With the use of cognitive radio (CR) technology, wireless devices can utilize licensed spectrum when it is unused by the licensed users. CR was developed as a response to the wireless spectrum shortage issue caused by...
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Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Ma...
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Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms *** define various frameworks on smart farming and monitoring system and still lacks to define effective data management *** IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the ***,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved ***,a conceptual framework of the proposed scheme and statistical design model has beenwell *** information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data *** simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.
Profanity detection has become increasingly important in various industries, including media and online content moderation. In this paper, we propose a novel approach to identifying profane words from audio using natu...
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In order to meet the pressing demand for early diagnosis in healthcare, we proposed a unique hybrid architecture in this study that is intended for the categorization of gastrointestinal (GI) illnesses. With fewer tra...
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Melanoma is a skin disease with high mortality rate while earlydiagnoses of the disease can increase the survival chances of patients. Itis challenging to automatically diagnose melanoma from dermoscopic skinsamples. ...
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Melanoma is a skin disease with high mortality rate while earlydiagnoses of the disease can increase the survival chances of patients. Itis challenging to automatically diagnose melanoma from dermoscopic skinsamples. computer-Aided Diagnostic (CAD) tool saves time and effort indiagnosing melanoma compared to existing medical approaches. In this background,there is a need exists to design an automated classification modelfor melanoma that can utilize deep and rich feature datasets of an imagefor disease classification. The current study develops an Intelligent ArithmeticOptimization with Ensemble Deep Transfer Learning Based MelanomaClassification (IAOEDTT-MC) model. The proposed IAOEDTT-MC modelfocuses on identification and classification of melanoma from dermoscopicimages. To accomplish this, IAOEDTT-MC model applies image preprocessingat the initial stage in which Gabor Filtering (GF) technique is *** addition, U-Net segmentation approach is employed to segment the lesionregions in dermoscopic images. Besides, an ensemble of DL models includingResNet50 and ElasticNet models is applied in this study. Moreover, AOalgorithm with Gated Recurrent Unit (GRU) method is utilized for identificationand classification of melanoma. The proposed IAOEDTT-MC methodwas experimentally validated with the help of benchmark datasets and theproposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.
Healthcare, the smart grid, and supply chain management are some of the industries where IoT(Internet of Things) technology is improving. Also, it improves the way humans communicate with each other, the atmosphere, a...
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This study investigates barely-supervised medical image segmentation where only few labeled data, i.e., single-digit cases are available. We observe the key limitation of the existing state-of-the-art semi-supervised ...
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Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material compositio...
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Scanning microscopy systems, such as confocal and multiphoton microscopy, are powerful imaging tools for probing deep into biological tissue. However, scanning systems have an inherent trade-off between acquisition ti...
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The purpose of this research is to gather and analyze a wide range of data related to weather and crop production in specific regions of India. The data includes information on temperature, rainfall, sand, grain, crop...
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ISBN:
(纸本)9783031889912
The purpose of this research is to gather and analyze a wide range of data related to weather and crop production in specific regions of India. The data includes information on temperature, rainfall, sand, grain, crop production, moisture content, and wind velocity, among others. The aim is to use this information to help farmers improve their crop yields and make more informed decisions about planting and harvesting crops. To achieve this goal, the study employs a method known as big data analytics for weather-based crop prediction. This approach utilizes historical weather data, crop yield, and soil moisture information to forecast future crop yields. Machine learning algorithms are applied to analyze large datasets and identify patterns and trends that can be used to make predictions about future crop yields. The research involves several stages. First, the data is processed using Python to remove any unwanted or noisy data. Next, the MapReduce framework is used to analyze and process the large amount of data collected. Then, k-means clustering is applied to the MapReduce outcomes to group similar data points together and obtain an expected average for the data, which helps in assessing its accuracy. The research also involves the development of a customized approach for predicting and displaying harvest forecasts through a user-friendly interface based on Flask. This approach allows users to make informed decisions about farming practices based on information such as temperature, rainfall, moisture content, and wind velocity. Furthermore, the study recommends three crops for farming and is configurable, allowing for the prediction of the best farms for each region. The research is essential for policymakers and farmers in India as it provides valuable insights into the potential impact of climate change on agricultural production. The study also offers a method for using data analytics to improve crop production, which can help increase food security in India an
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