Wajahat Ali Khan - Academia.edu (original) (raw)
Papers by Wajahat Ali Khan
Sustainability, 2021
In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as ... more 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...
Telemedicine and e-Health, 2013
Objective: Data interoperability among health information exchange (HIE) systems is a major conce... more Objective: Data interoperability among health information exchange (HIE) systems is a major concern for healthcare practitioners to enable provisioning of telemedicine-related services. Heterogeneity exists in these systems not only at the data level but also among different heterogeneous healthcare standards with which these are compliant. The relationship between healthcare organization data and different heterogeneous standards is necessary to achieve the goal of data level interoperability. We propose a personalizeddetailed clinical model (P-DCM) approach for the generation of customized mappings that creates the necessary linkage between organization-conformed healthcare standards concepts and clinical model concepts to ensure data interoperability among HIE systems. Materials and Methods: We consider electronic health record (EHR) standards, openEHR, and HL7 CDA instances transformation using P-DCM. P-DCM concepts associated with openEHR and HL7 CDA help in transformation of instances among these standards. We investigated two datasets: (1) data of 100 diabetic patients, including 50 each of type 1 and type 2, from a local hospital in Korea and (2) data of a single Alzheimer's disease patient. P-DCMs were created for both scenarios, which provided the basis for deriving instances for HL7 CDA and openEHR standards. Results: For proof of concept, we present case studies of encounter information for type 2 diabetes mellitus patients and monitoring of daily routine activities of an Alzheimer's disease patient. These reflect P-DCM-based customized mappings generation with openEHR and HL7 CDA standards. Customized mappings are generated based on the relationship of P-DCM concepts with CDA and openEHR concepts. Conclusions: The objective of this work is to achieve semantic data interoperability among heterogeneous standards. This would lead to effective utilization of resources and allow timely information exchange among healthcare systems.
White Leghorn layer breeder hens, 30 weeks of age, were divided into 12 groups (A-L). Group A was... more White Leghorn layer breeder hens, 30 weeks of age, were divided into 12 groups (A-L). Group A was kept on basal feed and served as control, while group B was offered feed supplemented with vitamin E (100 mg/Kg). Groups C-G were offered feed containing 100, 500, 2,500, 5,000 and 10,000 µg/Kg aflatoxin B1 (AFB1), respectively, whereas groups H-L were offered same dietary levels of AFB1 along with vitamin E (100 mg/Kg). The experimental feeds were offered for three weeks and afterward all the groups were switched over to basal feed for next two weeks. Body weight, absolute and relative weights of liver and kidneys of AF fed birds were significantly higher than control group. Pathological lesions in aflatoxin (AF) fed birds included enlarged, pale and friable liver, swollen kidneys and hemorrhages on different organs. Histopathological lesions in liver included fatty change, congestion and hemorrhages, while in kidneys tubular necrosis, cellular infiltration, congestion and hemorrhages were found in groups fed AFB1 at 500 µg/Kg and higher doses. In AF fed hens, no significant ameliorative effects of vitamin E could be observed upon AF induced decrease in feed intake, gross pathology and histopathological alterations and organ weight except body weights. It was concluded that the vitamin E ameliorated the AFB1 induced toxic effects in some of parameters studied.
2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), 2020
The diversity and proliferation of Knowledge bases have made data integration one of the key chal... more The diversity and proliferation of Knowledge bases have made data integration one of the key challenges in the data science domain. The imperfect representations of entities, particularly in graphs, add additional challenges in data integration. Graph dependencies (GDs) were investigated in existing studies for the integration and maintenance of data quality on graphs. However, the majority of graphs contain plenty of duplicates with high diversity. Consequently, the existence of dependencies over these graphs becomes highly uncertain. In this paper, we proposed graph probabilistic dependencies (GPDs) to address the issue of uncertainty over these large-scale graphs with a novel class of dependencies for graphs. GPDs can provide a probabilistic explanation for dealing with uncertainty while discovering dependencies over graphs. Furthermore, a case study is provided to verify the correctness of the data integration process based on GPDs. Preliminary results demonstrated the effectiveness of GPDs in terms of reducing redundancies and inconsistencies over the benchmark datasets.
Enhanced Quality of Life and Smart Living, 2017
A revolutionized wave of intelligent assistants has emerged in daily life of human over the recen... more 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.
최근 의학 기술이 눈부시게 발전함에 따라 사람들은 수명이 연장되고 삶의 질 향상에 많은 관심을 가지게 되었다. 더욱이 혁신적인 디지털 기술 발전과 함께 다양한 웨어러블 기기와... more 최근 의학 기술이 눈부시게 발전함에 따라 사람들은 수명이 연장되고 삶의 질 향상에 많은 관심을 가지게 되었다. 더욱이 혁신적인 디지털 기술 발전과 함께 다양한 웨어러블 기기와 수많은 헬스케어 어플리케이션이 출시되고 있으며, 이들은 어떻게 하면 개인의 성향이나 체질에 잘 맞는 맞춤형 (개인화) 서비스를 제공할 수 있을 것인가에 관심을 두고 진화하고 있다. 따라서 IoT 환경의 일상생활에서 입력되는 센서 데이터의 수집, 처리, 가공 기술, 일상 행위 및 라이프 스타일 인지, 지식 획득 및 관리 기술, 개인화 추천서비스 제공, 프라이버시 및 보안을 통합적으로 지원할 수 있는 프레임워크 개발에 대한 요구가 증대되고 있다. 이에 본 고에서는 저자가 개발중인 개인 맞춤 건강 및 웰니스 서비스를 제공하는 마이닝 마인즈 프레임워크를 소개한다. 마이닝 마인즈는 현존하는 최신 기술의 집약체로 개인화, 큐레이션, 빅 데이터 처리, 클라우드 컴퓨팅의 활용, 다양한 센서 정보의 수집과 분석, 진화형 지식의 생성과 관리, UI/UX를 통한 습관화 유도 등 다양한 요소를 포함한다. 그리고 건강 및 웰니스 프레임워크 요구사항 분석을 통해 마이닝 마인즈가 이러한 요구를 충족시킬 수 있으며, 개발된 프로토타입을 통해 개인화 서비스의 발전 가능성을 입증하고 향후 나아가야 할 방향을 제시한다.
2020 International Conference on Information Networking (ICOIN), 2020
No doubt that we are living in the era of Big Data, where we are noticing the expansion of smart ... more No doubt that we are living in the era of Big Data, where we are noticing the expansion of smart healthcare devices. The main obstacles for the Healthcare platform researchers in choosing the right Big Data tool to process unstructured data. Therefore, the current area of research is shifted from massive storage to efficiently analyze the data. This paper aims to present state-of-the-art Big Data analytics tools and presented the Intelligent Medical Platform (IMP) as a case study in dealing with the multimodal data. The result shows that the proposed platform is scalable in dealing with health care data.
2019 Conference on Next Generation Computing Applications (NextComp), 2019
User eXperience (UX) evaluation in the field of Mobile Augmented Reality (MAR) is a challenging t... more User eXperience (UX) evaluation in the field of Mobile Augmented Reality (MAR) is a challenging task, which requires the application of many heterogeneous methods, producing a variety of raw signals and subjective data. This multi-method approach is essential for capturing the holistic UX of any product, service or system. In order to convert this data into information and subsequently knowledge, a comprehensive and scalable system is required which can not only quantify the individual UX metrics but also produce a concise result, which is interpretable by anyone. We call this result, the User Experience Measurement Index, and in this paper we present the results of adopting the mixed method UX evaluation approach for evaluating a prototype MAR application using various methods and sensors, applied before, during, and after its usage. Additionally, we present the methodology and results for calculating the UXMI.
Advances in Intelligent Systems and Computing, 2019
Advanced Metering Infrastructure (AMI) is the aggregation of smart meters, communications network... more Advanced Metering Infrastructure (AMI) is the aggregation of smart meters, communications networks, and data management systems that are tailored to meet the efficient integration of renewable energy resources. The more complex features and soundless functionalities the AMI is enhanced with, the more cyber security concerns are raised and must be taken into consideration. It is imperative to assure consumer’s privacy and security to guarantee the proliferation of rolling out smart metering infrastructure. This research paper analyzes AMI from security perspectives; it discusses the possible vulnerabilities associated with different attack surfaces in the smart meter, their security and threat implications, and finally it recommends proper security controls and countermeasures. The research findings draw the foundation upon which robust security by design approach is geared for the deployment of the AMI in the future.
2020 International Conference on Information Networking (ICOIN), 2020
Data Interoperability is a critical part of achieving healthcare interoperability. In this paper,... more Data Interoperability is a critical part of achieving healthcare interoperability. In this paper, we present a novel methodology for automating the schema matching process at attribute level, among three non-standard and serialized schemas. We achieved 71.8% mappings, using a four stage process with string matching, longest common substring matching using suffix tree, and ConceptNet lookup.
Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 2019
Advancements in the field of healthcare information management have led to the development of a p... more Advancements in the field of healthcare information management have led to the development of a plethora of software, medical devices and standards. As a consequence, the rapid growth in quantity and quality of medical data has compounded the problem of heterogeneity; thereby decreasing the effectiveness and increasing the cost of diagnostics, treatment and follow-up. However, this problem can be resolved by using a semi-structured data storage and processing engine, which can extract semantic value from a large volume of patient data, produced by a variety of data sources, at variable rates and conforming to different abstraction levels. Going beyond the traditional relational model and by re-purposing state-of-the-art tools and technologies, we present, the Ubiquitous Health Profile (UHPr), which enables a semantic solution to the data interoperability problem, in the domain of healthcare1.
Computer Methods and Programs in Biomedicine, 2020
Acquiring guideline-enabled data driven clinical knowledge model using formally verified refined ... more Acquiring guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition method'. Computer Methods and Programs in Biomedicine, pp. 1-26.
Computing, 2020
The lack of Interoperable healthcare data presents a major challenge, towards achieving ubiquitou... more 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.
Computer, 2020
Titanium-coated surfaces are prone to tiny defects such as very small cracks, which are not easil... more Titanium-coated surfaces are prone to tiny defects such as very small cracks, which are not easily observable by the naked eye or optical microscopy. In this study, two new thresholding methods, namely contrast-adjusted Otsu's method and contrast-adjusted median-based Otsu's method, are proposed for automated defect detection system for titanium-coated aluminum surfaces. The two proposed methods were compared with four existing thresholding techniques in terms of accuracy and speed of defect detections for images of 700, 900, and 1000 dpi obtained using high-resolution scanning. Experimental results have shown that the proposed contrast-adjusting methods have performance similar to minimum error thresholding (MET) and are generally better than Otsu's method.
Applied Sciences, 2019
Label noises exist in many applications, and their presence can degrade learning performance. Res... more 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 ma...
Journal of Information Science, 2019
Recently, social media have been used by researchers to detect depressive symptoms in individuals... more Recently, social media have been used by researchers to detect depressive symptoms in individuals using linguistic data from users’ posts. In this study, we propose a framework to identify social information as a significant predictor of depression. Using the proposed framework, we develop an application called the Socially Mediated Patient Portal (SMPP), which detects depression-related markers in Facebook users by applying a data-driven approach with machine learning classification techniques. We examined a data set of 4350 users who were evaluated for depression using the Center for Epidemiological Studies Depression (CES-D) scale. From this analysis, we identified a set of features that can distinguish between individuals with and without depression. Finally, we identified the dominant features that adequately assess individuals with and without depression on social media. The model trained on these features will be helpful to physicians in diagnosing mental diseases and psychia...
International Journal of Medical Informatics, 2019
Background: Diabetic Retinopathy (DR) is considered a pathology of retinal vascular complications... more Background: 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,
Validation and verification are the critical requirements in the knowledge acquisition method for... more Validation and verification are the critical requirements in the knowledge acquisition method for 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 for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data in the Smart CDSS for treatment of oral cavity cancer. The final knowledge model was created by combining knowledge models obtained from CPGs and patient data after passing through a rigorous validation process. However, detailed analysis shows that 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. Therefore, it is required to enhance a hybrid ...
IEEE Access, 2018
Data-driven knowledge acquisition is one of the key research fields in data mining. Dealing with ... more 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. INDEX TERMS Knowledge engineering, data mining, features ranking, algorithm selection, decision tree, production rule, user experience. I. INTRODUCTION Knowledge systems have come a long way, from manual knowledge curation to automatic data-driven knowledge generation. The major drivers of this transition were the size and complexity of data. Since large datasets cannot be efficiently analyzed manually, the automation process is essential [2]. Initially in this process of knowledge automation, knowledge engineers followed ad-hoc procedures [3]. Later on, more systematic methodologies were devised, which can be referred to as data-driven knowledge acquisition systems. Knowledge extraction from structured sources such as databases is an active area of research in the information
Expert Systems with Applications, 2018
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights A method for cross-context interpretations of health and wellness recommendations. A mechanism of refining generalized recommendations to personalized recommendations. The contextual interpretations are made for increasing the user acceptability of a system.
Sustainability, 2021
In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as ... more 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...
Telemedicine and e-Health, 2013
Objective: Data interoperability among health information exchange (HIE) systems is a major conce... more Objective: Data interoperability among health information exchange (HIE) systems is a major concern for healthcare practitioners to enable provisioning of telemedicine-related services. Heterogeneity exists in these systems not only at the data level but also among different heterogeneous healthcare standards with which these are compliant. The relationship between healthcare organization data and different heterogeneous standards is necessary to achieve the goal of data level interoperability. We propose a personalizeddetailed clinical model (P-DCM) approach for the generation of customized mappings that creates the necessary linkage between organization-conformed healthcare standards concepts and clinical model concepts to ensure data interoperability among HIE systems. Materials and Methods: We consider electronic health record (EHR) standards, openEHR, and HL7 CDA instances transformation using P-DCM. P-DCM concepts associated with openEHR and HL7 CDA help in transformation of instances among these standards. We investigated two datasets: (1) data of 100 diabetic patients, including 50 each of type 1 and type 2, from a local hospital in Korea and (2) data of a single Alzheimer's disease patient. P-DCMs were created for both scenarios, which provided the basis for deriving instances for HL7 CDA and openEHR standards. Results: For proof of concept, we present case studies of encounter information for type 2 diabetes mellitus patients and monitoring of daily routine activities of an Alzheimer's disease patient. These reflect P-DCM-based customized mappings generation with openEHR and HL7 CDA standards. Customized mappings are generated based on the relationship of P-DCM concepts with CDA and openEHR concepts. Conclusions: The objective of this work is to achieve semantic data interoperability among heterogeneous standards. This would lead to effective utilization of resources and allow timely information exchange among healthcare systems.
White Leghorn layer breeder hens, 30 weeks of age, were divided into 12 groups (A-L). Group A was... more White Leghorn layer breeder hens, 30 weeks of age, were divided into 12 groups (A-L). Group A was kept on basal feed and served as control, while group B was offered feed supplemented with vitamin E (100 mg/Kg). Groups C-G were offered feed containing 100, 500, 2,500, 5,000 and 10,000 µg/Kg aflatoxin B1 (AFB1), respectively, whereas groups H-L were offered same dietary levels of AFB1 along with vitamin E (100 mg/Kg). The experimental feeds were offered for three weeks and afterward all the groups were switched over to basal feed for next two weeks. Body weight, absolute and relative weights of liver and kidneys of AF fed birds were significantly higher than control group. Pathological lesions in aflatoxin (AF) fed birds included enlarged, pale and friable liver, swollen kidneys and hemorrhages on different organs. Histopathological lesions in liver included fatty change, congestion and hemorrhages, while in kidneys tubular necrosis, cellular infiltration, congestion and hemorrhages were found in groups fed AFB1 at 500 µg/Kg and higher doses. In AF fed hens, no significant ameliorative effects of vitamin E could be observed upon AF induced decrease in feed intake, gross pathology and histopathological alterations and organ weight except body weights. It was concluded that the vitamin E ameliorated the AFB1 induced toxic effects in some of parameters studied.
2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), 2020
The diversity and proliferation of Knowledge bases have made data integration one of the key chal... more The diversity and proliferation of Knowledge bases have made data integration one of the key challenges in the data science domain. The imperfect representations of entities, particularly in graphs, add additional challenges in data integration. Graph dependencies (GDs) were investigated in existing studies for the integration and maintenance of data quality on graphs. However, the majority of graphs contain plenty of duplicates with high diversity. Consequently, the existence of dependencies over these graphs becomes highly uncertain. In this paper, we proposed graph probabilistic dependencies (GPDs) to address the issue of uncertainty over these large-scale graphs with a novel class of dependencies for graphs. GPDs can provide a probabilistic explanation for dealing with uncertainty while discovering dependencies over graphs. Furthermore, a case study is provided to verify the correctness of the data integration process based on GPDs. Preliminary results demonstrated the effectiveness of GPDs in terms of reducing redundancies and inconsistencies over the benchmark datasets.
Enhanced Quality of Life and Smart Living, 2017
A revolutionized wave of intelligent assistants has emerged in daily life of human over the recen... more 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.
최근 의학 기술이 눈부시게 발전함에 따라 사람들은 수명이 연장되고 삶의 질 향상에 많은 관심을 가지게 되었다. 더욱이 혁신적인 디지털 기술 발전과 함께 다양한 웨어러블 기기와... more 최근 의학 기술이 눈부시게 발전함에 따라 사람들은 수명이 연장되고 삶의 질 향상에 많은 관심을 가지게 되었다. 더욱이 혁신적인 디지털 기술 발전과 함께 다양한 웨어러블 기기와 수많은 헬스케어 어플리케이션이 출시되고 있으며, 이들은 어떻게 하면 개인의 성향이나 체질에 잘 맞는 맞춤형 (개인화) 서비스를 제공할 수 있을 것인가에 관심을 두고 진화하고 있다. 따라서 IoT 환경의 일상생활에서 입력되는 센서 데이터의 수집, 처리, 가공 기술, 일상 행위 및 라이프 스타일 인지, 지식 획득 및 관리 기술, 개인화 추천서비스 제공, 프라이버시 및 보안을 통합적으로 지원할 수 있는 프레임워크 개발에 대한 요구가 증대되고 있다. 이에 본 고에서는 저자가 개발중인 개인 맞춤 건강 및 웰니스 서비스를 제공하는 마이닝 마인즈 프레임워크를 소개한다. 마이닝 마인즈는 현존하는 최신 기술의 집약체로 개인화, 큐레이션, 빅 데이터 처리, 클라우드 컴퓨팅의 활용, 다양한 센서 정보의 수집과 분석, 진화형 지식의 생성과 관리, UI/UX를 통한 습관화 유도 등 다양한 요소를 포함한다. 그리고 건강 및 웰니스 프레임워크 요구사항 분석을 통해 마이닝 마인즈가 이러한 요구를 충족시킬 수 있으며, 개발된 프로토타입을 통해 개인화 서비스의 발전 가능성을 입증하고 향후 나아가야 할 방향을 제시한다.
2020 International Conference on Information Networking (ICOIN), 2020
No doubt that we are living in the era of Big Data, where we are noticing the expansion of smart ... more No doubt that we are living in the era of Big Data, where we are noticing the expansion of smart healthcare devices. The main obstacles for the Healthcare platform researchers in choosing the right Big Data tool to process unstructured data. Therefore, the current area of research is shifted from massive storage to efficiently analyze the data. This paper aims to present state-of-the-art Big Data analytics tools and presented the Intelligent Medical Platform (IMP) as a case study in dealing with the multimodal data. The result shows that the proposed platform is scalable in dealing with health care data.
2019 Conference on Next Generation Computing Applications (NextComp), 2019
User eXperience (UX) evaluation in the field of Mobile Augmented Reality (MAR) is a challenging t... more User eXperience (UX) evaluation in the field of Mobile Augmented Reality (MAR) is a challenging task, which requires the application of many heterogeneous methods, producing a variety of raw signals and subjective data. This multi-method approach is essential for capturing the holistic UX of any product, service or system. In order to convert this data into information and subsequently knowledge, a comprehensive and scalable system is required which can not only quantify the individual UX metrics but also produce a concise result, which is interpretable by anyone. We call this result, the User Experience Measurement Index, and in this paper we present the results of adopting the mixed method UX evaluation approach for evaluating a prototype MAR application using various methods and sensors, applied before, during, and after its usage. Additionally, we present the methodology and results for calculating the UXMI.
Advances in Intelligent Systems and Computing, 2019
Advanced Metering Infrastructure (AMI) is the aggregation of smart meters, communications network... more Advanced Metering Infrastructure (AMI) is the aggregation of smart meters, communications networks, and data management systems that are tailored to meet the efficient integration of renewable energy resources. The more complex features and soundless functionalities the AMI is enhanced with, the more cyber security concerns are raised and must be taken into consideration. It is imperative to assure consumer’s privacy and security to guarantee the proliferation of rolling out smart metering infrastructure. This research paper analyzes AMI from security perspectives; it discusses the possible vulnerabilities associated with different attack surfaces in the smart meter, their security and threat implications, and finally it recommends proper security controls and countermeasures. The research findings draw the foundation upon which robust security by design approach is geared for the deployment of the AMI in the future.
2020 International Conference on Information Networking (ICOIN), 2020
Data Interoperability is a critical part of achieving healthcare interoperability. In this paper,... more Data Interoperability is a critical part of achieving healthcare interoperability. In this paper, we present a novel methodology for automating the schema matching process at attribute level, among three non-standard and serialized schemas. We achieved 71.8% mappings, using a four stage process with string matching, longest common substring matching using suffix tree, and ConceptNet lookup.
Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 2019
Advancements in the field of healthcare information management have led to the development of a p... more Advancements in the field of healthcare information management have led to the development of a plethora of software, medical devices and standards. As a consequence, the rapid growth in quantity and quality of medical data has compounded the problem of heterogeneity; thereby decreasing the effectiveness and increasing the cost of diagnostics, treatment and follow-up. However, this problem can be resolved by using a semi-structured data storage and processing engine, which can extract semantic value from a large volume of patient data, produced by a variety of data sources, at variable rates and conforming to different abstraction levels. Going beyond the traditional relational model and by re-purposing state-of-the-art tools and technologies, we present, the Ubiquitous Health Profile (UHPr), which enables a semantic solution to the data interoperability problem, in the domain of healthcare1.
Computer Methods and Programs in Biomedicine, 2020
Acquiring guideline-enabled data driven clinical knowledge model using formally verified refined ... more Acquiring guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition method'. Computer Methods and Programs in Biomedicine, pp. 1-26.
Computing, 2020
The lack of Interoperable healthcare data presents a major challenge, towards achieving ubiquitou... more 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.
Computer, 2020
Titanium-coated surfaces are prone to tiny defects such as very small cracks, which are not easil... more Titanium-coated surfaces are prone to tiny defects such as very small cracks, which are not easily observable by the naked eye or optical microscopy. In this study, two new thresholding methods, namely contrast-adjusted Otsu's method and contrast-adjusted median-based Otsu's method, are proposed for automated defect detection system for titanium-coated aluminum surfaces. The two proposed methods were compared with four existing thresholding techniques in terms of accuracy and speed of defect detections for images of 700, 900, and 1000 dpi obtained using high-resolution scanning. Experimental results have shown that the proposed contrast-adjusting methods have performance similar to minimum error thresholding (MET) and are generally better than Otsu's method.
Applied Sciences, 2019
Label noises exist in many applications, and their presence can degrade learning performance. Res... more 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 ma...
Journal of Information Science, 2019
Recently, social media have been used by researchers to detect depressive symptoms in individuals... more Recently, social media have been used by researchers to detect depressive symptoms in individuals using linguistic data from users’ posts. In this study, we propose a framework to identify social information as a significant predictor of depression. Using the proposed framework, we develop an application called the Socially Mediated Patient Portal (SMPP), which detects depression-related markers in Facebook users by applying a data-driven approach with machine learning classification techniques. We examined a data set of 4350 users who were evaluated for depression using the Center for Epidemiological Studies Depression (CES-D) scale. From this analysis, we identified a set of features that can distinguish between individuals with and without depression. Finally, we identified the dominant features that adequately assess individuals with and without depression on social media. The model trained on these features will be helpful to physicians in diagnosing mental diseases and psychia...
International Journal of Medical Informatics, 2019
Background: Diabetic Retinopathy (DR) is considered a pathology of retinal vascular complications... more Background: 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,
Validation and verification are the critical requirements in the knowledge acquisition method for... more Validation and verification are the critical requirements in the knowledge acquisition method for 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 for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data in the Smart CDSS for treatment of oral cavity cancer. The final knowledge model was created by combining knowledge models obtained from CPGs and patient data after passing through a rigorous validation process. However, detailed analysis shows that 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. Therefore, it is required to enhance a hybrid ...
IEEE Access, 2018
Data-driven knowledge acquisition is one of the key research fields in data mining. Dealing with ... more 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. INDEX TERMS Knowledge engineering, data mining, features ranking, algorithm selection, decision tree, production rule, user experience. I. INTRODUCTION Knowledge systems have come a long way, from manual knowledge curation to automatic data-driven knowledge generation. The major drivers of this transition were the size and complexity of data. Since large datasets cannot be efficiently analyzed manually, the automation process is essential [2]. Initially in this process of knowledge automation, knowledge engineers followed ad-hoc procedures [3]. Later on, more systematic methodologies were devised, which can be referred to as data-driven knowledge acquisition systems. Knowledge extraction from structured sources such as databases is an active area of research in the information
Expert Systems with Applications, 2018
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights A method for cross-context interpretations of health and wellness recommendations. A mechanism of refining generalized recommendations to personalized recommendations. The contextual interpretations are made for increasing the user acceptability of a system.