Presently, sensor-cloud based environment becomes highly beneficial due to its applicability in several domains. Wireless multimedia sensor network (WMSN) is one among them, which involves a set of multimedia sensors to collect data about the deployed region. Compared to traditional object tracking models, animal tracking in WMSN is a tedious process owing to the harsh, dynamic, and energy limited sensors. This paper introduces a new Reliable Multi-Object Tracking Model using Deep Learning (DL) and Energy Efficient WMSN. Initially, the fuzzy logic technique is employed to determine the cluster heads (CHs) to attain energy efficiency. Next, in the second stage, a novel tracking algorithm by the use of Recurrent Neural Network (RNN) with a tumbling effect called RNN-T is developed. The proposed RNN-T model gets executed by every sensor node and the CHs execute the tracking algorithm to track the animals. Finally, the tracking results are transmitted to the cloud server for investigation purposes. In order to assess the performance of the presented model, an extensive experimental analysis is carried out by the use of a real-time wildlife video. The obtained results ensured that the RNN-T model has achieved better performance over the compared methods in different aspects.

This paper presents a method to estimate gasoline fuel consumption using the onboard vehicle information system OBD-II (Onboard Diagnoses-II). Multiple vehicles were used on a test route so that their consumption can be compared. The relationships between fuel consumption and both of the engine speed are measured in RPM (revolutions per minute), and the throttle position sensor (TPS). The relationships are expressed as polynomial equations. The method which is composed of an SVM (support vector machine) classifier combined with Lagrange interpolation, is used to define the relationship between the two engine parameters and the overall fuel consumption. The relationship model is plotted using a surface fitting tool. In the experimental section, the proposed method is tested using the vehicles on a major highway between two cities in Jordan. The proposed model gets its sample data from the engine’s RPM, TPS, and fuel consumption. The method successfully has given precise fuel consumption with square root mean difference of 2.43, and the figures are compared with the values calculated by the conventional method.

Mobile information and communication technology changed how people and businesses can benefit from government public services at any time and from anyplace. The success or failure of mobile government services is becoming more dependent on satisfying the needs and the expectations of both citizens and business organizations. This paper reviews and analyses some existing empirical studies that examine m-Government acceptance in some developing countries. Then, a new integrated conceptual model for examining some important key factors that may affect m-Government acceptance in Jordan from user perspective was proposed. An empirical test was conducted using a questionnaire to explore the effect of the following factors: Trust in mobile channel, trust in government, perceived usefulness, perceived ease of use, relative advantage, compatibility, complexity, service quality and user satisfaction on the behavioural intention to use m-Government applications. Finally, justification of the proposed integrated model and formulation of the associated hypotheses was conducted.

Cyber-attacks on cyber-physical systems (CPSs) resulted to sensing and actuation misbehavior, severe damage to physical object, and safety risk. Machine learning (ML) models have been presented to hinder cyberattacks on the CPS environment; however, the non-existence of labelled data from new attacks makes their detection quite interesting. Intrusion Detection System (IDS) is a commonly utilized to detect and classify the existence of intrusions in the CPS environment, which acts as an important part in secure CPS environment. Latest developments in deep learning (DL) and explainable artificial intelligence (XAI) stimulate new IDSs to manage cyberattacks with minimum complexity and high sophistication. In this aspect, this paper presents an XAI based IDS using feature selection with Dirichlet Variational Autoencoder (XAIIDS-FSDVAE) model for CPS. The proposed model encompasses the design of coyote optimization algorithm (COA) based feature selection (FS) model is derived to select an optimal subset of features. Next, an intelligent Dirichlet Variational Autoencoder (DVAE) technique is employed for the anomaly detection process in the CPS environment. Finally, the parameter optimization of the DVAE takes place using a manta ray foraging optimization (MRFO) model to tune the parameter of the DVAE. In order to determine the enhanced intrusion detection efficiency of the XAIIDS-FSDVAE technique, a wide range of simulations take place using the benchmark datasets. The experimental results reported the better performance of the XAIIDS-FSDVAE technique over the recent methods in terms of several evaluation parameters.

Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images. Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist. Therefore, automated cervical cancer diagnosis using automated methods are necessary. This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis (ODLIM-CCD) using pap smear images. The proposed ODLIM-CCD technique incorporates median filtering (MF) based pre-processing to discard the noise and Otsu model based segmentation process. Besides, deep convolutional neural network (DCNN) based Inception with Residual Network (ResNet) v2 model is utilized for deriving the feature vectors. Moreover, swallow swarm optimization (SSO) based hyperparameter tuning process is carried out for the optimal selection of hyperparameters. Finally, recurrent neural network (RNN) based classification process is done to determine the presence of cervical cancer or not. In order to showcase the improved diagnostic performance of the ODLIM-CCD technique, a series of simulations occur on benchmark test images and the outcomes highlighted the improved performance over the recent approaches with a superior accuracy of 0.9661.