In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of autoencoders (AE) for cell outage detection. First, we briefly introduce deep learning (DL) and also shed light on why it is a promising technique to make self organizing networks intelligent, cognitive, and intuitive so that they behave as fully self-configured, self-optimized, and self-healed cellular networks. The concept of SON is then explained with applications of intrusion detection and mobility load balancing. Our empirical study presents a framework for cell outage detection based on an autoencoder using simulated data obtained from a SON simulator. Finally, we provide a comparative analysis of the proposed framework with the existing frameworks.

In the realm of fourth-generation industrialization, there will be great demand for a skilled workforceTo produce a skilled workforce, we need sustainable education with quality and equity. Conventional ways of delivering and managing education would not fulfil the demands of the fourth industrial revolution (4IR). Disruptive technologies, such as Internet of Things (IoT), have great potential in revolutionizing the current educational setup. Therefore, this research work aims to present an overview of the capabilities of IoT applications in educational settings. Our research article digs into recent research carried out referring to IoT applications in education and provides a detailed insight into the topic from three different perspectives, i.e., from the perspective of school management, teachers, and learners. The present research explains the ways in which IoT has been applied for the benefit of school managers, teachers, and learners, showcased in the recent literature. The paper also sheds light on bottlenecks for IoT applications and explains security, privacy, scalability, reliability, and dehumanization as main constraints in IoT applications in educational settings.

The increased risk of caesarean section after induced labour is well documented. Rate of induction of labour has doubled in the past decade from 10 to 20%. Low Amniotic Fluid Index (AFI) as an isolated finding leads to increased obstetrical interventions but without any improvement in outcome.Objectives:  To determine the frequency of caesarean section due to failed induction in pregnancies at term with borderline AFI.Patients and Methods:  This cross-sectional study was conducted at Department of Obstetrics and Gynaecology, Unit-III, SIMS/Services Hospital, Lahore. The duration of study was one year from January, 2015 to December, 2015. A total of 150 patients were included in this study. AFI was measured by recent obstetric ultrasound. All patients with borderline AFI (5 – 8 cm) were included in the study. They were induced by glandin E2 gel. If induction of patients failed with two doses of glandin E2 gel, given vaginally 6 hours apart, patients were considered for cesarean section. The outcome measure was rate of caesarean section due to failed induction. All data were analyzed by SPSS version 20.Results:  Mean age of the patients was 30.34 ± 6.68 years. Mean gestational age was noted 38.34 ± 1.05 weeks. Out of 150 patients, 103 (68.7%) were para 1 – 3 and 47 patients (31.3%) were para 4 – 6. Caesarean section due to failed induction with borderline AFI was performed in 27 patients (18.0%). Stratification with regard to age, gestational age and parity was carried out and was found significant only for gestational age being > 39 weeks.Conclusion:  It is concluded that failed induction of labour at term in women with borderline AFI is not associated with increased risk of caesarean delivery.

Internet of things (IoT) is making its way in every field of life. Education is not an exception. IoT is making landmark achievements in its applications in the field of education. This paper presents an overview of the key IoT applications in the field of education from the perspective of school management, teachers, and learners enabling smart school concept and challenges and limitations in embarking IoT in educational settings.

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