3. Producción
Browse
16 results
Search Results
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, A feature extraction using probabilistic neural network and BTFSC-Net model with deep learning for brain tumor classification(Multidisciplinary Digital Publishing Institute (MDPI), 2022-12-31)BACKGROUND AND OBJECTIVES: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. MATERIALS AND METHODS: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images' slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. RESULTS: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. CONCLUSIONS: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Low-dose COVID-19 CT image denoising using batch normalization and convolution neural network(Multidisciplinary Digital Publishing Institute (MDPI), 2022-10-01)Computed tomography (CT) is used in medical applications to produce digital medical imaging of the human body and is acquired by the reconstruction process, where X-rays are the key component of CT imaging. The present coronavirus outbreak has spawned new medical device and technology research fields. COVID-19 most severely affects people with poor immunity; children and pregnant women are more susceptible. A CT scan will be required to assess the infection’s severity. As a result, to reduce the radiation levels significantly there is a need to minimize the CT scan noise. The quality of CT images may degrade in the form of noisy images due to low radiation levels. Hence, this study proposes a novel denoising methodology for COVID-19 CT images with a low dose, where a convolution neural network (CNN) and batch normalization were utilized for denoising. From different output metrics such as peak signal-to-noise ratio (PSNR) and image quality index (IQI), the accuracy of the resulting CT images was checked and evaluated, where IQI obtained the best results in terms of 99% accuracy. The findings were also compared with the outcomes of related recent research in the domain. After a detailed review of the findings, it was noted that the proposed algorithm in the present study performed better in comparision to the existing literature. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Cyber security of smart metering infrastructure using median absolute deviation methodology(Hindawi Limited, 2022-01-01)To combat cyber threats in the smart grid, an intrusion detection system can be integrated into the advanced metering infrastructure. Anomaly-based intrusion detection can detect even the tiniest changes in the parameter under investigation, whereas signature-based intrusion detection only recognises known attacks. The growing usage of smart grids necessitates the classification, identification, and implementation of countermeasures to threats. At the absolute least, smart grids must be protected against cyberattacks; thus, the highest level of information security must be offered. As a result of digitisation and the usage of more smart applications, the research looked at a variety of attack types, smart grid assaults, and major cyber threats on the voltage regulation. Machine learning techniques that analyse data in real time and formulate patterns to recognise an attack and scan through huge data for anomalies can be implemented into the advanced metering infrastructure (AMI) for intrusion detection for anomaly-based intrusion detection. The comparative test study done for the research found that the proposed method, median absolute deviation for anomaly identification in smart metering datasets, produced the most accurate and precise differentiations with the highest accuracy and precision. The median absolute deviation (MAD) algorithm model is trained using test data, and raw predictions are made, before true data are used to derive final test result parameters, precision, recall, and F1 scores. The methodology of the entire study is discussed in this paper, as well as how the MAD algorithm is best suited for anomaly-based intrusion detection, as well as result comparisons of other machine learning algorithms. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Erratum: Expression of concern: DFT assessments of BN, AlN, and GaN decorated carbon cage scaffolds for sensing the thiamazole drug, [Diam. Relat. Mater. 135 (May 2023) 109800]. (Diamond & Related Materials (2023) 135, (S0925963523001255), (10.1016/j.diam(Elsevier Ltd, 2024-06-01)This temporary Expression of Concern relates to the above article. Concerns have been brought to the attention of the journal regarding the potential sale of authorship positions on the article, and the validity of the authors' contributions. The journal editors are investigating the concerns, including contacting the authors, in line with Committee on Publication Ethics (COPE) guidelines and Elsevier's policies. The Expression of Concern will remain appended to the article until the investigation has been completed. If the editors can reach a conclusion, they will take any action that is deemed necessary. If the editors have determined that they cannot reach a satisfactory conclusion with the information available to them, a further notification will be published to update the community. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Developing Role of Extracellular Vesicles in Autoimmune Diseases: Special Attention to Mesenchymal Stem Cell-Derived Extracellular Vesicles(Elsevier B.V., 2023-09-01)Autoimmune diseases are complex, chronic inflammatory conditions initiated by the loss of immunological tolerance to self-antigens. Nowadays, there is no effective and useful therapy for autoimmune diseases, and the existing medications have some limitations due to their nonspecific targets and side effects. During the last few decades, it has been established that mesenchymal stem cells (MSCs) have immunomodulatory functions. It is proposed that MSCs can exert an important therapeutic effect on autoimmune disorders. In parallel with these findings, several investigations have shown that MSCs alleviate autoimmune diseases. Intriguingly, the results of studies have demonstrated that the effective roles of MSCs in autoimmune diseases do not depend on direct intercellular communication but on their ability to release a wide spectrum of paracrine mediators such as growth factors, cytokines and extracellular vehicles (EVs). EVs that range from 50 to 5,000 nm were produced by almost any cell type, and these nanoparticles participate in homeostasis and intercellular communication via the transfer of a broad range of biomolecules such as modulatory proteins, nucleic acids (DNA and RNA), lipids, cytokines, and metabolites. EVs derived from MSCs display the exact properties of MSCs and can be safer and more beneficial than their parent cells. In this review, we will discuss the features of MSCs and their EVs, EVs biogenesis, and their cargos, and then we will highlight the existing discoveries on the impacts of EVs from MSCs on autoimmune diseases such as multiple sclerosis, arthritis rheumatic, inflammatory bowel disease, Type 1 diabetes mellitus, systemic lupus erythematosus, autoimmune liver diseases, Sjögren syndrome, and osteoarthritis, suggesting a potential alternative for autoimmune conditions therapy. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Intelligent Engineering Management Modeling for Computer Technology and Data Mining(World Scientific, 2023-04-14)The author suggests using computer technology and data mining in intelligent engineering management to increase the management capacity of engineering projects and decrease the consumption of construction expenses. The emphasis is mostly on theoretical and analytical approaches to problems of practical concern for data mining, perhaps in combination with other conventional tools, and the associated applications to engineers and managers of various industrial sectors. This has immediate benefits for both academic and applied data mining researchers as well as research students. The author developed a BIM project management system based on Browser/Server architecture and Client/Server architecture in conjunction with BIM technology and the BIM 4D model. To execute complete project management of business management, real-time control, and decision support, the system must establish and use its business logic and data exchange link. Each module in the 4D construction dynamic management subsystem recognizes dynamic management of the construction process, and the working duration of each module is determined by calculating by using the unified engineering decomposition principle, rationalizing the relevant data through time parameters, recording the acquired management data and storing it in the BIM database, and connecting the BIM database bi-directionally through the web server through the system integration. The results showed that when the time is 20[Formula: see text]min, the throughput of the system is higher than [Formula: see text][Formula: see text]kb/s. The system can effectively improve the project management ability, reduce the construction cost and construction period of the project, and the system responds quickly. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Exosomal Non-Coding RNAs’ Role in Immune Regulation and Potential Therapeutic Applications(Elsevier GmbH, 2023-07-01)Exosomes are now significant players in both healthy and unhealthy cell-to-cell communication. Exosomes can mediate immune activation or immunosuppression, which can influence the growth of tumors. Exosomes affect the immune responses to malignancies in various ways by interacting with tumor cells and the environment around them. Exosomes made by immune cells can control the growth, metastasis, and even chemosensitivity of tumor cells. In contrast, exosomes produced by cancer cells can encourage immune responses that support the tumor. Exosomes carry circular RNAs, long non-coding RNAs, and microRNAs (miRNAs), all involved in cell-to-cell communication. In this review, we focus on the most recent findings concerning the role of exosomal miRNAs, lncRNAs, and circRNAs in immune modulation and the potential therapeutic implications of these discoveries. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, DFT Assessments of BN, AlN, and GaN Decorated Carbon Cage Scaffolds for Sensing the Thiamazole Drug(Elsevier Ltd, 2023-05-01)Sensing drug substances by nanostructures are very important in accordance with the management of targeted drug delivery processes and drug substances detections. Boron nitride (BN), aluminum nitride (AlN), and gallium nitride (GaN) decorated carbon cage (BN-C, AlN-C, and GaN-C) scaffolds were assessed towards sensing the thiamazole (TMZ) drug through the wB97XD/6–31 + G* level of density functional theory (DFT) computations. The singular models were optimized and their combinations to each other were stabilized to obtain the interacting TMZ@Scaffold bimolecular complexes and their corresponding features. The results indicated the existence of non-covalent physical interactions between the substances and their electronic features indicated possibility of sensing function for the investigated scaffolds. Based on the variations of values of adsorption energy and energy gap, the features of recovery time and conductance rate were achieved to predict a sensing function for the models; TMZ@GaN-C was found at the highest suitability in comparison with TMZ@AlN-C and TMZ@BN-C models. The obtained thermochemistry results indicated a spontaneous process for the formation of TMZ@Scaffold complexes. Based on all the obtained results, an order of TMZ@GaN-C > TMZ@AlN-C > TMZ@BN-C was found for describing stability, formation, and electronic features suitability by assigning specific features for each of the singular BN-C, AlN-C, and GaN-C scaffolds towards the TMZ drug. As a consequence, two purposes of detections and adsorptions were approached for the investigated scaffolds to develop sensing functions of BN-C, AlN-C, and GaN-C scaffolds for the TMZ drug. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Delay Optimization and Energy Balancing Algorithm for Improving Network Lifetime in Fixed Wireless Sensor Networks(Elsevier B.V., 2023-06-01)Since wireless in terms of energy-restricted processes, dispersion radii, processing power limitations, buffers, bandwidth-limited connections, active network topologies, and network stream of traffic outlines, sensor networks provide difficult design issues. The number of hops and latency are decreased if there is a relay mote because it interacts directly with relay motes that are closer to the destination mote. The tremendous intensive research in the area of Wireless Sensor Networks (WSN) has gained a lot of significance among the technical community and research. The job of WSN is to sense the data using sensor motes, pass on the data to the destination detection mote which is associated with a processing center and can be used in multiple spans of Internet of Things (IoT) applications. Wireless sensor network has a set of sensor motes. By making use of sensor mote placement strategy all the sensor motes are spread in an area with each mote having its own exceptional location. Internet of things applications are delay sensitive those applications have a challenge of forming the complete path at a lower delay constraint. The proposal is to modify the game theory energy balancing algorithm by making use of relay motes so that overall network lifetime is increased. It has been proved that modified GTEB is better with respect to existing algorithms in terms of delay, figure of hops, energy depletion, figure of alive motes, figure of dead motes, lifespan ratio, routing overhead and throughput. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deep Learning-Based Computer-Aided Diagnosis Model for the Identification and Classification of Mammography Images(Springer, 2023-09-01)Cancer of the breast is an illness that has the potential to be fatal for females all over the world. Even with the advancements that have been made in treatment, breast cancer cannot be prevented or cured; however, with early identification, one's life expectancy can be increased. A woman's overall health can be improved, which can add years to her life expectancy, if breast cancer is detected at an earlier stage. Radiological screening is a well-known method that is utilised for cancer prevention and detection in significant amounts. Mammograms have the ability to detect breast cancer as well as tumours that may be present in the breast. Recent study has demonstrated that DL-based CAD models can assist radiologists in establishing automated diagnosis of breast cancer in patients. The DL-based CAD model helps radiologists diagnose breast cancer automatically, according to recent research. DL techniques utilising convolutional neural network have gained interest because to their effectiveness in automating data feature representation and maximising accuracy by merging classification and feature representations. It successfully diagnoses clinical pictures. The research aims to build DL-based breast cancer diagnosis models and to review state-of-the-art ML and DL models for breast cancer diagnosis and classification. The research also examines the performance of the proposed models on the benchmark dataset. Sensitivity, specificity, accuracy, and F-measure measure performance. The experimental results showed that the proposed models are effective compared to modern methods. The proposed models are effective for breast cancer diagnosis and categorization.