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Development of Light-Weight High Entropy Alloys for Aerospace Applications: Perspective of Artificial intelligence 1

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3rd international conference on Innovation in Engineering, ICIE 2024

作者： Balaji, V. Xavior, M. Anthony Machado, Jose Joel, J. Jeyapandiarajan, P. Batako, Andre Burduk, Anna Vellore Institute of Technology Vellore632014 India University of Minho Braga Portugal Liverpool John Moores University Liverpool United Kingdom Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (数字)9783031626845

ISBN: (纸本)9783031626838

The primary driver of growing demand for innovation in materials particularly for aerospace applications is energy efficient and lightweight without sacrificing mechanical properties. One of such group of materials developed and researched across the globe is the high entropy alloys (HEAs). These alloys comprise a minimum of five elements to a maximum of thirteen elements in it. As the name indicates, the level of entropy has to decide the stability of the alloy system followed by Gibbs free energy. To increase the entropy level, more alloying elements can be added this is the simple concept of High Entropy alloy. This state-of-the-art article focuses on HEAs based on lightweight materials, their development, and mechanical property evaluation, and the application of Artificial intelligence (AI) techniques in predicting the properties and performance based on various machine learning techniques based on sustainable development goals. The role of artificial intelligence in optimizing the composition, the number of elements for designing the HEAs, the predicted properties, and the performance of the same are compiled and presented in this review article. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sustainable development

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ML_BrainDetection: An Intelligent Model for Brain Tumor Identification Using machine Learning 3

ML_BrainDetection: An Intelligent Model for Brain Tumor Iden...

引用

3rd international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Farag, Mohamed S Gomaa, Ibrahim A Abd-Elghany Zeid, Magdy Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

ISBN: (纸本)9798350306231

Brain tumors are one of the greatest causes of death worldwide. Due to that, early diagnosis and classification of the tumor would surely help increase the chance of survival for patients worldwide. However, classifying and identifying brain tumors requires certain tools, including using Magnetic Resonance Imaging (MRI) to detect brain tumors. Moreover, many researchers in the past have also built up Computer-Aided Diagnosis (CAD) systems to aid radiologists in their purpose of detecting brain abnormalities and tumors effectively. In this research, extracted multi-modal features like texture, morphological, entropy-based, Scale Invariant Features are proposed, and consequently, certain robust machine learning techniques, which are k-nearest neighbors (KNN), Decision Tree (DT), Support Vector machine (SVM), Naive Bayes, as well as the Logistic Regression classifier were used to analyze the two data-sets included in the paper, detect tumors found in the brain, not to mention, examining their results, which have then shown the highest accuracy when using the Logistic regression classifier in both data sets, with the Support Vector machine (SVM) classifier following it, which further support the argument made in the paper. © 2023 IEEE.

关键词： Support vector machines

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Penetration of Deep Learning in Human Health Care and Pharmaceutical Industries;the Opportunities and Challenges 3

Penetration of Deep Learning in Human Health Care and Pharma...

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3rd international conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Raman, Ramakrishnan Radha, H.R. Inbamalar, T.M. Subhahan, D. Abdus Kumar, Ashok Bathrinath, S. Sarkar, Swagata Symbiosis Institute of Business Management Pune & Symbiosis International Maharashtra Pune India K. S. School of Engineering and Management Department of Chemistry Karnataka Bangalore India R.M.K. College of Engineering Department of Electronics and Communication Tamil Nadu Thiruvallur India B v Raju Institute of Technology Department of Computer Science Telangana Narsapur India Banasthali Vidyapith Department of Computer Science Rajasthan India Kalasalingam Academy of Research Department of Mechanical Engineering Tamil Nadu Krishnankoil India Sri Sai Ram Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350336238

Computational medicine has emerged as a result of the advancement of medical technology, which has led to the emergence of the big data era in the biomedical area, which is supported by artificial intelligence technology. To advance the development of precision medicine, people must be able to extract the valuable information from this vast biomedical data. In the past, professionals in the field of feature engineering and domain knowledge were typically utilised to extract the features from the biological data using machine learning techniques, which took a lot of time and resources. Modern machine learning techniques like deep learning (DL) have an advantage over them in that they can automatically find strong, complex features from fresh data without the necessity for succeeding engineering. The study of DL's applications in the fields of genomics, drug development, electronic health records, and medical imaging suggests that deep learning has clear advantages in maximising the use of biomedical data. Deep learning is becoming increasingly important in the field of medicine and health due to its large range of potential applications. The lack of data, interpretability, data privacy, and heterogeneity are some of the limitations of deep learning in computational medical health. A resource for improving the use of deep learning in medical health is provided by the analysis and discussion of these difficulties. © 2023 IEEE.

关键词： Health care

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Analysis and research on acceleration measurement methods for indoor large length standard devices 3

Analysis and research on acceleration measurement methods fo...

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3rd international conference on machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Hu, Changan Zhou, Chengyi Li, Xingxing Li, Li Lv, Fei Hu, Baoquan National Institute of Measurement and Testing Technology Chengdu610021 China School of Computer Science Chengdu Normal University Chengdu611130 China School of Engineering Xi’an International University Xi’an710077 China

ISBN: (纸本)9781510681880

The indoor long length standard device is an important large-size measuring standard device, which has an acceleration and deceleration process during the starting and stopping stages of the slide. Studying the acceleration of the indoor long length standard device is of great physical significance for understanding its operating rules. In the article, a laser tracker was used to study the acceleration of indoor large length standards. Through data analysis using a laser tracker at equal sampling frequencies of 50Hz and 100Hz, some conclusions were drawn as follows: the acceleration value significantly increases when the measurement frequency of the laser tracker is increased. The maximum acceleration at 50Hz is 579.75 mm/s2, and the maximum acceleration at 100Hz is 920 mm/s2. After the acceleration phase is completed, the sliding table enters a constant speed phase, and the data oscillates in a zigzag pattern with zero values. After the deceleration phase is completed, the acceleration value of the sliding table steadily approaches zero. © 2024 SPIE.

关键词： Deceleration

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Defects Classification on Garment Fabrics and Application of Artificial intelligence to Detect Defects During Fabric Inspection 3rd

Defects Classification on Garment Fabrics and Application of...

引用

3rd international conference on Material, machines and Methods for Sustainable Development, MMMS 2022

作者： Ngoc, Nguyen Thi Thuy Lan, Nguyen Thi Ngoc Hieu, Nguyen Minh Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9783031318238

This report presents the study results of defects classification that often appear on garment fabrics and a defects identification model made by artificial intelligence. The classification system of fabric defects is created for the artificial intelligence recognition model to ensure a comprehensive and general overview in many aspects: the type of garment fabric, the stage and causes of the defects, objects and the degree of defects. The recognition model for identification of fabric defects is built on the experimental method using the YOLO algorithm version 5. The data for model training and testing are selected and made by the algorithm's requirements, including 261 samples of fabric defect on 2D images of fabrics that have different characteristics such as weaves, textures, thicknesses, weights, yarn densities, and compositions. The testing results show that the model achieves an average of over 65% accuracy depending on the difficulty of the detected defect types. This model is the initial basis for designing an automatic detection system of the defects for many different types of fabrics. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Defects

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Proceedings - 2023 international conference on Intelligent Computing, Communication and Convergence, ICI3C 2023

Proceedings - 2023 International Conference on Intelligent C...

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3rd international conference on Intelligent Computing, Communication and Convergence, ICI3C 2023

ISBN: (纸本)9798350343922

The proceedings contain 93 papers. The topics discussed include: game design and learning effectiveness evaluation of English teaching based on reinforcement learning algorithm;research on automatic pricing and replenishment decision of vegetable products based on association model;research on the carbon asset management service model of power grid company;research on the influence of academic demands on depression of high school students based on machine learning and principal component analysis;a comparative study between economic models and machine learning models for bitcoin return volatility prediction;research on the design scheme construction of industrial big data platform based on enterprise portrait;examining the fuzzy control of the dynamic coupling compensating cold storage refrigeration system;and hybrid population intelligence and deep learning research for software defect prediction.

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GRU-Powered Emotion Detection: Breaking New Ground in Artificial intelligence 3

GRU-Powered Emotion Detection: Breaking New Ground in Artifi...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Jindal, Anirudh Gill, Kanwarpartap Singh Malhotra, Sonal Devliyal, Swati Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350354171

This study looks at how to understand emotions in language using a type of technology called bidirectional gated recurrent units (GRUs). Old models that only look at data in one direction are not as good as bidirectional GRUs because they can't understand relationships in data from both before and after. For jobs like understanding emotions, where the situation matters a lot, being able to consider things from both sides is really important. A step-by-step plan is used to create the proposed model. An embedding layer is the first step that changes word numbers into dense vectors, which are needed to work with text data. A dropout layer with a rate of 0. 5 is added to avoid overfitting. Next, there are two GRU layers that can process information in both directions. They are set up to return sequences, which helps to understand the timing and order of events better. A batch normalization layer helps make training more stable and faster. An extra bidirectional GRU layer helps the model better understand complex relationships from both sides. It has six parts and uses a softmax function to sort feelings into six categories. The last layer is fully connected. The Adam optimizer is added to the model, and it uses sparse categorical cross-entropy to measure how well the model is performing. Accuracy is a way to see how well something performs. This design shows that bidirectional GRUs are good for mood analysis, working accurately 94% of the time. It does a better job at guessing feelings because the model can see complicated connections in ordered information, as the results show. The model summary shows the different parts and options for the settings that help make the results really good. © 2024 IEEE.

关键词： Emotion recognition

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Predicting In-Service Teachers’ Epistemological Beliefs Based on Their TL Conception Using Ensemble Learning 15th

Predicting In-Service Teachers’ Epistemological Beliefs Bas...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Mishra, Pooja Manghirmalani Saboowala, Rabiya Machine Intelligence Research Labs Mumbai India Mumbai India

ISBN: (纸本)9783031889912

The purpose of this study was to determine the attitude of in-service school teachers towards their Teaching and Leaning conceptions. The teaching and learning conceptions questionnaire (TLCQ) was used to collect data from 241 teachers teaching in various school boards. Results depicted that irrespective of the school boards, school teachers preferred a traditional approach to teaching when compared to constructivist approach. In other words, school teachers have a Traditionalist Conception towards teaching and learning. This information is further used in ensemble algorithms to verify the results and to understand if the same prediction is attained using machine learning techniques. For this, Random Forest, XG-Boost Regression Algorithm and an Ensemble learning algorithm called Ensemble-Random Forest–Support Vector machine is implemented. The results obtained using the ensemble learning algorithm is most promising. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Random forests

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DEEP-COVID-SEV: AN ENSEMBLE 2D AND 3D CNN-BASED APPROACH FOR COVID-19 SEVERITY PREDICTION FROM 3D CT-SCANS

DEEP-COVID-SEV: AN ENSEMBLE 2D AND 3D CNN-BASED APPROACH FOR...

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IEEE international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bougourzi, Fares Dornaika, Fadi Nakib, Amir Distante, Cosimo Taleb-Ahmed, Abdelmalik Univ Paris Est Creteil Lab LISSI F-94400 Paris France Univ Basque Country Basque Fdn Sci Ikerbasque Bilbao Spain Natl Res Council Italy I-73100 Lecce Italy Univ Polytech Hauts France UPHF CNRS IEMNUMR 8520 Valenciennes France

ISBN: (纸本)9798350302615

Since the advent of Covid-19 in late 2019, medical image analysis with artificial intelligence (AI) has become an important research topic. CT-scan imaging is an important diagnostic tool for this disease. This study is part of the 3rd COV19D competition for Covid-19 Severity Prediction, where we aim to close the significant gap between validation and testing results of the previous competition. To achieve this, we proposed two methods based on 2D and 3D CNN, respectively. Our 2D-CNN approach, called 2B-InceptResnet, includes two paths for segmented lungs and for infection of all slices of the input CT-scan, respectively. Each path contains a ConvLayer and an Inception-ResNet model pre-trained on ImageNet. In contrast, our 3D-CNN approach, known as Hybrid-DeCoVNet, consists of four blocks: Stem, four 3D-ResNet layers, classification head, and decision layer. Decision-based ensemble models are also created using these two proposed solutions with six training subsets. Our proposed approaches outperformed the baseline approach by 36% in the validation data of the 3rd COV19D competition for predicting the severity of Covid-19. In addition, our approach ranked second in the testing phase with an improvement of 14% compared to the baseline approach. These promising results demonstrate the potential of our novel method to improve the diagnosis and prognosis of Covid-19, which could contribute to the development of better treatment strategies and ultimately save lives.

关键词： Covid-19 Deep Leaning CNN recognition Severity

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Proceedings of AI4S 2022: Artificial intelligence and machine Learning for Scientific Applications, Held in conjunction with SC 2022: The international conference for High Performance Computing, Networking, Storage and Analysis

Proceedings of AI4S 2022: Artificial Intelligence and Machin...

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3rd IEEE/ACM international Workshop on Artificial intelligence and machine Learning for Scientific Applications, AI4S 2022

ISBN: (纸本)9781665462075

The proceedings contain 8 papers. The topics discussed include: automated continual learning of defect identification in coherent diffraction imaging;a case study on coupling OpenFOAM with different machine learning frameworks;PhySRNet: physics informed super-resolution network for application in computational solid mechanics;determining HEDP foams' quality with multi-view deep learning classification;pattern-based autotuning of OpenMP loops using graph neural networks;ensuring ai for science is science: making randomness portable;practical federated learning infrastructure for privacy-preserving scientific computing;and scalable integration of computational physics simulations with machine learning.

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