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共 7138 条记录，以下是121-130 订阅

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Samarjini - Revolutionizing Household Cleaning with an Affordable IoT-Based Automatic Mopper 3

Samarjini - Revolutionizing Household Cleaning with an Affor...

引用

3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Joshi, Siddharth Kate, Vandana Bhalerao, Shilpa Tripathi, Prasoon Sharma, Sudhanshu Acropolis Institute of Technology and Research Dept. of Computer Science and Information Technology Indore India

ISBN: (纸本)9798350343274

The title 'Samarjini' draws its inspiration from the Sanskrit word for a cleaning tool, and this research paper introduces the development of an automatic mopper machine designed to streamline household cleaning tasks while maintaining affordability. In a market filled with high-cost alternatives, this IoT-based device stands out by offering autonomous navigation, obstacle detection and avoidance, and effective floor cleaning at a lower price. The paper explores the machine's design, hardware and software components, evaluates its performance in various scenarios, and underscores its relevance in today's fast-paced lifestyles. Extensive experimentation was conducted to assess the machine's performance in diverse environments and scenarios. The outcomes affirm that 'Samarjini' excels in floor cleaning, adept.y avoiding obstacles and autonomously navigating through spaces. © 2023 IEEE.

关键词： Floors

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A Convolution Neural Network Based Classifier for Discerning AI Generated Images and Real Images 4

A Convolution Neural Network Based Classifier for Discerning...

引用

4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Kumar, Uppala Eswar Basava Vivekreddy, Chintakuntla Sujatha, Kamepalli Vignan's Foundation for Science Technology & Research Deemed to be University Dept. Information Technology & Computer Applications Guntur India

ISBN: (纸本)9798350354218

The exponential advancement of artificial intelligence (AI) has led to the creation of synthetic images that closely resemble real-world photographs. However, the widespread distribution of AI-generated content poses significant challenges in distinguishing between genuine and synthetic imagery, particularly in cybersecurity, content moderation, and image forensics. Traditional image classification methods struggle with this task due to the striking visual similarities between the two types of images. Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, leveraging their ability to extract complex features from raw pixel data. In this study, we propose an enhanced CNN methodology to differentiate between AI-generated and real images. Specifically, we focus on three popular CNN architectures: ResNet-50, RexNet-150, and RexNet-200. By augmenting these models with advanced architectural enhancements and training strategies, we aim to improve their discriminative capabilities. Our experimental findings reveal that RexNet-150 surpasses ResNet-50 and RexNet-200, achieving superior accuracy in distinguishing between AI-generated and real images. RexNet-150 attained an impressive 97.9% accuracy on the test dataset, outperforming ResNet-50 (93.1%) and RexNet-200 (90.3%). Moreover, RexNet-150 demonstrated enhanced training convergence and speed, indicating its effectiveness in learning discriminative features for image classification tasks. The superior performance of RexNet-150 holds significant implications for online safety, content moderation, and image authenticity detection. These advancements in CNN technology foster trust in digital imagery and enhance confidence in AI applications across diverse domains. © 2024 IEEE.

关键词： Convolutional neural networks

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Stock Market Prediction Using Deep Learning

Stock Market Prediction Using Deep Learning

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Johnshaida, S.K. Kumar, G. Shiva Vignan's Foundation for Science Technology & Research Deemed to be University Dept. Information Technology & Computer Applications Guntur India

ISBN: (纸本)9798350379525

This project focuses on leveraging One kind of deep learning model is the Long Short-Term Memory (LS TM) networks., to forecast stock prices by analyzing historical data and technical indicators. The methodology involves comprehensive steps such as data collection, preprocessing, feature selection, model selection (including LSTM networks), training, and evaluation taking advantage of performance indicators such as Mean Squared Error (MSE) and Mean Absolute Error (MAE). It utilizes a dataset comprising historical gold stock prices and trading volume to train and test the LSTM model. Furthermore, Naive Bayes, Support Vector Machine (SVM), Linear Regression, and other machine learning algorithms are examined in the study, along with more complex methods like Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). Results reveal diverse accuracy levels across models, with LSTM demonstrating a tendency to overpredict stock prices. Future endeavors aim to refine model performance, investigate ensemble methods, and integrate additional data sources like economic news and sentiment analysis to bolster prediction accuracy, thereby offering investors and analysts enhanced decision-making tools in the dynamic stock market landscape. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Shopping Cart Using Radio Frequency Identification

Smart Shopping Cart Using Radio Frequency Identification

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Kesava, Gudipati Rishi Reddy, Vanukuri Karthik Trivikram, Sadineni Santhi Sri, K. Vignan's Foundation for Science Technology & Research Deemed to Be University Dept. Information Technology & Computer Applications Guntur India

ISBN: (纸本)9798350348460

The "Smart Shopping Cart Using Radio Frequency Identification"project revolutionizes the shopping experience through RFID technology, offering real-time cost updates to shoppers. This innovative system incorporates RFID readers, a microcontroller for instant cost computations, and an LCD screen for displaying the total expense. By tackling common issues associated with traditional shopping carts, like prolonged checkout procedures and the absence of live cost monitoring, our project significantly enhances the efficiency and convenience of shopping. Shoppers can now enjoy a seamless experience as the RFID technology effortlessly scans tagged products, allowing the microcontroller to promptly calculate the overall cost, which is then displayed on the LCD screen. This breakthrough not only streamlines the shopping process but also eliminates the need for manual price entries, reducing errors and enhancing overall customer satisfaction. The successful integration of these technologies marks a promising step towards the future of retail, where smart shopping carts pave the way for quicker, more accurate, and enjoyable shopping ventures. © 2024 IEEE.

关键词： Efficiency and Convenience Radio Frequency Identification (RFID) Real-time Cost Updates RFID technology Integration Seamless Shopping Experience Smart Shopping Cart

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Foreign Exchange Market Analysis based on Novel Grey Wolf based Extreme Learning Machine Predictor 4

Foreign Exchange Market Analysis based on Novel Grey Wolf ba...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Dash, Swaty Sahu, Pradip Kumar Mishra, Debahuti Veer Surendra Sai University of Technology Burla Dept. of Information Technology Odisha Sambalpur India University Dept. of Computer Science & Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350349856

Foreign exchange trading basically bridges a gap between buyer and seller to transact at a set of prices of the currencies to make profit out of it by the traders and investors. In this paper, foreign exchange predictive models are explored based on basic Extreme Learning Machine (ELM) along with optimized version of ELM. Initially, this work tried to obtain the best optimized ELM network based upon the four nature-inspired optimization methodologies such as;particle swarm, ant colony, moth flame and grey wolf optimization (GWO). From this phase, it has been observed that the ELM network optimized with GWO outperforms rest of other experimented optimized ELM networks with respect to error graphs and performance metrics. In order to evaluate the performance, the ELM-GWO currency exchange predictor, the recognition performance metrics such as;EVS, MAE, MSE and R2 Score are computed, the error graphs are obtained and the average computational time taken are compared. © 2024 IEEE.

关键词： Ant colony optimization

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Unmasking the AI Hand: A Machine Learning Approach to Deciphering Authorship 3

Unmasking the AI Hand: A Machine Learning Approach to Deciph...

引用

3rd International Conference for Innovation in technology, INOCON 2024

作者： Bhandarkar, Abhay Mushtaq Ahmed, D.M. Vishwachetan, D. Mushtaq, Arfaat Kadam, Dhrithi Saxena, Salaj Ramaiah Institute of Technology Dept. of Computer Science and Engg Bangalore54 India Ramaiah Institute of Technology Dept. of Information Science and Engg Bangalore54 India

ISBN: (纸本)9798350381931

In an era of explosive data growth, accurate text analysis tools are paramount. The surge of AI-generated texts presents a pressing challenge: discerning human-written content from AI-generated content. This affects text analysis accuracy, necessitating innovative solutions. We introduce an AI-driven model within the Model-View-Controller (MVC) architecture, categorising text as AI-generated or human-authored. Leveraging supervised learning, the model uses Count Vectorizers and Multinomial Naive Bayes algorithm for training and classification. The proposed model shows a good accuracy of 86.2%. This, with enhanced precision and F1 scores, underscores efficacy in classifying text origins. Beyond its immediate contributions, our model promises to reshape content authenticity verification, from AI-generated social media posts to verifying news sources. The model offers future directions, advancing AI-generated content detection. As AI-generated content evolves, our approach ensures credibility and reliability in text analysis within the MVC framework, addressing the critical need to navigate the AI-generated content landscape. © 2024 IEEE.

关键词： Reliability analysis

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6G on the Horizon:Technologies, Requirements, Trends, and Potential Techniques 4

6G on the Horizon:Technologies, Requirements, Trends, and Po...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Yunus, Nur Arzilawati Md Hanapi, Zurina Mohd Kamarudin, Shafinah Universiti Putra Malaysia Faculty of Computer Science and Information Technology Dept. of Communication Technology and Network Selangor 43400 Malaysia

ISBN: (纸本)9798350395914

The advance of sixth generation (6G) wireless communication technology represents a paradigm shift in the field of telecommunications, promising unprecedented levels of connectivity, speed, and reliability. This review delves into the fundamental requirements driving the development of 6G, highlighting the need for ultra-reliable and low latency. By examining cutting-edge technologies such as terahertz communication, massive Multiple-Input Multiple-Output (MIMO), and Artificial Intelligence (AI) driven network optimization, this paper identifies key trends shaping the future of wireless communication networks. Moreover, it explores potential innovations including holographic communication, self-organizing networks, and quantum communication that have the potential to redefine the capabilities of 6G networks and unlock new opportunities for connectivity and collaboration. © 2024 IEEE.

关键词： Quantum communication

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Enhancing Solar Energy Efficiency: A Deep Learning Approach for Dust Detection

Enhancing Solar Energy Efficiency: A Deep Learning Approach ...

引用

2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Vigna Harish, R. Ramya, A. Siva, S. Santhi, S. Kalaiselvi, S. Anitha, V. National Engineering College Dept. of Information Technology Tuticorin India National Engineering College Dept. of Computer Science and Engineering Tuticorin India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528126

In recent decades, power generation from PV systems has become increasingly popular. However, several environmental variables, such as dust deposition on PV panels, have significantly reduced PV energy production. Several approaches are under investigation to identify dust and decrease power loss. These include thermal imaging, image processing, sensors, and IoT devices, machine learning, and deep learning. The orientation of a solar panel and the angular inclination relative to the horizontal plane affect one of the environmental factors that impact its performance: since they have an impact on how much solar energy that enters into the photovoltaic panel's surface. Dust, for one, is another example of environmental factors: the quantity of energy output can be influenced by coming into contact with dust. Dust particles lying on the panel's surface have the potential to reduce light from reaching the solar modules, which would drastically reduces the energy output. Cleaning or reducing modules is critical, but these procedures must be continuously monitored and assessed for their effects. Image processing algorithms can be used to detect dust to optimize solar panels. In this paper, a novel dataset from Kaggle comprising images of dusty and clean solar panels is presented, aiming to facilitate the research on automated dust detection algorithms. The dataset is utilized to evaluate the performance of existing state-of-the-art (SOTA) image classification methods. Subsequently, a new Convolutional Neural Network (CNN) architecture, ResNet-50 with an additional attention mechanism, is proposed, specifically designed for solar panel dust detection. The experimental results shows that the proposed method outperforms other SOTA algorithms, achieving an accuracy level of 96.2%. © 2024 IEEE.

关键词： Thermography (imaging)

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Real-Time Sign Language to Speech Converter Using OpenCV and MediaPipe 5

Real-Time Sign Language to Speech Converter Using OpenCV and...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Pathak, Anvisha Patil, Niraj Padhy, Prashant Jadhav, Adarsh Rukhande, Smita Gadhikar, Lakshmi Fr. Conceicao Rodrigues Institute of Technology Dept. of Information Technology Navi Mumbai India Fr. Conceicao Rodrigues Institute of Technology Dept. of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798331522100

Communication barriers between deaf individuals and hearing people can lead to several significant problems such as social integration, reduced access to information and reduced opportunities thereby affecting quality of life of deaf individuals. Use of sign language for communication serves as the primary means to address these challenges. However, understanding of the sign language is very difficult for hearing individuals. To address this issue, we present a Real-Time Sign Language to text and Speech Converter to facilitate effective communication between deaf and hearing individuals. This paper presents a novel approach to sign language recognition using OpenCV and MediaPipe. The main aim of the paper is to develop real time computer vision that can translate sign language gestures into text in real-time. We use image processing techniques to segment and analyze sign gestures, OpenCV to record and process these gestures, MediaPipe for hand tracking and gesture prediction and a text-to-speech engine to translate the text into speech for auditory communication. Thus, this study offers a comprehensive sign language to speech converter system that has the potential to significantly improve communication between the deaf community and the hearing people. © 2025 IEEE.

关键词： Gesture recognition

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Evaluation of Machine Learning Models in Student Academic Performance Prediction 5

Evaluation of Machine Learning Models in Student Academic Pe...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Sandeepa, A.G.R. Mohottala, Sanka General Sir John Kotelawala Defence University Dept. of Computer Engineering Ratmalana Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Science Malabe Sri Lanka

ISBN: (纸本)9798331530983

This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations while for train set, it was 99.45%. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train set, it was 99.65%. MLP's better performance over other machine learning models strongly suggest the potential use of neural networks as data-efficient models. Feature selection approach played a crucial role in improving the performance and multiple evaluation approaches were used in order to compare with existing literature. Explainable machine learning methods were utilized to demystify the black box models and to validate the feature selection approach. © 2025 IEEE.

关键词： Students

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