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.

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.

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.

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.

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.

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.

Effect of interstitial doping on atomic structure and magnetic properties of FeCo alloy

Imran Khan,Jicheol Son,Jisang Hong 한국자기학회 2014 한국자기학회 학술연구발표회 논문개요집 Vol.2014 No.11

Integrating Software Security into Agile-Scrum Method

( Imran Ghani ),( Zulkarnain Azham ),( Seung Ryul Jeong ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.2

Scrum is one of the most popular and efficient agile development methods. However, like other agile methods such as Extreme Programming (XP), Feature Driven Development (FDD), and the Dynamic Systems Development Method (DSDM), Scrum has been criticized because of lack of support to develop secure software. Thus, in 2011, we published research proposing the idea of a security backlog (SB). This paper represents the continuation of our previous research, with a focus on the evaluation in industry-based case study. Our findings highlight an improved agility in Scrum after the integration of SB. Furthermore, secure software can be developed quickly, even in situations involving requirement changes of software. Based on our experimental findings, we noticed that, when integrating SB, it is quite feasible to develop secure software using an agile Scrum model.

Cryptotanshinone promotes commitment to the brown adipocyte lineage and mitochondrial biogenesis in C3H10T1/2 mesenchymal stem cells via AMPK and p38-MAPK signaling

Imran, Khan Mohammad,Rahman, Naimur,Yoon, Dahyeon,Jeon, Miso,Lee, Byong-Taek,Kim, Yong-Sik Elsevier 2017 Biochimica et biophysica acta, Molecular and cell Vol.1862 No.10

<P>Although white adipose tissue (WAT) stores triglycerides and contributes to obesity, brown adipose tissue (BAT) dissipates energy as heat. Therefore, browning of WAT is regarded as an attractive way to counteract obesity. Our previous studies have revealed that treatment with cryptotanshinone (CT) during adipogenesis of 3T3-L1 cells inhibits their differentiation. Here, we found that pretreatment of C3H10T1/2 mesenchymal stem cells with CT before exposure to adipogenic hormonal stimuli promotes the commitment of these mesenchymal stem cells to the adipocyte lineage as confirmed by increased triglyceride accumulation. Furthermore, CT treatment induced the expression of early B-cell factor 2 (Ebf2) and bone morphogenetic protein 7 (Bmp7), which are known to drive differentiation of C3H10T1/2 mesenchymal stem cells toward preadipocytes and to the commitment to brown adipocytes. Consequently, CT treatment yielded brown-adipocyte-like features as evidenced by elevated expression of brown-fat signature genes including Ucp1, Prdm16, Pgc-1 alpha, Cidea, Zic1, and beige-cell-specific genes such as CD137, Hspb7, Cox2, and Tmem26. Additionally, CT treatment induced mitochondrial biogenesis through upregulation of Sirt1, Tfam, Nrf1 and Cox7a and increased mitochondrial mass and DNA content. Our data also showed that cotreatment with CT and BMP4 was more effective at activating brown-adipocyte-specific genes. Mechanistic experiments revealed that treatment with CT activated AMPK alpha and p38-MAPK via their phosphorylation: the two major signaling pathways regulating energy metabolism. Thus, these findings suggest that CT is a candidate therapeutic agent against obesity working via activation of browning and mitochondrial biogenesis in C3H10T1/2 mesenchymal stem cells.</P>

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.