The extensive progression in the Internet of Vehicles (IoV) and the exponential upsurge in data consumption reflect the importance of big data in IoV. In general, big data has gained a significant attraction in academia and industry to provide valuable business intelligence and evidence-based decisions. This has been a key enabler for the advancement of the Internet of Vehicles (IoV) in which big data

In this world of big data, the development and exploitation of medical technology is vastly increasing and especially in big biomedical imaging modalities available across medicine. At the same instant, acquisition, processing, storing and transmission of such huge medical data requires efficient and robust data compression models. Over the last 2 decades, numerous compression mechanisms, techniques

Due to the great challenges of measuring forces directly, identifying dynamic loads based on accessible responses is a crucial problem in engineering, helping ensure integrity and reliability of mechanical structures. Dynamic load identification is a difficult inverse problem due to matrix ill-posedness, noise-sensitivity and computational scale, especially in uncertain structures. Unexpected inaccurate

Food has a direct impact on an individual's life and is a significant area for the research community. Computational techniques in food-related computing are performed to address various food-related issues in the field of agronomy, medicine, biology etc. In this paper, we present a comprehensive review of research exclusively on segmentation techniques used for food computing. This paper illustrates

Plant disease detection is a critical issue that needs to be focused on for productive agriculture and economy. Detecting plant disease using traditional methods is a tedious job as it requires a tremendous amount of work, time, and expertise. Automatic plant disease detection is an important research area that has recently gained a lot of attention among the academicians, researchers, and practitioners

While considered a relatively emergent area of research (Bloch and Sacks in Autom Constr 91:256–272, 2018), Semantic Enrichment (SE) and Semantic Web services are among the prominent topics and trends in BIM research (BIM handbook a guide to building information modeling for owners, designers, engineers, contractors, and facility managers, John Wiley & Sons Inc, New Jersey). SE computational approaches

The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue

With the rapid development of logistics industry, vehicle scheduling is the key to the optimization of transportation links. Vehicle transportation route planning is becoming more and more important to reduce logistics costs. In recent decades, the research on VRP and related derivative problems has become more and more extensive. Based on the basic VRP, this paper classifies VRP according to its characteristics

The design of complex mechanical components is a time-consuming process which involves many design variables with multiple interacted objectives and constraints. Traditionally, the design process of mechanical components is performed manually depending on the intuition and experience of the designer. In recent decades, automatic methods have been proposed to effectively search diverse and large parameter

In today Internet era, confidential information transmitted over an insecure channel. With the significant development in the area of quantum computing, there is a need for unconditional security in confidential information. Quantum key distribution protocols are proven secure if all devices are perfect (in terms of technologies and proper protocol operations). The major challenges in quantum communication

This work bridges two research fields i.e. metaheuristics and wind farm layout design. Comparative performance of twelve metaheuristics (MHs) on wind farm layout optimisation (WFLO) was conducted. Four WFLO problems are proposed for benchmarking the various metaheuristics while the design problem is an attempt to simultaneously minimise wind farm cost and maximise wind farm totally produced power.

Quantum Computing is an emerging technology that can perform high intensive computing tasks which are needed in different areas such as data search from high volume data repository, prime factorization, number theory, cybersecurity, polynomial evaluation, interpolation, Machine learning, Artificial Intelligence and many more applications. Any computational problem that can be solved by classical computer

Diabetes, one of the top 10 causes of death worldwide, is associated with the interaction between lifestyle, psychosocial, medical conditions, demographic, and genetic risk factors. Predicting type 2 diabetes is important for providing prognosis or diagnosis support to allied health professionals, and aiding in the development of an efficient and effective prevention plan. Several works proposed machine-learning

The objective of this study is to present a state-of-the-art review on applications and trends of meta-heuristics for remanufacturing problems. This literature review mainly encompass the most popular and frequently employed meta-heuristics like genetic algorithm, artificial bee colony, particle swarm optimization, ant colony optimization, simulated annealing, tabu search, variable neighborhood search

Bio-particulate matter includes grains, cereal crops, and biomass that are considered discrete materials with irregular size and shape. Although the flow of these particles can behave like a continuum fluid at times, their discontinuous behavior cannot be simulated with traditional continuum-based modeling. The Discrete Element Method (DEM), coupled with Computational Fluid Dynamics (CFD), is considered

In the past, knowledge in the fields of Architecture, Engineering and Construction (AEC) industries mainly come from experiences and are documented in hard copies or specific electronic databases. In order to make use of this knowledge, a lot of studies have focused on retrieving and storing this knowledge in a systematic and accessible way. The Building Information Modeling (BIM) technology proves

This review article revisits and outlines the perfectly matched layer (PML) method and its various formulations developed over the past 25 years for the numerical modeling and simulation of wave propagation in unbounded media. Based on the concept of complex coordinate stretching, an efficient mixed displacement-strain unsplit-field PML formulation for second-order (displacement-based) linear elastodynamic

This paper simply presents a state of the art survey among the machine learning based approaches for the Glioma classification. As Glioma classification is a very challenging task in the field of medical science and this task is well addressed and taken by the fraternity of machine learning experts, who are working day and night to devise automated approaches that can automate this whole process of

The dawn of the new era in precise diagnostic devices and personalized cancer treatment is intertwined with computational models capable of integrating biochemical factors and biophysical processes to simulate and predict cancer progression. In the last decade, thanks to the increase in the computational power, the development of more sophisticated and realistic models has gained attention in cancer

Several new technologies such as the smart cities, the Internet of Things (IoT), and 5G Internet need services offered by cloud computing for processing and storing more information. Hence, the heterogeneity of the new companies that used the above-mentioned technologies will add many vulnerabilities and security concerns for the cloud paradigm. Presently, cloud computing involves every component such

Data mining, data analytics and data processing are three inter-related processes that are carried out on large volume of datasets. Data can be of any form such as text, numeric, ontology, alpha-numeric, images, video, and other multi-dimensional datasets. People dataset is one of the famous datasets from the above datasets. Crowdsourcing is used to solve the large size of data with people. The crowdsourcing

In the course of driving, due to the influence of environment, signal acquisition device and the degree of unevenness of road surface, the collected signals fluctuate within certain error range and have randomness. To predict the fatigue life of mechanical structures more accurately, the processing method of random load signals for uncertainty is proposed. The collected load signals are converted into

The survey paper summarizes the recent applications and developments in the domain of Generative Adversarial Networks (GANs) i.e. a back propagation based neural network architecture for generative modeling. GANs is one of the most highlighted research avenue due to its synthetic data generation capabilities and benefits of representations comprehended irrespective of the application. While several

Cloud Computing has many facets to it which makes it diverse. Investigation of a cyber-crime that involves the cloud as a subject, object, or as an environment, is cloud forensics. Challenges in cloud forensics exist at every phase of digital forensics. To provide a holistic solution to overcome these challenges we must first understand the cloud forensics landscape (the Cloud). The elements which

Agriculture is one of the most crucial field contributing to the development of any nation. It not only affects the economy of nation but also has impact on the world food grain statistics. For agriculturist obtaining sustainable production of crop is always a challenge. Achieving optimum crop yield has always been a challenge for the farmer due to ever changing environmental conditions. The major

The objective of this article is to summarise the work that we have been doing as a group in the context of stabilised finite element formulations for viscoelastic fluid flows. Viscoelastic fluids are complex non-Newtonian fluids, characterised by having an irreducible constitutive equation that needs to be solved coupled with the momentum and continuity equations. The finite element approximation

The coordinated or selective power system can be considered as a sequence procedure among two protective devices installed in series and having certain features. The coordination of protective devices in protection system is a very substantial procedure to guarantee a strength and reliable power system. Directional overcurrent relays coordination is considered as an optimization problem. Due to the

Micro scale experiments demonstrated that the material deformation behaviors of some metals and polymers deformed elasticity are size dependence, and the modified couple stress theory (MCST), which introduce an additional material length scale parameter, could capture the size dependences. The article reviews the available size-dependent models of micro-beam and micro-plate based on the MCST. For the

Exotic materials play a key role in high-end applications. Thus, Wire Electric Discharge Machining (WEDM) stands out due to superior surface roughness and high precision. Currently, WEDM is found in rigid bed-based CNC machines constraining the workpieces in geometry and size. Parallelly, the stochastic nature of WEDM and the lack of complete mathematical models make it complex to control. As a result

Accurate prediction and control of diesel engine-out emissions are vital areas of interest for automotive manufacturers and researchers. This article presents an investigative review of performance and emission control improvements in diesel engines over the past few decades. A brief background of environmental organizations like the Environmental Protection Agency has been included because they initiated

Code smells tend to compromise software quality and also demand more effort by developers to maintain and evolve the application throughout its life-cycle. They have long been catalogued with corresponding mitigating solutions called refactoring operations. Researchers have argued that due to the subjectiveness of the code smells detection process, proposing an effective use of automatic support for

This paper is devoted to a literature review for various treatments of dynamic analysis of concrete gravity and arch dams. The dynamic analysis of dams can be carried out by using analytical, semi-analytical, and numerical approaches, which may include nonlinear effects or ignore them. This kind of analysis can be conducted in the time or frequency domain. Different parameters significantly affect

Glioma is the deadliest brain tumor with high mortality. Treatment planning by human experts depends on the proper diagnosis of physical symptoms along with Magnetic Resonance (MR) image analysis. Highly variability of a brain tumor in terms of size, shape, location, and a high volume of MR images make the analysis time-consuming. Automatic segmentation methods achieve a reduction in time with excellent

The method of speckle reduction is widely used in synthetic aperture radar (SAR) imagery over the last three decades. The SAR images are inherently speckled in nature. Speckle noise is a granular pattern distribution, usually modeled as a multiplicative noise that affects the SAR images, as well as all coherent images. Other SAR related problems are also discussed in this paper. Therefore, despeckling

Computer simulations are used to model of complex physical systems. Often, these models represent the solutions (or at least approximations) to partial differential equations that are obtained through costly numerical integration. This paper presents a survey of two important statistical/machine learning approaches that have shaped the field of scientific modeling. Firstly we survey the developments

The emergence of nanotechnology and existence of computational techniques for measuring the thermophysical properties of nanolubricants has culminated in a substantial number of experimental studies over past two decades. Development of nanoparticles is a field that has more proximity to innovations taking place in the domain of physics and chemistry but their application aspects have attracted the

Present work explores the quantitative and qualitative performance investigation of nine recently developed optimization algorithm along with state-of-the-art differential evolution and its variants for the economic optimization of cooling tower. Quantitative performance of the competitive algorithms evaluated based on the best solution, mean solution, success rate, and standard deviation results.

Deep learning is associate degree future field of machine learning (ML) analysis. It consists of variety of numerous concealed layers of artificial neural networks ANN). Deep learning methods applies nonlinear transformation and high-level model abstraction to giant databases. Recent advances in deep learning design among several fields have already contributed considerably to computer science. This

Since early publications in the late 1980s and early 1990s, the finite volume method has been shown suitable for solid mechanics analyses. At present, there are several flavours of the method, which can be classified in a variety of ways, such as grid arrangement (cell-centred vs. staggered vs. vertex-centred), solution algorithm (implicit vs. explicit), and stabilisation strategy (Rhie–Chow vs. J

Neural engineering, an emerging interdisciplinary subject, is aimed at using engineering techniques to investigate the function and manipulate the behavior of the nervous system. The development of technology along with the advancement in Science helps to apply increasing multimodal research into the field of neural engineering, which has promoted the development of neural engineering. In this paper

In this paper, we are interested in high-order algorithms for time discretization and focus upon the high-order implicit/explicit algorithm designs. Five high-order unconditionally stable implicit algorithms, derived by the time continuous Galerkin method, weighting parameter method, collocation method, differential quadrature method, and the modified time-weighted residual method, in first-/second-order

Over the last decade, data processing shifted from cloud computing to the local processing environment named Fog computing. Fog computing is an extension of cloud computing, which process data nearby device instead of sending to the cloud this will reduce the burden from the Internet and quick processing. In this paper, we describe fog computing technology, infrastructure, and applications. Specifically

Image dehazing play a vital role in several applications related to computer vision. The prime motive of this paper is to provide the overview of the existing deep learning algorithms associated with image defogging. In beginning, the main issues preset in the existing single image technique based on physical models are discussed. Thereafter, the basic concept of atmospheric scattering model and deep

The heterogeneous and high-dimensional nature of omics data presents various challenges in gaining insights while analysis. In the era of big data, omics data is available as genome, proteome, transcriptome, and metabolome. Apart from the single omics data type, integrative omics known as multi-omics, and omics imaging data known as radiomics approaching to big data are being used for predictive analysis

Large-scale structural topology optimization has always suffered from prohibitively high computational costs that have till date hindered its widespread use in industrial design. The first and major contributor to this problem is the cost of solving the Finite Element equations during each iteration of the optimization loop. This is compounded by the frequently very fine 3D models needed to accurately

Partially or fully buried explosive on detonation releases a large amount of kinetic energy a part of which gets dissipated during creation of crater and the rest gets converted into ground shock. Both phenomena are of complex nature with involvement of non-linearity in both loading and material characteristics. This review aims at providing an insight into mechanisms involved during an event of buried

Several methods to derive accurate Timoshenko beam finite elements are presented and compared. Two application problems are examined: linear elastostatics and linearized prebuckling (LPB) stability analysis. Accurate elements can be derived for both problems using a well known technique that long preceeds the Finite Element Method: using homogeneous solutions of the governing equations as shape functions

Advancement in genome sequencing technology has empowered researchers to think beyond their imagination. Researchers are trying their hard to fight against various genetic diseases such as cancer. Artificial intelligence has empowered research in the healthcare sector. The availability of open-source healthcare datasets has motivated the researchers to develop applications which helps in early diagnosis

The necessity of image fusion is growing in recently in image processing applications due to the tremendous amount of acquisition systems. Fusion of images is defined as an alignment of noteworthy Information from diverse sensors using various mathematical models to generate a single compound image. The fusion of images is used for integrating the complementary multi-temporal, multi-view and multi-sensor

The development of low dimensional explicit based topology optimization approaches such as moving morphable components method increased the hopes to develop and expand evolutionary based solutions in the topology optimization of continuum structures. Despite the low dimensionality of the parametrization which helps to increase the efficiency, due to the multimodal behavior of the objective function

For several decades now, the construction industry is suffering from low productivity, especially in comparison to manufacturing industries which have succeeded to benefit from digitalization of their processes. Furthermore, scarceness of qualified workforce is expected in the near future. Construction automation is introduced as a solution to these challenges. The capabilities of construction robots

Almost all real-world and engineering problems involve multi-objective optimization of some sort that is often constrained. To solve these constrained multi-objective optimization problems, constrained multi-objective optimization evolutionary algorithms (CMOEAs) are enlisted. These CMOEAs require specific constraint handling techniques. This study aims to address and test the most successful constraint

Multi-objective truss optimisation is a research topic that has been less investigated in the literature compared to the single-objective cases. This paper investigates the comparative performance of fourteen new and established multi-objective metaheuristics when solving truss optimisation problems. The optimisers include multi-objective ant lion optimiser, multi-objective dragonfly algorithm, multi-objective

To describe the behaviours of geomaterials such as time-dependency, anisotropy and destructuration, multiple hardening parameters and laws are generally needed for application in advanced elasto-viscoplastic models. Time integration with stress updating is a key step in the application of elasto-viscoplastic models to engineering practice. However, the robustness of time integration algorithms for

Computation of penetration, scabbing and perforation depth under projectile impact is a highly non-linear and complex phenomenon. There exist various formulae, mainly empirical in nature, to estimate these depths under a given scenario. However, there exist a large difference between these depths computed using available empirical formulae and those observed during actual tests. In the present study

The optimized coordination of directional over current relays (DOCRs) in power systems is still a challenging task. This optimization problem has been augmented in the literature through coping with various methodologies and the progress is not over yet. Thanks to the advance in technology, this task is mitigated in terms of time consuming and computational errors via applying different metaheuristic

The accurate result of heuristic models combined by social inspired optimization methods is interesting issue for optimizations of hierarchical stiffened shells (HSS). In this paper, six heuristic combined by social-inspired optimization is compared for both ability and accuracy in optimization of load-carrying capacities of HSS. A three level optimization method is employed as (1) explicit dynamic

The deep learning has become the key for artificial intelligence applications development. It was successfully used to solve computer vision tasks. But the deep learning algorithms are based on Deep Neural Networks (DNN) with many hidden layers which need a huge computation effort and a big storage space. Thus, the general-purpose graphical processing units (GPGPU) are the best candidate for DNN development

This work compares Mean-CVaR portfolio optimization models with variable cardinality constraint and rebalancing process. It considers integer and continuous decision variables, the number of asset lots and asset investment rate, respectively, and the linear and non-linear formulations of CVaR. Exact methods are used to solve the linear models and parallel evolutionary algorithms are used to solve the

Online reviews are becoming increasingly important for decision-making. Consumers often refer to online reviews for opinions before making a purchase. Marketers also acknowledge the importance of online reviews and use them to improve product success. However, the massive amount of online review data, as well as its unstructured nature, is a challenge for anyone wanting to derive a conclusion quickly