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This paper explores the data carrying capacity enhancement in free space optics (FSO) transmission systems by employing orbital angular momentum (OAM) multiplexing technique. Specifically, simulative investigation of OAM-FSO terrestrial transmission is carried out under varying weather conditions. A net data capacity of 40 Gb/s over free space transmission medium is demonstrated using four distinct OAM beams (LG0,0, LG0,13, LG0,40, and LG0,80). The performance is compared for alternate mark inversion (AMI), return-to-zero (RZ), and non-return-to-zero (NRZ) encoding schemes. The results showed that the best performance occurred when using NRZ modulation and a favorable 40 Gb/s transmission along a range varying from 64 to 800 m depending on the external weather conditions is reported.

Let h 1 z and h 2 z be two nonvanishing holomorphic functions in the open unit disc with h 1 0 = h 2 0 = 1 . For some holomorphic function q z , we consider the class consisting of normalized holomorphic functions f whose ratios f z / z q z and q z are subordinate to h 1 z and h 2 z , respectively. The majorization results are obtained for this class when h 1 z is chosen either h 1 z = cos z or h 1 z = 1 + sin z or h 1 z = 1 + z and h 2 z = 1 + sin z .

The goal of this article is to determine sharp inequalities of certain coefficient-related problems for the functions of bounded turning class subordinated with a petal-shaped domain. These problems include the bounds of first three coefficients, the estimate of Fekete-Szegö inequality, and the bounds of second- and third-order Hankel determinants.

Smart City indeed become a vision of most of the countries. The city will have robots, drone, smart car, the house entirely operated Intelligently. However, managing how the human crowd and machine can live together is a challenging task; social and emotional interaction between human may affect the crowd behaviour. Therefore, Crowd simulation has the potential to demonstrate the behaviour of a massive people that gathering on a particular location during a specified period. The size of the crowd, geographical site condition and agent’s personality potentially make crowd simulation more believable. This paper aims to observe the potential of reinforcement learning for controlling Socio-emotional crowd behaviour by adding the parameter of emotion towards the crowds. The Tree algorithm more superior compare to other machine learning algorithm that capable of predicting the female agent with accuracy 88.1% and male agent around 85.3%. The simulation performed with the railway station scene that has four platform and six lanes to accommodate a passenger. Simulation is initiated with passengers walking toward the main entrance and went to the desired platform. The train is set up to arrive every minute, and the on-board passenger will move toward the entrance door and exit the central station. The reinforcement learning with socio-emotional control expected to bring the crowd simulation to provide realistic and human mimicked behaviour.

Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.

One of the most commonly used operating systems for smartphones is Android. The open-source nature of the Android operating system and the ability to include third-party Android apps from various markets has led to potential threats to user privacy. Malware developers use sophisticated methods that are intentionally designed to bypass the security checks currently used in smartphones. This makes effective detection of Android malware apps a difficult problem and important issue. This paper proposes a novel fuzzy integral-based multi-classifier ensemble to improve the accuracy of Android malware classification. The proposed approach utilizes the Choquet fuzzy integral as an aggregation function for the purpose of combining and integrating the classification results of several classifiers such as XGBoost, Random Forest, Decision Tree, AdaBoost, and LightGBM. Moreover, the proposed approach utilizes an adaptive fuzzy measure to consider the dynamic nature of the data in each classifier and the consistency and coalescence between each possible subset of classifiers. This enables the proposed approach to aggregate the classification results from the multiple classifiers. The experimental results using the dataset, consisting of 9476 Android goodware apps and 5560 malware Android apps, show that the proposed approach for Android malware classification based on the Choquet fuzzy integral technique outperforms the single classifiers and achieves the highest accuracy of 95.08%.

Background:The amino acid residues, in protein, undergo post-translation modification (PTM) during protein synthesis, a process of chemical and physical change in an amino acid that in turn alters behavioral properties of proteins. Tyrosine sulfation is a ubiquitous posttranslational modification which is known to be associated with regulation of various biological functions and pathological processes. Thus its identification is necessary to understand its mechanism. Experimental determination through site-directed mutagenesis and high throughput mass spectrometry is a costly and time taking process, thus, the reliable computational model is required for identification of sulfotyrosine sites.Methodology:In this paper, we present a computational model for the prediction of the sulfotyrosine sites named iSulfoTyr-PseAAC in which feature vectors are constructed using statistical moments of protein amino acid sequences and various position/composition relative features. These features are incorporated into PseAAC. The model is validated by jackknife, cross-validation, self-consistency and independent testing.Results:Accuracy determined through validation was 93.93% for jackknife test, 95.16% for crossvalidation, 94.3% for self-consistency and 94.3% for independent testing.Conclusion:The proposed model has better performance as compared to the existing predictors, however, the accuracy can be improved further, in future, due to increasing number of sulfotyrosine sites in proteins.

Real-time video communication has become one of the most significant applications extensively used by homogeneous/heterogeneous wireless network technologies, such as Wi-Fi, the Internet of things, the wireless sensor network (WSN), 5G, etc. This leads to enhanced deployment of multimedia streaming applications over wireless network technologies. In order to accomplish the optimal performance of real-time multimedia streaming applications over the homogeneous/heterogeneous wireless network, it is therefore necessary to develop a simulation tool-set that effectively measures the quality of service (QoS) for different multimedia streaming applications over transport layer protocols. This paper proposes an autonomous simulation tool (AST) that is entirely independent from the source code of transport layer protocols. Furthermore, the AST is integrated into NS-2 to evaluate the QoS of real-time video streaming over numerous transport layer protocols and it uses new QoS measurement tools to test the video delivery quality based on I-frames to speeds up the assessment of multimedia streaming quality and ensure high accuracy of performance metrics. The simulation results show that using the AST to simulate real-time multimedia stream results in between 13% and 36% higher delivery ratio and 150–250% less cumulative jitter delay compared with using baseline simulation tools. Also, the AST guarantees an optimal QoS performance measurements in terms of the peak signal-to-noise Ratio and visual quality of the received video.

Evaluation of customer satisfaction is an important area of marketing research in which products are defined by attributes that can be grouped into different categories depending on their contribution to customer satisfaction. It is important to identify the category of an attribute so that it can be prioritized by a manager. The Kano model is a well-known method to perform this task for an individual customer. However, it requires filling in a form, which is a difficult and time-consuming exercise. Many existing methods require less effort from the customer side to perform data collection and can be used for a group of customers; however, they are not applicable to individuals. In the present study, we develop a data-analytic method that also uses the dataset; however, it can identify the attribute category for an individual customer. The proposed method is based on the probabilistic approach to analyze changes in the customer satisfaction corresponding to variations in attribute values. We employ this information to reveal the relationship between an attribute and the level of customer satisfaction, which, in turn, allows identifying the attribute category. We considered the synthetic and real housing datasets to test the efficiency of the proposed approach. The method correctly categorizes the attributes for both datasets. We also compare the result with the existing method to show the superiority of the proposed method. The results also suggest that the proposed method can accurately capture the behavior of individual customers.

Most of the technologies of today’s world, which are enriched with various powerful features and amazing quality characteristics, enables software developers to come up with best possible software solutions, no matter what the context of the particular issue. Technologies such as Augmented Reality (AR), is utilized almost every kind of fields in today’s society. As computers become more advanced through mobile devices and wearable technology, augmented reality will become a seamless experience that is a part of our everyday lives. In the context of this work, an Intelligence mobile navigation application for the King Abdul Aziz University Rabigh is developed enabling the user to find specific locations on campus and offers the ability to explore the campus environment via AR. Furthermore, the system, Mobile Campus Navigation with Augmented Reality application is capable of giving guidance in outdoor location navigating and retrieving details of campus officials and lecturers. With the proposed system, it is expected to serve as a useful and informative navigate helper for both students of King Abdul Aziz University and for the visitors, at outdoor locations and to use as an application to check officials and lecturer availability and retrieve detail about them when they are not available at the office at any time.

The paper utilizes the Karnaugh map in a pedagogical calculation of the Shapley-Shubik index of voting power, which is perhaps the most prominent such index for managerial, political, and engineering voting systems. The Karnaugh map has features that allow it to readily implement this calculation, namely: (a) it can conveniently represent pseudo-switching functions of the form g= B_2^n →R, (b) it can immediately detect the number of orderings in going from one of its configurations (cells) to another, and (c) it visually identifies a pair of configurations that is critical w. r. t. a certain voter, which are a primitive winning coalition of which this voter is a member and a corresponding primitive losing coalition that results when this voter defects. The paper offers a detailed description of these three features and of the associated calculations using the celebrated system of the European Economic Community as a running example.

Spherical fuzzy set (SFS) is one of the most important and extensive concept to accommodate more uncertainties than existing fuzzy set structures. In this article, we will describe a novel enhanced TOPSIS-based procedure for tackling multi attribute group decision making (MAGDM) issues under spherical fuzzy setting, in which the weights of both decision-makers (DMs) and criteria are totally unknown. First, we study the notion of SFSs, the score and accuracy functions of SFSs and their basic operating laws. In addition, defined the generalized distance measure for SFSs based on spherical fuzzy entropy measure to compute the unknown weights information. Secondly, the spherical fuzzy information-based decision-making technique for MAGDM is presented. Lastly, an illustrative example is delivered with robot selection to reveal the efficiency of the proposed spherical fuzzy decision support approach, along with the discussion of comparative results, to prove that their results are feasible and credible.

Internet of Things (IoT) has become one of the most challenging issues in many researches to connect physical things through the internet by creating a virtual identity for everything. Traffic congestion in Riyadh city is chosen due to the proliferation in the number of vehicles on Riyadh roads that is resulting in grumbling by citizens. Currently, there is no reliable service offered to Riyadh citizens from the traffic department enabling them to access traffic information. A new traffic congestion framework for Riyadh is proposed so that it can help the development of traffic congestion services. This framework aims to benefit from the current Riyadh road infrastructure and apply the IoT paradigm for detecting traffic congestion. Sensing devices are used to identify the congestion of the traffic flow through providing multiple proposed services to the citizens such as a vehicle counting, live streaming video and rerouting services. Citizens are able to access all the services by using proposed mobile application connected to the internet, as all those services are integrated with public map Service. By using the services, the citizens are able to identify the exact location where congestion occurs and an alternate solution can be provided easily. To achieve this, Business Process Execution Language (BPEL) is embedded in one of the framework layers. Due to the effectiveness of BPEL, workflows are built to combine the proposed services with the legacy Riyadh services. This approach clearly defines how the services are executed through BPEL models.

The West Nile virus (WNV) infections are generally asymptomatic and are considered as immediate concerns of biodefense due to the lack of any therapeutic remedies. In this work, we created an interaction network of 1159 differentially expressed genes to detect potential hub genes from WNV infected primary human macrophages. We go on to explore the genetic variations that can alter the expression and function of identified hub genes (HCLS1, SLC15A3, HCK, and LY96) using the PROVEAN Protein Batch tool and PolyPhen-2. Community analysis of the network revealed that these clusters were enriched in GO terms such as inflammatory response and regulation of proliferation. Analysis of hub genes can aid in determining their degree of conservation and may help us in understanding their functional roles in biological systems. The nsSNPs proposed in this work may be further targeted through experimental methods for improving treatment towards the infection of WNV.

Average surface roughness value ( R a ) is an important measure of the quality of a machined work piece. Lower the R a value, the higher is the work piece quality and vice versa. It is therefore desirable to develop mathematical models that can predict the minimal R a value and the associated machining conditions that can lead to this value. In this paper, real experimental data from an end milling process is used to develop models for predicating minimum R a value. Two techniques, model tree and sequential minimal optimization based support vector machine, which have not been used before to model surface roughness, were applied to the training data to build prediction models. The developed models were then applied to the test data to determine minimum R a value. Results indicate that both techniques reduced the minimum R a value of experimental data by 4.2% and 2.1% respectively. Model trees are found to be better than other approaches in predicting minimum R a value.

Android malware is rapidly becoming a potential threat to users. The number of Android malware is growing exponentially; they become significantly sophisticated and cause potential financial and information losses for users. Hence, there is a need for effective and efficient techniques to detect the Android malware applications. This paper proposes an intelligent hybrid approach for Android malware detection using the permissions and API calls in the Android application. The proposed approach consists of two steps. The first step involves finding the most significant permissions and Application Programming Interfaces (API) calls that leads to efficient discrimination between the malware and good ware applications. For this purpose, two features selection algorithms, Information Gain (IG) and Pearson CorrCoef (PC) are employed to rank the individual permissions and API’s calls based on their importance for classification. In the second step, the proposed new hybrid approach for Android malware detection based on the combination of the Adaptive neural fuzzy Inference System (ANFIS) with the Particle Swarm Optimization (PSO), is employed to differentiate between the malware and goodware Android applications (apps). The PSO is intelligently utilized to optimize the ANFIS parameters by tuning its membership functions to generate reliable and more precise fuzzy rules for Android apps classification. Using a dataset consists of 250 goodware and 250 malware apps collected from different recourse, the conducted experiments show that the suggested method for Android malware detection is effective and achieved an accuracy of 89%.

Wireless sensor network (WSN) is a network communication technology that is characterized by its small size; low power consumption and limited memory capacity. It provides communication in a broadcast manner. These sensor nodes have no specific infrastructure and they are placed randomly throughout the network. The use of WSNs is to collect data from physical environment and in real time. Error in the collected data or malfunctioning of the sensors can create problems on large scale. There are few ways and methods through which WSNs can be attacked. One of them is the Denial of Service Attack (DoS), which captures the nodes and takes its important data, modifies it or halt it from responding to a bona fide user. Various techniques are used to tackle DoS attack on WSNS. By using the multiple base station approach we can reduce and minimize the drastic effect of DoS attack on WSNs and makes our network secure and functioning in its normal conditions

The diversity of applications developed with different programming languages, application/data architectures, database systems and representation of data/information leads to heterogeneity issues. One of the problem challenges in the problem of heterogeneity is about heterogeneity data in term of semantic aspect. The semantic aspect is about data that has the same name with different meaning or data that has a different name with the same meaning. The semantic data mapping process is the best solution in the current days to solve semantic data problem. There are many semantic data mapping technologies that have been used in recent years. This research aims to compare and analyze existing semantic data mapping technology using five criteria’s. After comparative and analytical process, this research provides recommendations of appropriate semantic data mapping technology based on several criteria’s. Furthermore, at the end of this research we apply the recommended semantic data mapping technology to be implemented with the real data in the specific application. The result of this research is the semantic data mapping file that contains all data structures in the application data source. This semantic data mapping file can be used to map, share and integrate with other semantic data mapping from other applications and can also be used to integrate with the ontology language.

In this paper we deal with approximate transient analysis of Product Form Queuing Networks. In particular, we exploit the idea of flow equivalence to reduce the size of the model. It is well-known that flow equivalent servers lead to exact steady state solution in many cases. Our goal is to investigate the applicability of flow equivalence to transient analysis. We show that exact results can be obtained even in the transient phase, but the definition of the equivalent server requires the analysis of the whole original network. We propose thus to use approximate aggregate servers whose characterization demands much less computation. Specifically, the characterization corresponds to the steady state equivalent server of the stations that we aim to aggregate and thus can be achieved by analyzing the involved stations in isolation. This way, approximations can be derived for any queuing network, but the precision of the results depends heavily on the topology and on the parameters of the model. We illustrate the approach on numerical examples and briefly discuss a set of criteria to identify the cases when it leads to satisfactory approximation.

The research on human computer interaction. Human already thinking to overcome the way of interaction towards natural interaction. Kinect is one of the tools that able to provide user with Natural User Interface (NUI). It has capability to track hand gesture and interpret their action according to the depth data stream. The human hand is tracked in point of cloud form and synchronized simultaneously.The method is started by collecting the depth image to be analyzed by random decision forest algorithm. The algorithm will choose set of thresholds and features split, then provide the information of body skeleton. In this project, hand gesture is divided into several actions such as: waiving to right or left toward head position then it will interpret as next or previous slide. The waiving is measured in degree value towards head as center point. Moreover, pushing action will trigger to open new pop up window of specific slide that contain more detailed information. The result of implementations is quite fascinating, user can control the PowerPoint and event able to design the presentation form in different ways. Furthermore, we also present a new way of presentation by presenting WPF form that connected to database for dynamic presentation tools

Mobile ad-hoc networks (MANETs) are temporary networks without any pre-existing infrastructure in which nodes are connected through wireless channels. There is no centralized administration in this network because nodes are not fixed at their locations, they are continuously moving to different locations. Each node in MANETs has dynamic capabilities because at one point acts as a host while in some other time becomes a router which can forward messages on the network to other nodes. One of the complicated issues in MANETs is routing due to their dynamic topology which is frequently changes due to the mobility of nodes. In order to forward packets in this complex topology using optimal routes, there is a need for efficient routing protocols. There are three categories of routing protocols in MANETs named as reactive, proactive and hybrid respectively. We have used a hybrid type protocol named Zone Routing Protocol (ZRP) for routing in our research. In order to control unwanted flooding in the overlapping zones of ZRP, we have proposed a selective broadcasting technique for route discovery. In this technique route discovery exchange messages are highly reduced in the outer zone of the ZRP.