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-c

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The most famous thing a person does is talk. He loves and hates, and continues with it confirming relationships, and with it, too, comes out of disbelief into faith. Marry a word and separate with a word. He reaches the top of the heavens with a kind word, with which he will gain the pleasure of God, and the Lord of a word that the servant speaks to which God writes with our pleasure or throws him on his face in the fire. Emotions are inflamed, the United Nations is intensified with a word, and relations between states and war continue with a word.

What comes out of a person’s mouth is a translator that expresses the repository of his conscience and reveals the place of his bed, for it is evidence of

The ligand Schiff base [(E)-3-(2-hydroxy-5-methylbenzylideneamino)- 1- phenyl-1H-pyrazol-5(4H) –one] with some metals ion as Mn(II); Co(II); Ni(II); Cu(II); Cd(II) and Hg(II) complexes have been preparation and characterized on the basic of mass spectrum for L, elemental analyses, FTIR, electronic spectral, magnetic susceptibility, molar conductivity measurement and functions thermodynamic data study (∆H°, ∆S° and ∆G°). Results of conductivity indicated that all complexes were non electrolytes. Spectroscopy and other analytical studies reveal distorted octahedral geometry for all complexes. The antibacterial activity of the ligand and preparers metal complexes was also studied against gram and negative bacteria.

The ligand Schiff base [(E)-3-(2-hydroxy-5-methylbenzylideneamino)- 1- phenyl-1H-pyrazol-5(4H) –one] with some metals ion as Mn(II); Co(II); Ni(II); Cu(II); Cd(II) and Hg(II) complexes have been preparation and characterized on the basic of mass spectrum for L, elemental analyses, FTIR, electronic spectral, magnetic susceptibility, molar conductivity measurement and functions thermodynamic data study (∆H°, ∆S° and ∆G°). Results of conductivity indicated that all complexes were non electrolytes. Spectroscopy and other analytical studies reveal distorted octahedral geometry for all complexes. The antibacterial activity of the ligand and preparers metal complexes was also studied against gram and negative bacteria.

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

Aerial manipulation of objects has a number of advantages as it is not limited by the morphology of the terrain. One of the main problems of the aerial payload process is the lack of real-time prediction of the interaction between the gripper of the aerial robot and the payload. This paper introduces a digital twin (DT) approach based on impedance control of the aerial payload transmission process. The impedance control technique is implemented to develop the target impedance based on emerging the mass of the payload and the model of the gripper fingers. Tracking the position of the interactional point between the fingers of gripper and payload, inside the impedance control, is achieved using model predictive control (MPD) approach.