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Crafting a Machine Learning Model for Discerning Synthetic and Authentic Images

Crafting a Machine Learning Model for Discerning Synthetic a...

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Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： Misha Shah Gautam Jaiswal Dolly Sharma Amity School of Engineering and Technology Amity university Noida India Department of Computer Science and Engineering Amity university Noida India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

This paper shows the development of a Convolutional Neural Network (CNN) model with an attention mechanism and data augmentation for discerning synthetic and authentic images. Given the increase in AI-generated content there is a need for robust methods to identify an image's authenticity. The model employs ensemble learning by using ResNet50 and VGG16 architectures which are combined with a custom CNN. The project has detailed explanations of CNNs, data augmentation, TensorFlow, CUDA, ensemble models and the mathematical concepts underlying these techniques. With a dataset of $\mathbf{2 . 7}$ million images, the model aims for high accuracy in classification tasks.

关键词： Training Attention mechanisms Accuracy Computational modeling Data augmentation Data models Convolutional neural networks Ensemble learning Load modeling Residual neural networks

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Predictive Analytics in Sports: Using Machine Learning to Forecast Outcomes and Medal Tally Trends at the 2024 Summer Olympics

Predictive Analytics in Sports: Using Machine Learning to Fo...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Jhankar Moolchandani Vikrant Chole Santosh Sahu Rahul Kumar Anshita Shukla Ashok Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Gwalior Madhya Pradesh India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

This paper focuses on using machine learning approaches in predicting the medal projections and analyzing the medal distribution pattern in the 2024 Summer Olympics. Due to the availability of a large number of variables both at the individual athlete level and the country level as well as event specific measures, various statistical models are trained to predict the number of medals and unearthing traits that are associated with success in Olympics. The evaluation of the predictions of the models is done using linear regression and random forest along with support vector classifier, as well as artificial neural network classifiers. Using the research findings, it becomes evident that predictive analytics has the capacity of offering a better understanding of the performance of sport teams and formulation of strategies for future competition events. As is observed from the above findings the random forest model sampled the highest accuracy of 99. 034 percent in the general set out of the four models; thus, it is recommended ideal for complicated set data investigations Based on the results of the experiments, it is clearly observed that the random forest has the highest accuracy of 99. 034.

关键词： Analytical models Adaptation models Accuracy Linear regression Predictive models Market research Data models Predictive analytics Random forests Sports

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A Nobel Approach to Recommend Online Courses by using Hybrid Recommendation System 10

A Nobel Approach to Recommend Online Courses by using Hybrid...

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10th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022

作者： Gupta, Sumita Majumdar, Rana Gambhir, Sapna Amity School of Engineering & Technology Amity University Department of Computer Science & Engineering Noida India Sister Nivedita University New Town Department of Computer Science and Engineering Kolkata India J.C. Bose University of Science and Technology Department of Computer Engineering Faridabad India

ISBN: (数字)9781665474337

ISBN: (纸本)9781665474337

The Recommender systems, also known as recommendation engines are a relatively new but extremely indemand machine learning application being extensively used by websites. They achieve the function of a highly intelligent and responsive virtual salesman on the website by recommending or pitching an idea to the customers in which he/she might be potentially interested. The primary purpose of recommender systems is broadening the profit of the enterprise by simulating sale of maximum products to a customer and to enhance the user experience of the website. Such recommender systems are being widely developed using machine learning concepts, implemented by the Python language, by online enterprises big and small all across the globe. This work exhibits a recommender scheme for an e-learning website which bids various online courses to its users for learning new skills on an online platform. A composite method is functional to implement the same which in turn uses the two basic methods namely content based and collaborative filtering in a non-inclusive manner. © 2022 IEEE.

关键词： Recommender systems

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Towards Smart Agriculture: Automated Detection and Diagnosis of Bean Leaf Diseases Using ResNext50 Model 14

Towards Smart Agriculture: Automated Detection and Diagnosis...

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

作者： Mahajan, Manish Sharma, Rishabh Singh, Amandeep Kukreja, Vinay Vats, Satvik Amity University Amity School of Engineering and Technology Punjab Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Gulzar Group of Institutions Punjab Ludhiana India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350335095

Bean leaf halo blight is a widespread plant disease that causes large output losses by affecting the leaves of bean plants. This disease is known as bean leaf halo blight. To develop successful methods for illness management, it is essential to determine the seriousness levels of this disease in a precise and timely manner. The proposed research study present a multi-classification framework that makes use of the ResNeXt50 model to categorize bean leaf halo blight according to one of five distinct seriousness levels. The authors want to achieve the goal of developing a dependable and effective system by utilizing the capabilities of deep learning (DL) and transfer learning (TL). This will allow us to provide farmers with assistance in making well-informed decisions on disease management methods. To make the model more robust, we amped up the data by collecting and processing a collection of bean leaf pictures and by using data augmentation strategies. After being trained and tested, the ResNeXt50 model was found to have an accuracy level of 96.37% overall. Following the investigation of performance parameters for each layer, a confusion matrix was developed to evaluate the classification results produced by the model. In addition, the ResNeXt50 model was judged to be superior to other cutting-edge models under its ability to effectively categorize bean leaf halo blight. This was demonstrated by a comparison with other models. The framework that has been developed has the potential to greatly improve disease control practices, contribute to the enhancement of crop yields, and ensure that agriculture remains sustainable. Future research may concentrate on improving the model and evaluating its performance in real-world settings;this will contribute to the practical implementation of the suggested system. © 2023 IEEE.

关键词： Crops

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Review of Crowdfunding: Historical Evolution, Societal Implications, and Architectural Perspectives

Review of Crowdfunding: Historical Evolution, Societal Impli...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Kajal Joseph Deepa Parasar Computer Science and Engineering Department Amity School of Engineering and Technology Amity University Maharashtra Mumbai Maharashtra India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

This study performs an analysis on crowdfunding. It explores how crowdfunding began, how it's impacting society, the challenges it faces, and how these platforms are designed. Following a detailed historical review of crowdfunding platforms' emergence, the paper explores their significant impacts on investors, enterprises, and society at large. By reviewing relevant literature, the study identifies research gaps, thus establishing a foundation for future investigation. The analysis delves into architectural designs and frameworks, while simultaneously investigating the difficulties faced by businesses during crowdsourcing campaigns. Finally, the study synthesizes key findings and their implications for academics, practitioners, and policymakers within the domain of crowdsourcing. Decentralized crowdfunding has fundamentally transformed the investment landscape, revolutionizing traditional models and unlocking novel funding avenues for early-stage ventures. Examining this evolution in detail, with a specific focus on the AngelicCrowd platform and its impact on angel investors, this study aims to provide a holistic understanding of the dynamics within the decentralized crowdfunding ecosystem. Through analysis of key trends, obstacles, and opportunities, the research investigates factors influencing adoption, the role of blockchain technology and smart contracts, and impediments to widespread implementation. The analysis is informed by a diverse dataset, incorporating responses from angel investors utilizing AngelicCrowd and similar platforms. Additionally, the paper explores the repercussions of these developments on conventional funding strategies and the democratization of investment.

关键词： Surveys Technological innovation Crowdfunding Uncertainty Reviews Social networking (online) Smart contracts

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Using Swin Transformer and SIFT Algorithm to Detect Bone Abnormalities

Using Swin Transformer and SIFT Algorithm to Detect Bone Abn...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Simran Makwane Anjali Kiran Shweta Bhardwaj Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

This research paper investigates the combination of cutting-edge deep learning architectures, specifically the Swin Transformer, with conventional feature extraction methods, such as the Scale-Invariant Feature Transform (SIFT) algorithm, for the identification and classification of fractures. We seek to improve the accuracy and robustness of fracture identification from medical pictures by integrating characteristics acquired from SIFT with the capabilities of Swin Transformer during a 16- week period, we show the efficacy of our hybrid technique through thorough experimentation and evaluation, providing insights into its performance in comparison to traditional methods. Our research advances fracture diagnosis techniques, which may lead to more precise and effective clinical decision-making in hospital settings.

关键词： Hospitals Decision making Deep architecture Transforms Transformers Feature extraction Market research

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Bone Fracture Segmentation Using Cascaded Convolutional Neural Networks 10

Bone Fracture Segmentation Using Cascaded Convolutional Neur...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Mittal, Ruchi Malik, Varun Kumar, Manoj Chaturvedi, Prateek N Rao, A.L. Khan, Akhilesh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Manav Rachna University Department of Computer Science and Engineering Haryana India Amity School of Engineering and Technology Amity University Department of Mechanical Engineering Greater Noida India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

In order to effectively treat bone fractures, an appropriate diagnosis must be made quickly after the injury occurs. Medical picture segmentation is an area where deep learning methods, especially Convolutional Neural Networks (CNNs), have made significant strides in recent years. Cascaded Convolutional Neural Networks (C-CNNs) are proposed as a new method for segmenting bone fractures in this research. The cascaded architecture enables the segmentation model to focus on refining the boundaries and intricate details of fractures, leading to improved accuracy and reliability. The first stage of our proposed framework consists of a dedicated CNN designed to detect potential fracture regions within X-ray images. By efficiently identifying the candidate fracture areas, the model reduces the search space for subsequent stages, enhancing computational efficiency. In the second stage, a refined CNN further processes the detected regions to accurately delineate the boundaries of the fractures. The network can learn complicated fracture patterns and nuances seen in the X-ray pictures because to the cascaded architecture that allows for specialized emphasis at each level. We used a large dataset of annotated X-ray images encompassing various fracture kinds and severities to train and assess the proposed C-CNN architecture. In terms of segmentation accuracy, sensitivity, and specificity, our experimental findings show that the cascaded technique excels above typical single-stage CNN models. The suggested technique also shows resilience over a wide range of fracture types, from the most frequent to the rarest, making it excellent for use in the clinic. © 2023 IEEE.

关键词： Bone Fractures Deep Learning Medical Image Segmentation Segmentation Accuracy

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Bayesian-Enhanced EEG Signal Analysis for Epileptic Seizure Recognition: A 1D-CNN LSTM Approach

Bayesian-Enhanced EEG Signal Analysis for Epileptic Seizure ...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Mantra Jain Harshit Bhardwaj Ansh Srivastav Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

The intricate neurological condition known as epilepsy, which is common across the world, presents consid-erable difficulties in accurately identifying and differentiating between non-epileptic and epileptic activity using electroen-cephalograms (EEGs). To customize successful therapies, it is essential to accurately identify the kinds of epileptic activity. Since epilepsy affects about 50 million people worldwide according to latest update of WHO and is typified by spontaneous seizures, early identification and prediction are vital in enabling people to minimize possible harm. The current seizure prediction methods lacks, with particular emphasis on their limited performance on small training datasets and their disregard for time-series data. To identify small patterns in EEG data, this study combines cutting edge technologies, like Long Short-Term Memory (LSTM) and 1D-CNN (Convolutional Neural Network) leveraging data from five hundred patients acquired from the UCI Machine Learning Repository. To optimize the 1D-CNN LSTM architec-ture and hyper-parameters, Bayesian optimization is employed, allowing for efficient exploration of the parameter space. It's effectiveness is not only limited to enhancing the performance metrics of a particular model but also minimizing the computing power required for fine tuning. The research evaluates the effectiveness of the 1D-CNN LSTM-based model, showcasing its potential as a reliable tool for automated epilepsy detection with accuracy of 99.47% (~ ~ 100%), average sensitivity of 99.45%, and average specificity of 99.57%. This approach, emphasizes the significance of anticipating seizures in advance, attempts to provide epileptics the tools they need to control and avoid seizures in advance, so ultimately enhancing their quality of life for patients.

关键词： Training Computational modeling Epilepsy Brain modeling Electroencephalography Data models Reliability

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Machine Learning-Based Comparative Analysis of COVID-19 Infected Cases with GDP and World Happiness Report 6th

Machine Learning-Based Comparative Analysis of COVID-19 Infe...

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6th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2022

作者： Gupta, Meenu Kumar, Rakesh Sharma, Shanon Bansal, Mitashi Obaid, Ahmed J. Chandigarh University Mohali India Amity School of Engineering and Technology Noida India Faculty of Computer Science and Mathematics University of Kufa Kufa Iraq

ISBN: (纸本)9789811995118

Today, the world has been struck by an indomitable virus and an unprecedented situation arising due to COVID-19 sickness. The current circumstance is an extremely extraordinary circumstance, and each country in this world is attempting to relieve and reduce its spread. In this paper, we will research on how to pre-process and combine datasets to ascertain required measures and set them up for an investigation. This paper aims at reviewing the correlation between the above-mentioned factors. COVID-19 dataset was imported and set up for examination by dropping segments and collecting columns. After deciding on and computing a decent measure, the examination results were visualized using seaborn. The factors like gross domestic product (GDP), happiness, and social support are the factors that played a decisive role in the impact of the coronavirus on a nation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Comparative Analysis of LSTM and Random Forest Algorithms for Sentiment Classification in Movie Reviews 3

Comparative Analysis of LSTM and Random Forest Algorithms fo...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Purohit, Shardha Rajput, Aarfa Vats, Satvik Mudgal, Rusha Shah, Owais Ahmad Verma, Aditya Noida International University School of Journalism & Mass Communication Gautam Budh Nagar India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Amity University Amity School of Communication Manesar Gurugram India K. R. Mangalam University School of Engineering & Technology Gurugram India

ISBN: (纸本)9798350375190

This study conducts sentiment analysis on 50,000 movie reviews, comparing the traditional Random Forest classifier with a Long Short-Term Memory (LSTM) network. The Random Forest achieved robust metrics across accuracy, precision, recall, and F1-score (0.85), while the LSTM excelled in capturing the complex language patterns inherent in the reviews through its sequence-based processing. This study has refined the data by removing irrelevant characters and employing tokenization and lemmatization to enhance model training. Despite its higher computational requirements, the LSTM showed potential in detailed sentiment analysis. Future directions include exploring other neural architectures, enlarging the dataset, and practical applications to improve model efficacy. The study highlights key insights into sentiment analysis methodologies and their implications for both academic and practical realms. © 2024 IEEE.

关键词： Long short-term memory

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