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Bitcoin Price Prediction Using XGBoost Classifier: A Machine Learning Approach 7th

Bitcoin Price Prediction Using XGBoost Classifier: A Machine...

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7th International Conference on Soft Computing and Signal Processing, ICSCSP 2024

作者： Sai, P. H. V. Sesha Talpa Acharya, Ganesh Yellappa Pramod, S. Ganesan, Shanmuga Canaraj Bangarraju, Saripelli Chaitanya Tiwari, Kishan Jaswanthi, Katakam Bhaumik, Amiya Department of Mechanical Engineering Malla Reddy College of Engineering and Technology Hyderabad India School of Mechanical Engineering Lincoln University College Petaling Jaya Malaysia Department of Computer Science and Engineering Malla Reddy University Hyderabad India Department of Computer Science and Data Science Engineering Chalapathi Institute of Engineering and Technology Andhra Pradesh Guntur India Lincoln University College Petaling Jaya Malaysia

ISBN: (纸本)9789819609239

Cryptocurrency, particularly Bitcoin, has become a significant component of the financial asset market over the last years with its unstable price. For Traders, investors, and other market participants, the potential movement in Bitcoin’s price is becoming more important. This study focuses on mitigating the unpredictability of Bitcoin prices by using the classifier of XGBoost. This work uses historical data of Bitcoin’s price and data of relevant factors like trading volume, sentiment analysis of the market, and technical indicator. data pre-processing techniques such as missing value treatment, normalization, and the split of data into training and testing are performed first. XGBoost is then trained in the developing set to identify the patterns in the input variables. The test data is then employed to assess the model with the help of various metrics including accuracy, precision, recall, and F1-score. The XGBoost model has also been compared with the other base models and the traditional forecasting models based on time series. Our results also suggest that the proposed model outperformed the benchmark model and reported 97% accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cost estimating

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Security Challenges and Elucidations in Cloud Storage and File Systems: An Advanced Investigation Review

Security Challenges and Elucidations in Cloud Storage and Fi...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： S. P. Santhoshkumar S. Hariharasudhan S. Poorna Prakash Jim Mathew Philip S. Haritha T. Nalini Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai India Artificial Intelligence & Data Science Akshaya College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore Tamil Nadu India Master of Computer Application Rathinam Technical Campus. Coimbatore India Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

With the widespread adoption of cloud storage and file systems, security concerns have become paramount. This paper presents an advanced investigation into the security challenges encountered in cloud storage and file systems. We explore the vulnerabilities and threats that these systems face, ranging from unauthorized access to data breaches and insider attacks. Additionally, we examine the evolving landscape of security solutions, including encryption techniques, access control mechanisms, and intrusion detection systems. Through a comprehensive review of existing literature and case studies, we identify emerging trends and best practices for mitigating security risks in cloud storage and file systems. Our analysis provides valuable insights for researchers, practitioners, and organizations seeking to enhance the security posture of their cloud-based infrastructure. Cloud storage and file systems have revolutionized data management, offering scalability, flexibility, and accessibility. However, alongside their benefits, they present formidable security challenges. This paper conducts an advanced investigation into the security landscape of cloud storage and file systems, aiming to interpret key challenges and propose effective solutions. Through a thorough examination of prevalent security threats, including unauthorized access, data breaches, and insider attacks, this study identifies vulnerabilities inherent in cloud environments. Moreover, it scrutinizes emerging security technologies such as encryption, access control mechanisms, and intrusion detection systems, assessing their efficacy in mitigating risks. By synthesizing insights from existing literature and real-world case studies, this investigation provides valuable guidance for enhancing the security posture of cloud-based infrastructure. Ultimately, this paper contributes to the ongoing discourse on cloud security by offering nuanced analyses and actionable recommendations to researchers, practitioners,

关键词： Cloud computing File systems Reviews Scalability Cloud computing security Intrusion detection data breach Market research Encryption Security

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Attention Span and it's Correlation with Mental Health

Attention Span and it's Correlation with Mental Health

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AIML-Applications for engineering & Technology (ICAET), International Conference on

作者： Richa Shah Laxmi Bewoor Sarthak Rak Ayush Patne Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune India Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

This paper delves into the correlation between attention span and mental health. Attention span is the ability to focus on a task before being distracted by certain factors. It ranges from 2 seconds to more than 20 minutes. Meanwhile, mental health constitutes a person's psychological, sociological, and emotional well-being. Using a clustering algorithm, this research seeks to provide a concrete relation between the two across an age group of 18–25.

关键词： Correlation Anxiety disorders Clustering algorithms Mental health Depression Prediction algorithms Concrete

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Predictive Maintenance in IoT Networks Using a Temporal Attention Mechanism Based Federated Convolutional Neural Network 4

Predictive Maintenance in IoT Networks Using a Temporal Atte...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Guduri, Naga Venkata Ramakrishna Charaan, R.M. Dilip Kalaiyarasi, R. Samuthira Pandi, V. Reddy, D.Laxma Selvaprabhu, T. University of Technology and Applied Sciences Faculty of Engineering Al Musannah Oman Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad India Dhanalakshmi College of Engineering Department of Cse Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

In the era of Industry 4.0, edge devices in industrial settings generate vast amounts of data crucial for process optimization and Predictive Maintenance (PM). Traditional cloud-based approaches for handling this data are becoming untenable due to cost, latency, and privacy issues. Federated learning emerges as a viable solution, offering localized and efficient data processing while mitigating these challenges. This paper presents a Federated Convolutional Neural Network with Temporal Attention Mechanism (FedCNN-TAM) for PM tasks in Internet of Things (IoT) networks. This model leverages temporal features in sensor data to enhance predictive accuracy. Evaluated on the CMAPSS dataset, FedCNN-TAM outperforms representative models with a significant performance gain. This model exhibits the potential for broader applications in industrial IoT, setting the stage for future research on its scalability and adaptability to multi-modal data types. © 2024 IEEE.

关键词： Spatio-temporal data

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Knowledge-augmented Self-training of A Question Rewriter for Conversational Knowledge Base Question Answering

Knowledge-augmented Self-training of A Question Rewriter for...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Ke, Xirui Zhang, Jing Lv, Xin Xu, Yiqi Cao, Shulin Li, Cuiping Chen, Hong Li, Juanzi School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China Department of Computer Science and Technology Tsinghua University Beijing China

The recent rise of conversational applications such as online customer service systems and intelligent personal assistants has promoted the development of conversational knowledge base question answering (ConvKBQA). Different from the traditional single-turn KBQA, ConvKBQA usually explores multi-turn questions around a topic, where ellipsis and coreference pose great challenges to the single-turn KBQA systems which require self-contained questions. In this paper, we propose a rewrite- and-reason framework to first produce a full-fledged rewritten question based on the conversation history and then reason the answer by existing single-turn KBQA models. To overcome the absence of the rewritten supervision signals, we introduce a knowledge-augmented self-training mechanism to transfer the question rewriter from another dataset to adapt to the current knowledge base. Our question rewriter is decoupled from the subsequent QA process, which makes it easy to be united with either retrieval-based or semantic parsing-based KBQA models. Experiment results demonstrate the effectiveness of our method and a new state-of-the-art result is achieved. The code and dataset are available online now. © 2022 Association for Computational Linguistics.

关键词： Knowledge based systems

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Intensity Confusion Matters: An Intensity-Distance Guided Loss For Bronchus Segmentation

Intensity Confusion Matters: An Intensity-Distance Guided Lo...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Haifan Gong Wenhao Huang Huan Zhang Yu Wang Xiang Wan Hong Shen Guanbin Li Haofeng Li Shenzhen Research Institute of Big Data China School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) China InferVision China School of Computer Science and Engineering Sun Yat-sen University China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Automatic segmentation of the bronchial tree from CT imaging is important, as it provides structural information for disease diagnosis. Despite the merits of previous automatic bronchus segmentation methods, they have paied less attention to the issue we term as Intensity Confusion, wherein the intensity values of certain background voxels approach those of the foreground voxels within bronchi. Conversely, the intensity values of some foreground voxels are nearly identical to those of background voxels. This proximity in intensity values introduces significant challenges to neural network methodologies. To address the issue, we introduce a novel Intensity-Distance Guided loss function, which assigns adaptive weights to different image voxels for mining hard samples that cause the intensity confusion. The proposed loss estimates the voxel-level hardness of samples, on the basis of the following intensity and distance priors. We regard a voxel as a hard sample if it is in: (1) the background and has an intensity value close to the bronchus region; (2) the bronchus region and is of higher intensity than most voxels inside the bronchus; (3) the background region and at a short distance from the bronchus. Extensive experiments not only show the superiority of our method compared with the state-of-the-art methods, but also verify that tackling the intensity confusion issue helps to significantly improve bronchus segmentation. Project page: https://***/lhaof/ICM.

关键词： Image segmentation Image resolution Accuracy Computed tomography Neural networks Focusing Benchmark testing

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Contrail Analysis through Advanced Neural Network Architectures: Image Segmentation and Classification

Contrail Analysis through Advanced Neural Network Architectu...

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International Conference on computer and Information Technology (ICCIT)

作者： Ashraful Alam Nirob Shahriar Ahmed Tahmidul Karim Takee Adnan Rahman Eshan Shadik Ul Haque Ehsanur Rahman Rhythm Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bangladesh

The aviation industry’s immense expansion is having an impact on global warming and has resulted in some significant environmental issues. When an airplane passes directly over them, tiny crisscross patterns, often known as contrails, may be visible. They are to blame for this effect. Contrails are really just airborne particles that have been compressed with water. They are uncommon since ice can only form under particular climatic circumstances, such as extremely cold, hot, humid, and saturating air. Even worse, because of the cooler climate at night, it is more dangerous because it has more time to live. They gather heat from the sun and store it, then release it into the atmosphere. Some experts have also warned the public that the radiation these contrails produce may be more damaging to the atmosphere than previously predicted. For this reason, scientists are looking for methods to reduce these contrails by comprehending their behaviors and patterns. Now, the proposed study segments and classifies images of contrails acquired from satellite data. In this study, complex neural network architectures, including U-Net, DeepLab, Attention Mechanism, and ResNet50 with CNN, are used to segment and binary classify those photos. These architectural frameworks will aid this research in effectively classifying and segmenting those contrails from the satellite images so that further research can comprehend and observe their patterns and behaviors.

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Evaluating Question generation models using QA systems and Semantic Textual Similarity

Evaluating Question generation models using QA systems and S...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Safwan Shaheer Ishmam Hossain Sudipta Nandi Sarna Md Humaion Kabir Mehedi Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bangladesh

Question generation based on conversational context is a difficult problem to solve. A widely used technique for generating quality questions using fine-tuned models relies on a suitable answer and the context, usually the passage. But when it comes to conversational settings, the questions generated are not of the highest quality as they lack the contextual element in the question, especially due to the lack of co-reference resolution of the entity. Furthermore, in most of the evaluation techniques for generating questions, there seems to be a lack of utilizing powerful question-answering systems to judge the answerability of the questions generated. The most prevalent metric used for judging machine-generated text against the human gold standard, BLUE, unfortunately doesn't factor in whether a question answering system would be able to answer the question, but instead focuses mostly on the number of substrings that match against each other. Various question generation models following a generalized encoder-decoder architecture were evaluated using semantic textual similarity for both the generated questions and the generated answers. Although higher parameters in a model usually lend to better performance, our experiment displayed that such is not always the case, at least when there is a massive amount of context missing.

关键词： Measurement Computational modeling Conferences Semantics computer architecture Question answering (information retrieval) Context modeling

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Distributed Dual Objective Sampled-data Control of Energy Storage Systems

Distributed Dual Objective Sampled-data Control of Energy St...

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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Xin Zhang, Ying Cai, He Su, Youfeng Chen, Xiaofeng Fuzhou University School of Mathematics and Statistics Fuzhou350108 China Fuzhou University College of Computer and Data Science Fuzhou350108 China South China University of Technology School of Automation Science and Engineering Guangzhou510641 China

ISBN: (纸本)9781665465335

In this work, we take into account the dual objective control issue for an energy storage system (ESS). For one thing, the total ESS's power output ought to meet a certain reference value. For another, all energy storage units (ESUs) must have a balanced state-of-energy (SOE) in order to preserve the system power capacity at its highest level. To this end, a sampled-data distributed control scheme is proposed so as to achieve both state-ofenergy balancing and power tracking subject to the reliable and fixed communication network, where the range of sampling period is obtained by the standard procedure. An illustrative example is offered to support the efficacy of the provided control approach. © 2022 IEEE.

关键词： Energy storage

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Impact of energy efficiency on the morphology and behaviour of evolved robots 21

Impact of energy efficiency on the morphology and behaviour ...

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2021 Genetic and Evolutionary Computation Conference, GECCO 2021

作者： Rebolledo, Margarita Zeeuwe, Daan Bartz-Beielstein, Thomas Eiben, A.E. Institute for Data Science Engineering and Analytics Th Koln Gummersbach Germany Department of Computer Science Vrije Universiteit Amsterdam Netherlands

ISBN: (纸本)9781450383516

Most evolutionary robotics studies focus on evolving some targeted behavior without considering energy usage. In this paper, we extend our simulator with a battery model to take energy consumption into account in a system where robot morphologies and controllers evolve simultaneously. The results show that including the energy consumption in the fitness in a multi-objective fashion reduces the average size of robot bodies while reducing their speed. However, robots generated without size reduction can achieve speeds comparable to robots from the baseline set. © 2021 Owner/Author.

关键词： Energy efficiency

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