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检索条件"机构=Artificial Intelligence and Machine Learning Engineering"

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ProductGuard: Empowering Informed Choices for Safe Living 3

ProductGuard: Empowering Informed Choices for Safe Living

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vadlamudi, Sandyarani Shashikala, K.S. Patil, Suyash Ganesh Shetty, Rajath U. Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798331527549

In a landscape where consumer decisions significantly influence personal health and well being, the necessity for informed choices regarding product safety is paramount. This project introduces ProductGuard, a mobile application designed to empower users in making conscientious and informed decisions about skincare and personal care products. The project integrates four core components aimed at assisting users in evaluating product safety, comparing ingredients, providing personalized recommendations based on skin type, and offering expert guidance through a chatbot interface. The first component employs advanced image analysis technology utilizing a dataset extracted from the Environmental Working Group (EWG) website, enabling users to capture product ingredient images and receive detailed safety analyses. The second component allows comparative analysis by uploading images of two product ingredient lists, swiftly determining the safer option. Leveraging deep learning models and MobileNetV2, the third component performs skin type classification based on facial images, ensuring accurate categorization into dry, oily, normal, or sensitive skin, further providing tailored product recommendations. Lastly, a chatbot powered by ChatGPT serves as the fourth component, furnishing users with real-time guidance on skincare, ingredient queries, and safe living practices. Developed using React Native, Flask integration, and Python backend, this application amalgamates cutting-edge technology with user-centric design for seamless navigation and accessibility across various devices. ProductGuard surpasses existing systems by offering a holistic approach to skincare analysis, integrating advanced image recognition, personalized recommendations, and real-time expert assistance, thereby empowering users to make informed and safer skincare choices. © 2025 IEEE.

关键词： Bottles

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Fake Social Media Detection using machine learning 15

Fake Social Media Detection using Machine Learning

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Thangamani, R. Vimaladevi, M. Mohammed Rifath, J. Harini, G. Hari, S. Kongu Engineering College Department of Artificial Intelligence Tamil Nadu India Kongu Engineering College Artificial Intelligence And Machine Learning Perundurai India

ISBN: (纸本)9798350370249

Social media platforms such as Facebook, Twitter, Instagram and others have become an integral part of our lives, facilitating global connectivity. But the proliferation of fake profiles on these platforms poses serious risks, including the spread of false information, phishing attacks and identity theft. This research project focuses on identifying fake Twitter profiles using machine learning techniques to distinguish them from genuine accounts. In the dataset used in this study, Twitter profiles are classified into genuine accounts (TFP and E13) and fake accounts (INT, TWT and FSF).. The main objective is to evaluate the effectiveness of the ensemble learning technique Random Forest algorithm in classifying social media profiles. In-depth discussions include algorithm architecture, hyperparameters, and selection of critical features such as follower and friend counts, status changes, and other relevant features. Additionally, alternative classifiers including neural networks, LSTM, XG Boost and others are briefly discussed in the summary. The Random Forest algorithm has a commendable 95.54% accuracy after rigorous training. This high accuracy underlines its ability to effectively distinguish between real and fake Twitter profiles. By assigning a value of 0 to genuine profiles and 1 to fake profiles, the algorithm provides a practical solution to detect and mitigate fake accounts, thereby solving the cyber security issues associated with social media. This research contributes to ongoing efforts to create a safer online environment by identifying and removing fake profiles. © 2024 IEEE.

关键词： Fake News LSTM Profiles ROC Social Media

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Enhancing Pneumonia Treatment in Children by machine learning Techniques 4

Enhancing Pneumonia Treatment in Children by Machine Learnin...

引用

4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Gajbhiye, Ashwini Gundewar, Swapnil Verma, Prateek Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Pneumonia is a health challenge that affects many people more so the children, thus leading to high death rates across the world. There is a persuading evidential proof which shows that the techniques in the application of the machine learning (ML) have helped in improving the diagnosis and treatment of pneumonia. This paper discusses a number of ML techniques such as CNNs, SVMs and Random Forest that can be used to enhance the reliability and effectiveness of detecting pneumonia from enhanced chest x-ray images. The classification shows how other methods like Min-Max normalization and a specific type of image pre-processing called Contrast-Limited Adaptive Histogram Equalization (CLAHE) can make image quality even better as well as increase the accuracy of the model. Here, by using the sophisticated dalliance of ML models it is possible to dramatically minimize the incidence of diagnostic errors and enhance the results of pneumonia treatments thus witnessing an important evolutionary leap in the specialty. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhancing E-commerce Fashion Sales through Personalized Recommendation Systems 18

Enhancing E-commerce Fashion Sales through Personalized Reco...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Subaranjani, T. Kannaiyan, Sathiyapriya Parvathy, S. Shwetha, P.R. The Oxford College of Engineering Dept. of Artificial Intelligence and Machine Learning Bangalore India

ISBN: (纸本)9789380544519

Personalized recommendation systems are pivotal in the thriving e-commerce fashion sector, enriching customer experiences and driving sales. This paper introduces an innovative method to enhance e-commerce fashion sales through personalized recommendations. The model merges Singular Value Decomposition (SVD) Reranking with customer grouping, yielding tailored product suggestions for distinct customer segments. The study comprehensively explores multiple recommendation techniques, assessing their performance using Mean Average Precision (MAP) scores. Results demonstrate the superiority of the proposed model, achieving a validation score of 0.032007. This success is attributed to the model's ability to capture latent features and offer personalized suggestions based on customer group traits. This research underscores the significance of personalized recommendations in boosting ecommerce fashion sales. The proposed approach, SVD Reranking with customer grouping, excels in providing personalized suggestions across diverse customer segments. These findings contribute to the advancement of recommendation systems in fashion, fostering customer satisfaction, minimizing returns, and bolstering sustainability. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Customer Experience Data Analytics Ecommerce Fashion Sales Personalized Recommendation Systems Privacy Concerns Singular Value Decomposition (SVD) Reranking Time-based Popularity Scores User Engagement

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Unlocking Stock Market Potential: machine learning Predictions for NIFTY50's Most Profitable Companies 4

Unlocking Stock Market Potential: Machine Learning Predictio...

引用

4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Sanda, Preetham Sagar Thota, Revanth Kodimala, Akhil Gadari, Rishab Babu Aurora, Gagandeep Padmaja, Naga Vardhaman College of Engineering Dept. Artificial Intelligence and Machine Learning Hyderabad India

ISBN: (纸本)9798350364729

The use of machine learning (ML) to recognize the stock market trend as well as companies, which are expected to provide the most profit with-in NIFTY50 index is a focus area work in the past 5 years of historical data. We use many ML models (linear regression, decision trees, and neural networks) to predict stock price movements in order to decide which are the top 5 companies with the best return. In summary, according to our experiment the ML models can be good at predicting stock trends and are thus very useful for any investors. It demonstrates how deep learning and machine learning could be used to empower the levels of investment strategies with non-linear irregularities not captured by traditional models. The dictionary tells you what kind of processing has been performed on the data (such as missing value handling, normalization etc.), which keeps things more organized. We also use feature engineering techniques such as moving averages, relative strength index (RSI), and moving average convergence divergence (MACD) to improve the predictability of these models. © 2024 IEEE.

关键词： Marketplaces

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A Hybrid Deep learning Approach for Accurate and Transparent Maize Plant Disease Classification 18

A Hybrid Deep Learning Approach for Accurate and Transparent...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Madhavi, P. Bindhu Subaranjani, T. Sigtia, Sumith Mehta, Vidhan The Oxford College of Engineering Dept. of Artificial Intelligence and Machine Learning Bangalore India

ISBN: (纸本)9789380544519

Within the domain of agricultural progress, accurately identifying instances of plant diseases emerges as a pivotal hurdle. We introduce a fresh approach to classify diseases in maize plant leaves by combining a hybrid model that merges Convolutional Neural Networks (CNNs) for visual recognition with Multi-Layer Perceptrons (MLPs) for transparency. This amalgamation aims to bridge the gap between precision and comprehensibility. Our study begins by scrutinizing the constraints of prevailing CNN and MLP-based models in the context of disease classification. Subsequently, we present our hybrid architecture, elucidating its benefits and addressing inherent challenges. Through rigorous experimentation, we showcase the model's remarkable performance, achieving training, validation, and test accuracies of 98.98%, 96.19%, and 95.76%, respectively. The significance of our proposed model lies in its potential to transform disease management in agriculture. The fusion of accuracy and interpretability not only equips farmers with actionable insights but also establishes a model for ethical and transparent AI implementation. This research propels the field forward by providing an inventive hybrid solution that excels not only in accuracy but also harmonizes with practical agricultural scenarios, thus paving the path for a more sustainable and enlightened future. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Accuracy Agriculture CNN-MLP Hybrid Convolutional NeuralNetworks Disease Classification Interpretability Multi-Layer Perceptrons Sustainability

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Innovating Drug Design for Alzheimer’s Disease via Reinforcement learning for Enhanced Molecular Generation 4th

Innovating Drug Design for Alzheimer’s Disease via Reinforc...

引用

4th International Conference on Innovations in Computational intelligence and Computer Vision, ICICV 2024

作者： Satish, Nishank Bukapindi, Manikanta K, Shreyas Akhil, Guru Malagi, Vindhya P. Department of Artificial Intelligence and Machine Learning Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9789819769919

Alzheimer’s disease is a neurodegenerative disorder that affects the brain’s intricate functions. With an estimated 50 million people worldwide affected by Alzheimer’s disease, innovative approaches to drug discovery are imperative. Developing drugs for Alzheimer's disease is challenging due to the intricacies of the brain and prolonged clinical trial durations. These challenges underscore the critical need for innovative approaches in the pursuit of effective Alzheimer's treatments. In this study, a Random Forest predictor is utilized to refine features for predictive modeling. Employing a stacked Gated Recurrent Unit architecture in the generative phase, diverse Simplified Molecular Input Line Entry System (SMILES) format molecular drugs are produced. Reinforcement learning optimizes the model for higher bioactivity. This approach yields robust outcomes, with an 84% validity rate for generated SMILES and a Tanimoto similarity coefficient of 0.70 to known molecules. This methodology showcases promising results, emphasizing its efficacy in computational drug discovery for Alzheimer’s disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Drug discovery

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Data-Driven Urban Safety: A CNN-Based Predictive Model for Manhole Hazard Detection

Data-Driven Urban Safety: A CNN-Based Predictive Model for M...

引用

2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Kumar, Sudabathula Vijay Sai Padmaja, Jagini Naga Mattaparty, Sri Harsh Ismail, Shaik Varma, Nadimpalli Madana Kailash Vaishnavi, Polasani Vardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad India

ISBN: (纸本)9798350374711

The safety of urban environments is a paramount concern, with manholes serving as critical components of infrastructure by regulating sewages but also susceptible to pose hazard when improperly maintained or obscured. The passing years have shown the level of threat an open manhole poses. In this research paper, we try to introduce a novel approach to address these concerns by leveraging Convolutional Neural Networks (CNNs) for real-time manhole status prediction. This research offers a practical and effective tool for authorities to promptly address hazardous conditions and ultimately minimizing the risks to the public. © 2024 IEEE.

关键词： Convolutional neural networks

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Transforming Digital Interaction: An In-Depth Exploration of Extended Reality(XR) Technologies 5

Transforming Digital Interaction: An In-Depth Exploration of...

引用

5th International Conference on Data intelligence and Cognitive Informatics, ICDICI 2024

作者： Mahalakshmi, S. Aruna, S. Yogeswaran, S. Ajay Suriya, K.T. Rajalakshmi Engineering College Dept. of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

In the rapidly evolving technology field, Extended Reality (XR) represents a transformative innovation. Far from being just a trendy term, XR fundamentally changes the way digital content is experienced and interacted with. Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) all fall under the umbrella of XR, which provides immersive experiences that are revolutionizing sectors such as education, healthcare, and entertainment. The paper presents a comprehensive exploration of XR by addressing its conceptual framework, historical development, current applications, and the limitations or obstacles that impede its full-scale adoption. Various industry applications of XR are examined, alongside the challenges preventing widespread integration of this technology. Future transformations and advancements in XR devices are also predicted, with the expectation that these gadgets will eventually blend seamlessly into everyday life, making digital interactions more engaging and reshaping how virtual experiences are perceived. © 2024 IEEE.

关键词： Virtual environments

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Time Series Forecasting for Energy Consumption Using XGBoost and LSTM

Time Series Forecasting for Energy Consumption Using XGBoost...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Rajasree, R.S. Yashwanth Pavankumar, K. Sree Dinesh Reddy, P. Yashas, D. D'Souza, Sonia Maria New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350361537

The increasing demand for accurate energy consumption forecasting has spurred a surge of interest in leveraging advanced machine learning techniques. This project focuses on the application in XG Boost, a powerful and versatile gradient-boosting algorithm, time series forecasting (TSF) in context of energy consumption. LSTM networks are applied for forecasting of predicted energy consumption. We analyze the abilities of LSTM models in predicting patterns of consumption, which is very crucial for structured resource management and sustainability initiatives and it exhibits a performance of 82% aacuracy, Intending to improve the prediction accuracy and address the challenges posed by the dynamic nature of energy consumption patterns, we employ XG Boost to model intricate dependencies and capture non-linear relationships within the series data and it shows a performance of 89.05% accuracy. XGBoost has oupperformed LSTM for TSF in energy consumption. © 2024 IEEE.

关键词： Data Science Energy Consumption Gradient Boosting LSTM machine learning Predictive Analytics Resource Management Sustainability Time Series Forecasting (TSF) XGBoost

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