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As Unified Modeling Language (UML) activity diagrams capture the key system behavior, the UML activity diagram is well suited for the system level testing of systems. In this paper, first an activity flow graph is derived from activity diagram. Then, all the required information is extracted from the activity flow graph (AFG). The activity flow graph (AFG) for the activity diagram is created by traversing the activity diagram from beginning to end, showing choices, conditions, concurrent executions, loop statements. From the graph different control flow sequence are identified by traversing the AFG by depth first traversal technique. Next, an algorithm is proposed to generate all activity paths. Finally, test cases are generated using activity path coverage criteria. Here, a case study on Soft drink Vending Machine (SVM) has been presented to illustrate our approach.

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Prediction and analysis of stock market data have a vital role in current time’s economy. The various methods used for the prediction can be classified into 1) Linear Algorithms like Moving Average (MA) and Auto-Regressive Integrated Moving Average (ARIMA). 2) Non-Linear Models like Artificial Neural Networks and Deep Learning. In this work, we are using the results of previous research papers to demonstrate the potential of some models like ARIMA, Multi-Layer Perception (MLP) ), Convolutional Neural Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long-Short Term Memory (LSTM) for forecasting the stock price of an organization based on its available historical data. Then, implementing some of these methods to check and compare their efficiency within the same issue. We used Independently RNN (IndRNN) to explore a better efficiency for stock prediction and we found that it gives better accuracy prevailing methods in the current time. We also proposed an enhancement to IndRNN by replacing its default activation function with a more effective function called Parametric Rectified Linear Unit (PreLU). Our proposed approach can be used as an alternative method for predicting time series data efficiently other than the typical approaches today

Service-Oriented Architecture (SOA) has changed the way business enterprises get aligned with technology with a very fast pace keeping the demand of re-alignment time very short. Testing of services becomes equally important in SOA implementation in the context of enterprise architecture, business re-alignment and so on maintaining quality. Unit and integration testing though holds a key testing strategy but regression test becomes inevitable when services are orchestrated, choreographed and bound dynamically as per the change in requirement as well as when services are upgraded. In this paper a survey work has been done for regression testing method for SOA. Here the changed scenario of implementation of SOA enabled applications has been considered. General TermsTesting, Survey, SOA research.

The paper’s emphasis is on the imagined speech decoding of electroencephalography (EEG) neural signals of individuals in accordance with the expansion of the brain-computer interface to encompass individuals with speech problems encountering communication challenges. Decoding an individual’s imagined speech from nonstationary and nonlinear EEG neural signals is a complex task. Related research work in the field of imagined speech has revealed that imagined speech decoding performance and accuracy require attention to further improve. The evolution of deep learning technology increases the likelihood of decoding imagined speech from EEG signals with enhanced performance. We proposed a novel supervised deep learning model that combined the temporal convolutional networks and the convolutional neural networks with the intent of retrieving information from the EEG signals. The experiment was carried out using an open-access dataset of fifteen subjects’ imagined speech multichannel signals of vowels and words. The raw multichannel EEG signals of multiple subjects were processed using discrete wavelet transformation technique. The model was trained and evaluated using the preprocessed signals, and the model hyperparameters were adjusted to achieve higher accuracy in the classification of imagined speech. The experiment results demonstrated that the multiclass imagined speech classification of the proposed model exhibited a higher overall accuracy of 0.9649 and a classification error rate of 0.0350. The results of the study indicate that individuals with speech difficulties might well be able to leverage a noninvasive EEG-based imagined speech brain-computer interface system as one of the long-term alternative artificial verbal communication mediums.

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