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

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A Case Study to Predict Structural Health of a Gasoline Pipeline Using ANN and GPR Approaches 7th

A Case Study to Predict Structural Health of a Gasoline Pipe...

引用

7th International Conference on Production, Energy and Reliability, ICPER 2020

作者： Shaik, Nagoor Basha Pedapati, Srinivasa Rao Othman, Abdul Rahim Dzubir, Faizul Azly B.A. Mechanical Engineering Department Universiti Teknologi PETRONAS Perak Bandar Seri Iskandar32610 Malaysia Mechanical Department Group Technical Solutions Project Delivery and Technology Division Petroliam Nasional Berhad Kuala Lampur50050 Malaysia Artificial Intelligence and Machine Learning Research Unit Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

ISBN: (纸本)9789811919381

Almost all fluid transportation in oil and gas facilities from one point to another are mainly using simple and complex pipelines *** pipelines system is vital as it generates significant cash revenue to the ***, the structural health of pipeline deteriorates over time due to various damage *** Loss of Primary Containment (LOPC) to the pipelines can lead to heavy business *** engineers will normally apply the most common approach to mitigate the LOPC by providing schedule maintenance, perform repair activities as well as system *** activities are mostly controlled manually by the *** paper will focus on the prediction of structural health monitoring of gasoline pipeline located in one of the facilities in Malaysia, using historical inspection reports by means artificial Neural Networks (ANN) and Gaussian Process Regression (GPR) regression *** collected data was analyzed and applied on these two methods to determine the best fitness and *** is noticed that both Bayesian-regularization ANN and exponential GPR model demonstrate better performance compared to the others when assessed based on the highest R2 *** results were deemed satisfactory as the R2 value for both methods were close to *** results also showed that both ANN and GPR models were almost equal in predicting the structural health of a pipeline with an accuracy of 99% and 97.3%, *** work may help in controlling/monitoring the inspection cost and to preplan the maintenance scheduling for gasoline pipeline network in oil and gas industry. © 2023, Institute of technology PETRONAS Sdn Bhd.

关键词： Forecasting

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A Review on Applications of IoT in Healthcare

A Review on Applications of IoT in Healthcare

引用

Electronics and Renewable Systems (ICEARS), International Conference on

作者： Vedant Thakare Prateek Verma Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This article examines how the Internet of Things (IoT) is rapidly transforming the medical field. IoT technologies are revolutionizing patient care, increasing productivity, and boosting results in a variety of ways, from wearable technology and telemedicine to smart hospitals and personalized medicine. Predictive analytics, remote patient monitoring, and the use of AI and machine learning for more precise diagnosis and treatment regimens are important applications. The future of IoT in healthcare is extremely bright, with developments in 5G, AI, and other cutting-edge technologies set to further transform the sector and enhance human health, even while issues like data security and privacy still need to be resolved.

关键词： Productivity Data privacy Renewable energy sources 5G mobile communication Reviews Telemedicine Medical services Transforms Internet of Things Public healthcare

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Text to Image Generation Using Attentional Generative Adversarial Network 1

引用

1st International Conference on Advanced Computing, machine learning, Robotics and Internet Technologies, AMRIT 2023

作者： Rao, Supriya B. Shetty, Shailesh S. Singh, Chandra M, Srinivas P. Bekal, Anush Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore Karnataka Mangaluru India Department of Computer Science and Business System Srinivas Institute of Technology Karnataka Mangaluru India Department of Electronics & Communication Engineering Sahyadri College of Engineering & Management Karnataka Mangaluru India Department of Computer Science Engineering Sahyadri College of Engineering & Management Karnataka Mangaluru India

ISBN: (数字)9783031472213

ISBN: (纸本)9783031472206

AttnGan – Attentional Generative Adversarial Network (GAN) is a type of GANs that is used for text to image generation. Unlike other art models, it focuses on fine-grained word level information and once the model is trained by mapping the images and the corresponding text in the dataset, the model will generate image part-by-part using the image description given by the user as an input to the model. Current paper, focuses on the conversion of text into images based on the description provided by the user. This enables them to translate their ideas in the form of text into images of their required choices. The model is initially trained by the description of the text and images and when the user provides the text as an input to the system, it is used as a testing data. By analyzing the text word-by-word, the respective images pertaining to the text is generated part-by-part by considering the text description into account. This can heavily help the users in a applications such as in editing aids, documental archives, etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Generative adversarial networks

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Revolutionizing Quality Control: A machine learning Approach for Precise Surface Defect Detection and Localization in Industrial Components

Revolutionizing Quality Control: A Machine Learning Approach...

引用

Emerging Technologies in Computer Science for Interdisciplinary Applications (ICETCS), International Conference on

作者： A. Ajina K.G Jaya Christiyan Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bangalore Department of Mechanical Engineering M S Ramaiah Institute of Technology Bangalore

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

This paper focuses on the creation of a machine learning application designed to efficiently identify and categorize surface imperfections in a variety of components. Conventional methods, relying on visual examination or manually crafted characteristics are prone to inaccuracies and are time-intensive. Leveraging the capabilities of artificial intelligence architecture, we not only achieve precise defect classification but also accurate defect localization. In contrast to traditional approaches, artificial intelligence can adeptly learn optimal visual features in a supervised manner. In real image data set we performed our experiments which illustrate that our approach outperforms current methods while maintaining computational efficiency. We have developed a machine learning model for surface defect detection in steel flat surfaces through image segmentation. This advancement has resulted in reduced defects, heightened manufacturing accuracy, increased production rates and enhanced product quality.

关键词： Location awareness Visualization machine learning Quality control Production Product design Quality assessment

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Early-Stage Detection and Diagnosis of Dental Caries in Oral Health Using machine learning

Early-Stage Detection and Diagnosis of Dental Caries in Oral...

引用

technology & engineering Management Conference - Asia Pacific (TEMSCON-ASPAC), IEEE

作者： Sri Dharshana T Pravin P S Department of Artificial Intelligence and Machine Learning Coimbatore Institute of Technology Coimbatore India Department of Electronics & Instrumentation Engineering National Institute of Technology Silchar Assam India

ISBN: (数字)9798331515881

ISBN: (纸本)9798331515898

Dental caries detection is a critical aspect of oral health monitoring, with significant implications for preventive dentistry and patient care. In this paper, we present a novel approach to dental caries early detection utilizing digital imaging and machine learning techniques. The proposed method leverages enabling accurate early detection of dental caries and proactive treatment and monitoring of oral health. Through the integration of convolutional neural networks (CNNs) and data augmentation techniques, our model achieves robust performance in identifying carious lesions from dental images. Additionally, we explore the use of ensemble learning methods, such as the Random Forest algorithm, to enhance the accuracy of caries detection. Exper-imental results demonstrate the effectiveness of our approach in accurately diagnosing dental caries, with a test accuracy of 87.5% for the CNN model and 93.75% for the Random Forest model. Furthermore, we present a comprehensive analysis of model performance through confusion matrices, classification reports, and receiver operating characteristic (ROC) curves. Our findings underscore the potential of digital twin-based approaches in revolutionizing dental diagnostics and facilitating personalized treatment strategies tailored to individual patients.

关键词： Accuracy engineering management Digital images Receivers Dentistry Convolutional neural networks Lesions Ensemble learning Random forests Monitoring

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Sentiment-Driven Live Face Detection in Kindergarten Environments Using PSO

Sentiment-Driven Live Face Detection in Kindergarten Environ...

引用

IEEE India Conference (INDICON)

作者： M S Asharani G Tejesh Sanjeevakumar Hatture Rashmi P Karchi Department of Information science and Engineering Nagarjuna College of Engineering and Technology Bengaluru Karnataka India Department of Artificial Intelligence and Machine Learning Nagarjuna College of Engineering and Technology Bengaluru Karnataka India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

This research discusses a new means of improving the kindergarten learning environment. It comprises live face detection, relies on Particle Swarm Optimization, and finally uses Sentiment Analysis. The system will record a live video that adjusts the face detection settings using algorithms from the PSO technique before it will concentrate on the faces of expressions of kindergarten children. It can play special songs according to the emotions recognized by the system, making the learning process more interesting. Face detection using PSO technology makes the system perform effectively in various environments as the changing atmosphere does not reduce reliability in a kindergarten. Apart from the perceived benefit of using technology, this approach develops young children's emotional intelligence due to individualized experiences through playing, and it is fun and educational at the same time. This holistic combination of flexible face detection and the response about real-time emotional contact is aimed at linking technology and early childhood education. Its primary purpose is to cultivate the thought and emotional development of kindergarten students in their early years.

关键词： Sentiment analysis machine learning algorithms Mood machine learning Real-time systems Robustness Safety Face detection Particle swarm optimization Optimization

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Anomaly Detection in Traffic Surveillance Videos using Differential Evolution Dragonfly Algorithm with VGG16 1

Anomaly Detection in Traffic Surveillance Videos using Diffe...

引用

1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Balassem, Zaid Ajzan Silvia, Ensteih Thind, Harpreet Kaur Harirajkumar, J. Kavitha, P. The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Dayananda Sagar College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India Dayananda Sagar College of Engineering Department of Computer Science and Engineering Bengaluru India Sona College of Technology Department of Electronics and Communication Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

Anomaly detection in video surveillance systems is an eminently vital field due to the widespread utility of the method, ranging from public security to monetary safety. Numerous architectures can be used for detection, extraction, selection and classification of the anomalous sequences. However, traditional optimization algorithms suffer from poor initialisation conditions and premature convergence in feature selection. Further, existing network architectures show a saturation in accuracy with an increasing number of layers. This work proposes a Differential Evolution Dragonfly algorithm (DE-DA) with VGG16 which helps to prevent local trapping for anomaly detection in traffic surveillance video. In the preprocessing step, keyframes are extracted using Histogram of Differences (HOD) to find outliers in the images and then, an auto-encoder architecture is used to perform codebook generation. DE-DA is used to select the features and VGG16 network performs classification with the help of residual blocks with skip connections. The proposed DE-DA with VGG16 achieves 31 %of Equal Error Rate (EER) compared to the existing auto-encoder. © 2024 IEEE.

关键词： Anomaly detection

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Leveraging Cutting-Edge Deep learning Techniques for Superior Prostate Cancer Detection

Leveraging Cutting-Edge Deep Learning Techniques for Superio...

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International Conference on Computational intelligence and Networks (CINE)

作者： Karedla Chandana Reddy Cheripally Shreza Lakkars Akhil Ruqsar Zaitoon Kottu Santosh Kumar Saroja Kumar Rout Department of Information Technology Vardhaman College of Engineering (Autonomous) Hyderabad India Department of Artificial Intelligence and Machine Learning Vardhaman College of Engineering (Autonomous) Hyderabad India

ISBN: (数字)9798331516796

ISBN: (纸本)9798331516802

Prostate cancer is one of the main causes of death due to cancer in men, so diagnostic procedures for this disease must be effective and timely. This paper presents research into the possibility of diagnosing prostate cancer from MRI images of a prostate gland using the YOLOv8 deep learning system. Run on hundreds of images regarding cancer, it showed positive findings with an F1 confidence curve of 0.95, precision confidence of 0.99 with recall confidence of 0.99 and a precision-recall confidence of 0.98. Results show how excellent and effective the algorithm is in detecting prostate cancer. The results further portray that integrating YOLOv8 algorithm can improve diagnosis such that faster and more accurate diagnosis of prostate cancer be made. The study will further be tailored to optimize the algorithm, expanding the dataset to incorporate more prostate cancer variations and introducing new data from MRI. Deployment of the model would demand genomic data, actual time clinical usage of the model, improved interpretability of the model, and compliance with legal retention policy. It opens up possibilities for further research in this field in the future towards betterment in prostate cancer detection and therapy.

关键词： Deep learning Accuracy Law Magnetic resonance imaging Medical treatment Glands Genomics Data models Real-time systems Prostate cancer

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A Review on the Role of artificial intelligence in Personalized learning

A Review on the Role of Artificial Intelligence in Personali...

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Innovation in technology (APCIT), Asia Pacific Conference on

作者： Rukaiyya Qureshi Shital Hajare Prateek Verma Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Meghe Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Meghe Wardha Maharashtra

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

This article explores the revolutionary role of artificial intelligence (AI) in personalized learning, focusing on its potential to revolutionize educational processes and improve learning outcomes. This paper provides insights into how AI technologies can be used to tailor instruction, identify learning needs, and provide specific strategies that cater to different students' psychological, cognitive, and motivational preferences. The article examines case studies and examples to demonstrate different uses of AI in personalized learning settings, such as autonomous tutoring systems, personalized education channels, and analysis based on data. However, the use of AI in education raises ethical, technological, and policy concerns that require careful study and proactive actions to assure fair access, preserve learner privacy, and eliminate algorithmic biases. Ultimately, finally, the article emphasizes AI's transformative potential in personalized learning and advocates for coordinated efforts among educators, academics, and policymakers to leverage its benefits while tackling accompanying obstacles

关键词： Technological innovation Privacy Ethics Reviews Education Psychology Focusing

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Navigating Neural Landscapes: A Deep Dive into Brain Tumor Detection and Localization Using Advanced Deep learning Techniques

Navigating Neural Landscapes: A Deep Dive into Brain Tumor D...

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International Conference on Computational intelligence and Networks (CINE)

作者： Cheruku Devakichaitanya Vagicherla Vasaviharshitha Akula Anirudh Vivek Kulkarni Kottu Santosh Kumar Saroja Kumar Rout Department of Information Technology Vardhaman College of Engineering (Autonomous) Hyderabad India Department of Artificial Intelligence and Machine Learning Vardhaman College of Engineering (Autonomous) Hyderabad India

ISBN: (数字)9798331516796

ISBN: (纸本)9798331516802

Brain tumors pose a grave risk to the health of the patient and become a problem of complexity which is quite diagnostic in this sense. In the conclusions of this paper, the YOLOv9 algorithm, which is one of the advanced deep learning approaches, is used to detect and locate abdominal tumors. A massive amount of brain tumor images was considered and preprocessed for detection by using the YOLOv9 object detection system this system is well acclaimed as being faster and more accurate. Our results of $\mathbf{0. 6 5}$ for the final $\mathbf{F} 1$ curve, 99 accuracy confidence, recall confidence: 96, and 0. 93 precision-recall confidence take measures demonstrating the inexhaustible and robust algorithm in differentiating and locating the brain tumor disagglutination consistently and at the right place. Therefore, this research work clearly shows that there is a considerable enhancement in the diagnosis of brain tumors using the YOLOv9 algorithm which ensures the early time of diagnosis thus helping patients with higher prognosis. Following this focal theme, there is a need to emphasize the ingredients of the tool that diagnoses brain tumors, including improvement of the algorithm, increase in data sets, great tumor diversity, and addition of multi-modal data. Successes in the use of real-time clinical applications and other certifications must be accomplished if this technology will do well in healthcare delivery. This lays the basis for further studies and, thereby, forms the basis for application in the detection and identification of the location of brain tumors.

关键词： Deep learning Accuracy Navigation Brain tumors Object detection Medical services Prediction algorithms Real-time systems Regulation Prognostics and health management

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