Natural radioactivity radionuclides in building materials, such as^(226)Ra,^(232)Th and^(40)K, cause indoor exposure due to their gamma-rays. In this research, in a standard dwelling room(5.0 m 9 4.0 m 9 2.8 m), with ...Natural radioactivity radionuclides in building materials, such as^(226)Ra,^(232)Th and^(40)K, cause indoor exposure due to their gamma-rays. In this research, in a standard dwelling room(5.0 m 9 4.0 m 9 2.8 m), with the floor covered by various granite stones, was set up to simulate the dose rates from the radionuclides using MCNP4 C code. Using samples of granite building products in Iran, activities of the^(226)Ra,^(232)Th and^(40)K were measured at 3.8–94.2, 6.5–172.2 and 556.9–1529.2 Bq kg^(-1),respectively. The simulated dose rates were26.31–184.36 n Gy h^(-1), while the measured dose rates were 27.70–204.17 n Gy h^(-1). With the results in good agreement, the simulation is suitable for any kind of dwelling places.展开更多
The internet of medical things(IoMT)empowers patients to get adaptable,and virtualized gear over the internet.Task scheduling is the most fundamental problem in the IoMT-cloud since cloud execution commonly relies on ...The internet of medical things(IoMT)empowers patients to get adaptable,and virtualized gear over the internet.Task scheduling is the most fundamental problem in the IoMT-cloud since cloud execution commonly relies on it.Thus,a proposition is being made for a distinct scheduling technique to suitably meet these solicitations.To manage the scheduling issue,an artificial intelligence(AI)method known as a hybrid genetic algorithm(HGA)is proposed.The proposed AI method will be justified by contrasting it with other traditional optimization and AI scheduling approaches.The CloudSim is utilized to quantify its effect on various parameters like time,resource utilization,cost,and throughput.The proposed AI technique enhanced the viability of task scheduling with a better execution rate of 32.47ms and a reduced time of 40.16ms.Thus,the experimented outcomes show that the HGA reduces cost as well as time profoundly.展开更多
Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools.Tuberculosis can be detected from microsco...Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools.Tuberculosis can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis,this method can be tedious for both Microbiologists and Radiologists and can lead to miss-diagnosis.These challenges can be solved by employing Computer-Aided Detection(CAD)via Al-driven models which learn features based on convolution and result in an output with high accuracy.In this paper,we described automated discrimination of X-ray and microscope slide images into tuberculosis and non-tuberculosis cases using pretrained AlexNet Models.The study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East University Hospital and Kaggle repository.For classification of tuberculosis using microscopic slide images,the model achieved 90.56%accuracy,97.78%sensitivity and 83.33%specificity for 70:30 splits.For classification of tuberculosis using X-ray images,the model achieved 93.89%accuracy,96.67%sensitivity and 91.11%specificity for 70:30 splits.Our result is in line with the notion that CNN models can be used for classifying medical images with higher accuracy and precision.展开更多
文摘Natural radioactivity radionuclides in building materials, such as^(226)Ra,^(232)Th and^(40)K, cause indoor exposure due to their gamma-rays. In this research, in a standard dwelling room(5.0 m 9 4.0 m 9 2.8 m), with the floor covered by various granite stones, was set up to simulate the dose rates from the radionuclides using MCNP4 C code. Using samples of granite building products in Iran, activities of the^(226)Ra,^(232)Th and^(40)K were measured at 3.8–94.2, 6.5–172.2 and 556.9–1529.2 Bq kg^(-1),respectively. The simulated dose rates were26.31–184.36 n Gy h^(-1), while the measured dose rates were 27.70–204.17 n Gy h^(-1). With the results in good agreement, the simulation is suitable for any kind of dwelling places.
文摘The internet of medical things(IoMT)empowers patients to get adaptable,and virtualized gear over the internet.Task scheduling is the most fundamental problem in the IoMT-cloud since cloud execution commonly relies on it.Thus,a proposition is being made for a distinct scheduling technique to suitably meet these solicitations.To manage the scheduling issue,an artificial intelligence(AI)method known as a hybrid genetic algorithm(HGA)is proposed.The proposed AI method will be justified by contrasting it with other traditional optimization and AI scheduling approaches.The CloudSim is utilized to quantify its effect on various parameters like time,resource utilization,cost,and throughput.The proposed AI technique enhanced the viability of task scheduling with a better execution rate of 32.47ms and a reduced time of 40.16ms.Thus,the experimented outcomes show that the HGA reduces cost as well as time profoundly.
文摘Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools.Tuberculosis can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis,this method can be tedious for both Microbiologists and Radiologists and can lead to miss-diagnosis.These challenges can be solved by employing Computer-Aided Detection(CAD)via Al-driven models which learn features based on convolution and result in an output with high accuracy.In this paper,we described automated discrimination of X-ray and microscope slide images into tuberculosis and non-tuberculosis cases using pretrained AlexNet Models.The study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East University Hospital and Kaggle repository.For classification of tuberculosis using microscopic slide images,the model achieved 90.56%accuracy,97.78%sensitivity and 83.33%specificity for 70:30 splits.For classification of tuberculosis using X-ray images,the model achieved 93.89%accuracy,96.67%sensitivity and 91.11%specificity for 70:30 splits.Our result is in line with the notion that CNN models can be used for classifying medical images with higher accuracy and precision.
