Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

Lung cancer is a leading cause of global mortality rates.Early detection of pulmonary tumors can significantly enhance the survival rate of patients.Recently,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high accuracy.Nevertheless,the existing method-ologies cannot obtain a high level of specificity and sensitivity.The present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet architecture.The LCSC model comprises two distinct stages.The first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung lobes.Subsequently,an improved AlexNet architecture is employed to classify lung cancer.During the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate nodules.The suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or malignant.The proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

Effect of Light Emitting Diodes (LEDs) on Growth, Mineral Composition, and Nutritional Value of Wheat & Lentil Sprouts

Sprouts are ready-to-eat and are recognized worldwide as functional components of the human diet.Recent advances in innovative agricultural techniques could enable an increase in the production of healthy food.The use of light-emitting diode(LED)in indoor agricultural production could alter the biological feedback loop,increasing the functional benefits of plant foods such as wheat and lentil sprouts and promoting the bioavailability of nutrients.The effects of white(W),red(R),and blue(B)light were investigated on the growth parameters and nutritional value of wheat and lentil sprouts.In the laboratory,seeds were sown under three different LED treat-ments:white,red,and blue light,while normal incandescent light served as a control.Percentage seed germina-tion improved by 18.34%and 12.67%for wheat and 18.34%and 12.67%for lentil sprouts under LED treatments R and B,respectively.An increase in total soluble protein and sugar by 33.4%and 9.23%in wheat and by 31.5%and 5.87%in lentils was observed under the R LED treatment.Vitamin C concentrations in wheat and lentils were significantly increased by R LED compared to all other treatments.Other parameters,including potassium and sodium concentrations,were significantly increased under red and blue light compared to the control;white light,on the other hand,significantly decreased all these parameters.According to the experimental data,red and blue LED light could be beneficial in the production of functional wheat and lentil sprouts with high nutrient concentrations.

Accuracy and Computational Cost of Interpolation Schemes While Performing <i>N</i>-Body Simulations

The continuous approximations play a vital role in N-body simulations. We constructed three different types, namely, one-step (cubic and quintic Hermite), two-step, and three-step Hermite interpolation schemes. The continuous approximations obtained by Hermite interpolation schemes and interpolants for ODEX2 and ERKN integrators are discussed in this paper. The primary focus of this paper is to measure the accuracy and computational cost of different types of interpolation schemes for a variety of gravitational problems. The gravitational problems consist of Kepler’s two-body problem and the more realistic problem involving the Sun and four gas-giants—Jupiter, Saturn, Uranus, and Neptune. The numerical experiments are performed for the different integrators together with one-step, two-step, and three-step Hermite interpolation schemes, as well as the interpolants.

Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.

Hydro-meteorological characteristics of Chitral River basin at the peak of the Hindukush range

This paper presents the impact of mean maximum temperature on Chitral river basin situated at Chitral district and high altitude (>6000 m) peaks of the Hindukush range under changing climate in Pakistan. The analysis of Chitral River as one of the tributary of Kabul River—the second largest river of Pakistan—revealed that change in temperature has a profound influence on the snow/glacial melt in comparison to the mean monthly rainfall. This is because the studied river is faded by the snow and glacial melt and receives a lot of snowfall from winter (DecFeb) to pre-monsoon (April-May). In monsoon period (Jul-Sep), 30% of the time the discharge rate remains above the mean while 60% of the time the discharge is less than the mean in the pre-monsoon (April-May) period. It means that 10% of the time the discharge is in reach of 300% to 900% of the mean flow, showing a rise in water yield and river discharge rate due to increase in mean monthly maximum temperature. Due to this significant increase (p < 0.05), the glaciers start melting faster and disappear in early summer, hence, reducing their residency period to convert into ice. This shows the signals of changing climate transfer into hydrological changes in Pakistan. Our findings are important for agriculture, hydropower and water management sectors for future planning especially in dry season for sustainable food security and for operation of ydrological installations in the country.

Using UML Behavioral Model to Support Aspect Oriented Model

Aspect oriented software development is an emerging paradigm of software development. The notion of this technique is separation of concerns which means to implement each concern in a single object in object oriented programming but still there are concerns which are distributed on different objects and are called crosscutting concerns while another form is Core concerns are the core functionality provided by the system but crosscutting concerns are the concerns like logging, performance etc. Modeling of aspect oriented software is different from the normal modeling of object-oriented or procedural language software, because aspects don’t have the independent identity or existence and they are tightly coupled to their woven context so it is difficult to model them. The one aim of our research paper is to explore the domain of Modeling of the aspect-oriented software. The goal of this research paper is to give a UML Behavioral modeling techniques in the domain of aspect oriented software development. This technique of generating UML Behavioral Model for aspects will give better understating of separations concerns.

Jovian Problem: Performance of Some High-Order Numerical Integrators

N-body simulations of the Sun, the planets, and small celestial bodies are frequently used to model the evolution of the Solar System. Large numbers of numerical integrators for performing such simulations have been developed and used;see, for example, [1,2]. The primary objective of this paper is to analyse and compare the efficiency and the error growth for different numerical integrators. Throughout the paper, the error growth is examined in terms of the global errors in the positions and velocities, and the relative errors in the energy and angular momentum of the system. We performed numerical experiments for the different integrators applied to the Jovian problem over a long interval of duration, as long as one million years, with the local error tolerance ranging from 10-16 to 10-18.

Optoelectronic and elastic properties of metal halides double perovskites Cs_(2)InBiX_(6)(X = F, Cl, Br, I)

Structural geometry, electronic band gaps, density of states, optical and mechanical properties of double perovskite halides Cs2InBiX6(X = F, Cl, Br, I) are investigated using the density functional theory. These compounds possess genuine perovskite stoichiometry, evaluated using various geometry-based indices like tolerance factor, octahedral factor, and formation energy. The fundamental electronic band gaps are direct and valued in the range 0.80–2.79 e V. These compounds have narrow band gaps(except Cs2InBiX6) due to strong orbital coupling of the cations. The valence band maximum and conduction band minimum are confirmed to be essentially of In 5 s and Bi 6 p characters, respectively. The splitting of Bi 6 p bands due to strong spin-orbit coupling causes reduction in the band gaps. These compounds have large dispersion in their bands and very low carrier effective masses. The substitution of halogen atoms has great influence on the optical properties. The mechanical properties reveal that Cs2InBiX6(X = F, Cl, Br, I) satisfy the stability criteria in cubic structures.