There are currently considerable challenges concerning data security and privacy, particularly in relation to modern technologies. This includes the virtual world known as the Metaverse, which consists of a virtual sp...
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Smart agriculture systems leverage the possibilities offered by cutting-edge technologies such as IoT, AI, and remote sensing to revolutionize conventional farming by enhancing resource utilization, production, and cr...
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ISBN:
(纸本)9798331509675
Smart agriculture systems leverage the possibilities offered by cutting-edge technologies such as IoT, AI, and remote sensing to revolutionize conventional farming by enhancing resource utilization, production, and crop damage mitigation. Real-time monitoring of soil and crop health, predictive analytics, pest control, and precision irrigation measures are all enabled by these systems. They are able to address major Indian agriculture issues, consequently boosting yield and profitability and promoting environmental sustainability. The largescale deployment of intelligent agriculture systems will change the agriculture landscape in India and will assure long-term food security for an ever-growing population. Challenges include adequate research and future studies in order to better install and achieve smart agricultural systems to protect crops. Intelligent agriculture involves all advanced research, including science and innovations, in national development through space technologies to enhance soil quality, conserve water, and facilitate agriculture information. Space ventures will undergo improved modernization through the introduction of crop sprayers, precision gene editors, epigenetics, big data analytics, IoT, wind and photovoltaic smart energy, AI-enabled robotic applications, and wide-scale desalination technologies. Implementing digital farming systems in developing economies will help their sectors as 85 percent of the global population is set to live in developing countries by 2030. Automation will prove to be necessary since food scarcity is on the rise along with resource wastage. Control strategies such as the IoT, aerial imagery, machine learning, and artificial intelligence will boost production and prevent soil degradation. These advancedtechnologies are also able to alleviate such issues as plant disease detection, pesticide management, and water application. The introduction of the Internet of Things in the agricultural research world has started
This paper explores the application of artificial intelligence (AI) technologies in the engineering design domain. Two experiments were designed for examining the abilities of diverse generative AI platforms in engine...
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Hand Gesture Recognition (HGR) systems have garnered significant attention due to their applications in human-computer interaction, virtual reality, and assistive technologies. However, achieving adaptable, accurate, ...
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Drilling is a significant cutting method employed in manufacturing industries. With the development of computer technology, the finite element method (FEM) has been widely applied in the research of cutting. By simula...
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Drilling is a significant cutting method employed in manufacturing industries. With the development of computer technology, the finite element method (FEM) has been widely applied in the research of cutting. By simulating the drilling using FEM, it is possible to reveal the cutting mechanism and optimize the drilling process without conducting extensive experiment. This paper introduces the finite element software commonly used in drilling, summarizes the key techniques in finite element simulation of drilling, and reviews the applications of the finite element simulation of drilling in the research of chip morphology, cutting temperature, cutting force, and burr prediction. The problems existing in current research and the future development direction are pointed out. The research is helpful to promote the development and application of finite element simulation technology of drilling.
This research investigates the feasibility of utilizing Mobile Ad Hoc Networks (MANETs) in conjunction with Raspberry Pi-equipped Unmanned Aerial Vehicles (UAV) swarms. The primary objective is to overcome the limitat...
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This paper contains the way of making a portable acquisition system of a sEMG signal from the extensor digitorum muscle with real-time processing of this signal to generate hand grip force information. The system repr...
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paper provides an overview of the historical evolution of speech recognition, synthesis, and processing technologies, highlighting the transition from statistical models to deep learning-based models. Firstly, the pap...
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paper provides an overview of the historical evolution of speech recognition, synthesis, and processing technologies, highlighting the transition from statistical models to deep learning-based models. Firstly, the paper reviews the early development of speech processing, tracing it from the rule-based and statistical models of the 1960s to the deep learning models, such as deep neural networks (DNN), convolutional neural networks (CNN), and recurrent neural networks (RNN), which have dramatically reduced error rates in speech recognition and synthesis. It emphasizes how these advancements have led to more natural and accurate speech outputs. Then, the paper examines three key learning paradigms used in speech recognition: supervised, self-supervised, and semi-supervised learning. Supervised learning relies on large amounts of labeled data, while self-supervised and semi-supervised learning leverage unlabeled data to improve generalization and reduce reliance on manually labeled datasets. These paradigms have significantly advanced the field of speech recognition. Furthermore, the paper explores the wide-ranging applications of AI-driven speech processing, including smart homes, intelligent transportation, healthcare, and finance. By integrating AI with technologies like the Internet of Things (IoT) and big data, speech technology is being applied in voice assistants, autonomous vehicles, and speech-controlled devices. The paper also addresses the current challenges facing intelligent speech processing, such as performance issues in noisy environments, the scarcity of data for low-resource languages, and concerns related to data privacy, algorithmic bias, and legal responsibility. Overcoming these challenges will be crucial for the continued progress of the field. Finally, the paper looks to the future, predicting further improvements in speech processing technology through advancements in hardware and algorithms. It anticipates increased focus on personalized servic
This study explores automation and prompt engineering to enhance productivity by leveraging both emerging and existing technologies. It covers topics such as automated bug fixing, AI-driven office tasks, web data extr...
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The accurate detection and monitoring of whales in the Indian Ocean are vital for marine conservation but face significant challenges due to the complexities of underwater environments. advanced remote sensing technol...
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