Search Results (44)

Background: The dental healthcare private sector in Riyadh city has been growing rapidly over the past few years; however, there is a lack of information on the accessibility and spatial distribution of private dental healthcare facilities (PDHFs) in the area. This study aimed to evaluate the spatial distribution of PDHFs in Riyadh city in relation to population density in each sub-municipality. Methods: The current information regarding the number, location, and operability of PDHFs in Riyadh city was obtained from the Ministry of Health. A total of 632 operating PDHFs were included with the precise location plotted on Quantum Geographic Information System software (version 3.32.1, Essen, Germany) using Google Earth. Four levels of buffer zones—1 km, 3 km, 5 km, and >5 km—were determined. The population statistics and mean monthly individual income per district were gathered from Zadd.910ths. Microsoft Excel (version 16.0, Microsoft, Redmond, WA, USA) and RStudio software (version 4.1.3, Posit Software, PBC, Boston, MA, USA) were used for additional data analysis. Results: There was an overall ratio of one PDHF per 9958 residents in Riyadh city. Olaya and Maather sub-municipalities had the largest PDHF-to-population ratios: (1:4566) and (1:4828), respectively. Only 36.3% of the city’s total area was within a 1 km buffer zone from a PDHF. There was an overall weak positive correlation between the number of PDHFs and the total area in each sub-municipality (r = 0.29), and the distribution of PDHFs was uneven corresponding to the area (G* = 0.357). Conclusions: There was an uneven distribution of PDHFs in Riyadh city. Some areas were underserved while others were overserved in several sub-municipalities. Policy-makers and investors are encouraged to target underserved areas rather than areas with significant clustering to improve access to care. Full article

Cloud computing services have become indispensable to people’s lives. Many of their activities are performed through cloud services, from small companies to large enterprises and individuals to government agencies. It has enabled clients to use companies’ services on demand at the lowest cost anywhere, anytime, over the Internet. Despite these advantages, cloud networks are vulnerable to many types of attacks. However, as the adoption of cloud services accelerates, the risks associated with these services have also increased. For this reason, solutions have been implemented to improve cloud security, such as monitoring networks, the backbone of the cloud infrastructure, and detecting and classifying cyberattacks. Therefore, an intrusion detection system (IDS) is one of the essential defenses for detecting attacks in the cloud computing network. Current IDSs encounter some challenges in handling and simultaneously analyzing the large scale of traffic found in the cloud environment, and this affects the accuracy of cyberattack detection. Therefore, this research proposes a deep learning-based model by leveraging advanced convolutional neural networks (CNNs)-based model architecture to detect cyberattacks in the cloud environment efficiently. The proposed CNN-based model for intrusion detection consists of multiple significant stages: dataset collection, preprocessing, the SMOTE balance data strategy, feature selection, model training, testing, and performance evaluation. Experiments have demonstrated that the proposed model is highly effective in protecting cloud networks against various potential attacks. With over 98.67% accuracy, precision, and recall, the model has proven its ability to detect and classify network intrusions. Detailed analyses show that the model is proficient in securing cloud security measures and mitigating the risks associated with evolving security threats. Full article

The increasing use of IoHT devices in healthcare has brought about revolutionary advancements, but it has also exposed some critical vulnerabilities, particularly in cybersecurity. IoHT is characterized by interconnected medical devices sharing sensitive patient data, which amplifies the risk of cyber threats. Therefore, ensuring healthcare data’s integrity, confidentiality, and availability is essential. This study proposes a hybrid deep learning-based intrusion detection system that uses an Artificial Neural Network (ANN) with Bidirectional Long Short-Term Memory (BLSTM) and Gated Recurrent Unit (GRU) architectures to address critical cybersecurity threats in IoHT. The model was tailored to meet the complex security demands of IoHT and was rigorously tested using the Electronic Control Unit ECU-IoHT dataset. The results are impressive, with the system achieving 100% accuracy, precision, recall, and F1-Score in binary classifications and maintaining exceptional performance in multiclass scenarios. These findings demonstrate the potential of advanced AI methodologies in safeguarding IoHT environments, providing high-fidelity detection while minimizing false positives. Full article

This article discusses the importance of cross-platform UX/UI designs and frameworks and their effectiveness in building web applications and websites. Third-party libraries (TPL) and plug-ins are also emphasized, as they can help developers quickly build and compose applications. However, using these libraries can also pose security risks, as a vulnerability in any library can compromise an entire server and customer data. The paper proposes using multi-authentication with specific parameters to analyze third-party applications and libraries used in cross-platform development. Based on multi-authentication, the proposed model will make setting up web desensitization methods and access control parameters easier. The study also uses various end-user and client-based decision-making indicators, supporting factors, and data metrics to help make accurate decisions about avoiding and blocking unwanted libraries and plug-ins. The research is based on experimentation with five web environments using specific parameters, affecting factors, and supporting data matrices. Full article

Knee osteoarthritis is a challenging problem affecting many adults around the world. There are currently no medications that cure knee osteoarthritis. The only way to control the progression of knee osteoarthritis is early detection. Currently, X-ray imaging is a central technique used for the prediction of osteoarthritis. However, the manual X-ray technique is prone to errors due to the lack of expertise of radiologists. Recent studies have described the use of automated systems based on machine learning for the effective prediction of osteoarthritis from X-ray images. However, most of these techniques still need to achieve higher predictive accuracy to detect osteoarthritis at an early stage. This paper suggests a method with higher predictive accuracy that can be employed in the real world for the early detection of knee osteoarthritis. In this paper, we suggest the use of transfer learning models based on sequential convolutional neural networks (CNNs), Visual Geometry Group 16 (VGG-16), and Residual Neural Network 50 (ResNet-50) for the early detection of osteoarthritis from knee X-ray images. In our analysis, we found that all the suggested models achieved a higher level of predictive accuracy, greater than 90%, in detecting osteoarthritis. However, the best-performing model was the pretrained VGG-16 model, which achieved a training accuracy of 99% and a testing accuracy of 92%. Full article

This study aimed to examine the antidepressant properties of apigenin in an experimental mouse model of chronic mild stress (CMS). Three weeks following CMS, albino mice of either sex were tested for their antidepressant effects using the tail suspension test (TST) and the sucrose preference test. The percentage preference for sucrose solution and the amount of time spent immobile in the TST were calculated. The brain malondialdehyde (MDA) levels, catalase activity, and reduced glutathione levels were checked to determine the antioxidant potential of treatments. When compared to the control, animals treated with apigenin during the CMS periods showed significantly shorter TST immobility times. Apigenin administration raised the percentage preference for sucrose solution in a dose-dependent manner, which put it on par with the widely used antidepressant imipramine. Animals treated with apigenin displayed a significantly (p ˂ 0.05) greater spontaneous locomotor count (281) when compared to the vehicle-treated group (245). Apigenin was also highly effective in significantly (p ˂ 0.01) lowering plasma corticosterone levels (17 vs. 28 µg/mL) and nitrite (19 vs. 33 µg/mL) produced by CMS in comparison to the control group. During CMS, a high dose (50 mg/kg) of apigenin was given, which greatly increased the reduced glutathione level while significantly decreasing the brain’s MDA and catalase activity when compared to the control group. As a result, we infer that high doses of apigenin may have potential antidepressant effects in animal models via various mechanisms. Full article

The swarm-based Artificial Bee Colony (ABC) algorithm has a significant range of applications and is competent, compared to other algorithms, regarding many optimization problems. However, the ABC’s performance in higher-dimension situations towards global optima is not on par with other models due to its deficiency in balancing intensification and diversification. In this research, two different strategies are applied for the improvement of the search capability of the ABC in a multimodal search space. In the ABC, the first strategy, Patron–Prophet, is assessed in the scout bee phase to incorporate a cooperative nature. This strategy works based on the donor–acceptor concept. In addition, a self-adaptability approach is included to balance intensification and diversification. This balancing helps the ABC to search for optimal solutions without premature convergence. The first strategy explores unexplored regions with better insight, and more profound intensification occurs in the discovered areas. The second strategy controls the trap of being in local optima and diversification without the pulse of intensification. The proposed model, named the PP-ABC, was evaluated with mathematical benchmark functions to prove its efficiency in comparison with other existing models. Additionally, the standard and statistical analyses show a better outcome of the proposed algorithm over the compared techniques. The proposed model was applied to a three-bar truss engineering design problem to validate the model’s efficacy, and the results were recorded. Full article

Recent innovations in digital image capturing techniques facilitate the capture of stationary and moving objects. The images can be easily captured via high-end digital cameras, mobile phones and other handheld devices. Most of the time, captured images vary compared to actual objects. The captured images may be contaminated by dark, grey shades and undesirable black spots. There are various reasons for contamination, such as atmospheric conditions, limitations of capturing device and human errors. There are various mechanisms to process the image, which can clean up contaminated image to match with the original one. The image processing applications primarily require detection of accurate noise type which is used as input for image restoration. There are filtering techniques, fractional differential gradient and machine learning techniques to detect and identify the type of noise. These methods primarily rely on image content and spatial domain information of a given image. With the advancements in the technologies, deep learning (DL) is a technology that can be trained to mimic human intelligence to recognize various image patterns, audio files and text for accuracy. A deep learning framework empowers correct processing of multiple images for object identification and quick decision abilities without human interventions. Here Convolution Neural Network (CNN) model has been implemented to detect and identify types of noise in the given image. Over the multiple internal iterations to optimize the results, the identified noise is classified with 99.25% accuracy using the Proposed System Architecture (PSA) compared with AlexNet, Yolo V5, Yolo V3, RCNN and CNN. The proposed model in this study proved to be suitable for the classification of mural images on the basis of every performance parameter. The precision, accuracy, f1-score and recall of the PSA are 98.50%, 99.25%, 98.50% and 98.50%, respectively. This study contributes to the development of mural art recovery. Full article

Economic Load Dispatch (ELD) belongs to a non-convex optimization problem that aims to reduce total power generation cost by satisfying demand constraints. However, solving the ELD problem is a challenging task, because of its parity and disparity constraints. The Pigeon-Inspired Optimizer (PIO) is a recently proposed optimization algorithm, which belongs to the family of swarm intelligence algorithms. The PIO algorithm has the benefit of conceptual simplicity, and provides better outcomes for various real-world problems. However, this algorithm has the drawback of premature convergence and local stagnation. Therefore, we propose an Oppositional Pigeon-Inspired Optimizer (OPIO) algorithm—to overcome these deficiencies. The proposed algorithm employs Oppositional-Based Learning (OBL) to enhance the quality of the individual, by exploring the global search space. The proposed algorithm would be used to determine the load demand of a power system, by sustaining the various equality and inequality constraints, to diminish the overall generation cost. In this work, the OPIO algorithm was applied to solve the ELD problem of small- (13-unit, 40-unit), medium- (140-unit, 160-unit) and large-scale (320-unit, 640-unit) test systems. The experimental results of the proposed OPIO algorithm demonstrate its efficiency over the conventional PIO algorithm, and other state-of-the-art approaches in the literature. The comparative results demonstrate that the proposed algorithm provides better results—in terms of improved accuracy, higher convergence rate, less computation time, and reduced fuel cost—than the other approaches. Full article

Alfalfa (Medicago sativa L.) is one of the most important perennial forage crops to build effective diets for livestock producers. Forage crop improvement depends largely on the availability of diverse germplasms and their efficient utilization. The present investigation was conducted at Ismailia Agricultural Research Station to assess twenty-one alfalfa genotypes for yield components, forage yield and quality traits during 2019/2020 and 2020/2021. The genotypes were evaluated in field experiments with three replicates and a randomized complete block design, using analysis of variance, estimate of genetic variability, estimate of broad sense heritability (h_b²) and cluster analysis to identify the inter relationships among the studied genotypes as well as principal component analysis (PCA) to explain the majority of the total variation. Significant differences were found among genotypes for all studied traits. The general mean of the studied traits was higher in the second year than the first year. Moreover, the combined analysis showed highly significant differences between the two years, genotypes and the year × gen. interaction for the traits studied. The genotype F18 recorded the highest values for plant height, number of tiller/m², total fresh yield and total dry yield, while, the genotype F49 ranked first for leaf/stem ratio. The results showed highly significant variation among the studied genotypes for crude protein %, crude fiber % and ash %. Data revealed that the genotypes P13 and P5 showed the highest values for crude protein %, whereas, the genotype F18 recorded the highest values for crude fiber % and ash content. The results revealed high estimates of genotypic coefficient and phenotypic coefficient of variation (GCV% and PCV%) with high hb2, indicating the presence of genetic variability and effective potential selection for these traits. The cluster analysis exhibited considerable genetic diversity among the genotypes, which classified the twenty one genotypes of alfalfa into five sub-clusters. The genotypes F18, F49, K75, S35, P20, P5 and P13 recorded the highest values for all studied traits compared with other clusters. Furthermore, the PC analysis grouped the studied genotypes into groups and remained scattered in all four quadrants based on all studied traits. Ultimately, superior genotypes were identified can be utilized for crop improvement in future breeding schemes. Full article

A Wireless Sensor Network (WSN) is a group of autonomous sensors that are distributed geographically. However, sensor nodes in WSNs are battery-powered, and the energy drainage is a significant issue. The clustering approach holds an imperative part in boosting the lifespan of WSNs. This approach gathers the sensors into clusters and selects the cluster heads (CHs). CHs accumulate the information from the cluster members and transfer the data to the base station (BS). Yet, the most challenging task is to select the optimal CHs and thereby to enhance the network lifetime. This article introduces an optimal cluster head selection framework using hybrid opposition-based learning with the gray wolf optimization algorithm. The hybrid technique dynamically trades off between the exploitation and exploration search strategies in selecting the best CHs. In addition, the four different metrics such as energy consumption, minimal distance, node centrality and node degree are utilized. This proposed selection mechanism enhances the network efficiency by selecting the optimal CHs. In addition, the proposed algorithm is experimented on MATLAB (2018a) and validated by different performance metrics such as energy, alive nodes, BS position, and packet delivery ratio. The obtained results of the proposed algorithm exhibit better outcome in terms of more alive nodes per round, maximum number of packets delivery to the BS, improved residual energy and enhanced lifetime. At last, the proposed algorithm has achieved a better lifetime of ≈20%, ≈30% and ≈45% compared to grey wolf optimization (GWO), Artificial bee colony (ABC) and Low-energy adaptive clustering hierarchy (LEACH) techniques. Full article

Sustainable Cloud Computing is the modern era’s most popular technology. It is improving daily, offering billions of people sustainable services. Currently, three deployment models are available: (1) public, (2) private, and (3) hybrid cloud. Recently, each deployment model has undergone extensive research. However, relatively little work has been carried out regarding clients’ adoption of sustainable public cloud computing (PCC). We are particularly interested in this area because PCC is widely used worldwide. As evident from the literature, there is no up-to-date systematic literature review (SLR) on the challenges clients confront in PCC. There is a gap that needs urgent attention in this area. We produced an SLR by examining the existing cloud computing models in this research. We concentrated on the challenges encountered by clients during user adoption of a sustainable PCC. We uncovered a total of 29 obstacles that clients confront when adopting sustainable PCC. In 2020, 18 of the 29 challenges were reported. This demonstrates the tremendous threat that PCC still faces. Nineteen of these are considered critical challenges to us. We consider a challenge a critical challenge if its occurrence in the final selected sample of the paper is greater than 20%. These challenges will negatively affect client adoption in PCC. Furthermore, we performed three different analyses on the critical challenges. Our analysis may indicate that these challenges are significant for all the continents. These challenges vary with the passage of time and with the venue of publication. Our results will assist the client’s organization in understanding the issue. Furthermore, it will also help the vendor’s organization determine the potential solutions to the highlighted challenges. Full article

According to United Nations (UN) 2030 agenda, the pollution detection system needs to be improved for the establishment of fresh air to obtain healthy life of living things. There are many reasons for the pollution and one of the reasons for pollution is from the emissions of the vehicles. Currently digital technologies such as the Internet of Things and Long-Range are showing significant impact on establishment of smart infrastructure for achieving the sustainability. Based on this motivation, this study implemented a sensor node and gateway-based Internet of Things architecture to monitor the air quality index value from any location through Long-Range communication, and Internet connectivity. To realize the proposed system, a customization of hardware is carried out and implemented the customized hardware i.e., sensor node and gateway in real-time. The sensor node is powered with node mapping to minimize the data redundancy. In this study, the evaluation metrics such as bit rate, receiver sensitivity, and time on air are evaluated by spreading factor (SF), code rate (CR), bandwidth, number of packets, payload size, preamble, and noise figure. The real-time sensor values are logged on the cloud server through sensor node and gateway. The sensor values recorded in the cloud server is compared with optimal values and concluded that the PM₁₀, PM₂.₅ are high in the air and remaining values of NO₂, O₃, CO are optimal in the air. Along with this an architecture is proposed for interfacing the hardware with blockchain network through cloud server and API for node authentication. Full article

Vehicle identification and classification are some of the major tasks in the areas of toll management and traffic management, where these smart transportation systems are implemented by integrating various information communication technologies and multiple types of hardware. The currently shifting era toward artificial intelligence has also motivated the implementation of vehicle identification and classification using AI-based techniques, such as machine learning, artificial neural network and deep learning. In this research, we used the deep learning YOLOv3 algorithm and trained it on a custom dataset of vehicles that included different vehicle classes as per the Indian Government’s recommendation to implement the automatic vehicle identification and classification for use in the toll management system deployed at toll plazas. For faster processing of the test videos, the frames were saved at a certain interval and then the saved frames were passed through the algorithm. Apart from toll plazas, we also tested the algorithm for vehicle identification and classification on highways and urban areas. Implementing automatic vehicle identification and classification using traditional techniques is a highly proprietary endeavor. Since YOLOv3 is an open-standard-based algorithm, it paves the way to developing sustainable solutions in the area of smart transportation. Full article

Software-defined networking (SDN) is an innovative network architecture that splits the control and management planes from the data plane. It helps in simplifying network manageability and programmability, along with several other benefits. Due to the programmability features, SDN is gaining popularity in both academia and industry. However, this emerging paradigm has been facing diverse kinds of challenges during the SDN implementation process and with respect to adoption of existing technologies. This paper evaluates several existing approaches in SDN and compares and analyzes the findings. The paper is organized into seven categories, namely network testing and verification, flow rule installation mechanisms, network security and management issues related to SDN implementation, memory management studies, SDN simulators and emulators, SDN programming languages, and SDN controller platforms. Each category has significance in the implementation of SDN networks. During the implementation process, network testing and verification is very important to avoid packet violations and network inefficiencies. Similarly, consistent flow rule installation, especially in the case of policy change at the controller, needs to be carefully implemented. Effective network security and memory management, at both the network control and data planes, play a vital role in SDN. Furthermore, SDN simulation tools, controller platforms, and programming languages help academia and industry to implement and test their developed network applications. We also compare the existing SDN studies in detail in terms of classification and discuss their benefits and limitations. Finally, future research guidelines are provided, and the paper is concluded. Full article