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

共 2036 条记录，以下是51-60 订阅

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Secure Photo Sharing Across Platforms Using Blockchain Technology 4

Secure Photo Sharing Across Platforms Using Blockchain Techn...

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

作者： Nivetha, K. Harini, M. Harshinni, V. Nivetha, N. Tamilselvan, P. M Kumarasamy College of Engineering Department of Artificial Intelligence and Data Science Karur India M Kumarasamy College of Engineering Department of Artificial Intelligence and Machine Learning Karur India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

With the rapid expansion of online social networks (OSNs), photo sharing has become increasingly prevalent, raising significant privacy and security concern. Existing approaches, like centralized access control mechanisms and encryption-based privacy solutions, suffer from inconsistent policy enforcement, unauthorized access risk, and lack of transparency. Moreover, traditional OSN platforms afford limited control over the dissemination of content with respect to counteracting illegal reproductions. In order to address these challenges, Photo Chain is designed. The relevance of this name derives from its concept of using a blockchain mechanism to enable photo sharing in social networks securely and in a controlled manner. The system has smart contracts for dynamic access control, PreHash for photo integrity verification, and Gaussian Blur for the protection of sensitive regions. Dissemination control also provides various ways to prevent unauthorized sharing of photos. Ethereum-based smart contracts maintain an immutable log of transactions to guarantee privacy and transparency through user-defined access policies. A functional prototype has also been developed and tested for security, privacy, and controlled dissemination of pictures through OSNs. This prototype has demonstrated the usefulness of Photo Chain. © 2025 IEEE.

关键词： Differential privacy

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Enhancing Career Development: An AI-Driven Mentorship Platform for Student-Mentor Connections

Enhancing Career Development: An AI-Driven Mentorship Platfo...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Charanya, P. Nidhish, M. Prajesh, U.R. Saktheeswar, K. Pillai, Sanjana S. Sri Eshwar College of Engineering Intelligence and Machine Learning Department of Artificial Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798331512965

Thunai is an AI-powered mentorship platform that connects users with personalized mentors for career guidance. Key features include automated calendar booking, video communication with privacy-enhancing dynamic emojis, and AI-driven mentor matching aligned with mentee goals. The AI assistant, GURU, supports task management, resource recommendations, and LinkedIn-based evaluations to foster growth. Thunai offers subscription plans and partnerships with educational and mental health organizations, enhancing engagement, leadership development, and networking. Leveraging AI technologies like sentiment analysis and feedback, Thunai improves mentor matching, user satisfaction, and mentee engagement for career and emotional well-being. © 2025 IEEE.

关键词： Professional aspects

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Avoidance of Ship Collision Using Deep Reinforcement learning with Bi-Directional Long Short-Term Memory in Continuous Action Spaces 2

Avoidance of Ship Collision Using Deep Reinforcement Learnin...

引用

2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Krishnamurthy, Revatthy Adnan, Myasar Mundher Sunil Kumar, V. Srinivas, T. Aditya Sai Aluvala, Srinivas Rajalakshmi Engineering College Artificial Intelligence & Machine Learning Department Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India School of Computer Science and Engineering Sr University Warangal India

ISBN: (纸本)9798350373110

Recent days, with the globalization of the world economy the ships count are increasing for marine transportation and the Waterways are growing increasingly overcrowded than previous. This situation may leads to problem of collision of ships which may cause losing of life and damage for property and to nature. Many automatically collision models of ships are implemented but many focused only on the ship-ship encounter situation only. By using a grid sensor which is virtual sensor, agents of Deep Reinforcement learning (DRL) classify approach of a multiple ships. This framework introduces an automatically collision detection algorithm for ships using DRL in continuous action spaces. DRL is used for avoidance of collision with a maximum distance of safe passing between ships. A unique method is developed named inside Obstacle Zone by Target (OZT) used to change learning capability that expands the OZT. Using Bi-directional Long Short-Term Memory (BI-LSTM) cell, network is redesigned and continuous action spaces training is carried out to train a model with longer safe distance of ships. In collision detection model that the bow cross range is effective for COLREGs compliant collision avoidance that is proposed in this model. The propose model also validates a scenario that included more ships and have passed that Imazu problem. The proposed BI-LSTM model achieved 80.43 % of accuracy, 95.67% of precision, 85.89% of recall and 93.54% of f1 score values. © 2024 IEEE.

关键词： Deep reinforcement learning

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Weather Forecasting Using machine learning Approach 15

Weather Forecasting Using Machine Learning Approach

引用

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

作者： Nazareth, Pradeep Konnur, Aman Khadirsab Chavan, Ankit Manohar Shetty, KeerthanRaj M.D. Department of Artificial Intelligence and Machine Learning Alva’s Institute of Engineering and Technology Karnataka Moodbidri India

ISBN: (纸本)9798350370249

Weather forecasting has become increasingly difficult due to the increasing consequences of climate change. Many industries depend on accurate weather forecasts. Accurate forecasts for the near future are quite valuable. The unpredictable nature of climate change often makes adhering to traditional protocols difficult. To increase the accuracy of weather forecasts, this paper designs a technique to predict the weather using machine learning. The technique uses Long Short-Term Memory (LSTM) model, a kind of artificial neural network. The goal of the model was to predict both favourable and adverse weather. The technique uses various meteorological parameters, such as temperature, pressure, humidity, wind speed, and precipitation. The performance of the model is evaluated using a range of metrics and visualizations from the training and validation sets. This technique achieved greater results in Huber loss, Mean Absolute Error (MAE), and Nash-Sutcliffe Efficiency. The results indicate that proposed LSTM-based neural networks outperforms state-of-the-art forecasting systems like artificial Neural Networks (ANN) in terms of accuracy, MAE and MSE in predicting weather. ©2024 IEEE.

关键词： Wind forecasting

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Real Time Object Detection and Tracking Using SSD Mobilenetv2 on Jetbot GPU 8

Real Time Object Detection and Tracking Using SSD Mobilenetv...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Ramesh, G. Jeswin, Y. Rao, Divith R. Suhaag, B.R. Uppoor, Daksh Kiran Raj, K.M. Alva's Institute of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Mangalore India

ISBN: (纸本)9798350350593

The results of the experiments show that dataset size has a substantial impact on the accuracy of deep learning applications. For example, the Single Shot Detector (SSD) MobileNetV2 architecture, when trained on a dataset of 45,000 samples, achieved an impressive 97.8 % accuracy with an inference latency of 5.545 milliseconds (ms) on the Jetson Nano platform. In line with these discoveries, my research intends to combine the SSD MobileNetV2 model with Jetbot, a mobile robotics platform powered by NVIDIA Jetson Nano, to enable effective object tracking and detection. By using a pipeline that includes data collection, model training, inference, and motor control, the system optimizes performance by using pre-trained weights that may have been modified for certain datasets. Using tracking algorithms for constant movement monitoring, the system quickly recognizes and categorizes objects by utilizing real-time inference on the Jetson Nano. The system's practical usefulness in many circumstances, such as navigation, is shown via experimental validation, which also highlights the system's potential for enhancing autonomous systems and robotics. © 2024 IEEE.

关键词： Deep learning

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EmoLens: Research on Enhancing Recommendation Systems through Facial Emotion Recognition with CNN and MobileNetV2 15

EmoLens: Research on Enhancing Recommendation Systems throug...

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

作者： Lobo, Deltan Gleran Goutham, J.S. Aman, Mohammed Vishma, D. Kunder, Harish Kiranraj, K.M. Alva's Institute of Engineering and Technology Department of Artificial Intelligence and Machine Learning Karnataka Moodbidri India

ISBN: (纸本)9798350370249

In order to construct customized recommendation systems, this study presents EmoLens, a state-of-the-art framework that seamlessly integrates Convolutional Neural Networks (CNNs), MobileNetV2 architecture, and Facial Emotion Recognition (FER). EmoLens distinguishes itself by using deep learning techniques to identify subtle emotional states and provide recommendations that are specifically targeted to the user. This is in addition to recording emotional indicators from facial expressions in real-time. EmoLens increases user engagement and happiness by predicting emotional reactions to different content, goods, or services based on the analysis of users' facial expressions. Moreover, this study investigates the future directions of FERbased recommendation systems, imagining its incorporation into a broad range of commonplace gadgets and uses. EmoLens foresees developments in emotion-aware interfaces, which will allow recommendation engines to dynamically adjust in real time to the emotional states of users. This will transform user experiences, content distribution, and product suggestions. The ethical aspects of responsible emotional data collection and usage are explored in detail, with a focus on the significance of user consent and transparency in this dynamic environment. By means of comprehensive testing and evaluation in various fields like ecommerce, entertainment, and social media, EmoLens exhibits its effectiveness in cultivating more profound user relationships and influencing the trajectory of tailored suggestions with unparalleled accuracy and understanding. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring the Efficacy of Mental Health Care Chatbots: A Comprehensive Review 15

Exploring the Efficacy of Mental Health Care Chatbots: A Com...

引用

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

作者： Nazareth, Pradeep Nikhil, G.B. Chirag, G. Prathik, N.R. Pratham, P. Alva's Institute of Engineering and Technology Department of Artificial Intelligence and Machine Learning Karnataka Moodbidri India

ISBN: (纸本)9798350370249

This comprehensive review paper examines the land-scape of mental health care chatbots, focusing on their design, effectiveness, ethical considerations, and future directions. Mental health care chatbots have emerged as innovative tools to support individuals in managing their mental well-being, offering a range of services from diagnosis to therapy. Furthermore, the paper discusses the importance of meeting standards, regulations, and protecting user privacy in implementing AI-based mental health interventions. It addresses the need to inform mental health providers and users about the main features and potential benefits of chatbots, while also considering the challenges of oversight and accountability. It emphasizes the importance of social and emotional aspects in chatbot design, highlighting dimensions like sociability, empathy, and understandable medical vocabulary. Ethical considerations in digital mental health care, including assessment, diagnosis, and treatment, are also addressed in the review. It discusses the educational implications of mental health care chatbots and offers research recommendations for future studies, including investigations into mobile apps for mental health and addressing challenges in digital mental health research. © 2024 IEEE.

关键词： AI-driven interventions Cognitive behavior therapy Emotion recognition technology Mental health care chatbots Rasa Architecture Telemedicine integration

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Optimizing Crop Yields with Multilayer Perceptrons: A Data-Driven Approach 8

Optimizing Crop Yields with Multilayer Perceptrons: A Data-D...

引用

8th International Conference on Inventive Systems and Control, ICISC 202

作者： Nivetha, N. Usharani, S. Department of Computer Science and Engineering Villupuram India Department of Artificial Intelligence and Machine Learning Villupuram India

ISBN: (纸本)9798350386578

Precision agriculture has become a major change in crop farming. It utilises cutting-edge technologies to maximise field-level management. Precision agriculture has completely transformed crop production by leveraging the latest developments to maximize field-level management. Predicting crop yields with accuracy helps farmers reduce their environmental impact, increase productivity, and make well-informed decisions. Accurate and timely insights are frequently lacking in traditional agricultural yield prediction approaches. The study offers a deep learning method for precisely predicting agricultural yields. Accurate crop yield forecasts assist farmers in minimizing their negative environmental effects, boosting productivity, and making educated choices. However, there are many obstacles because conventional agricultural yield prediction methods frequently need more timely and precise insights. Despite their success, several challenges still exist. These include handling heterogeneous data, dealing with missing values, and the complexity of capturing non-linear relationships in the data. To determine whether decision trees or Multi-Layer Perceptrons (MLP) are ideal in crop yield prediction, these models are compared with each other. Multi-layer perceptrons (MLP) are prominent among these techniques. Even though the MLP model was more accurate, decision trees also are relevant to the prediction process. This means have the capability of understanding multi-layer intra-data intricacies through their structure whereas decision trees may overfit on noisy data or grow too deep hence leading to many splits also known as being bushy unless they are pruned to reduce this bushiness. The study suggests a novel method for predicting agricultural productivity using a machine learning model Decision Tree and Multi-Layer Perceptrons (MLP). A web interface is also created to enable smooth communication with the prediction model. Through the usage of this interface, farmers and agr

关键词： Prediction models

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AI for Career Growth: Advanced Resume Analysis and LinkedIn Scraping for Personalized Job Recommendations 2

AI for Career Growth: Advanced Resume Analysis and LinkedIn ...

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2nd International Conference on Self Sustainable artificial intelligence Systems, ICSSAS 2024

作者： Suresh Kumar, K. Srihari, P. Raman, C.J. St. Joseph's College of Engineering Department of Artificial Intelligence and Machine learning Chennai India

ISBN: (纸本)9798350368413

In today's dynamic job market, individuals seeking career advancement face the challenge of efficiently navigating through vast amounts of information to identify suitable job opportunities. To address this challenge, we present an innovative approach leveraging artificial intelligence (AI) technologies for career growth. Our system combines advanced resume analysis techniques with LinkedIn scraping capabilities to provide personalized job recommendations tailored to individual skillsets and career aspirations. The system uses an AI-powered resume analyser and Selenium based web scraping to extract key information from resumes. It uses natural language processing to identify strengths, weaknesses, qualifications, experience, and achievements, and Google Gemini AI Model to generate personalized job title recommendations. The system also extracts job listings from LinkedIn, enhancing the job search process. Experiments show the system improves efficiency and accuracy, enabling individuals to make informed career decisions and accelerate professional growth. © 2024 IEEE.

关键词： Employment

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Towards CodeBlizz: Developing an AI-Driven IDE Plugin for Real-Time Code Suggestions, Debugging, and learning Assistance with Generative AI and machine learning Models 2

Towards CodeBlizz: Developing an AI-Driven IDE Plugin for Re...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Preethi, P. Ragavan, V. Abinandhana, C. Umamaheswari, G. Suvethaa, D.R. Sri Eshwar College of Engineering Artificial Intelligence and Machine Learning Department of CSE Coimbatore641202 India

ISBN: (纸本)9798331534950

This article unveils CodeBlizz, an innovative AI-powered add-on for IDEs (Integrated Development Environments) like Visual Studio Code. This software program makes use of generative AI to provide developers with seamless access to educational assets along with support for real-time debugging, and context-aware code suggestions. Compared to various code completion tools on the market, CodeBlizz takes a complete strategy to dramatically lower the learning curve for inexperienced workers while simultaneously increasing coding productivity. This is achieved through the direct integration of learning resources into the working environment. CodeBlizz utilizes proficient transformer-based models, such as CodeBERT for comprehending syntax and producing useful snippets and GPT-3 to generate context-aware code referrals. Also, the plugin employs T5 models to provide real-time problem detection and debugging support by turning malfunctioning code into error messages and possible fixes. Added to that, adaptive learning from user feedback is feasible with reinforcement learning models such as Deep Q-Networks (DQN), which boosts the accuracy of the model over time. Together, these machine learning elements allow CodeBlizz to provide developers with solid and dynamic support from within the IDE, which helps to boost accuracy and speed up code completion. Without pushing users to leave the IDE, the plugin renders functionalities encompassing dynamically generated code snippets, syntax correction, error pattern identification, and real-time tutorials for learning at ease. On the survey, users reported a thirty percent reduction in time spent on error correction and tutorial seeks from online websites, which further validates the benefit of incorporating AI that generates code for immediate guidance within the coding routine. Early testing demonstrates a significant boost in coding accuracy. © 2024 IEEE.

关键词： Software testing

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