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Stemming is an essential step in varied Natural Language Processing (NLP) applications. It is used to reduce different variants of the query words to a standard form to avoid the vocabulary mismatch issue in Information Retrieval (IR) systems. There are a lot of stemming algorithms in English language, but Urdu NLP still in its infancy. A stemmer develops and evaluates in a language cannot be efficiently used for other languages . Urdu has the highly inflected and complex morphological structure, and most of the current Urdu stemmers remove a few numbers of affixes and leads to inefficient results. In this context, according to the best of our knowledge, this is a first comprehensive effort towards the proposition and evaluation of a novel Urdu Text Stemmer (UTS) that cope with multi-level inflection and derivation forms in Urdu text. The experimental evaluation of the proposed scheme has been conducted on the text-based and words-based custom developed corpus. The proposed stemming technique is rigorously evaluated and compared with state-of-the-art stemming algorithms. Experimental results demonstrate that UTS outperforms existing Urdu stemmers and achieves an accuracy of 94.92% and 91.8% on word and text corpus, respectively.

Text stemming is one of the basic preprocessing step for Natural Language Processing applications which is used to transform different word forms into a standard root form. For Arabic script based languages, adequate analysis of text by stemmers is a challenging task due to large number of ambigious structures of the language. In literature, multiple performance evaluation metrics exist for stemmers, each describing the performance from particular aspect. In this work, we review and analyze the text stemming evaluation methods in order to devise criteria for better measurement of stemmer performance. Role of different aspects of stemmer performance measurement like main features, merits and shortcomings are discussed using a resource scarce language i.e. Urdu. Through our experiments we conclude that the current evaluation metrics can only measure an average conflation of words regardless of the correctness of the stem. Moreover, some evaluation metrics favor some type of languages only. None of the existing evaluation metrics can perfectly measure the stemmer performance for all kind of languages. This study will help researchers to evaluate their stemmer using right methods.

Several software design patterns have cataloged either with canonical or as variants to solve a recurring design problem. However, novice designers mostly adopt patterns without considering their ground reality and relevance to design problems, which causes to increase the development and maintenance efforts. The existing automated systems to select the design patterns need either high computing effort and time for the formal specification or precise learning through the training of several classifiers with large sample size to select the right design patterns realized on the base of their ground reality. In order to discuss this issue, we propose a method on the base of a supervised learning technique named ‘Learning to Rank’, to rank the design patterns via the text relevancy with the description of the given design problems. Subsequently, we also propose an evaluation model to assess the effectiveness of the proposed method. We evaluate the efficacy of the proposed method in the context of several design pattern collections and relevant design problems. The promising experimental results indicate the applicability of the proposed method as a recommendation system to select the right design pattern(s).

Though, Unified Modeling Language (UML), Ontology, and Text categorization approaches have been used to automate the classification and selection of design pattern(s). However, there are certain issues such as time and effort for formal specification of new patterns, system context-awareness, and lack of knowledge which needs to be addressed. We propose a framework (i.e. Three-phase method) to discuss these issues, which can aid novice developers to organize and select the correct design pattern(s) for a given design problem in a systematic way. Subsequently, we propose an evaluation model to gauge the efficacy of the proposed framework via certain unsupervised learning techniques. We performed three case studies to describe the working procedure of the proposed framework in the context of three widely used design pattern catalogs and 103 design problems. We find the significant results of Fuzzy c-means and Partition Around Medoids (PAM) as compared to other unsupervised learning techniques. The promising results encourage the applicability of the proposed framework in terms of design patterns organization and selection with respect to a given design problem.

The imbalance energy consumption and high data traffic at intermediate nodes degrade the network performance. In this paper, we propose: energy grade and balance load distribution corona, EG without corona and DA without corona based schemes to distribute data traffic across the network nodes for efficient energy consumption. The dynamic adjustment of transmission range in first scheme helps in reducing data load. Additionally, the transmission range is purely based on distance, energy and data load of the receiver node for achieving maximum network lifetime. Second scheme divides a data packet into three fractions; small, medium and large for transmitting from various paths to evenly distribute the data load on the network nodes. In third scheme, depth adjustment of void node is performed to resume network operations, whereas, the load distribution and transmission range mechanisms are the same. The extensive simulations are carried out to show the effectiveness of proposed schemes in terms of PDR, energy consumption, and load distribution against the baseline scheme.

There are a number of research challenges associated with Internet of Things (IoT) security, and one of these challenges is to design novel frameworks to mine malicious frequent patterns for identifying misuse and detecting anomalies without incurring high computational costs (e.g., due to generation and analysis of unnecessary patterns and gap creation between patterns). Association rule mining is a popular approach in the literature; hence, in this paper, we critically analyze existing association rule mining techniques. We then present a framework for mining malicious frequent patterns in an IoT deployment, prior to evaluating the utility of the proposed framework using data from a Pakistan-based organization.

As an emerging and promising approach, crowdsourcing-based software development has become popular in many domains due to the participation of talented pool of developers in the contests, and to promote the ability of requesters (or customers) to choose the ‘wining’ solution with respect to their desired quality levels. However, due to lack of a central mechanism for team formation, continuity in the developer’s work on consecutive tasks and risk of noise in submissions of a contest, there is a gap between the requesters of a domain and their quality concerns related to the adaptation of a crowdsourcing-based software development platform. In order to address concerns and aid requesters, we describe three measures; Quality of Registrant Developers (QRD), Quality of Contest (QC) and Quality of Support (QS) to compute and predict the quality of a crowdsourcing-based platform through historical information on its completed tasks. We evaluate the capacity of the QRD, QC and QS as assessors to predict the quality. Subsequently, we implement a crawler to mine the information of completed development tasks from the TopCoder platform to inspect the proposed measures. The promising results of our QRD, QC, and QS measures suggest to use the proposed measures to the requesters and researchers of other domains such as pharmaceutical research and development, in order to investigate and predict the quality of crowdsourcing-based software development platforms.

Demand side management (DSM) in smart grid authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. This results in reduced carbon emission, consumer electricity cost, and increased grid sustainability. Most of the existing DSM techniques ignore priority defined by consumers. In this paper, we present priority-induced DSM strategy based on the load shifting technique considering various energy cycles of an appliance. The day-ahead load shifting technique proposed is mathematically formulated and mapped to multiple knapsack problem to mitigate the rebound peaks. The autonomous energy management controller proposed embeds three meta-heuristic optimization techniques; genetic algorithm, enhanced differential evolution, and binary particle swarm optimization along with optimal stopping rule, which is used for solving the load shifting problem. Simulations are carried out using three different appliances and the results validate that the proposed DSM strategy successfully shifts the appliance operations to off-peak time slots, which consequently leads to substantial electricity cost savings in reasonable waiting time, and also helps in reducing the peak load demand from the smart grid. In addition, we calculate the feasible regions to show the relationship between cost, energy consumption, and delay.

Presently, most of the software development organizations are adopting the phenomena of Global Software Development (GSD), mainly because of the significant return on investment it produces. However, GSD is a complex phenomenon and there are many challenges associated with it, especially that related to Software Process Improvement (SPI). The aim of this work is to identify humans' related success factors and barriers that could impact the SPI process in GSD organizations and proposed a theoretical framework of the factors in relation to SPI implementation. We have adopted the Systematic Literature Review (SLR) method in order to investigate the success factors and barriers. Using the SLR approach, total ten success factors and eight barriers were identified. The paper also reported the Critical Success Factors (CSFs) and Critical Barriers (CBs) for SPI implementation following the criteria of the factors having a frequency ≥ 50% as critical. Our results reveal that five out of ten factors are critical for SPI program. Moreover, total three barriers were ranked as the most critical barriers. Based on the analysis of the identified factors, we have presented a theoretical framework that has highlighted an association between the identified factors and the implementation of the SPI program in GSD environment.

This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load, which is difficult to process with conventional computational models. These data are known as energy big data. The analysis of big data divulges the deeper insights that help experts in the improvement of smart grid&rsquo, s (SG) operations. Processing and extracting of meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand for big data using Deep Long Short-Term Memory (DLSTM). Due to the adaptive and automatic feature learning mechanism of Deep Neural Network (DNN), the processing of big data is easier with LSTM as compared to the purely data-driven methods. The proposed model was evaluated using well-known real electricity markets&rsquo, data. In this study, day and week ahead forecasting experiments were conducted for all months. Forecast performance was assessed using Mean Absolute Error (MAE) and Normalized Root Mean Square Error (NRMSE). The proposed Deep LSTM (DLSTM) method was compared to traditional Artificial Neural Network (ANN) time series forecasting methods, i.e., Nonlinear Autoregressive network with Exogenous variables (NARX) and Extreme Learning Machine (ELM). DLSTM outperformed the compared forecasting methods in terms of accuracy. Experimental results prove the efficiency of the proposed method for electricity price and load forecasting.

Short-Term Electricity Load Forecasting (STELF) through Data Analytics (DA) is an emerging and active research area. Forecasting about electricity load and price provides future trends and patterns of consumption. There is a loss in generation and use of electricity. So, multiple strategies are used to solve the aforementioned problems. Day-ahead electricity price and load forecasting are beneficial for both suppliers and consumers. In this paper, Deep Learning (DL) and data mining techniques are used for electricity load and price forecasting. XG-Boost (XGB), Decision Tree (DT), Recursive Feature Elimination (RFE) and Random Forest (RF) are used for feature selection and feature extraction. Enhanced Convolutional Neural Network (ECNN) and Enhanced Support Vector Regression (ESVR) are used as classifiers. Grid Search (GS) is used for tuning of the parameters of classifiers to increase their performance. The risk of over-fitting is mitigated by adding multiple layers in ECNN. Finally, the proposed models are compared with different benchmark schemes for stability analysis. The performance metrics MSE, RMSE, MAE, and MAPE are used to evaluate the performance of the proposed models. The experimental results show that the proposed models outperformed other benchmark schemes. ECNN performed well with threshold 0.08 for load forecasting. While ESVR performed better with threshold value 0.15 for price forecasting. ECNN achieved almost 2% better accuracy than CNN. Furthermore, ESVR achieved almost 1% better accuracy than the existing scheme (SVR).

The emergence of the Demand Response (DR) program optimizes the energy consumption pattern of customers and improves the efficacy of energy supply. The pricing infra-structure of the DR program is dynamic (time-based). It has rather complex features including marginal costs, demand and seasonal parameters. There is variation in DR price rate. Sometime prices go high (peak load) if the demand of electricity is more than the generation capacity. The main objective of DR is to encourage the consumer to shift the peak load and gets incentives in terms of cost reduction. However, prices remain the same for all the users even if they shift the peak load or not. In this work, Game Theory (GT)-based Time-of-Use (ToU) pricing model is presented to define the rates for on-peak and shoulder-peak hours. The price is defined for each user according to the utilize load. At first, the proposed model is examined using the ToU pricing scheme. Afterward, it is evaluated using existing day-ahead real-time pricing scheme. Moreover, shifting load from on-peak hours to off-peak hours may cause rebound peak in off-peak hours. To avert this issue, we analysis the impact of Salp Swam Algorithm (SSA) and Rainfall Algorithm (RFA) on user electricity bill and PAR after scheduling. The experimental results show the effectiveness of the proposed GT-based ToU pricing scheme. Furthermore, the RFA outperformed SSA.