Papers by Muhammad Asif Razzaq
Multimedia Systems, 2020
Human activity recognition (HAR) is an important branch of human-centered research. Advances in w... more Human activity recognition (HAR) is an important branch of human-centered research. Advances in wearable and unobtrusive technologies offer many opportunities for HAR. While much progress has been made in HAR using wearable technology, it still remains a challenging task using unobtrusive (non-wearable) sensors. This paper investigates detection and tracking of multi-occupant HAR in a smart-home environment, using a novel low-resolution Thermal Vision Sensor (TVS). Specifically, the research presents the development and implementation of a two-step framework, consisting of a Computer Vision (CV) based method to detect and track multiple occupants combined with Convolutional Neural Network (CNN) based HAR. The proposed algorithm uses frame-difference over consecutive frames for occupant detection, a set of morphological operations to refine identified objects, and features are extracted before applying a Kalman filter for tracking. Laterally, a 19-layer CNN architecture is used for HAR and afterward the results from both methods are fused using time interval based sliding window. This approach is evaluated through a series of experiments based on benchmark Thermal Infrared datasets (VOT-TIR2016) and multi-occupant data collected from TVS. Results demonstrate that the proposed framework is capable of detecting and tracking 88.46% of multi
— In recent years, healthcare and wellness platforms are developed rapidly with the advent of sma... more — In recent years, healthcare and wellness platforms are developed rapidly with the advent of smart devices which possess diverse sensors. Existing systems are limited to provide simple health status visualization services from single device or single sensor, which make them unable to provide timely high quality services. This paper proposes Human Centric Awareness Framework based on diverse IoT devices to provide high quality services timely.
—The combination of ontology based context-awareness and machine learning context classification ... more —The combination of ontology based context-awareness and machine learning context classification is an interesting research area. The determined contexts are obtained using semantic reasoning based on context ontology developed by expert using domain specific rules. This reasoning suffer challenges of soundness and completeness in real-time deployment. This paper addresses the aforementioned challenges from semantic reasoning by embracing machine learning modeling and classification benefits. Machine learning relies on data, for this we developed training and deployment phase for ontological ABox assertions. Approximately 99.99% precision through machine learning approach was achieved over 91.5% accuracy with semantic reasoning. The statistical evaluation proves the improvement in terms of accuracy for context prediction and overall performance.
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.
Abstract—A revolutionized wave of intelligent assistants has emerged in daily life of human ove... more Abstract—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. This paper proposes Context-aware Dialogue Management (CADM) framework using speech based interaction with Healthcare systems such as CDSS.
A B S T R A C T Case-Based Learning (CBL) has become an effective pedagogy for student-centered l... more A B S T R A C T Case-Based Learning (CBL) has become an effective pedagogy for student-centered learning in medical education, which is founded on persistent patient cases. Flippped learning and Internet of Things (IoTs) concepts have gained significant attention in recent years. Using these concepts in conjunction with CBL can improve learning ability by providing real evolutionary medical cases. It also enables students to build confidence in their decision making, and efficiently enhances teamwork in the learning environment. We propose an IoT-based Flip Learning Platform, called IoTFLiP, where an IoT infrastructure is exploited to support flipped case-based learning in a cloud environment with state of the art security and privacy measures for personalized medical data. It also provides support for application delivery in private, public, and hybrid approaches. The proposed platform is an extension of our Interactive Case-Based Flipped Learning Tool (ICBFLT), which has been developed based on current CBL practices. ICBFLT formulates summaries of CBL cases through synergy between students' and medical expert knowledge. The low cost and reduced size of sensor device, support of IoTs, and recent flipped learning advancements can enhance medical students' academic and practical experiences. In order to demonstrate a working scenario for the proposed IoTFLiP platform, real-time data from IoTs gadgets is collected to generate a real-world case for a medical student using ICBFLT.
The emerging research on automatic identification of user's contexts from the cross-domain enviro... more 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.
Recent years have witnessed a huge progress in the automatic identification of individual primiti... more Recent years have witnessed a huge progress in the automatic identification of individual primitives of human behavior, such as activities or locations. However, the complex nature of human behavior demands more abstract contextual information for its analysis. This work presents an ontology-based method that combines low-level primitives of behavior, namely activity, locations and emotions, unprecedented to date, to intelligently derive more meaningful high-level context information. The paper contributes with a new open ontology describing both low-level and high-level context information, as well as their relationships. Furthermore, a framework building on the developed ontology and reasoning models is presented and evaluated. The proposed method proves to be robust while identifying high-level contexts even in the event of erroneously-detected low-level contexts. Despite reasonable inference times being obtained for a relevant set of users and instances, additional work is required to scale to long-term scenarios with a large number of users.
There is sufficient evidence proving the impact that negative lifestyle choices have on people's ... more There is sufficient evidence proving the impact that negative lifestyle choices have on people's health and wellness. Changing unhealthy behaviours requires raising people's self-awareness and also providing healthcare experts with a thorough and continuous description of the user's conduct. Several monitoring techniques have been proposed in the past to track users' behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific component of context, such as activity counters. This work presents an innovative multimodal context mining framework to inspect and infer human behaviour in a more holistic fashion. The proposed approach extends beyond the state-of-the-art, since it not only explores a sole type of context, but also combines diverse levels of context in an integral manner. Namely, low-level contexts, including activities, emotions and locations, are identified from heterogeneous sensory data through machine learning techniques. Low-level contexts are combined using ontological mechanisms to derive a more abstract representation of the user's context, here referred to as high-level context. An initial implementation of the proposed framework supporting real-time context identification is also presented. The developed system is evaluated for various realistic scenarios making use of a novel multimodal context open dataset and data on-the-go, demonstrating prominent context-aware capabilities at both low and high levels. The last World Health Organization (WHO) global status report on noncommunicable diseases reveals that illnesses associated with lifestyle choices are currently the leading cause of death worldwide [1]. As a matter of fact, non-communicable diseases are responsible for more than two-thirds of the world's deaths, with more than 40% of them representing premature deaths under the age of 70 years. Recognizing this seriously worrying epidemic scenario, the WHO has defined a clear roadmap to alter the course of the so-called " slow-moving public health disaster ". Most of the policies
Case-Based Learning (CBL) has become an effective pedagogy for student-centered learning
in medic... more Case-Based Learning (CBL) has become an effective pedagogy for student-centered learning
in medical education, which builds its foundation on persisted patient cases. Flip learning and
Internet of Things (IoTs) concepts have gained much attention in the recent years. These concepts with CBL can improve learning capabilities by providing real and evolutionary medical
cases. It also enables students to build confidence in decision making, and to enhance teamwork environment efficiently. This paper proposes an IoT-based Flip Learning Platform, called
IoTFLiP, where IoT infrastructure is exploited to support Flipped case-based learning in cloud
environment with state of the art security and privacy measures for the potential personalized
medical data. It also provides the support for application delivery in private, public, and hybrid
approaches. The proposed platform is the extension of our Interactive Case-Based Flip Learning Tool (ICBFLT) that is developed based on the current CBL practices. ICBFLT formulates the
summaries of CBL cases through synergies of students’ as well as medical experts’ knowledge.
Due to low cost and with reduced sensing devices’ size, support of IoTs, and recent flip learning concepts can enhance medical students’ academic and practical experiences. To demonstrate
the working scenario of proposed IoTFLiP platform, a real-time data through IoTs gadgets is
collected to generate a real-life situation case for a medical student using ICBFLT.
While reasoners are year after year scaling up in the classical, time invariant domain of ontolog... more While reasoners are year after year scaling up in the classical, time invariant domain of ontological knowledge, reasoning upon rapidly dynamic information has been neglected, forgotten or less attention given. On the contrary, processing of data streams has been largely investigated and specialized Database Stream Management Systems (DSMS) and Complex Event Processing (CEP) also exist. In this paper, by coupling reasoners with powerful, reactive, throughput-efficient DSMS, we propose conceptual model for Real time stream reasoning over RDF streams obtained from heterogeneous data streams. This conceptual model is believed to be having capability of handling continuous query answering in real-time with an efficient way and having better throughput.
2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 2014
The significance of social media has already been proven in provoking transformation of public op... more The significance of social media has already been proven in provoking transformation of public opinion for developed countries in improving democratic process of elections. On the contrary, developing countries lacking basic necessities of life possess monopolistic electoral system in which candidates are elected based on tribes, family backgrounds, or landlord influences. They extort voters to cast votes against their promises for the provision of basic needs. Similarly voters also poll votes for personal interests being unaware of party manifesto or national interest. These issues can be addressed by social media, resulting as ongoing process of improvement for presently adopted electoral procedures. People of Pakistan utilized social media to garner support and campaign for political parties in General Elections 2013. Political leaders, parties, and people of Pakistan disseminated party's agenda and advocacy of party's ideology on Twitter without much campaigning cost. To study effectiveness of social media inferred from individual's political behavior, large scale analysis, sentiment detection & tweet classification was done in order to classify, predict and forecast election results. The experimental results depicts that social media content can be used as an effective indicator for capturing political behaviors of different parties Positive, negative and neutral behavior of the party followers as well as party's campaign impact can be predicted from the analysis. The analytical findings proved to be having considerable correspondence with actual results as published by Election Commission of Pakistan..
The emergence of smartness in many domains for facilitating the life has boomed in recent years, ... more The emergence of smartness in many domains for facilitating the life has boomed in recent years, one of prominent area is Healthcare. Many of the developed applications for healthcare, use diagnosis/recommendation mechanism for facilitating physicians and patients. We propose a novel approach that reinforces physician diagnosis, guidelines and recommendation by Semantic knowledge inferencing. Upon receiving any diagnosis/recommendation our model unveils hidden knowledge through reasoning over Domain Specific Clinical Model DSCM thus enhancing authenticity of clinical decisions. This approach provides first-time ever combination of rule-based reasoning, automatic reasoner selection and updation of DSCMs. The clinical domain consists of heterogeneous DSCMs also termed as Ontologies.
Lecture Notes in Computer Science, 2015
International Journal of Computer Theory and Engineering, 2013
Today in the era of fast, modern and continuous changing world, we need to enhance and improve th... more Today in the era of fast, modern and continuous changing world, we need to enhance and improve the existing routing and switching protocols for better performance. OpenFlow switches enable researchers to test and examine the behavior of newly designed protocol in their local environment without disturbing the existing production flow. This allows us to provide the minimize delay in production packet forwarding and maximum flexibility in controlling the flow of experimental packets through the network. In this paper, we have demonstrated to run a network topology on OpenFlow Virtual Machine Simulator (VMS) using NOX controller. Monitoring of flow tables on various switches has been analyzed. Later, we have developed a number of NOX controller components using C++ code to restrict Address resolution Protocol (ARP) on our network and to define dynamic programmable flows on OpenFlow switches.
2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 2014
The significance of social media has already been proven in provoking transformation of public op... more The significance of social media has already been proven in provoking transformation of public opinion for developed countries in improving democratic process of elections. On the contrary, developing countries lacking basic necessities of life possess monopolistic electoral system in which candidates are elected based on tribes, family backgrounds, or landlord influences. They extort voters to cast votes against their promises for the provision of basic needs. Similarly voters also poll votes for personal interests being unaware of party manifesto or national interest. These issues can be addressed by social media, resulting as ongoing process of improvement for presently adopted electoral procedures. People of Pakistan utilized social media to garner support and campaign for political parties in General Elections 2013. Political leaders, parties, and people of Pakistan disseminated party's agenda and advocacy of party's ideology on Twitter without much campaigning cost. To study effectiveness of social media inferred from individual's political behavior, large scale analysis, sentiment detection & tweet classification was done in order to classify, predict and forecast election results. The experimental results depicts that social media content can be used as an effective indicator for capturing political behaviors of different parties Positive, negative and neutral behavior of the party followers as well as party's campaign impact can be predicted from the analysis. The analytical findings proved to be having considerable correspondence with actual results as published by Election Commission of Pakistan..
2013 IEEE 9th International Conference on Emerging Technologies (ICET), 2013
Social media is gaining popularity due to its information spreading feature. Twitter is one of th... more Social media is gaining popularity due to its information spreading feature. Twitter is one of the most powerful source of information sharing because of its massive users. Consequently, Twitter has become a popular resource in order to analyze the data for different research purposes like social engineering, sentiment analysis, business purposes etc. due to its easy data availability. In Twitter, the information may be categorized as important or un-important Whatever information spreads through re-tweets becomes important or popular. As popular messages contain vital information for the users, one has to study the characteristics of such messages since it is related to breaking news identification, viral marketing and other similar tasks. In this research, we investigate the prediction of the popularity of messages by the number of re-tweets. We transform this task into a classification problem and existing Similarity Learning Algorithm (SiLA) is applied. SiLA, an extension of voted perceptron algorithm, learns the similarity matrix for kNN classification before classifying tweets as either popular or un-popular based on the content features. We classify tweets in binary as well as multi-class classification. For the former case, we consider that either the tweet has been re-tweeted (meaning popular) or not (unpopular). However, in the case of multi-class classification, SiLA uses different popular bands, defined by the number of re-tweet count. The binary classification algorithm achieved 85% accuracy and the multi-class classification achieved 73% accuracy. Experimental results show that learning similarity measures improve the accuracy when compared with other kNN based methods like cosine similarity and Euclidean distance. AfeywonJs-Similarity learning; SiLA algorithm; kNN classification; popular tweets; social networks
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Papers by Muhammad Asif Razzaq
in medical education, which builds its foundation on persisted patient cases. Flip learning and
Internet of Things (IoTs) concepts have gained much attention in the recent years. These concepts with CBL can improve learning capabilities by providing real and evolutionary medical
cases. It also enables students to build confidence in decision making, and to enhance teamwork environment efficiently. This paper proposes an IoT-based Flip Learning Platform, called
IoTFLiP, where IoT infrastructure is exploited to support Flipped case-based learning in cloud
environment with state of the art security and privacy measures for the potential personalized
medical data. It also provides the support for application delivery in private, public, and hybrid
approaches. The proposed platform is the extension of our Interactive Case-Based Flip Learning Tool (ICBFLT) that is developed based on the current CBL practices. ICBFLT formulates the
summaries of CBL cases through synergies of students’ as well as medical experts’ knowledge.
Due to low cost and with reduced sensing devices’ size, support of IoTs, and recent flip learning concepts can enhance medical students’ academic and practical experiences. To demonstrate
the working scenario of proposed IoTFLiP platform, a real-time data through IoTs gadgets is
collected to generate a real-life situation case for a medical student using ICBFLT.
in medical education, which builds its foundation on persisted patient cases. Flip learning and
Internet of Things (IoTs) concepts have gained much attention in the recent years. These concepts with CBL can improve learning capabilities by providing real and evolutionary medical
cases. It also enables students to build confidence in decision making, and to enhance teamwork environment efficiently. This paper proposes an IoT-based Flip Learning Platform, called
IoTFLiP, where IoT infrastructure is exploited to support Flipped case-based learning in cloud
environment with state of the art security and privacy measures for the potential personalized
medical data. It also provides the support for application delivery in private, public, and hybrid
approaches. The proposed platform is the extension of our Interactive Case-Based Flip Learning Tool (ICBFLT) that is developed based on the current CBL practices. ICBFLT formulates the
summaries of CBL cases through synergies of students’ as well as medical experts’ knowledge.
Due to low cost and with reduced sensing devices’ size, support of IoTs, and recent flip learning concepts can enhance medical students’ academic and practical experiences. To demonstrate
the working scenario of proposed IoTFLiP platform, a real-time data through IoTs gadgets is
collected to generate a real-life situation case for a medical student using ICBFLT.