The increasing traffic congestion problem can be solved by an adaptive traffic signal control (ATSC) system as it utilises real-time traffic information to control traffic signals. Recently, deep reinforcement learning (DRL) has shown its potential in solving the traffic signal timing. However, one of the main challenges of DRL is to design a proper reward function and special attention needs for a multi-objective reward design. Since the feedback to the agent depends on the reward function, a proper design of reward function is needed for fast and stable learning. In this study, the authors proposed a new reward architecture called composite reward architecture (CRA) for multi-objective ATSC to optimise multiple objectives. It calculates multiple rewards in parallel for each action and applies the majority voting method to choose the desired action. Since the traffic signal of one intersection affects the adjacent intersections, a new coordination approach is proposed to get the overall smooth traffic flow. The proposed reward architecture CRA is compared with several existing reward functions used in the literature for different traffic scenarios. The new coordinated approach is compared with the non-coordinated approach. The authors demonstrated that the proposed approaches outperform the others concerning waiting time, halting the number of vehicles, and so on.

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For software quality assurance, software defect prediction (SDP) has drawn a great deal of attention in recent years. Its goal is to reduce verification cost, time and effort by predicting the defective modules efficiently. In SDP, proper attribute selection plays a significant role. However, selection of proper attributes and their representation in an efficient way are very challenging due to the lacking of standard set of attributes. To address these issues, we introduce Selection of Attribute with Log filtering (SAL) to select a proper set of attributes. Our proposed attribute selection process can effectively select the best set of attributes, which are relevant for the discrimination of defected and non-defected software modules. Further, we adopt log filtering to pre-process the input data. We have evaluated the proposed attribute selection method using several widely used publicly available datasets. The simulation results demonstrate that our method is more effective to improve the accuracy of SDP than the existing state-of-the-art methods.

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