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International Conference on computer and Communications Technologies (ICCCT)

作者： Sathya T Tharun A K Arun S Laith Aravind P Y Saraswathy S Department of Computer Science and Business System Sri Sairam Engineering college Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Road safety is a significant concern worldwide, with a high number of fatalities occurring due to the negligence of helmet usage by two-wheeler riders. According to statistics, thousands of lives are lost annually due to non-compliance with helmet regulations. This paper proposes an automated helmet detection system using image processing techniques to enforce safety measures. The system captures real-time images of the rider using a camera and processes them with a pretrained machine learning model on a Raspberry Pi. If a helmet is detected, the vehicle's ignition is enabled; otherwise, it remains disabled. This method ensures compliance with safety regulations and contributes to reducing road accidents. The model achieves 95% accuracy in helmet detection, making it a reliable and practical solution for real-world deployment. Experimental results validate the effectiveness of the proposed system, highlighting its potential in improving road safety and reducing fatalities.

关键词： Head Road accidents Image processing Machine learning Ignition Road safety Regulation Real-time systems Safety Reliability

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Impact of Advanced Innovations on Solar Panel Efficiency and Adoption 3

Impact of Advanced Innovations on Solar Panel Efficiency and...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Supriyono, Ignatius Agus Sudaryono Azizah, Nur Zakaria, Noor Azura Madani, Muchlishina University of Raharja Department of Computer System Tangerang Indonesia University of Raharja Department of Informatic Engineering Tangerang Indonesia University of Raharja Department of Information Systems Tangerang Indonesia International Islamic University Malaysia Department of Computer Science Selangor Kuala Lumpur Malaysia

ISBN: (纸本)9798350367492

The rapid advancement of new technologies has significantly impacted the solar energy industry. This study explores how innovative technologies such as artificial intelligence (AI), the Internet of Things (IoT), and advanced materials affect the performance and adoption of solar panels. Using data from global solar panel manufacturers and adoption rates from 2010 to 2023, we aim to show how these technologies improve efficiency, reduce costs, and drive widespread adoption. Our findings indicate that integrating AI and IoT into solar panel systems significantly boosts efficiency by optimizing energy output and predictive maintenance. Advanced materials, especially in photovoltaic cells, have greatly improved energy conversion rates. These advancements lower production and maintenance costs and enhance the reliability and lifespan of solar panels, making them more attractive to consumers and investors. Regions with supportive regulations and financial incentives see higher adoption rates and better performance improvements. This research provides valuable insights for policymakers, industry stakeholders, and researchers, highlighting the importance of technological innovation in achieving sustainable energy goals. By addressing key factors that influence solar panel performance and adoption, this study contributes to the broader discussion on renewable energy development and the transition to a low carbon economy. Future research should focus on long-term studies to assess the lasting impacts of these technologies on the solar energy market. © 2024 IEEE.

关键词： Adoption Rates Advanced Materials Cost Reduction Efficiency Impact Innovative Technologies Regulatory Frameworks

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A Study Of Dynamic Thresholds Power Detection Spectrum Sensing Techniques In CRN 9

A Study Of Dynamic Thresholds Power Detection Spectrum Sensi...

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9th International Conference on Advanced Computing and Communication systems, ICACCS 2023

作者： Keerthika, J. AnandaKumar, H. Priya, V. Yuvalatha, S. Gayathri, N. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Dr NGP Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Business System Sathyamangalam India

ISBN: (纸本)9798350397376

A Cognitive radio is the mobile communication technologies which help to utilize the efficient need of radio spectrum origin. The performance of CRN is significantly impacted by the spectrum sensing function. The problem of bandwidth has risen as a more specific one with breakthroughs in remote communications. By forgiving unlicensed users to usage the available spectrum in primary networks is the solution to the CR Technology. An innovative method for enhancing the electromagnetic spectrum's use efficiency, cognitive radio is a encouraging technology. Spectrum sensing has high spectral resolution capabilities and aids in the detection of the spectrum holes. The various methodologies are used to enhance the spectrum sensing essence. These techniques are distinguished by a sensing threshold, which is crucial to the performance of the sensing. The most important element in selecting the best threshold to optimize spectrum sensing performance is the dynamic threshold in the proposed system's spectrum detection algorithm. The energy earned from the licensed user over a precise time of observation, then the dynamic threshold is commutated between two levels. The estimation of the noise ambiguity factor forms the basis for threshold evaluations. These criteria are used to hike the likelihood of detection and contraction the possibility of false alarms. © 2023 IEEE.

关键词： Cognitive radio

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Review of Cloud AI for Real-Time Fraud Detection 10

Review of Cloud AI for Real-Time Fraud Detection

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10th International Conference on Automation, Robotics, and Applications, ICARA 2024

作者： Aabitova, Gulnara Abalkanov, Miras Abitova, Gulnara A. Shuteyeva, Gulnur Aitmukhanbetova, Elvira Kulniyazova, Korlan Astana IT University Dep. Computer Engineeering Astana Kazakhstan State Univerity of New York at Binghamton Dept. Electical Engineering and Computer Science Binghamton United States Eurasian National University Dept. System Anslysis and Managemnt Astana Kazakhstan

ISBN: (纸本)9798350394245

The paper presents a comprehensive analysis of the integration of Cloud AI in real-time fraud detection, highlighting significant advancements and practical applications. It is identifying a key challenge such as data privacy and the need for scalable solutions, while also exploring the potential of innovative AI algorithms and strategic partnerships to enhance fraud detection capabilities. Through detailed case studies, the work is demonstrated the effective application of Cloud AI across various sectors. The study concludes that Cloud AI, despite not being a complete solution, plays a crucial role in strengthening fraud detection systems. Our findings suggest that strategic implementation of Cloud AI can lead to more adaptive and efficient systems, thereby significantly contributing to the economic sector's resilience against fraudulent activities. This paper underscores the transformative impact of Cloud AI in fraud detection, offering insights into its future potential and laying the groundwork for further advancements in this field. © 2024 IEEE.

关键词： Crime

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A Hybrid Approach for Maximum Power Point Tracker on PV systems

A Hybrid Approach for Maximum Power Point Tracker on PV Syst...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Ely Ondo Ekogha Pius Owolawi Computer system engineering Tshwane University of Technology Pretoria Gauteng South Africa

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

As the usage of photovoltaic is emerging to palliate the degradation of the atmosphere and the natural environment. Photovoltaic (PV) systems appear to be the key for green energy production for households and industrial sectors. However, due to the dynamic change of load for a PV system or the variations of weather conditions, most PV systems are equipped with a maximum power point tracker (MPPT) to operate at their optimum capabilities. However, the efficiency of the MPPT controller varies from the techniques and algorithms used thus affecting the operations of the PV system. This paper proposes a hybrid technique based on Artificial neural network (ANN) combines with perturbation and observation (P&O) or incremental conductance (IC) algorithms using MATLAB Simulink for tracking the ideal maximum power point under uniform and sudden change of weather conditions. A comparison is depicted with conventional techniques, the findings suggest that the hybrid ANN-IC technique has an improved accuracy of $98 \%$ and response time 0,154 seconds to MPP under uniform weather. The model presents a fast-tracking response under rapidly changing conditions for a convergence time of $1.013 \mu \mathrm{s}$. Keywords-Maximum power point (MPP) tracker, artificial neural network (ANN), perturbation and observation (P&O), incremental conductance (IC).

关键词： Maximum power point trackers Photovoltaic systems Software packages Perturbation methods Artificial neural networks Hybrid power systems Time factors

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IoT and AI-Driven Conceptual Model of Museum Ecosystem

IoT and AI-Driven Conceptual Model of Museum Ecosystem

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2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Puspasari, Shinta Siradjuddin, Indah Agustien Rachmansyah Fahtu Rahman, M. Ade Haversyalapa, Ditho Universitas Indo Global Mandiri Faculty of Computer Science Department of Informatics Palembang Indonesia Universitas Trunojoyo Faculty of Engineering Department of Informatics Madura Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Department of Computer System Palembang Indonesia

ISBN: (纸本)9798350381290

Museum ecosystems need digital transformation to provide value added to museums. This study discusses the digital museum ecosystem conceptual model based on artificial intelligence (AI) technology and the Internet of Things (IoT). The new conceptual model integrates three elements of the museum ecosystem, people, collections & infrastructure, and museum services collaborating and communicating to achieve outputs in supporting the achievement of museum organizational goals through IoT and AI-based digital technology. The conceptual model describes various forms of implementing IoT and AI in the museum ecosystem for museum management along with the value added or results generated for museum management. The development of this ecosystem conceptual model into a physical museum has an impact on optimizing museum performance by providing value added in the form of convenience and prosperity for users and various museum stakeholders, especially in Society 5.0. © 2023 IEEE.

关键词： Internet of things

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Optimizing optical pulse breakup for efficient supercontinuum generation in an all-fiber system

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Optics Continuum 2024年第2期3卷 164-175页

作者： Chang, Kuan-Yuan Gong, Chen-Jia Liu, Jia-Ming Institute of Photonic System National Yang Ming Chiao Tung University Tainan Taiwan Institute of Imaging and Biomedical Photonics National Yang Ming Chiao Tung University Tainan Taiwan Electrical and Computer Engineering Department University of California Los AngelesCA United States Graduate Institute of Optoelectronic Engineering National Chung Hsing University Taichung Taiwan

Ultra-broadband supercontinuum (SC) is generated by an all-fiber system with well-defined pulses (WDPs) as a seed laser. Through properly adjusting the lengths of the fiber segments in the system, sub-pulses with high peak powers are generated through the process of optical pulse breakup. Then, the broken optical pulses are launched into a hybrid nonlinear, which consists of a highly nonlinear optical fiber and a photonic crystal fiber, generating a SC of 1.8 W covering a spectral range from 554 nm to 2.17 µm. In this study, it is demonstrated through experimental observation that optical pulse breakup is an essential process for WDPs before the pulses are launched into a nonlinear optical fiber to generate SC. However, pulse breakup must not happen before the amplification of the WDPs;otherwise, the amplification efficiency is decreased, leading to a subsequent low efficiency of SC generation. The proper breakup of the WDPs after they are efficiently amplified is accomplished by sending these pulses through an optimum length of regular optical fiber, which depends on the peak power of the amplified WDPs. An optical fiber of an insufficient length leads to insufficient pulse breakup, which does not significantly increase the peak power of the pulses, resulting in little enhancement of SC generation. An excessively long fiber leads to excessive pulse breakup, which stretches the pulsewidth and splits the pulse energy, resulting in decreasing the efficiency of SC generation. © 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Supercontinuum generation

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An Adaptive Traffic Signal Management system Incorporating Reinforcement Learning

An Adaptive Traffic Signal Management System Incorporating R...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent systems, AICERA/ICIS 2023

作者： Masfequier Rahman Swapno, S.M. Chhabra, Gunjan Kaushik, Keshav Nobel, S.M. Nuruzzaman Islam, Md Babul Shahiduzzaman, Md. Bangladesh University of Business and Technology Dept of Computer Science and Engineering Dhaka Bangladesh Graphic Era Hill University Dehradun India University of Petroleum and Energy Studies Dehradun India Unical Dept. of Computer Modeling Electronic and System Engineering Rende Italy

ISBN: (纸本)9798350303452

Although there has been a lot of study done on traffic monitoring, control, modeling, and the use of artificial intelligence to handle these problems, there are still a lot of critical problems that need to be solved. In existing traffic signal research, a major problem is longer delays and queues, causing significant traffic congestion. Our research aims to minimize these issues, successfully reducing delays and queues. This has led to improved traffic flow and a significant decrease in congestion. Reinforcement learning for traffic signal control uses RL techniques to create intelligent agents that make real-time decisions about changing traffic signals at intersections. Its goal is to enhance traffic flow, reduce congestion, and improve overall transportation efficiency by optimizing signal phases dynamically. Reinforcement property state contain the current circumstances at an intersection, actions correspond to the choices made by the traffic signal controller, and rewards reflect the controller's performance and are in line with traffic objectives like minimizing delays, reducing congestion, cutting down on travel times, and improving safety. We scrutinize Deep Q learning by reason of Deep Q Learning gives Q value, By estimating and updating Q-values, the agent can learn which actions are more beneficial in different traffic conditions. We are able to cut the queue length by 9.7% to shorten wait times and relieve traffic. Get a happy outcome of traffic signal control and improve rewards by 9.7%. This research study showcases the promising capabilities of reinforcement learning in enhancing the synchronization of traffic light controllers, effectively mitigating the adverse consequences of traffic congestion within urban environments. The adoption of this innovative approach holds the promise of fostering a more sustainable and streamlined transportation system for the future. © 2023 IEEE.

关键词： Reinforcement learning

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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

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SN computer Science 2024年第5期5卷 549页

作者： Hamza, Manar Ahmed Hashim, Aisha Hassan Abdalla Motwakel, Abdelwahed Elhameed, Elmouez Samir Abd Osman, Mohammed Kumar, Arun Singla, Chinu Munjal, Muskaan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Management Information Systems College of Business Administration in Hawtat bani Tamim Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science College of Post-Graduated Studies University of Science and Technology Omdurman Sudan Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Machine learning Natural language processing Sentiment analysis Social media

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Revolutionizing Digital Media Content using a Smart Drone and an Innovative ML Technique 22

Revolutionizing Digital Media Content using a Smart Drone an...

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22nd International Learning and Technology Conference, L and T 2025

作者： Almalki, Faris A. Almarzouki, Rahaf A Technology Taif University College of Computers and Information Department of Computer Engineering Taif Saudi Arabia King Abdulaziz University Collage of Economics and Admissions Department of Management Information System Jeddah21589 Saudi Arabia

ISBN: (纸本)9798331532215

In the contemporary digital age, the fields of journalism and social media assume a pivotal role in disseminating information about current events and issues to the public. However, conventional techniques for gathering news and capturing images frequently encounter constraints and limitations. In recent years, technological advancements have transformed the creation and consumption of digital media content. This study presents an innovative solution that combines a smart drone with a reinforcement learning (RL) machine learning technique to enhance digital media content in a timely manner. The proposed framework has been subjected to validation and testing in a real-world scenario in Taif City through the transfer of rich digital media content. The results demonstrate promising outcomes, which support Saudi Arabia's efforts to enrich digital media content for tourism. © 2025 IEEE.

关键词： Adversarial machine learning

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