Comparison of characteristics and ventilatory course between coronavirus disease 2019 and Middle East respiratory syndrome patients with acute respiratory distress syndrome

Imran Khalid,Romaysaa M Yamani,Maryam Imran,Muhammad Ali Akhtar,Manahil Imran,Rumaan Gul,Tabindeh Jabeen Khalid,Ghassan Y Wali 대한중환자의학회 2021 Acute and Critical Care Vol.36 No.3

Background: Both coronavirus disease 2019 (COVID-19) and Middle East respiratory syndrome (MERS) can cause acute respiratory distress syndrome (ARDS); however, their ARDS course and characteristics have not been compared, which we evaluate in our study. Methods: MERS patients with ARDS seen during the 2014 outbreak and COVID-19 patients with ARDS admitted between March and December 2020 in our hospital were included, and their clinical characteristics, ventilatory course, and outcomes were compared. Results: Forty-nine and 14 patients met the inclusion criteria for ARDS in the COVID-19 and MERS groups, respectively. Both groups had a median of four comorbidities with high Charlson comorbidity index value of 5 points (P>0.22). COVID-19 patients were older, obese, had significantly higher initial C-reactive protein (CRP), more likely to get trial of high-flow oxygen, and had delayed intubation (P≤0.04). The postintubation course was similar between the groups. Patients in both groups experienced a prolonged duration of mechanical ventilation, and majority received paralytics, dialysis, and vasopressor agents (P>0.28). The respiratory and ventilatory parameters after intubation (including tidal volume, fraction of inspired oxygen, peak and plateau pressures) and their progression over 3 weeks were similar (P>0.05). Rates of mortality in the ICU (53% vs. 64%) and hospital (59% vs. 64%) among COVID-19 and MERS patients (P≥0.54) were very high. Conclusions: Despite some distinctive differences between COVID-19 and MERS patients prior to intubation, the respiratory and ventilatory parameters postintubation were not different. The higher initial CRP level in COVID-19 patients may explain the steroid responsiveness in this population.

Using Machine Learning Algorithms for Housing Price Prediction: The Case of Islamabad Housing Data

Imran, Imran,Zaman, Umar,Waqar, Muhammad,Zaman, Atif Institute of Information Science and Technology 2021 Soft computing and machine intelligence Vol.1 No.1

House price prediction is a significant financial decision for individuals working in the housing market as well as for potential buyers. From investment to buying a house for residence, a person investing in the housing market is interested in the potential gain. This paper presents machine learning algorithms to develop intelligent regressions models for House price prediction. The proposed research methodology consists of four stages, namely Data Collection, Pre Processing the data collected and transforming it to the best format, developing intelligent models using machine learning algorithms, training, testing, and validating the model on house prices of the housing market in the Capital, Islamabad. The data used for model validation and testing is the asking price from online property stores, which provide a reasonable estimate of the city housing market. The prediction model can significantly assist in the prediction of future housing prices in Pakistan. The regression results are encouraging and give promising directions for future prediction work on the collected dataset.

Challenges in Distributed Agile Software Development Environment: A Systematic Literature Review

( Imran Ghani ),( Angelica Lim ),( Muhammad Hasnain ),( Israr Ghani ),( Muhammad Imran Babar ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.9

Due to increasing interest in distributed agile software development, there is a need to systematically review the literature on challenges encountered in the agile software development environment. Using the Systematic Literature Review (SLR) approach, 32 relevant publications, dated between 2013 and 2018 were selected from four electronic databases. Data from these publications were extracted to identify the key challenges across the system development life cycle (SDLC) phases, which essentially are short phases in each agile-based iteration. 5 types of key challenges were identified as impacting the SDLC phases; these challenges are Communication, Coordination, Cooperation, Collaboration and Control. In the context of the SLDC phases, the Communication challenge was discussed the most often (79 times, 33%). The least discussed challenges were Cooperation and Collaboration (26 times, 11% each). The 5 challenges occur because of distances which occur in distributed environment. This SLR identified 4 types of distances which contribute to the occurrence of these key challenges - physical, temporal, social-cultural and knowledge/experience. Of the 32 publications, only 4 included research which proposed new solutions to address challenges in agile distributed software development. The authors of this article believe that the findings in this SLR are a resource for future research work to deepen the understanding of and to develop additional solutions to address the challenges in distributed agile software development.

Time-limited Gramians Based Model Reduction Framework for 1-D and 2-D Systems

Muhammad Imran,Muhammad Imran,Syeda Fizza Hamdani 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.11

Gawronski and Juang provide an unstable reduced-order model formulation without a priori error bounds for the original one- and two-dimensional models. Several strategies were put forth for the standard linear discretetime one-dimensional models to guarantee the stability of the reduced-order model over a given time-intervals. These frameworks produce significant truncation mistakes and lack time-domain error-bound expressions. For discrete-time, two-dimensional Gramians models, there are no stability-preserving frameworks that the authors are aware of. This study suggests a Gramian-based model reduction strategy for discrete-time models. One- and two-dimensional discrete-time models can be employed with the framework. The suggested model reduction approach is applied using time-limited Gramians after the discrete-time two-dimensional causal recursive separable denominator models are split into two sub-models (two one-dimensional cascaded models). The framework ensures reduced-order model stability and offers time-domain a priori error-bound expressions for one- and twodimensional models. Comparisons and numerical results demonstrate the usefulness of the proposed framework.

Effect of increasing dietary metabolizable protein on nitrogen efficiency in Holstein dairy cows

Muhammad Imran,Talat Naseer Pasha,Muhammad Qamer Shahid,Imran Babar,Muhammad Naveed ul Haque 아세아·태평양축산학회 2017 Animal Bioscience Vol.30 No.5

Objective: The objective of the study was to determine the effects of increasing levels of metabolizable protein (MP) on lactation performance and nitrogen (N) efficiencies in lactating dairy cows. Methods: Nine multiparous cows in mid lactation [113±25 days in milk] received three treatments in a 3×3 Latin square design with a period length of 21 days. The treatments were three diets, designed to provide similar energy and increasing supply of MP (g/d) (2,371 [low], 2,561 [medium], and 2,711 [high] with corresponding crude protein levels [%]) 15.2, 18.4, and 20.9, respectively. Results: Increasing MP supplies did not modify dry matter intake, however, it increased milk protein, fat, and lactose yield linearly. Similarly, fat corrected milk increased linearly (9.3%) due to an increase in both milk yield (5.2%) and milk fat content (7.8%). No effects were observed on milk protein and lactose contents across the treatments. Milk nitrogen efficiency (MNE) decreased from 0.26 to 0.20; whereas, the metabolic efficiency of MP decreased from 0.70 to 0.60 in low to high MP supplies, respectively. The concentration of blood urea nitrogen (BUN) increased linearly in response to increasing MP supplies. Conclusion: Increasing MP supplies resulted in increased milk protein yield; however, a higher BUN and low MNE indicated an efficient utilization of dietary protein at low MP supplies.

Feasibility Study of Case-Finding for Breast Cancer by Community Health Workers in Rural Bangladesh

Chowdhury, Touhidul Imran,Love, Richard Reed,Chowdhury, Mohammad Touhidul Imran,Artif, Abu Saeem,Ahsan, Hasib,Mamun, Anwarul,Khanam, Tahmina,Woods, James,Salim, Reza Asian Pacific Journal of Cancer Prevention 2015 Asian Pacific journal of cancer prevention Vol.16 No.17

Background: Mortality from breast cancer is high in low- and middle-income countries, in part because most patients have advanced stage disease when first diagnosed. Case-finding may be one approach to changing this situation. Materials and Methods: We conducted a pilot study to explore the feasibility of population-based case finding for breast cancer by community health workers (CHWs) using different data collection methods and approaches to management of women found to have breast abnormalities. After training 8 CHWs in breast problem recognition, manual paper data collection and operation of a cell-phone software platform for reporting demographic, history and physical finding information, these CHWs visited 3150 women >age 18 and over they could find-- from 2356 households in 8 villages in rural Bangladesh. By 4 random assignments of villages, data were collected manually (Group 1), or with the cell-phone program alone (Group 2) or with management algorithms (Groups 3 and 4), and women adjudged to have a serious breast problem were shown a motivational video (Group 3), or navigated/accompanied to a breast problem center for evaluation (Group 4). Results: Only three visited women refused evaluation. The manual data acquisition group (1) had missing data in 80% of cases, and took an average of 5 minutes longer to acquire, versus no missing data in the cell phone-reporting groups (2,3 and 4). One woman was identified with stage III breast cancer, and was appropriately treated. Conclusions: Among very poor rural Bangladeshi women, there was very limited reluctance to undergo breast evaluation. The estimated rarity of clinical breast cancer is supported by these population-based findings. The feasibility and efficient use of mobile technology in this setting is supported. Successor studies may most appropriately be trials focusing on improving the suggested benefits of motivation and navigation, on increasing the numbers of cases found, and on stage of disease at diagnosis as the primary endpoint.

Blind Adaptive Multiuser Detection for the MC-CDMA Systems Using Orthogonalized Subspace Tracking

Imran Ali,김덕년,Jong Soo Lim 한국전자통신연구원 2009 ETRI Journal Vol.31 No.2

In this paper, we study the performance of subspace-based multiuser detection techniques for multicarrier code-division multiple access (MC-CDMA) systems. We propose an improvement in the PASTd algorithm by cascading it with the classical Gram-Schmidt procedure to orthonormalize the eigenvectors after their sequential extraction. The tracking of signal subspace using this algorithm, which we call OPASTd, has a faster convergence as the eigenvectors are orthonormalized at each discrete time sample. This improved PASTd algorithm is then used to implement the subspace blind adaptive multiuser detection for MC-CDMA. We also show that, for multiuser detection, the complexity of the proposed scheme is lower than that of many other orthogonalization schemes found in the literature. Extensive simulation results are presented and discussed to demonstrate the performance of the proposed scheme.

Energy‐balance node‐selection algorithm for heterogeneous wireless sensor networks

Imran Khan,Dhananjay Singh 한국전자통신연구원 2018 ETRI Journal Vol.40 No.5

To solve the problem of unbalanced loads and the short network lifetime of heterogeneous wireless sensor networks, this paper proposes a node‐selection algorithm based on energy balance and dynamic adjustment. The spacing and energy of the nodes are calculated according to the proximity to the network nodes and the characteristics of the link structure. The direction factor and the energy‐adjustment factor are introduced to optimize the node‐selection probability in order to realize the dynamic selection of network nodes. On this basis, the target path is selected by the relevance of the nodes, and nodes with insufficient energy values are excluded in real time by the establishment of the node‐selection mechanism, which guarantees the normal operation of the network and a balanced energy consumption. Simulation results show that this algorithm can effectively extend the network lifetime, and it has better stability, higher accuracy, and an enhanced data‐receiving rate in sufficient time.

얼굴과 발걸음을 결합한 인식

Imran Fareed Nizami,안성제(Sungje An),홍성준(Sungjun Hong),이희성(Heesung Lee),김은태(Euntai Kim),박민용(Minnon Park) 한국지능시스템학회 2008 한국지능시스템학회논문지 Vol.18 No.1

개인 식별 연구는 보안, 감시 시스템에서 중요한 부분이다. 최선의 성능을 가진 시스템을 설계하기 위하여 감지기들로부터 최대 정보를 이용할 수 있도록 설계한다. 다양한 생체 인식 시스템은 등록, 확인, 또는 개인 식별을 위하여 생리 특성이나 행동 특성을 하나이상 활용한다. 발걸음 인식만을 가지고는 아직 개인별 변별적 특징을 안정적으로 나타내지 못하므로, 본 논문에서는 얼굴과 발걸음을 결합한 개인 식별 시스템을 제안한다. 본 논문에서 우리는 한 개의 카메라를 이용한다. 즉, 얼굴과 발걸음 인식 모두 하나의 카메라를 이용하여 획득된 같은 이미지 셋을 사용한다. 본 논문의 중점은 이미지들에서 이용할 수 있는 최대 정보량을 활용하는 것으로 시스템의 성능을 향상시키는 것이다. 결합은 결정 단계에서 고려된다. 제안된 알고리듬은 NLPR 데이터베이스를 사용한다. Identification of humans from multiple view points is an important task for surveillance and security purposes. For optimal performance the system should use the maximum information available from sensors. Multimodal biometric systems are capable of utilizing more than one physiological or behavioral characteristic for enrollment, verification, or identification. Since gait alone is not yet established as a very distinctive feature, this paper presents an approach to fuse face and gait for identification. In this paper we will use the single camera case i.e. both the face and gait recognition is done using the same set of images captured by a single camera. The aim of this paper is to improve the performance of the system by utilizing the maximum amount of information available in the images. Fusion in considered at decision level. The proposed algorithm is tested on the NLPR database.

FastMap Projection for High-Dimensional Data: A Cluster Ensemble Approach

Imran Khan,Kamen Ivanov,Qingshan Jiang 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.12

High-dimensional data with many features present a significant challenge to current clustering algorithms. Sparsity, noise, and correlation of features are common properties of high-dimensional data. Another essential aspect is that clusters in such data often exist in various subspaces. Ensemble clustering is emerging as a leading technique for improving robustness, stability, and accuracy of high-dimensional data clusterings. In this paper, we propose FastMap projection for generating subspace component data sets from high-dimensional data. By using component data sets, we create component clusterings and provides a new objective function that ensembles them by maximizing the average similarity between component clusterings and final clustering. Compared with the random sampling and random projection methods, the component clusterings by FastMap projection showed high average clustering accuracy without sacrificing clustering diversity in synthetic data analysis. We conducted a series of experiments on real-world data sets from microarray, text, and image domains employing three subspace component data generation methods, three consensus functions, and a proposed objective function for ensemble clustering. The experiment results consistently demonstrated that the FastMap projection method with the proposed objection function provided the best ensemble clustering results for all data sets.