A metaverse can bring many benefits (i.e., self-sustaining and proactive analytics (e.g., analysis before user requests)) to wireless applications; however, its deployment is very challenging due to simultaneous quality of service (QoS) and quality of physical experience (QoE) constraints. Furthermore, the computing and communication resources of end-nodes are limited. For instance, immersive experience devices (e.g., augmented reality (AR) headsets) in a metaverse have limited computing power and therefore, might not be able to perform rendering tasks. Consequently, this paper proposes a novel task offloading framework for metaverse-empowered wireless systems. Our formulated problem aims at minimizing the cost of task offloading in the metaverse while considering both QoS and QoE constraints by optimizing the task offloading, resource allocation, and transmit power allocation variables. For QoS, we consider latency and reliability, whereas for QoE, we consider both immersive experience and packet error rate. To optimize the formulated problem, we use a decomposition-based scheme that further uses modified block-successive upper-bound minimization (BSUM), convex optimization, and multi-agent reinforcement learning (MARL) for transmit power allocation, resource allocation, and task offloading, respectively. Our solution of using convex optimization-assisted MARL for joint resource allocation and task offloading significantly improves the performance of learning in terms of reward and attaining fast QoS as well as QoE. Furthermore, BSUM significantly improves transmit power allocation when used in conjunction with a convex optimizer and MARL. Other than that, we also use a dueling (i.e., it is a reinforcement learning architecture combining the dueling network structure with the double deep Q-learning network method for more stable and efficient Q-value learning) concept to further improve the performance of MARL. Our analyses show that convex optimization, BSUM, and dueling help in significantly improving the performance of MARL. Compared to traditional MARL, our proposal results in significant improvement in terms of reward and cost, as illustrated by the results.

This paper introduces an automatic segmentation system designed for precise outlining of the pulmonary area within 3D computed tomography (CT) scans, utilizing a combination of unsupervised and supervised models. Initially, an unsupervised model is utilized to depict the empirical distribution of Hounsfield units in both lung and chest regions within the 3D CT volume. This representation takes the form of a probability model based on a linear combination of Gaussians, determined through a modified expectation maximization (EM) algorithm. Subsequently, the LCG-based segmentation is refined by modeling it with a spatial probabilistic model using a 3D Markov Gibbs random field (MGRF) with analytically estimated potentials. Finally, a supervised deep learning model is introduced and integrated with the proposed unsupervised model to achieve superior segmentation results. The efficacy of the proposed method is assessed using 3D chest scans from 29 patients confirmed with varying degrees of severity in COVID-19. This evaluation employs four distinct metrics: Dice similarity coefficient (DSC), overlap coefficient, Hausdorff distance (HD), and absolute volume difference (AVD), achieving remarkable results of $97.35_{ \pm 1.51} \%, 94.89_{ \pm 2.80} \%$, $3.39_{ \pm 1.61}$, and $2.70_{ \pm 2.88}$, respectively. When compared to four state-of-the-art deep learning-based methods, the proposed system demonstrated outstanding performance in segmenting pathological lung tissues, highlighting its potential and efficacy.

A novel unsupervised-based segmentation method is introduced to accurately delineate the lung region in 3D CT images based on appearance and geometric models. First, a probabilistic model that utilized a linear combination of Gaussian (LCG) tuned by a modified expectation maximization (EM) algorithm, is employed to model the density distribution of 3D CT chest volume. Subsequently, the initial labeling of the 3D CT chest volume is mapped to a probability distribution based on a 3D Markov Gibbs random field (MGRF) for refining. Finally, a geometric model is employed to refine the proposed segmentation by interpolating/connecting two points on its boundary with high curvature. The effectiveness of the proposed approach on 3D computed tomography (CT) chest scans of 26 patients diagnosed with different severity of coronavirus disease 2019 (COVID-19) is evaluated using four different metrics: overlap coefficient, Dice similarity coefficient (DSC), absolute lung volume difference (ALVD), and 95th-percentile bidirectional Hausdorff distance (95thHD). The proposed method achieved 94.89%±2.39%, 97.36%±1.27%, 1.79±1.89, and 4.75±2.3, respectively. Compared to three state-of-the-art methods based on deep learning approaches, the proposed method achieved superior performance in segmenting pathological lung tissues, demonstrating the promising of the proposed segmentation system.

Abstract Disease diagnosis is an exciting task due to many associated factors. Inaccuracy in the measurement of a patient’s symptoms and the medical expert’s expertise has some limitations capacity to articulate cause affects the diagnosis process when several connected variables contribute to uncertainty in the diagnosis process. In this case, a decision support system that can assist clinicians in developing a more accurate diagnosis has a lot of potentials. This work aims to deploy a fuzzy inference-based decision support system to diagnose various diseases. Our suggested method distinguishes new cases based on illness symptoms. Distinguishing symptomatic disorders becomes a time-consuming task in most cases. It is critical to design a system that can accurately track symptoms to identify diseases using a fuzzy inference system (FIS). Different coefficients were used to predict and compute the severity of the predicted diseases for each sign of disease. This study aims to differentiate and diagnose COVID-19, typhoid, malaria and pneumonia. The FIS approach was utilized in this study to determine the condition correlating with input symptoms. The FIS method demonstrates that afflictive illness can be diagnosed based on the symptoms. Our decision support system’s findings showed that FIS might be used to identify a variety of ailments. Doctors, patients, medical practitioners and other healthcare professionals could benefit from our suggested decision support system for better diagnosis and treatment.

Cell-penetrating peptides (CPPs) are special peptides capable of carrying a variety of bioactive molecules, such as genetic materials, short interfering RNAs and nanoparticles, into cells. Recently, research on CPP has gained substantial interest from researchers, and the biological mechanisms of CPPS have been assessed in the context of safe drug delivery agents and therapeutic applications. Correct identification and synthesis of CPPs using traditional biochemical methods is an extremely slow, expensive and laborious task particularly due to the large volume of unannotated peptide sequences accumulating in the World Bank repository. Hence, a powerful bioinformatics predictor that rapidly identifies CPPs with a high recognition rate is urgently needed. To date, numerous computational methods have been developed for CPP prediction. However, the available machine-learning (ML) tools are unable to distinguish both the CPPs and their uptake efficiencies. This study aimed to develop a two-layer deep learning framework named DeepCPPred to identify both CPPs in the first phase and peptide uptake efficiency in the second phase. The DeepCPPred predictor first uses four types of descriptors that cover evolutionary, energy estimation, reduced sequence and amino-acid contact information. Then, the extracted features are optimized through the elastic net algorithm and fed into a cascade deep forest algorithm to build the final CPP model. The proposed method achieved 99.45 percent overall accuracy with the CPP924 benchmark dataset in the first layer and 95.43 percent accuracy in the second layer with the CPPSite3 dataset using a 5-fold cross-validation test. Thus, our proposed bioinformatics tool surpassed all the existing state-of-the-art sequence-based CPP approaches.

The automated techniques enabled with Artificial Neural Networks (ANN), Internet of Things (IoT), and cloud‐based services affect the real‐time analysis and processing of information in a variety of applications. In addition, multihoming is a type of network that combines various types of networks into a single environment while managing a huge amount of data. Nowadays, the big data processing and monitoring in multihoming networks provide less attention while reducing the security risk and efficiency during processing or monitoring the information. The use of AI‐based systems in multihoming big data with IoT‐ and AI‐integrated systems may benefit in various aspects. Although multihoming security issues and their analysis have been well studied by various scientists and researchers; however, not much attention is paid towards big data security processing in multihoming especially using automated techniques and systems. The aim of this paper is to propose an IoT‐based artificial network to process and compute big data processing by ensuring a secure communication multihoming network using the Bayesian Rule (BR) and Levenberg‐Marquardt (LM) algorithms. Further, the efficiency and effect on multihoming information processing using an AI‐assisted mechanism are experimented over various parameters such as classification accuracy, classification time, specificity, sensitivity, ROC, and F‐measure.

One thing that assists in automatic environmental monitoring is leaf segmentation. By segmenting a leaf, image-based leaf health assessment can be performed which is crucial in maintaining the effectiveness of the environmental balance. This paper presents a technique that serves an accurate framework for diseased leaf segmentation from Coloured imaged. In other words, this method works to use information generated from RGB images that we have stored in our data base to represent the current input image. To achieve such technique, four main steps were constructed: 1) Using contrast variations to characterize the region of interest (ROI) of a given leaf which enhances the accuracy of the segmentation using minimal time. 2) using linear combination of discrete Gaussians (LCDG) to represent the visual appearance of the input image and to assume the marginal probability distributions of the three regions of interest classes. 3) Using information generated from RGB images that we have stored in our data base to calculate the probabilities of the three classes on a pixel basis in step two. 4) Lastly, clarifying the labels with Gauss-Markov random field model (GGMRF) to maintain the continuity. After all these steps, the experimental validation promised high accuracy.

Security and the privacy features concerning e-banking needs to be improved rapidly to continue its growing. It is really difficult to ensure enough adequate security by using the conventional algorithms for a long time period, due to recent advances such as high progress in cryptanalysis techniques, improvement of computing skills and continuous hacking trials. This paper refers important issues regarding how to enhance the transition to more secure cryptographic and encryption algorithms in the financial sector. This paper recommends that adopting and implementing open source applications following international standards can be considered as a good replacement to the conventional algorithms to offer more enhancement security techniques and highest performance encryption algorithms for e-banking transaction services. We proposed a modified algorithm for AES, in which substitute byte, shift row will remain as in the original AES while mix column operation is replaced by 128 permutation operation followed by add round key operation. Comparative study with traditional encryption algorithms is shown the superiority of the modified algorithm and its high ability to overcome the problem of computational overhead. We additionally suggested another level of e-banking security services using Confidence Building Metric (CBM). The CBMs are computed based on certain parameters and can be implemented on any platform at the client side.

Although the College of Information Technology (CIT) at Zayed University follows the ACM guidelines for Information Security curricula, its graduates are not able to fully meet employers' requirements. In this paper, we illustrate a new approach for teaching and engaging students in the context of a real experience related to the Information Security field using ISO 27001. Ten IT students at the college were supervised throughout their capstone projects, in which they investigated the use of ISO standards related to IT in the UAE. They expressed a great deal of satisfaction with their projects, and, created five case studies. Three of these are related to ISO 27001 implementation. In addition, three of the students were hired to work in this area after graduation. Our results reveal the importance of integrating international standards into the curricula of educational institutions.

This research adopts a new scenario-based black box testing methodology for testing web applications. It combines a black box testing strategy with the functions (scenarios) measured by the COSMIC-FFP measurement procedure (ISO/IEC 19761 standard) to produce an optimal set of test cases. This testing approach shows its applicability during all the development phases. Moreover, it can be applied during the early development phase once the specifications have been documented as well as after the development phase where we don't have the access to the code. This paper also considers the use of a functional complexity measure for assigning priorities to the generated test cases. Finally, those concepts have been applied on part of Online Banking System as a case study.

Function Point Analysis (FPA) measurement method was proposed primarily to measure the functional size of management information systems. The method has been criticized as an inadequate method to measure the functional size of Real Time (RT) applications. Many extensions have been made to the original method to extend its applicability to size RT applications. The Common Software Measurement International Consortium (COSMIC) designed COSMIC function point measurement method to meet the requirement of measuring a wider scope of software domains, particularly, RT applications. This paper aims to derive a convertibility equation between the two methods for small-size RT applications. A systematic conversion approach was applied using two types of conversion (using the unadjusted FPA size and transaction functions size) using 13 small-size RT applications, considering the quality of requirements, via linear and non-linear regression models. The results show that RT applications have a varied functional size for processes, in which FPA method could not quantify it adequately, mainly because of the FPA weighting system tables and the inadequate representation of data files. Moreover, the results show that there is a significant correlation between the two methods, but the conversion factor is slightly larger than the previous studies, in which the conversion factor is more than 1. However, some limitations of FPA method rules relative to RT applications cast some doubts on the possibility to extend the conversion for larger applications size.

Functional Size Measurement (FSM) has been widely adopted by software organizations over the past years. In particular, Function Point Analysis (FPA) measurement method as the eldest and the most globally FSM method used. In the middle of 90s, COSMIC measurement method was designed to cover the limitations of previous FSM methods, and to size wider scope of application domains. COSMIC measurement method had received special attention due to its novelties and merits it offers. In this respect, many organizations have moved from using FPA to the COSMIC measurement method, mainly because FPA cannot size as many software project types as COSMIC. Since there have been many projects sized with FPA are available in various databases, researchers undertook to compare the Unadjusted FPA (UFP) size and COSMIC Function Point (CFP) size unit statistically, towards converting FPA data in to its corresponding value in CFP. In the literature, there is only one conversion model that converts the size at the level of Basic Functional Components (BFCs) between the two methods. This paper examines the accuracy of the conversion model that converts the size at the level of BFCs. Moreover, this paper presents a theoretical and probabilistic conversion model that converts the FPA transaction functions size to the most likely corresponding CFP size at the level of BFCs. The proposed model considers the primary intent and the processing logic forms of each FPA transaction function BFC, measurement best practices and COSMIC method rules. The proposed model has been validated using 14 industrial business applications. The model was found to adequately and high accurately convert all the projects tested precisely. The model might be useful for companies that are using both methods or planning to adopt the COSMIC method as a conversion or validation procedure.

This paper presents an overview of quantitative analysis techniques for software quality and their applicability during the software development life cycle (SDLC). This includes the Seven Basic Tools of Quality, Statistical Process Control, and Six Sigma, and it highlights how these techniques can be used for managing and controlling the quality of software during specification, design, implementation, testing, and maintenance. We verify whether or not these techniques, which are generally accepted for most projects, most of the time, and have value that is recognized by the peer community, have indeed been included in the SWEBOK Guide.

Software component technology has become a major pillar of the IT evolution. The benefits of this technology, such as reuse, enhanced quality and relatively short application development time, have been key drivers of its industrial adoption. However, in its progress towards maturity, component technology has suffered from a number of limitations, such as unused component members (data and functionalities). For instance, a reusable software component incorporates a set of members, a sizevarying subset of which is actually used to satisfy the functional requirements of a particular software application. This means that a complementary subset of unused members will persist in the deployed application, where this subset provides no functional value to the host application. Furthermore, these unused members can consume memory and network resources and might compromise application integrity and/or security if they are exploited inappropriately. In this paper, we propose CoMet, a prototype tool which applies CUMM (Component Unused Member Measurement) method to measure unused component members (attributes and functionalities) and their usage percentages in a software application.