Breast cancer is a leading cause of cancer type for death among women in most of popular countries, breast cancer detection is important and challenging role in worldwide to save women's life. Due to inexperience to detect cancer, the doctors and radio logistic can miss the abnormality, which leads to death. Mammography is the most used method for breast cancer detection used by the radiologists. In this experiment, the MIAS (Mammogram Image Analysis Society) database is used and the MIAS database consists of normal and abnormal type of 322 mammograms. The pre-processing is most important step to capture quality mammogram image for next study and processing in mammogram analysis. Texture analysis plays important role to identify normal and abnormal types. Texture feature extraction can be done by local binary patterns (LBP) operator and by using LBP we can consider only sign parameters, it may loss the some texture information. The local binary pattern is a rotation invariant approach for the texture analysis. In this experiment famous completed LBP (CLBP) method used for extracting texture features. Completed LBP considering the sign, magnitude and centre gray level values. By using the joint or hybrid distributions combine CLBP_Sign, CLBP_Magnitude and CLBP_Center gray level values.LBP is one type of Completed LBP for texture analysis, advantage of CLBP is rotation invariant. Finally extracted texture features can be trained and classified by using the SVM classifier for identifying the normal and abnormal cancer type.
Green chemistry approaches for the synthesis of metallic nanoparticles have become a new and promising field of research in recent years. Synthesis of metal nanoparticles [like gold (Au), silver (Ag), lead (Pb), platinum (Pt), copper (Cu), iron (Fe), cadmium (Cd), and other metal oxides such as titanium oxide (TiO), zinc oxide (ZnO), etc.] by various chemical and physical approaches as well as the biological approaches mediated by number of microorganisms have been actively found. Plant-mediated synthesis approaches are found to be more reliable and economic route to synthesize these metal nanoparticles. Owing to the biodiversity of plant biomasses, the actual mechanism by which the plant constituents have contributed to the synthetic process is yet to be fully known. Although the feasibility of controlling, the size and shape of nanoparticles by variation in reaction conditions have been demonstrated in many studies. Conventionally, nanoparticles are synthesized by chemicals and physicochemical methods using several chemicals which later on become accountable for various risk due to their general toxicity, so that solving the objective biological approaches is coming up to fill these gaps. The plant-mediated synthesis process undergoes highly controlled approaches for making them suitable for metal nanoparticle synthesis. In addition, biological synthesis of metallic nanoparticles is inexpensive, one-step, and eco-friendly method. In addition, the plant-mediated nanoparticles are used as potential pharmaceutical agents for various diseases such as malaria, HIV, cancer, hepatitis, and other diseases. Including this some other relevant information regarding nanopharmaceutical products, companies that are involve in the manufacturing and commercialization process and their clinical trial status are also discussed. This review article gives an overview of the plant-mediated synthesis of metal nanoparticles, possible compounds, and mechanisms that might be responsible for the reduction process as well as the potential pharmacological applications, currently available nanopharmaceutical products and their marketing status.
This paper presents SPICE modeling of Phase-change Random Access Memory (PCRAM). Different models of PCRAM have been already proposed but those models still lack capability to exactly model the behavior of PCRAM. In this paper we have introduced various physical parameters in the programming, to accurately model PCRAM behavior. The modeling of PCRAM cell has been done in Verilog-A and simulation results have been extensively verified using SPICE. Further, we have integrated the proposed model with the modified CNFET based read-write circuit. This paper emerges with the successful modeling of R-I characteristics of PCRAM. Read and write operations with proper programming pulses have also been demonstrated in this paper.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.