An Extended Stable Marriage Problem Algorithm for Clone Detection

Code cloning negatively affects industrial software and threatens intellectual property. This paper presents a novel approach to detecting cloned software by using a bijective matching technique. The proposed approach focuses on increasing the range of similarity measures and thus enhancing the precision of the detection. This is achieved by extending a well-known stable-marriage problem (SMP) and demonstrating how matches between code fragments of different files can be expressed. A prototype of the proposed approach is provided using a proper scenario, which shows a noticeable improvement in several features of clone detection such as scalability and accuracy.Comment: 20 pages, 10 figures, 6 table

A Machine Learning Strategy for the Quantitative Analysis of the Global Warming Impact on Marine Ecosystems

It is generally observed that aquatic organisms have symmetric abilities to produce oxygen (O2) and fix carbon dioxide (CO2). A simulation model with time-dependent parameters was recently proposed to better understand the symmetric effects of accelerated climate change on coastal ecosystems. Changes in environmental elements and marine life are two examples of variables that are expected to change over time symmetrically. The sustainability of each equilibrium point is examined in addition to proving the existence and accuracy of the proposed model. To support the conclusions of this research compared to other studies, numerical simulations of the proposed model and a case study are investigated. This paper proposes an integrated bibliographical analysis of artificial neural networks (ANNs) using the Reverse-Propagation with Levenberg–Marquaradt Scheme (RP-LMS) to evaluate the main properties and applications of ANNs. The results obtained by RP-LMS show how to prevent global warming by improving the management of marine fish resources. The reference dataset for greenhouse gas emissions, environmental temperature, aquatic population, and fisheries population (GAPF) is obtained by varying parameters in the numerical Adam approach for different scenarios. The accuracy of the proposed RP-LMS neural network is demonstrated using mean square error (MSE), regression plots, and best-fit output. According to RP-LMS, the current scenario of rapid global warming will continue unabated over the next 50 years, damaging marine ecosystems, particularly fish stocks

Symmetrical Model of Smart Healthcare Data Management: A Cybernetics Perspective

Issues such as maintaining the security and integrity of data in digital healthcare are growing day-by-day in terms of size and cost. The healthcare industry needs to work on effective mechanisms to manage these concerns and prevent any debilitating crisis that might affect patients as well as the overall health management. To tackle such critical issues in a simple, feasible, and symmetrical manner, the authors considered the ideology of cybernetics. Working towards this intent, this paper proposes a symmetrical model that illustrates a compact version of the adopted ideology as a pathway for future researchers. Furthermore, the proposed ideology of cybernetics specifically focuses on how to plan the entire design concept more effectively. It is important for the designer to prepare for the future and manage the design structure from a product perspective. Therefore, the proposed ideology provides a symmetric mechanism that includes a variety of estimation and evaluation techniques as well as their management. The proposed model generates a symmetric, variety-issue, reduced infrastructure that can produce highly effective results due to an efficient usability, operatability, and symmetric operation execution which are the benefits of the proposed model. Furthermore, the study also performed a performance simulation assessment by adopting a multi-criteria decision-making approach that helped the authors compare the various existing and proposed models based on their levels of effectiveness

Advancing Sustainable Healthcare through Enhanced Therapeutic Communication with Elderly Patients in the Kingdom of Saudi Arabia

Effective communication in nursing, particularly with older patients, is critical to providing high-quality care. The purpose of this research is to fill key gaps in the existing literature by emphasizing the importance of therapeutic communication in the setting of mental nursing care for elderly patients in Saudi Arabia. Building on the study’s foundation, which recognizes the various issues faced by cultural, religious, and linguistic diversity, this research adopted a rigorous research methodology incorporating a broad group of senior healthcare professionals as experts. We analyze various therapeutic communication approaches used by mental health nurses using extensive surveys and observations. This empirical study’s findings are likely to make a significant addition to the field by throwing light on the most efficient methods for improving nurse–elderly-patient communication. The study identifies Simulation-Based Training as the most viable technique, with potentially far-reaching implications for improving care for older patients in Saudi Arabia. This study paves the way for significant advances in healthcare practices, with a focus on mental health nursing, ultimately helping both nurses and elderly patients by developing trust, understanding, and increased communication

On the Computational Study of a Fully Wetted Longitudinal Porous Heat Exchanger Using a Machine Learning Approach

The present study concerns the modeling of the thermal behavior of a porous longitudinal fin under fully wetted conditions with linear, quadratic, and exponential thermal conductivities surrounded by environments that are convective, conductive, and radiative. Porous fins are widely used in various engineering and everyday life applications. The Darcy model was used to formulate the governing non-linear singular differential equation for the heat transfer phenomenon in the fin. The universal approximation power of multilayer perceptron artificial neural networks (ANN) was applied to establish a model of approximate solutions for the singular non-linear boundary value problem. The optimization strategy of a sports-inspired meta-heuristic paradigm, the Tiki-Taka algorithm (TTA) with sequential quadratic programming (SQP), was utilized to determine the thermal performance and the effective use of fins for diverse values of physical parameters, such as parameter for the moist porous medium, dimensionless ambient temperature, radiation coefficient, power index, in-homogeneity index, convection coefficient, and dimensionless temperature. The results of the designed ANN-TTA-SQP algorithm were validated by comparison with state-of-the-art techniques, including the whale optimization algorithm (WOA), cuckoo search algorithm (CSA), grey wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm, and machine learning algorithms. The percentage of absolute errors and the mean square error in the solutions of the proposed technique were found to lie between 10−4 to 10−5 and 10−8 to 10−10, respectively. A comprehensive study of graphs, statistics of the solutions, and errors demonstrated that the proposed scheme’s results were accurate, stable, and reliable. It was concluded that the pace at which heat is transferred from the surface of the fin to the surrounding environment increases in proportion to the degree to which the wet porosity parameter is increased. At the same time, inverse behavior was observed for increase in the power index. The results obtained may support the structural design of thermally effective cooling methods for various electronic consumer devices

A Machine Learning Strategy for the Quantitative Analysis of the Global Warming Impact on Marine Ecosystems

It is generally observed that aquatic organisms have symmetric abilities to produce oxygen (O2) and fix carbon dioxide (CO2). A simulation model with time-dependent parameters was recently proposed to better understand the symmetric effects of accelerated climate change on coastal ecosystems. Changes in environmental elements and marine life are two examples of variables that are expected to change over time symmetrically. The sustainability of each equilibrium point is examined in addition to proving the existence and accuracy of the proposed model. To support the conclusions of this research compared to other studies, numerical simulations of the proposed model and a case study are investigated. This paper proposes an integrated bibliographical analysis of artificial neural networks (ANNs) using the Reverse-Propagation with Levenberg–Marquaradt Scheme (RP-LMS) to evaluate the main properties and applications of ANNs. The results obtained by RP-LMS show how to prevent global warming by improving the management of marine fish resources. The reference dataset for greenhouse gas emissions, environmental temperature, aquatic population, and fisheries population (GAPF) is obtained by varying parameters in the numerical Adam approach for different scenarios. The accuracy of the proposed RP-LMS neural network is demonstrated using mean square error (MSE), regression plots, and best-fit output. According to RP-LMS, the current scenario of rapid global warming will continue unabated over the next 50 years, damaging marine ecosystems, particularly fish stocks

Classification of the Human Protein Atlas Single Cell Using Deep Learning

Deep learning has made great progress in many fields. One of the most important fields is the medical field, where we can classify images, detect objects and so on. More specifically, deep learning algorithms entered the field of single-cell classification and revolutionized this field, by classifying the components of the cell and identifying the location of the proteins in it. Due to the presence of large numbers of cells in the human body of different types and sizes, it was difficult to carry out analysis of cells and detection of components using traditional methods, which indicated a research gap that was filled with the introduction of deep learning in this field. We used the Human Atlas dataset which contains 87,224 images of single cells. We applied three novel deep learning algorithms, which are CSPNet, BoTNet, and ResNet. The results of the algorithms were promising in terms of accuracy: 95%, 93%, and 91%, respectively

Classification of the Human Protein Atlas Single Cell Using Deep Learning

Deep learning has made great progress in many fields. One of the most important fields is the medical field, where we can classify images, detect objects and so on. More specifically, deep learning algorithms entered the field of single-cell classification and revolutionized this field, by classifying the components of the cell and identifying the location of the proteins in it. Due to the presence of large numbers of cells in the human body of different types and sizes, it was difficult to carry out analysis of cells and detection of components using traditional methods, which indicated a research gap that was filled with the introduction of deep learning in this field. We used the Human Atlas dataset which contains 87,224 images of single cells. We applied three novel deep learning algorithms, which are CSPNet, BoTNet, and ResNet. The results of the algorithms were promising in terms of accuracy: 95%, 93%, and 91%, respectively

Comparison Between Cloud and Grid Computing: Review Paper

Cloud computing is the most recent announced technology that has been launched on the network world.Clouds are considered as a new generation of Grid computing. Clouds consist of data centres which areowned by the same institute. The homogeneity within each data centre in the infrastructure is the mainfeature for the cloud computing compared to grid computing. This paper provides a definition for thecloud, it discusses many aspects of Cloud Computing, and describes architectures for the cloud (by lookingat Amazon’s application (GrepTheWeb)) and how its cost definition differs from that of Grid computing.This paper focuses on comparing Cloud Computing to previous generations such as Grid Computing, byreviewing some Security and Policy Issues in Cloud and Grid Computing. At the end, this paper describesthe similarities and differences between the Grid and Cloud approaches.<br/

A Smart Card-Based Two-Factor Mutual Authentication Scheme for Efficient Deployment of an IoT-Based Telecare Medical Information System

The integration of the Internet of Things (IoT) and the telecare medical information system (TMIS) enables patients to receive timely and convenient healthcare services regardless of their location or time zone. Since the Internet serves as the key hub for connection and data sharing, its open nature presents security and privacy concerns and should be considered when integrating this technology into the current global healthcare system. Cybercriminals target the TMIS because it holds a lot of sensitive patient data, including medical records, personal information, and financial information. As a result, when developing a trustworthy TMIS, strict security procedures are required to deal with these concerns. Several researchers have proposed smart card-based mutual authentication methods to prevent such security attacks, indicating that this will be the preferred method for TMIS security with the IoT. In the existing literature, such methods are typically developed using computationally expensive procedures, such as bilinear pairing, elliptic curve operations, etc., which are unsuitable for biomedical devices with limited resources. Using the concept of hyperelliptic curve cryptography (HECC), we propose a new solution: a smart card-based two-factor mutual authentication scheme. In this new scheme, HECC’s finest properties, such as compact parameters and key sizes, are utilized to enhance the real-time performance of an IoT-based TMIS system. The results of a security analysis indicate that the newly contributed scheme is resistant to a wide variety of cryptographic attacks. A comparison of computation and communication costs demonstrates that the proposed scheme is more cost-effective than existing schemes