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Stock Market Prediction Using Machine Learning Based Techniques

Stock Market Prediction Using Machine Learning Based Techniq...

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Dubey, Aditya Sharma, Ramnaresh Bisen, Dhananjay Venu, Nookala Jaiswal, Varshali Raghuwanshi, Bhagat Singh Shakywar, Khemchand Madhav Institute of Technology & Science Centre for Internet of Things Gwalior India Madhav Institute of Technology & Science Centre for AI Gwalior India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India School of Engineering Avantika University MP Ujjain India

ISBN: (纸本)9798350379525

Many methods have been used to forecast stock market trends in the big data age, including real numbers, fuzzy time series data, fuzzy sets design, and conventional time series data. The control of semantic value data is made possible by the use of fuzzy time series data in stock market prediction, which makes accurate value predictions possible. These techniques are mostly used to forecast dynamic, non-linear datasets in a variety of industries, such as stock markets and cryptocurrencies. In order to predict stock prices, artificial intelligence (AI) methods such as neural networks (NN) have become increasingly important. Many methods have been developed to validate parametric and non-parametric theories for predicting trends and gains in the stock market using the feed-forward (FF) NN technique. A variety of soft computing techniques have been investigated in order to provide ways for decision-making based on profit and loss criteria without relying on artificial intelligence. This paper's main goal is to perform a comprehensive examination of the different prediction techniques used to estimate price and return in the stock market. The study includes a comparative review of several approaches as well as a variety of stock market forecasting strategies. © 2024 IEEE.

关键词： Marketplaces

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Towards Efficient Discrete Controller Synthesis: Semantics-Aware Stepwise Policy Design via LLM

Towards Efficient Discrete Controller Synthesis: Semantics-A...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Ishimizu, Yusei Li, Jialong Yamauchi, Takuto Chen, Sinan Cai, Jinyu Hirano, Takanori Tei, Kenji School of Computing Tokyo Institute of Technology Tokyo Japan Waseda University Department of Computer Science and Engineering Tokyo Japan Kobe University Center of Mathematical and Data Sciences Kobe Japan

ISBN: (纸本)9798331530839

Discrete Controller Synthesis (DCS) is an automated generation technique to generate controllers from environment models and requirements. The time and space complexities of DCS are critical because they directly govern the feasibility of using DCS for real-time generation of controllers to cope with environmental changes. Stepwise DCS is considered a promising approach to prevent the construction of ineffective state spaces in the subsequent analysis of the full controller by analyzing sub-controllers that meet part of the requirements at an early stage. However, constructing a stepwise policy is a challenge, as the current methods often require domain experts to manually build it by understanding and reasoning the semantics of each sub-environment model and sub-requirement. To reduce the effort of manual construction, this paper introduces a method that leverages large language models (LLMs) to automate the generation of stepwise policies by utilizing the LLM's ability to understand and reason about the semantics of state machines. The evaluation using GPT-4 on a drone control scenario demonstrates the effectiveness of our method, with an average reduction of 82.2% in states and 91.2% in transitions. © 2024 IEEE.

关键词： Control system synthesis

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Fast Parallel Recovery for Transactional Stream Processing on Multicores 40

Fast Parallel Recovery for Transactional Stream Processing o...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zhao, Jianjun Liu, Haikun Zhang, Shuhao Duan, Zhuohui Liao, Xiaofei Jin, Hai Zhang, Yu School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350317152

Transactional stream processing engines (TSPEs) have gained increasing attention due to their capability of processing real-time stream applications with transactional semantics. However, TSPEs remain susceptible to system failures and power outages. Existing TSPEs mainly focus on performance improvement, but still face a significant challenge to guarantee fault tolerance while offering high-performance services. We revisit commonly-used fault tolerance approaches in stream processing and database systems, and find that these approaches do not work well on TSPEs due to complex data dependencies. In this paper, we propose a novel TSPE called MorphStreamR to achieve fast failure recovery while guaranteeing low performance overhead at runtime. The key idea of MorphStreamR is to record intermediate results of resolved dependencies at runtime, and thus eliminate data dependencies to improve task parallelism during failure recovery. MorphStreamR further mitigates the runtime overhead by selectively tracking data dependencies and incorporating workload-aware log commitment. Experimental results show that MorphStreamR can significantly reduce the recovery time by up to 3.1 x while experiencing much less performance slowdown at runtime, compared with other applicable fault tolerance approaches. © 2024 IEEE.

关键词： Fault tolerance

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Enhancing Phishing Website Detection with Machine Learning Algorithms

Enhancing Phishing Website Detection with Machine Learning A...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Weijie Pang Ayrton Joseph DiPina Ashar Neyaz School of Computing & Data Science Wentworth Institute of Technology Boston MA USA

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

This paper examines the impact of feature selection on phishing website detection using machine learning techniques. We utilize the UCI Phishing Website dataset, which consists of 11,055 instances and 30 features with no missing values. Our results show that the random forest outperforms logistic regression, achieving an accuracy of 97.11%. By analyzing feature importance, we optimize the model by removing detrimental features, further improving accuracy to 97.74%. This study highlights the effectiveness of the random forest in phishing detection and underscores the importance of feature selection in enhancing model performance.

关键词： Logistic regression Analytical models Accuracy Machine learning algorithms Phishing Digital forensics Feature extraction Security Random forests

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Chung’s functional law of the iterated logarithm for the Brownian sheet

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Frontiers of Mathematics in China 2022年第6期17卷 1015-1024页

作者： Yonghong LIU Ting ZHANG Yiheng TANG Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation School of Mathematics and Computing ScienceGuilin University of Electronic TechnologyGuilin541004China

In this paper,we investigate functional limit problem for path of a Brownian sheet,Chung’s functional law of the iterated logarithm for a Brownian sheet is *** main tool in the proof is large deviation and small devi... 详细信息

关键词： Brownian sheet Chung’s functional law of the iterated logarithm

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Research on Information System Cloud computing Scheduling Method Based on Swarm Intelligence Algorithm 3

Research on Information System Cloud Computing Scheduling Me...

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3rd International Signal Processing, Communications and Engineering Management Conference, ISPCEM 2023

作者： Xiang, Wang Yancheng, Wang School of Computing and Data Science Xiamen University Malaysia Jalan Sunsuria Bandar Sunria Selangor Sepang43900 Malaysia

ISBN: (纸本)9798350307023

With the advancement of cloud computing technology, research on cloud computing task scheduling has become a hot topic. This paper mainly studies the cloud computing scheduling method of information systems based on swarm intelligence algorithm, including cloud computing scheduling model based on particle swarm optimization(PSO), improved Invasive Tumor Growth Optimization(ITGO) model structure, and multi-objective cloud computing task scheduling based on improved artificial bee colony algorithm. This paper concludes that by introducing multi-objective optimization terms, replacing multi-objective optimization terms with fitness functions, initializing tumor cell set functions, and improving search strategies and selection strategies, the efficiency and quality of cloud computing task scheduling can be improved. In order to realize the optimal utilization of cloud computing resources and meet the needs of users. © 2023 IEEE.

关键词： Multiobjective optimization

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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike Information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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Upconversion luminescence and temperature sensing performance of Er^(3+) ions doped self-activated KYb(MoO_(4))_(2) phosphors

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Journal of Rare Earths 2024年第8期42卷 1507-1513,I0004页

作者： Ye Tong Wenpeng Liu Shoujun Ding School of Microelectronics and Data Science Anhui University of TechnologyMaanshan243002China Advanced Laser Technology Laboratory of Anhui Province Hefei230037China Anhui Institute of Optics and Fine Mechanics Chinese Academy of SciencesHefei230031China

In this work,a series of self-activated KYb(MoO_(4))_(2) phosphors with various x at% Er^(3+) doping concentrations(x=0.5,1,3,5,8,10,15) was synthesized by the solid-state reaction *** phase structure of the as-prepared samples was analyzed by X-ray diffraction(XRD),XRD Rietveld refinement and Fourier transform infrared(FT-IR) *** as-prepared samples retain the orthorhombic structure with space group of Pbcn even Er^(3+) doping concentration up to 15 at%.High-purity upconversion(UC) green emission with green to red intensity ratio of 55 is observed from the as-prepared samples upon the excitation of 980 nm semiconductor laser and the optimum doping concentration of Er^(3+) ions in the self-activated KYb(MoO_(4))_(2) host is revealed as 3 at%.The strong green UC emission is confirmed as a two-photon process based on the power-dependent UC *** addition,the fluorescence intensity ratios(FIRs) of the two thermally-coupled energy levels,namely ^(2)H_(11/2) and ^(4)S_(3/2).of Er^(3+) ions were investigated in the temperature region 300-570 K to evaluate the optical temperature sensor behavior of the *** maximum relative sensitivity(S_(R)) is determined to be 0.0069 K^(-1) at300 K and the absolute sensitivity(S_(A)) is determined to be 0.0126 K^(-1) at 300 *** S_(A) of self-activated KYb(MoO_(4))2:Er^(3+)is almost twice that of traditional KY(MoO_(4))2:Er^(3+)/Yb^(3+)codoping *** results demonstrate that Er^(3+) ions doped self-activated KYb(MoO_(4))2 phosphor has promising application in visible display,trademark security and optical temperature sensors.

关键词： Er^(3+)ions doped KYb(MoO_(4))_(2) Self-activated phosphors Upconversion luminescence Solid-state reaction method Optical temperature sensors Rare earths

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A Modified EACOP Implementation for Real-Parameter Single Objective Optimization Problems 13

A Modified EACOP Implementation for Real-Parameter Single Ob...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Tangherloni, Andrea Coelho, Vasco Buffa, Francesca M. Cazzaniga, Paolo Bocconi Institute for Data Science and Analytics Bocconi University Department of Computing Sciences Milan Italy University of Milano-Bicocca Department of Informatics Systems and Communication Milan Italy Bocconi University Department of Computing Sciences Milan Italy Bocconi Institute for Data Science and Analytics Italy University of Bergamo Department of Human and Social Sciences Bergamo Italy

ISBN: (纸本)9798350308365

Evolutionary algorithms are effective techniques for optimizing non-linear and complex high-dimensional problems. However, most of them require a precise fine-tuning of their functioning settings to achieve satisfactory results. In this work, we propose a modified version of an evolutionary approach called the Evolutionary Algorithm for COmplex-process oPtimization (EACOP), designed to have a limited number of hyper-parameters. The base version of EACOP (bEACOP) combines different strategies, including the scatter search methodology, local searches, and a novel combination method based on path relinking to balance the exploration and exploitation phases. Our improved version (iEACOP) intensifies the exploration phase to escape from suboptimal search space areas where, on the contrary, bEACOP gets stuck. Our results show that iEACOP outperforms bEACOP on 27 out of 29 CEC 2017 test suite benchmark functions, exhibiting comparable performance against the three best algorithms of the CEC 2017 competition on single-objective bound-constrained real-parameter numerical optimization. The source code of bEACOP and iEACOP will be made publicly available on GitHub upon acceptance. © 2024 IEEE.

关键词： Optimization algorithms

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Detecting Criminal Activities and Promoting Safety Using Deep Learning

Detecting Criminal Activities and Promoting Safety Using Dee...

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2022 IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2022

作者： Mathur, Rohan Chintala, Tejas Rajeswari, D. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu 603203 India

ISBN: (纸本)9781665495295

Automation and autonomous systems are among the few powerhouses of innovation that drive entire domains towards advancing further in leaps and bounds. Great technological innovations can be attributed to tasks that are made easier and more perceptible by automation, and artificial intelligence is here to make these automated systems smart enough to perform their tasks with the power of decision-making, thereby greatly reducing human intervention in redundant processes. Our project follows the aforementioned ideals: building a product to minimize manual labor (both physical and mental) for tasks that can be seamlessly automated and processed while solving the main problem statement at hand. Currently, surveillance cameras play a vital role to ensure the safety of the people, yet they are plain video-providing entities with no smart decision making mechanisms of their own. Because of this growth of data composed from surveillance cameras, automated video streams have become a requisite for automatically detecting abnormal events. The main aim of the project focuses on promoting safety on campus by employing deep learning techniques to automate the task of monitoring and reporting crimes from the physical Closed-Circuit Television (CCTV), assigning the responsibility of detecting criminal activity to a framework that can identify patterns to differentiate them for smarter monitoring. The model in this paper that we propose was able to distinguish between certain crimes with a 0.94 and 0.95 precision for Assault and Abuse respectively. © 2022 IEEE.

关键词： Crime

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