The Majority of previous research on new technology acceptance has been conducted with single-step Structural Equation Modeling (SEM) based methods. The primary purpose of the study is to enhance the new technology acceptance based research with the Artificial Neural Network (ANN) method to enable more precise and in-depth research results as compared to the single-step SEM method. This study measures the relation between technology readiness dimension (optimism, innovativeness, discomfort, insecurity) and the technology acceptance (perceived ease of use and perceived usefulness) – and the intention to use cryptocurrency, such as bitcoin. The contribution of this study include the use of a multi-analytical approach by combining Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis. First, PLS-SEM was applied to assess which factor has significant influence toward intention to use cryptocurrency. Second, an ANN was employed to rank the relative influence of the significant predictor variables attained from the PLS-SEM. The findings of the two-step PLS-SEM and ANN approach confirm that the use of ANN further verifies the results obtained by the PLS-SEM analysis. Also, ANN is capable of modelling complex linear and non-linear relationships with high predictive accuracy compared to SEM methods. Also, an Importance-Performance Map Analysis (IPMA) of the PLS-SEM results provides a more specific understanding of each factor–s importance-performance.

Wireless Body Area Network (WBAN) is an interconnection of small bio-sensor nodes that are deployed in/on different parts of human body. It is used to sense health-related data such as rate of heart beat, blood pressure, blood glucose level, Electro-cardiogram (ECG), Electro-myography (EMG) etc. of human body and pass these readings to real-time health monitoring systems. WBANs is an important research area and is used in different applications such as medical field, sports, entertainment, social welfare etc. Bio-Sensor Nodes (BSNs) or simply called as Sensor Nodes (SNs) are the main backbone of WBANs. SNs normally have very limited resources due to its smaller size. Therefore, minimum consumption of energy is an essential design requirement of WBAN schemes. In the proposed work, Energy-efficient Harvested-Aware clustering and cooperative Routing Protocol for WBAN (E-HARP) are presented. The presented protocol mainly proposes a novel multi-attribute-based technique for dynamic Cluster Head (CH) selection and cooperative routing. In the first phase of this two-phased technique, optimum CH is selected among the cluster members, based on calculated Cost Factor (CF). The parameters used for calculation of CF are; residual energy of SN, required transmission power, communication link Signal-to-Noise-Ratio (SNR) and total network energy loss. In order to distribute load on one CH, E-HARP selects new CH in each data transmission round. In the second phase of E-HARP, data is routed with cooperative effort of the SN, which saves the node energy by prohibiting the transmission of redundant data packets. To evaluate the performance of the proposed technique, comprehensive experimentations using NS-2 simulation tool has been conducted. The results are compared with some latest techniques named as EH-RCB, ELR-W, Co-LAEEBA, and EECBSR. The acquired results show a significant enhancement of E-HARP in terms of network stability, network life time, throughput, end-to-end delay and packet delivery ratio.

Image segmentation is considered as a key research topic in the area of computer vision. It is pivotal in a broad range of real-life applications. Recently, the emergence of deep learning drives significant advancement in image segmentation; the developed systems are now capable of recognizing, segmenting, and classifying objects of specific interest in images. Generally, most of these techniques primarily focused on the asymmetric field of view or frontal view objects. This work explores widely used deep learning-based models for person segmentation using top view data set. The first model employed in this work is Fully Convolutional Neural Network (FCN) with Resnet-101 architecture. The network consists of a set of max-pooling and convolution layers to identify pixel-wise class labels and prediction of the mask. The second model is based on FCN called U-Net with Encoder-Decoder architecture. The encoder is mainly comprised of a contracting path, also called an encoder, which captures the context in the image and symmetric expanding path called decoder to enable accurate location. The third model used for top view person segmentation is a DeepLabV3 model also with encoder-decoder architecture. The encoder consists of trained Convolutional Neural Network (CNN) to encode feature maps of the input image. The decoder is used for up-sampling and reconstruction of output using important information extracted by the encoder. All segmentation models are firstly tested using pre-trained models (trained on frontal view data set). To improve the performance, these models are further trained using person data set captured from a top view. The output of all models consists of a segmented person in the top view images. The experimental results reveal the effectiveness and performance of segmentation models by achieving IoU of 83%, 84%, and 86% and mIoU of 80% 82%and 84% for FCN, U-Net, and DeepLabv3 respectively. Furthermore, the discussion is provided for output results with possible future guidelines.

Due to distributed nature, dynamic topology and resources constraints of tiny sensing nodes in wireless sensor networks (WSNs), the quality of service (QoS) support is a challenging issue. However, satisfying the stringent QoS requirements is an open problem. QoS aware protocols for WSNs have gained recently considerable attention of the researchers. In this paper, we focus on the QoS satisfaction in WSNs, basics of QoS support in WSNs, and more importantly challenge, requirements of QoS at each layer. Furthermore, we review the QoS protocols and categorize the QoS aware protocols and elaborate their pros and cons. We also discuss the QoS parameters with respect to each protocol performance parameters. A survey and comprehensive discussion on the QoS aware protocols of WSNs are presented, including their strengths and limitations. Finally, we also survey some computational intelligence (CI) techniques and find the basic requirements of such techniques. Moreover, we study these CI techniques in the light of QoS management and tabulate the level of each CI technique for QoS management. The paper is concluded with open research issues.