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Lung cancer is one of the most prevalent cancers in the world and is responsible for most cancer-related fatalities. The present methods for identifying lung cancer, such as CT scans and biopsies, have drawbacks, such as being expensive, intrusive, and exposing patients to radiation. Convolutional neural networks (CNN), deep learning, and other machine learning approaches have recently demonstrated promising outcomes in medical image analysis, including lung cancer identification. The openly accessible Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) used a dataset of 1,010 lung nodules. The nodules in the dataset were both benign and malignant, and they were different sizes and shapes. The photos were preprocessed by being uniformly resized to 32x32 pixels and normalizing the pixel values to be between 0 and 1. The database was separated into a data-training set (80%) and a data-validating set (20%) at random. We built a CNN model with three convolutional layers, each followed by a max-pooling layer and a dropout layer to prevent overfitting. With a batch size of 32 and a learning rate of 0.001, the Adam optimizer was trained to perform model training. The model's sensitivity, specificity, and overall precision on the validation set were 96.6%, 94.7%, and 95.8%, respectively. To assess the accuracy of our CNN model, we compared it to the performance of four radiologists with varying levels of experience in the diagnosis of lung cancer.In terms of sensitivity, our CNN model outscored all four radiologists while matching their specificity levels. AUC of 0.98, showing great diagnostic accuracy, was reached by our CNN model according to a receiver operating characteristic (ROC) analysis we also carried out. Our research shows the promise of CNN models for precise and effective lung cancer nodule detection on CT scans. CNN models can help radiologists identify lung nodules with high accuracy, lowering the possibility of patients being ignored or misdiagnosed. Future research might examine the potential use of Deep networks in clinical decision-making and lung disease detection initiatives. [ABSTRACT FROM AUTHOR]

Agriculture is the backbone of success of rural countries like India. In this way, the proposed procedure is to utilize mechanization and web of thing (IoT) innovation to form agribusiness smart. There are currently several commercial IoT frameworks for water system planning that create differentiators for water monitoring and storage. However, this framework focuses on exploiting newly available water resources without controlling the selected water resources. Think about it, an inexpensive Arduino-based IoT framework was developed to design a computerized water system, and it will provide estimation of water requirements by using low-cost available sensors. This paper is around IOT based keen cultivating and water structure framework. The extreme motivation of this paper is to computerize the method of watering to plants. This work makes a difference us to notice the values of different parameters like moisture, temperature, humidity of plants and water them appropriately. This can be done utilizing Arduino board, voltage controller and transfer which commands the engine. In this paper, we have created an brilliantly water system framework that will control the water apportioning to the trim intelligently without any administrator and endeavors low-cost Arduino-based water system gadgets, sensors, particularly advanced procedures actualized in water system frameworks and farming, such as shrewdly water system frameworks. The main vision of this research is to detect minimum amount of water needed to nourish plants. Mass of the ranchers waste time instead of focusing on providing water to the crops at the time of need. The proposed framework find out the desired quantity of water on the basis of information received through the sensors - The main advantage of the system is the use of precision agribusiness and cloud platforms, which can expand the water use compost, which also helps in decision making. to increase crop yields. the weather conditions of the field. This article moreover points to create the water system framework for domestic gardening and this framework can be kept up without any human checking. It consequently checks the dampness of the soil beneath the tree and plants within the domestic cultivate and in case the dampness is moo it demonstrates the message and pours water naturally to the plants from the water capacity to which it is associated. The exact utilization of water system strategy is needed as there may be a requirement of precipitation or there may be dried regions. For this cause, a mechanized water system framework is utilized in which it is appropriate in all weather conditions. The soil dampness detector will sense the water substance and demonstrate either to pump the water or not. This will offer assistance to diminish the wastage of water additionally makes a difference to grow the plants indeed in the event that the human isn't physically present. The developed IoT framework has been tested in several field tests to calculate sensor accuracy, independent monitoring and operation, and usability monitoring, and to evaluate its capabilities and functionality. This framework appears to have been used effectively for the management and control of various water resources: water, groundwater, seawater and wastewater. By extension, it has been shown that the frame can be used for long periods without a source of control, which will make it a ideal device for using in annual crops. [ABSTRACT FROM AUTHOR]