Factors Influencing the Successful Adoption of Human Resource Information System: The Content of Aqaba Special Economic Zone Authority

This study examines the key factors that have impact on the successful adoption of Human Resource Information System (HRIS) within the Aqaba Special Economic Zone Authority (ASEZA)/Jordan. In order to accomplish the purpose of the study four critical factors are inquired. So, four critical factors are inquired: First, TAM Model (Perceived Ease of Use (PEOU) and Perceived Usefulness (PU)). Second, Information Technology Infrastructure (ITI). Third, Top Management Support (TMS). Finally, Individual Experience with Computer (IEC). The research model was applied to collect data from the questionnaires answered by 45 users of HRIS as a source of primary data, based on a convenience sample the response rate was about 91%. In addition, the results were analyzed by utilizing the Statistical Package for Social Software (SPSS). Furthermore, the findings were analyzed;multiple Regression analysis indicated that all research variables have significant relationship on successful adoption of HRIS. The findings indicated IT infrastructures have a positive and significant effect on the successful adoption of HRIS. But there is no significant of PU, PEOU, TMS, and IEC on the successful adoption of HRIS. Finally, the results indicated that no significant statistical differences of demographic characteristics on HRIS adoption. Depending on the research’s findings;the researchers proposed a set of recommendations for better adoption of HRIS in SEZA.

A Review of Anomaly Detection Systems in Cloud Networks and Survey of Cloud Security Measures in Cloud Storage Applications

Cloud computing has become one of the most projecting words in the IT world due to its design for providing computing service as a utility. The typical use of cloud computing as a resource has changed the scenery of computing. Due to the increased flexibility, better reliability, great scalability, and decreased costs have captivated businesses and individuals alike because of the pay-per-use form of the cloud environment. Cloud computing is a completely internet dependent technology where client data are stored and maintained in the data center of a cloud provider like Google, Amazon, Apple Inc., Microsoft etc. The Anomaly Detection System is one of the Intrusion Detection techniques. It’s an area in the cloud environment that is been developed in the detection of unusual activities in the cloud networks. Although, there are a variety of Intrusion Detection techniques available in the cloud environment, this review paper exposes and focuses on different IDS in cloud networks through different categorizations and conducts comparative study on the security measures of Dropbox, Google Drive and iCloud, to illuminate their strength and weakness in terms of security.

Forecasting promising technology using analysis of patent information:Focused on next generation mobile communications

In order to forecast promising technologies in the field of next generation mobile communication, various patent indicators were analyzed such as citation per patent, patent family information, patent share, increase rate, and patent activity. These indicators were quantified into several indexes and then integrated into an evaluation score to provide promising technologies. As a result of the suggested patent analysis, four technologies out of twenty two in details classification were selected, which showed outstanding technology competitiveness, high patent share and increasing rates as well as high recent-patent-ratios and triad-patent-family-ratios. Each of the selected technologies scored more than 10 points in total, and the following four technologies were suggested as promising ones in the field of next generation mobile communication: 1) 3GPP based mobile communication, 2) beyond 4G mobile communication, 3) IEEE 802.16 based mobile communication, which are in medium classification of broadband mobile communication system, and 4) testing/certification system of mobile communication, which is in medium classification of mobile communication testing/certification system.

A case study of customer satisfaction for information technology solutions

There are numerous information technology solutions including hardware and software. A company that provides the solution should have knowledge of the customer needs in the purpose of sailing strategy or upgrade policy. The needs are also directly connected to the user satisfaction. However, the users have respective points of view in the needs as well as they may not identify the requirements to improve the solution. SERVQUAL can be an appropriate method to define and measure the customer satisfaction for the information technology solutions. As a case study of the customer satisfaction, the modified SERVQUAL items and scoring method are applied to a cyber-infrastructure system named CyberL ab in Korea. The measurement results of user satisfaction for CyberL ab are provided to confirm that our proposed method performs as we intended. From the results, we can score the satisfaction level of users and identify their needs in the various aspects. The total user satisfaction level for CyberL ab is scored by 88.3.

Improving Computation Efficiency of Source Authentication by Elliptic Curve Cryptosystem

The overload of traditional cryptosystems is too high for real-time applications so there is a need to design a new encryption and signature scheme for the multicast applications. In this paper, we use the elliptic curve cryptosystem to design a source authentication scheme for real-time applications. The proposed scheme uses the message recovery signature to reduce the computation cost. Thus, the proposed source authentication scheme is more suitable for real-time applications, such as online meeting, online movie, and online music.

Dissemination of Information Communication Technologies: Mobile Government Practices in Developing States

Information Communication Technologies (ICT) has offered m-government applications as an intermediate technology to provide effective and efficient government services to the public. Due to high rate of corruptions in developing states, government policies diversified governmental services from offline to virtualized perspective to expose accessibility, transparency, accountability and accessibility through mobile government. Deployment of such ICT tool also exposed a unique opportunity for the recovery of the public confidence against government which has damaged due to corruption activities in country. Virtualization of the government services became compulsory due to high rate of corruption that occurred in the economic context and it became a serious obstacle for economic development of developing states. The virtualized services aimed to harmonize governmental services into mobile platform in order to become more transparent to the public. This research paper comparatively investigates the mobile government services that are located in Malta and Singapore which are classified as developing countries. The criteria of the comparison have done based on demographic structure of the country, M-government policies and ICT infrastructure of the country. The findings of this study exposed the impact of e-government practices and differences between them in terms of applicability and provide a specific point of view for m-government adoption policy.

Design of Machine Learning Based Smart Irrigation System for Precision Agriculture

Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable agriculture.The use of state-of-the-art technologies,such as the Internet of Things,transform traditional cultivation practices,like irrigation,to modern solutions of precision agriculture.To achieve effectivewater resource usage and automated irrigation in precision agriculture,recent technologies like machine learning(ML)can be employed.With this motivation,this paper design an IoT andML enabled smart irrigation system(IoTML-SIS)for precision agriculture.The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation.The proposed IoTML-SIS model involves different IoT based sensors for soil moisture,humidity,temperature sensor,and light.Besides,the sensed data are transmitted to the cloud server for processing and decision making.Moreover,artificial algae algorithm(AAA)with least squares-support vector machine(LS-SVM)model is employed for the classification process to determine the need for irrigation.Furthermore,the AAA is applied to optimally tune the parameters involved in the LS-SVM model,and thereby the classification efficiency is significantly increased.The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.

Resolution of Relationship between Organizational Performance and Human Resource Management through Nonlinear Modeling

The relation between the HRM and the firm performance is analyzed statistically by many researchers in the literature. However, there are very few nonlinear approaches in literature for finding the relation between Human Resource Management (FIRM) and firm performance. This paper exposes the relationship between human resource management and organizational performance through the use of nonlinear modeling technique. The modeling is proposed based on Radial Basis Function (RBF) which is nonlinear modeling technique in literature. The relation between 12 input and 9 output parameters is investigated in this research that is collected between 54 companies in Turkey which indicated that the relationship between organizational management performance and relationship management can be modelled through nonlinearly.

Transformation of MRI Images to Three-Level Color Spaces for Brain Tumor Classification Using Deep-Net

In the domain ofmedical imaging,the accurate detection and classification of brain tumors is very important.This study introduces an advanced method for identifying camouflaged brain tumors within images.Our proposed model consists of three steps:Feature extraction,feature fusion,and then classification.The core of this model revolves around a feature extraction framework that combines color-transformed images with deep learning techniques,using the ResNet50 Convolutional Neural Network(CNN)architecture.So the focus is to extract robust feature fromMRI images,particularly emphasizingweighted average features extracted fromthe first convolutional layer renowned for their discriminative power.To enhance model robustness,we introduced a novel feature fusion technique based on the Marine Predator Algorithm(MPA),inspired by the hunting behavior of marine predators and has shown promise in optimizing complex problems.The proposed methodology can accurately classify and detect brain tumors in camouflage images by combining the power of color transformations,deep learning,and feature fusion via MPA,and achieved an accuracy of 98.72%on a more complex dataset surpassing the existing state-of-the-art methods,highlighting the effectiveness of the proposed model.The importance of this research is in its potential to advance the field ofmedical image analysis,particularly in brain tumor diagnosis,where diagnoses early,and accurate classification are critical for improved patient results.

Artificial Neural Network Modeling for Predicting Thermal Conductivity of EG/Water-Based CNC Nanofluid for Engine Cooling Using Different Activation Functions

A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature range.In most radiators that are used to cool an engine,water serves as a cooling fluid.The performance of a radiator in terms of heat transmission is significantly influenced by the incorporation of nanoparticles into the cooling water.Concentration and uniformity of nanoparticle distribution are the two major factors for the practical use of nanofluids.The shape and size of nanoparticles also have a great impact on the performance of heat transfer.Many researchers are investigating the impact of nanoparticles on heat transfer.This study aims to develop an artificial neural network(ANN)model for predicting the thermal conductivity of an ethylene glycol(EG)/waterbased crystalline nanocellulose(CNC)nanofluid for cooling internal combustion engine.The implementation of an artificial neural network considering different activation functions in the hidden layer is made to find the bestmodel for the cooling of an engine using the nanofluid.Accuracies of the model with different activation functions in artificial neural networks are analyzed for different nanofluid concentrations and temperatures.In artificial neural networks,Levenberg–Marquardt is an optimization approach used with activation functions,including Tansig and Logsig functions in the training phase.The findings of each training,testing,and validation phase are presented to demonstrate the network that provides the highest level of accuracy.The best result was obtained with Tansig,which has a correlation of 0.99903 and an error of 3.7959×10^(–8).It has also been noticed that the Logsig function can also be a good model due to its correlation of 0.99890 and an error of 4.9218×10^(–8).Thus ourANNwith Tansig and Logsig functions demonstrates a high correlation between the actual output and the predicted output.

Exact solutions of nonlinear fractional differential equations by (G'/G)-expansion method

In this paper, we use the fractional complex transform and the （G＇/G）-expansion method to study the nonlinear fractional differential equations and find the exact solutions. The fractional complex transform is proposed to convert a partial fractional differential equation with Jumarie＇s modified Riemann-Liouville derivative into its ordinary differential equation. It is shown that the considered transform and method are very efficient and powerful in solving wide classes of nonlinear fractional order equations.

A comparative study of seismic provisions between International Building Code 2003 and Uniform Building Code 1997

This study focuses on the comparison of the Uniform Building Code （UBC） 1997 and International Building Code （IBC） 2003 in relation to the seismic design and analysis of special steel moment resisting frame buildings （SMRF）. This paper formulates a numerical study of a steel SMRF building, studied in four different situations, namely： as an office building in San Francisco; as an office building in Sacramento; as an essential facility in San Francisco, and as an essential facility in Sacramento. The analytical results of the model buildings are then compared and analyzed taking note of any significant differences. This case study explores variations in the results obtained using the two codes, particularly the design base shear and drift ratios as they relate to different locations and occupancy use. This study also proves that IBC 2003 is more stringent for the redundancy factor under design category E for the SMRF building, and drift limits for essential facilities.

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models:An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.

Profile Evolution and Energy Dissipation for Internal SolitonTransmitting over Different Submarine Ridges

Fundamental experiments were carried out in a wave flume on internal solitary wave （ISW） of depression-type propagating over a submerged ridge. The seabed ridge included either triangular or semicircular shape - regarded as topographic obstacles. Influenced by the submarine ridge, the transmitted waves were found to always consist of a leading pulse （a solitary wave） followed by a dispersive wave train. The wave profile propagating over a triangular ridge was similar to that caused by a semicircular obstacle. Apparently, the smooth face of a semicircular ridge produced time lag of wave propagation. From experimental results available, the reduction in wave energy induced by a semicircular ridge was larger than that by a triangular one. The events of wave distortion, strong breaking, internal bolus, and stratification mixing happened in case that the crest of an ISW was great enough to interact with the topographic obstacle. The reduction in wave energy was induced by strong breaking, and it depended on the ridge height rather than the geometric shape of the ridge.

Fault Tolerance Mechanisms in Distributed Systems

The use of technology has increased vastly and today computer systems are interconnected via different communication medium. The use of distributed systems in our day to day activities has solely improved with data distributions. This is because distributed systems enable nodes to organise and allow their resources to be used among the connected systems or devices that make people to be integrated with geographically distributed computing facilities. The distributed systems may lead to lack of service availability due to multiple system failures on multiple failure points. This article highlights the different fault tolerance mechanism in distributed systems used to prevent multiple system failures on multiple failure points by considering replication, high redundancy and high availability of the distributed services.

A Novel Technique for Early Detection of COVID-19

COVID-19 is a global pandemic disease,which results from a dangerous coronavirus attack,and spreads aggressively through close contacts with infected people and artifacts.So far,there is not any prescribed line of treatment for COVID-19 patients.Measures to control the disease are very limited,partly due to the lack of knowledge about technologies which could be effectively used for early detection and control the disease.Early detection of positive cases is critical in preventing further spread,achieving the herd immunity,and saving lives.Unfortunately,so far we do not have effective toolkits to diagnose very early detection of the disease.Recent research findings have suggested that radiology images,such as X-rays,contain significant information to detect the presence of COVID-19 virus in early stages.However,to detect the presence of the disease in in very early stages from the X-ray images by the naked eye is not possible.Artificial Intelligence(AI)techniques,machine learning in particular,are known to be very helpful in accurately diagnosing many diseases from radiology images.This paper proposes an automatic technique to classify COVID-19 patients from their computerized tomography(CT)scan images.The technique is known as Advanced Inception based Recurrent Residual Convolution Neural Network(AIRRCNN),which uses machine learning techniques for classifying data.We focus on the Advanced Inception based Recurrent Residual Convolution Neural Network,because we do not find it being used in the literature.Also,we conduct principal component analysis,which is used for dimensional deduction.Experimental results of our method have demonstrated an accuracy of about 99%,which is regarded to be very efficient.

Preparation of Microcapsules Containing Water Droplets Stabilized with Solid Powder and Application to Blowing Agent

We have tried to prepare the microcapsules containing water droplets stabilized with solid powder by utilizing the (W/O)/W emulsion. The water droplets as core material were stabilized in the monomer droplets with titanium dioxide (TiO2) as a particulate surfactant. Before adding the TiO2 powder into the monomer phase, the powder was modified with triethoxyvinylsilane to adjust the degree of hydrophobicity and to promote adhesion on the interface between the inner water phase and the monomer phase in the (W/O) emulsion. It was investigated how the degree of hydrophobicity of the TiO2 powder affected the stability of water droplets in the (W/O) emulsion and the (W/O) droplets in the (W/O)/W emulsion. Moreover, the microcapsule diameters were measured before and after the expansion operation where the water droplets microencapsulated were applied as a blowing agent. The expansion ratio was increased with increase in the stability of the water droplets and the amount of water microencapsulated.

Preparation of Microcapsules with Liquid Droplet Coalescence Method Followed by Phase Separation

Novel preparation method of microencapsules was developed on the basis of the liquid coalescence method followed by phase separation. Oil droplets of limonene dissolving expanded polystyrene as a shell material were forced to collide and coalesce with the Isopar oil droplets of core material in the continuous wates phase. When two kinds of oil droplets are collided and coalesced with each other, expanded polystyrene dissolved in the limonene oil may be phase-separated in the oil droplets newly formed to form the microcapsule shell, because the Isopar oil was a poor solvent for expanded polystyrene but a good solvent for the limonene oil. In the experiment, the diameter (or number) of limonene oil droplets dissolving expanded polystyrene was mainly changed, because the coalescence frequency between the droplets is strongly dependent on the number of droplets. Favorable core shell types of microcapsules with the shell thickness from 1.0 to 5.0 μm were able to be prepared under all the experimental conditions adopted here.

Accuracy Evaluation of A Diagnostic Test by Detecting Outliers and Influential Observations

Logit regression analysis is widely applied in scientific studies and laboratory experiments, where skewed observations on a data set are often encountered. A number of problems with this method, for example, oudiers and influential observations, can cause overdispersion when a model is fitted. In this study a systematic statistical approach, including the plotting of several indices is used to diagnose the lack-of-fit of a logistic regression model. The outliers and influential observations on data from laboratory experiments are then detected. Specifically we take account of the interaction of an internal sohtary wave （ISW） with an obstacle, i.e., an underwater ridge, and also analyze the effects of the ridge height, the lower layer water depth, and the potential energy on the amplitude-based transmission rate of the ISW. As concluded, the goodness-of-fit of the revised logit regression model is better than that of the model without this approach.

Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector Machines

Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers.In this paper,we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance.Research have mostly focused the problem of human detection in thin crowd,overall behavior of the crowd and actions of individuals in video sequences.Vision based Human behavior modeling is a complex task as it involves human detection,tracking,classifying normal and abnormal behavior.The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e.,fill hole inside objects algorithm.Human detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction algorithm.The classification task is achieved using binary and multi class support vector machines.The proposed technique is validated through accuracy,precision,recall and F-measure metrics.