This paper established a novel multi-input multi-output (MIMO) communication network, in the presence of full-duplex (FD) transmitters and receivers with the assistance of dual-side intelligent omni surface (IOS). Com...
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Aging allows the occurrence and progression of cerebrovascular diseases, highlighting the importance of detecting senescent cells and tissues in the nervous system. Detecting senescent cells/tissues using nanoparticle...
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This study explores the impact of Quality of Service (QoS) on consumer satisfaction in basic wireline telecom services in Assam, India. The aim of this research is to identify key QoS indicators that influence consume...
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ISBN:
(数字)9798350391107
ISBN:
(纸本)9798350391114
This study explores the impact of Quality of Service (QoS) on consumer satisfaction in basic wireline telecom services in Assam, India. The aim of this research is to identify key QoS indicators that influence consumer satisfaction in the region. A qualitative research method is adopted, utilizing secondary data available for public disclosure, including government reports, telecom regulatory authority publications, and consumer feedback from various platforms. The study focuses on understanding how these indicators such as call quality, internet connectivity, customer support response time, and pricing transparency affect the satisfaction levels of consumers in Assam. The findings indicate that call quality and internet reliability are the most critical factors for consumers, followed by effective customer service and pricing fairness. These results highlight the need for telecom providers in Assam to prioritize service improvements in these areas to enhance consumer satisfaction and loyalty. This study offers valuable insights for policy makers and telecom companies to improve service delivery.
The paper presents a detailed assessment of the energy performance of a construction site in Germany, where realistic electricity consumption patterns are obtained using metering devices installed in the site switchge...
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We study the common continual learning setup where an overparameterized model is sequentially fitted to a set of jointly realizable tasks. We analyze the forgetting—loss on previously seen tasks—after k iterations. ...
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We consider an opinion dynamics model coupled with an environmental dynamics. Based on a forward invariance argument, we can simplify the analysis of the asymptotic behavior to the case when all the opinions in the so...
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Within this context, this research is aimed at enhancing the sales forecasting in e-commerce using the newer approaches of predictive analytics and deep neural networks. There usually is significant variability of res...
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ISBN:
(数字)9798331542375
ISBN:
(纸本)9798331542382
Within this context, this research is aimed at enhancing the sales forecasting in e-commerce using the newer approaches of predictive analytics and deep neural networks. There usually is significant variability of results as compared to traditional approaches that are based on historical data and simple econometric models. This research evaluates how deep learning approaches, LSTM and Transformer models, improve precisely the forecast by noticing the behaviour dynamics and more complex patterns in the business environment. The actual approach includes sales history data, understanding customers, trends, and related promotions to train as well as test neural networks. The basic hybrid approach integrates deep learning and statistical methods to forecast the maximum potential outcomes. The performance of models in this study is evaluated by Mean Absolute Error (MAE), Root Mean Squared Error(RMSE) and R-Squared to compare the forecasted sales values with the actual ones. It is established that incorporating the deep learning models that have been described enhances the forecast precision concerning the baseline and conventional methods of approach significantly, especially the hybrid model. The precise accuracy, and factors that cause it to fluctuate from one product category to another, and the impact of promotional activities on the forecast's precision, are addressed. Thus, the study contributes to the knowledge that adopting innovative techniques such as advanced predictive analytics and deep learning to improve sales forecasting can potentiate strategic business courses of action and the firm's performance in today's cut-throat e-commerce marketplace.
Object detection is an integral piece of autonomous cars learning their surroundings and making safe judgements about where and how to drive in real time. This research compares and contrasts multiple machine learning...
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ISBN:
(数字)9798331512965
ISBN:
(纸本)9798331512972
Object detection is an integral piece of autonomous cars learning their surroundings and making safe judgements about where and how to drive in real time. This research compares and contrasts multiple machine learning models for object identification in autonomous cars in order to assess accuracy, computational economy, and real time performance. Then, we present how various well known proposed models (CNN, YOLO, SSD, R-CNN) perform in changing driving conditions. It researches the models capabilities to run real time applications within autonomous cars, as well as the trade off of speed and accuracy between different models. We also study how hyperparameter adjustment, data augmentation and transfer learning impact these models’ performance. Through thorough research on benchmark datasets, this work shows the benefits of the adoption of hybrid models, which mix speed and accuracy, to increase item recognition in real world contexts. This study contributes to the expanding corpus of knowledge on AI driven solutions for autonomous systems by having useful insights regarding the improvement of autonomous vehicle object identification algorithms.
Millimeter-wave(MMW) signals in 60 GHz band have shown immense potential for accurate range estimation with precise time and multipath resolution. Nonline of sight(NLOS) propagation is a primary factor affecting the r...
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Millimeter-wave(MMW) signals in 60 GHz band have shown immense potential for accurate range estimation with precise time and multipath resolution. Nonline of sight(NLOS) propagation is a primary factor affecting the range precision. To improve range estimation,an Energy detector(ED) based normalized threshold algorithm which employs a Neural network(NN) is developed on the basis of NLOS identification. The maximum curl and standard deviation of the received energy block values are used to determine NLOS environment and the normalized thresholds for different Signal-to-noise ratios(SNRs). The effects of the channel and integration period are *** results are presented which show that the proposed approach provides better precision and is more robust than other solutions over a wide range of SNRs for the CM1.1 and CM2.1 channel models in the IEEE802.15.3 c standard.
This work demonstrates the benefits of using tool-tissue interaction forces in the design of autonomous systems in robot-assisted surgery (RAS). Autonomous systems in surgery must manipulate tissues of different stiff...
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