Reinforcement learning for delay tolerance and energy saving in mobile wireless sensor networks

Reinforcement Learning (RL) has emerged as a promising approach for improving the performance of Wireless Sensor Networks (WSNs). The Q-learning technique is one approach of RL in which the algorithm continuously learns by interacting with the environment, gathering information to take certain actions. It maximizes performance by determining the optimal result from that environment. In this paper, we propose a data gathering algorithm based on a Q-learning approach named Bounded Hop Count - Reinforcement Learning Algorithm (BHC-RLA). The proposed algorithm uses a reward function to select a set of Cluster Heads (CHs) to balance between the energy-saving and data-gathering latency of a mobile Base Station (BS). In particular, the proposed algorithm selects groups of CHs to receive sensing data of cluster nodes within a bounded hop count and forward the data to the mobile BS when it arrives. In addition, the CHs are selected to minimize the BS tour length. Extensive experiments by simulation were conducted to evaluate the performance of the proposed algorithm against another traditional heuristic algorithm. We demonstrate that the proposed algorithm outperforms the existing work in the mean of the length of a mobile BS tour and a network's lifetime

[In Press] Knowledge graph for recommendation system : enhanced relation reliability and prediction probability (ERRaPP)

With the current explosion of information, the end-users find it challenging to filter this information. Recommendation systems present solutions to filter and prioritize the information to overcome the problem of information overloading. However, one of the main challenges associated with RS is accuracy. A knowledge graph (KG) is one solution to improve the recommendation system’s performance. The existing solutions do not consider the side information and semantic relationship from the knowledge graph, which results in the problem of accuracy of the recommendation and the processing time. Our proposed solution aims to increase the accuracy and decrease the processing time by exploring semantic relations between entities and considering the importance of relationships. The proposed system consists of a collaborative knowledge graph (GCN) with Enhanced Relation Reliability and Prediction Probability (ERRaPP) algorithm to enhance the recommendation accuracy and minimize the processing time. This algorithm includes the importance of relation specialized in an entity to get more reliable paths. It also has an attention mechanism with a sigmoid function to replace the inner product between entities embedding to improve the prediction. The results are obtained for different model stages (training, evaluation, test) for 4 other datasets (Book-Crossing, MovieLens-20 M, MovieLens-1 M and Last.FM). The results show that the proposed solution achieves better recommendation accuracy with less processing time for all three stages and 4 datasets. The proposed solution provides the recommendation accuracy of 0.705 against the current accuracy of 0.665 on average for the Book-Crossing dataset and a processing time of 7.884 seconds against the current processing time of 12 seconds on average for the testing stage. The proposed solution focuses on enhancing the overall accuracy and reducing the processing time of the knowledge graph-based recommendation system by using the ERRaPP algorithm. Finally, the solution with enhanced relation reliability and score prediction improves the recommendation accuracy by considering semantic relations between entities

[In Press] Modified anisotropic diffusion and level-set segmentation for breast cancer

Breast cancer is frequent among women and its early diagnosis using thermography is not been widely practiced in medical facilities due to its limitation in classification accuracy, sensitivity, and specificity. This research aims to improve the accuracy, sensitivity, and specificity of breast cancer classification in thermal images. The proposed system is composed of the Least Square Support Vector Machine (LSSVM) to improve the classification and prediction accuracy of breast thermography images using optimized hyperparameters. Multi-view breast thermal images are pre-processed using Gaussian Filtering (GF) with a standard deviation value of 1.4 which is followed by anisotropic diffusion while trying to enhance the image by removing noise. Interested regions are segmented by the level-set segmentation technique, and canny edge detection is applied to the segmented output to limit the amount of data and filter useless information. Texture features are extracted from 1370 healthy and 645 sick subjects fetched from Database for Mastology Research (DBR) which is an online free thermogram database. The features from different views of thermograms are later reduced with a t-test. Significant features are added together to obtain feature vector which produces vectors that are further supplied to the Vector Support Machine that utilizes optimized hyper-parameters for the breast thermogram classification. Compared to the state of art solution, the proposed system increased the accuracy by 9% while sensitivity and specificity get increased by 5.75% and 7.25% respectively. The proposed method focuses on modifying the anisotropic diffusion function and enhancing the segmentation of breast thermograms for classification analysis