This paper investigates the ability of utilizing nature-inspired optimization techniques alongside machine learning (ML) to enhance resource adjustment in Hadoop clusters. The use of ML models with adaptive optimizati...
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This is a research paper based on a transfer learning approach with a primary aim at the analysis of chest Xrays for accurate detection and interpretation of lung diseases. The proposed method relies heavily on the us...
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This paper investigates the ability of utilizing nature-inspired optimization techniques alongside machine learning (ML) to enhance resource adjustment in Hadoop clusters. The use of ML models with adaptive optimizati...
详细信息
ISBN:
(数字)9798331539948
ISBN:
(纸本)9798331539955
This paper investigates the ability of utilizing nature-inspired optimization techniques alongside machine learning (ML) to enhance resource adjustment in Hadoop clusters. The use of ML models with adaptive optimization methodology is aimed at minimizing the time incurred to complete a task in addition to enhancing the efficiency of resource utilization. It reviews the existing Hadoop scheduling approaches and focusing on machine learning and bio-inspired techniques. For the categorization of workloads and prediction of resource needs for a specific future time, the suggested approach includes using ML algorithms like supervised/unsupervised or reinforcement ***, new efficient work schedules and resources distribution will be used due to the nature-inspired optimization algorithms, for example, the GA, PSO. This research framework for scheduling in Hadoop will be created that will incorporate elements of artificial intelligence by using machine learning to provide expectations along with bio-inspired techniques. The tests will indicate how much resources will be utilised, how long certain jobs take, how well and scalable or the system is using actual or simulated data sets. Comprehensive comparative assessment with the traditional and improved approaches will reveal the efficiency of the proposed methodology. The results part consist of optimised approach for resource distribution, efficient flexibility in handling the workloads, and increased Hadoop specialised cluster reinforcement for scalability and heterogeneously challenging data processing activities.
This is a research paper based on a transfer learning approach with a primary aim at the analysis of chest Xrays for accurate detection and interpretation of lung diseases. The proposed method relies heavily on the us...
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ISBN:
(数字)9798331515911
ISBN:
(纸本)9798331515928
This is a research paper based on a transfer learning approach with a primary aim at the analysis of chest Xrays for accurate detection and interpretation of lung diseases. The proposed method relies heavily on the use of pretrained deep learning models to enhance diagnostic accuracy and reduce the time and computational resources taken during training. Applying transfer learning to a large chest X-ray dataset, the model successfully detects key patterns associated with common lung diseases, such as pneumonia and tuberculosis. The manuscript encompasses data preprocessing, model finetuning, and performance evaluation and demonstrates huge improvements over the traditional methods both in terms of accuracy and interpretability. It has been experimentally proven that the model is competent enough to provide localization of disease areas, as it can be visualized through heatmaps obtained from predictions, which might further help the radiologists perform their diagnosis tasks. This work advocates for medical imaging automation for the early and efficient detection of lung disease.
The energy plays a prominent role in the development of a country's economic conditions. This helps to increase the overall performance index of a country. Thus a continuous energy system is needed to develop a co...
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The energy plays a prominent role in the development of a country’s economic conditions. This helps to increase the overall performance index of a country. Thus a continuous energy system is needed to develop a coun...
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The energy plays a prominent role in the development of a country’s economic conditions. This helps to increase the overall performance index of a country. Thus a continuous energy system is needed to develop a country’s performance index. Thus to have an advancement in the energy management system, an improved system called smart grid is implemented. This helps in efficient usage of energy management through meeting the demand side managing in the smart buildings. This assistances to decrease the additional procedure of energy consumption through optimization techniques such as genetic algorithm used in smart buildings. This is done with the machine learning through optimization algorithm. This helps to proper scheduling of appliances. This is done through the priority scheduling. The overall performance is done through the internet of things with sensors. The sensors helps in the process of sensing the external physical parameters such as temperature, humidity, moisture, heat and light. This helps to enhance the energy efficiency in the smart buildings through the machine learning and IoT.
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