User Transmit Power Minimization through Uplink Resource Allocation and User Association in HetNets

The popularity of cellular internet of things (IoT) is increasing day by day and billions of IoT devices will be connected to the internet. Many of these devices have limited battery life with constraints on transmit power. High user power consumption in cellular networks restricts the deployment of many IoT devices in 5G. To enable the inclusion of these devices, 5G should be supplemented with strategies and schemes to reduce user power consumption. Therefore, we present a novel joint uplink user association and resource allocation scheme for minimizing user transmit power while meeting the quality of service. We analyze our scheme for two-tier heterogeneous network (HetNet) and show an average transmit power of -2.8 dBm and 8.2 dBm for our algorithms compared to 20 dBm in state-of-the-art Max reference signal received power (RSRP) and channel individual offset (CIO) based association schemes

Improvement of Soil Health through Residue Management and Conservation Tillage in Rice-Wheat Cropping System of Punjab, Pakistan

In South Asia, soil health degradation is affecting the sustainability of the rice-wheat cropping system (RWCS). Indeed, for the sustainability of the soil quality, new adaptive technologies, i.e., conservation tillage and straw management resource conservation, are promising options. This investigation was focused on the interaction of tillage and straw management practices and their effects on Aridisols, Yermosols soil quality, and nutrients dynamics with different soil profiles within RWCS. The long-term field experiment was started in 2014 with the scenarios (i) conventional tillage (SC1), (ii) residue incorporation (SC2), (iii) straw management practices (SC3 and SC4) and conservation tillage (SC5). Conservation tillage practice (SC5) showed significant impact on properties of soil and availability of nutrients in comparison with that of conventional farmers practice (SC1) at the studied soil depths. The SC5 showed significant results of gravitational water contents (25.34%), moderate pH (7.4), soil organic-matter (7.6 g kg(-1)), total nitrogen (0.38 g kg(-1)), available phosphate (7.4 mg kg(-1)), available potassium (208 mg kg(-1)) compared to SC1 treatment at 0 to 15 cm soil depth. Whereas, DTPA-extractable-Cu, Mn, and Zn concentration were significantly higher, i.e., 1.12 mg kg(-1), 2.14 mg kg(-1), and 4.35 mg kg(-1), respectively under SC5 than conventional farmer's practices, while DTPA (diethylene triamine pentaacetic acid) extractable Fe (6.15 mg kg(-1)) was more in straw management practices (SC4) than conventional and conservation tillage. Therefore, conservation tillage (SC5) can surge the sustainability of the region by improving soil assets and nutrients accessibility and has the potential to minimize inorganic fertilizers input in the long run

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)

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

Compromised user credentials detection using temporal features: A prudent based approach

© 2017 ACM. This study exposes a serious and rapidly growing cyber threat of compromised legitimate user credentials which is very effective for cyber-criminals to gain trusted relationships with the account owners. Such a compromised user\u27s credentials ultimately result in damage incurred by the attacker at large-scale. Moreover, the detection of compromised legitimate user activities is crucial in competitive and sensitive organizations because wrong data is more difficult to clean from the database. The proposed study presents a novel approach to detect compromised users\u27 activity in a live database. Our approach uses a composition of prudence analysis, ripple down rules (RDR) and simulated experts (SE) to detect and identify accounts that experience a sudden change in behavior. We collected data from a sensitive running database for a period of Six months and evaluate the proposed technique. The results show that this combined model can fully detect outlier user\u27s activity and can provide useful information for the concerned decision maker

EFFECTIVENESS OF READING ALOUD STRATEGIES FOR DEVELOPING READING HABITS

Read aloud strategy is essential; it determines the success of the future. The focus of present study was on read-aloud strategy and other reading strategies. Mainly the purpose of this study was to check the effectiveness of different reading strategies, which could be helpful at elementary level. The present study was based on reading habits of students at elementary level. The study was conducted in a Govt. Girls Elementary School Fateh Wala, Multan. Data was collected from grade 8 students. Data was collected with the help of questionnaire; pretest, posttest was also used to collect data from students. A sample size of 25 students was taken in which ten were female, and 15 male students had participated. Ten teachers also participated in this research work in which 5 were male teachers and five were female. A comparative analysis was conducted for students reading habits with reading-aloud and silent reading strategy. Atlas ti 8 updated version was used for literature review. Results include the finding that read aloud strategy is better than silent reading and other reading strategies at elementary level.  Article visualizations

Securing Cognitive Radio Networks using blockchains

Due to the increase in industrial applications of Internet of Things (IoT), number of internet connected devices have been increased accordingly. This has resulted in big challenges in terms of accessibility, scalability, connectivity and adaptability. IoT is capable of creating connections between devices on wireless medium but the utilization of scarce spectrum in efficient manner for the establishment of these connections is the biggest concern. To accommodate spectrum allocation problem different radio technologies are being utilized. One of the most efficient technique being used is cognitive radio, which dynamically allocate the unlicensed spectrum for IoT applications. Spectrum sensing being the fundamental component of Cognitive Radio Network (CRN) is threatened by security attacks. Process of spectrum sensing is disturbed by the malicious user (MU) which attacks the primary signal detection and affects the accuracy of sensing outcome. The presence of such MU in system, sending false sensing data can degrade the performance of cognitive radios. Therefore, in this article a blockchain based method is proposed for the MU detection in network. By using this method an MU can easily be discriminated from a reliable user through cryptographic keys. The efficiency of the proposed mechanism is analyzed through proper simulations using MATLAB. Consequently, this mechanism can be deployed for the validation of participating users in the process of spectrum sensing in CRN for IoTs.publishe

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)

Effectiveness of reading aloud strategies for developing reading habits

Read aloud strategy is essential; it determines the success of the future. The focus of present study was on read-aloud strategy and other reading strategies. Mainly the purpose of this study was to check the effectiveness of different reading strategies, which could be helpful at elementary level. The present study was based on reading habits of students at elementary level. The study was conducted in a Govt. Girls Elementary School Fateh Wala, Multan. Data was collected from grade 8 students. Data was collected with the help of questionnaire; pretest, posttest was also used to collect data from students. A sample size of 25 students was taken in which ten were female, and 15 male students had participated. Ten teachers also participated in this research work in which 5 were male teachers and five were female. A comparative analysis was conducted for students reading habits with reading-aloud and silent reading strategy. Atlas ti 8 updated version was used for literature review. Results include the finding that read aloud strategy is better than silent reading and other reading strategies at elementary level

Hospital-Based Cancer Registry

Background :. To  determine the frequency of different types of malignancies in different gender and age groups, presenting at a surgical unit. Methods:  In this observational study  cancer patients of both genders were included to determine frequencies of different malignancies that presented to a surgical unit.  All the patients with age greater than 12 years and being admitted in surgical unit 1 with the diagnosis of malignancy, were included. The variables recorded included age, sex, address, diagnosis, biopsy, date of biopsy, treatment timeline, stage at presentation, final outcome and referral to other care units. Data was entered and analyzed using the statistical package for social sciences (SPSS) software, version 22. Results: A total of 150 malignant tumours were analyzed. There were 50 (33.3%) male patients and 100 (66.7%) females. Malignant tumours of breast 67 (44.7%) and esophagus 16 (10.7%), were found to be the most common whereas malignant melanoma  (0.7%), submandibular tumours (0.7%), and parotid tumours (0.7%), were least common. The most common malignancy in males were of stomach (16.0%) and rectum (16.0%), whereas in females it was the breast malignancies (67.0%). Dividing the age distribution of the patients into 15-year bands, the peak age-category was 41-60 years (46.0%), while only 3 (2.0%) patients were above 80 years.  Conclusion: Cancer trends were found to be similar as that of other studies in Pakistan with a few differences. Data management is sub optimal. There is a dire need of integrated system of Cancer Registry