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Solar Spectrum – Understanding Solar Activity Patterns Within a Solar Cycle Using Machine Learning Techniques

Solar Spectrum – Understanding Solar Activity Patterns With...

引用

Communication & Computing (WCONF), World Conference on

作者： U H Devika Disha K Sanketh R Gayathri Deeksha Deepak M S Bhargavi Dept. of Computer Science and Engineering Bangalore Institute of Technology Bengaluru India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

The Sun's overpowering impact on our planet and space behavior is undeniable. Solar cycles are characterized by variations in the number and distribution of sunspots, and other solar activities like solar flares and coronal mass ejections (CME). Solar cycle prediction involves forecasting the overall behavior and activity levels of the Sun over the course of several solar cycles. These activities play a crucial role in shaping space weather, influencing satellite communications, power systems, and Earth's climate. Consequently, accurately predicting and classifying solar phenomena using machine learning methods has garnered significant attention in astrophysics. This research compares the efficiency of Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures in forecasting solar activities, focusing on sunspots, solar flares, and CMEs. The study aims to identify the superior model for the Solar Activity Prediction System, by employing advanced machine learning techniques. Sunspot prediction, based on a univariate dataset, revealed minimal overfitting for both models, with LSTM showing closer alignment with validated data and GRU producing a consistent average. Multivariate solar flare prediction showcased varied accuracies, with LSTM at 72.76% and GRU at 71.43%, while combined models reached up to 83.89%. Additionally, the binary classification of CMEs using LSTM and GRU models resulted in an AUC of 55 percent and 47 percent, respectively. These findings offer significant perspectives into the efficacy of Recurrent Neural Network architectures in solar activity forecasting, useful for advancing solar activity forecasting systems.

关键词： Accuracy Memory architecture Weather forecasting Machine learning Predictive models Logic gates Forecasting Sun Long short term memory Satellite communications

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Decentralized e-KYC System for Secure Identity Verification in Bangladesh

Decentralized e-KYC System for Secure Identity Verification ...

引用

Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Sagar Karmoker Md. Adib Khan Nur Adnan Chowdhury Anik Md. Sabbir Hossain Habibur Rahman Ummay Khadiza Rumpa Mahamudul Hasan Dept. of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

In this modern age where everything is developing and moving forward to achieve new heights, Fraud and scam-related cases are also increasing. That’s why we need to be more careful than ever before. nowadays whether we want to open a bank account or use a financial app we first need to verify ourselves by doing e-KYC. But the problem is this e-KYC verification is needed in so many sectors and we have to do it multiple times. This paper shows how this situation can be solved by using a blockchain-based e-KYC (Electronic Know Your Customer) system. This will be a one-stop solution for e-KYC, where people need to do KYC only one time and then they can use it in multiple organizations. Because of blockchain technology, our system will be transparent, decentralized, efficient, and trustworthy. Where users can choose whom to give the e-KYC information and can transfer all the information or the state of the information within a second. The proposed e-KYC systems’ motive is to create a more secure and widespread digitalized ecosystem that will bring benefits to any country.

关键词： Technological innovation Scalability Ecosystems Organizations Blockchains Fraud Security Stakeholders Testing Resilience

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A Hierarchical BRAM/URAM Buffer for SNN

A Hierarchical BRAM/URAM Buffer for SNN

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Computing and Machine Intelligence (ICMI), International Conference on

作者： Rasha Karakchi Dept. Computer Science and Engineering University of South Carolina Columbia SC

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Applications such facial recognition, social networking and automated translation are well-known by their exponential growth in size and level of complexity. Such applications are the real-world examples of machine learning which are computationally modeled using neural network. Executing such operations are bounded by the needed memory to store and transfer datasets into computational units. Access rate and storage capacity are related, in somehow, to the type of memory and its structure. In domain-specific architectures, on_chip memories are usually utilized as data memory because they allow wider busses and higher operation rate, however they are less dense that off_chip memories such as DRAMs. Examples of on_chip memories are Block RAM (BRAMs) and UltraRAMs (URAMs). While both memories are equivalent in latency and speed, URAM is considered denser because of its capability to cascade and create larger memory without reversely impacting the frequency. In this work, we propose a hierarchical hybrid input buffer to store weight and spikes of spiking neural network accelerator. The buffer is designed as a combination of BRAMs and URAMs where BRAMs represent the lower layer (closer to processing unit) while URAM are the upper layer. We tested our design on Xilinx Ultrascale Zynq 104 FPGA device and we found that our design can run on a higher frequency with less hardware resources and BRAM/URAM utilization comparing with the state-of-the-art.

关键词： Power demand Social networking (online) Memory management Neural networks Memory architecture Random access memory Spiking neural networks

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Detecting Near-Surface Melt-Water and Basal Ice-Water Interfaces by VHF Radar Sounder data

Detecting Near-Surface Melt-Water and Basal Ice-Water Interf...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sanchari Thakur Lorenzo Bruzzone Dept. of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Very high-frequency (VHF) radar sounding from an orbital platform is a promising mission concept for subsurface observations of the Earth’s polar caps for mapping the ice sheets and ice shelves from the surface to the base. Previous feasibility studies have analyzed the detectability of the basal interface, the internal layers and the subglacial lakes in simulated VHF radargrams. However, there are few studies on the detection of near-surface melt-water and the ice-ocean interface, which are important for predicting the stability of the polar ice. This paper both presents a novel semi-supervised basal ice-water detection algorithm and exploits a state-of-the-art dielectric inversion technique to detect surface melt for the analysis of simulated VHF radargrams. Results show that about 90% of the ice-ocean interfaces are correctly detected with the proposed detection algorithm and that the dielectric inversion revealed a pool of melt-water in the Antarctic peninsula.

关键词： Radar remote sensing Radar detection Radar Lakes Ice Stability analysis Dielectrics

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Privacy-Preserving Techniques in AI-Based Healthcare: A Comprehensive Review

Privacy-Preserving Techniques in AI-Based Healthcare: A Comp...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Nandini Sharma Pritaj Yadav Dept. of Computer Science & Engineering Rabindranath Tagore University Bhopal India

ISBN: (数字)9798350378726

ISBN: (纸本)9798350378733

The incorporation of Artificial Intelligence (AI) in the healthcare has transformed patient care, diagnostics, and personalized Medicare in the today's era. As a result, it also increases significant privacy concerns due to sensitive healthcare data. This paper provides the comprehensive review of privacy-preserving methods, including federated learning, homomorphic encryption, differential privacy, blockchain, and secure multiparty computation that analyzing them in terms of privacy guarantees, computational complexity, scalability, and real-time applicability. Future work focuses on developing hybrid frameworks, lightweight privacy solutions for edge computing, and quantum-resistant cryptography to address evolving security threats. These advancements will play the pivotal role in ensuring secure, scalable, and ethically sound AI-driven healthcare systems.

关键词： Privacy Ethics Differential privacy Quantum computing Reviews Scalability Medical services Real-time systems Artificial intelligence Edge computing

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Study on Networking Models of SDN using Mininet and MiniEdit

Study on Networking Models of SDN using Mininet and MiniEdit

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Sreebha Bhaskaran Supriya M. Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

Software Defined Networking (SDN) has brought about a revolutionary shift in network management and control. The driving force behind SDN's development is the need for enhanced network programmability and scalability to accommodate rapidly changing applications and services. By decoupling the control plane from the data plane, network managers gain centralized control and programmability over resources. This study explores the architecture, encompassing application, control layer, and data layer, while highlighting the pivotal role of controllers. In addition, this work also explores SDN using Mininet and MiniEdit emulators for designing custom topologies built based on real time scenarios. Consequently, the research aims to pinpoint the optimal topology suited for various applications.

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An Innovative Approach for Detection of Motorcyclists Helmet Non-Compliance using Edge Computing

An Innovative Approach for Detection of Motorcyclists Helmet...

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data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Mulpuri Jithendra M. Madhava Krishna M. Charan Sai P. Sri Harsha Supriya M Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Using innovative technologies and creative strategies can help reduce fatalities and lessen the financial impact of accidents. This suggests a significant improvement in global traffic law enforcement and road safety advocacy. The study proposed in this paper uses an innovative approach to address the challenge of motorcyclists neglecting traffic regulations through a deep learning-driven framework. Using advanced pre-trained object detection models, the system accurately identifies helmetless motorcyclists and number plates. In addition, the proposed model does the Optical Character Recognition (OCR) to extract the license plate information from the identified motorcyclists who are not wearing helmets. The architecture was tested using an NVIDIA Jetson as the edge device, delivering quick performance, efficiently managing large data volumes, and optimizing resource use. Extensive testing and in-dept. analysis show that the proposed approach is robust and highly beneficial. The proposed system integrates OCR algorithms, edge computing capabilities, and advanced deep learning techniques to offer a scalable solution for assisting law enforcement entities and enhancing traffic safety initiatives.

关键词： Law enforcement Computational modeling Writing Helmets Road safety Safety Time factors License plate recognition Edge computing Testing

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Exploring the Efficacy of Deep Learning and Statistical Approaches in Temperature Forecasting 1

Exploring the Efficacy of Deep Learning and Statistical Appr...

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1st International Conference on Emerging Technologies and Innovation for Sustainability, EmergIN 2024

作者： Tarunkumar, K. Umesh, Aditya Humbarwadi, Maithri Sohan, B.S. Bhargavi, M.S. Dept. of Computer Science and Engineering Bangalore Institute of Technology Bengaluru India

ISBN: (纸本)9798350391268

Temperature forecasting has become increasingly critical due to the rapidly changing climate, especially in urban environments where temperature fluctuations impact public health, infrastructure, and overall quality of life. Accurately predicting temperature patterns is difficult owing to the complexity of climate systems and the increasing variability caused by global climate change. This study addresses the issue by incorporating key weather features to forecast temperatures. The study investigates conventional statistical models, such as ARIMA and SARIMA, and cutting-edge methodologies for deep learning, such as Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Transformers. Among these, the Transformer model stands out because it uses its attention mechanism to detect precise trends in longitudinal data, making it ideal for high-precision temperature forecasting. By enhancing the accuracy of these predictions, the system provides an optimal tool for mitigating the impacts of climate change, helping urban areas better prepare for and adapt to temperature fluctuations. The study highlights the importance of advanced forecasting models in addressing the growing challenge of climate variability, contributing to more effective climate adaptation strategies. © 2024 IEEE.

关键词： Global warming

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Hand Gesture Recognition using OpenCV 10

Hand Gesture Recognition using OpenCV

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Gupta, Rohan Singh, Akashdeep University Institute of Engineering Chandigarh University Deptt. of Electronics and Communication Engineering Punjab Mohali India University Institute of Engineering Chandigarh University Dept. of Computer Science Engineering Punjab Mohali India

ISBN: (纸本)9789380544472

For regulating the gestures the module which is mainly employed is Opencv. To regulate the structure the hand gesture will act as input. A hand gesture recognition technology allows us to communicate nonverbally in a natural, groundbreaking, and ultramodern way. The goal of the program is to spread a revolutionary gesture reputation strategy based on the discovery of a few form grounded traits. One digital digicam is included in the arrangement to capture the stoner's gesture and use it as an entry point into the device. One of the most important aspects of this program is to provide a device that can recognize particular physical motions and send information for tools operation. For example, a user can control a computer by performing a particular action next to a videotape digital digicam connected to a computer. Using the OpenCV module, we can extend the range of a hand gesture device in this arrangement. The device can then be controlled with hand gestures rather than using a physical component. The system utilizes high definition cameras for identifying the gestures. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Palmprint recognition

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Earthquake Prediction Through Machine Learning Approach

Earthquake Prediction Through Machine Learning Approach

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Pratik Shrote Priya Dasarwar Snehlata Dongre Prashant Khobragade Intern Center of Excellence Data Science GH Raisoni College of Engineering Nagpur India CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Dept. of Computer Science and Engineering GH Raisoni College of Engineering Nagpur India Dept. of Computer Science and Engineering H Raisoni College of Engineering Nagpur India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

The population of earthquake-prone areas is growing, as is the number of people killed and injured by earthquakes. The problem of the increasing number of populated regions that are vulnerable to earthquakes is being addressed by a shortage of experts in earthquake engineering. This implies the requirement for automated intelligence-monitored mitigation and warning systems. Predicting earthquakes is still a major problem for disaster management around the globe. The objective of this work is to build a machine learning (ML) approach that uses geographic coordinates (latitude, longitude) and elevation (height) as inputs to forecast earthquake magnitude. The estimated magnitude is used to classify the seismic risk level for a given site into high, low, or no risk categories using Richter's scale. The study offers a thorough analysis of the methodology, covering feature engineering, data collecting, preprocessing, and model construction. A variety of machine learning methods are investigated, and relevant measures are used to assess their effectiveness.

关键词： Seismic measurements Prevention and mitigation Earthquake engineering Disasters Earthquakes Machine learning Real-time systems Hazards Stakeholders Resilience

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