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Advanced Stock Market Prediction Using Unsupervised Federated Learning techniques

Advanced Stock Market Prediction Using Unsupervised Federate...

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international CSI Computer conference, CSICC

作者： Mohsen Tajgardan Atena Shiranzaei Mahtab Jamali Reza Khoshkangini Mahdi Rabbani Faculty of Electrical and Computer Engineering Qom University of Technology Qom Iran Department of Computer Engineering Faculty of Industry and Mining (Khash) University of Sistan and Baluchestan Sistan and Baluchestan Iran Department of Computer Science and Media Technology Sustainable Digitalisation Research Centre Malmö University Malmö Sweden Canadian Institute for Cybersecurity University of New Brunswick NB Canada

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

In the realm of stock market prediction, traditional supervised learning approaches often struggle with the vast and diverse nature of financial data, coupled with privacy concerns. This paper explores a novel methodology that combines unsupervised learning techniques with federated learning system to enhance stock market prediction models. We present a comprehensive system where local models, trained using unsupervised methods, contribute to a global model through federated aggregation. By leveraging federated learning, our approach allows multiple financial institutions to collaboratively train models on their decentralized data while preserving data privacy. This approach addresses the challenges of data heterogeneity and communication efficiency, providing a robust and scalable solution for advanced stock market forecasting. Our experiments demonstrate that integrating unsupervised learning with federated learning not only improves predictive accuracy but also enhances the model's ability to identify emerging market trends and anomalies. Finally, we compare our distributed data model with other machine learning models that use local data.

关键词： Data privacy Federated learning Supervised learning Predictive models parallel processing Data models Security Stock markets Forecasting Unsupervised learning

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Optimizing Sentiment Analysis on Twitter for Improved Customer Insights: Integrating Bagged CNN and Flamingo Search

Optimizing Sentiment Analysis on Twitter for Improved Custom...

引用

distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), international conference on

作者： C. Umarani Jyoti Metan Piyush Kumar Pareek Mahantesh Mathapati Department of Computer Science and Applications Christ Academy Institute for Advanced Studies Bengaluru India Department of ISE Atria Institute of Technology Bengaluru India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE Amruta Institute of Engineering & Management Sciences Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Twitter helps you understand public opinion by revealing what people think, feel, and believe. This study provides a framework for businesses, particularly food delivery companies, to analyse social media data competitively and gain actionable insights. A novel combination of Flamingo Search Optimisation (FSA) and a one-dimensional bagged CNN is applied to Twitter data to improve customer satisfaction research. Our method uses advanced machine learning to analyse and interpret consumer attitudes and trends using Twitter's massive, real-time customer feedback stream. We found that sentiment analysis can now better predict consumer preferences and satisfaction. The study suggests developing more advanced optimisation algorithms, cross-platform sentiment analysis, real-time processing, and natural language processing techniques. Average kappa score: 0.9941, sensitivity: 0.9969, specificity: 0.9972, accuracy: 0.9971, precision: 0.9973. This study prepares companies to improve customer engagement and respond quickly to digital consumer sentiments.

关键词： Sentiment analysis Accuracy Sensitivity Social networking (online) Blogs Customer satisfaction Companies Real-time systems Convolutional neural networks Optimization

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A Novel Big Data processing Approach to Feature Extraction for Electrical Discharge Machining based on Container Technology

A Novel Big Data Processing Approach to Feature Extraction f...

引用

22nd IEEE/ACIS international conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD-Fall)

作者： Alimadji, Denata Rizky Hung, Min-Hsiung Lin, Yu-Chuan Suryajaya, Benny Chen, Chao-Chun Chinese Culture Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Cheng Kung Univ Inst Mfg Informat & Syst Tainan Taiwan

ISBN: (纸本)9781665404037

EDM (Electrical Discharge Machining) is a process to remove metal from conductive materials using electrical sparks. To monitor the EDM process using virtual metrology (VM), we need to obtain the electrode's voltage and current signals of a machine tool. Due to the nature of EDM, the sensors installed on the machine tool acquire the signals at a high sampling rate and generate a vast amount of data in a short time, thereby raising the big-data processing issue. Our previous work proposed an efficient approach called BEDPS to process the EDM big data in a Hadoop distributed cluster. This paper presents a novel big data processing approach to feature extraction for EDM by using container technology (i.e., Docker and Kubernetes). We reimplement some Spark algorithms of BEDPS in Python (originally in Scala) and then run the refined BEDPS in containers in a Kubernetes cluster. Testing results show that the refined BEDPS developed in this study can reduce the execution time by almost half, compared to the original Scala version (9.6577 minutes vs. 19.2735 minutes). The adoption of Python in Spark is also shown to have similar performance with Scala, although there are some cases where Python performance falls short, for example, parallel processing using Python parallel processing library. The results also show that the Kubernetes cluster is promising to be an alternative way, other than the Hadoop, for processing big data. At the same time, it can bring some advantages to the big data processing applications, such as easy deployment, robustly running, load balance, self-healing, failover, and horizontal auto-scaling for containerized applications.

关键词： Electrical Discharge Machining (EDM) Virtual Metrology Feature Extraction Big Data Hadoop Docker Container Kubernetes Python

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Enhancing MPI Communication Efficiency for Grid-Based Stencil Computations

Enhancing MPI Communication Efficiency for Grid-Based Stenci...

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Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC), international conference on

作者： Santosh Iranagouda Goudar Premkumar SJ Nayaka K K Girish Biswajit Bhowmik Department of Computer Science and Engineering Maharshi Sushrut CAS Lab BRICS Lab National Institute of Technology Karnataka Surathkal Mangalore Bharat

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517908

In parallel computing, where efficiency and speed are crucial, the Message Passing Interface (MPI) is a fundamental paradigm for managing large-scale distributed memory systems. MPI is critical to complex computational tasks, particularly in grid-based computations that solve intricate numerical problems by discretizing spatial domains into structured grids. However, MPI Cartesian communicators exhibit limitations in handling these computations effectively, especially when managing large-scale data exchanges and complex stencil patterns. This paper addresses these challenges by presenting an integrated approach that combines MPI collective and Cartesian communication methods. The proposed solution simplifies data distribution, eliminates redundant interfaces, and enhances communication efficiency. Experimental results show a 43% reduction in execution time and a 40% decrease in communication overhead, with scalability improvements achieving 12.5x speedup using 64 processes. These quantitative outcomes demonstrate the advan-tages of the proposed method over conventional MPI Cartesian approaches, establishing it as a reliable framework for advancing High-Performance Computing (HPC) capabilities in grid-based applications.

关键词： Mechatronics Scalability Message passing Memory management Reinforcement learning parallel processing Real-time systems Computational efficiency Reliability Intelligent systems

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Comparative Analysis of Sequential and parallel Computing for Object Detection Using Deep Learning Model

Comparative Analysis of Sequential and Parallel Computing fo...

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Information Technology (ACIT)

作者： Fathi Amsaad Shorouq Al-Eidi Omar Darwish Computer Science and Engineering Wright State University Ohio USA Computer Science Department Tafila Technical University Tafila Jordan Information Security and Applied Computing Eastern Michigan University Michigan USA

Recently, parallel and distributed computing has become indispensable in various application domains, such as cloud computing and extensive data analysis, to meet the growing demands for efficiency and reliability. In this study, we compared sequential and parallel computing techniques applied to a deep learning model for object detection. By leveraging multiple evaluation metrics, such as F 1 score, execution time, and accuracy, we meticulously evaluated the model's performance. Our analysis revealed that parallel execution considerably decreased the training time of model, resulting in an average time savings of 40 % while maintaining the training process's accuracy and quality. These finding have significant implications for real world applications, as they demonstrate tremendously the potential for expedited deep learning model training without compromising accuracy. This can tremendously benefit medical imaging and realtime video analysis, where accuracy and efficiency are paramount.

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Scalable SAT Solving in the Cloud 24th

Scalable SAT Solving in the Cloud

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24th international conference on Theory and applications of Satisfiability Testing (SAT)

作者： Schreiber, Dominik Sanders, Peter Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9783030802233;9783030802226

Previous efforts on making Satisfiability (SAT) solving fit for high performance computing (HPC) have led to super-linear speedups on particular formulae, but for most inputs cannot make efficient use of a large number of processors. Moreover, long latencies (minutes to days) of job scheduling make large-scale SAT solving on demand impractical for most applications. We address both issues with Mallob, a framework for job scheduling in the context of SAT solving which exploits malleability, i.e., the ability to add or remove processing power from a job during its computation. Mallob includes a massively parallel, distributed, and malleable SAT solving engine based on HordeSat with a more succinct and communication-efficient approach to clause sharing and numerous further improvements over its precursor. Experiments with up to 2560 cores show that Mallob outperforms an improved version of HordeSat and scales significantly better. Moreover, Mallob can solve many formulae in parallel while dynamically adapting the assigned resources, and jobs arriving in the system are usually initiated within a fraction of a second.

关键词： parallel SAT solving distributed computing Malleable load balancing

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IIIT-Speech Twins 1.0: An English-Hindi parallel Speech Corpora for Speech-to-Speech Machine Translation and Automatic Dubbing

IIIT-Speech Twins 1.0: An English-Hindi Parallel Speech Corp...

引用

Oriental COCOSDA international conference on Speech Database and Assessments

作者： Anindita Mondal Anil Kumar Vuppala Chiranjeevi Yarra Language Technologies Research Center IIIT Hyderabad India

ISBN: (数字)9798331506032

ISBN: (纸本)9798331506049

The demand for high-quality parallel speech data has been increasing as deep-learning based Speech to Speech Machine Translation (SSMT) and automatic dubbing approaches gain popularity in speech applications. Traditional and well-established speech applications such as Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) heavily rely on large corpus of monolingual speech and the corresponding text. While there is a wealth of parallel text data available for both English and Indic languages, parallel speech data is available only for English and other European languages, yet it often lacks natural prosody and semantic alignment between the languages. For achieving cross-lingual prosody transfer, end-to-end SSMT models, and high-quality dubbing from English to Hindi, in this work, an English-Hindi parallel bilingual speech-text corpus named lIlT-Speech Twins 1.0 is created. This data contains twin-like English and Hindi speech-text pairs obtained from publicly available children's stories in both the languages, through manual and automatic processing. Starting with 8 stories in each language, totaling around 4 hours of audio, the final outcome was a 2-hour dataset. This was achieved through systematic segmentation, re-moval of non-speech background audio, and sentence-by-sentence alignment to ensure accurate meaning in both languages. In addition to ensuring proper alignment and transcription, this dataset offers a rich source of natural prosody, expressions, and emotions, due to the narrative diversity within the stories. The dataset also provides sig-nificant speaker variability, with different characters being voiced by various speakers, enhancing the richness of the lIlT-Speech Twins 1.0 corpus.

关键词： Systematics Accuracy Databases Semantics Europe Manuals Text to speech Machine translation Automatic speech recognition

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Power Quality Analytical Methodology Using Signal processing and Machine Learning techniques for Grid Fault Classification and Location Detection

Power Quality Analytical Methodology Using Signal Processing...

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IEEE international conference on Smart Energy Grid Engineering (SEGE)

作者： Abdullah A. Jabbar Mohd. Zamri Che Wanik Antonio P. Sanfilippo HBKU-QEERI

ISBN: (数字)9798350377378

ISBN: (纸本)9798350377385

The increase in global energy demand with a growing dynamic power system has made today's electrical network a complex infrastructure where increased faults and grid violations are being reported at higher rates. Also, with an increased penetration of intermittent distributed Energy Resource (DER), the detection of transient events occurring in a large network is becoming challenging and therefore encouraging potential areas of research in this field. Furthermore, the deployment of high-resolution devices such as PMU gives access to vast amounts of data and carries vital information to ensure grid safety and protection. This paper analyzes a methodology to detect and predict power system disturbances and fault location in electrical power network (distribution & transmission) using data-driven & machine learning techniques. It uses signal processing techniques based on multi-level discrete wavelet transforms, with wavelet spectral energy/entropy-based feature extraction stage to train ANN (Artificial Neural Network) classifiers to predict and classify faults in power system applications. Machine learning classification techniques are used based on a dataset which is generated with single measurement point in simulation environment where grid model is used with varying fault conditions such as alternating phase & neutral impedance, inception angle & distance. A comparative analysis is performed with ANN and other potential classifiers to identify potential limitation & improvements in feature extraction stage to better classify faults and location. Furthermore, PQ events such as voltage sag, voltage swell, and transient event have been analyzed and the propagating effect of faults in the network, medium voltage to low voltage network are also discussed.

关键词： Electric potential Accuracy Artificial neural networks Machine learning Signal processing Transmission line measurements Feature extraction Wavelet analysis Discrete wavelet transforms Transient analysis

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GraphScope: A Unified Engine For Big Graph processing 47th

GraphScope: A Unified Engine For Big Graph Processing

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47th international conference on Very Large Data Bases (VLDB)

作者： Fan, Wenfei He, Tao Lai, Longbin Li, Xue Li, Yong Li, Zhao Qian, Zhengping Tian, Chao Wang, Lei Xu, Jingbo Yao, Youyang Yin, Qiang Yu, Wenyuan Zhou, Jingren Zhu, Diwen Zhu, Rong Alibaba Grp Hangzhou Peoples R China Univ Edinburgh Edinburgh Midlothian Scotland Shenzhen Inst Comp Sci Shenzhen Peoples R China

GraphScope is a system and a set of language extensions that enable a new programming interface for large-scale distributed graph computing. It generalizes previous graph processing frameworks (e.g., Pregel, GraphX) and distributed graph databases (e.g., Janus-Graph, Neptune) in two important ways: by exposing a unified programming interface to a wide variety of graph computations such as graph traversal, pattern matching, iterative algorithms and graph neural networks within a high-level programming language;and by supporting the seamless integration of a highly optimized graph engine in a general purpose data-parallel computing system. A GraphScope program is a sequential program composed of declarative data-parallel operators, and can be written using standard Python development tools. The system automatically handles the parallelization and distributed execution of programs on a cluster of machines. It outperforms current state-of-the-art systems by enabling a separate optimization (or family of optimizations) for each graph operation in one carefully designed coherent framework. We describe the design and implementation of GraphScope and evaluate system performance using several real-world applications.

关键词： Engines

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Secure Coded Computation for Efficient distributed Learning in Mobile IoT 18

Secure Coded Computation for Efficient Distributed Learning ...

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18th Annual IEEE international conference on Sensing, Communication, and Networking (SECON)

作者： Yang, Yilin D'Oliveira, Rafael G. L. El Rouayheb, Salim Yang, Xin Seferoglu, Hulya Chen, Yingying Rutgers State Univ New Brunswick NJ 08854 USA MIT Cambridge MA 02139 USA Univ Illinois Chicago IL USA

ISBN: (纸本)9781665441087

distributed computation plays an essential role in cloud and edge computing. Data such as images, audio, and text can be represented as matrices to facilitate efficient computation, especially in the domains of distributed machine learning, computer vision, and signal processing. Many coded computation algorithms have been proposed for big data applications to securely partition and distribute matrices to parallel worker devices. However, these proposals have yet to he adapted for mobile platforms beyond theoretical means. Mobile IoT networks can greatly benefit from secure distributed computing, however, commercial devices such as smartphones and tablets are much more limited in resources compared to platforms in data centers, requiring special design considerations. We investigate existing distribution schemes from an operational complexity and security viewpoint and study their perlbrmance in several mobile IoT networks, identifying performance bottlenecks in regards to communication and computation costs. From our findings, we propose new, scalable algorithms optimized to handle the unique constraints of mobile loT. Extensive evaluations of our proposals on publicly available image classification datasets show how distributed learning can be specially optimized to enhance runtime and battery performance on mobile loT by over 10x.

关键词： distributed computing coded computations edge device mobile IoT

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