.5 million serialized medical fragments for 390,101 patients while maintaining a good scalablity ratio between amount of data and its retrieval speed.
Fahad Ahmed Satti; Taqdir Ali; Jamil Hussain; Wajahat Ali Khan; Asad Masood Khattak; Sungyoung Lee. Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability. Computing 2020, 102, 2409 -2444.
AMA StyleFahad Ahmed Satti, Taqdir Ali, Jamil Hussain, Wajahat Ali Khan, Asad Masood Khattak, Sungyoung Lee. Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability. Computing. 2020; 102 (11):2409-2444.
Chicago/Turabian StyleFahad Ahmed Satti; Taqdir Ali; Jamil Hussain; Wajahat Ali Khan; Asad Masood Khattak; Sungyoung Lee. 2020. "Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability." Computing 102, no. 11: 2409-2444.
Background and Objective: Validation and verification are the critical requirements for the knowledge acquisition method of the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method with the support of a rigorous validation process for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data for the treatment of oral cavity cancer. However, due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts. Methods: This paper presents the refined knowledge acquisition (ReKA) method, which uses the Z formal verification process. The ReKA method adopts the verification method and explores the mechanism of theorem proving using the Z notation. It enhances a hybrid knowledge acquisition method to thwart the inconsistencies using formal verification. Results: ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. These criteria ensure the validity of the final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the additional criteria by the ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57% compared to a similar approach with an accuracy of 69.7%. Furthermore, the ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs. Conclusion: ReKA refined the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. As a formally proven method, it always yields a valid knowledge model having high quality, supporting local practices, and influenced by standard CPGs. Furthermore, the final knowledge model obtained from ReKA also preserves the performance such as the accuracy of the individual source knowledge models.
Maqbool Hussain; Muhammad Afzal; Khalid M. Malik; Taqdir Ali; Wajahat Ali Khan; Muhammad Irfan; Arif Jamshed; Sungyoung Lee. Acquiring guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition method. Computer Methods and Programs in Biomedicine 2020, 197, 105701 .
AMA StyleMaqbool Hussain, Muhammad Afzal, Khalid M. Malik, Taqdir Ali, Wajahat Ali Khan, Muhammad Irfan, Arif Jamshed, Sungyoung Lee. Acquiring guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition method. Computer Methods and Programs in Biomedicine. 2020; 197 ():105701.
Chicago/Turabian StyleMaqbool Hussain; Muhammad Afzal; Khalid M. Malik; Taqdir Ali; Wajahat Ali Khan; Muhammad Irfan; Arif Jamshed; Sungyoung Lee. 2020. "Acquiring guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition method." Computer Methods and Programs in Biomedicine 197, no. : 105701.
The proposed Intelligent Medical Platform is a dialoguebased medical decision-making system that provides medical coaching and recommendation services, based on incremental learning methodology. The prototype demonstrates 90% accuracy for knowledge acquisition, 80% satisfaction level of user interaction with the system, and 95% accuracy for system integration with the legacy system.
Taqdir Ali; Jamil Hussain; Muhammad Amin; Musarrat Hussain; Usman Akhtar; Wajahat Ali Khan; Sungyoung Lee; Byeong Ho Kang; Maqbool Hussain; Muhammad Afzal; Hyeong Won Yu; Ubaid Ur Rehman; Ho-Seong Han; June Young Choi; Arif Jamshed. The Intelligent Medical Platform: A Novel Dialogue-Based Platform for Health-Care Services. Computer 2020, 53, 35 -45.
AMA StyleTaqdir Ali, Jamil Hussain, Muhammad Amin, Musarrat Hussain, Usman Akhtar, Wajahat Ali Khan, Sungyoung Lee, Byeong Ho Kang, Maqbool Hussain, Muhammad Afzal, Hyeong Won Yu, Ubaid Ur Rehman, Ho-Seong Han, June Young Choi, Arif Jamshed. The Intelligent Medical Platform: A Novel Dialogue-Based Platform for Health-Care Services. Computer. 2020; 53 (2):35-45.
Chicago/Turabian StyleTaqdir Ali; Jamil Hussain; Muhammad Amin; Musarrat Hussain; Usman Akhtar; Wajahat Ali Khan; Sungyoung Lee; Byeong Ho Kang; Maqbool Hussain; Muhammad Afzal; Hyeong Won Yu; Ubaid Ur Rehman; Ho-Seong Han; June Young Choi; Arif Jamshed. 2020. "The Intelligent Medical Platform: A Novel Dialogue-Based Platform for Health-Care Services." Computer 53, no. 2: 35-45.
Diabetic Retinopathy (DR) is considered a pathology of retinal vascular complications, which stays in the top causes of vision impairment and blindness. Therefore, precisely inspecting its progression enables the ophthalmologists to set up appropriate next-visit schedule and cost-effective treatment plans. In the literature, existing work only makes use of numerical attributes in Electronic Medical Records (EMR) for acquiring such kind of DR-oriented knowledge through conventional machine learning techniques, which require an exhaustive job of engineering most impactful risk factors. In this paper, an approach of deep bimodal learning is introduced to leverage the performance of DR risk progression identification. In particular, we further involve valuable clinical information of fundus photography in addition to the aforementioned systemic attributes. Accordingly, a Trilogy of Skip-connection Deep Networks, namely Tri-SDN, is proposed to exhaustively exploit underlying relationships between the baseline and follow-up information of the fundus images and EMR-based attributes. Besides that, we adopt Skip-Connection Blocks as basis components of the Tri-SDN for making the end-to-end flow of signals more efficient during feedforward and backpropagation processes. Through a 10-fold cross validation strategy on a private dataset of 96 diabetic mellitus patients, the proposed method attains superior performance over the conventional EMR-modality learning approach in terms of Accuracy (90.6%), Sensitivity (96.5%), Precision (88.7%), Specificity (82.1%), and Area Under Receiver Operating Characteristics (88.8%). The experimental results show that the proposed Tri-SDN can combine features of different modalities (i.e., fundus images and EMR-based numerical risk factors) smoothly and effectively during training and testing processes, respectively. As a consequence, with impressive performance of DR risk progression recognition, the proposed approach is able to help the ophthalmologists properly decide follow-up schedule and subsequent treatment plans.
Cam-Hao Hua; Thien Huynh-The; Kiyoung Kim; Seung-Young Yu; Thuong Le-Tien; Gwang Hoon Park; Jaehun Bang; Wajahat Ali Khan; Sung-Ho Bae; Sungyoung Lee. Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification. International Journal of Medical Informatics 2019, 132, 103926 .
AMA StyleCam-Hao Hua, Thien Huynh-The, Kiyoung Kim, Seung-Young Yu, Thuong Le-Tien, Gwang Hoon Park, Jaehun Bang, Wajahat Ali Khan, Sung-Ho Bae, Sungyoung Lee. Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification. International Journal of Medical Informatics. 2019; 132 ():103926.
Chicago/Turabian StyleCam-Hao Hua; Thien Huynh-The; Kiyoung Kim; Seung-Young Yu; Thuong Le-Tien; Gwang Hoon Park; Jaehun Bang; Wajahat Ali Khan; Sung-Ho Bae; Sungyoung Lee. 2019. "Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification." International Journal of Medical Informatics 132, no. : 103926.
Label noises exist in many applications, and their presence can degrade learning performance. Researchers usually use filters to identify and eliminate them prior to training. The ensemble learning based filter (EnFilter) is the most widely used filter. According to the voting mechanism, EnFilter is mainly divided into two types: single-voting based (SVFilter) and multiple-voting based (MVFilter). In general, MVFilter is more often preferred because multiple-voting could address the intrinsic limitations of single-voting. However, the most important unsolved issue in MVFilter is how to determine the optimal decision point (ODP). Conceptually, the decision point is a threshold value, which determines the noise detection performance. To maximize the performance of MVFilter, we propose a novel approach to compute the optimal decision point. Our approach is data driven and cost sensitive, which determines the ODP based on the given noisy training dataset and noise misrecognition cost matrix. The core idea of our approach is to estimate the mislabeled data probability distributions, based on which the expected cost of each possible decision point could be inferred. Experimental results on a set of benchmark datasets illustrate the utility of our proposed approach.
Donghai Guan; Maqbool Hussain; Weiwei Yuan; Asad Masood Khattak; Muhammad Fahim; Wajahat Ali Khan. Enhanced Label Noise Filtering with Multiple Voting. Applied Sciences 2019, 9, 5031 .
AMA StyleDonghai Guan, Maqbool Hussain, Weiwei Yuan, Asad Masood Khattak, Muhammad Fahim, Wajahat Ali Khan. Enhanced Label Noise Filtering with Multiple Voting. Applied Sciences. 2019; 9 (23):5031.
Chicago/Turabian StyleDonghai Guan; Maqbool Hussain; Weiwei Yuan; Asad Masood Khattak; Muhammad Fahim; Wajahat Ali Khan. 2019. "Enhanced Label Noise Filtering with Multiple Voting." Applied Sciences 9, no. 23: 5031.
The objective of this study is to help a team of physicians and knowledge engineers acquire clinical knowledge from existing practices datasets for treatment of head and neck cancer, to validate the knowledge against published guidelines, to create refined rules, and to incorporate these rules into clinical workflow for clinical decision support. A team of physicians (clinical domain experts) and knowledge engineers adapt an approach for modeling existing treatment practices into final executable clinical models. For initial work, the oral cavity is selected as the candidate target area for the creation of rules covering a treatment plan for cancer. The final executable model is presented in HL7 Arden Syntax, which helps the clinical knowledge be shared among organizations. We use a data-driven knowledge acquisition approach based on analysis of real patient datasets to generate a predictive model (PM). The PM is converted into a refined-clinical knowledge model (R-CKM), which follows a rigorous validation process. The validation process uses a clinical knowledge model (CKM), which provides the basis for defining underlying validation criteria. The R-CKM is converted into a set of medical logic modules (MLMs) and is evaluated using real patient data from a hospital information system. We selected the oral cavity as the intended site for derivation of all related clinical rules for possible associated treatment plans. A team of physicians analyzed the National Comprehensive Cancer Network (NCCN) guidelines for the oral cavity and created a common CKM. Among the decision tree algorithms, chi-squared automatic interaction detection (CHAID) was applied to a refined dataset of 1229 patients to generate the PM. The PM was tested on a disjoint dataset of 739 patients, which gives 59.0% accuracy. Using a rigorous validation process, the R-CKM was created from the PM as the final model, after conforming to the CKM. The R-CKM was converted into four candidate MLMs, and was used to evaluate real data from 739 patients, yielding efficient performance with 53.0% accuracy. Data-driven knowledge acquisition and validation against published guidelines were used to help a team of physicians and knowledge engineers create executable clinical knowledge. The advantages of the R-CKM are twofold: it reflects real practices and conforms to standard guidelines, while providing optimal accuracy comparable to that of a PM. The proposed approach yields better insight into the steps of knowledge acquisition and enhances collaboration efforts of the team of physicians and knowledge engineers.
Maqbool Hussain; Muhammad Afzal; Taqdir Ali; Rahman Ali; Wajahat Ali Khan; Arif Jamshed; Sungyoung Lee; Byeong Ho Kang; Khalid Latif. Data-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntax. Artificial Intelligence in Medicine 2018, 92, 51 -70.
AMA StyleMaqbool Hussain, Muhammad Afzal, Taqdir Ali, Rahman Ali, Wajahat Ali Khan, Arif Jamshed, Sungyoung Lee, Byeong Ho Kang, Khalid Latif. Data-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntax. Artificial Intelligence in Medicine. 2018; 92 ():51-70.
Chicago/Turabian StyleMaqbool Hussain; Muhammad Afzal; Taqdir Ali; Rahman Ali; Wajahat Ali Khan; Arif Jamshed; Sungyoung Lee; Byeong Ho Kang; Khalid Latif. 2018. "Data-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntax." Artificial Intelligence in Medicine 92, no. : 51-70.
The Linked Open Data (LOD) cloud is a global information space with a wealth of structured facts, which are useful for a wide range of usage scenarios. The LOD cloud handles a large number of requests from applications consuming the data. However, the performance of retrieving data from LOD repositories is one of the major challenge. Overcome with this challenge, we argue that it is advantageous to maintain a local cache for efficient querying and processing. Due to the continuous evolution of the LOD cloud, local copies become outdated. In order to utilize the best resources, improvised scheduling is required to maintain the freshness of the local data cache. In this paper, we have proposed an approach to efficiently capture the changes and update the cache. Our proposed approach, called Application- Aware Change Prioritization (AACP), consists of a change metric that quantifies the changes in LOD, and a weight function that assigns importance to recent changes. We have also proposed a mechanism to update policies, called Preference-Aware Source Update (PASU), which incorporates the previous estimation of changes and establishes when the local data cache needs to be updated. In the experimental evaluation, several state-ofthe- art strategies are compared against the proposed approach. The performance of each policy is measured by computing the precision and recall between the local data cache update using the policy under consideration and the data source, which is the ground truth. Both cases of a single update and iterative update are evaluated in this study. The proposed approach is reported to outperform all the other policies by achieving an F1-score of 88% and effectivity of 93.5%.
Usman Akhtar; Muhammad Asif Razzaq; Ubaid Ur Rehman; Muhammad Bilal Amin; Wajahat Ali Khan; Eui-Nam Huh; Sungyoung Lee. Change-Aware Scheduling for Effectively Updating Linked Open Data Caches. IEEE Access 2018, 6, 65862 -65873.
AMA StyleUsman Akhtar, Muhammad Asif Razzaq, Ubaid Ur Rehman, Muhammad Bilal Amin, Wajahat Ali Khan, Eui-Nam Huh, Sungyoung Lee. Change-Aware Scheduling for Effectively Updating Linked Open Data Caches. IEEE Access. 2018; 6 (99):65862-65873.
Chicago/Turabian StyleUsman Akhtar; Muhammad Asif Razzaq; Ubaid Ur Rehman; Muhammad Bilal Amin; Wajahat Ali Khan; Eui-Nam Huh; Sungyoung Lee. 2018. "Change-Aware Scheduling for Effectively Updating Linked Open Data Caches." IEEE Access 6, no. 99: 65862-65873.
The user experience (UX) is an emerging field in user research and design, and the development of UX evaluation methods presents a challenge for both researchers and practitioners. Different UX evaluation methods have been developed to extract accurate UX data. Among UX evaluation methods, the mixed-method approach of triangulation has gained importance. It provides more accurate and precise information about the user while interacting with the product. However, this approach requires skilled UX researchers and developers to integrate multiple devices, synchronize them, analyze the data, and ultimately produce an informed decision. In this paper, a method and system for measuring the overall UX over time using a triangulation method are proposed. The proposed platform incorporates observational and physiological measurements in addition to traditional ones. The platform reduces the subjective bias and validates the user’s perceptions, which are measured by different sensors through objectification of the subjective nature of the user in the UX assessment. The platform additionally offers plug-and-play support for different devices and powerful analytics for obtaining insight on the UX in terms of multiple participants.
Jamil Hussain; Wajahat Ali Khan; Taeho Hur; Hafiz Syed Muhammad Bilal; Jaehun Bang; Anees Ul Hassan; Muhammad Afzal; Sungyoung Lee. A Multimodal Deep Log-Based User Experience (UX) Platform for UX Evaluation. Sensors 2018, 18, 1622 .
AMA StyleJamil Hussain, Wajahat Ali Khan, Taeho Hur, Hafiz Syed Muhammad Bilal, Jaehun Bang, Anees Ul Hassan, Muhammad Afzal, Sungyoung Lee. A Multimodal Deep Log-Based User Experience (UX) Platform for UX Evaluation. Sensors. 2018; 18 (5):1622.
Chicago/Turabian StyleJamil Hussain; Wajahat Ali Khan; Taeho Hur; Hafiz Syed Muhammad Bilal; Jaehun Bang; Anees Ul Hassan; Muhammad Afzal; Sungyoung Lee. 2018. "A Multimodal Deep Log-Based User Experience (UX) Platform for UX Evaluation." Sensors 18, no. 5: 1622.
A considerable number of frameworks and platforms are available to model terminologies in the clinical domains, but wellness domain lacks a development framework. The objective of this paper is to develop a clinically influenced and harmonized wellness concepts model (WCM) in order to support diverse wellness applications and services. This model is supported by a novel framework in the wellness domain. In order to develop wellness concepts model, the proposed framework is divided into four processes; start-up and initiation process, WCM modeling and evolution process, WCM production process, and release process. The WCM modeling and evolution process extracts top level hierarchical concepts from the existing published literature using a systematic review process. The framework also extends its scope to evolution using the wellness recommendations guidelines. The evolution process is supported by clinical concepts harmonization with the help of terminology standard, SNOMED CT. We validated the top level hierarchical concepts using a group of experts based on a decision-making method known as the nominal group technique (NGT). In the final decision of the NGT, 14.7% of the hierarchical concepts are eliminated from the model due to their voting score of less than 70% in the expert panel. The top level concepts of the model are cross-validated using structured equation modeling (SEM). The chi-square (χ2) test and root mean square error of approximation test results demonstrated the acceptable goodness of fit indices for the WCM with respect to experts' and users' opinions. In order to fill the gap that existed in wellness and clinical domain, this paper systematically investigated concepts for building a clinically harmonized model called the WCM. The proposed WCM development framework is validated through the NGT and SEM.
Taqdir Ali; Maqbool Hussain; Muhammad Afzal; Wajahat Ali Khan; Taeho Hur; Muhammad Bilal Amin; Dohyeong Kim; Byeong Ho Kang; Sungyoung Lee; Dohyang Kim. Clinically Harmonized Wellness Concepts Model for Health and Wellness Services. IEEE Access 2018, 6, 26660 -26674.
AMA StyleTaqdir Ali, Maqbool Hussain, Muhammad Afzal, Wajahat Ali Khan, Taeho Hur, Muhammad Bilal Amin, Dohyeong Kim, Byeong Ho Kang, Sungyoung Lee, Dohyang Kim. Clinically Harmonized Wellness Concepts Model for Health and Wellness Services. IEEE Access. 2018; 6 ():26660-26674.
Chicago/Turabian StyleTaqdir Ali; Maqbool Hussain; Muhammad Afzal; Wajahat Ali Khan; Taeho Hur; Muhammad Bilal Amin; Dohyeong Kim; Byeong Ho Kang; Sungyoung Lee; Dohyang Kim. 2018. "Clinically Harmonized Wellness Concepts Model for Health and Wellness Services." IEEE Access 6, no. : 26660-26674.
Muhammad Afzal; Syed Imran Ali; Rahman Ali; Maqbool Hussain; Taqdir Ali; Wajahat Ali Khan; Muhammad Amin; Byeong Ho Kang; Sungyoung Lee. Personalization of wellness recommendations using contextual interpretation. Expert Systems with Applications 2018, 96, 506 -521.
AMA StyleMuhammad Afzal, Syed Imran Ali, Rahman Ali, Maqbool Hussain, Taqdir Ali, Wajahat Ali Khan, Muhammad Amin, Byeong Ho Kang, Sungyoung Lee. Personalization of wellness recommendations using contextual interpretation. Expert Systems with Applications. 2018; 96 ():506-521.
Chicago/Turabian StyleMuhammad Afzal; Syed Imran Ali; Rahman Ali; Maqbool Hussain; Taqdir Ali; Wajahat Ali Khan; Muhammad Amin; Byeong Ho Kang; Sungyoung Lee. 2018. "Personalization of wellness recommendations using contextual interpretation." Expert Systems with Applications 96, no. : 506-521.
Data-driven knowledge acquisition is one of the key research fields in data mining. Dealing with large amounts of data has received a lot of attention in the field recently, and a number of methodologies have been proposed to extract insights from data in an automated or semi-automated manner. However, these methodologies generally target a specific aspect of the data mining process, such as data acquisition, data preprocessing, or data classification. However, a comprehensive knowledge acquisition method is crucial to support the end-to-end knowledge engineering process. In this paper, we introduce a knowledge acquisition system that covers all major phases of the cross-industry standard process for data mining. Acknowledging the importance of an end-to-end knowledge engineering process, we designed and developed an easy-to-use data-driven knowledge acquisition tool (DDKAT). The major features of the DDKAT are: (1) a novel unified features scoring approach for data selection; (2) a user-friendly data processing interface to improve the quality of the raw data; (3) an appropriate decision tree algorithm selection approach to build a classification model; and (4) the generation of production rules from various decision tree classification models in an automated manner. Furthermore, two diabetes studies were performed to assess the value of the DDKAT in terms of user experience. A total of 19 experts were involved in the first study and 102 students in the artificial intelligence domain were involved in the second study. The results showed that the overall user experience of the DDKAT was positive in terms of its attractiveness, as well as its pragmatic and hedonic quality factors.
Maqbool Ali; Rahman Ali; Wajahat Ali Khan; Soyeon Caren Han; Jaehun Bang; Taeho Hur; Dohyeong Kim; Sungyoung Lee; Byeong Ho Kang. A Data-Driven Knowledge Acquisition System: An End-to-End Knowledge Engineering Process for Generating Production Rules. IEEE Access 2018, 6, 15587 -15607.
AMA StyleMaqbool Ali, Rahman Ali, Wajahat Ali Khan, Soyeon Caren Han, Jaehun Bang, Taeho Hur, Dohyeong Kim, Sungyoung Lee, Byeong Ho Kang. A Data-Driven Knowledge Acquisition System: An End-to-End Knowledge Engineering Process for Generating Production Rules. IEEE Access. 2018; 6 ():15587-15607.
Chicago/Turabian StyleMaqbool Ali; Rahman Ali; Wajahat Ali Khan; Soyeon Caren Han; Jaehun Bang; Taeho Hur; Dohyeong Kim; Sungyoung Lee; Byeong Ho Kang. 2018. "A Data-Driven Knowledge Acquisition System: An End-to-End Knowledge Engineering Process for Generating Production Rules." IEEE Access 6, no. : 15587-15607.
Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN). The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods.
Muhammad Ahmad; Stanislav Protasov; Adil Mehmood Khan; Rasheed Hussain; Asad Masood Khattak; Wajahat Ali Khan. Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers. PLOS ONE 2018, 13, e0188996 .
AMA StyleMuhammad Ahmad, Stanislav Protasov, Adil Mehmood Khan, Rasheed Hussain, Asad Masood Khattak, Wajahat Ali Khan. Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers. PLOS ONE. 2018; 13 (1):e0188996.
Chicago/Turabian StyleMuhammad Ahmad; Stanislav Protasov; Adil Mehmood Khan; Rasheed Hussain; Asad Masood Khattak; Wajahat Ali Khan. 2018. "Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers." PLOS ONE 13, no. 1: e0188996.
Muhammad Bilal Amin; Muhammad Sadiq; Wajahat Ali Khan; Asad Masood Khattak; Usman Akhtar; Sungyoung Lee. Configurable Data Acquisition for Cloud-centric IoT. Proceedings of the 12th International Conference on Interaction Design and Children 2018, 23 .
AMA StyleMuhammad Bilal Amin, Muhammad Sadiq, Wajahat Ali Khan, Asad Masood Khattak, Usman Akhtar, Sungyoung Lee. Configurable Data Acquisition for Cloud-centric IoT. Proceedings of the 12th International Conference on Interaction Design and Children. 2018; ():23.
Chicago/Turabian StyleMuhammad Bilal Amin; Muhammad Sadiq; Wajahat Ali Khan; Asad Masood Khattak; Usman Akhtar; Sungyoung Lee. 2018. "Configurable Data Acquisition for Cloud-centric IoT." Proceedings of the 12th International Conference on Interaction Design and Children , no. : 23.
The emerging research on automatic identification of user’s contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user’s contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts.
Muhammad Asif Razzaq; Claudia Villalonga; Sungyoung Lee; Usman Akhtar; Maqbool Ali; Eun-Soo Kim; Asad Masood Khattak; Hyonwoo Seung; Taeho Hur; Jaehun Bang; Dohyeong Kim; Wajahat Ali Khan. mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification. Sensors 2017, 17, 2433 .
AMA StyleMuhammad Asif Razzaq, Claudia Villalonga, Sungyoung Lee, Usman Akhtar, Maqbool Ali, Eun-Soo Kim, Asad Masood Khattak, Hyonwoo Seung, Taeho Hur, Jaehun Bang, Dohyeong Kim, Wajahat Ali Khan. mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification. Sensors. 2017; 17 (10):2433.
Chicago/Turabian StyleMuhammad Asif Razzaq; Claudia Villalonga; Sungyoung Lee; Usman Akhtar; Maqbool Ali; Eun-Soo Kim; Asad Masood Khattak; Hyonwoo Seung; Taeho Hur; Jaehun Bang; Dohyeong Kim; Wajahat Ali Khan. 2017. "mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification." Sensors 17, no. 10: 2433.
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.
Taqdir Ali; Maqbool Hussain; Wajahat Ali Khan; Muhammad Afzal; Jamil Hussain; Rahman Ali; Waseem Hassan; Arif Jamshed; Byeong Ho Kang; Sungyoung Lee. Multi-model-based interactive authoring environment for creating shareable medical knowledge. Computer Methods and Programs in Biomedicine 2017, 150, 41 -72.
AMA StyleTaqdir Ali, Maqbool Hussain, Wajahat Ali Khan, Muhammad Afzal, Jamil Hussain, Rahman Ali, Waseem Hassan, Arif Jamshed, Byeong Ho Kang, Sungyoung Lee. Multi-model-based interactive authoring environment for creating shareable medical knowledge. Computer Methods and Programs in Biomedicine. 2017; 150 ():41-72.
Chicago/Turabian StyleTaqdir Ali; Maqbool Hussain; Wajahat Ali Khan; Muhammad Afzal; Jamil Hussain; Rahman Ali; Waseem Hassan; Arif Jamshed; Byeong Ho Kang; Sungyoung Lee. 2017. "Multi-model-based interactive authoring environment for creating shareable medical knowledge." Computer Methods and Programs in Biomedicine 150, no. : 41-72.
Unhealthy behavior, constitutes of unhealthy diet, smoking, physical inactivity and alcohol intake, increases the risk of chronic diseases and premature mortality. These unhealthy behaviors can be avoided by little intention and guidance. Diet is an influential factor of healthcare. Healthy and balanced diet selection is related to the better life expectancy and decreases the chances of chronic diseases. The Ubiquitous computing revolutionized the wellness domain towards user centric preference based health management. In this study we proposed a method for monitoring and indication of users’ unhealthy nutrition consumption. We evaluated 3 different timings of indication to user for induction of healthy dietary pattern. The “location and time based indication” depicts very promising result of 78% in the adoption of healthy diet pattern and has positive impact on the intake of fat nutrient in diet.
Hafiz Syed Muhammad Bilal; Wajahat Ali Khan; Sungyoung Lee. Unhealthy Dietary Behavior Based User Life-Log Monitoring for Wellness Services. Computer Vision 2017, 10461, 73 -84.
AMA StyleHafiz Syed Muhammad Bilal, Wajahat Ali Khan, Sungyoung Lee. Unhealthy Dietary Behavior Based User Life-Log Monitoring for Wellness Services. Computer Vision. 2017; 10461 ():73-84.
Chicago/Turabian StyleHafiz Syed Muhammad Bilal; Wajahat Ali Khan; Sungyoung Lee. 2017. "Unhealthy Dietary Behavior Based User Life-Log Monitoring for Wellness Services." Computer Vision 10461, no. : 73-84.
A revolutionized wave of intelligent assistants has emerged in daily life of human over the recent years, therefore huge progress has been witnessed for development of healthcare assistants having the capability to communicate with users. However, the conversational complexities demand building more personalized and user-oriented dialogue process systems. To support human-computer dialogue process many models have been proposed. Considering personalization aspect, this research work presents novel Context-aware Dialogue Manager (CADM) model with its foundation based on well-known JDL fusion model. The proposed model addresses modern techniques for multi-turn dialogue process, by identifying dialogue intents, contexts and fusing personalized contexts over them. The model also maintains the dialogue context for progressing complex and multi-turn dialogue. It also helps using intent-context relationship in identifying optimized knowledge source for accurate dialogue expansion and its coherence. CADM functionality is discussed using support of Intelligent Medical Assistant in healthcare domain, which has the speech-based capability to communicate with users.
Muhammad Asif Razzaq; Wajahat Ali Khan; Sungyoung Lee. Intent-Context Fusioning in Healthcare Dialogue-Based Systems Using JDL Model. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 61 -72.
AMA StyleMuhammad Asif Razzaq, Wajahat Ali Khan, Sungyoung Lee. Intent-Context Fusioning in Healthcare Dialogue-Based Systems Using JDL Model. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; ():61-72.
Chicago/Turabian StyleMuhammad Asif Razzaq; Wajahat Ali Khan; Sungyoung Lee. 2017. "Intent-Context Fusioning in Healthcare Dialogue-Based Systems Using JDL Model." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 61-72.