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Did COVID-19 Pandemic Influence the Ether Cryptocurrency Market? Perception and Analysis

Did COVID-19 Pandemic Influence the Ether Cryptocurrency Mar...

引用

2022 International Conference on Breakthrough in Heuristics and Reciprocation of Advanced Technologies, BHARAT 2022

作者： Mithillesh Kumar, P. Jeyanthi, R. Deepa, K. Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Computer Science and Engg. Bengaluru India

ISBN: (数字)9781665436250

ISBN: (纸本)9781665436250

In recent days, DeFi tokens have gained popularity as an investment option in the pandemic period and has gained a significant amount of investment. Cryptocurrency trading is a type of DeFi that has gained a lot of attention in the global market. The value of such currencies is increasing on a daily basis and peaked during the pandemic. One of these significant cryptocurrencies is the ether cryptocurrency, which ranks second only to the bitcoin cryptocurrency in terms of the value of a single coin. The ARIMA model will be used to forecast the price of ether. In this paper, an hourly forecast and a short term period forecast are performed. The forecast clearly shows that the ARIMA model performed better on log transformed data than on the original data. It's also evident that COVID-19 pandemic has also aided the growth of ethereum when compared to previous year. © 2022 IEEE.

关键词： Ethers

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Bounded Simultaneous Messages 43

Bounded Simultaneous Messages

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43rd IARCS Annual Conference on Foundations of Software Technology and Theoretical computer science, FSTTCS 2023

作者： Bogdanov, Andrej Dinesh, Krishnamoorthy Filmus, Yuval Ishai, Yuval Kaplan, Avi Sekar, Sruthi School of EECS University of Ottawa Canada Dept. of Computer Science and Engineering Indian Institute of Technology Palakkad India The Henry and Marylin Taub Faculty of Computer Science Technion Haifa Israel University of California BerkeleyCA United States

ISBN: (纸本)9783959773041

We consider the following question of bounded simultaneous messages (BSM) protocols: Can computationally unbounded Alice and Bob evaluate a function f(x, y) of their inputs by sending polynomial-size messages to a computationally bounded Carol? The special case where f is the mod-2 inner-product function and Carol is bounded to AC0 has been studied in previous works. The general question can be broadly motivated by applications in which distributed computation is more costly than local computation. In this work, we initiate a more systematic study of the BSM model, with different functions f and computational bounds on Carol. In particular, we give evidence against the existence of BSM protocols with polynomial-size Carol for naturally distributed variants of NP-complete languages. © 2023 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Automata theory

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Exploring the Impact of Univariate Feature Selection Method on Machine Learning Algorithms for Heart Disease Prediction

Exploring the Impact of Univariate Feature Selection Method ...

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Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Mainul Islam Rafiqul Islam Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Bangladesh

This study compares various machine learning techniques such as logistic regression, support vector machine (SVM), random forest, Naïve Bayes, and decision tree. Later, a feature selection approach that considers the Univariate Feature Selection (UFS) method was applied to select the best features based on statistical testing. In this research, we employed the ANOVA F-test to pick the most significant features, and we used a cutting-edge dataset for experimental analysis. The ANOVA F-test was used to exploit the target variable's dependency on the continuous variables. The results show that employing the UFS approach for picking features improves the performance of the algorithms. According to the experimental results, using UFS with the logistic regression technique yields 93.3% accuracy, whereas the standalone Naïve Bayes algorithm achieved the best accuracy.

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Improved Dual Haptic Controller with Extra Virtual Damper for Z-Width Enhancement 3

Improved Dual Haptic Controller with Extra Virtual Damper fo...

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3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Tiwari, Arpan R. Saha, S.K. Kumar, Subodh Indian Institute of Technology Delhi Dept. of Mechanical Engineering New Delhi India Indian Institute of Technology Delhi Dept. of Computer Science Engineering New Delhi India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Haptic simulation systems use the sensation of touch to allow users to experience virtual surroundings. In interfaces that are based on impedance, haptics controllers are sampled data that use angular position and velocity to provide torque feedback. The sample and hold data collecting strategy, which connects the continuous and discrete worlds, hinders the haptic model's stability. Increasing the sampling rate enlarges the Z-width of the system but increases sampling frequencies, deteriorates the velocity information, reducing the stable range of damping. In this work, implementation of a concept similar to virtual coupling has been used by introducing an extra layer of the damper in between the virtual system and the Haptic device. Sampling the velocity at lower frequencies and position at higher one help in maximizing the stiffness range. This dual control of the improved architecture of the dual-rate controller is discussed in this paper. The suggested scheme's effectiveness was validated by theoretical stability analyses utilizing linear control methods and compared with passivity theory. © 2022 IEEE.

关键词： Controllers

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Enhancing Hyperledger Fabric Security with Lightweight Post-Quantum Cryptography and National Cryptographic Algorithms

Enhancing Hyperledger Fabric Security with Lightweight Post-...

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Open Innovations Association (FRUCT), Conference of

作者： Vinay Kumar Kasula Sarath Babu Rakki Rajkumar Banoth Dept. Information Technology University of the Cumberlands Williamsburg KY USA Dept. of Computer Science and Engineering JNTUH University College of Engineering Hyderabad TG India Dept. of Computer Science University of Texas at San Antonio San Antonio TX USA

ISBN: (数字)9789526524634

ISBN: (纸本)9798331595425

Hyperledger Fabric is a scalable and modular consortium blockchain platform designed for enterprise applications, where cryptographic algorithms play a fundamental role in ensuring data security and integrity. However, the native Fabric framework lacks support for national cryptographic standards, necessitating the integration of secure cryptographic mechanisms. This study proposes an enhanced approach to embedding national cryptographic algorithms into the Fabric platform. First, Graph-based Dependency Analysis is employed to investigate the interaction logic among Fabric components and the invocation of cryptographic functions, facilitating an efficient integration strategy. Next, the Lightweight Post-Quantum Cryptography (L-PQC) framework, which enhances resistance to quantum threats while maintaining computational efficiency, is utilized to integrate SM2, SM3, and SM4 cryptographic algorithms into Fabric’s Blockchain Cryptographic Service Provider (BCCSP) module. Subsequently, a Microservices-Based Cryptography Integration mechanism is designed to establish seamless mapping between Fabric’s cryptographic function calls and the national cryptographic algorithm interfaces, ensuring compatibility and interoperability. Finally, the implementation is evaluated using Blockchain Simulation Environments, where a fabric-gm consortium blockchain instance is deployed to validate the correctness and efficiency of the embedded cryptographic modules. Comparative analysis with the original Fabric platform reveals that the enhanced system introduces a 2.5% increase in network startup time, a 1.8× rise in transaction latency, and a 7.5% increase in dynamic certificate generation time, while maintaining operational performance within acceptable limits. The proposed integration strategy ensures secure and efficient cryptographic support within Hyperledger Fabric, making it resilient to emerging cryptographic threats.

关键词： Resistance Technological innovation Quantum computing Distributed ledger Heuristic algorithms System performance Fabrics Blockchains Cryptography Standards

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Self-Adaptive Smart Lighting System using Yolov8 and ESP32

Self-Adaptive Smart Lighting System using Yolov8 and ESP32

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International Conference on Electrical Energy Systems (ICEES)

作者： Virum Ranka Dwisha Shah Devam Shah Pradnya Saval Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

In the pursuit of sustainable living and enhanced user comfort, the integration of smart technologies into everyday devices has become paramount. This paper highlights the importance of dynamic lighting control in improving energy efficiency and the overall quality of life. Traditional lighting systems often fail to account for the dynamic nature of human activities, resulting in unnecessary energy wastage and suboptimal lighting conditions. The proposed system seeks to bridge the gap between existing lighting systems and the evolving needs of modern indoor spaces, with its focus on adaptability, efficiency, and user-centricity, promoting a more comfortable and nurturing environment. Although earlier studies have explored various aspects of smart lighting technologies, there remains a paucity of comprehensive solutions that integrate machine learning with sensor technologies to create truly adaptive environments. Novel algorithms have been utilized to optimize both visual comfort and energy efficiency. This system achieves up to 45-70% energy savings compared to existing lighting systems, making it a promising solution for both environmental and economic concerns.

关键词： Visualization Machine learning algorithms Energy conservation Smart homes Activity recognition Energy efficiency Real-time systems Trajectory Sensors Smart lighting

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Respiratory Estimation During Sleep Using dept. Camera in Real Environment

Respiratory Estimation During Sleep Using Depth Camera in Re...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Daisuke Kushida Ryuga Kodani Dept. Electorical Engineering and Computer Science Faculty of Engineering Tottori University Tottori Japan Dept. Engineering Graduate School of Sustainability Science Tottori University Tottori Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Sleep apnea syndrome (SAS) is common but people are generally unaware of this condition, as it occurs during sleep. A method was previously proposed to estimate respiration during sleep in a non-contact and non-constraint manner using a dept. camera that is suitable for everyday use. The method was validated on subjects who were awake but remains untested under sleeping conditions. In this study, 12 men aged 65 years or older were tested under actual sleeping conditions for eight days. The effectiveness and accuracy of the proposed method were confirmed through a comparison with commercially available products.

关键词： Accuracy Estimation Aging Cameras Sleep apnea Synthetic aperture sonar Consumer electronics

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Quantum-Enhanced Machine Learning for Precision Breast Cancer Diagnosis

Quantum-Enhanced Machine Learning for Precision Breast Cance...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Agniva Banerjee Dept. Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Bhopal India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. This research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes.

关键词： Image segmentation Accuracy Machine learning algorithms Software algorithms Artificial neural networks Machine learning Feature extraction Breast cancer Software Tumors

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Beyond the Blink: IoT's Next-Gen Drowsiness Detection System for Drivers 3

Beyond the Blink: IoT's Next-Gen Drowsiness Detection System...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Routray, Sidheswar Patel, Nachiket Patel, Dhruv Srivastava, Jyoti Dash, Rudra Narayan Pandit Deendayal Energy University Dept. of Computer Science & Engg. Gandhinagar India Indrashil University Dept. of Computer Science & Engg. Mehsana India Ganpat University Dept. of Computer Engineering Mehsana India School of Electrical Engineering KIIT University Bhubaneswar India

ISBN: (纸本)9798331533205

Drowsiness during driving includes high risks for the driver, the co-passengers, and the other people on the road. Advance Driver Assistance Systems (ADAS) have been proposed to reduce these risks. This work introduces a new Internet of Things (IoT) seizure detection system to improve driver safety using Raspberry Pi, which is equipped with various sensors and utilizes machine learning for real-time monitoring and analysis. Using sensor fusion technology, including monitoring yawning and eye-closing patterns, the system aims to detect early signs of driver sleepiness. Being a low-cost, versatile, and tiny device, it becomes an ideal core processor for integration into an in-car environment. Additionally, the simplicity enables seamless communication with various sensors and facilitates effective data collection. Extensive tests and simulations were conducted to evaluate the effectiveness and reliability of the proposed system for real-world driving situations. The result outcomes prove the system's accuracy and efficiency in detecting early signs of driver fatigue to enhance driver safety. The integration of Raspberry Pi and sensor fusion technology makes it economical as well as scalable approaches supporting multiple platforms. © 2025 IEEE.

关键词： Advanced driver assistance systems

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Identification of Affective States from Malayalam Audios Based on Valence Arousal Prediction

Identification of Affective States from Malayalam Audios Bas...

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Brain computer Interface & Healthcare Technologies (iCon-BCIHT), International Conference on

作者： Talit Sara George Jini George Pournami P N Dept. of Computer Science and Engg. National Institute of Technology Calicut India Dept. of Computer Science and Engg. Providence College of Engineering Chengannur India

ISBN: (数字)9798331540067

ISBN: (纸本)9798331540074

Affective state plays a key role in emotion recognition, influencing various applications from mental health evaluation to human-machine interaction. These states can be described using two primary aspects: valence, which indicates the pleasantness of the speech, and arousal, which relates to the intensity of the emotion. This study aims to automate the prediction of valence and arousal values from speech data, addressing the limitations of manual annotation and human error. The IIITSAINT-EMOMDB dataset, created from Malayalam films, uses the Circumplex Model of Affect to map emotions such as happy, sad, angry, and calm. The approach leverages machine learning models, including Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), for prediction. Experimental results demonstrate that the traditional models like SVR and ANN are outperformed by the CNN model, which achieved a mean squared error (MSE) of 0.3 for valence and 0.1 for arousal. These findings highlight the power of automated emotion recognition in large-scale audio datasets, offering promising applications in affective computing and psychological analysis.

关键词： Emotion recognition Affective computing Accuracy Annotations Computational modeling Machine learning Predictive models Data models Convolutional neural networks Cultural differences

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