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This study investigates an energy utilization optimization strategy in a smart home for charging electric vehicles (EVs) with/without a vehicle-to-home (V2H) and/or household energy storage system (HESS) to improve household energy utilization, smooth the load profile, and reduce electricity bills. The proposed strategy detects EV arrival and departure time, establishes the priority order between EV and HESS during charge and discharge, and ensures that the EV battery state of energy at the departure time is sufficient for its travel distance. It also ensures that the EV and HESS are charged when electricity prices are low and discharged in peak hours to reduce net electricity expenditure. The proposed strategy operates in different modes to control the energy amount flowing from the grid to EV and/or HESS and the energy amount drawn from the HESS and/or EV to feed the demand to maintain the load curve level within the average limits of the daily load curve. Four different scenarios are presented to investigate the role of HESS and EV technology in reducing electricity bills and smoothing the load curve in the smart house. The results demonstrate that the proposed strategy effectively reduces electricity costs by 12%, 15%, 14%, and 17% in scenarios A, B, C, and D, respectively, and smooths the load profile. Transferring valley electricity by V2H can reduce the electricity costs better than HESS, whereas HESS is better than EV at flattening the load curve. Transferring valley electricity through both V2H and HESS gives better results in reducing electricity costs and smoothing the load curve than transferring valley electricity by HESS or V2H alone. [ABSTRACT FROM AUTHOR]

Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women. This disease is classified into two subtypes: invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS). The advancements in artificial intelligence (AI) and machine learning (ML) techniques have made it possible to develop more accurate and reliable models for diagnosing and treating this disease. From the literature, it is evident that the incorporation of MRI and convolutional neural networks (CNNs) is helpful in breast cancer detection and prevention. In addition, the detection strategies have shown promise in identifying cancerous cells. The CNN Improvements for Breast Cancer Classification (CNNI-BCC) model helps doctors spot breast cancer using a trained deep learning neural network system to categorize breast cancer subtypes. However, they require significant computing power for imaging methods and preprocessing. Therefore, in this research, we proposed an efficient deep learning model that is capable of recognizing breast cancer in computerized mammograms of varying densities. Our research relied on three distinct modules for feature selection: the removal of low-variance features, univariate feature selection, and recursive feature elimination. The craniocaudally and medial-lateral views of mammograms are incorporated. We tested it with a large dataset of 3002 merged pictures gathered from 1501 individuals who had digital mammography performed between February 2007 and May 2015. In this paper, we applied six different categorization models for the diagnosis of breast cancer, including the random forest (RF), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), support vector classifier (SVC), and linear support vector classifier (linear SVC). The simulation results prove that our proposed model is highly efficient, as it requires less computational power and is highly accurate. [ABSTRACT FROM AUTHOR]

GPS datasets in the big data regime provide rich contextual information that enable efficient implementation of advanced features such as navigation, tracking, and security in urban computing systems. Understanding the hidden patterns in large amount of GPS data is critically important in ubiquitous computing. The quality of GPS data is the fundamental key problem to produce high quality results. In real world applications, certain GPS trajectories are sparse and incomplete; this increases the complexity of inference algorithms. Few of existing studies have tried to address this problem using complicated algorithms that are based on conventional heuristics; this requires extensive domain knowledge of underlying applications. Our contribution in this paper are two-fold. First, we proposed deep learning based bidirectional convolutional recurrent encoder-decoder architecture to generate the missing points of GPS trajectories over occupancy grid-map. Second, we interfaced attention mechanism between enconder and decoder, that further enhance the performance of our model. We have performed the experiments on widely used Microsoft geolife trajectory dataset, and perform the experiments over multiple level of grid resolutions and multiple lengths of missing GPS segments. Our proposed model achieved better results in terms of average displacement error as compared to the state-of-the-art benchmark methods. [ABSTRACT FROM AUTHOR]