Drivers can be identified through patterns in their routine driving behaviours, as observed by analysing the timing and sequence of various manoeuvres. In contemporary mobility contexts, comprehending and accurately predicting drivers’ behaviours are crucial for informing efficient transportation planning, enhancing traffic safety, reducing emissions, and improving driving efficiency. An increasing number of researchers have explored a variety of machine learning (ML) models to identify, classify, and predict drivers’ behaviours. However, the reliability of these results is often undermined by the complexities associated with the data characteristics, contexts, and the authors’ expertise. Additionally, there is a lack of comprehensive investigation into the effect of driving behaviour on vehicles’ performance, driver identity, and driving activities. This research aims to compare various ML methods to establish a conclusive and generalisable empirical benchmark. The experiments were divided into three phases: estimation of fuel consumption, driver identification, and driver actions’ prediction from drivers’ behaviour during motion. The experiments evaluate prediction accuracy, performance, and computational cost using a different range of temporal and nontemporal ML models and eight datasets from diverse sources, which resulted in 9 tables of outputs. The results have been gauged and scored precisely, and then high-rated and ineffective algorithms were pinpointed for each task. This study is the most in-depth investigation, providing an exhaustive comparison of different ML models for predicting three main criteria of driving behaviour, marking it as the most detailed investigation in this field.

In this paper, we have evaluated the effectiveness of scheduling strategies in Linked Open Data based application for keeping local data caches up-to-date. We argue that the healthcare organizations are publishing data publicly, but consuming of data is difficult due to rapid growth of the linked data cloud. Most of the applications that are consuming linked data suffer from challenges such as change estimation and accuracy of index for keeping the data fresh for visualization. In this work, we have evaluated the quality of the data updates performed by the scheduling strategies. We have implemented the state-of-the-art web scheduling approaches; ChangeRatio and ChangeRate on linked dataset. We have concluded our evaluation that the strategies based on ChangeRate performed better than the ChangeRatio.

The combination of ontology based context-awareness and machine learning context classification is an interesting research area. The determined contexts are obtained using semantic reasoning based on context ontology developed by expert using domain specific rules. This reasoning suffer challenges of soundness and completeness in real-time deployment. This paper addresses the aforementioned challenges from semantic reasoning by embracing machine learning modeling and classification benefits. Machine learning relies on data, for this we developed training and deployment phase for ontological ABox assertions. Approximately 99.99% precision through machine learning approach was achieved over 91.5% accuracy with semantic reasoning. The statistical evaluation proves the improvement in terms of accuracy for context prediction and overall performance.

Future mobile radio systems, like 5G, are setting extremely demanding targets with respect to the number of served users, data rates, and latency etc. Advanced techniques such as massive MIMO and joint cooperation over several distributed radio stations are being developed to achieve these targets. Channel prediction has been deemed to be a potential main enabler for these techniques. Here, we exploit the correlation present in the multi-dimensional massive MIMO channel and apply HOSVD-based techniques to improve the prediction of weak channels by devising a denoising step before the state-of-the-art channel predictor. We show that at low signal-to-noise ratios and prediction horizons of 1 ms, there is a gain of more than 7 dB in prediction performance.

For future 5G systems significant performance benefits are expected from massive MIMO, especially in combination with tight inter-cell cooperation including joint-transmission using cooperative multi-point transmission. Most massive MIMO evaluations concentrate on the base station side with the goal to achieve high spectral efficiency by MU-MIMO or large coverage by strong beamforming gains. Especially for the, here interesting, below 6 GHz RF-bands user equipment (UE) sided analysis is typically limited to four or mostly eight antenna elements per UE, which can be justified by the limited space to place more antenna elements as well as the related UE complexity. At the same time, there would be many benefits from UE-sided beamforming, ranging from improved channel estimation and prediction accuracy, effective interference suppression up to coverage and spectral efficiency gains on the system level. In our previous work, we have already proposed virtual beamforming for channel estimation and prediction, while here we extend the concept to user data transmission over virtually generated beams. Virtual beamforming directly applied to user data can be very inefficient, a challenge we overcome by parallel transmission over a set of coded virtual beams.

Personalized healthcare envisions providing customized treatment and management plans to individuals at their doorstep. Key factors to ensure personalized healthcare is to involve with the individual in their daily life activities and process the gathered information to provide recommendations. We identified the mostly exposed domains for gathering chronic disease patients information that includes: clinical, social media, and daily life activities. Clinical data is related to the health-care of the patients while social media, sensory, and wearables data is related to the wellness data of the patients. A framework is required to monitor the health and wellness information of the patients for health and wellness analytics provisioning to the physicians for better decision making. We propose Personalized, Ubiquitous Life-care Decision Support System (PULSE); a state of the art decision support system that helps physicians and patients in life-style management of chronic disease patients such as Diabetes. The proposed approach not only utilizes clinical information but also personalized information by correlation to find hidden information using big data health analytic for improvement of life-care. PULSE provides health analytics by utilizing and processing clinical information of the patient. In the same way, it provides wellness analytics to the patients by using their social, activities, emotions and daily life information. The co-relation between clinical and personalized analytics is performed for better recommendations to the patients. This eventually results in improved life-care and healthy living of the individuals.

MPI provides high performance parallel file access API called MPI-IO. ROMIO library implements MPI-IO specifications thus providing this facility to C and Fortran programmers. Similarly, object-oriented languages such as Java and C# have adapted MPI specifications and their implementations provide HPC facility to its programmers. These implementations, however, lack parallel file access capability which is very important for large-scale parallel applications. In this paper, we propose a Java based parallel file access API called MPJ-IO and describe its reference implementation. We describe design details and performance evaluation of this implementation. We use JNI calls in our code to utilize functions from ROMIO library. In addition, we highlight the reasons for using JNI calls in our code.

Mobile cloud computing aims at improving user experience through enhancing the ability of mobile applications by doing intensive tasks in the cloud. In this paper we consider an environment similar to a hybrid cloud in which the mobile device works as a private cloud. Given that the mobile phone has both limited processing resources and battery time, the proposed mobile application architecture has been designed with the capability of sending specified data/parameters to the cloud. This data is subsequently used for further processing/mining and visualization to assist in inferring further information through mapreduce. This information gives details about resource and battery consumption which will help in optimizing the relationship between the mobile device and cloud. It will also be beneficial to the optimization of the mobile application through the trends visualized in the cloud. In this paper we created an activity recognition health application as an example and helped the user about his health along with giving an insight into abnormal behavior and lifestyle trends.

Cloud Computing has been adopted by health-care industry for their data's storage, manipulation, and secured sharing needs. However, cloud's distributed nature can be exploited to the use of scientific applications that are designed for health-care data evaluation. These scientific applications, such as, Medical Imaging, Gene and Protein annotation, Mapdrug therapy and Clinical Decision Support Systems (CDSS), require high-performance messaging libraries with minimum computational and communication overhead, and efficient resource utilization. The proposed High Performance Java Sockets (HPJS) encapsulates the needs of high-performance messaging of scientific applications for cloud platforms. HPJS effectively uses Java's socket implementation for high-performance inter-process communication. With single-copy protocol, thread re-usability and reduced communication overhead, HPJS can perform messaging twice as fast to conventional buffered-base communication libraries.