Due to the large usage of internet, our environment is transformed into digital society, in which everything is connected together and can be accessed from anywhere. This is the Internet of things (IoT), which refers to the usage of intelligently connected devices and systems. These devices usually collect their data from sensors and actuators in machines and other physical objects. This makes Wireless Sensor Network (WSN) a subset of an Internet of things (IoT) topology, and hence it acts like a bridge that connects the real world to the digital world. So it is important to find a flexible

Computer vision is shaping a new era with its constant development of SOTA algorithms. One heavily contested sub-field of computer vision is face detection, due to its versatile usage in many fields such as security, medical diagnosis, entertainment, and military applications. As the technology develops, it aims to run faster and more accurately on mobile devices and remote computers. In this paper, we aim to compare a number of the best face recognition algorithms and analyze the performance of each of them by deploying each algorithm on a Jetson Nano Developer Kit. Among the 6 algorithms

The issues of multitude of noisy, irrelevant, misleading features, and the capability to tackle inaccurate and inconsistent data in real world topics are the justification to turn into one of the most significant needs for feature selection. This paper proposes an intelligent hybrid approach using Rough Set Theory (RST), Chaos Theory and Binary Grey Wolf Optimization Algorithm (CBGWO) for feature selection problems. Ten different chaotic maps are used to estimate and tune GWO parameters. Experiments are applied on complex medical datasets with various uncertainty features and missing values

The degree to which innovators adopt Blockchain as a tool to create relevant and useful business solutions will determine how fast and far the platform moves into our daily lives. There are limitless opportunities for technology to define and shape future innovation. In a world dominated by digital technology, IoT plays a prominent role in our lives. It has created an ecosystem that links many systems to give smart performances in every task. The proliferation of the IoT has created a new evolution of cell phones, home and other embedded applications that are all connected to the internet

Conceptual cost estimates, are prepared at the very early stages of a project, and generally before the construction drawings and specifications are available. At this stage, cost estimates are needed by the owner, contractor, designer, or funding agencies for determination of the feasibility of a project, financial evaluation of a number of alternative projects, or establishment of an initial budget. Traditional approaches rely heavily on experienced engineers. This paper presents a method using fuzzy clustering technique for pump station projects cost estimation. The proposed conceptual cost

Mental depression is associated with an increased risk of cardiovascular mortality, thus provisioning generic simple nonlinear mathematical models for normal and depressed cases using only heart rate (HR) or stroke volume (SV) as a single input to produce cardiac output (CO) as a single output instead of using both HR and SV as two inputs. The proposed models could be in the future an effective tool to investigate the effect of neuroleptic medication, especially depression, and it reduces the time of processing. Seventy-four depressed cases, 74 normal peers and autoregressive considered as a

Daily, a big extent of colored, partially treated textile effluents drained into the sanitation systems causing serious environmental concerns. Therefore, the decolorization treatment process of wastewater is crucial to improve effluent quality. In the present study, 3 different sorbent materials, nano zerovalent iron (nZVI), activated carbon (AC), and green-synthesized nano zerovalent iron (GT-nZVI), have been prepared for raw textile wastewater decolourization. The prepared nanomaterials were characterized via X-ray diffraction (XRD) spectroscopy, scanning electron microscopy (SEM), energy

Understanding of deterioration mechanisms in sewers helps asset managers in developing prediction models for estimating whether or not sewer collapse is likely. Effective utilisation of deterioration prediction models along with the development and use of life cycle maintenance cost analysis contribute to reducing operation and maintenance costs in sewer systems. This article presents a model for life-cycle maintenance planning of deteriorating sewer network as a multi-objective optimisation problem that treats the sewer network condition and service life as well as life-cycle maintenance cost

This paper presents a framework for optimizing condition assessment policies by balancing the revealed value of information with the cost of obtaining such information. The computational platform is based on augmenting the asset condition state with an expected level of accuracy. Inaccuracies due to condition assessment reliability are evaluated using the partially observable Markov decision process. The single objective genetic algorithm is used to select the most cost-effective assets to assess considering information inaccuracy under a fixed budget. The model is extended using

With ageing water and sewer infrastructure in North America, assessing the condition of these assets has received increased attention in the past few years. Condition assessment is an integral component in any asset management program. Determining the condition of buried infrastructure tends to be more cumbersome, costly and error-prone compared to other surface infrastructure like roads and buildings. For sewers, CCTV is considered the industry standard for condition assessment technologies. For pressurized water pipelines, technologies tend to be more costly and uncertain (e.g