Image denoising is a critical problem in low-level computer vision, where the aim is to reconstruct a clean, noise-free image from a noisy input, such as a mammogram image. In recent years, deep learning, particularly convolutional neural networks (CNNs), has shown great success in various image processing tasks, including denoising, image compression, and enhancement. While CNN-based approaches dominate, Transformer models have recently gained popularity for computer vision tasks. However, there have been fewer applications of Transformer-based models to low-level vision problems like image denoising. In this study, a novel denoising network architecture called DeepTFormer is proposed, which leverages Transformer models for the task. The DeepTFormer architecture consists of three main components: a preprocessing module, a local-global feature extraction module, and a reconstruction module. The local-global feature extraction module is the core of DeepTFormer, comprising several groups of ITransformer layers. Each group includes a series of Transformer layers, convolutional layers, and residual connections. These groups are tightly coupled with residual connections, which allow the model to capture both local and global information from the noisy images effectively. The design of these groups ensures that the model can utilize both local features for fine details and global features for larger context, leading to more accurate denoising. To validate the performance of the DeepTFormer model, extensive experiments were conducted using both synthetic and real noise data. Objective and subjective evaluations demonstrated that DeepTFormer outperforms leading denoising methods. The model achieved impressive results, surpassing state-of-the-art techniques in terms of key metrics like PSNR, FSIM, EPI, and SSIM, with values of 0.41, 0.93, 0.96, and 0.94, respectively. These results demonstrate that DeepTFormer is a highly effective solution for image denoising, combining the power of Transformer architecture with convolutional layers to enhance both local and global feature extraction.

Channel contention significantly contributes to TCP's poor performance in wireless ad hoc networks (WANET), where the IEEE 802.11 medium access control (MAC) protocol is used to access the medium. This article aims to boost the functionality of the IEEE 802.11 MAC protocol, which guarantees the effective and fair utilization of shared channel resources. Therefore, a novel method known as the channel usage based backoff (CHUBB) algorithm has been proposed to fine-tune the binary exponential backoff (BEB) algorithm of the IEEE 802.11 MAC protocol. Using the CHUBB algorithm, each node's contention window (CW) is computed based on channel usage. Extensive simulation results demonstrate that by employing the suggested CHUBB algorithm, TCP has gained higher throughput and decreased the unfair impact on the network, such as the improvement in throughput ranges from 3.66% to 31.01% in a chain topology, 6.52% to 11.56% in a 7x7 grid topology, and 5.86% to 11.23% in a 9x9 grid topology. Compared to the BEB algorithm, the suggested approach makes more efficient use of channel resources, leading to fewer retransmissions. Even when the user datagram protocol (UDP) was utilized, it functioned admirably at the transport layer.

Osteosarcoma is the most normal kind of cancer that arises in bones, which appears on the surface to resemble earlier types of bone cells that assist in forging new bone tissues, but the tissue in osteosarcoma is weaker and softer than normal bone tissue. The usage of automated techniques for the detection of osteosarcoma has the potential to mitigate the obligations and burdens confronted by pathologists owing to its abundant quantity of cases. Artificial intelligence (AI) has an emerging progress in diagnostic pathology. In recent years, numerous studies using deep learning (DL) techniques to histopathological images (HI) have been published. While several studies claim higher accuracy, they might lack generalization and fall into the pitfall of overfitting owing to the wide range of HI. The study objective is to enhance the diagnosis and detection of osteosarcoma by employing computer-assisted detection (CAD) and diagnoses (CADx). Technique like convolutional neural networks (CNN) make better prognoses for patient conditions and considerably reduce the surgeon’s workload. CNN needs to be trained on the massive quantity of data to accomplish a remarkable performance. Therefore, the study presents a novel Group Teaching Optimization Algorithm with Deep Learning-Driven Osteosarcoma Detection on Histopathological Images (GTOADL-ODHI) technique. The purpose of the GTOADL-ODHI technique is to examine the HIs for the detection and classification of osteosarcoma. To accomplish this, the GTOADL-ODHI algorithm applies the Gaussian filtering (GF) method for image pre-processed to become rid of the noise. Besides, the capsule network (CapsNet) model is utilized for the extractor of the feature vector. Furthermore, the hyperparameter selection of the CapsNet approach takes place using the GTOA. Finally, the self-attention bidirectional long short-term memory (SA-BiLSTM) model can be employed for osteosarcoma recognition and classification. The widespread experimental analysis of the GTOADL-ODHI method is tested on the benchmark datasets. The simulation validation reported the optimum solution of the GTOADL-ODHI algorithm related to existing systems concerning distinct aspects.

The usage of cloud computing service platforms are exponentially growing to provide on-demand services for end-users for using advanced technologies. These platform services are achieved through resource virtualization to maximize the resource usage and minimize energy requirements. Energy consumption is a key factor for designing efficient and manageable cloud data centers. Optimal techniques are used for placing virtual machines in physical machines to reduce the energy consumption ratio of physical hosts. This paper proposes a novel efficient virtual machines placement algorithm for a cloud computing environment. This method exploits a modified artificial bee colony optimization algorithm for identifying under-utilized physical machines based on energy consumption and resource allocation charts. An adaptive threshold method is then proposed to select suitable threshold levels for energy consumption to identify under-utilized physical host machines. A comparative analysis with state of art methods is carried out by using the CloudSim 3.0 simulator. Simulation results show the superiority of our method, able to achieve the highest accuracy values of 97.2% for accuracy and of 97.9% for precision rate, thus confirming the efficacy of our approach for virtual machine placement in cloud environments.

Electrocardiogram (ECG) signal is a measure of the heart's elec-trical activity. Recently, ECG detection and classification have benefited from the use of computer-aided systems by cardiologists. The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization (DTO) and Differential Evolution Algorithm (DEA) into a unified algorithm to optimize the hyperparameters of neural network (NN) for boosting the ECG classification accuracy. In addition, we proposed a new feature selection method for selecting the significant feature that can improve the overall performance. To prove the superiority of the proposed approach, several experiments were conducted to compare the results achieved by the proposed approach and other competing approaches. Moreover, sta-tistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests. Experimental results confirmed the superiority and effectiveness of the proposed approach. The classification accuracy achieved by the proposed approach is (99.98%).

Metamaterial Antennas are a type of antenna that uses metamaterial to enhance performance. The bandwidth restriction associated with small antennas can be solved using metamaterial antennas. Machine learning is gaining popularity as a way to improve solutions in a range of fields. Machine learning approaches are currently a big part of current research, and they're likely to be huge in the future. The model utilized determines the accuracy of the prediction in large part. The goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna's bandwidth and gain. The basic models employed in the developed ensemble are Support Vector Regression (SVR), K-NearestRegression (KNR), Multi-Layer Perceptron (MLP), Decision Trees (DT), and Random Forest (RF). The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization (DTO) algorithm. To choose the best features from the dataset, the binary (bDTO) algorithm is exploited. The proposed ensemble model is compared to the base models and results are recorded and analyzed statistically. In addition, two other ensembles are incorporated in the conducted experiments for comparison. These ensembles are average ensemble and K-nearest neighbors (KNN)-based ensemble. The comparison is performed in terms of eleven evaluation criteria. The evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.

Governments around the world have invested significant sums of money on Information and Communication Technology (ICT) to improve the efficiency and effectiveness of services being provided to their citizens. However, they have not achieved the desired results because of the lack of interoperability between different government entities. Therefore, many governments have started shifting away from the original concept of e-Government towards a much more transformational approach that encompasses the entire relationship between different government departments and users of public services, which can be termed as transformational government (t-Government). This implementation of t-Government requires a high level of interoperability between government organisations. In this paper, a model is proposed to explore and investigates the key factors that influence interoperability required for the implementation of t-Government in Saudi Arabian context from four key areas, namely, organisational, technological, political and social using institutional theory as a lens. This model was developed comprising the effect of six main constructs: technological compatibility, organizational compatibility, governance readiness, citizen centricity and e-Government program on interoperability required for the implementation of t-Government. The model factors, relationships, and hypotheses stemmed from the literature on Information Sharing, Information Integration, G2G, interoperability and t-Government models.The results show that technological compatibility, organizational compatibility, and governance readiness have a positive impact on the interoperability required for the implementation of t-Government in this particular context. Unexpectedly, it indicates that citizen centricity has negative impact on the interoperability required for the implementation of t-Government. It also shows that there is a direct and positive impact from e-Government program (Yesser) to technological compatibility and governance readiness. Moreover, it shows that there is a direct and positive impact from citizen centricity to e-Government program (Yesser). Unexpectedly, the results indicate that e-Government program (Yesser) has no impact on the interoperability required for the implementation of t-Government. It also indicates that the e-Government program (Yesser) doesn’t affect organizational compatibility.This paper provides a model for creating interoperability between government organisations to help e-Government officials and policy makers to identify the key factors that can affect the interoperability level required for the implementation of t-Government, and examines how these issues could be treated in practice. It also provides a guideline to researchers with regard to the impact of these identified factors on interoperability required for t-Government implementation.

Governments around the world have invested significant sums of money on Information and Communication Technology (ICT) to improve the efficiency and effectiveness of services been provided to their citizens. However, they have not achieved the desired results because of the lack of interoperability between different government entities. Therefore, many governments have started shifting away from the original concept of e-Government towards a much more transformational approach that encompasses the entire relationship between different government departments and users of public services, which can be termed as transformational government (t- Government). In this paper, a model is proposed for governing factors that impact the implementation of t-Government such as strategy, leadership, stakeholders, citizen centricity and funding in the context of Saudi Arabia. Five constructs are hypothesised to be related to the implementation of t-Government. To clarify the relationships among these constructs, a structural equation model (SEM) is utilised to examine the model fit with the five hypotheses. The results show that there are positive and significant relationships among the constructs such as the relationships between strategy and t-Government; the relationships between stakeholders and t-Government; the relationships between leadership and t-Government. This study also showed an insignificant relationship between citizens’ centricity and t-Government and also an insignificant relationship between funding and t-Government. document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.

Many countries around the world are currently investing significant sums of money on technology to modernise their services and improve the quality of their services being offered. They are seeking to adopt e-Government platforms by using information and communication technology (ICT) to improve the efficiency and effectiveness of services being provided to their citizens, as well as other government agencies. However, they have not achieved the desired results because of the lack of integration between different government organisations. Therefore, an increasing range of public services are being offered over the Internet. In many countries a plateau has been reached regarding the delivery of new services and now the focus seems to be on looking for improvements by integrating governmental processes over the Internet. In other words the focus is no longer on the increasing the range of services, but on integrating existing services together in order to create ease of access and improvements in service. Many nations have started shifting away from the original concept of e-Government towards a much more transformational approach (t-Government). This study addresses the Governance factors that influence t-Government based on a literature review of the studies related to t-Government.

Governments around the world have invested significant sums of money on Information and Communication Technology (ICT) to improve the efficiency and effectiveness of services being provided to their citizens. However, they have not achieved the desired result because of the lack of interoperability between different government entities. Interoperability is a critical success factor for achieving a mature level of e-government to enable a citizen centric approach to the provision of services. This paper presents a better understanding of transformational governments and the need for t-government; it investigates the various dimensions to t-government, and addresses the Organisational, Technological and Governance factors that influence t-government based on a literature review of the studies related to t-government.