rizwana irfan

Comsats University Islamabad, Computer Science, Faculty Member

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Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

Frontiers in Medicine, 2021

Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic p... more Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds—both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy...

IEEE TRANSACTIONS ON CLOUD COMPUTING 1 MobiContext: A Context-aware Cloud-Based Venue Recommendation Framework

— In recent years, recommendation systems have seen significant evolution in the field of knowled... more — In recent years, recommendation systems have seen significant evolution in the field of knowledge engineering. Most of the existing recommendation systems based their models on collaborative filtering approaches that make them simple to implement. However, performance of most of the existing collaborative filtering-based recommendation system suffers due to the challenges, such as: (a) cold start, (b) data sparseness, and (c) scalability. Moreover, recommendation problem is often characterized by the presence of many conflicting objectives or decision variables, such as users ’ preferences and venue closeness. In this paper, we proposed MobiContext, a hybrid cloud-based Bi-Objective Recommendation Framework (BORF) for mobile social networks. The MobiContext utilizes multi-objective optimization techniques to generate personalized recommendations. To address the issues pertaining to cold start and data sparseness, the BORF performs data preprocessing by using the Hub-Average (HA) i...

doi: 10.1049/iet-net.2013.0009 Survey

on social networking services

Integrated Architecture of Data Warehouse with Business Intelligence Technologies

2018 24th International Conference on Automation and Computing (ICAC), 2018

Business Intelligence (BI) is the process to extract information from data then get knowledge fro... more Business Intelligence (BI) is the process to extract information from data then get knowledge from that information to take the decisions. This paper shows the effectiveness of BI technologies with data warehouse, for decision making. In this paper, we deploy the Integrated Proposed Architecture (IPA) on W category hospital in order to manage and monitor the data effectively for analysis and decision making. The accuracy of IPA is 93% in term of information analysis that is 6% better than Traditional Data warehouse Architecture (TDA). The IPA is also able to support dashboard management, multidimensional data model, perform online analytical processing, perform user authentication and generate dynamic reports via BI technologies.

Framework for Embedding Tacit Knowledge in Pedagogical Model to Enhance E-Learning

2008 New Technologies, Mobility and Security, 2008

AbstractGlobalization has brought with it acute need of knowledge availability, due to which E-l... more

Enhance Knowledge Management Process for Group Decision Making

2010 Second International Conference on Computer Engineering and Applications, 2010

... Rizwana Irfan Dept of Computer Sciences, COMSATS Institute of Information Technology, Pakista... more

AVTMT - The Effect of Multimodal Patterns on Human Memory

Journal of Advances in Information Technology, 2013

This paper presents a computer game which consists of multiple tiles at three different levels. W... more This paper presents a computer game which consists of multiple tiles at three different levels. When users click on each tile, it flips or un-flips. Each tile, upon flipping, reveals a different patterns e.g. audio, image or text. The system matches those patterns. If two flipped tiles have same pattern, they disappear otherwise they un-flip. This research work 1) examines the effects of audio, visual and text patterns on sighted, deaf, and blind users 2) analyzes the working of human's memory depending upon the appearance of audio, visual and text patterns and 3) calculates the performance of each user based on the number of previous interactions made with the game. The total number of clicks and the time taken to finish the game was recorded. The results of the four experiments indicate that different types of users behave differently when presented with specific type of information (audio, visual and text).

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Integrated Architecture of Data Warehouse with Business Intelligence Technologies

Contextualization in Large-Scale Social Networks

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion

Diagnostics

Breast cancer is becoming more dangerous by the day. The death rate in developing countries is ra... more Breast cancer is becoming more dangerous by the day. The death rate in developing countries is rapidly increasing. As a result, early detection of breast cancer is critical, leading to a lower death rate. Several researchers have worked on breast cancer segmentation and classification using various imaging modalities. The ultrasonic imaging modality is one of the most cost-effective imaging techniques, with a higher sensitivity for diagnosis. The proposed study segments ultrasonic breast lesion images using a Dilated Semantic Segmentation Network (Di-CNN) combined with a morphological erosion operation. For feature extraction, we used the deep neural network DenseNet201 with transfer learning. We propose a 24-layer CNN that uses transfer learning-based feature extraction to further validate and ensure the enriched features with target intensity. To classify the nodules, the feature vectors obtained from DenseNet201 and the 24-layer CNN were fused using parallel fusion. The proposed ...

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Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks

Personal and Ubiquitous Computing

The novel human coronavirus disease COVID-19 has become the fifth documented pandemic since the 1... more The novel human coronavirus disease COVID-19 has become the fifth documented pandemic since the 1918 flu pandemic. COVID-19 was first reported in Wuhan, China, and subsequently spread worldwide. Almost all of the countries of the world are facing this natural challenge. We present forecasting models to estimate and predict COVID-19 outbreak in Asia Pacific countries, particularly Pakistan, Afghanistan, India, and Bangladesh. We have utilized the latest deep learning techniques such as Long Short Term Memory networks (LSTM), Recurrent Neural Network (RNN), and Gated Recurrent Units (GRU) to quantify the intensity of pandemic for the near future. We consider the time variable and data non-linearity when employing neural networks. Each model's salient features have been evaluated to foresee the number of COVID-19 cases in the next 10 days. The forecasting performance of employed deep learning models shown up to July 01, 2020, is more than 90% accurate, which shows the reliability of the proposed study. We hope that the present comparative analysis will provide an accurate picture of pandemic spread to the government officials so that they can take appropriate mitigation measures.

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SocialRec: A Context-aware Recommendation Framework with Explicit Sentiment Analysis

IEEE Access

In recent years, recommendation systems have seen significant evolution in the field of knowledge... more In recent years, recommendation systems have seen significant evolution in the field of knowledge engineering. Usually, the recommendation systems match users' preferences based on the star ratings provided by the users for various products. However, simply relying on users' ratings about an item can produce biased opinions, as a user's textual feedback may differ from the item rating provided by the user. In this paper, we propose SocialRec, a hybrid context-aware recommendation framework that utilizes a rating inference approach to incorporate users' textual reviews into traditional collaborative filtering methods for personalized recommendations of various items. We apply text-mining algorithms on a large-scale user-item feedback dataset to compute the sentiment scores. We propose a greedy heuristic to produce ranking of items based on users' social similarities and matching preferences. To address challenges resulting from cold start and data sparseness, SocialRec introduces pre-computation models based on Hub-Average (HA) inference. Rigorous evaluations of SocialRec (on large-scale datasets) demonstrate high accuracy, especially in comparison with previous related frameworks.

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Ontology Learning in Text Mining for Handling Big Data in Healthcare Systems

Journal of Medical Imaging and Health Informatics

Scalability Issues in Online Social Networks

ACM Computing Surveys, 2016

The last decade witnessed a tremendous increase in popularity and usage of social network service... more The last decade witnessed a tremendous increase in popularity and usage of social network services, such as Facebook, Twitter, and YouTube. Moreover, advances in Web technologies coupled with social networks has enabled users to not only access, but also generate, content in many forms. The overwhelming amount of produced content and resulting network traffic gives rise to precarious scalability issues for social networks, such as handling a large number of users, infrastructure management, internal network traffic, content dissemination, and data storage. There are few surveys conducted to explore the different dimensions of social networks, such as security, privacy, and data acquisition. Most of the surveys focus on privacy or security-related issues and do not specifically address scalability challenges faced by social networks. In this survey, we provide a comprehensive study of social networks along with their significant characteristics and categorize social network architect...

Risk Analysis-Relative Impact Ratio

Survey on social networking services

IET Networks, 2013

The social computing, such as social networking services (SNSs) and social Networking Platforms (... more The social computing, such as social networking services (SNSs) and social Networking Platforms (SNPs) provide a coherent medium through which people can be interactive and socialize. The SNP is a Web-based social space, specifically designed for end user-driven applications that facilitate communication, collaboration and sharing of the knowledge through a variety of SNSs, such as text, video and audio streams. In the conventional SNPs, such as Facebook, LinkedIn and MySpace, computers are not capable of acquiring the information based on the common intelligence and human behaviour. This survey provides a comprehensive overview of the current SNSs and discusses different possibilities of incorporating the existing SNSs into the context-aware techniques that include semantic Web, social search and social recommendations. The contextaware computing provides services customisation based on the individual human characteristics, such as human preferences, mood, behaviours, and emotions. The Integration of contextual information with SNSs can be more useful and productive for the development of the intelligent social communicational services. This survey provides several possible future research directions especially in the field of social search and recommendation that can provide better social communication capabilities. Moreover, this study will provide new directions to the future researcher for designing future generation contextbased SNSs that provide services based on the on-demand collaboration.

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MobiContext: A Context-aware Cloud-based Recommendation Framework

IEEE Transactions on Cloud Computing, 2015

A survey on text mining in social networks

The Knowledge Engineering Review, 2015

In this survey, we review different text mining techniques to discover various textual patterns f... more In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networking websites is inherently unstructured and fuzzy in nature. In everyday life conversations, people do not care about the spellings and accurate grammatical construction of a sentence that may lead to different types of ambiguities, such as lexical, syntactic, and semantic. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several surveys have been conducted to analyze different methods for the information extraction. Most of the surveys emphasized on the application of different text mining techniques for unstructured data sets reside in the form of text documents, but do not specifically target ...

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