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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Krishnamurthy, K.T. Managuli, Manjunath Malipatil, Sushma Bagyalakshmi, K. Salake, Sudha V. Kadalagi, Pankaja S. Patil, Sheetal B. Bms College of Engineering Vtu Dept. of Electronics & Instrumentation Belagavi India Kls Gogte Institute of Technology Vtu Dept. of Electronics & Communication Belagavi India Amc College of Engineering Vtu Dept. of Computer Science Belagavi India Sri Ranganathar Institute of Engineering & Technology Dept. of Electrical & Electronics Coimbatore India Kls Gogte Institute of Technology Vtu Dept. of Computer Science Belagavi India Sgb Institute of Technology Vtu Dept. of Computer Science Belagavi India

ISBN: (纸本)9798350359688

One of the greatest significant and fastest-growing financial sectors of India's agriculture industry at the moment is poultry. Chicken output has been steadily rising globally due to superior manufacturing processes and uniform farming management. Automation is essential in today's environment, and the Internet of Things (IoT) is a rapidly developing idea. Traditional systems are being transformed into automated systems. The project's main goal is to automate chicken farms by utilizing Internet of Things technology to carry out several management-related tasks. This study examines how a poultry farm's environmental factors, similar to humidity, temperature, and ammonia gas, are automatically observed and managed to promote chicken growth. The water position is also controlled and covered with the help of a detector module. All detector values are acquired by connecting all the detector modules to the microcontroller, and also, using the WIFI module, they will be uploaded to the webpage. Being in the care of the loud ranch can get the inner eco-friendly condition over a PC using the internet. This system will control temperature, moisture, ammonia gas and water situation with the help of a cooling addict, exhaust addict, ventilation window and DC motor without any moral interface. Grounded on the threshold values, it'll switch on the bias. therefore, this system design provides automated flesh, reduces the pool, and increases the product of healthy cravens. © 2024 IEEE.

关键词： DC motors

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The Evolution of Mobile Phone Batteries: From Ni-Cd to Li-ion

The Evolution of Mobile Phone Batteries: From Ni-Cd to Li-io...

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2024 International Conference on Science, technology, engineering and Management, ICSTEM 2024

作者： Manoharan, S. Mahalakshmi, B. Govindaraj, V. Vidyalakshmi, R. Dept. of Electrical and Electronics Engineering JCT College of Engineering and Technology Coimbatore India Dept. of Physics Hindustan College of Engineering and Technology Tamilnadu Coimbatore India Dept. of Electrical and Electronics Engineering Karpagam College of Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350376913

The evolution of mobile phones has been nothing short of remarkable. The style, functionality, and capabilities of mobile phones have changed significantly over time, from the heavy and pricey gadgets of the 1980s to the elegant and sophisticated smartphones of today. One of the key components that have played a crucial role in this evolution is the battery. Without a reliable and efficient source of power, mobile phones would not have been able to become the ubiquitous devices that we know today. Extended battery life, quicker charging times, and more efficiency are the outcomes of notable breakthroughs in the evolution of mobile phone batteries over time. This study will examine the switch from Ni-Cd to Li-ion batteries used in mobile phones and how it affected the development of these devices. © 2024 IEEE.

关键词： Environmental impact

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Decomposition Algorithm for Security-Constrained Unit Commitment with AC Optimal Power Flows

Decomposition Algorithm for Security-Constrained Unit Commit...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Renl, Weihang Li, Mingze Fan, Lei Guan, Yongpei University of Florida Dept. of Industrial and Systems Engineering FL United States University of Houston Dept. of Electrical and Computer Engineering TX United States University of Houston Engineering Technology TX United States

ISBN: (纸本)9798331541125

This paper presents a comprehensive approach to solving the security-constrained unit commitment with alternating current power flows (SCUC-ACPF) problem in contemporary power systems. We introduce algorithms that decompose the problem into subproblems suitable for specialized solvers, so that the large-scale mixed-integer nonlinear program-ming SCUC-ACPF problem can be solved. In case studies, we validate the efficiency and effectiveness of our algorithms on synthetic and industry-scale power system networks. © 2025 IEEE.

关键词： Lagrange multipliers

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Effects of Mismatch on IC-Equipped Programmable Metasurfaces with Multibeam Functionality

Effects of Mismatch on IC-Equipped Programmable Metasurfaces...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Kossifos, Kypros M. Georgiou, Julius Antoniades, Marco A. University of Cyprus Dept. of Electrical & Computer Engineering Nicosia Cyprus Toronto Metropolitan University Dept. of Electrical Computer & Biomedical Engineering ON Canada

ISBN: (纸本)9781665442282

Integrated circuit (IC) technology can be incorporated in programmable metasurfaces (PMSFs) and reduce their cost and power consumption while increasing their electromagnetic manipulation capabilities. Such improvements can succour the incorporation of PMSFs as a common telecommunication technology. In order for this to be realised, the reproducibility and reliability of IC-equipped PMSFs need to be addressed. A study is presented on effects of IC mismatch on the performance of a PMSF with multibeam functionality. A mismatch of 10% to 40% of the nominal IC response is used and the effects on the far field are presented. © 2023 IEEE.

关键词： Timing circuits

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Deep Learning Assisted Intelligent Human computer Interaction for Next Generation Internet Applications

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第2期48卷 145-146页

作者： Hasan, Al Mehedi Hwang, Yong Seok Shin, Jungpil Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology Bangladesh Kwangwoon University Seoul Korea Republic of School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan

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Optimizing Renewable Energy Management and Demand Response with Ant Colony Optimization: A Pathway to Enhanced Grid Stability and Efficiency

Optimizing Renewable Energy Management and Demand Response w...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Islam, Riadul Kabir, Shaharier Shufian, Abu Rabbi, Md Shahiduzzaman Akteruzzaman, Md Lamar University Dept. of Electrical and Computer Engineering Texas United States American International University-Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798331541125

The integration of renewable energy sources into the power grid presents significant challenges due to their inherent variability and unpredictability. Efficient management of these sources and the effective implementation of demand response strategies are crucial for maintaining grid stability and optimizing energy usage. This study presents a comprehensive evaluation of the performance of Ant Colony Optimization (ACO) in optimizing microgrid operations compared to other modern algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). The primary metrics of analysis include energy cost, renewable energy utilization, grid stability, and demand response effectiveness. Through a detailed dataset analysis and algorithmic implementation, the results indicate that ACO consistently outperforms its counterparts across all key performance indicators. ACO achieved the lowest energy cost, maximized renewable energy utilization, and maintained superior grid stability and demand response effectiveness. These findings highlight the potential of ACO as a robust optimization technique for enhancing the efficiency and sustainability of microgrid systems. The study underscores the importance of selecting appropriate algorithms to meet the complex and dynamic challenges of modern energy systems, promoting a transition towards greener and more reliable energy infrastructure. © 2025 IEEE.

关键词： Demand response

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Text-Conditioned Image Synthesis - A Review

Text-Conditioned Image Synthesis - A Review

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2023 IEEE Silchar Subsection Conference, SILCON 2023

作者： Saha, Piyush Ghosh, Sumon Bhandari, Dinabandhu Calcutta Institute of Engineering and Management Dept. of Electrical Engineering Kolkata India Heritage Institute of Technology Dept. of Computer Applications Kolkata India Heritage Institute of Technology Dept. of Computer Science Kolkata India

ISBN: (纸本)9798350314144

With the advent of Generative Adversarial Networks and diffusion models, image synthesis conditioned on text description has been an active area of research. Generative Adversarial Networks are a flexible and intuitive way of conditional image synthesis and significant progress has been made in the last few years regarding visual realism, diversity, and semantic alignment. In recent years, Diffusion probabilistic models have been shown to perform better in the task of image synthesis and have been used extensively for the task of text-to-image synthesis, significantly improving visual realism, semantic alignment, and generation of high-resolution images. However, the field still faces many challenges such as the generation of high-resolution images with multiple objects and the development of suitable and reliable evaluation metrics. In this review, we contextualize the state-of-the-art text-conditioned image generation models, and critically examine current evaluation strategies, architectures, model training, and datasets. This review complements previous surveys on text-conditioned image synthesis which we believe will help researchers to further advance this field. © 2023 IEEE.

关键词： Generative adversarial networks

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Cybersecurity Threat Detection Using Deep Learning and Anomaly Detection Techniques

Cybersecurity Threat Detection Using Deep Learning and Anoma...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Rajendran, T. Mohamed Imtiaz, N. Jagadeesh, K. Sampathkumar, Balaji Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Nagarjuna College of Engineering and Technology Dept. of Information Science and Engineering Bangalore India Sri Venkateswaraa College of Technology Dept. of Computer Science and Engineering Chennai India Akshaya College of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350359688

Through the use of combined deep learning and anomaly detection approaches, this research investigates the area of cybersecurity threat detection. The study proves the framework's extraordinary success in recognizing as well as mitigating diverse cyber dangers, including insider threats and zero-day attacks, through practical testing and case studies. The framework establishes itself as a proactive cybersecurity solution adaptive to the changing threat landscape due to its high accuracy of 95%, low amount of false positives 5%, and quick reaction times. The recommendations cover user training, scalability, regulatory compliance, including cross-disciplinary collaboration. Behavioral analysis, AI explainability, IoT security, and countering quantum computing concerns should be the main areas of future research. These revelations help the cybersecurity industry become more resilient and well-prepared. In this research investigation, experiments on threat detection were conducted using deep learning algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The findings of the study reveal that a detection accuracy of 93% was achieved by CNN, while a higher accuracy of 96% in threat detection was exhibited by RNN. © 2024 IEEE.

关键词： Zero-day attack

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An Overview of Machine Learning Applications in Blockchain for Green Energy Exchanges 33

An Overview of Machine Learning Applications in Blockchain f...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Nemakonde, Pfano Nemangwele, Fhulufhelo Ratshitanga, Mukovhe University of Venda Faculty Science Engineering and Agriculture Dept. of Physics Thohoyandou South Africa Cape Peninsula University of Technology Dept. of Electrical Electronic and Computer Engineering Bellville South Africa

ISBN: (纸本)9798331535162

There is an increasing demand for affordable, decentralised and distributed electricity, which is one of the key motivating factors that has incentivised this review article's writing. Industrial 4.0 has been a conduit through which the conventional grids shift towards a self-autonomous system, and blockchain platforms are emerging as viable solutions to facilitate that paradigm. With the advent of peer-to-peer (P2P) energy trading, community-based energy exchanges have significantly contributed to reduced grid dependency. Machine learning (ML) is being implemented in predictive analytics for energy forecasting (EF), thereby aiding both distribution network operators (DNOs) and aggregators to precisely and predictably monitor, optimise and stabilise grid operations through smart agents. The main aim of this review paper is to provide a detailed analysis of the existing research and implementation approaches on blockchain-based P2P energy trading and a comparative overview of how ML applications aid predictive analytics in community-based energy exchanges. © 2025 IEEE.

关键词： Predictive analytics

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ML Assisted Web Application Firewall 12

ML Assisted Web Application Firewall

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Kalariya, Pranav Jethva, Manthan Alginahi, Yasser University of Windsor Electrical and Computer Engineering Dept. Windsor Canada Computer Science Dept. Adrian College MI United States

ISBN: (纸本)9798350330366

This study presents the development and implementation of a sophisticated Web Application Firewall (WAF) empowered by machine learning techniques to bolster cybersecurity measures. Traditional WAFs primarily rely on rule-based systems, which may struggle to adapt to the evolving nature of web-based threats. In contrast, our proposed solution leverages machine learning algorithms to dynamically analyze and respond to emerging cyber threats, providing a more proactive and adaptive defense mechanism. The core functionality of the system involves the continuous monitoring of incoming web traffic, extracting relevant features, and utilizing a machine learning model to classify the traffic as either benign or malicious. The model is trained on historical data to recognize patterns and behaviors indicative of various cyber threats, including SQL injection, cross-site scripting, and other common attack vectors. Through this learning process, the system becomes adept.at discerning malicious activities and adapting its defense strategies accordingly. The proposed model helps achieve higher precision in identifying the threat requests from normal requests. © 2024 IEEE.

关键词： Learning algorithms

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