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Enhancing Distribution System Efficiency Through Vector Space control of Power Inverters for Improved Power Factor

Enhancing Distribution System Efficiency Through Vector Spac...

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advances in Electrical, computing, communication and Sustainable Technologies (ICAECT), international conference on

作者： Adriàn Mullo Manuel Jaramillo Diego Carrion Electrical Engineering Department Universidad Politécnica Salesiana Quito Ecuador

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Regarding stability and efficiency, DG, characterized by decentralized energy production on a small scale, presents unique challenges and opportunities. This study investigates the integration of dynamic loads into the test system, validating the effective operation of the implemented systems and controls while emphasizing improvements in power *** factor (PF) is a vital indicator of efficiency and is essential for optimizing electrical loads. This paper proposes utilizing space vector pulse width modulation (SVPWM) control strategies in power inverters to enhance the PF. The research implements distributed generation (DG) and dynamic reactive power compensation control to improve system conditions further. The study presents theoretical foundations, experimental results, and comparative analyses focused on PF enhancement. Regarding stability and efficiency, DG, characterized by decentralized energy production on a small scale, presents unique challenges and opportunities. This study investigates the integration of dynamic loads into the test system, validating the effective operation of the implemented systems and controls while emphasizing improvements in power factor.

关键词： Space vector pulse width modulation Reactive power Production Aerospace electronics control systems Vectors Stability analysis Inverters Distributed power generation Voltage control

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Fighting the Illusion: Exploring Consumer Defense Against Deepfake Technology Using Protection Motivation Theory

Fighting the Illusion: Exploring Consumer Defense Against De...

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advances in Electrical, computing, communication and Sustainable Technologies (ICAECT), international conference on

作者： Kanchan Pranay Patil Dhanya Pramod Sumon Mitra Suraj Sarma Pranavi Sodagiri Sudhansu Saraswat Symbiosis Centre for Information Technology Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

With an emphasis on comprehending and mitigating the adverse effects of Deepfake in the metaverse, this study seeks to create a thorough understanding of the nuances of how people protect themselves from the adverse effects of Deepfake. Drawing from Protection Motivation Theory (PMT), we propose a framework to explore protection motivation from deepfakes'. Deepfakes, acting as stressors, can trigger anxiety, fear, and selfdoubt. Studying the interplay between deepfake threats and coping mechanisms can reveal interventions for protection motivation. This can guide the development of secure measures for a safer, more enjoyable virtual experience. This empirical research relies on quantitative data collected from 291 consumers, which was analysed using structural equation modeling. Perceived Severity, Perceived Vulnerability, and Self-Efficacy, had significant influence on Protective Motivation from Deepfakes. However, Response Efficacy and Response Cost influence on protection motivation was found insignificant. This Research paper shall help Technology practitioners, Metaverse Designers, IT professionals in Improving Deepfake Detection and Mitigation, Enhancing User Awareness and Education, designing user-friendly deepfake detection tools, and Strengthening Metaverse Security and Trust. Research is essential to creating safeguards, defending marginalised populations, and guaranteeing a secure and wholesome virtual future.

关键词： Deepfakes Ethics Costs Metaverse Social networking (online) Prevention and mitigation Mathematical models Appraisal Security Protection

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Secure and Optimized Cloud Storage with Privacy Preservation using Improved SHA-256

Secure and Optimized Cloud Storage with Privacy Preservation...

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Computational Intelligence, communication Technology and networking (CICTN), international conference on

作者： Aditi Joshi Ganesh Gopal Devarajan Marimuthu Karuppiah Yogesh Kumar Sharma Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad Uttar Pradesh India Presidency School of Computer Science and Engineering Presidency University Bengaluru Karnataka India Department of CSE Dewan V S Institute of Technology Meerut U.P India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

With the increased reliance of enterprises on cloud storage solutions for data management, maintaining safe and efficient storage while emphasizing privacy protection is crucial. With an emphasis on user privacy, this study examines the methods, tools, and difficulties currently involved in attaining safe and effective cloud storage. It looks at access control protocols, encryption techniques like SHA-256, and new technologies that are intended to improve the security and effectiveness of cloud storage systems. The study also addresses the changing trends influencing the security of cloud storage in the future and emphasizes the significance of adhering to privacy laws.

关键词： Access control Cloud computing Privacy Data privacy Technological innovation Shape Regulation Encryption Servers Secure storage

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Ensemble Features Selection and GRU-Based Detection DDoS Attack on Software-Defined networking controller

Ensemble Features Selection and GRU-Based Detection DDoS Att...

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advances in Computer Science, Electrical, Electronics, and communication Technologies (CE2CT), international conference on

作者： Nawal Rifka Annisa Muhammad Aidiel Rachman Putra Adifa Widyadhani Chanda D'Layla Tohari Ahmad Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Informatics Department Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The increasing adoption of Software-Defined networking (SDN) has introduced a more flexible and programmable network architecture. However, this shift has also exposed SDN controllers to Distributed Denial of Service (DDoS) attacks, which can disrupt the entire network by overloading the control plane. Previous detection methods struggle with low accuracy due to the use of irrelevant features and unrealistic datasets. Additionally, previous study RNN-based model, which suffered from long processing times. The feature combination approach used based on intersection only. As a result, utilizing other feature combination techniques could produce more relevant features, enhancing the effectiveness of the DDoS detection process. This research proposes a novel approach for detecting DDoS attacks on SDN controllers using a Gated Recurrent Unit (GRU)-based model with ensemble feature selection The proposed approach is validated using DDoS Attack SDN datasets, with best performance evaluated through metrics are 0.989 detection accuracy, 0.988 precision, 0.983 recall, and 0.985 F1-Score.

关键词： Measurement Training Accuracy Prevention and mitigation Network security Feature extraction Denial-of-service attack Computer crime Software defined networking Testing

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Design of Wide Neural Network Model for controlling Tunable LED Luminaire: (Neural Network based Luminaire control)

Design of Wide Neural Network Model for Controlling Tunable ...

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advances in Electrical, computing, communication and Sustainable Technologies (ICAECT), international conference on

作者： Ancy Princia Aranha Anna Merine George Vedavyasa Kamath Ciji Pearl Kurian Padmashree K. S Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Electronics and Communication Engineering BMS Institute of Technology & Management Bengaluru India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

This paper explores machine learning models to design adjustable light spectra for supporting circadian entrainment using LED luminaires. The approach combines practical experimental work with sophisticated machine learning methods to enhance lighting solutions. There is a growing demand for adjustable sources to improve efficiency and reduce costs, leading to the development of multichannel LED drivers capable of operating at different brightness levels and spectra for each LED channel. The multichannel LED driving circuitry is a critical component of human-centric lighting, which aims to enhance the well-being and health of occupants. The experiment utilized a four-channel warm white and cool white combination LED luminaire under dimming and Correlated Colour Temperature (CCT) variation. Current-controllable LEDs for different CCTs and intensities were tested with the help of DMX 512 Decoder and master control. The Wide Neural Network model designed outperforms other models in performance, helping to determine the current requirement of the lighting control system to set the desired circadian entrainment conditions.

关键词： Accuracy Neural networks Lighting Machine learning Predictive models Light emitting diodes Data models Lighting control Decoding Mixers

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Enhanced Coffee Bean Defect Detection Using Super-Resolution GANs and Deep Learning

Enhanced Coffee Bean Defect Detection Using Super-Resolution...

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Computational Intelligence, communication Technology and networking (CICTN), international conference on

作者： S. Prabu P.S. Thanigaivelu S. Poonkodi M. Anand Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai TamilNadu India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Image Super-Resolution (SR) is a key challenge in computer vision, focusing on enhancing low-resolution images while preserving details. This paper presents a novel method using Generative Adversarial Networks (GANs) for improving image quality, specifically targeting coffee bean defect detection. The proposed SR-GAN model includes a generator that upscales low-resolution inputs and a discriminator that differentiates between generated high-resolution images and real ones. Trained on a coffee bean dataset, the model optimizes loss functions to balance perceptual quality and fidelity, capturing subtle defects like cracks and discolorations. Enhanced images are processed by two Convolutional Neural Networks (CNNs): one identifies broad defect categories, such as defective black beans, brown beans, broken beans and non-defective high-quality beans. This layered approach ensures more precise defect identification and classification, overcoming limitations of previous methods that struggled with subtle defects. Performance evaluations show that SR-GAN and CNN significantly outperforms existing models in resolution enhancement and classification accuracy, offering a robust solution for improving quality control in coffee production.

关键词： Deep learning Performance evaluation Image quality Computer vision Computational modeling Superresolution Quality control Production Convolutional neural networks Defect detection

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Data-Driven Credit Scoring: Integrating Genetic Algorithms With Ensemble Learning for Multi Class Classification

Data-Driven Credit Scoring: Integrating Genetic Algorithms W...

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advances in Electrical, computing, communication and Sustainable Technologies (ICAECT), international conference on

作者： Arinjay Jain Dhanya Pramod Symbiosis Centre for Information Technology (SCIT) Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

In the unexpectedly dynamic financial world, credit scoring plays a pivotal role in assessing the creditworthiness of people and agencies. Traditional statistical models, though effective, regularly face obstacles in managing complex and large datasets. This study introduces a unique method to credit score scoring by using integrating genetic algorithms with ensemble learning strategies to improve class accuracy. The research utilizes a dataset of consumer financial and demographic variables to broaden predictive models, mainly the use of XGBoost and Random Forest classifiers. Through hyperparameter optimization using genetic algorithms, the models acquire achieve improvements in overall performance. Further, ensemble techniques, consisting of weighted ensembling and stacking, are hired to leverage the strengths of a couple of models. This research promotes the capability of combining genetic algorithms and superior ensemble techniques in growing strong and correct credit score scoring models, contributing to extra efficient risk control inside the financial zone. The findings suggest large implications for banks and monetary establishments in improving decision-making tactics and customer segmentation.

关键词： Accuracy Stacking Propulsion Predictive models Hyperparameter optimization Ensemble learning Reliability Optimization Random forests Genetic algorithms

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Edge computing for Real-Time Decision-Making in Industrial Automation Systems - A Comprehensive Review

Edge Computing for Real-Time Decision-Making in Industrial A...

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Advancements in Electrical, Electronics, communication, computing and Automation (ICAECA), international conference on

作者： R. Raffik V. J. Balamurugan N. Gopinath Pandian Department of Mechatronics Engineering Kumaraguru College of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The real-time decision-making potential brought by edge computing is disrupting industrial automation, transcending the speed-and-bandwidth limitations of traditional cloud computing. By enabling on-site data processing, edge computing effectively low-latency and responsive applications, e.g., predictive maintenance, process optimization, and autonomous robotics. This paper discusses edge computing architecture and deployment models, its hardware and software components, and networking infrastructure. The objectives to review are to analyze how edge computing facilitates real-time decision-making in automated industrial processes. The discourse is then fully addressed around the different core technologies, advantages, concerns, real-life instances, and future trends toward increasing efficiencies, lowering latencies, and making smart manufacturing possible. Integration of AI and machine learning at the edge levels make real-time analytics, thus improving efficiency and agility. Key challenges under consideration include security, scalability, and economic impact, along with their applications in the real world. Future direction encompasses AI-based analytics, energy-efficient edge devices, and seamless interoperability poised to change its status from enabler to disrupter concerning industrial automation.

关键词： Automation Reviews Decision making Process control Real-time systems Energy efficiency Security Optimization Interoperability Edge computing

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DCTCP-FQ: Enhancing Fairness and Convergence Time in Data Center Congestion control

DCTCP-FQ: Enhancing Fairness and Convergence Time in Data Ce...

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international conference on computing, networking and communications (ICNC)

作者： Haoyu Wang Xiaoqian Zhang Allen Yang Bo Sheng Department of Computer Science University of Massachusetts Boston Boston USA Department of Computer Science University of Nebraska Omaha Omaha USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

Efficient congestion control is vital for the performance of modern data centers, which host diverse and latency-sensitive applications. Data Center TCP (DCTCP) is a well-established algorithm that utilizes Explicit Congestion Notification (ECN) to achieve low latency and high throughput. However, DCTCP has drawbacks regarding convergence time and fairness, especially when managing flows that start at different times when there is already congestion. To address these limitations, we introduce DCTCP-FQ, an enhanced version of DCTCP that incorporates an active queue management strategy. DCTCP-FQ employs selective packet marking at congestion points, targeting flows with transmission rates exceeding the average sustainable rate. This strategy enables faster convergence for laterstarted flow and improved fairness without compromising the throughput and latency benefits of DCTCP. Extensive simulations demonstrate that DCTCP-FQ achieves significant improvements in convergence time and flow fairness compared to traditional DCTCP in the scenario where a congestion point adds a new flow. According to our evaluations, DCTCP-FQ archives about an average of 64.82% faster in flow completion time than DCTCP for smaller size flow ranging from 200KB to 1000kb under a 1Gbps congestion point and about 43.91% increase in Jain’s fairness index in multi-hop multi-bottleneck flows topology for mixed starting flows, making it a robust solution for next-generation data center networks. This paper details the design, implementation, and evaluation of DCTCP-FQ, highlighting its potential to enhance network performance and fairness in data center environments.

关键词： Data centers Network topology Simulation Spread spectrum communication Throughput Topology Proposals Low latency communication Next generation networking Convergence

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A Comparative Analysis of Depression Detection with and without Natural Language Processing

A Comparative Analysis of Depression Detection with and with...

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international conference on ent computing and control Systems (ICICCS)

作者： L.Mary Gladence Chukka Saipooja Pranathi Naman Raj Merlin Mary Jenitha School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

In the recent times, intensifying struggles for sustenance and education, have sharply escalated competitive tensions among individuals, leaving many students especially the adolescents in prolonged states of stress and apprehension, due to the lack of proper guidance, trauma or other aspects leading to a significant surge in mental health issues. The recent advancement in social media like Instagram, Facebook, Twitter, etc. has made the communication easy but also has become something that is offering these teenagers a fresh opportunity or method to release or express their emotions which they might otherwise keep to themselves. This research focuses on analyzing adolescents' Twitter posts for detection of depression. Textual data will be harvested from these posts and transformed into input suitable for machine learning algorithms. Different forms of machine learning algorithms are going to be experimented upon, using and not using NLP— Natural Language Processing, to design a system for the recognition of patterns of depression in individuals.

关键词： Support vector machines Analytical models Machine learning algorithms Social networking (online) Blogs Mental health Nearest neighbor methods Depression Natural language processing Web sites

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