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Fabrication and characterization of magnesium-based nanocomposites reinforced with Baghdadite and carbon nanotubes for orthopaedical applications

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Journal of Magnesium and Alloys 2024年第12期12卷 5144-5163页

作者： Mojtaba Ansari Shiva Mahdavikia Hossein Eslami Mozhdeh Saghalaini Hamid Taghipour Fatemeh Zare Shahin Shirani Mohammad Hossein Alizadeh Roknabadi Department of Biomedical Engineering Meybod UniversityMeybodIran Department of Biomedical Engineering Amirkabir University of TechnologyTehranIran Center for Education and Research on Macromolecules(CERM) CESAM Research UnitDepartment of ChemistryUniversity of LiègeLiègeBelgium Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTXUSA Department of Aerospace Engineering Amirkabir University of TechnologyTehranIran

This study explores the potential of Mg/Carbon Nanotubes/Baghdadite composites as biomaterials for bone regeneration and repair while addressing the obstacles to their clinical *** powder was synthesized using the sol-gel method to ensure a fine distribution within the Mg/CNTs ***/1.5 wt.%CNT composites were reinforced with BAG at weight fractions of 0.5,1.0,and 1.5 wt.%using spark plasma sintering at 450℃and 50 MPa after homogenization via ball *** cellular bioactivity of these nanocomposites was evaluated using human osteoblast-like cells and adipose-derived mesenchymal stromal *** proliferation and attachment of MG-63cells were assessed and visualized using the methylthiazol tetrazolium(MTT)assay and SEM,while AD-MSC differentiation was measured using alkaline phosphatase activity *** were also generated to visualize the diameter distributions of particles in SEM images using image processing *** Mg/CNTs/0.5 wt.%BAG composite demonstrated optimal mechanical properties,with compressive strength,yield strength,and fracture strain of 259.75 MPa,180.25 MPa,and 31.65%,*** learning models,including CNN,LSTM,and GRU,were employed to predict stress-strain relationships across varying BAG amounts,aiming to accurately model these curves without requiring extensive physical *** shown by contact angle measurements,enhanced hydrophilicity promoted better cell adhesion and ***,corrosion resistance improved with a higher BAG *** study concludes that Mg/CNTs composites reinforced with BAG concentrations below 1.0 wt.%offer promising biodegradable implant materials for orthopedic applications,featuring adequate load-bearing capacity and improved corrosion resistance.

关键词： Biodegradable material Magnesium-based composite Carbon nanotube Baghdadite Spark plasma sintering

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A Review Paper on Object-Detection using the DeepLearning Approach 11

A Review Paper on Object-Detection using the DeepLearning Ap...

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11th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Bodke, Minal Patil, Chetan Chopade, Pratik Patil, Yashodhan Patil, Omkar Pimpri Chinchwad College of Engineering and Research Department of Computer Engineering Pune India

ISBN: (纸本)9798350348422

Because of their close association with object identification, video analysis and picture comprehension have drawn a lot of interest in recent years. Detection of conventional objects the solution is built on handcrafted functions and architecture that seems to be trainable. The accumulation produces a little stall in performance. A complicated set made up of numerous low-level pictures and Scene Classifier features with item detectors and high-level context. As the deep learning field matures, more semantic, high-level, Deeper features are developed to solve existing challenges in conventional architecture. In terms of network design, training technique, and optimization function, for example, these models behave differently. This white paper gives an introduction to Deep. A framework for object identification based on learning. our examination starts A short history of deep learning, the representative tool, the Convolutional Neural Network (CNN). Then we concentrate on a standard generic object identification architecture with certain modifications and handy methods. Improve detecting performance even further. particularly specific the features of discovery tasks vary. Monitoring of particular tasks, such as detection of prominent items, face recognition, and pedestrian identification. Examination of experimental data It is also possible to compare various methodologies and get some significant findings. Finally, as guidance for future effort, several intriguing areas and tasks are presented. Both object identification and a neural network-based learning system are involved. One of the biggest difficulties of object detection is that an object viewed from different angles may look completely different. For example, the images of the cakes that you can see below differ from each other because they show the object from different sides. © 2023 IEEE.

关键词： Object detection

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Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT

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Digital Communications and Networks 2023年第2期9卷 411-421页

作者： Anichur Rahman Md Jahidul Islam Shahab S.Band Ghulam Muhammad Kamrul Hasan Prayag Tiwari Department of Computer Science and Engineering National Institute of Textile Engineering and Research(NITER)Constituent Institute of the University of DhakaSavarDhaka1350Bangladesh Department of Computer Science and Engineering Green University of BangladeshDhakaBangladesh Future Technology Research Center College of FutureNational Yunlin University of Science and TechnologyDouliouTaiwanChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Information and Communication Engineering Hankuk University of Foreign StudiesSouth Korea Department of Computer Science Aalto UniversityFinland

Some of the significant new technologies researched in recent studies include BlockChain(BC),Software Defined Networking(SDN),and Smart Industrial Internet of Things(IIoT).All three technologies provide data integrity,confidentiality,and integrity in their respective use cases(especially in industrial fields).Additionally,cloud computing has been in use for several years *** information is exchanged with cloud infrastructure to provide clients with access to distant resources,such as computing and storage activities in the *** are also significant security risks,concerns,and difficulties associated with cloud *** address these challenges,we propose merging BC and SDN into a cloud computing platform for the *** paper introduces“DistB-SDCloud”,an architecture for enhanced cloud security for smart IIoT *** proposed architecture uses a distributed BC method to provide security,secrecy,privacy,and integrity while remaining flexible and *** in the industrial sector benefit from the dispersed or decentralized,and efficient environment of ***,we described an SDN method to improve the durability,stability,and load balancing of cloud *** efficacy of our SDN and BC-based implementation was experimentally tested by using various parameters including throughput,packet analysis,response time,bandwidth,and latency analysis,as well as the monitoring of several attacks on the system itself.

关键词： Smart IIoT Blockchain SDN IoT Security Privacy OpenFlow SDN-Controller Data security Cloud computing Cloud management

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CNN-Based Detection of Tampered Digital Images 2

CNN-Based Detection of Tampered Digital Images

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Kumar, S. Prem Thirumalesh, M. Sai Kumar, T. Pramod Reddy, P. Subba Angadi, Sanjeevkumar Venu, Nookala Computer Science and Engineering G.Pullaiah College of Engineering and Technology Kurnool India Nutan College of Engineering and Research Computer Science and Engineering Talegaon Dabhade Pune India Madhya Pradesh Gwalior474 005 India

ISBN: (纸本)9798350384277

This project presents a novel approach for detecting tampered digital images using Convolutional Neural Networks (CNN). Our methodology leverages the power of deep learning to analyse image content and identify alterations introduced through manipulation. The key innovation lies in the integration of CNN architectures, enabling robust and accurate detection of tampering in various image types. Additionally, the project incorporates Error Level Analysis (ELA) as a complementary feature to enhance the detection accuracy. ELA helps identify areas of an image with inconsistent compression levels, aiding in the identification of potential tampering artifacts. The synergy between CNN-based analysis and ELA creates a comprehensive solution for the reliable detection of tampered digital images. This approach demonstrates promising results in differentiating between authentic and manipulated images, making it a valuable tool for forensic image analysis, and ensuring the integrity of digital content. © 2024 IEEE.

关键词： Convolutional neural networks

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Med Assistant Android Application

Med Assistant Android Application

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2023 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2023

作者： Turkar, Hemant Tiwari, Shubham Mate, Aman Naranje, Atish Jaulkar, Jay Hadke, Kunal Rajiv Gandhi College of Engineering and Research Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350398748

Fitness apps that can help people improve their health and stamina by increasing incentive for physical activity utilising gamification principles and wearables have been made possible because to the rise in 'smartphone ownership.' However, compared to other app kinds, these apps have a low retention rate. In this research, the most popular fitness mobile apps were reviewed, and various gamification strategies were compared. The research revealed that most fitness applications are difjicult to use, have an excessive number of features, and don't motivate users enough. A straightforward fitness application that links to a watch and employs levels and avatars to boost motivation was created to address some of the aforementioned drawbacks. A team of volunteers evaluated the application, and the findings were generally favourable. The outcomes demonstrated that there is room for future advancement in application development. © 2023 IEEE.

关键词： Android (operating system)

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A Blended Learning Strategy for English-Based Programming Courses in China's Higher Education 13

A Blended Learning Strategy for English-Based Programming Co...

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13th International Conference on Educational and Information Technology, ICEIT 2024

作者： Wu, Mei Wang, Xining Lu, Tao O'Hare, Gregory M.P School of Computer Science and Engineering School of Artificial Intelligence Wuhan Institute of Technology China School of Education School of Computer Science and Statistics Trinity College Dublin Dublin Ireland School of Computer Science and Statistics Trinity College Dublin Dublin Ireland

ISBN: (纸本)9798350372663

As English becomes the mainstream language in communication and education worldwide, more and more Chinese universities emphasize the importance of adding English content to the higher education curriculum. This paper explores the integration of English content into computer Science education in Chinese universities, with a focus on bilingual programming courses. It proposes a blended learning strategy that combines face-to-face classroom interaction with platform-based self-directed learning. It designed the experiment and the number of participants is 84. The experimental results show that the adoption of this blended teaching strategy can enhance students' enthusiasm for bilingual programming courses and facilitate students in achieving the desired learning effect. © 2024 IEEE.

关键词： Students

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Relevant Musical Schema-based Human Emotion Controller using Deep Learning Techniques 3

Relevant Musical Schema-based Human Emotion Controller using...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Nagaraj, P. Sudar, K. Muthamil Krishna, Gollamandala Vamsi Yeswanth, Grandhi Srinivas Charan, Gurrala Sai Charan, Nidiganti Kalasalingam Academy of Research and Education Krishnankoil Department of Computer Science and Engineering Virudhunagar India Mepco Schlenk Engineering College Department of Computer Science and Engineering Virudhnagar India

ISBN: (纸本)9798350361186

Musical Schema negotiation describes how a user's facial expressions may convey their emotional state or mood. You may see these expressions in the live video from the system camera. Many efforts are being made to teach machines to recognize various human emotions or moods in the field of computer vision and machine learning (ML). There are several techniques available in machine learning to identify human emotions. We propose a system that detects mood in real time and generates a mood-based music player that recommends songs depending on the observed mood. This is now a function that comes pre-installed on the traditional music player applications on our phones. One major benefit of mood detection integration is customer satisfaction. Among this system's objectives are to analyze the user's look, forecast their expression, and suggest music based on how they seem. © 2024 IEEE.

关键词： Deep learning

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GRU combined model based on multi-objective optimization for short-term residential load forecasting

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10423-10440页

作者： Yi, Lingzhi Peng, Xinlong Fan, Chaodong Wang, Yahui Li, Yunfan Liu, Jiangyong College of Automation and Electronic Engineering Xiangtan University Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Hunan Xiangtan China School of Computer Science and Technology Hainan University Hainan China College of Electrical and Information Engineering Hunan University Changsha China

Reliable and accurate short-term forecasting of residential load plays an important role in DSM. However, the high uncertainty inherent in single-user loads makes them difficult to forecast accurately. Various traditional methods have been used to address the problem of residential load forecasting. A single load forecast model in the traditional method does not allow for comprehensive learning of data characteristics for residential loads, and utilizing RNNs faces the problem of long-term memory with vanishing or exploding gradients in backpropagation. Therefore, a gated GRU combined model based on multi-objective optimization is proposed to improve the short-term residential load forecasting accuracy in this paper. In order to demonstrate the effectiveness, GRUCC-MOP is first experimentally tested with the unimproved model to verify the model performance and forecasting effectiveness. Secondly the method is evaluated experimentally with other excellent forecasting methods: models such as DBN, LSTM, GRU, EMD-DBN and EMD-MODBN. By comparing simulation experiments, the proposed GRU combined model can get better results in terms of MAPE on January, April, July, and November load data, so this proposed method has better performance than other research methods in short-term residential load forecasting. © 2024 - IOS Press. All rights reserved.

关键词： Multiobjective optimization

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An Autoencoder-BiLSTM framework for classifying multiple types of lung diseases from CXR images

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Multimedia Tools and Applications 2024年第38期83卷 86091-86120页

作者： Ankayarkanni, B. Sangeetha, P. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Jeppiaar Nagar Chennai600 119 India Rajiv Gandhi College of Engineering Research and Technology Maharashtra Chandrapur442403 India

Millions of people die from lung illness each year as a result of its rise in recent years. CXR imaging is one of the most widely used and reasonably priced diagnostic techniques for the diagnosis of many illnesses. Unfortunately, even for seasoned radiologists, accurately diagnosing sickness from Chest X-Rays (CXR) samples is challenging. To combat the pandemic, a reliable, affordable, and efficient way to diagnose lung disease has become essential. Consequently, a unique optimized Auto Encod-BI Long-Short Term Memory (Bi-LSTM) model is proposed in this research work. Pre-processing, segmentation, feature extraction, and multiple types of lung illness diagnosis are the four main stages of the suggested model. First, Laplacian filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to pre-process the gathered CXR pictures. Next, the Region of Interest (ROI) from the previously processed images are recognized by means of the newly enhanced MobileNetV2. The new Self-Improved Slime Mould Algorithm (SI-SMA) is used to fine-tune the hyper-parameters of MobileNetV2 in order to precisely identify the afflicted locations. Based on the phenomenon of slime mould oscillation, the conventional Slime Mould Algorithm (SMA) model has been modified with the creation of the SI-SMA model. Next, characteristics like the Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) are taken out. Finally, a unique AutoEncod-BiLSTM Framework—which is divided into three categories—is shown to automate the process of identifying illnesses in CXR pictures: pneumonia, COVID-19, and normal. The autoencoder and Bi-LSTM are combined to create the suggested AutoEncod-BiLSTM model. The retrieved features are used to train the AutoEncod-BiLSTM Framework. Moreover, the proposed model enhanced the disease detection efficiency than the existing models and the disease detection accuracy of the proposed model is about 99.1%. Furthermore, the suggested model attains better

关键词： Equalizers

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Graphical Password Authentication Using Decentralized Single-Sign-On

Graphical Password Authentication Using Decentralized Single...

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2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Shinde, Rishikesh Dnyandev Jathar, Kishori Ravindra Pardeshi, Abhay Dilip Thorat, S.A. Government College of Engineering and Research Avasari Department of Computer Engineering Pune India

ISBN: (纸本)9798350311204

Authentication is a crucial process that verifies the identity of an individual or system seeking access to resources or services. Password-based authentication systems, which are the most common and widely used, can be complex, difficult to remember, and vulnerable to various attacks. Graphical passwords offer a more user-friendly alternative to text-based passwords and have been validated by software companies and psychology research. However, existing password authentication systems, including OAuth2.0, Google Auth, and Facebook Auth, have limitations, such as single-point failure, centralized architecture, privacy concerns, and resource-intensive computation systems. This paper proposes a decentralized platform, Visual-D-Auth, that leverages blockchain and IPFS technologies to provide secure and reliable access to users through a Decentralized Single Sign-On (DSSO) technique. The Visual-D-Auth system eliminates the need for remembering multiple usernames and passwords in a decentralized environment while providing improved security, user privacy, and convenience. The paper introduces a unique graphical password generation algorithm that generates highly secure passwords using a combination of images and a user-selected username. Finally, the Visual-D-Auth solutions address the most common weaknesses of graphical password systems, such as shoulder-surfing and smudge attacks, offering a more secure and user-friendly solution. Overall, this research presents a novel approach to authentication that could potentially revolutionize the way we authenticate access to resources and services. © 2023 IEEE.

关键词： Authentication

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