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Resource Allocation in Cloud Computing Using Genetic Algorithm and neural network

Resource Allocation in Cloud Computing Using Genetic Algorit...

引用

IEEE International conference on Smart Cloud (SmartCloud)

作者： Mahdi Manavi Yunpeng Zhang Guoning Chen Department of Computer Science University of Houston Houston USA Department of Information Science Technology University of Houston Houston USA

Cloud computing is one of the most used distributed systems for data processing and data storage. Due to the continuous increase in the size of the data processed by cloud computing, scheduling multiple tasks to maintain efficiency while reducing idle becomes more and more challenging. Efficient cloud-based scheduling is also highly sought by modern transportation systems to improve their security. In this paper, we propose a hybrid algorithm that leverages genetic algorithms and neural networks to improve scheduling. Our method classifies tasks with the neural network Task Classification (N2TC) and sends the selected tasks to the Genetic Algorithm Task Assignment (GATA) to allocate resources. It is fairness aware to prevent starvation and considers the execution time, response time, cost, and system efficiency. Evaluations show that our approach outperforms the state-of-the-art method by 3.2 % at execution time, 13.3 % in costs, and 12.1 % at response time.

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Learning Knowledge Graph Embedding in Semantic Space: A Novel Bi-linear Semantic Matching Method

Learning Knowledge Graph Embedding in Semantic Space: A Nove...

引用

International Joint conference on neural networks (IJCNN)

作者： Chen, Kai Wang, Ye Li, Aiping Li, ChenChen Zhao, Xiaojuan Ding, Ruidong Natl Univ Def Technol Coll Comp Changsha Peoples R China

ISBN: (纸本)9780738133669

Knowledge Graphs represent facts with triples containing head entity h, tail entity t and relation r which facilitate applications of many scenarios, for example intelligent web search, community detection and question answering. Knowledge Graph Embedding (KGE) represents elements of triples in a low-dimensional continuous vector space. Though it has been widely studied by both academic and industry communities, most researches focus on learning embeddings of entities and relations separately rather than considering the interactive semantic information between them. However, neither relations nor entities exist lonely out of context. In this paper, we define an interactive semantic space to model the context of triples and propose a novel Bi-linear Semantic Matching Method using Convolutional networks (BiSC). Specifically, we use 1D convolutional neural networks to extract features of the interactive semantics and then compute the similarity scores in the bi-linear space. Compared to existing complex graph network methods, BiSC needs lower computational cost to reach competitive results on link prediction task. The consistent state-of-the-art performance through extensive experiments over two benchmarks demonstrates the advantages of the proposed BiSC model. Further analysis on convergence study and case study of interactive semantic space show the efficiency of our model.

关键词： Adaptation models Computational modeling Semantics neural networks Benchmark testing Feature extraction Knowledge discovery

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Design and Application System of Machine Translation Language Model Based on Cloud LM Algorithm 6

Design and Application System of Machine Translation Languag...

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6th International conference on Futuristic Trends in networks and Computing Technologies, FTNCT 2024

作者： Jia, Tianyang School of International Studies Shenyang Jianzhu University Liaoning Shenyang110001 China

Language modeling is the core of natural language processing, especially in areas such as speech recognition and machine translation. This article is based on Cloud LM (Cloud Language Model), using Apache Solr as the platform, and cloud computing as the foundation, with the goal of improving the efficiency and scalability of machine translation. This article designed an open-source cloud environment management platform that enables users to quickly and effectively retrieve distributed language models, while also achieving real-time updates and maintenance of the models. Through comparative experiments, research and analysis were conducted on the translation efficiency, network latency, and resource utilization based on Cloud LM. Even when processing translation tasks of up to 1000 sentences, the CPU (Central processing Unit) usage and memory consumption of the model remained within a reasonable range. The research findings of this article contribute to improving the efficiency of machine translation work and applying it for scalability and practicality in cloud computing environments, providing strong technical support for future fields such as machine translation. © 2025 The Author(s). Published by Elsevier B.V.

关键词： neural machine translation

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Intelligent Image Captioning with InceptionV3, LSTM, and PySpark Integration

Intelligent Image Captioning with InceptionV3, LSTM, and PyS...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International conference

作者： Thanu Kurian Jimsha K Mathew Syam Dev R S Anand P K Vignesh A Rakshith B S Dept of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Image captioning is a challenging task in artificial intelligence that involves generating descriptive captions for images automatically. In this project, we propose a novel approach leveraging advanced technologies such as PySpark, LSTM, and InceptionV3 to develop an effective image captioning system. We harness the power of PySpark, a distributed computing framework, to efficiently process large-scale image datasets and extract high-level features from images using the InceptionV3 convolutional neural network (CNN) model. These features capture the semantic information present in the images and serve as input to the caption generation model. The caption generation model utilizes LSTM neural network as the decoder component. LSTM is well-suited for sequential data processing and is capable of generating coherent and contextually relevant captions based on the extracted image features. The InceptionV3 model acts as the encoder, extracting meaningful visual features from input images, while the LSTM decoder generates captions by decoding these features into natural language descriptions. This multimodal approach enables the model to understand and describe the content of images accurately. Through experimentation and evaluation on diverse image datasets, our proposed system demonstrates promising results in generating accurate and human-like captions for a wide range of images. By integrating PySpark, LSTM, and InceptionV3, our image captioning system achieves state-of-the-art performance, highlighting the effectiveness of leveraging advanced technologies in solving complex AI tasks.

关键词： Visualization Electric potential Accuracy Computational modeling Feature extraction Decoding Convolutional neural networks Data mining Artificial intelligence Long short term memory

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Neuromorphic Photonic networks

Neuromorphic Photonic Networks

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Optical Fiber Communications conference and Exhibition (OFC)

作者： Shastri, Bhavin J. Bilodeau, Simon Marquez, Bicky A. Tait, Alexander N. de Lima, Thomas Ferreira Huang, Chaoran Chrostowski, Lukas Shekhar, Sudip Prucnal, Paul R. Queens Univ Dept Phys Engn Phys & Astron Kingston ON K7L 3N6 Canada Princeton Univ Dept Elect Engn Princeton NJ 08544 USA Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

Neuromorphic photonics exploit optical device physics for neuron models, and optical interconnects for distributed, parallel, and analog processing for high-bandwidth, low-latency and low switching energy applications... 详细信息

ISBN: (纸本)9781943580866

关键词： (200.3050) Information processing (200.4700) Optical neural systems (250.5300) Photonic integrated circuits

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Demonstrating NADA: A Workflow for distributed CNN Training on FPGA Clusters

Demonstrating NADA: A Workflow for Distributed CNN Training ...

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International conference on Field Programmable Logic and Applications

作者： Justin Knapheide Philipp Kreowsky Benno Stabernack Fraunhofer Institute for Telecommunications Heinrich Hertz Institute Berlin Germany University of Potsdam Embedded Systems Architectures for Signal Processing Potsdam Germany

We introduce our network Attached Deep learning Accelerator called NADA, which consists of a novel and flexible HW/SW framework for efficient training of deep neural networks on FPGA clusters. NADA is centered around layer parallelism, instantiating a specific implementation for each layer. These implementations are placed across the desired number of network attached FPGAs in the cluster. The NADA hardware architecture relies on a high-speed UDP/IP pure hardware network stack. We demonstrate the usability of our approach by training a couple of demo networks on an Arria 10 FPGA cluster.

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Drowsiness Detection for Drivers using Hybrid T-distributed Stochastic Neighbor Embedding with Convolutional neural network

Drowsiness Detection for Drivers using Hybrid T-Distributed ...

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International conference on Futuristic Technologies (INCOFT)

作者： D. Balakrishnan Umasree Mariappan Balabhadra Nikhitha Bokkisam Akshitha Bedamsetty Visali Bandikattu Snehalatha Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Department of Computer Science and Engineering Sri Vidya College of Engineering and Technology Virudhunagar Tamil Nadu India

Drowsiness Detection (DD) is the procedure of identifying signs of drowsiness in individuals, especially in critical situations like driving, heavy machinery operation, or aircraft piloting. Hybrid Bi-directional Long Short Term Memory with Convolutional neural network (HBLSTM-CNN) was proposed for driver's DD. At first, an eye dataset was gathered and subsequently subjected to pre-processing using a shape predictor. The pre-processed images are initially analyzed using a BiLSTM to identify the most prominent characteristics, which are subsequently subjected to additional processing in a CNN to serve the purpose of DD. Nonetheless, as the data's dimensionality grows, BiLSTM demands more computational resources, including memory and processing power. So in this paper, Hybrid t-distributed Stochastic Neighbor Embedding with CNN (Ht-SNE-CNN) is introduced for driver's DD. In Ht-SNE-CNN, t-SNE is utilized for reducing dimensionality because of its inherent simplicity, obviating the requirement to train a sophisticated model. It directly computes a lower-dimensional depiction of the data while emphasizing the preservation of local similarities. This inherent simplicity frequently leads to faster and more direct utilization, particularly when handling high-dimensional data. The reduced data are given as input to CNN for driver's DD. The experimental findings demonstrate that the suggested HtSNE-CNN outperforms HBLSTM-CNN with regard to exactness, precision, and recall for detecting driver drowsiness.

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Medical CT Image Cutting and Diagnostic Discrimination Model based on Decision Tree and Deep neural network

Medical CT Image Cutting and Diagnostic Discrimination Model...

引用

distributed Systems, Computer networks and Cybersecurity (ICDSCNC), International conference on

作者： Jiaxiang Li Xinru Fu School of Mathematics and Physics China University of Geosciences Wuhan China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Cancer, with a high mortality rate, remains one of the top priorities in medical research. Rectal cancer is a common and significant threat in the research studies of medical image processing. It is still a challenge to determine the accuracy of lymph node metastasis preoperatively using universally accepted methods with preoperative CT images. The report argues that technology that applies deep learning to precisely identify lymph node metastasis in patients suffering from rectal cancer when CT scan images are analyzed should be used. We present advanced strategies with deep learning algorithms for understanding and classifying CT images, which demonstrate a more improved probability of image classification over traditional methods. This paper will improve image classification accuracy over traditional methods and open a more promising way for preoperative-based assessment in the treatment of rectal cancer.

关键词： Deep learning Image segmentation Computed tomography Feature extraction Data models Metastasis Decision trees Medical diagnostic imaging Lymph nodes Image classification

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An Uncertainty Aware Semi-Supervised Federated Learning Framework for Radar-based Hand Gesture Recognition

An Uncertainty Aware Semi-Supervised Federated Learning Fram...

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European Radar conference (EURAD)

作者： Tobias Sukianto Matthias Wagner Sarah Seifi Cecilia Carbonelli Mario Huemer Infineon Technologies AG Germany Johannes Kepler University Linz Austria Technical University of Munich Germany

ISBN: (数字)9782874870798

ISBN: (纸本)9798350385137

neural network-based (NN) radar gesture recognition sensors are operated in different domains. The NNs can be trained in a centralized fashion, by using datasets from different individuals and environments. Centralized training faces challenges, such as the risk of data privacy leakage. Federated learning (FL) is a distributed optimization field where data collection, processing, and training of the NN are carried out across multiple clients. A common assumption in FL is that all clients can access ground truth labels. In realistic scenarios, the clients possess partially labeled data, or only a fraction of clients has labeled data. The challenge is known as semi-supervised federated learning (SSFL). For SSFL, one issue is the dependence on the quality of the unlabeled *** this work, we present a radar-based SSFL framework based on probabilistic pseudo-labeling. It is shown that our framework counteracts poor quality data in the unlabeled dataset during training in gesture sensing.

关键词： Training Uncertainty Federated learning Artificial neural networks Gesture recognition Radar Probabilistic logic Robustness Sensors Optimization

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A 43pJ per Inference CBNN-based Compute-in-sensor Associative Memory in 28nm FDSOI 51

A 43pJ per Inference CBNN-based Compute-in-sensor Associativ...

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47th IEEE European Solid State Circuits conference (ESSCIRC)

作者： Larras, Benoit Frappe, Antoine Univ Lille Univ Polytech Hauts France UMR 8520 IEMN CNRS Cent Lille F-59000 Lille France

ISBN: (纸本)9781665437516

distributed smart sensors are more and more used in applications such as biomedical or domestic monitoring. However, each sensor broadcasts data wirelessly to the others or to an aggregator, which leads to energy-hungry sensor nodes and an increased latency at the network level. To tackle both challenges, this work proposes to distribute part of the processing elements in each sensor node and presents a 28nm FDSOI ASIC implementation of an associative memory using clique-based neural networks (CBNNs) coupled with an integrated SRAM memory. It consumes 43pJ for a single inference, which is 6.5 times better than state-of-the-art associative memories implementations, for the same memory size.

关键词： Wireless communication Associative memory neural networks Memory management Silicon-on-insulator Random access memory Body area networks

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