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The Internet has seen substantial growth of regional language data in recent years. It enables people to express their opinion by incapacitating the language barriers. Urdu is a language used by 170.2 million people for communication. Sentiment analysis is used to get insight of people opinion. In recent years, researchers’ interest in Urdu sentiment analysis has grown. Application of deep learning methods for Urdu sentiment analysis has been least explored. There is a lot of ground to cover in terms of text processing in Urdu since it is a morphologically rich language. In this paper, we propose a framework for Urdu Text Sentiment Analysis (UTSA) by exploring deep learning techniques in combination with various word vector representations. The performance of deep learning methods such as Long Short-Term Memory (LSTM), attention-based Bidirectional LSTM (BiLSTM-ATT), Convolutional Neural Networks (CNN) and CNN-LSTM is evaluated for sentiment analysis. Stacked layers are applied in sequential model LSTM, BiLSTM-ATT, and C-LSTM. In CNN, various filters are used with single convolution layer. Role of pre-trained and unsupervised self-trained embedding models is investigated on sentiment classification task. The results obtained show that the BiLSTM-ATT outperformed other deep learning models by accomplishing 77.9% accuracy and 72.7% F1 score.

Load forecasts are fundamental inputs for the reliable and resilient operation of a power system. Globally, researchers endeavor to improve the accuracy of their forecast models. However, lack of studies detailing standardized model development procedures remains a major issue. In this regard, this study advances the knowledge of the systematic development of short-term load forecast (STLF) models for electric power utilities. The proposed model has been developed by using hourly load (time series) of five years of an electric power utility in Pakistan. Following the investigation of previously developed load forecast models, this study addresses the challenges of STLF by utilizing multiple linear regression, bootstrap aggregated decision trees, and artificial neural networks (ANNs) as mutually competitive forecasting techniques. The study also highlights both rudimentary and advanced elements of data extraction, synthetic weather station development, and the use of elastic nets for feature space development to upscale its reproducibility at global level. Simulations showed the superior forecasting prowess of ANNs over other techniques in terms of mean absolute percentage error (MAPE), root mean squared error (RMSE) and R2 score. Furthermore, an empirical approach has been taken to underline the effects of data recency, climatic events, power cuts, human activities, and public holidays on the model’s overall performance. Further analysis of the results showed how climatic variations, causing floods and heavy rainfalls, could prove detrimental for a utility’s ability to forecast its load demand in future.

Smart community setups nowadays are subjected to complicated issues such as instability, intermittent integration of the load at the demand side, and lack of intelligent two-way communication process. These issues need to be addressed in terms of a balanced power demand dispatch (DD) in the real-time or day-ahead duplex signal regime under multi-microgrids. This paper offers an intelligent multi-agent-based approach that works between different levels of communication and their respective layers for a community-based system to optimize the power in community-based multi-microgrids model. This will further enhance user personal comfort. Constraints relative to cost minimization also have a relation with this model. A three-level structure with various layers of autonomous agents take intelligent decisions based on prioritized particle swarm optimization (P-PSO), prioritized plug and play (PPnP), and knapsack; considering DD as the main driver of the system to address objectives like price and power consumption uncertainties. Distinct smart home models, depending upon their living habits, are keenly observed providing their power infrastructure and personal comfort. Load appliances considered as load agents are individually contemplated for maximum proficiency. Furthermore, two-way communication between utility and consumers lowers down the risk of the inefficiency of the system.

Generating intuitions from data using data mining and machine learning algorithms to predict outcomes is useful area of computing. The application area of data mining techniques and machine learning is wide ranging including industries, healthcare, organizations, academics etc. A continuous improvement is witnessed due to an ongoing research, as seen particularly in healthcare. Several researchers have applied machine learning to develop decision support systems, perform analysis of dominant clinical factors, extraction of useful information from hideous patterns in historical data, making predictions and disease classification. Successful researches created opportunities for physicians to take appropriate decision at right time. In current study, we intend to utilize the learning capability of machine learning methods towards the classification of birth data using bagging and boosting classification algorithms. It is obvious that differences in living styles, medical assistances, religious implications and the region you live in collectively affect the residents of that society. This motive has encouraged the researchers to conduct studies at regional levels to comprehensively explore the associated medical factors that contribute towards complications among women during pregnancy. The current study is a comprehensive comparison of bagging and boosting classification algorithms performed on birth data collected from the government hospitals of city Muzaffarabad, Kashmir. The experimental tasks are carried out using caret package in R which is considered an inclusive framework for building machine learning models. Accuracy based results with different evaluation measures are presented. Bagging functions including Adabag and BagFda performed marginally better in terms of accuracy, precision and recall. Improvements are observed in comparison to previous study performed on same dataset.

The increasing demand of energy in the traditional grids is getting more complex, less feasible, harmful, uneconomical and high in power losses. This paper presents an efficient energy management approach to mitigate such issues with smart micro grid (SMG) and aims at a solution that is both cost effective and eco-friendly, within energy market paradigm. Goals are achieved with the help of Home Energy Management Controller (HEMC), Energy Market Management Controller (EMMC) and Control Agent (CA). The individual load is managed in the presence of local generation, storage system, user comfort, DGs and Utility within energy market paradigm. Two level energy management approach is proposed to achieve concerned goals. First is to manage load and schedule storage with respect to individual local generation and market pricing. Second is to manage energy market with the help of four different types of priorities and control agent input. The problem is solved with a variant of meta-heuristic method, Multi Objective Grey Wolf Optimization (MOGWO), which gives more comprehensive solution by comparing with Particle Swarm Optimization (PSO). The proposed methodology is implemented on a SMG based-community test system. Homes within that community have different economic conditions and personal priorities. Simulation results demonstrates achievement of aimed goals in presented work.

In the field of medical, each and every analysis is decisive as the study links to life of the subject under observation. One of the most vital area in the field of medical is the healthcare of expecting women in low income countries. High mortality rate due to increased number of caesarean section is evident because of poor medical infrastructure in the region, misunderstood religious teachings, low education and lack of proper decision making at the right time. The root cause analysis of situations demanding caesarean section is a tough job, however in the presence of historical data, one may extract useful information that will help supporting a medical decision by predicting the outcome. It is obvious that regional disparities have a huge impact on the residents of that region. A study performed on any region cannot be all applicable to the residents of some other distant region. This motive has established grounds to conduct a local study upon the data collected from expecting women in city Muzaffarabad, Kashmir. It is believed that the findings of this study will be significant for women that share more or less similar physical, social and maternal traits. Keeping this in mind, study presents an analysis of two clustering techniques for the investigation of appropriate algorithm that groups data into relevant clusters robustly. Firstly, we analyzed K-means and K-medoids algorithms' capability to cluster the data using different distance metrics. Secondly, data transformation techniques including scale, range and Yeo-Johnson are applied. Finally, transformed data are used in K-means and K-medoids algorithms' to generate cluster accuracy. It is observed that the results produced from transformed data are better than using raw data. Yeo-Johnson transformation method is found best for k-means (Hartigan & Wang), K-medoids (SEV distance function) and Rank k-medoids (SEV distance function) with mean accuracy 67.58%, 69.58% and 72.64% respectively.

There are several physical and social factors that are associated to the maternal health and may be considered influential towards the C-Section across the world. Several studies have been conducted in different regions of the world, mostly targeting the pregnant women in specific region. The dynamicity of pregnancy and differences among women with respect to region and social life enforces the researchers to widen the sphere of research at regional level to comprehensively explore the significant risk factors associated to mother and expected child. In this paper, the region of interest is the native city of authors lacking medical facilities and proper pregnant women healthcare infrastructure. As compared to advanced countries, no such study is ever conducted in this region that involves cause analysis of factors resulting in enhanced cases of C-sections and assisting physicians via providing decision support systems based on knowledge induced from machine learning approaches. The aim of this paper is twofold. The first objective is to collect data regionally, in order to conduct local study, first of its kind in this region, and acquire results that are helpful for public health offices in decision making. Second, it is desired to produce different birth classification models and study their applicability on birth data collected previously. The best approach on the basis of correct classification may later be used to produce decision support systems to assist physicians to gain knowledge from the hidden patterns in data. The success of such study is crucial as it will open the doors of interdisciplinary research in two distinct fields of the region.