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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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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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Design of a High Voltage Common Mode Resilient FlexRay Receiver in 180nm/5V CMOS Process 6

Design of a High Voltage Common Mode Resilient FlexRay Recei...

引用

6th Iranian International Conference on Microelectronics, IICM 2024

作者： Mansoury, Hamid Sadat Saeedi, Saeed Atarodi, Mojtaba Sharif University of Technology Dept. of Electrical Engineering Tehran Iran Tarbiat Modares University Dept. of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331511371

A FlexRay receiver that is capable of operating within [-60V, 60V] common mode range using a common mode cancellation method is presented in this paper. The receiver uses 5V MOSFET transistors and incorporates a digital block to reduce the required number of comparators to 2, potentially leading to lower power consumption and die area. Extensive simulations have been conducted to ensure the receiver's functionality within a temperature range of [-40°C, 150°C], which is particularly crucial for automotive applications. Furthermore, corner and mismatch simulations have been performed to ensure the circuit's process tolerance and suitability for mass production. Additionally, Electromagnetic Immunity simulations have been performed to ensure that the receiver, intended for automotive applications, meets the necessary standards. The layout of the receiver occupies an area of 153.6 × 128.7 μm2.. © 2024 IEEE.

关键词： CMOS integrated circuits

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Smart Shopping Cart Using Radio Frequency Identification

Smart Shopping Cart Using Radio Frequency Identification

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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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Exploring emerging spreaders through GCN-based link prediction and a novel centrality method 17

Exploring emerging spreaders through GCN-based link predicti...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Nandi, Suman Maji, Giridhar Dutta, Animesh National Institute of Technology Dept. of Computer Science and Engineering West Bengal Durgapur India Asansol Polytechnic Dept. of Electrical Engineering West Bengal India

ISBN: (纸本)9798331531195

Exploring influential spreaders and predicting missing links in complex networks is essential for understanding and effectively controlling network dynamics. This paper presents a Graph Convolutional Network (GCN)-based link prediction method to estimate the probability of future link formation. We incorporate node features that capture local and global topological connectivity structures and feed these into the GCN model, where convolutional layers aggregate neighboring information and transform node features. This approach enables the model to capture structural patterns by integrating local and global information from neighboring nodes. In the final layer, the GCN model computes a prediction score representing the likelihood of an edge's existence, using insights gained during training. Finally, considering the predicted links, we update the network structure and introduce a novel centrality method called Emerging Spreader Centrality (ESC) to identify emerging spreaders within this augmented network. We conduct two separate experiments to evaluate the performance of the GCN-based link prediction and the ESC method, comparing their effectiveness with various state-of-the-art methods. Results demonstrate that our approach not only effectively predicts future links but also identifies emerging spreaders in the augmented networks. © 2025 IEEE.

关键词： Prediction models

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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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Research on Suspended Sediment Concentration Detection Based on the ISSA-RF Model

Research on Suspended Sediment Concentration Detection Based...

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2024 OES China Ocean Acoustics, COA 2024

作者： Ning, Gengxin Lei, Zihan Li, Yuan Xiao, Ruojun Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources South China University of Technology Dept. of Electronic and Information Engineering Guangzhou China South China University of Technology Dept. of Electronic and Information Engineering Guangzhou China South China University of Technology Dept. of Civil Engineering and Transportation Guangzhou China

ISBN: (纸本)9798350340297

Traditional methods for detecting suspended sediment concentration, such as the weighing method, optical method, and remote sensing monitoring method have high equipment requirements and complex detection processes. To address these issues, a multi-frequency acoustic suspended sediment concentration detection device is constructed. Based on the characteristics of the received signals, the use of frequency domain features is proposed to improve the random forest algorithm for detecting the suspended sediment concentration, namely the Random Forest Based on Improved Sparrow Search Algorithm (ISSA-RF) algorithm. This algorithm enhances the traditional sparrow search algorithm by optimizing its initialization process to mitigate issues related to reduced population diversity and convergence to zero during the search process. Additionally, this improved algorithm is utilized to select the optimal parameters of the random forest, thereby reducing the time required for constructing the random forest model. We use the ISSA-RF algorithm to simulate the detection of suspended sediment concentration, and the results show that the algorithm effectively improves the efficiency of constructing the random forest model and the accuracy of detection. © 2024 IEEE.

关键词： Suspended sediments

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A Multiple Branch Fusion Network for Feature Learning and Hyperspectral Image Classification 14

A Multiple Branch Fusion Network for Feature Learning and Hy...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kanthi, Murali Rao, K. Venkateshwara Pavani K, Sudha Bhaskar, Nuthanakanti Bhukya, Shankar Nayak Hitendra Sarma, T. Cmr Technical Campus Telangana Hyderabad India CMR College of Engineering & Technology Dept. of Computer Science & Engineering Telangana Hyderabad India Dept. of Computer Science & Engineering Cmr Technical Campus Telangana Hyderabad India Vasavi College of Engineering Dept. of Information Technology Telangana Hyderabad India

ISBN: (纸本)9798350335095

Hyperspectral image (HSI) classification has made extensive use of deep learning approaches, specifically Convolutional Neural Network (CNN). CNN's ability to fully capture local contextual aspects of HSI makes it an excellent HSI classifier. Although, a significant factor in the poor classification performance is the scarcity of training data. Conventional CNN-based methods don't use the integrate-band relationships of HSI as much as they could because they typically employ 2D-CNNs to extract features. On the other hand, 3D-CNNs use a similar procedure to get both spectral and spatial data. This gets around the problem with 2D-CNNs, but it could mean that not enough features are extracted. As a solution, we offer a multi-scale 3D-CNN with multi branch feature fusion, which we call multi branch fusion CNN (MBF-CNN), for HSI classification. We begin by applying 3D-CNN on multiple scales to extract HSI features. After that, the multiple distinct characteristics are streamlined and combined together. The fused features then go through several fully connected layers to get the classification outcomes. The experiments are executed on both the new data sets and the publicly accessible benchmark data sets to expose the efficiency and durability of the proposed model. Empirically, it has been demonstrated that the presented model succeeds over the remaining state-of-the-art approaches in terms of classification accuracy. © 2023 IEEE.

关键词： Classification (of information)

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3D-CAN: A 3D Convolution Attention Network for Feature Extraction and Classification of Hyperspectral Images

3D-CAN: A 3D Convolution Attention Network for Feature Extra...

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2023 IEEE International Conference on Network, Multimedia and information technology, NMITCON 2023

作者： Kanthi, Murali Rao, K. Venkateshwara Sekhar Reddy, L. Chandra Sarma, T. Hitendra Bhaskar, Nuthanakanti Vasundhara, Nam Telangana Hyderabad India Cmr College of Engineering and Technology Dept. of Computer Science and Engineering Telangana Hyderabad India Telangana Hyderabad India Vasavi College of Engineering Dept. of Information Technology Telangana Hyderabad India Cmr Technical Campus Dept. of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350300826

In recent years, the deep learning method has gained widespread popularity in hyperspectral image classification (HSI) tasks because of remarkable feature extraction capability. However, conventional deep learning approaches often yield suboptimal classification performance due to the limited discrimination ability of the extracted features. Moreover, the challenge of obtaining high classification performance with a limited number of samples in hyperspectral images (HSIs) has emerged as a prominent research area of interest. To address this concern, this letter provides a novel 3D convolution attention network (3D-CAN) for feature extraction and HSIs classification. The attention mechanism employed in this context enables capturing both the long-range spatial dependencies of HSIs in the horizontal and vertical directions and the varying significance levels across distinct spectral channels. The experiments are executed on both the new data sets and the publicly accessible benchmark data sets to expose the efficiency and durability of the proposed model. Empirically, it has been demonstrated that the presented model succeeds over the remaining state-of-the-art approaches in terms of classification accuracy. © 2023 IEEE.

关键词： Convolution

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