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检索条件"机构=Dept. of Computer Science and Engineering Chalmers University of Technology"

共 9562 条记录，以下是151-160 订阅

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SHELL-BASED COMPUTATIONAL HOMOGENISATION WITH IMMERSED METHODS FOR MESO-SCALE MODELLING OF FIBRE REINFORCED COMPOSITES 23

SHELL-BASED COMPUTATIONAL HOMOGENISATION WITH IMMERSED METHO...

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23rd International Conference on Composite Materials, ICCM 2023

作者： Börjesson, Elias Verhoosel, Clemens Remmers, Joris J.C. Fagerström, Martin Dept. Industrial and Materials Science Chalmers University of Technology Gothenburg Sweden Dept. Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands

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Contribution of Algorithm Visualizations to Students' Learning Skills: A Pedagogical Approach 12

Contribution of Algorithm Visualizations to Students' Learni...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Kheder, Harem Ali Varol, Asaf College of Engineering and Computer Sciences Lebanese French University Dept. of Computer Engineering Erbil Iraq College of Engineering and Computer Science The University of Tennessee at Chattanooga Dept. of Engineering Management and Technology ChattanoogaTN United States

ISBN: (纸本)9798350330366

The Domain Algorithm visualizations are multimedia-based representations of algorithms that help students learn how they operate and behave. Algorithm visualizations provide numerous benefits to instructors and students, including an improved understanding of difficult algorithms in programming. By stripping down the complexities of algorithms and presenting them in simplified visual ways, students can obtain a more thorough knowledge of how they perform. Instructional approaches, such as guided instruction or discovery learning, are critical to the pedagogical effectiveness of algorithm representations. Guided instructions can considerably boost learners' overall performance in problem-solving activities that incorporate visualizations. At the same time, discovery education promotes exploration and experimentation, yet it may result in longer learning times and increased mental demands. A combination of these approaches is required for optimal effectiveness of instruction. In this paper, we investigate how the pedagogical effectiveness of algorithm visualizations should be determined by a variety of elements, including student characteristics, visual and cognitive processing ability, algorithm representation, and instructional context based on a literature review and interviews with field experts, and a bunch of recommendations is provided that has witnessed by authors during their long teaching periods. © 2024 IEEE.

关键词： Students

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BSCIAM: A Blockchain based Secure Cloud Identity and Access Management Framework

BSCIAM: A Blockchain based Secure Cloud Identity and Access ...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Das, Swatisipra Sahil, Mohammad Pandit, Naveen Kumar Priyadarshini, Rojalina Gochhayat, Sarada Prasad Fakir Mohan University Postgraduate Dept. of Computer Science Odisha India C.V. Raman Global University Dept. of Computer Science and Engineering Odisha India Indian Institute of Technology Jammu Dept. of Computer Science and Engineering J&K India

ISBN: (纸本)9798350348460

Cloud computing enables on-demand computation on remote servers and computers. Thanks to the adaptability and scalability of the infrastructure for data storage, processing, and management. To solve the security problems arising in cloud identity management techniques such as, dependency on a third-party token provider, leakage of user's details, single point of failure, etc., decentralized cloud identity management systems came into the picture. Blockchain is a decentralized database providing immutability and transparency to recorded transaction data. The current token-based decentralized cloud identity management systems have limitations, including the absence of data access management procedures and a lack of security features. This paper suggests a Blockchain based Secure Cloud Identity and Access Management (BSCIAM) model for secure identity and access management, utilizing encryption and key sharing techniques. The proposed model has been implemented using Ethereum smart contracts for token-based identity management. © 2024 IEEE.

关键词： access management blockchain cloud identity management

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Enhancing Autism Spectrum Disorder Identification: A Machine Learning Approach using CatBoost

Enhancing Autism Spectrum Disorder Identification: A Machine...

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2024 IEEE International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Rathod, Vijayalaxmi N. Goudar, R.H. Dhananjaya, G.M. Patil, Minal Hukkeri, Geetabai S. Kaliwal, Rohit B. Dept. of Computer Science and Engineering Visvesvaraya Technological University Karnataka Belagavi India Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Bengaluru India

ISBN: (纸本)9798331505134

Autism is a complex neurodevelopment condition that affects an individual's behavior, communication, and social interaction. Identification is a critical endeavor in healthcare, necessitating accurate and efficient diagnostic methodologies, early identification is pivotal for timely intervention and improved outcomes in affected individuals. This paper investigates the use of machine learning algorithms, specifically CatBoost, for Autism trait identification using heterogeneous datasets from toddlers, children, adolescents, and adults. The research investigates the performance of CatBoost in handling mixed data types, including categorical features and missing values, without extensive preprocessing. Utilizing gradient boosting on decision trees, CatBoost demonstrates its efficacy in capturing complex relationships between features, facilitating high predictive accuracy in autism identification. Through rigorous evaluation metrics such as accuracy, precision, recall, and F1 score, the designed system achieves a precise accuracy of 92% for adult datasets and 88% for child and adolescent datasets. This study delineates CatBoost's robustness across diverse age groups, providing insightful information on its applicability for Autism Spectrum Disorder diagnosis in the healthcare domain. © 2024 IEEE.

关键词： Diseases

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Deep Analysis of Machine Learning Techniques for the Effective Retrieval of Images Based on Content 2

Deep Analysis of Machine Learning Techniques for the Effecti...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Jain, Ashish Varshney, Sudeep Set Sharda University Dept. of Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

Our research aims to investigate how machine learning techniques can be applied for efficient image retrieval based on content. It is a very difficult process to obtain accurate images from vast digital image collections. CBIR has been particularly interested in this area. It has been shown that content- or feature-based approaches are more effective than text-based query techniques. Research has shown that the performance of these methods varies depending on the type and size of the data set analyzed, with some algorithms performing well for specific small data sets and others for larger data sets. This article provides a comprehensive and systematic review of effective methods for obtaining the image. The paper's objective is to examine different ML classifiers that are applied via the researchers and compare their results based on accuracy and performance. These algorithms include SVM, KNN, random forest, BoVW, Naive Bayes, and LDA. This review paper can assist researchers in selecting the best classifier for their research, as well as in improving existing classifiers by combining them with other algorithms. © 2024 IEEE.

关键词： CBIR classifier feature extraction Random Forest SVM

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Smart Waste Management System Using IoT

Smart Waste Management System Using IoT

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Dhar, Mainak Roy, Alak Mandal, Purnendu Icfai University Dept. of Computer Science and Engineering Tripura Agartala India Tripura University Dept. of Information Technology Agartala India

ISBN: (纸本)9798331540821

It is thereby noticed that the rapid rate of urbanization has increased the quantum of waste generation manifold, which imposes significant challenges in waste management for traditional systems. In this paper, a Smart Waste Management System (SWMS) is proposed at a time when Internet of Things (IoT) and Artificial Intelligence (AI) technologies are about to reach the mainstream. The SWMS will reduce operations costs and its impact on the environment with its architecture, which provides real-time monitoring, data analytics, and intelligent decision-making methods to enhance efficiency. The IoT part of SWMS involves setting up sensor nodes in the waste bins to observe fill levels and other environmental parameters. The sensors transmit the data without wires to a central cloud platform for storage and analysis. AI algorithms on the cloud process this information to produce optimized disposal schedules, fill-level predictions, and anomaly detection, like fires or spills. Moreover, by using techniques of machine learning, system performance will be improved further by available historical data and real-time feedback. © 2024 IEEE.

关键词： Decision making

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Prediction and Modelling of Suicidal Mental Tendencies 8

Prediction and Modelling of Suicidal Mental Tendencies

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

作者： Guruprasad, Sunitha Rao, Arnav G. Shreeraksha Shivani, K. Varsha, S.N. Yashoda, K. St Joseph Engineering College Dept. of Computer Science & Engineering Mangaluru India Visvesvaraya Technological University Karnataka Belagavi India Ramaiah Institute of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350350593

Suicide remains a pressing public health concern worldwide, necessitating effective predictive and preventive strategies. Predicting suicidal tendencies is a complex endeavor, as it involves multifaceted interactions between biological, psychological, and environmental factors. Advances in computational modeling and machine learning have provided promising avenues for understanding and forecasting these tendencies. Such models often integrate clinical data, including psychiatric history, symptom severity, and demographic information, with biological markers and psychosocial variables. The objective of our work is to identify patterns and risk factors that may indicate heightened suicidal risk. Ongoing research seeks to refine predictive models, incorporate real-time monitoring, and develop targeted interventions, aiming to reduce the incidence of suicide and provide timely support to those at risk. The application of suicide text analysis extends to user-friendly interfaces, allowing individuals to input text, receive predictions, and access relevant support. The analysis involves preprocessing techniques such as text normalization, stop-word removal, and stemming to enhance the quality of input data. Various machine learning algorithms, ranging from Naive Bayes to sophisticated ensemble methods, are explored to determine their effectiveness in predicting suicidal ideation. © 2024 IEEE.

关键词： Prediction models

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A Deep Learning Based Image Processing Technique for Early Lung Cancer Prediction

A Deep Learning Based Image Processing Technique for Early L...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Tasnim, Nowshin Noor, Kazi Rifah Islam, Mursalina Huda, Mohammad Nurul Sarker, Iqbal H. Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chittagong4349 Bangladesh United International University Dept. of Computer Science & Engineering Dhaka1212 Bangladesh Edith Cowan University PerthWA6027 Australia

ISBN: (纸本)9798350372229

Lung cancer is the primary cause of cancer mor-tality all over the world due to the increase of tobacco consumption, and industrialization in developing nations. As the early-stage diagnosis can reduce the mortality rate significantly, early detection with the availability of high-tech Medical facilities is highly necessary. In this research, we used deep learning (DL) methods initially on patient's 1190 CT scan images from the Kaggle IQ-OTH lung cancer dataset, and after significant image preprocessing steps we found augmented images including normal, malignant, and benign cases to identify high-risk in-dividuals to detect lung cancer and also predict the malignancy and thus, taking early actions to prevent long-term consequences. A thorough performance comparison between several classifiers, including the conventional CNN, Resnet50, and InceptionV3, has been presented. Here, affine transformation, gaussian noise, and other rigorous image preprocessing techniques are used. The contribution obtained a 98% validation accuracy while reducing the model's complexity with the previous preprocessing stage. The comparison method shows that the suggested preprocessing method yields a higher F1 score value of 97%, validating our suggested methodology. © 2024 IEEE.

关键词： CT scans deep learning healthcare analytics image processing lung cancer prediction

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Adaptive Contour Noise Generation Network for Semi-supervised Medical Data Segmentation 6

Adaptive Contour Noise Generation Network for Semi-supervise...

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6th International Conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Ye, Shunlong Nanjing University of Science and Technology Dept. of Computer Science and Engineering Nanjing China

ISBN: (纸本)9781665491259

When training convolutional neural networks, noise distribution in data can affect segmentation accuracy. Therefore, many neural networks will strengthen the segmentation ability of the model by artificially adding noise. However, because the noise distribution of the input data is not known, the model training process can only generate random noise, e.g., gaussian noise, but cannot simulate real-world noise. In the medical imaging domain, more attention is paid to the image contours, which can also be used for noising simulation. Meanwhile, considering the existence of rich unlabeled data sets in medical images, this paper proposes a semi-supervised framework for uncertainty estimation by using Monte Carlo Dropout and generates adaptive noise using the uncertainty map. Specifically, the framework consists of three parts, including a noise generation model, a teacher model, and a student model. The noise generation part processes the data input to the other two parts and uses the contour feature identified by the posteriori uncertainty map to simulate priori noise to guide the process of training segmentation model. Meanwhile, the student model updates its parameters based on the teacher model output and the ground truth annotations by minimizing a joint loss. Segmentation experiments on left atrium 3D data show that our method has competitive results with other SOTA semi-supervised methods. © 2023 IEEE.

关键词： Medical imaging

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Blockchain Based Secure and Decentralized Smart Licensing of Charging Vehicles for Rajshahi City Corporation 2

Blockchain Based Secure and Decentralized Smart Licensing of...

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2nd International Conference on Information and Communication technology for Sustainable Development, ICICT4SD 2023

作者： Haque, Momenul Paul, Subrata Kumer Islam, Kamrul Della, Mursheda Nusrat Paul, Rakhi Rani Fahim, Sultan Bangladesh Army University of Engineering and Technology Dept. of Computer Science and Engineering 6431 Bangladesh University of Rajshahi Dept. of Computer Science and Engineering Rajshahi6205 Bangladesh Rabindra Maitree University Dept. of Computer Science and Engineering Kushtia7000 Bangladesh Bangladesh Army University of Engineering and Technology Dept. of Information and Communication Engineering Bangladesh

ISBN: (纸本)9798350358667

The adoption of charging vehicles in Rajshahi has emerged as a significant step towards reducing air pollution and promoting a healthy, green city. However, the current centralized licensing system poses challenges such as time-consuming processes and the potential for fraudulent activities. To address these issues, this paper proposes the implementation of a secure and decentralized smart licensing system for charging vehicles in Rajshahi City Corporation, leveraging the smart capabilities of blockchain technology. By utilizing Hyperledger Fabric, the implementation generates blockchain based smart contracts that automate and streamline the licensing process smartly and efficiently. This approach ensures the immutability, transparency, and integrity of license records, minimizing the risk of fraud and enhancing overall efficiency. Additionally, the system incorporates the use of QR codes and an inquiry system to enhance the credibility of paper-based licenses. Compared to other digital licenses, this blockchain-based method significantly reduces risks and contributes to the development of a greener and more sustainable Rajshahi city environment. Furthermore, the introduction of a sustainable business policy, utilizing the G2C model, facilitates revenue generation and distribution fairly and equitably for all stakeholders involved in the charging vehicle ecosystem. This implementation not only offers cost benefits and efficiency improvements for drivers but also promotes a sustainable and environmentally friendly city. © 2023 IEEE.

关键词： Smart contract

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