As a key pillar of smart transportation in smart city applications, electric vehicles (EVs) are becoming increasingly popular for their contribution in reducing greenhouse gas emissions. One of the key challenges, however, is the strain on power grid infrastructure that comes with large-scale EV deployment. The solution to this lies in utilization of smart scheduling algorithms to manage the growing public charging demand. Using data-driven tools and machine learning algorithms to learn the EV charging behavior can improve scheduling algorithms. Researchers have focused on using historical charging data for predictions of behavior such as departure time and energy needs. However, variables such as weather, traffic, and nearby events, which have been neglected to a large extent, can perhaps add meaningful representations, and provide better predictions. Therefore, in this paper we propose the usage of historical charging data in conjunction with weather, traffic, and events data to predict EV session duration and energy consumption using popular machine learning algorithms including random forest, SVM, XGBoost and deep neural networks. The best predictive performance is achieved by an ensemble learning model, with SMAPE scores of 9.9% and 11.6% for session duration and energy consumptions, respectively, which improves upon the existing works in the literature. In both predictions, we demonstrate a significant improvement compared to previous work on the same dataset and we highlight the importance of traffic and weather information for charging behavior predictions.

The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions. In this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context. The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor. Incorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called “FLEXc” and available online at: http://hpcr.cs.odu.edu/flexc .

Medical imaging plays a crucial role in the diagnosis of various diseases, including cancer, cardiovascular conditions, and respiratory disorders such as asthma, pneumonia, and even COVID-19. Nowadays, Deep Learning (DL) has demonstrated remarkable accuracy in disease detection and diagnosis using medical imaging such as X-rays, CT scans, and MRIs. However, DL models mostly require access to sensitive patient data not only to capture the images but also to store them in the data cloud, which raises major privacy concerns. To solve this problem, several techniques such as Differential Privacy (DP) and Federated Learning (FL), have been developed. These methods allow models to learn useful patterns without exposing personal details. Building on these developments, the key gap this review targets is the lack of a recent, side-by-side analysis that connects medical tasks, DL architectures, FL designs, and DP budgets with real-world performance and resilience against privacy attacks. Motivated by this gap, we conducted a systematic review of the literature (SLR) of studies published between 2023 and 2025 that used DL principles with privacy-preserving methods for medical diagnosis. Utilizing the PRISMA framework, we reviewed 23 peer-reviewed articles and grouped them into four categories: included COVID-19, skin lesions, Alzheimer’s disease, and other conditions such as diabetic retinopathy, kidney disease, and heart problems. We sort results by modality, model family, FL topology, DP mechanism, and reported threat model. Furthermore, the review significantly represents that CNN-based models with DP and FL methods are the most common choices, and developed pipelines with these models often achieve accuracy above 90%. At the same time, the review found gaps such as limited real-world testing, a lack of diverse datasets, and underuse of advanced DP approaches. Based on these results, we recommend building larger and more varied datasets, improving privacy-utility trade-offs, and testing models in clinical environments.

The Internet of Things (IoTs) is attracting the attention of scientists worldwide. With its explosive growth along with applications that require real-time computing power, a new technology called edge computing has emerged. As a result, edge computing has changed the way data are processed and handled back and forth for millions of devices worldwide, such as autonomous vehicles and electric cars. The confinement of cloud computing technology, such as a content delivery network (CDN), contributed significantly to edge computing development. Currently, this technology can meet the demands of ever-growing mobile devices and the IoTs. This paper describes a novel secure framework consisting of a hybrid storage architecture consisting of CDN, edge computing, and centralized storage. Centralized storage consists of multilevel storage systems that comprise solid-state drivers (SSDs) and hard disk drivers (HDDs), which provide optimal data storage solution for a wide variety of real-time data processing applications. Transforming the data back and forth between SSDs and HDDs is crucial for achieving high performance while meeting the edge device request deadline. Additionally, a new dynamic solid-state disk partitioning mechanism is introduced to optimize security for the proposed framework among hard disk drives. A partition from the solid-state disks to hard disk drives is assigned based on hard disk drive workloads.

Insider attacks have the potential to inflict severe damage to an organizations reputation, intellectual property and financial assets. The primary difference between the external intrusions and the insider intrusions is that an insider wields power of knowledge about the information system resources, their environment, policies. We present an approach to detecting abnormal behavior of an insider by applying Dynamical System Theory to the insiders computer usage pattern. This is because abnormal system usage pattern is one of the necessary precursors to actual execution of an attack. A base profile of system usage pattern for an insider is created via applying dynamical system theory measures. A continuous monitoring of the insiders system usage and its comparison with this base profile is performed to identify considerable deviations. A sample system usage in terms of application system calls is collected, analyzed, and graphical results of the analysis are presented. Our results indicate that dynamical system theory has the potential of detecting suspicious insider behavior occurring prior to the actual attack execution.