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Predictive Modeling of Stock Prices Using Time Series Analysis

Predictive Modeling of Stock Prices Using Time Series Analys...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Vrisha Shah Krish Bilakhiya Devam Shah Kriti Srivastava Pradnya Saval Dept. of Electronics and Telecommunication Engineering Dwarkadas J. Sanghvi College of Engineering Mumbai India Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Technical analysis of stocks is crucial in the finance industry to make investment and purchase decisions. Forecasting the patterns of the stock market is a complex task as it’s erratic in nature. The stock prices exhibit non-stationarity and are subject to significant noise due to the influence of several factors. Commonly held beliefs propose that investing in the stock market entails notable risks, often discouraging many from considering it as a viable investment option. However, leveraging Time Series Analysis on historical stock data presents an opportunity to construct predictive models capable of predicting future trends in the closing prices of individual stocks. Traditionally, trend forecasting or stock price forecasting has been a tedious task with little accuracy. However, current technological advances and new techniques offer the opportunity to use historical data to make accurate predictions. Despite the challenges posed by using elements like investor behavior, government policies, global events and geopolitical tensions that make accurate predictions difficult to obtain, this paper contributes to the development of foundations for long-term stock price forecasts. This research specializes in the prediction of stock price to help investors in making informed decisions in this ever-changing market. Unique to this study is the comprehensive evaluation of multiple models - Exponential Smoothing, ARIMA, SARIMA and VAR - conducted on the Facebook stock data. By evaluating these models based on various metrics and comparing their results, this research provides a detailed analysis of their capabilities within the dynamic panorama of the stock marketplace.

关键词： Analytical models Reactive power Accuracy Social networking (online) Time series analysis Predictive models Market research data models Forecasting Stock markets

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P-YOLOv8: Efficient and Accurate Real-Time Detection of Distracted Driving

P-YOLOv8: Efficient and Accurate Real-Time Detection of Dist...

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2024 IEEE High Performance Extreme Computing Conference, HPEC 2024

作者： Elshamy, Mohamed R. Emara, Heba M. Shoaib, Mohamed R. Badawy, Abdel-Hameed A. Klipsch School of ECE New Mexico State University Las CrucesNM88003 United States Pyramids Higher Inst. for Engineering & Technology Dept. of Electronics and Communications Engineering Egypt College of Computing and. Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798350387131

Distracted driving is a critical safety issue that leads to numerous fatalities and injuries worldwide. This study addresses the urgent need for efficient and real-time machine learning models to detect distracted driving behaviors. Leveraging the Pretrained-YOLOv8 (P-YOLOv8) model, a real-time object detection system is introduced, optimized for both speed and accuracy. This approach addresses the computational constraints and latency limitations commonly associated with conventional detection models. The study demonstrates P-YOLOv8's versa-tility in both object detection and image classification tasks using the Distracted Driver Detection dataset from state farm, which includes 22,424 images across ten behavior categories. Our research explores the application of P-YOLOv8 for image classification, evaluating its performance compared to deep learning models such as VGG16, VGG19, and ResNet. Some traditional models often struggle with low accuracy, while others achieve high accuracy but come with high computational costs and slow detection speeds, making them unsuitable for real-time applications. P-YOLOv8 addresses these issues by achieving competitive accuracy with significant computational cost and efficiency advantages. In particular, P-YOLOv8 generates a lightweight model with a size of only 2.84 MB and a lower number of parameters, totaling 1, 451, 098, due to its innovative architecture. It achieves a high accuracy of 99.46% with this small model size, opening new directions for deployment on inexpensive and small embedded devices using Tiny Machine Learning (TinyML). The experimental results show robust performance, making P-YOLOv8 a cost-effective solution for real-time deployment. This study provides a detailed analysis of P-YOLOv8's architecture, training, and performance benchmarks, highlighting its potential for real-time use in detecting distracted driving. © 2024 IEEE.

关键词： Convolutional neural networks

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Performance Analysis for Biometric Identification Systems with Nonlegitimate Users

Performance Analysis for Biometric Identification Systems wi...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Yagi, Hideki Hirasawa, Shigeichi University of Electro-Communications Dept. of Computer and Network Engineering Tokyo Japan Waseda University Center for Data Science Tokyo Japan

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

The biometric identification system, introduced by Willems et al., is a mathematical model to identify users based on their physical features. Although the maximum rate of the number of users which are reliably dealt with in the system (identification capacity) and the exponential behavior of the average error probability (error exponents) of the legitimate users have been revealed via information theoretic approaches, optimum error exponents has not been shown when there exists a nonlegitimate user in the system. In this paper, we formally define the reliability function as the optimum error exponent for legitimate users for a given rate of the number of legitimate users and a given error exponent for the nonlegitimate users. It is shown that the reliability function can be completely characterized by the well-known random coding exponent and the hypothesis testing error exponent. © 2022 IEEE.

关键词： Biometrics

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Recognition of data Breach Method from Story Using Advanced Deep Learning 3

Recognition of Data Breach Method from Story Using Advanced ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Jeba, Samiha Maisha Aurpa, Tanjim Taharat Alom, Md Samiul Joy, Md. Wadud Mahmud Ahamed, Asm Shakil Dash, Satya Ranjan Ahmed, Md Shoaib International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science Bangladesh Kiit University School of Computer Applications Odisha India Boise State University Department of Computer Science BoiseID United States

ISBN: (纸本)9798350381931

We often open our eyes in the morning with the appalling news of data breaches of different popular companies. This is a significant threat not only to giant companies but also to the general people's privacy. Various methods are maneuvered for data breaches, and identifying these methods in a short can help understand the loss and take proper steps against these incidents. Researchers nowadays focus on the data breaches issue to reduce its threats to the tech industry. In this research, we have used advanced deep learning methods(e.g., BERT, XLNet, and Electra) to predict data breach methods using the data breach story. The proposed classifier has provided an accuracy of 92.86%. © 2024 IEEE.

关键词： Deep learning

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Sarcasm Prediction Using Different Learning Approaches on User Behavior and Contextual Evaluation 5

Sarcasm Prediction Using Different Learning Approaches on Us...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Ahire, Lomesh Kautik Babar, S.D. Mahalle, P.N. Dept of Computer Engineering Smt. Kashibai Navale College of Engineering Savitribai Phule Pune University Pune India Dept of Computer Engineering Savitribai Phule Pune University Pune India Dept. of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798400709418

A large number of people have been using social media platforms extensively to communicate their thoughts and feelings in the recent era of social networking. Both the user base and data volume on social networks are growing quickly every day. Any time an event or activity occurs nearby, nearby individuals express their thoughts and reactions on social media. When a new product is introduced, users on social media platforms also comment on it. Some people express their views or feelings using informal or complex language which makes it difficult to understand for another user. It is challenging to ascertain the true thoughts because different people express their opinions in complex ways. In this study, the various factors that affect these feelings are briefly discussed. In order to identify sarcasm on Twitter, a generic technique is also necessary in addition to the tweet's content. The proposed approach uses contents of tweet in association with important aspects like user behavior and context of tweet. By users' behavior we can identify its influence on other users and context is required to identify user behavior while detecting sarcasm. Proposed approach uses user behavior pattern and personality features along with contextual data. This all information and the already known sarcasm prediction mechanism will help us to set up the generic approach to detect sarcasm on Twitter. © 2023 ACM.

关键词： Behavioral research

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Research Concept Link Prediction via Graph Convolutional Network 8

Research Concept Link Prediction via Graph Convolutional Net...

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8th International Conference on Big data and Information Analytics, BigDIA 2022

作者： Wang, Xiangyu Chen, Lyuzhou Lyu, Derui Ban, Taiyu Guan, Yifeng Chen, Qiuju School of Data Science Ustc Hefei China School of Computer Science and Technology Ustc Hefei China Dept. Electronic Engineering and Information Science Ustc Hefei China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

As interdisciplinary research develops, numerous publications are uploaded to the research community. This makes researchers spend a lot of time searching for valuable information. Some methods have been proposed to provide more accurate recommendations, such as domain terminology-based or graph pattern-based methods. However, due to the continuous emergence of new research concepts and new research fields, it is difficult to apply it to practical tasks. To tackle these issues, this paper constructs the research keywords of different publications into a topological network, and then predicts the possibility of connection between different keywords. To analyze the associations between research concepts, graph convolution technology are employed to design method. It could provide domain-independent recommendations for the relevance of research concepts. Experiments demonstrate that the proposed method can predict associations between research concepts more effectively than traditional methods. © 2022 IEEE.

关键词： Convolution

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Leveraging ReRAM Sequence Graphs for Efficient Mapping of Binary Adders in ReRAM Crossbars

Leveraging ReRAM Sequence Graphs for Efficient Mapping of Bi...

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International Symposium on VLSI Design and Test (VDAT)

作者： Pragnya Dhal Arighna Deb Subrata Das Debesh Kumar Das School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Dept. of Computer Science & Engineering Supreme Knowledge Foundation Group of Institutions Hooghly India Dept. of Computer Science & Engineering Jadavpur University Kolkata India

ISBN: (数字)9798350380101

ISBN: (纸本)9798350380118

Memristor, also known as Resistive Random Access Memory (ReRAM), enables in-memory computing by processing data directly within storage locations, thus mitigating the data transfer bottleneck between the processor and memory in high-speed applications. However, existing methods for implementing addition of two n-bit operands within ReRAM/memristor crossbar structures do not ensure minimal device utilization or minimal ReRAM/memristor read-write cycles. In this paper, we propose a novel approach for implementing n-bit addition in ReRAM crossbars to optimize the usage of the ReRAM device and minimize read-write cycles. We develop the ReRAM Sequence Graph (ReSG) to efficiently represent adder functionality and systematically allocate ReRAM devices to ReSG nodes. Furthermore, we present a scheduling mechanism for implementing ReSG nodes within ReRAM crossbars, accelerating the execution of the addition operation. Experimental evaluations show that our method surpasses current approaches, achieving better device utilization and reduced read-write cycles.

关键词： Schedules Resistive RAM Memristors Very large scale integration In-memory computing data transfer Adders

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Design and Investigate the Deep Learning Models for White Blood Cell Classification 3

Design and Investigate the Deep Learning Models for White Bl...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Gaikwad, Prajwal. S. Deshmukh, Rakesh K. Pise, Priya Bhopale, Shrikant D. Kalinga University Raipur India AISSMS's Institute of Information Technology Dept. of Computer Engineering Pune India Artificial Intelligence and Data Science Indira College of Engineering and Management Pune India Dr. D.Y. Patil Pratisthan's Colleg of Engineering Salokhenagar Kolhapur India

ISBN: (纸本)9798350381931

For the purpose of illness prediction, the objective of this study is to develop automated models for the categorization of white blood cells (WBCs). In the human immune system, white blood cells (WBCs) function to combat infections and shield the body from external substances. Eosinophils, neutrophils, monocytes, basophils, and lymphocytes are the components that make up these cells;each of these cell types accounts for a different proportion and is responsible for a distinct set of functions overall. The clinical laboratory technique for counting the specific types of white blood cells (WBCs) has long been an essential component of a testing procedure known as a complete blood count (CBC), which is used to aid in the monitoring of individuals' health. However, such manual processes are time-consuming and error-prone for disease detection. The recently proposed machine and deep learning-based solutions for the blood cell classification delivered better results, however, suffered from challenges as well. These challenges motivate us to propose a novel AI-based approach for automatic WBCs classification in this research work. © 2024 IEEE.

关键词： Cells

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Investigating Patient Behavior and Experience in Healthcare Settings to Improve Service Quality

Investigating Patient Behavior and Experience in Healthcare ...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chandra, Pradeep Kumar Ramesh, B. Anandhi, R.J. Singla, Atul Prakash Raj, M. Al-Ghazi, Hussein Jamil Hamid LLloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India St. Martin's Engineering College Dept. of Cse Telangana Secunderabad India National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

This study explains ways to enhance patient healthcare experiences. The proposed system integrates real-time patient feedback analysis, predictive modeling, individualized therapeutic concepts, digital user interface improvements, and sensitivity training. After comprehensive empirical research, the proposed strategy consistently outperformed well-known strategies in key performance indicators. With sentiment analysis tools, clinicians may instantly reply to patient remarks in real time. Predictive modeling using advanced machine learning made patient happiness predictions easier. Personalized treatment concepts employ combined filtering to adapt healthcare to each person. Digital tools will be simple and effective, DEO stated. Empathy training for healthcare personnel demonstrated the method's human-centeredness. Regular poll analysis, less complex predictive modeling, generic therapy, little digital interface optimization, and less empathy training were used. The proposed approach outperformed them. The recommended strategy worked because of real-time responsiveness, independence, and patient concentration. Because it considers how each patient contact is unique and varies over time, the recommended strategy transforms healthcare delivery. This strategy shows how to provide patient-centered care as healthcare evolves. © 2024 IEEE.

关键词： Patient treatment

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WelQrate: defining the gold standard in small molecule drug discovery benchmarking 24

WelQrate: defining the gold standard in small molecule drug ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yunchao (Lance) Liu Ha Dong Xin Wang Rocco Moretti Yu Wang Zhaoqian Su Jiawei Gu Bobby Bodenheimer Charles David Weaver Jens Meiler Tyler Derr Computer Science Dept. Vanderbilt University (VU) Neural Science Dept. Amherst College Chemistry Dept. VU Computer Science Dept. University of Oregon Data Science Institute VU MD Anderson Cancer Center Computer Science Dept. Vanderbilt University (VU) and Electrical and Computer Engineering Dept. VU and Psychology Dept. VU Chemistry Dept. VU and Institute of Chemical Biology VU Chemistry Dept. VU and Center for Structural Biology VU and Pharmacology Dept. VU and Institute for Drug Discovery Leipzig University (LU) and Computer Science Dept. LU and Chemistry Dept. LU. Computer Science Dept. Vanderbilt University (VU) and Data Science Institute VU

ISBN: (纸本)9798331314385

While deep learning has revolutionized computer-aided drug discovery, the AI community has predominantly focused on model innovation and placed less emphasis on establishing best benchmarking practices. We posit that without a sound model evaluation framework, the AI community's efforts cannot reach their full potential, thereby slowing the progress and transfer of innovation into real-world drug discovery. Thus, in this paper, we seek to establish a new gold standard for small molecule drug discovery benchmarking, WelQrate. Specifically, our contributions are threefold: WelQrate dataset collection - we introduce a meticulously curated collection of 9 datasets spanning 5 therapeutic target classes. Our hierarchical curation pipelines, designed by drug discovery experts, go beyond the primary high-throughput screen by leveraging additional confirmatory and counter screens along with rigorous domain-driven preprocessing, such as Pan-Assay Interference Compounds (PAINS) filtering, to ensure the high-quality data in the datasets; WelQrate Evaluation Framework - we propose a standardized model evaluation framework considering high-quality datasets, featurization, 3D conformation generation, evaluation metrics, and data splits, which provides a reliable benchmarking for drug discovery experts conducting real-world virtual screening; Benchmarking - we evaluate model performance through various research questions using the WelQrate dataset collection, exploring the effects of different models, dataset quality, featurization methods, and data splitting strategies on the results. In summary, we recommend adopting our proposed WelQrate as the gold standard in small molecule drug discovery benchmarking. The WelQrate dataset collection, along with the curation codes, and experimental scripts are all publicly available at ***.

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