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stock Market Price Prediction Using GRU and XGB 21

Stock Market Price Prediction Using GRU and XGB

引用

21st OITS international conference on Information Technology, OCIT 2023

作者： Shrestha, Aashmit Kumari, Rina Jena, Ajay Kumar Parida, Ajaya Kumar Parida, Nirupama Parida, Raj Kumar Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India National Institute of Technology Computer Science and Engineering Meghalaya Shillong India Kalinga Institute of Industrial Technology School of Computer Engineering India

ISBN: (纸本)9798350358230

Predicting the future course of the stock market is challenging because of its volatility and multifaceted dependencies. This study introduces a novel approach that harnesses the power of Deep learning Recurrent Neural Network (RNNs), including established models like LstM, Bidirectional LstM, GRU, and XGBOOst, with a unique focus on improving prediction accuracy. By integrating historical data and technical indicators, such as Moving Averages, our framework refines prediction strategies. Our key innovation lies in the strategic combination of GRU, specialized for capturing long-Term trends, and XGBoost, which generates final forecasts with remarkable precision. This novel approach enhances the overall strategy and substantially increases forecast accuracy, making a significant contribution to the field of stock market prediction. © 2023 IEEE.

关键词： Forecasting

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Detection of Deepfakes: Protecting Images and Vedios Against Deepfake 21

Detection of Deepfakes: Protecting Images and Vedios Against...

引用

21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Tian, Ying Zhou, Wang Haq, Amin Ul Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province LuZhou China School of Computer and Software Engineering Xihua University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331519254

In recent years, with the rapid development of deep learning and computer vision technology, the forgery technology of images and videos has become increasingly mature, posing new challenges to information security and social stability. Behind the re-evolution of deepfake lies the rampant proliferation of fake content, which is used for election tampering, identity fraud, fraud, spreading fake news, and so on. To address these challenges, researchers are constantly exploring and developing image-based deepfake detection techniques, which aim to effectively identify and prevent deepfake content in images and videos. This article will introduce the current development status of deepfake detection technology, present its principles and methods in detail, and look ahead to its future development directions. © 2024 IEEE.

关键词： Convolutional neural networks

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Classification of Multilingual Medical Documents using Deep learning 21

Classification of Multilingual Medical Documents using Deep ...

引用

21st IEEE/ACIS international conference on Software engineering Research, Management and Applications, SERA 2023

作者： Wahiba, Ben Abdessalem Karaa Kawther, Dridi The High Institute of Management of Tunis RIADI Tunis Tunisia

ISBN: (纸本)9798350345889

Due to a large number of documents available on the web, operations such as finding a set of information contained in a document has become a difficult task, especially with multilingual documents. Hence the necessity to have performance tools for finding, organizing and classifying information. A variety of classification methods are proposed to resolve this kind of problem but these techniques suffer from limits such as the loss of information, and the loss of relations between words that affects the effectiveness and the performance of the classification process. So, this paper attempts to support the idea of multilingual document classification, especially in the biomedical domain using a new approach, based on deep learning. The key idea is to generate a new conceptual representation of textual multilingual medical documents to facilitate the classification task. In this context, a deep learning technique will be exploited for a good representation. To show the feasibility of our approach, we implemented a system related to a domain that attracts more and more attention from the data mining community: The biomedical domain. An experimental study is performed, using documents extracted from the biomedical benchmark corpus, called Oshumed, which contains documents distributed by different categories. © 2023 IEEE.

关键词： Classification (of information)

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Evaluating the Impact of In-band ECC on GPU Performance 21

Evaluating the Impact of In-band ECC on GPU Performance

引用

21st international System-on-Chip Design conference, ISOCC 2024

作者： Park, Soyoung Kim, Jungrae Sungkyunkwan University Dept. of Artificial Intelligence Suwon Korea Republic of Sungkyunkwan University Dept. of Semiconductor Systems Engineering Suwon Korea Republic of

ISBN: (纸本)9798350377088

In the rapidly evolving landscape of GPU applications, ensuring reliability against memory errors is paramount. Memory errors are a significant source of hardware failures, necessitating robust GPU memory protection mechanisms like Error Correcting Codes (ECC). Current ECC implementations in GPUs, particularly in-band ECC used with Graphics DDR (GDDR) memories, aim to safeguard against these errors. However, in-band ECC significantly hampers performance due to the necessity of separate data and redundancy transfers, leading to increased latency and reduced throughputs. This paper examines performance impact of in-band ECC on GPUs. © 2024 IEEE.

关键词： Program processors

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Research on virtual human swarm football collaboration technology based on reinforcement learning 21

Research on virtual human swarm football collaboration techn...

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21st IEEE international conference on Ubiquitous Computing and Communications / 5th IEEE international conference on data Science and Computational Intelligence / 21st IEEE international conference on Computer and Information Technology

作者： Mo, Xiaoshan Hu, Bin Xu, Caixu Yang, Yuling Wuzhou Univ Guangxi Key Lab Machine Vision & Intelligent Cont Wuzhou 543002 Peoples R China Macao Univ Sci & Technol Fac Arts & Human Macau Peoples R China

ISBN: (纸本)9781665477260

Reinforcement learning is used in many applications in artificial intelligence fields such as computer vision and environmental contextualized decision-making scenarios;virtual human swarms provide research directions for multi-intelligent body collaboration and environmental field detection in multi-intelligent body swarms. Football is a group sport, which is characterized by its holistic nature, confrontation, versatility, and ease of implementation, as well as the characteristics of both individual intelligence and group intelligence, and is a typical application scenario for multi-intelligence collaboration. This paper takes football as the research object to study the team collaboration problem and team gaming problem of the virtual human swarm in a specific environment and solves the cooperation and competition relationship between intelligence with reinforcement learning of multi-intelligence training.

关键词： Virtual human clusters Reinforcement learning Multi-intelligence training Decentralization.

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21st international conference on Service-Oriented Computing, ICSOC 2023

21st International Conference on Service-Oriented Computing,...

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21st international conference on Service-Oriented Computing, ICSOC 2023

ISBN: (纸本)9783031484230

The proceedings contain 48 papers. The special focus in this conference is on Service-Oriented Computing. The topics include: IDLGen: automated Code Generation for Inter-parameter Dependencies in Web APIs;time-Aware Log Anomaly Detection Based on Growing Self-organizing Map;an Empirical Evaluation of the Energy and Performance Overhead of Monitoring Tools on Docker-Based Systems;chainsFormer: A Chain Latency-Aware Resource Provisioning Approach for Microservices Cluster;energy-Efficient and Communication-Aware Resource Allocation in Container-Based Cloud with Group Genetic Algorithm;engineering Self-adaptive Microservice Applications: An Experience Report;FUSE: Fault Diagnosis and Suppression with eBPF for Microservices;serviceSim: A Modelling and Simulation Toolkit of Microservice Systems in Cloud-Edge Environment;2DPChain: Orchestrating Transactions in Order-Execute Blockchain to Exploit Intra-batch and Inter-batch Parallelism;deep learning Model for Personalized Web Service Recommendations Using Attention Mechanism;a Dynamical Model for the Nonlinear Features of Value-Driven Service Ecosystem Evolution;a Middleware for Hybrid Blockchain Applications: Towards Fast, Affordable, and Accountable Integration;An AI Chatbot for Explaining Deep Reinforcement learning Decisions of Service-Oriented Systems;BEAR: Revolutionizing Service Domain Knowledge Graph Construction with LLM;dependency-Aware Resource Allocation for Serverless Functions at the Edge;distributing Quantum Computations, by Shots;energy-Efficient Task Offloading with statistic QoS Constraint Through Multi-level Sleep Mode in Ultra-Dense Network;enhancing Blockchain Performance via On-chain and Off-chain Collaboration;deep Reinforcement learning-Based Scheduling for Same Day Delivery with a Dynamic Number of Drones;designing Reconfigurable intelligent Systems with Markov Blankets;exploiting Category Information in Sequential Recommendation;Niagara: Scheduling DNN Inference Services on Heterogeneous Edge Proc

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Enhancing Climate Change Understanding: A Novel Deep learning Framework with the Climate Change Parameter Model(CCPM)

Enhancing Climate Change Understanding: A Novel Deep Learnin...

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2024 IEEE international conference on Modeling, Simulation and intelligent Computing, MoSICom 2024

作者： Ashraf, Tajamul Bashir, Janibul Indian Institute of Technology Delhi School of Information Technology New Delhi India National Institute of Technology Srinagar Department of Information Technology Srinagar India

ISBN: (纸本)9798331533311

One of the severe and pressing problems which humanity is facing in the 21st Century is climate change. It is adversely affecting the entire globe as is evident with the rising ocean levels, accelerated melting of glaciers, frequent storms, and many more. The need of the hour is to limit climate change by reducing greenhouse gas emissions. Machine learning algorithms have proven to be extremely effective in numerous scenarios and the experts are working to extend their uses to limit the effects of climate change. The domain is vast but in the current literature, the works in this domain are very limited. In this paper, we have tried to work in this particular direction by applying deep learning methodologies to analyze the effect of different climate factors on overall climate change. We proposed a novel climate change parameter model to predict the correlation between different climate parameters. We evaluated our scheme by validating the model on the data specific to the Jammu and Kashmir region and the model loss was found to be under 2%. In addition, based on our model we proposed some reactive strategies that can be implemented to keep the climate change effects under control. © 2024 IEEE.

关键词： Adversarial machine learning

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Digital Twins of Smart Campus: Performance Evaluation Using Machine learning Analysis 21

Digital Twins of Smart Campus: Performance Evaluation Using ...

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21st IEEE/ACIS international conference on Software engineering Research, Management and Applications, SERA 2023

作者： Hussaini, Adamu Qian, Cheng Guo, Yifan Lu, Chao Yu, Wei Towson University Dept. of Computer & Information Sciences United States

ISBN: (纸本)9798350345889

The Internet of Things (IoT) paradigm is gradually becoming more prevalent through numerous devices and technologies, including sensors, actuators, microcontrollers, cloud-enabled services, and analytics. IoT objects gain intelligence by integrating with wireless sensor networks (WSNs), mobile computing and communication, and others. With sensors, smart things can be enabled by monitoring and identifying environmental changes related to motion, temperature, humidity, pressure, light, vibration, etc. To timely keep track of state changes, researchers are considering developing a cyber replicator, denoted as Digital Twin (DT), of real physical systems as a way to visualize, model, and work with complex cyber-physical systems (CPS). In this paper, we first refine the dataset to a format that can be easily used for deep learning (DL) experiments, IoT data pipeline development, data modeling and simulation, data aggregation, etc. We then demonstrate that DT data can be used to determine space occupancy based on the ambient light sensor, which tends to indicate occupancy in particular spaces because the building has smart lighting that will switch off when rooms are unoccupied after a certain time. Given the apparent developments in machine learning technology, it is clear that machine learning-based prediction has the ability to enhance resource utilization and further forecast future events. Particularly, we use a DT-based dataset and Long-Short-Term Memory (LstM) neural network architecture to forecast the campus building's internal temperature. © 2023 IEEE.

关键词： Internet of things

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Proceedings - 2023 IEEE international conference on Parallel and Distributed Processing with Applications, Big data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking, ISPA/BDCloud/SocialCom/SustainCom 2023

Proceedings - 2023 IEEE International Conference on Parallel...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

ISBN: (纸本)9798350329223

The proceedings contain 153 papers. The topics discussed include: transaction data management optimization based on multi-partitioning in blockchain systems;semi-asynchronous federated learning optimized for NON-IID data communication based on tensor decomposition;HKTGNN: hierarchical knowledge transferable graph neural network-based supply chain risk assessment;DQR-TTS: semi-supervised text-to-speech synthesis with dynamic quantized representation;deep reinforcement learning-based network moving target defense in DPDK;iNUMAlloc: towards intelligent memory allocation for AI accelerators with NUMA;and predictive queue-based low latency congestion detection in data center networks.

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IoT-Based Smart Irrigation System for Optimal Water-Resource Utilization in Indian Agriculture 21

IoT-Based Smart Irrigation System for Optimal Water-Resource...

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21st OITS international conference on Information Technology, OCIT 2023

作者： Bhoi, Ashutosh Baliarsingh, Santos Kumar Dev, Prabhu Prasad Ghugar, Umashankar Biswal, Manas Ranjan GITAM Deemed to Be University Department of Computer Science and Engineering Visakhapatnam 530045 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India OP Jindal University Department of Computer Science and Engineering Raigarh 496109 India

ISBN: (纸本)9798350358230

Agriculture holds paramount significance for human civilization. In India, the agricultural sector is the primary source of income, with 70% of farmers and the general populace dependent on it. Conventional irrigation methods like overhead sprinklers and flooding show inefficiency, resulting in significant water wastage and promoting issues such as soil fungus due to excessive moisture. To address these challenges, IoT-based smart irrigation management systems offer a solution by optimizing water resource utilization. Our current study involves developing a smart irrigation system that automates field irrigation through the integration of various sensors like DHT11 (humidity and temperature) and soil moisture with the Raspberry Pi 3B model. We are using MongoDB (database) on VPC to store and process data on the cloud and NodeJS web server to deploy our model. Then machine learning algorithms are implemented to analyze these sensor data and predict the irrigation. We also integrate weather conditions using an open weather API for rain prediction. data from cloud databases is to be fetched using an Android application. © 2023 IEEE.

关键词： Machine learning

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