One of the fatal diseases in the world is heart disease. Every year, millions of people die of cardiovascular diseases. However, one can decrease the mortality rates if the heart disease was detected and treated early...
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ISBN:
(纸本)9789811977527
One of the fatal diseases in the world is heart disease. Every year, millions of people die of cardiovascular diseases. However, one can decrease the mortality rates if the heart disease was detected and treated early. Usually, people do an electrocardiogram (ECG) test to know about the well-being of their heart. Some kind of irregular functioning and illness in the heart can be found in an ECG test. When the heart malfunctions or if there is any improper beating of the heart, then it results in arrhythmia. there are several types of arrhythmia and some of them are fatal. the process to identify the correct type of arrhythmia is quite difficult and effort-taking process. Even the small changes in the ECG relate to another kind of arrhythmia. It takes experience and patience to recognize the type of arrhythmia accurately. therefore, deep learning techniques should be employed to analyze the test. Machine learningthat involves many levels of processing is known as deep learning. From computer vision to natural language processing, there’s a lot to learn. It has been used in various applications. this method is receiving more popularity because of extreme accuracy, provided the numerous amount of data. the interesting feature is that it analyses the examples and distinguishes the classes and levels automatically. this study is regarding arrhythmia prediction in ECG and the attention it deserves in deep learning community. Providing CNN model, we are going to elaborate the process of detecting cardiac arrhythmia using ECG dataset in this study. the model is executed by rendering CNN with cardiac arrhythmia recognition database. Purpose: About one-third of the world’s population is affected by arrhythmia. Hence, the development of new and successful methodologies is highly in demand in the field of arrhythmia prediction. Further, the need of a cost-effective wearable monitoring gadget to identify the condition of arrhythmia is highly recommended. It assures the trouble-
this paper comprehensively reviews the profound impact of Artificial Intelligence (AI) on laboratory sessions within supply chain management education. the primary objective is to elucidate the diverse applications of...
this paper comprehensively reviews the profound impact of Artificial Intelligence (AI) on laboratory sessions within supply chain management education. the primary objective is to elucidate the diverse applications of AI in educational settings and assess how AI has reshaped traditional laboratory sessions. this paper uses a systematic review methodology to analyse literature sources, identifying key trends, methodologies, and outcomes. the analysis categorises and scrutinises supply chain education laboratory sessions, including inventory management, demand forecasting, logistics optimisation, and procurement strategies. the paper delves into how AI technologies, such as machine learningalgorithms, optimisation models, and simulation tools, have revolutionised these laboratory sessions. Findings reveal that AI integration has substantially improved the quality and effectiveness of laboratory experiments. AI-powered sessions empower students with enhanced problem-solving skills and a deeper understanding of real-world supply chain challenges. Moreover, AI-driven experiments create dynamic and adaptive learning environments, fostering student engagement and critical thinking. the implications extend beyond the classroom, as AI in laboratory sessions enhances supply chain management education and prepares future professionals for an AI-driven industry. this paper underscores the need for continued research and innovation in AI applications for supply chain management education and emphasises the importance of educators staying updated with evolving AI technologies.
In the rapidly advancing domain of wireless communication networks, the delivery of high-speed data services has entered a heightened phase of competition. As the demand for uninterrupted user connectivity experiences...
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ISBN:
(数字)9798350309249
ISBN:
(纸本)9798350309256
In the rapidly advancing domain of wireless communication networks, the delivery of high-speed data services has entered a heightened phase of competition. As the demand for uninterrupted user connectivity experiences continual growth, the precise prediction of downlink user throughput emerges as a pivotal challenge within the field of network optimization. In this research endeavor, we harness the capabilities of cutting-edge machine learning techniques to discern the most pertinent and impactful Key Performance Indicators (KPIs) governing the evolution of downlink throughput. Our overarching objective is to construct a pioneering predictive model withthe acumen to accurately estimate throughput, grounded in these indispensable KPIs.
the rapid development of facial recognition technology has brought great convenience to daily life, but also serious security risks, especially in the case of occlusion and loud noise. Faced withthis limitation, this...
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this paper proposes a reinforcement learning-based optimization framework that defines a structured state space (real-time conversion rates, channel ROI, historical CTR), action space (budget-compliant allocations), a...
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ISBN:
(数字)9798331510664
ISBN:
(纸本)9798331510671
this paper proposes a reinforcement learning-based optimization framework that defines a structured state space (real-time conversion rates, channel ROI, historical CTR), action space (budget-compliant allocations), and reward function (balancing revenue, cost, and placement effectiveness). To enhance adaptability, we introduce a multi-channel synergy mechanism using behavioral correlation matrices and a time-sequence update model for predictive, real-time budget adjustment. Trained with Proximal Policy optimization (PPO) in a high-fidelity simulation, the model outperforms traditional rule-based and DQN baselines in CTR (+8.7%), ROI (+12.4%), and policy stability, while reducing latency and memory usage.
To ensure food security, advanced agriculture and the Green Movement haveincreased overall output, but with a yield gap. However, the use of agrochemicals such as fertilizers and pesticides, as well as modern cultivar...
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the current terrain classification of transmission lines is generally target classification or fixed-point classification, which lacks comparability and leads to the reduction of the optimization frequency of a single...
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ISBN:
(数字)9798350368314
ISBN:
(纸本)9798350368321
the current terrain classification of transmission lines is generally target classification or fixed-point classification, which lacks comparability and leads to the reduction of the optimization frequency of a single transmission line, so we propose to study the terrain classification and distance optimization of transmission lines based on deep learning. According to the current measurement, data acquisition and terrain feature extraction are carried out first to improve the contrast of optimization in a multistage way, and the multistage classification of terrain and preliminary planning of paths are carried out. Based on this, a deep learning transmission line distance optimization model is designed, and dynamic correction scheduling is adopted to realize the optimization process. the test results show that the deep learning transmission line terrain classification and distance optimization method is higher compared withthe renewable energy transmission line protection method under the prediction of traveling wave arrival time, and the efficient hybrid strategy of FFT and fuzzy logic technology in the transmission line fault diagnosis method, which indicates that the optimization effect of the present study has a stronger coverage, better processing speed, and a more accurate completion of the preset tasks.
Recidivism prediction has become an essential tool in criminal justice systems, aiding decision-making in areas such as sentencing, parole, and rehabilitation. Machine learning (ML) algorithms have been widely employe...
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ISBN:
(数字)9798331532970
ISBN:
(纸本)9798331532987
Recidivism prediction has become an essential tool in criminal justice systems, aiding decision-making in areas such as sentencing, parole, and rehabilitation. Machine learning (ML) algorithms have been widely employed to improve the accuracy of recidivism risk assessments. However, concerns about fairness and algorithmic bias have been raised, particularly in high-stakes applications. this study focuses on the Greek prison system, utilizing a dataset from Greek prisons to analyze and mitigate biases in ML-based recidivism predictions. the study primarily investigates the impact of age as a sensitive attribute and employs fairness-aware optimization techniques to reduce disparities in predictive outcomes. By incorporating fairness constraints into the training process, we demonstrate that balancing fairness and accuracy is possible. the results indicate that implementing fairness-aware ML models can significantly reduce bias, particularly against younger offenders, while maintaining acceptable predictive performance. Our findings contribute to ongoing discussions on the ethical application of AI in criminal justice and highlight the necessity of fairness-aware methodologies for equitable decision-making.
In edge computing (EC) scenarios such as traffic flow prediction and social relationship recommendations, the emerging Graph Neural Networks (GNNs) is the promising deep learning model to tackle these applications. Ho...
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ISBN:
(数字)9798350378542
ISBN:
(纸本)9798350378559
In edge computing (EC) scenarios such as traffic flow prediction and social relationship recommendations, the emerging Graph Neural Networks (GNNs) is the promising deep learning model to tackle these applications. However, since GNNs can cause a large amount of cross-server communication during their inference process, there is still a big challenge to provide cost-efficient intelligent services in EC for GNN processing. To address this issue, this paper proposes Edgraph, an efficient EC architecture for GNN computing, withthe goal of minimizing the system's energy and time cost for processing user tasks. Specifically, at each time step, the architecture first perceives the user topology and represents their data association as a graph layout. then the graph layout is optimized by calling our proposed hierarchical traversal graph cutting algorithm (HTGC). Finally, based on the optimized graph layout, our proposed deep reinforcement learning (DRL) based graph offloading algorithm (DRLGO) is executed to obtain the optimal offloading strategy for the tasks of users. Based on this strategy, the tasks of users are offloaded to edge servers and processed by the deployed GNN model. Experimental results show the effectiveness of our proposed architecture because it can fully learn dynamic scenario information and minimize the energy and time cost of the GNN-based EC system.
this paper proposes a deep reinforcement learning (DRL) based algorithm for the stochastic dynamic microgrid energy management. First, we consider AC power flow constraints which makes the problem non-convex and uncer...
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