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检索条件"任意字段=5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022"

共 261 条记录，以下是11-20 订阅

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AI-Enhanced Unmanned Aerial Vehicles for Search and Rescue Operations 5

AI-Enhanced Unmanned Aerial Vehicles for Search and Rescue O...

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5th international conference on Innovative Trends in Information Technology (ICITIIT)

作者： Farsath, Rashida K. Jitha, K. Marwan, Mohammed V. K. Jouhar, Muhammed Ali A. Farseen, Muhammed K. P. Musrifa, K. A. MEA Engn Coll Dept Comp Sci & Engn Malappuram India

ISBN: (纸本)9798350386813;9798350386820

this paper introduces a cutting-edge AI-empowered Unmanned Aerial Vehicle (UAV) system, enriched with stateof-the-art sensor technology, advanced image recognition algorithms, and autonomous navigation capabilities. the system represents a transformative approach to search and rescue operations, offering unparalleled precision and rapid response times. Our methodology encompasses multifaceted data collection techniques, including surveys, interviews, data mining, Internet of things (IoT) sensors, and sophisticated video analytics. machine learning and deep learning models are then applied to process and analyze this data, enabling real-time image recognition for precise target identification. the system's AI-driven autonomous navigation algorithms optimize mission planning, resulting in significantly reduced response times and heightened mission success rates. Extensive real-world tests and simulations validate the exceptional performance of the proposed AI-empowered UAV system. these tests underscore its capacity to expedite emergency response efforts in dynamic and challenging environments. In parallel, this paper addresses critical ethical considerations, ememphasizing responsible data handling practices, and robust security measures to ensure the system's integrity in sensitive contexts. As exemplified through a compelling case study of successful rescue operations, this technology represents a groundbreaking advancement in the field. By bridging the gap between cutting- edge technology and life-saving applications, it holds the potential to redefine the landscape of search and rescue missions, ushering in an era of heightened efficiency, precision, and impact.

关键词： Artificial Intelligence Unmanned Aerial Vehicles Sensor Technology Image Recognition Algorithms Autonomous Navigation Search and Rescue Operations Emergency Response Human-machine Collaboration Drones machine learning Deep learning data Privacy Ethics IOT Sensors Video analytic IoT Deep learning Random Forest Restricted Boltzmann Network Auto encoder Intrusion Detection System Smart City

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Better Qualitative Searching for Effecting the Performance of machine Translation 1

引用

5th international conference on applied machine learning and data analytics, amlda 2022

作者： Banerjee, Anasua Kumar, Vinay Banik, Debajyoty National Institute of Technology Jharkhand Jamshedpur India Kalinga Institute of Industrial Technology Odisha Bhubaneswar India

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

By improving a better comprehension of the human language for linguistically based human-computer communication, natural language processing (NLP) contributes to the empowerment of intelligent machines. Instead of hand-coding massive sets of rules, NLP can use machine learning to learn these rules automatically by examining a set of rules. With a view to better and clearer results in the endeavor to achieve success in the field of language translation, we have gone through some of the techniques like machine Translation, Punctuation Restoration, Name Entity Recognition, and Sentiment Analysis. Here we mentioned different state-of-the-art methods for different techniques of NLP. this paper offers an abstract analysis of the current state of language translation techniques. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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A Credit Card Fraud Detection Model Using machine learning Methods with a Hybrid of Undersampling and Oversampling for Handling Imbalanced datasets for High Scores 1

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5th international conference on applied machine learning and data analytics, amlda 2022

作者： Sinayobye, Omar Musabe, Richard Uwitonze, Alfred Ngenzi, Alexander School of ICT University of Rwanda Kigali Rwanda

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

Due to recent advancements in communication technology and e-commerce systems, credit cards are currently the most popular form of payment for online as well as offline purchases. As a consequence, there is substantially more fraud involved in the transaction processes. data mining has an issue for prediction, and data classification, therefore finding occurrences is essential. Unusual events are challenging to be identified due to their irregularity and casualness, but misclassifying unusual events can lead to large financial losses. In this study, an approach for detecting credit card fraud using machine learning methods, such as K-Nearest Neighbors, random forest, decision trees, logistic regression, and support vector machines, is proposed. the research attempts to look at the effectiveness of the classification models while applying both the Oversampling and Undersampling techniques to find instances of fraud in the dataset for fraudulent activities. the experimental study used two days of credit card transactions made by European cardholders in September 2013. To evaluate the models’ performances, confusion matrix, precision, recall, f1_score, cross-validation score, and ROC_AUC score metrics were used. From different experiments of the tested model, it can be easily observed that the performance of all models was better compared with previous literature thus the KNN was the best in almost all metrics used. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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HL7 FHIR Platform, Scalable, Reliable and Comprehensive of Clinical databases Analyzed with machine learning for ICU Public Healthcare Center 1

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5th international conference on Biomedical and Health Informatics (ICBHI)

作者： Chavez Plaza, Bernardo Briggs Luque, Jaime Chicuy Godoy, Luis Cuevas Figueroa, Boris Covarrubias Ganderat, Rodrigo Ramirez Izquierdo, Manuel Hosp El Salvador Crit Care Serv ICU Santiago Chile Artificial Intelligence Hub Hlth Santiago Chile Navy Hosp Neurosurg Vina Del Mar Chile

ISBN: (数字)9783031592164

ISBN: (纸本)9783031592157;9783031592164

Hospital facilities today have to deal with exponentially increasing volumes of batch and streaming data, comprising a variety of structured, unstructured, and semi-structured data types, originating from an ever-increasing number of sources and disparate devices located in healthcare facilities, such as intensive care units. At the same time, doctors demand faster and easier access to reliable and up-to-date data to make accurate decisions. Finding the right data sets and making them available for analysis is often a complicated process that further slows down clinical decisions. that is compounded by regulatory compliance and security controls that must be manually applied at every step of the data lifecycle, from data generation to analytics. Smart UPC is an interoperable solution that has emerged as an architecture for managing data with machine learning (ML) to overcome these challenges. the layered architecture focuses on making data readily available in a dashboard for Intensive Care Unit users, improving insights based on the use of AI and leveraging automation to simplify administration, the safety and quality of health care, complying with cybersecurity requirements and under very strict ethical standards. Smart UPC, based on a proprietary HL7 FHIR platform offers a unified, scalable, reliable and comprehensive view of ICU clinical data. It is a solution to support applications in decision making in a complex environment, with high critical demand.

关键词： data artificial intelligence HL7 FHIR platform intensive care units machine learning

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A Real-Time Face Recognition Attendance Using machine learning 1

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5th international conference on applied machine learning and data analytics, amlda 2022

作者： Adeline, Umurerwa Marie Gaspard, Harerimana Innocent, Kabandana College of Science and Technology University of Rwanda Kigali Rwanda College of Science and Technology University of Rwanda Kigali Rwanda

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

Systems for managing attendance are crucial for all organizations. One of the most often utilized biometrics for verifying human identity is the face. Most businesses in Rwanda utilize logbooks, cards, or fingerprints to track employees’ attendance at work. However, because of COVID-19, which is an infectious disease, attendance has been discontinued due to concerns over its spread. this study describes real-time Face recognition attendance using machine learning, an Internet of things (IoT)-based biometric face recognition solution. To capture the live streaming video, a high-quality camera with a Sony IMX477 sensor and a 16mm 10MP Telephoto lens connected to a Raspberry Pi 4 Model B, can send frames at a time to the cloud. the pre-trained FaceNet model is employed by the system to extract features from a face image after using MTCNN (Multi-Task Cascaded Convolutional Neural Networks) to recognize facial landmarks on images. Real-time image processing is done in the cloud, and attendance is recorded on a dashboard that is accessible from anywhere. the system sends the email to the employee using SMTP Protocol in case of arriving late /absent without permission. the result reveals that the system is safe, dependable, trustworthy, and does not require physical touch. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Face recognition

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Deep learning Methods to Automate Embryo Classification and Evaluation 1

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5th international conference on applied machine learning and data analytics, amlda 2022

作者： Shobha, Rani B R Bharathi, S. Pareek, Piyush Kumar Dr. Ambedkar Institute of Technology Karnataka Bengaluru India Nitte Meenakshi Institue of Technology Karnataka Bengaluru India

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

Every one in seven people face fertility issues in India due to surge in stress levels and changes in lifestyle, IVF (In Vitro Fertilization) comes as boon [1]. Artificial Intelligence methods increase the performance of assisted reproductive technology (ART) over past decade. Manual approaches in grading embryos are prone to human errors and show slight variations in grading embryos by different embryologists. Science and technology can improve the grading of embryos to some extent by using time lapse imaging systems use of high-resolution microscopes. Artificial intelligence, machine learning and deep learning in particular automates and standardizes the embryo grading process, their by minimising the variations and increasing the chances of successful pregnancy. this paper evaluates different approaches involving machine learning and deep learning techniques to identify embryo and extract the features of embryos such as ICM and TE which help in grading embryos. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Grading

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Retrieval of Weighted Lexicons Based on Supervised learning Method 1

引用

5th international conference on applied machine learning and data analytics, amlda 2022

作者： López-Chau, Asdrúbal Rojas-Hernández, Rafael Valle-Cruz, David Trujillo-Mora, Valentin Laboratorio de Investigación en Ingeniería y Ciencias Aplicadas CU UAEM Zumpango Universidad Autónoma del Estado de México Zumpango Estado de México55600 Mexico Unidad Académica Profesional Tianguistenco Universidad Autónoma del Estado de México Santiago Tianguistenco52640 Mexico

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

Lexicons are a lexical resource that has been used successfully in sentiment analysis and other areas of natural language processing. Although there are several unweighted lexicons and weighted lexicons, they all achieve poor performance in many applications. this is because they are created in general contexts, and adding more terms to an existing lexicon is complicated. Furthermore, current methods for generating weighted lexicons are complex and not very intuitive. In this article, we show the results of a method to generate weighted lexicons from a tagged corpus. the terms that make up the lexicon, as well as the corresponding weights, are obtained by means of a distance measure that is closely related to the probability that a document belongs to its label. the preliminary results obtained with a corpus of 405 documents show that the method reaches an accuracy of 92.3%. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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From Clicks to Class Participation: Demystifying Student Engagement Factors with Attribute Ranking and Predictive analytics 5

From Clicks to Class Participation: Demystifying Student Eng...

引用

5th international conference on Artificial Intelligence and data Sciences, AiDAS 2024

作者： Hanapi, Rozita Razali, Mohd Norhisham Yahya, Syazleena Seman, Sharidatul Akma Abu Halamy, Suhaida Rahim, Emelia Abdul Universiti Teknologi Mara Faculty of Business and Management Samarahan Campus Malaysia Kementerian Sumber Asli dan Kelestarian Alam Perak Malaysia Universiti Teknologi Mara Faculty of Business and Management Puncak Alam Campus Malaysia School of Information Science College of Computing Informatics and Mathematics Universiti Teknologi Mara Samarahan Campus Malaysia

ISBN: (纸本)9798331528553

Student engagement is important in virtual and physical learning environments to ensure students are connected with the learning materials and the learning environment. However, understanding student engagement is difficult due to limited observation and uncertain participation metrics. this paper explores the application of machine learning techniques to analyze student engagement levels in online and face-to-face learning environments to uncover actionable insights. the data about student engagement was collected comprising diverse student attributes such as class interest, attendance, effort, and technology proficiency. Firstly, the dataset underwent preprocessing to handle missing values and normalize attributes. Attributes selection using Information Gain and Correlation are employed to identify key predictors influencing student engagement. Subsequently, classification algorithms were applied to build predictive models. Model performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. Afterwards, trend analysis was carried out using a data visualization approach. the insights obtained from these analyses guide to the identification of critical factors such as class interest, attendance, and technology proficiency, which significantly impact engagement levels. the findings highlight the effectiveness of machine learning in discerning patterns and predicting student behaviors in virtual learning environments with Logistic Regression yielded 93.30% classification accuracy. Practical implications such as targeted interventions and curriculum adaptations can be designed to enhance engagement based on the identified predictors. © 2024 IEEE.

关键词： Contrastive learning

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Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Generator for Wind Turbine Based on ResNet-50 Transfer learning Model 5

Demagnetization Fault Diagnosis of Permanent Magnet Synchron...

引用

5th international conference on Sensing, Measurement and data analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Du, Siyu Chen, Yu Zhang, Sichao Liang, Feng Shahbaz, Nadeem Zhu, Guoqiang Zhao, Shouwang Wang, Shuang Ma, Yong Li, Chong Wang, Zhongjie Zhao, Yong School of Electrical Engineering Xi'an Jiaotong University Xi'an China Xi'an Thermal Power Research Institute Co. Ltd. Xi'an China

ISBN: (纸本)9798331529192

this paper utilizes Convolutional Neural Networks (CNN) with Residual Networks (ResNet) to identify and classify demagnetization faults in images. Firstly, we collected current feature datasets corresponding to two types of permanent magnet wind turbines, namely 25 kW and 2 MW, representing the source and target domains. Using the source domain dataset, we trained a ResNet50 model specifically for demagnetization fault diagnosis of the 25 kW wind turbine. Subsequently, we applied a network-based transfer learning approach to adapt this model to the demagnetization fault diagnosis for the 2 MW wind turbine, completing the fine-tuning training required for transfer learning. Compared to models without transfer learning, this method significantly improves the accuracy and efficiency of demagnetization fault diagnosis. © 2024 IEEE.

关键词： Permanent magnets

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data analytics in Farming: Crop Yield Projection in Telangana State Employing machine learning Methods 5th

Data Analytics in Farming: Crop Yield Projection in Telangan...

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5th international conference on data Science, machine learning and Applications, ICDSMLA 2023

作者： Himaanshu, V. Suryanarayana, G. Prakash, L.N.C.K. Vignesh, Vamshi Srikar, B. Sai Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Information Technology Vardhaman College of Engineering Hyderabad India CVR College of Engineering Hyderabad India

ISBN: (纸本)9789819780426

Telangana state is in the southern part of India where the Godavari and Krishna rivers flow providing important water resources for agriculture. Agriculture is the primary occupation of the major population in the state, and it is also one of the essential sectors that majorly contributes to the state’s economy. Accurate estimation of crop yield production is essential for sustainable agricultural development, food security, and poverty reduction. therefore, various supervised machine learning procedures have been used to forecast the crop yield production in Telangana State. In this work, various algorithms are applied to the Telangana crop yield dataset. the results generated by these algorithms have been analysed and compared. With the help of crop yield analysis, farmers know their yield before their farming practice so that they can reveal and resolve their crop yield challenges. this article suggests a machine learning algorithm for the farmers to detect and solve their crop yield problems. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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