Sentiment analysis (SA) is a crucial and difficult issue in the discipline of Artificial Intelligence (AI) because of the intricate nature of languages. systems utilizing rule-driven & machine learning (ML) method...
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ISBN:
(数字)9798350355338
ISBN:
(纸本)9798350355345
Sentiment analysis (SA) is a crucial and difficult issue in the discipline of Artificial Intelligence (AI) because of the intricate nature of languages. systems utilizing rule-driven & machine learning (ML) methods have gained popularity. Current approaches have been ineffective in accurately categorizing humour, jokes, including subjective in text. This research aims to implement and assess the effectiveness of cutting-edge ML sentiment evaluation approaches using a publicly available IMDB database. The collection contains numerous examples of both sarcasm & irony. Transformer-based models, Long-short term memory (LstM), convolutional neural networks (CNN) & bag of tricks (BoT) are created & assessed. Furthermore, we have analyzed how hyper parameter values impact the precision of the systems.
This research paper explores the evolution of Natural Language Processing (NLP) and Computational Linguistics, tracing the transition from grammar-based approaches to the adoption of machine learning methods. Initiall...
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ISBN:
(数字)9798350355338
ISBN:
(纸本)9798350355345
This research paper explores the evolution of Natural Language Processing (NLP) and Computational Linguistics, tracing the transition from grammar-based approaches to the adoption of machine learning methods. Initially rooted in linguistic theories, early NLP systems relied on rule-based methods for language parsing and processing, albeit with limitations in handling natural language complexities. The advent of machine learning brought about a paradigm shift, empowering NLP systems to learn patterns directly from data, leading to more adaptable and robust language processing capabilities. Hybrid approaches, blending rule-based techniques with machine learning, have emerged as a synergistic solution, combining the interpretability of rules with the learning capacity of algorithms. Recent advancements in NLP, including transformer models and multimodal techniques, have further accelerated progress, enabling state-of-the-art systems capable of human-level performance across diverse applications. Looking forward, future directions in NLP encompass neural symbolic approaches, self-supervised learning, multimodal NLP, continual learning, and ethical considerations, promising further innovation in the field. Through interdisciplinary collaboration and a commitment to responsible AI, the evolution of NLP continues to bridge the gap between human language and machine understanding, shaping a future where intelligent communication between humans and machines is seamless and ubiquitous.
Reinforcement Learning (RL) has emerged as a transformative technology for autonomous vehicles, enabling sophisticated decision-making systems that enhance driving safety, efficiency, & adaptability. This paper ex...
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ISBN:
(数字)9798350356816
ISBN:
(纸本)9798350356823
Reinforcement Learning (RL) has emerged as a transformative technology for autonomous vehicles, enabling sophisticated decision-making systems that enhance driving safety, efficiency, & adaptability. This paper explores the application of RL algorithms in the context of autonomous vehicle control, focusing on the development of intelligent agents capable of learning optimal driving policies through interaction with complex environments. The study reviews various RL techniques, including Deep Q-Learning, Policy Gradient methods, & Actor-Critic frameworks, evaluating their effectiveness in addressing key challenges such as real-time decision making, dynamic environment adaptation, & multi-agent interactions. Emphasis is placed on the design of reward functions, exploration strategies, & simulation environments, which are crucial for training robust & reliable autonomous driving systems. Case studies demonstrate the application of RL in scenarios such as lane-keeping, adaptive cruise control, & collision avoidance. The paper also discusses advancements in simulation platforms & hardware acceleration that facilitate scalable RL experiments. By integrating theoretical insights with practical implementations, this work provides a comprehensive overview of current developments in RL for autonomous vehicles & identifies future research directions aimed at overcoming limitations & achieving safer, more efficient.
Unmanned aerial vehicles, or UAVs, are the most sophisticated remote sensing instruments available today, revolutionizing the process of gathering vast amounts of data. In addition to serving as sensors, they assist i...
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ISBN:
(数字)9798350355338
ISBN:
(纸本)9798350355345
Unmanned aerial vehicles, or UAVs, are the most sophisticated remote sensing instruments available today, revolutionizing the process of gathering vast amounts of data. In addition to serving as sensors, they assist in decision-making and measure production processes in the industrial sector as proactive problem-solving tools. Together, the Industrial Internet of Things and Unmanned Aerial Vehicle (UAV) technologies have produced a powerful monitoring and control system. Cloud computing, which efficiently manages IIoT system data and ensures seamless operation, is a crucial component of this synergy. Fog computing is crucial to the integration of IoT gateways, which act as links between various objects and the internet. These gateways function as unified interfaces by facilitating network access and supporting a wide range of communication protocols. It has taken a lot of effort to create UAVs and multi-UAV systems. This work provides a unique fog, cloud, and UAV computing-based IIoT control and oversight system. What distinguishes it is the automatic integration of UAVs with industrial controls via an IoT gateway platform. In addition, UAV-taken images undergo rapid and thorough analysis in the public internet to provide visual plant monitoring in real time. To confirm this theory, the platform was tested in a concrete manufacturing plant. The results amply demonstrated the platform's efficacy in enhancing UAV the remote control, which improves product quality and lowers waste. A thorough examination of connection latency at every IIoT tier for IoT gateways verified the system's reliability and efficacy.
We consider the problem of recovering a signal x* ∈ ℝn, from magnitude-only measurements, yi = | 〈ai, x*〉 | for i = {1,2,..., m}. Also known as the phase retrieval problem, it is a fundamental challenge in nano-, b...
ISBN:
(纸本)9781510860964
We consider the problem of recovering a signal x* ∈ ℝn, from magnitude-only measurements, yi = | 〈ai, x*〉 | for i = {1,2,..., m}. Also known as the phase retrieval problem, it is a fundamental challenge in nano-, bio- and astronomical imaging systems, and speech processing. The problem is ill-posed, and therefore additional assumptions on the signal and/or the measurements are *** this paper, we firststudy the case where the underlying signal x* is s-sparse. We develop a novel recovery algorithm that we call Compressive Phase Retrieval with Alternating Minimization, or CoPRAM. Our algorithm is simple and can be obtained via a natural combination of the classical alternating minimization approach for phase retrieval, with the CoSaMP algorithm for sparse recovery. Despite its simplicity, we prove that our algorithm achieves a sample complexity of O (s2 logn) with Gaussian samples, which matches the best known existing results. It also demonstrates linear convergence in theory and practice and requires no extra tuning parameters other than the signal sparsity level *** then consider the case where the underlying signal x* arises from structured sparsity models. We specifically examine the case of block-sparse signals with uniform block size of b and block sparsity k = s/b. For this problem, we design a recovery algorithm that we call Block CoPRAM that further reduces the sample complexity to O (ks log n). For sufficiently large block lengths of b = Θ(s), this bound equates to O (s log n). To our knowledge, this constitutes the first end-to-end linearly convergent family of algorithms for phase retrieval where the Gaussian sample complexity has a sub-quadratic dependence on the sparsity level of the signal.
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