Lyme disease is one of the most common vector-borne infections. It typically causes cardiac illnesses, neurologic illnesses, musculoskeletal disorders, and dermatologic conditions. However, most of the time, it is poorly diagnosed due to many similarities with other diseases such as drug rash. Given the potentially serious consequences of unnecessary antimicrobial treatments, it is essential to understand frequent and uncommon diagnoses that explain symptoms in this population. Recently, deep learning models have been used for the diagnosis of various rash-related diseases. However, these models suffer from overfitting and color variation problems. To overcome these problems, an efficient stacked deep transfer learning model is proposed that can efficiently distinguish between patients infected with Lyme (+) or infected with other infections. 2nd order edge-based color constancy is used as a preprocessing approach to reduce the impact of multisource light from images acquired under different setups. The AlexNet pretrained learning model is used for building the Lyme disease diagnosis model. To prevent overfitting, data augmentation techniques are also used to augment the dataset. In addition, 5-fold cross-validation is also used. Comparative analysis indicates that the proposed model outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and area under the curve.
E-government policy initiatives for implementing citizen-centric integrated interoperable (CII) e-government services have gained international validity by governments worldwide. Despite extensive deliberations in e-government literature, however, successfully implementing strategic, institutional, and technological changes required by citizen-centric (vis-à-vis government-centric) e-government remains an unresolved theoretical and pragmatic conundrum. CII e-government systems are characterized by greater diversity in stakeholders, processes, technologies, applications, and big data, requiring greater cross-agency collaboration and process integration/standardization. Drawing from e-government interoperability and governance literatures, the authors examined the governance role in facilitating CII e-government implementation. The authors performed website and policy analyses of a successful implementation of Saudi Ministry portal, which exemplifies CII e-services. Results showed that government's earlier disconnected websites had not facilitated cross-agency information sharing required for citizen-centric e-government development. However, the authors found evidence that both e-government interoperability policy framework and collaborative governance had contributed to overcoming the implementation challenges and delivering CII e-government services to its diverse stakeholders.
Pathway reconstruction, which remains a primary goal for many investigations, requires accurate inference of gene interactions and causality. Non-coding RNA (ncRNA) is studied because it has a significant regulatory role in many plant and animal life activities, but interacting micro-RNA (miRNA) and long non-coding RNA (lncRNA) are more important. Their interactions not only aid in the in-depth research of genes' biological roles, but also bring new ideas for illness detection and therapy, as well as plant genetic breeding. Biological investigations and classical machine learning methods are now used to predict miRNA-lncRNA interactions. Because biological identification is expensive and time-consuming, machine learning requires too much manual intervention, and the feature extraction process is difficult. This research presents a deep learning model that combines the advantages of convolutional neural networks (CNN) and bidirectional long short-term memory networks (Bi-LSTM). It not only takes into account the connection of information between sequences and incorporates contextual data, but it also thoroughly extracts the sequence data's features. On the corn data set, cross-checking is used to evaluate the model's performance, and it is compared to classical machine learning. To acquire a superior classification effect, the proposed strategy was compared to a single model. Additionally, the potato and wheat data sets were utilized to evaluate the model, with accuracy rates of 95% and 93%, respectively, indicating that the model had strong generalization capacity.
The generation of robust global maps of an unknown cluttered environment through a collaborative robotic framework is challenging. We present a collaborative SLAM framework, CORB2I-SLAM, in which each participating robot carries a camera (monocular/stereo/RGB-D) and an inertial sensor to run odometry. A centralized server stores all the maps and executes processor-intensive tasks, e.g., loop closing, map merging, and global optimization. The proposed framework uses well-established Visual-Inertial Odometry (VIO), and can be adapted to use Visual Odometry (VO) when the measurements from inertial sensors are noisy. The proposed system solves certain disadvantages of odometry-based systems such as erroneous pose estimation due to incorrect feature selection or losing track due to abrupt camera motion and provides a more accurate result. We perform feasibility tests on real robot autonomy and extensively validate the accuracy of CORB2I-SLAM on benchmark data sequences. We also evaluate its scalability and applicability in terms of the number of participating robots and network requirements, respectively.
An intuitionistic fuzzy set (IFS) is a valuable tool to execute uncertain and indeterminate information. IFSs are more suitable to identify decision-maker’s evaluation data in decision-making problems. Intuitionistic fuzzy aggregation operators (AOs) are of enormous consequences in multiple attribute group decision-making (MAGDM) problems with an intuitionistic fuzzy environment. Consequently, the main impacts of this article are: firstly, to instigate various new novel generalized power AOs based on Schweizer-Sklar operational rules for IFS. Secondly, the study aims to discuss characteristics and particular cases of AOs. The core edge of proposed AOs is that they can eradicate the influence of uncomfortable data which could be too high or too low, making them more admirable for efficiently solving more and more complex MAGDM problems. Thirdly, we instigate two new algorithms to deal with MAGDM established on the generalized Schweizer-Sklar power AOs. Lastly, we appertain the anticipated method and algorithms to health care waste treatment technology selection (HCW-TT) to show the competence of the anticipated method and algorithms. The prevailing novelties of these items are duplex. Firstly, new generalized AOs established on Schweizer-Sklar operational rules are initiated for IFNs. Secondly, two new approaches for IF MAGDM are initiated, one for known decision-makers (DMs) and attributes weights, while the other for unknown DMs and attributes weights.
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