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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, 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, 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, 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Adsorption of Thiotepa Anticancer by the Assistance of Aluminum Nitride Nanocage Scaffolds: A Computational Perspective on Drug Delivery Applications(Elsevier B.V., 2023-06-05)This work was carried out by the importance of providing insights into the nano-based drug delivery of anticancers. To this aim, a representative model of aluminum nitride (AN) nanocage scaffold and two of its boron and gallium doped forms (BAN and GAN) were investigated towards the adsorption of thiotepa (Tep) anticancer. Density functional theory (DFT) calculations were performed to evaluate the stabilized structures and their corresponding electronic features. The results indicated the contribution of N-head and S-head of Tep to interactions with each scaffold resulting six complexes; NTep@AN and STep@AN, NTep@BAN and STep@BAN, NTep@GAN and STep@GAN. In each complex model, the characteristic features were obtained based on the interactions details and frontier molecular orbitals related features. In this regard, the investigated scaffolds were found suitable for adsorbing the Tep substance with different strengths and frontier molecular orbitals levels yielding the possibility of assigning specified recovery time and conductance. As a consequence, the models of investigated scaffolds were found suitable to work as possible carriers of Tep anticancer for approaching the nano-based drug delivery purposes. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Review on Composite Materials for Energy Harvesting in Electric Vehicles(MDPI, 2023-04-01)The field of energy harvesting is expanding to power various devices, including electric vehicles, with energy derived from their surrounding environments. The unique mechanical and electrical qualities of composite materials make them ideal for energy harvesting applications, and they have shown tremendous promise in this area. Yet additional studies are needed to fully grasp the promise of composite materials for energy harvesting in electric vehicles. This article reviews composite materials used for energy harvesting in electric vehicles, discussing mechanical characteristics, electrical conductivity, thermal stability, and cost-effectiveness. As a bonus, it delves into using composites in piezoelectric, electromagnetic, and thermoelectric energy harvesters. The high strength-to-weight ratio provided by composite materials is a major benefit for energy harvesting. Especially important in electric vehicles, where saving weight means saving money at the pump and driving farther between charges, this quality is a boon to the field. Many composite materials and their possible uses in energy harvesting systems are discussed in the article. These composites include polymer-based composites, metal-based composites, bio-waste-based hybrid composites and cement-based composites. In addition to describing the promising applications of composite materials for energy harvesting in electric vehicles, the article delves into the obstacles that must be overcome before the technology can reach its full potential. Energy harvesting devices could be more effective and reliable if composite materials were cheaper and less prone to damage. Further study is also required to determine the durability and dependability of composite materials for use in energy harvesting. However, composite materials show promise for energy harvesting in E.V.s. Further study and development are required before their full potential can be realized. This article discusses the significant challenges and potential for future research and development in composite materials for energy harvesting in electric vehicles. It thoroughly evaluates the latest advances and trends in this field. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Programmed Cardiac Arrhythmia Analysis System by Adaptive Cardiac Outlining(Springer, 2023-09-01)For inter- and intra-patient variation scenarios, high-classification accuracy is still an active topic of research in automated arrhythmia-diagnosis systems. However, there are significant flaws with this strategy. Because of this, the building of a “patient adjustable” classifier is necessary. A mathematical strategy based on altering window length in accordance with the distance from the adjacent R peak is used to reduce unwanted noise. The electrocardiogram's most prominent waveform is the QRS beat (ECG). Automated ECG analysis relies heavily on the detection of QRS beats. We suggested a method that is typically used for intrusion detection in networks to detect QRS beats in ECG signals. Use this network intrusion detection method for the detection of worms in networking applications, as well as for determining the most common strings. The proposed structure calculates various features, such as QR level, RS level, QR slope, and RS slope. The suggested algorithm's classification efficiency is much higher than that of existing methods. The proposed approach can also be used to detect patient-specific cardiac fluctuation. We have proposed a method to classify normal, right bundle branch block, left bundle branch block, atrial premature contractions, and ventricle premature contractions.