One of the basic requirements with adapting to cloud technology is to find an optimal resource allocation based on the dynamic workload. The default functioning of Kubernetes Horizontal Pod Auto-scaling in cloud is sc...
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One of the basic requirements with adapting to cloud technology is to find an optimal resource allocation based on the dynamic workload. The default functioning of Kubernetes Horizontal Pod Auto-scaling in cloud is scaling of its pods only when the threshold of the cluster/application is crossed in order to adapt to increasing workload. Rather we want to deploy a proactive provisioning framework based on machine learning based predictions. We have demonstrated a novel deep learning framework based on a transformer in the area of dynamic workload predictions and showed how to apply the results to a custom auto-scaler in cloud. Our Framework builds time-series predictive models in machine learning such as ARIMA, LSTM, Bi-LSTM and transformer models. The dynamic scaling framework applies machine learning algorithms and presents recommendations to make proactive and smart decisions. Though the transformer model has been used in NLP and Vision applications mostly, we showed that the transformer based model can produce the most effective results in cloud workload predictions.
In the latest days, study into the development of intelligent technologies has proven valuable, contributing to attempts to improve the quality of human existence. Smart glass is one of the intelligent wearable device...
In the latest days, study into the development of intelligent technologies has proven valuable, contributing to attempts to improve the quality of human existence. Smart glass is one of the intelligent wearable devices that can be used for various purposes, including healthcare monitoring, fall detection, sleep tracking, and human activity recognition (HAR). Smart-phones and smartwatches are the primary wearables utilized in sensor-based HAR to collect human motions for training recognition models based on physical movement. These wearable tools, nevertheless, are more intrusive than smart glasses. Using IMU sensor data acquired via smart glasses, we investigate deep learning algorithms for detecting people's activities of daily living (ADL). This work proposes a hybrid deep neural network that automatically extracts spatial-temporal information from raw data to enhance identification $\mathbf{p}$ erformance. We performed tests to evaluate deep learning models using a publically available benchmark dataset, UCA-EHAR, which included IMU sensor data from multiple ADL from smart eyewear. The recommended CNN-LSTM model achieved the best effectiveness with the highest F1-score of 93.20%, as determined by experimental findings.
The prevalence of intelligent wearable devices has significantly increased, offering advantages to individuals of all age groups. In the field of human activity recognition (HAR) research, wearable sensor data plays a...
The prevalence of intelligent wearable devices has significantly increased, offering advantages to individuals of all age groups. In the field of human activity recognition (HAR) research, wearable sensor data plays a crucial role in classifying various human actions. Sensor-based assessments are commonly employed in hazard analysis and risk assessments. However, traditional machine learning (ML) algorithms have faced challenges when applied to sports activities due to their unpredictable nature. These challenges have prompted the need for manual feature extraction, limiting the efficacy of ML methodologies in data categorization. To address this, our study introduces ResNet, a deep residual network, for effectively categorizing sports activities using wearable sensor data. To evaluate our proposed approach, we utilized a publicly available benchmark dataset consisting of sensor data collected from the dominant wrist, neck, and thigh of each study participant. The experimental results demonstrated the exceptional performance of the ResNet model, achieving an impressive accuracy rate of 99.83% in recognizing sport activities. Additionally, we compared the efficacy of our suggested model with five fundamental deep learning models, namely CNN, LSTM, BiLSTM, GRU, and BiGRU. The comparative analysis revealed that the ResNet model outperformed the other deep learning models, demonstrating superior performance in sport activity recognition.
Hyperledger Fabric stands as a leading framework for permissioned block-chain systems, ensuring data security and audit-ability for enterprise applications. As applications on this platform grow, understanding its com...
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ISBN:
(数字)9798350327939
ISBN:
(纸本)9798350327946
Hyperledger Fabric stands as a leading framework for permissioned block-chain systems, ensuring data security and audit-ability for enterprise applications. As applications on this platform grow, understanding its complex configuration concerning various block-chain parameters becomes vital. These configurations significantly affect the system’s performance and cost. In this research, we introduce a Stochastic Petri Net (SPN) model to analyze Hyper-ledger Fabric’s performance, considering variations in block-chain parameters, computational resources, and transaction rates. We provide case studies to validate the utility of our model, aiding block-chain administrators in determining optimal configurations for their applications. A key observation from our model highlights the block size’s role in system response time. We noted an increased mean response time, between 1 to 25 seconds, due to variations in transaction arrival rates.
The Internet of Vehicles (IoV) is an architecture of the intelligent transportation system that combines automotive, transportation, and information exchange to increase road safety. The categorization of roadways not...
The Internet of Vehicles (IoV) is an architecture of the intelligent transportation system that combines automotive, transportation, and information exchange to increase road safety. The categorization of roadways not only improves the passenger's comfort and safety but also provides autonomous cars with safe navigation paths. In this paper, we present an identification method based on movement data from smart glasses (electroocu-lography, acceleration, and angular velocity) to categorize four kinds of roads often experienced: highway, city road, highway, undeveloped region, and housing estate. We developed a deep pyramidal residual network that automatically recovers spatial-temporal data and efficiently identifies road kinds. We performed experiments to evaluate deep learning models utilizing a publicly available benchmark dataset, including sensor data acquired from intelligent eyewear. Experimentally, we discovered that the suggested 1D-PyramidNet model obtained the most incredible interpretation with the most increased accuracy (92.23%) and outperformed all other deep learning models.
Many services that are taken for granted in smart cities are not even remotely available in dislocated areas, i.e. "smart territories". With the aim to offer a practical and secure way to transport data in s...
Many services that are taken for granted in smart cities are not even remotely available in dislocated areas, i.e. "smart territories". With the aim to offer a practical and secure way to transport data in such constrained scenarios, we focus on the problem of incentivizing to Data Mules, i.e. devices dedicated to enable communication even in the absence of the Internet. We combine decentralized technologies and State-Channels to verify the correct behavior of participants in an offline scenario.
Many algorithms used are based on geometrical computation. There are several criteria in selecting appropriate algorithm from already known. Recently, the fastest algorithms have been preferred. Nowadays, algorithms w...
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In order to maximize efficiency, electric vehicles use Battery Management Systems (BMS) to manage their rechargeable batteries. The battery's safety and dependability are ensured by the battery management system, ...
In order to maximize efficiency, electric vehicles use Battery Management Systems (BMS) to manage their rechargeable batteries. The battery's safety and dependability are ensured by the battery management system, and the battery's age is extended without being damaged. Various monitoring methods are used to keep track of the battery pack's charging capacity, voltage-levels, current-ratings and ecological pressure ratio. A variety of analogue and digital sensors are employed with microcontrollers for this purpose. Several powerful batteries have been developed as a result of the tremendous advances in the field of batteries over the past ten years. High-power batteries with suitable battery management systems are essential for the safe and reliable operation of Electric Vehicles (EVs). This paper will address the power sources utilized by electric automobiles and the primary problems of battery control systems, as well as compare both the lithium ion battery as well as the metal based nickel-hydride batteries in regards to growing older along with the impact caused by climate through the use of the State-of-Charge (SOC) and closed device electricity. This study's goal is to compare and contrast different intelligence techniques along with the control methods used in electric vehicle battery management platforms. The study assesses the characteristics, structure, configuration, accuracy, benefits, and downsides of intelligent algorithms for estimating the condition of a battery. Focusing on parts, characteristics, aims, outcomes, advantages, and restrictions, this research analyses the various controllers used for warming, cooling, balancing, and protecting batteries.
作者：
Skala, VaclavUniversity of West Bohemia
Faculty of Applied Sciences Department of Computer Science and Engineering Univerzitni 8 PlzenCZ 306 14 Czech Republic
This paper presents a new approach to computation of geometric continuity for parametric bi-cubic patches, based on a simple mathematical reformulation which leads to simple additional conditions to be applied in the ...
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Deep machine learning approaches can enhance the forecasting and detection of rocks and minerals in water defense systems. This research focused on applying deep machine learning techniques to increase accuracy and ef...
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ISBN:
(数字)9798350373783
ISBN:
(纸本)9798350373790
Deep machine learning approaches can enhance the forecasting and detection of rocks and minerals in water defense systems. This research focused on applying deep machine learning techniques to increase accuracy and efficiency in rock and mineral prediction. The researchers employed a series of watermarks that appeared and then utilized a convolutional neural network (CNN) to extract significant characteristics. Using transfer learning approaches and state-of-the-art optimization algorithms, researchers suggested a multi-class solution to identify between rocks and minerals. The suggested technique accurately predicted and recognized rocks and mines in underwater imagery, even in complicated settings with complicated against and illumination circumstances. The finding was essential for water protection and maritime safety systems, as it can assist in the automatic identification of rocks and minerals, decreasing the time and effort necessary for manual inspection
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