One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details provided by the X-ray images dataset, the study showed that the using of X-ray data set in our deep learning algorithm could provide promising results by getting accuracy of validation for both Convolution Neural Network and SequeezeNet models 93%, 76%, respectively while the validation loss in both models Convolution Neural Network and SequeezeNet 34%, 30% respectively, these promise results will make the physician give a swift decision in diagnosis of lung cancer and keeping the patients away from exposing to unnecessary extra radiation dose during the Computed Tomograph exam as well as the low cost of X-ray examination comparing with Computed Tomograph exam.
The past several years have seen an increase in awareness of the pervasiveness of medications as pollutants in the aquatic environment. The main reason for concern regarding the release of pharmaceuticals into the environment is the possibility that biological agents may become opposing to them. The development of precise and reliable analytical techniques for pharmaceutical determination in a range of samples is necessary for their safe use in the pharmaceutical industry and medical treatments. This review offers a summary of chromatographic techniques for identifying and quantifying the examination of pharmaceuticals in a range of environmental samples. Both the general public and the scientific community are currently very intere
... Show MoreUltrasound has been used as a diagnostic modality for many intraocular diseases, due its safety, low cost, real time and wide availability. Unfortunately, ultrasound images suffer from speckle artifact that are tissue dependent. In this work, we will offer a method to reduce speckle noise and improve ultrasound image to raise the human diagnostic performance. This method combined undecimated wavelet transform with a wavelet coefficient mapping function: where UDWT used to eliminate the noise and a wavelet coefficient mapping function used to enhance the contrast of denoised images obtained from the first component. This methods can be used not only as a means for improving visual quality of medical images but also as a preprocessing
... Show MoreThe present study aims at knowing the effect of discussion method for students of fifth grade in preparatory school.
Methodology of the Study:
In order to achieve the objective of the study, the researcher chooses non-randomly the preparatory school affiliated to the District Chamchamal \ Suliemnaniya. The sample attained 64 students in 32 per group (control and experimental) groups. The researcher used the discussion method which was applied on experimental group. She uses the traditional method on the control group.
The researcher matched the two group in ago, intelligence, marks at the Kurdish Language in the previous year , pretest and posts for the indepe
... Show MoreFace recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security
... Show MoreBotnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet
... Show MoreImitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co
... Show MoreMany academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci
... Show MoreMany academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre
... Show MoreEpithelial and stromal communications are essential for normal uterine functions and their dysregulation contributes to the pathogenesis of many diseases including infertility, endometriosis, and cancer. Although many studies have highlighted the advantages of culturing cells in 3D compared to the conventional 2D culture system, one of the major limitations of these systems is the lack of incorporation of cells from non‐epithelial lineages. In an effort to develop a culture system incorporating both stromal and epithelial cells, 3D endometrial cancer spheroids are developed by co‐culturing endometrial stromal cells with cancerous epithelial cells. The spheroids developed by this method are phenot
This study will develop and implement the International Identification Card (IIC) to multi users (M-1). The IIC can be used in several methods such as an IC card, Passport, driver’s license, Visa card, Security Information system, Business part, all information about individuals/persons. The Smart Identification Card Technology (SICT) system will be using several new technology categories/tools such as Information Technology, Management Information Technology, Database management, internet service, Bluetooth service, NFC and mobile calling service. The target of SICT is to increase the efficiency of IC card to know the details for all citizens and foreigners from any country regardless their nationalities. What this means is the c
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