Livestock is one of the critical socioeconomic assets in developing countries like India. However, the lack of a reliable and timely diagnosis system for identifying livestock diseases has led to significant losses in...
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ISBN:
(纸本)9798350347579
Livestock is one of the critical socioeconomic assets in developing countries like India. However, the lack of a reliable and timely diagnosis system for identifying livestock diseases has led to significant losses in the livestock population, hindering efforts to achieve food security and reduce poverty in the country. To address this issue, a study proposed the integration of an expert system with machine learning and image processing. According to the 2019 Livestock Census, India has a total livestock population of around 535 million, which includes cattle, buffalo, sheep, goats, and pigs. The livestock sector is an important source of income for millions of households in India. As per the 2019 Livestock Census, there are around 145 million households involved in livestock farming and related activities in the country. The livestock sector contributes significantly to the Indian economy. In 2020, the total value of livestock output in India, excluding the value of horses, ponies, mules, donkeys, camels, and yak, was estimated to be around 9.37 trillion Indian rupees (around 125 billion US dollars), which accounts for around 4.2% of country's GDP. The dairy sector is a major contributor to the livestock economy in India. It accounts for around 70% of the total value of livestock output in the country. In addition to dairy, the livestock sector also provides meat, wool, leather, and other products. According to the latest available data from the Ministry of Statistics and Program Implementation, the contribution of the livestock sector to India's Gross Value Added (GVA) in 2020-21 was 5.04%. Since GVA is more accurate measure of the sector's contribution to the economy than GDP, it is concluded that livestock sector contributed around 5% to India's economy in 2020-21. According to a study by the National Dairy Development Board (NDDB) of India, there is an average of one veterinarian for every 5,000 cattle in India. This study improves the accuracy of livestock dis
In this research work image processing is done for direct application of digital images. Image processing is used for the enhancement of process includes with it. Adaptive histogram equalization is an appropriate meth...
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The Internet of Things (IoT) is revolutionizing healthcare by enabling remote patient monitoring. An IoT-based patient monitoring system is the main focus of this study that uses an electrocardiogram (ECG) sensor, a w...
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作者：
Vaghela, PriyalbaMakwana, Ashwin
Charotar University of Science and Technology Department of Computer Science and Engineering Gujarat Changa388421 India
Charotar University of Science and Technology Department of Computer Engineering Gujarat Changa388421 India
By choosing the most instructive segments of the video content, video summarization systems seek to produce a succinct and comprehensive summarization. The most advanced techniques are those that use contemporary Deep...
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Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which lim...
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Remote sensing (RS) image interpretation includes multi-label picture classification, a key problem. The complicated and diverse character of many remote sensing scenes, which are produced by the spatial combination a...
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During the COVID-19 outbreak, deep-learning techniques have been extensively studied for computer-assisted diagnosis. However, distinguishing COVID-19 from other types of pneumonia remains challenging. This research d...
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In the current landscape of online data services,data transmission and cloud computing are often controlled separately by Internet Service Providers(ISPs)and cloud providers,resulting in significant cooperation challe...
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In the current landscape of online data services,data transmission and cloud computing are often controlled separately by Internet Service Providers(ISPs)and cloud providers,resulting in significant cooperation challenges and suboptimal global data service *** this study,we propose an end-to-end scheduling method aimed at supporting low-latency and computation-intensive medical services within local wireless networks and healthcare *** approach serves as a practical paradigm for achieving low-latency data services in local private cloud *** meet the low-latency requirement while minimizing communication and computation resource usage,we leverage Deep Reinforcement Learning(DRL)algorithms to learn a policy for automatically regulating the transmission rate of medical services and the computation speed of cloud ***,we utilize a two-stage tandem queue to address this problem *** experiments are conducted to validate the effectiveness for our proposed method under various arrival rates of medical services.
A pandemic has broken out throughout the world since December 2019 and later it has been named COVID-19. The flow of normal life has collapsed due to this pandemic, especially in the economic, public health, and educa...
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In order to accurately and quickly recognize Bengali handwritten digits and characters, this paper suggests an FPGA-based hardware accelerator design of ANN for handwritten Bengali character recognition applications. ...
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