data security is becoming increasingly important as cloud computing advances. data security is the fundamental problem of all distributed computing systems. Cloud computing enables access to distributed applications a...
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Traffic prediction in urban areas is essential for smart city applications, enabling efficient transportation management, reduced congestion, and improved quality of life. Spatio-temporal traffic prediction is particu...
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ISBN:
(数字)9798331522667
ISBN:
(纸本)9798331522674
Traffic prediction in urban areas is essential for smart city applications, enabling efficient transportation management, reduced congestion, and improved quality of life. Spatio-temporal traffic prediction is particularly challenging due to complex spatial and temporal dependencies in traffic data. This study introduces a Convolutional-Temporal Neural Net- work System (CTNS) model that combines Convolutional Neural Networks (CNNs) andrecurrent Neural Networks (rNNs) to capture spatial and temporal features simultaneously for more accurate traffic forecasting. CNNs are employed to learn spatial correlations between different locations in the urban area, while rNNs, specifically Long Short-Term Memory (LSTM) networks, capture the sequential dependencies in temporal traffic patterns. Using real-world traffic data, the hybrid CTNS model demonstrates superior performance in accurately predicting traffic flow compared to traditional models. This approach highlights the potential of deep learning architectures for enhancing the accuracy of traffic predictions, which is critical for urban planning and intelligent transportation systems. Extensive experiments on real-world urban traffic datasets validate the model's ability to achieve high accuracy, scalability, androbustness in handling complex spatio-temporal interactions.
The Internet of Things (IoT) is booming, with research showing that billions of connecteddevices gatherdata, communicate, and automate. The widespread adoption of IoT devices has been hampered by one major challenge...
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SONAr (Sound Navigation andranging) is one of the critical techniques used fordetection and location of underwater objects. distinguishing harmless objects such as rocks from hazardous threats like mines is vital fo...
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ISBN:
(数字)9798350357530
ISBN:
(纸本)9798350357547
SONAr (Sound Navigation andranging) is one of the critical techniques used fordetection and location of underwater objects. distinguishing harmless objects such as rocks from hazardous threats like mines is vital for successful naval operations and maritime security. This article discusses how the introduction of machine learning techniques will bring about a difference in this process. The results have shown that 80-90% accuracy could be achieved in the process using an appropriately designed machine learning model. The improved classification of underwater objects is, no doubt the most significant benefit of incorporating machine learning into the SONArtechnology, which ensures the process both safer and efficient. Machine learning does not directly involve humans; hence, unlike traditional methods, it reduces risks to *** different machine learning algorithms were used, namely Logistic regression, K- Nearest Neighbors (KNN), random Forest, and Support Vector Machine (SVM). The model performance metrics analysis was done to ensure a detailed comparison of which algorithm is best. Besides proving the supremacy of machine learning, this comparative study provides an accurate performance evaluation to ensure that the best model is selected for practical applications. The integration of multiple algorithms underscores the adaptability androbustness of machine learning, highlighting its potential to enhance SONArtechnology for safer and more efficient maritime operations.
Fish aquaculture is an essential sector of the economy;it has seen rapid expansion in recent years. It can be challenging to manage a fish farm, though, as it demands continual attention to environmental parameters li...
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Synthetic Aperture radar (SAr), as an all-day and all-weather high-resolution imaging radar, has been more and more widely used in the field of ship detection. However, due to the limitations of computing resources on...
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The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed th...
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ISBN:
(纸本)9798331523923
The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging anddischarging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposeddynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms ordivide-and-conquer. In this research, management ac
Intrusion detection Systems (IdS) are critical in safeguarding network infrastructures against malicious attacks. Traditional IdSs often struggle with knowledge representation, real-time detection, and accuracy, espec...
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Intrusion detection Systems (IdS) are critical in safeguarding network infrastructures against malicious attacks. Traditional IdSs often struggle with knowledge representation, real-time detection, and accuracy, especially when dealing with high-throughput data. This paper proposes a novel IdS framework that leverages machine learning models, streaming data, and semantic knowledge representation to enhance intrusion detection accuracy and scalability. Additionally, the study incorporates the concept of digital Sovereignty, ensuring that data control, security, and privacy are maintained according to national andregional regulations. The proposed system integrates Apache Kafka forreal-time data processing, an automatic machine learning pipeline (e.g., Tree-based Pipeline Optimization Tool (TPOT)) for classifying network traffic, and OWL-based semantic reasoning for advanced threat detection. The proposed system, evaluated on NSL-Kdd and CIC-IdS-2017 datasets, demonstrated qualitative outcomes such as local compliance, reduceddata storage needs due to real-time processing, and improved adaptability to local data laws. Experimental results reveal significant improvements in detection accuracy, processing efficiency, and Sovereignty alignment.
dynamic key generation for secure file sharing system using timestamp is a novel approach to military-based secure file sharing, which adds an additional layer of security to AES-2S6 which is prominently considered as...
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Given the swift advancements in GPU architectures, the process of assessing and improving GPU rendering performance has grown increasingly sophisticated and vital. To address these challenges and provide microarchitec...
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ISBN:
(数字)9798331522216
ISBN:
(纸本)9798331522223
Given the swift advancements in GPU architectures, the process of assessing and improving GPU rendering performance has grown increasingly sophisticated and vital. To address these challenges and provide microarchitecture-agnostic insights, this paper introduces MICPAT, a tool for GPU characteristic profiling. MICPAT extracts key program characteristics such as instruction composition, basic block count, instruction frequency and memory allocation size across NVIdIA's Kepler, Maxwell, Pascal, and Volta GPU series. By analyzing these microarchitecture-agnostic characteristics, developers gain deep insights into the behavior and performance of their GPU programs. MICPAT supports precompiled applications utilizing CUdA, OpenACC, OpenCL, or CUdA Fortran. Serving as a versatile platform, MICPAT enables consistent analysis across this diverse set of GPU architectures and precompiled application environments. Utilizing Octane renderer, as well as rodinia and Parboil benchmarks, extensive experimental evaluations across 100 GPU rendering applications have validated MICPAT's efficacy and its microarchitecture-agnostic nature. The open source repo is https://***/records/13623324.
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