Wireless based sensor networks contain sensors for environment monitoring but have restricted resources. Many clustering protocols are designed to prolong network lifetime but have problems of inadequate cluster head selection criteria, fixed clustering, and static rounds which consume more energy. It is needed to develop an adaptive clustering strategy for better CH selection and load balancing. In this article, we introduced an energy-efficient mobility based cluster head selection mechanism to overcome these limitations. CH selection is based on dedicated parameters that have a huge impact on the sensor energy consumption. The weightage of each node is calculated on the base of the node's mobility level, residual energy, distance to sink, and density of neighbors. Inter-cluster communication uses single-hop/multi-hop. MATLAB is used to perform simulations. Results show that the proposed approach EEMCS performs better as compared to the existing algorithms CRPD, LEACH, and MODLEACH in terms of load balancing, network stability, energy depletion, and throughput. Energy utilization in the case of EEMCS is much less and the network lifetime is greater than other existing protocols.

In developing countries like Pakistan, cleft surgery is expensive for families, and the child also experiences much pain. In this article, we propose a machine learning–based solution to avoid cleft in the mother’s womb. The possibility of cleft lip and palate in embryos can be predicted before birth by using the proposed solution. We collected 1000 pregnant female samples from three different hospitals in Lahore, Punjab. A questionnaire has been designed to obtain a variety of data, such as gender, parenting, family history of cleft, the order of birth, the number of children, midwives counseling, miscarriage history, parent smoking, and physician visits. Different cleaning, scaling, and feature selection methods have been applied to the data collected. After selecting the best features from the cleft data, various machine learning algorithms were used, including random forest, k-nearest neighbor, decision tree, support vector machine, and multilayer perceptron. In our implementation, multilayer perceptron is a deep neural network, which yields excellent results for the cleft dataset compared to the other methods. We achieved 92.6% accuracy on test data based on the multilayer perceptron model. Our promising results of predictions would help to fight future clefts for children who would have cleft.

Quality education is necessary as it provides the basis for equality in society. It is also significantly important that educational institutes be focused on tracking and improving the academic performance of each student. Thus, it is important to identify the key factors (i.e., diverse backgrounds, behaviors, etc.) that help students perform well. However, the increasing number of students makes it challenging and leaves a negative impact on credibility and resources due to the high dropout rates. Researchers tend to work on a variety of statistical and machine learning techniques for predicting student performance without giving much importance to their spatial and behavioral factors. Therefore, there is a need to develop a method that considers weighted key factors which have an impact on their performance. To achieve this, we first surveyed by considering experts’ opinions in selecting weighted key factors using the Fuzzy Delphi Method (FDM). Secondly, a geospatial-based machine learning technique was developed which integrated the relationship between students’ location-based features, semester-wise behavioral features, and academic features. Three different experiments were conducted to prove the superiority and predict student performance. The experimental results reveal that Long Short-Term Memory (LSTM) achieved higher accuracy of 90.9% as compared to other machine learning methods, for instance, Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Multilayer Perceptron (MLP), and Decision Tree (DT). Scientific analysis techniques (i.e., Fuzzy Delphi Method (FDM)) and machine learning feature engineering techniques (i.e., Variance Threshold (VT)) were used in two different experiments for selecting features where scientific analysis techniques had achieved better accuracy. The finding of this research is that, along with the past performance and social status key factors, the semester behavior factors have a lot of impact on students’ performance. We performed spatial statistical analysis on our dataset in the context of Pakistan, which provided us with the spatial areas of students’ performance; furthermore, their results are described in the data analysis section.

No abstract

No abstract