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Integrating Local Learning to Improve Deep-Reinforcement-Learning-based Pairs Trading Strategies

Integrating Local Learning to Improve Deep-Reinforcement-Lea...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Wei-Che Chang Tian-Shyr Dai Ying-Ping Chen Chin-Yi Hsieh Yu-Wei Chang Yu-Han Huang Inst. of Computer Science and Engineering National Yang Ming Chiao Tung University Hsinchu City Taiwan Dept. of Information Management and Finance National Yang Ming Chiao Tung University Hsinchu City Taiwan Dept. of Computer Science National Yang Ming Chiao Tung University Hsinchu City Taiwan

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Instead of trying to predict unpredictable market trends, influenced by various complex factors that are challenged to be fully captured in a machine learning model, financial experts often adopt pairs trading. This strategy involves simultaneously trading two stocks to eliminate market trends. The portfolio value of a carefully selected stock pair oscillates around a mean level, with investors longing the underpriced and shorting the overpriced portfolios to profit or stop loss when the portfolio’s value reverts or diverges *** timing of trading actions highly depends on the characteristics of constituent stocks and significantly influences trading performance. Past literature either trains a single machine learning model with all stock pairs’ trading data or multiple models, each for a specific stock pair. While the former approach avoids overfitting due to sufficient data, it struggles to capture the unique characteristics of different stock pairs. Conversely, the latter approach focuses on specific pairs but faces limited training *** address this dilemma, our paper leverages local learning to recommend the best trading actions. We group trading data by similarity and train each model with data from a specific group. The trained model then predicts optimal actions for stock pairs in that group. Using Gaussian mixture models for data grouping in local learning outperforms other methods in scenarios with limited data for most stock pairs.

关键词： Training Machine learning Learning (artificial intelligence) Predictive models White noise Market research Data models

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Battery Optimization Using Machine Learning 3

Battery Optimization Using Machine Learning

引用

3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Patil, Aditya Lodha, Sanket Deshmukh, Sonal Joshi, Rupali S. Patil, Vaishali Chitnis, Sudhir Vishwakarma Institute of Information Technology Pune India Mgm University-Institute Of Management and Research Chh. Sambhajinagar India Mgm University-JavaharlalNehru Engineering College Chh. Sambhajinagar India Pvg College of Science and Commerce Pune India Vishwakarma Institute of Information Technology Pune India Vishwakarma College of Arts Commerce and Science Dept. of Computer Science Pune India

ISBN: (纸本)9798350354348

The ever-growing mountain of e-waste, fueled by short-lived batteries, threatens our planet's security. Discarded batteries leach toxic materials, jeopardizing environmental health and safety. This escalating crisis demands a paradigm shift. This research proposes a groundbreaking solution: leveraging Machine Learning to optimize battery lifecycles, dramatically reducing waste and its associated environmental security *** work integrates advancements in solid-state electrolytes with data-driven ML techniques to enhance battery management, contributing to sustainable security solutions. We employ clustering algorithms to categorize batteries for targeted optimization and implement regression models to predict and mitigate battery degradation. Our approach goes beyond traditional capacity and voltage metrics, incorporating crucial factors like temperature and dept. of discharge for a holistic E-waste security *** research paves the way for a secure and sustainable future by establishing a critical link between AI and responsible e-waste management. By promoting collaboration between researchers, policymakers, and consumers, we can leverage technology to decouple our reliance on electronics from the environmental burden of battery waste. This work presents a powerful solution with immense potential to secure a cleaner and more sustainable future. © 2024 IEEE.

关键词： Waste management

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3D Modelling and Rendering Using Autodesk 3ds Max

3D Modelling and Rendering Using Autodesk 3ds Max

引用

Annual Information Technology, Electromechanical engineering and Microelectronics Conference (IEMECON)

作者： Priyal Dangi Akshat Jain Debabrata Samanta Soumi Dutta Abhishek Bhattacharya Dept. of Computer Science CHRIST University Bangalore Karnataka India Dept. of Computing and Information Technologies RIT Kosovo (A.U.K) Prishtina Kosovo Europe Rochester Institute of Technology RIT Global Campus Prishtina Kosovo Europe Dept. of Computer Science & Applications Institute of Engineering & Management Kolkata India

This is outlined how to create a 3D custom kitchen design, including how to set up the sources, details, work with managing various modifiers like edit poly, slice, mesh select, turbo smooth, lattice, bend, shell modifier, so to provide the kitchen an authentic appearance. The method materials are fitted to the model output, together with illuminating the environment leveraging Arnold lights that are intended to be utilized with this renderer only. It has features that are optimised for rendering with Arnold. Procedures and methods regarding rendering are indeed specified. The final rendering was made out of several drawings. Our report’s intention is to develop a kitchen design that enriches models with materials and ample shapes from standard extended primitive along with the mostly utilization of pro-boolean. Finally, a material editor was used to improve the model. target illumination, too.

关键词： Solid modeling Three-dimensional displays Shape Education Lighting Lattices Microwave communication

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APOA based Multi-scale Parallel Convolution Blocks with Hybrid Deep Learning for Gastric Cancer Prediction from Endoscopic Images

APOA based Multi-scale Parallel Convolution Blocks with Hybr...

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Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： Adithya Padthe Mahantesh Mathapati Priyanka M S Priya Nandihal Department of Information Technology University of the Cumberlands USA Dept.of CSE Amruta Institute of Engineering and Management Sciences (AIeMS) Dept. of AIML Nitte Meenakshi Institute of Technology Bengaluru Dept of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore

In terms of mortality rates, gastric cancer is second only to lung cancer. Manual gastric slice pathology examination is labor-intensive and prone to observer bias. Endoscopy of the upper digestive tract is commonly used for the screening of gastric cancer. An object identification model, a kind of deep learning, was presented as a means of automating the diagnosis of early stomach cancer using endoscopic pictures. However, difficulties were encountered while attempting to reduce the sum of false positives in the identified findings. Tumour segmentation from the preprocessed pictures was carried out in this study, which is often more challenging and crucial. The research suggests a productive approach that makes use of multi-scale parallel convolution blocks (MPCs). Multi-scale parallel convolutions (MPCs) use filters of variable sizes to extract characteristics that are relevant across a range of tumour sizes. To further aid feature extraction with fewer parameters, residual connections and residual blocks can be used. In addition, the suggested study uses an Artificial plant optimisation algorithm (APOA) to fine-tune the segmentation model's parameters without resorting to post-processing approaches. Finally, gastric cancer detection from endoscopic pictures is accomplished using a hybrid classification strategy that incorporates Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Experiments employing 1208 photos from healthy people and 533 photographs from patients with stomach cancer examined detection performance using the 5-fold cross-validation approach. These findings show promise for the suggested method's application in automated early stomach cancer diagnosis using endoscopic images.

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A Machine Learning Approach to Classify Network Traffic 13

A Machine Learning Approach to Classify Network Traffic

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13th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2021

作者： Jadav, Nilesh Dutta, Nitul Sarma, Hiren Kumar Deva Pricop, Emil Tanwar, Supeep Marwadi University Dept. of Computer Engineering Rajkot India Sikkim Manipal Inst. of Technology Dept. of Information Technology Majitar India Petroleum-Gas University of Ploiesti Automatic Control Comp and Elect. Dept Ploiesti Romania Nirma University Dept. of Computer Science and Eng. Ahmedabad India

ISBN: (纸本)9781665425346

There is a significant increase of cloud and networking-enabled applications, leading to an exponential growth of network traffic. Monitoring all these applications and their generated traffic is a challenging and complex task, especially in regard to anonymous networks used to access the Dark Web or darknet. Manual investigation of the network traffic to determine patterns of malicious activity is a very difficult task and the results might not be satisfactory. The usage of machine learning is the most viable approach for the classification of the traffic as normal or malign activity. This paper analyzes CIC-Darknet 2020 dataset to classify the benign and darknet traffic. Before applying any classifiers to our dataset, we have balanced it using Synthetic Minority Oversampling Technique (SMOTE). We have applied PCA to reduce dimensionality, furthermore, ensemble techniques, logistic classifiers, tree-classifiers, and Naive Bayes have been compared and evaluated thoroughly with various evaluation metrics Accuracy, Precision, Recall, F1-Score, and Mathew's Correlation Coefficient (MCC). © 2021 IEEE.

关键词： Machine learning

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Image Segmentation Survey: Classical and Deep Learning Methods

Image Segmentation Survey: Classical and Deep Learning Metho...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Viren Bachani Anish Roychowdhury Abhijit Dasgupta Hazem EI-Alfy BSc. Data Science SP Jain School of Global Management Sydney Australia Data Science Dept. SP Jain School of Global Management Sydney Australia Data Science Dept. SP Jain School of Global Management Mumbai India School of Computer Science University of Sydney Sydney Australia Faculty of Engineering Alexandria University Alexandria Egypt

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Image segmentation is a prerequisite to almost all computer vision applications. It enables the extraction of meaningful information from visual inputs by partitioning images into segments with shared features. This review paper begins by outlining different image segmentation tasks. It then surveys a broad spectrum of segmentation techniques, ranging from classical frameworks to deep-learning (DL) architectures. We perform this through a systematic literature analysis across a handful of applications as part of our methodology. A key contribution of our survey is that it elaborates on a few situations where classical approaches outperform DL-based methods in accuracy, along with the added advantage of computational efficiency. In some cases, these findings also provoke interest in revisiting classical methods, prompting strategies that leverage the strengths of both paradigms.

关键词： Surveys Deep learning Image segmentation computer vision Visualization Accuracy Systematics Training data computer architecture Feature extraction

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Creation of Bookshelf Using Autodesk 3ds Max: 3D Modelling and Rendering

Creation of Bookshelf Using Autodesk 3ds Max: 3D Modelling a...

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Annual Information Technology, Electromechanical engineering and Microelectronics Conference (IEMECON)

作者： Akshat Jain Priyal Dangi Debabrata Samanta Soumi Dutta Abhishek Bhattacharya Niju P. Joseph Dept. of Computer Science CHRIST University Bangalore Karnataka India Dept. of Computing and Information Technologies RIT Kosovo (A.U.K) Prishtina Kosovo Europe Rochester Institute of Technology RIT Global Campus Prishtina Kosovo Europe Dept. of Computer Science & Applications Institute of Engineering & Management Kolkata India

The step-by-step process of creating a bookshelf design is specified, including the ProBoolean compound primitive, applying edit poly modifier, using detach option, making use of lattice modifier, using bend modifier, using twist modifier. The manner in which materials are added to the model, together with environment lighting and renderer configuration, is defined. Procedures and methods for rendering are also defined. What we aim to achieve through our research is to create a Bookshelf design that uses materials to enhance the models. The shapes used in the model were Box, Teapot, sphere, chamfercyl, Oiltank, ProBoolean compound. The modifiers used were edit poly, bend, twist, lattice. Afterwards we used the Arnold light and material editor to enhance and glorify the model.

关键词： Three-dimensional displays Shape Computational modeling Lattices Lighting Microwave communication Rendering (computer graphics)

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Home Care Assistance Solution based on a Software Multi-Agents System

Home Care Assistance Solution based on a Software Multi-Agen...

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International Conference on control Systems and computer Science (CSCS)

作者： Mohammad Rasras Iuliana Marin Şerban Radu Doctoral School of Automatic Control and Computers University POLITEHNICA of Bucharest Bucharest ROMANIA Dept. of Engineering in Foreign Languages University POLITEHNICA of Bucharest Bucharest ROMANIA Dept. of Computer Science University POLITEHNICA of Bucharest Bucharest ROMANIA

A smart home allows automated technology to control systems and appliances to assist daily life activities. In the current work, a prototype of a designed smart home health-aware system is presented. Three predefined ruled agents have been used to gather data, communicate between themselves, interpret the data based on several machine learning algorithms to predict abnormality for cardiac conditions. The paper also compares results obtained by running five machine learning algorithms. The agents function in accordance with predefined dataset describing residents ’ age, gender, height, weight, systolic and diastolic blood pressure, cholesterol, and glucose levels, smoking and alcohol consumption habits, and lastly, physical activity. Our plan is to simplify ubiquitous health monitoring for smart home’s residents. The proposed solution aims to monitor parametric measurements to alert health specialists in case of emergencies.

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Stock Market Growth and Decline Using ARIMA Model and Sentiment Analysis

Stock Market Growth and Decline Using ARIMA Model and Sentim...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Sandipan Biswas Bhaskar Roy Sujit Kumar Singh Nitin Nitin Saroj Kumar Sahoo Sanjaya Kumar Sarangi Dept. of CSE-AI Brainware University Kolkata India Dept. of CSE(AIML) Asansol Engineering College Asansol India Faculty of Information Technology and Computer Science Parul University Vadodara India Department of CSE Akhilesh Das Gupta Institute of Professional Studies Delhi India Department of CSE Centurion University of Technology and Management Bhubaneswar India Dept. of Computer Science Utkal University Bhubaneswar India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Researchers have been developing and entering the technological era since the 18th century. Before everyone else, social networks brought people from distant nations together. Social network applications such as Reddit, Twitter, Facebook, Instagram, LinkedIn, and others are examples of associate degree programs. Each application has a unique purpose. Investors who want to buy stocks take use of this social networking information. They stay up with a few companies thanks to this information. In order to assess people's sentiments regarding a certain stock, we will be using stock-related news data in this instance. Using TEXTBLOB sentiment analysis, this analysis will provide feedback on the corporate so that we may be prepared to see a rise or fall in relation to investors or firm performance.

关键词： Sentiment analysis Analytical models Social networking (online) Time series analysis Predictive models Data models Web sites Multimedia communication Stock markets Intelligent systems

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Real-time gesture control UAV with a low resource framework

Real-time gesture control UAV with a low resource framework

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2021 International Symposium of Asian control Association on Intelligent Robotics and Industrial Automation, IRIA 2021

作者： Bhat, Manohar Mahto, Gopikishan Kesaria, Smit Femandes, Vikrant Arya, Kavi BMS College of Engineering Dept. of Electrical and Electronics Bangalore560019 India UPU Government Polytechnic Dept. of Computer Science and Engg. Durg491001 India Indian Institute of Technology Dept. of Systems and Control Mumbai400076 India Indian Institute of Technology Dept. of Computer Science and Engg. Mumbai400076 India

ISBN: (纸本)9781665433235

This study showcases a low-resource framework that enables people with no technical know-how to interact with drones, it also explores the capabilities of 2D- computer vision and deep learning techniques for gesture based interface systems on a low-cost micro drone with an onboard RGB camera. This Human-Robot Interaction system processes the real-time human pose to allow a user to command the drone, i.e., by providing direction to move and execute actions. A linear PD controller and image processing techniques are implemented to track humans whilst maintaining a safe distance from the user by perceiving dept. information through pose estimation. We incorporated the gesture recognition results into a drone using the Robot Operating System (ROS) and evaluated system performance indoor and outdoor. This low computation framework can be applied further to control robotic arms or mobile robots. © 2021 IEEE.

关键词： Drones

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