An Observation and Analysis the role of Convolutional Neural Network towards Lung Cancer Prediction

يعد سرطان الرئة من أخطر الأمراض وأكثرها انتشارًا ، حيث يتسبب في العديد من الوفيات كل عام. على الرغم من أن صور التصوير المقطعي المحوسب تستخدم في الغالب في تشخيص السرطان ، إلا أن تقييم عمليات الفحص يعد مهمة معرضة للخطأ وتستغرق وقتًا طويلاً. يمكن للنموذج القائم على التعلم الآلي والذكاء الاصطناعي تحديد أنواع سرطان الرئة وتصنيفها بدقة تامة ، مما يساعد في الكشف المبكر عن سرطان الرئة الذي يمكن أن يزيد من معدل البقاء على قيد الحياة. في هذا البحث ، تُستخدم الشبكة العصبية التلافيفية لتصنيف السرطانة الغدية وسرطان الخلايا الحرشفية وصور المسح المقطعي المحوسب للحالة العادية من مجموعة بيانات صور مسح الصدر بالأشعة المقطعية باستخدام مجموعات مختلفة من الطبقة المخفية والمعلمات في نماذج CNN. تم تدريب النموذج المقترح على 1000 صورة مسح مقطعي للخلايا السرطانية وغير السرطانية للعثور على أفضل مزيج من المعلمات في CNN للتنبؤ بسرطان الرئة بدقة. سجل النظام المقترح أعلى دقة بلغت 92.79٪. بالإضافة إلى ذلك ، تتناول الورقة 192 ملاحظة تمت باستخدام نموذج CNN.Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-cancerous cells to find the best combination of parameters in CNN to predict lung cancer accurately.  The proposed system recorded the highest accuracy of 92.79%. In addition to that, the paper addresses 192 observations made using the CNN model.

EXPLORING THE SCIENTIFIC BENEFITS OF PHYTESTEROL ENRICHED SOYA-BUTTER- A COMPREHENSIVE REVIEW

Phytosterol-enriched soy butter presents a novel culinary option suitable for diverse food preparations. This plant-derived butter originates from soybeans, which furnish imperative amino acids, unsaturated fats, including oleic and linoleic acids, isoflavones, phyto-sterols, lecithins, saponins, and an array of minerals, folic acid, and B vitamins. The soybean-derived butter incorporates phytosterols extracted from vegetable oil. These compounds exhibit a structure akin to cholesterol, yet they function to lower blood cholesterol levels through competitive inhibition. This fortified soy butter holds potential advantages for individuals grappling with metabolic disorders such as cholesterolemia, juvenile diabetes, hypertension, obesity, and chronic ailments like osteoporosis, cancer, menopausal syndrome, and anemia. Consequently, this product stands to offer superior health benefits compared to alternative offerings in the marke

Practice and Philosophy of Climate Model Tuning Across Six U.S. Modeling Centers

Model calibration (or tuning) is a necessary part of developing and testing coupled ocean-atmosphere climatemodels regardless of their main scientific purpose. There is an increasing recognition that this process needs to become more transparent for both users of climate model output and other developers. Knowing how and why climate models are tuned and which targets are used is essential to avoiding possible misattributions of skillful predictions to data accommodation and vice versa. This paper describes the approach and practice of model tuning for the six major U.S. climate modeling centers. While details differ among groups in terms of scientific missions, tuning targets and tunable parameters, there is a core commonality of approaches. However, practices differ significantly on some key aspects, in particular, in the use of initialized forecast analyses as a tool, the explicit use of the historical transient record, and the use of the present day radiative imbalance vs. the implied balance in the pre-industrial as a target

Atmospheric Reanalyses-Recent Progress and Prospects for the Future. A Report from a Technical Workshop, April 2010

In April 2010, developers representing each of the major reanalysis centers met at Goddard Space Flight Center to discuss technical issues - system advances and lessons learned - associated with recent and ongoing atmospheric reanalyses and plans for the future. The meeting included overviews of each center s development efforts, a discussion of the issues in observations, models and data assimilation, and, finally, identification of priorities for future directions and potential areas of collaboration. This report summarizes the deliberations and recommendations from the meeting as well as some advances since the workshop

The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

The recent US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability" was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) then any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, an operational European system and there are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test Bed (CTB) NMME workshops (February 18, and April 8, 2011) a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data is readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (http://origin.cpc.ncep.noaa.gov/products/people/wd51yf/NMME/index.html). Moreover, the NMME forecast are already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, presents an overview of the multi-model forecast quality, and the complementary skill associated with individual models

Intercalation of shRNA-plasmid in Mg-Al layered double hydroxide nanoparticles and its cellular internalization for possible treatment of neurodegenerative diseases

In the present work, nanoconjugates of shRNA-plasmid and a non-viral nanoceramic vector, e.g., Mg-Al layered double hydroxide (Mg-Al LDH), were synthesized and intercalated. Subsequently, these particles with an average size of 40-60 nm, were transfected into mammalian neuroblastoma cells (SH-SY5Y). The as prepared Mg-Al LDH was able to protect the incorporated shRNA-plasmid against a range of pH values, DNaseI, endonucleases, and serum components. To test the applicability of the nanoconjugate for future in-vivo studies, serum from three different model experimental animals viz, mouse, rat and guinea pig was used for the serum protection study. Additionally, we showed that prolonged storage at different temperatures does not affect the quality of the nanoconjugate. Using this nanoconjugate to transform cells, a maximum internalization of similar to 26% at 24h was achieved. Lastly, we demonstrated effective and safe delivery of the plasmid by measuring GFP production and shRNA-induced knockdown of TNF alpha

The Experimental MJO Prediction Project

Weather prediction is typically concerned with lead times of hours to days, while seasonal-to-interannual climate prediction is concerned with lead times of months to seasons. Recently, there has been growing interest in 'subseasonal' forecasts---those that have lead times on the order of weeks (e.g., Schubert et al. 2002; Waliser et al. 2003; Waliser et al. 2005). The basis for developing and exploiting subseasonal predictions largely resides with phenomena such as the Pacific North American (PNA) pattern, the North Atlantic oscillation (NAO), the Madden-Julian Oscillation (MJO), mid-latitude blocking, and the memory associated with soil moisture, as well as modeling techniques that rely on both initial conditions and slowly varying boundary conditions (e.g., tropical Pacific SST). An outgrowth of this interest has been the development of an Experimental MJO Prediction Project (EMPP). Th project provides real-time weather and climate information and predictions for a variety of applications, broadly encompassing the subseasonal weather-climate connection. Th focus is on the MJO because it represents a repeatable, low-frequency phenomenon. MJO's importance among the subseasonal phenomena is very similar to that of El Nino-Southern Oscillation(ENSO) among the interannual phenomena. This note describes the history and objectives of EMPP, its status,capabilities, and plans

The GEWEX Water Vapor Assessment archive of water vapour products from satellite observations and reanalyses

The Global Energy and Water cycle Exchanges (GEWEX) Data and Assessments Panel (GDAP) initiated the GEWEX Water Vapor Assessment (G-VAP), which has the main objectives to quantify the current state of the art in water vapour products being constructed for climate applications and to support the selection process of suitable water vapour products by GDAP for its production of globally consistent water and energy cycle products. During the construction of the G-VAP data archive, freely available and mature satellite and reanalysis data records with a minimum temporal coverage of 10 years were considered. The archive contains total column water vapour (TCWV) as well as specific humidity and temperature at four pressure levels (1000, 700, 500, 300 hPa) from 22 different data records. All data records were remapped to a regular longitude–latitude grid of 2°  ×  2°. The archive consists of four different folders: 22 TCWV data records covering the period 2003–2008, 11 TCWV data records covering the period 1988–2008, as well as 7 specific humidity and 7 temperature data records covering the period 1988–2009. The G-VAP data archive is referenced under the following digital object identifier (doi): https://doi.org/10.5676/EUM_SAF_CM/GVAP/V001. Within G-VAP, the characterization of water vapour products is, among other ways, achieved through intercomparisons of the considered data records, as a whole and grouped into three classes of predominant retrieval condition: clear-sky, cloudy-sky and all-sky. Associated results are shown using the 22 TCWV data records. The standard deviations among the 22 TCWV data records have been analysed and exhibit distinct maxima over central Africa and the tropical warm pool (in absolute terms) as well as over the poles and mountain regions (in relative terms). The variability in TCWV within each class can be large and prohibits conclusions about systematic differences in TCWV between the classes