Customer churn prediction in telecommunication industry using data certainty

© 2018 Elsevier Inc. Customer Churn Prediction (CCP) is a challenging activity for decision makers and machine learning community because most of the time, churn and non-churn customers have resembling features. From different experiments on customer churn and related data, it can be seen that a classifier shows different accuracy levels for different zones of a dataset. In such situations, a correlation can easily be observed in the level of classifier\u27s accuracy and certainty of its prediction. If a mechanism can be defined to estimate the classifier\u27s certainty for different zones within the data, then the expected classifier\u27s accuracy can be estimated even before the classification. In this paper, a novel CCP approach is presented based on the above concept of classifier\u27s certainty estimation using distance factor. The dataset is grouped into different zones based on the distance factor which are then divided into two categories as; (i) data with high certainty, and (ii) data with low certainty, for predicting customers exhibiting Churn and Non-churn behavior. Using different state-of-the-art evaluation measures (e.g., accuracy, f-measure, precision and recall) on different publicly available the Telecommunication Industry (TCI) datasets show that (i) the distance factor is strongly co-related with the certainty of the classifier, and (ii) the classifier obtained high accuracy in the zone with greater distance factor\u27s value (i.e., customer churn and non-churn with high certainty) than those placed in the zone with smaller distance factor\u27s value (i.e., customer churn and non-churn with low certainty)

Portrayal of Pakistan Tehreek Insaf (PTI) government in Print Media: Analysis of The Nation & The Express Tribune

The article identifies and describes the coverage pattern of first 100 days of Pakistan Tehreek Insaf (PTI) government performance in the light of agenda setting and framing theories in two leading English dailies of Pakistan: The Nation and The Express Tribune. The front pages of the newspapers were selected for the duration of 100 days to measure the slant, frame, frequency and placement of news stories related to the performance of newly established Pakistan Tehreek Insaf government in the areas of economic affairs and foreign policy. The results indicate that the two leading English dailies cover the activities of the Pakistan Tehreek Insaf (PTI) differently. The Nation published 18 news stories related to economic affairs and foreign policy of the government while The Express Tribune published 24 news stories related to economic affairs and foreign policy of the newly established government on their front pages duration the 100 days study period. The language and theme of most of the articles published by The Nation were favorable towards Pakistan Tehreek Insaf (PTI). The language and theme of the articles of The Express Tribune were more neutral and critical towards the performance of Pakistan Tehreek Insaf (PTI) government in the areas of economic affairs and foreign policy

A prudent based approach for compromised user credentials detection

© Springer Science+Business Media New York 2018. Compromised user credential (CUC) is an activity in which someone, such as a thief, cyber-criminal or attacker gains access to your login credentials for the purpose of theft, fraud, or business disruption. It has become an alarming issue for various organizations. It is not only crucial for information technology (IT) oriented institutions using database management systems (DBMSs) but is also critical for competitive and sensitive organization where faulty data is more difficult to clean up. Various well-known risk mitigation techniques have been developed, such as authentication, authorization, and fraud detection. However, none of these methods are capable of efficiently detecting compromised legitimate users’ credentials. This is because cyber-criminals can gain access to legitimate users’ accounts based on trusted relationships with the account owner. This study focuses on handling CUC on time to avoid larger-scale damage incurred by the cyber-criminals. The proposed approach can efficiently detect CUC in a live database by analyzing and comparing the user’s current and past operational behavior. This novel approach is built by a combination of prudent analysis, ripple down rules and simulated experts. The experiments are carried out on collected data over 6 months from sensitive live DBMS. The results explore the performance of the proposed approach that it can efficiently detect CUC with 97% overall accuracy and 2.013% overall error rate. Moreover, it also provides useful information about compromised users’ activities for decision or policy makers as to which user is more critical and requires more consideration as compared to less crucial user based prevalence value

Capacity Dimensioning of HSDPA Urban Network

To launch a cellular network, prelaunch capacity dimensioning is performed which includes coverage estimation and throughput prediction. Cellular companies in developing countries like Pakistan are only providing 2G services, while 3G services are yet to be launched. Although a lot of research has been done on 3G services in developed countries but there is very little knowledge regarding practical aspects of planning and optimization of 3G networks in third world countries like Pakistan. This research paper includes a thorough analysis of factors that affect capacity of 3G networks, including radio propagation models. Various propagation models are studied and propagation constants of Standard Propagation Model are tuned according to topography of Islamabad. The performance analysis of these propagation models is done using Matlab and results are verified through planning tool Atoll and field measurements. Based on analysis of these results capacity dimensioning, in terms of number of sites, is carried out for an urban network of Islamabad

Clinical Outcomes of physiologically-guided revascularisation

Objective: To assess the clinical outcomes of revascularisation based on fractional flow reserve (FFR) and/or instantaneous wave-free ratio (iFR).Study design: Descriptive study.Place and duration of study: Department of Medicine, The Aga Khan University Hospital, Karachi from January 2012 to January 2020.Methodology: A cohort of patients having moderate to severe coronary stenosis, undergoing coronary revascularisation based on invasive physiological assessment (FFR or iFR) were assessed. The participants were divided into the revascularisation-deferred group and the revascularization-performed group, based on the physiological results. Cox-proportional hazard model building was done, using a stepwise approach by assessing all plausible interactions and considering p-value ≤0.05 as statistically significant.Results: The frequency of major adverse cardiac event (MACE) and target vessel revascularisation was 8.4% and 3.2% in the revascularisation-performed group as compared to 6.4% and 3.2% in the revascularisation-deferred group. In adjusted models, no statistically significant difference was noted in MACE when comparing the revascularisation-performed group with a deferred group.Conclusion: Revascularisation guided by invasive physiological assessment with FFR or iFR is clinically safe and led to better resource utilisation. Key Words: Fractional flow reserve, Instantaneous wave-free ratio, Invasive physiological assessment, Low-middle income country

A Survey of Routing Issues and Associated Protocols in Underwater Wireless Sensor Networks

Underwater Wireless Sensor Network is newly emerging wireless technology in which small size sensors with limited energy, limited memory and bandwidth are deployed in deep sea water and various monitoring operation like tactical surveillance, environmental monitoring and data collection are performed through these tiny sensor. Underwater Wireless Sensor Network is used for exploration of underwater resources, oceanographic data collection, flood or disaster prevention, tactical surveillance system and unmanned underwater vehicles. Sensor node consist of small memory, central processing unit and antenna. Underwater network is much different from terrestrial sensor network as radio waves cannot be used in Underwater Wireless Sensor Network. Acoustic channels are used for communication in deep sea water. Acoustic Signals carries with itself many limitation. Such as Limited bandwidth, higher end to end delay, network path loss, higher propagation delay and dynamic topology. Usually these limitation results in higher energy consumption with less number of packets delivered. The main aim now a days is to operate sensor node having smaller battery for a longer time in network. This survey has discussed the state of the art Localization based and Localization free routing protocols. Routing associated issues in the area of Underwater Wireless Sensor Network has also been discussed

Customer churn prediction in telecommunication industry using data certainty

Customer Churn Prediction (CCP) is a challenging activity for decision makers and machine learning community because most of the time, churn and non-churn customers have resembling features. From different experiments on customer churn and related data, it can be seen that a classifier shows different accuracy levels for different zones of a dataset. In such situations, a correlation can easily be observed in the level of classifier's accuracy and certainty of its prediction. If a mechanism can be defined to estimate the classifier's certainty for different zones within the data, then the expected classifier's accuracy can be estimated even before the classification. In this paper, a novel CCP approach is presented based on the above concept of classifier's certainty estimation using distance factor. The dataset is grouped into different zones based on the distance factor which are then divided into two categories as; (i) data with high certainty, and (ii) data with low certainty, for predicting customers exhibiting Churn and Non-churn behavior. Using different state-of-the-art evaluation measures (e.g., accuracy, f-measure, precision and recall) on different publicly available the Telecommunication Industry (TCI) datasets show that (i) the distance factor is strongly co-related with the certainty of the classifier, and (ii) the classifier obtained high accuracy in the zone with greater distance factor's value (i.e., customer churn and non-churn with high certainty) than those placed in the zone with smaller distance factor's value (i.e., customer churn and non-churn with low certainty)

Neuroinflammatory Triangle Presenting Novel Pharmacological Targets for Ischemic Brain Injury

Ischemic stroke is one of the leading causes of morbidity and mortality globally. Hundreds of clinical trials have proven ineffective in bringing forth a definitive and effective treatment for ischemic stroke, except a myopic class of thrombolytic drugs. That, too, has little to do with treating long-term post-stroke disabilities. These studies proposed diverse options to treat stroke, ranging from neurotropic interpolation to venting antioxidant activity, from blocking specific receptors to obstructing functional capacity of ion channels, and more recently the utilization of neuroprotective substances. However, state of the art knowledge suggests that more pragmatic focus in finding effective therapeutic remedy for stroke might be targeting intricate intracellular signaling pathways of the 'neuroinflammatory triangle': ROS burst, inflammatory cytokines, and BBB disruption. Experimental evidence reviewed here supports the notion that allowing neuroprotective mechanisms to advance, while limiting neuroinflammatory cascades, will help confine post-stroke damage and disabilities.Peer reviewe