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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Mohiuddin, Md. Ghouse Reddy, D. Laxmi Sreenivasa Ahmed, Aijaz University Pg College Palamamuru University Dept. of Computer Science Telangana State Mahabubnagar India Chaitanya Bharthi Institute of Technology - Cbit Dept. of It Telangana State Hyderabad India University of Technology & Applied Science Dept. of It Shinas Oman

ISBN: (纸本)9798331542573

The fastest spreading dieses among the cancer dieses throughout the world is the Breast cancer. Identifying it at the prior stage will be helpful for effective treatment. Convolutional Neural Networks (CNNs) is an efficient technique which has demonstrated significant ability in classifying and recognizing patterns in medical images. In this research paper we have explored the application of deep learning models for detecting cancer cells from histopathological images, and focused on various architectures, training methods, and assessment measures. Here we specifically evaluated the functioning of four well-known models of CNN comprising AlexNet, VGG16, InceptionV3 and, ResNet50 by employing the dataset from BreakHis for binary classification of the histopathological images into Malignant and Benign classes. Each model is fine-tuned and trained through transfer learning. Here we used cross-entropy loss with Adam optimizer for binary classification, through which the learning rate is adaptively adjusted, and facilitating efficient handling of large datasets. In this research work for training our models we applied Stratified k-fold cross-validation approach for ensuring stability and generalizability. The effectiveness and performance of the models are assessed with the help of Accuracy, Precision, & Recall, and F1-Score. Comparative analysis indicates that ResNet50 outperforms the other models in detecting cancer cells. Finally, we discussed the potencies and weakness of the applied models for offering valuable perceptions into their efficacy for medical image classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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An app-based Spaced Repetition Learning Environment with User Generated Video Content

An app-based Spaced Repetition Learning Environment with Use...

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2023 IEEE International Symposium on Multimedia, ISM 2023

作者： Schimanke, Florian Mertens, Robert Kolker, Lars Mertens, Robert Hsw University of Applied Sciences Dept. of Computer Science and Engineering Hameln Germany Bhh University of Applied Sciencees Hamburg Germany

ISBN: (纸本)9798350395761

Spaced repetition is a learning task that helps users repeat learning items at optimally computed intervals. It is best suited for small learning units such as learning cards. In a typical learning scenario these learning cards do not stand by themselves, rather they are the last step in the students' work process, summarizing the students' interaction with the learning material. In this paper a framework is developed that allows learning material curators and students to create spaced repetition learning units with back-references to multimedia learning content, i.e., learning videos. With the advent of video learning nuggets, a wealth of content was created that can now be used in spaced repetition learning. The approach also allows for linking to specific parts of those learning videos to enable students to quickly review learning content specific to the learning unit currently worked on. © 2023 IEEE.

关键词： Students

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Leveraging Support Vector Machine for Sports Injury Classification 15

Leveraging Support Vector Machine for Sports Injury Classifi...

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15th International Conference on Information Technology and Electrical Engineering, ICITEE 2023

作者： Surasak, Thattapon Praking, Phornnatee Kitchat, Kotcharat King Mongkut's University of Technology North Bangkok Faculty of Applied Science Dept. of Computer and Information Science Bangkok Thailand National Central University Dept. of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350304466

This study explores the utility of Support Vector Machines (SVM) for the classification of injuries in Athletes including tennis, table tennis, and badminton. The primary objective of this research was to develop an automated method to detect between normal and injury pose, providing a poten-tially powerful tool for early detection. In other words, this research is the pioneer study to represent one another solution for sports injury classification using the image data apart from the current approaches that might be focused on the sensor-based. Therefore, we collect a dataset from various social media across the three sports, labeled into two categories: 'Normal' and 'Injury'. The SVM model was trained on this dataset, and tuned to optimize its performance in distinguishing the two classes. Achieving an accuracy of 96.63%, the result demonstrates that the SVM model can effectively identify injury patterns in the considered sports. The implications of this study are sig-nificant, presenting the initial steps towards applying computer vision tasks in sports topics. By demonstrating the feasibility of an SVM-based approach to injury detection in these racket sports. © 2023 IEEE.

关键词： Sports

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Real-Time Human Body Sensors Data Classification using Supervised Machine Learning 2

Real-Time Human Body Sensors Data Classification using Super...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Farooq, Muhammad Sajid Khan, Muhammad Farhan Lahore Garrison University Dept. of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan University of Health Sciences Dept. of Forensic Sciences Lahore Pakistan

ISBN: (纸本)9798350335644

Supervised learning algorithms have recently been brought to the classification of medical data in an effort to boost diagnostic throughput and eliminate human-caused diagnostic errors. This measure was taken to improve diagnostic precision. To be carried out, the fully supervised learning process needs a large amount of accurately labelled data. Annotating data material, on the other hand, demands a large expenditure of time, effort, and resources. Using the self- supervised learning approach to solve this challenge has the potential to provide a variety of beneficial outcomes for everyone. When there is no direct supervision, pretext projects are regularly employed in independent study scenarios. This is done so that massive volumes of unsupervised data may be utilized to obtain supervised knowledge. Furthermore, by utilizing specialized, supervised information, the network is prepared to obtain representations that will be beneficial for still-growing professions. The two-step CPC-based classification framework (TCC) model is built completely using contrastive predictive coding and is divided into two distinct phases that are carried out in sequential order. Making use of the contrastive predictive coding (CPC) model, the initial step is to build a fictional position. It must be completed as quickly as possible. This activity must utilize the encoder to extract appropriate features from a broad variety of classes in order to achieve the goal that it has set out to achieve. The encoder will employ the skills they learned while working on the pretext in the second half, which will take place in a training setting under the guidance of an expert teacher. Finally, we assess the proposed framework by comparing it to the most recent and innovative research in the field. This allows us to highlight the framework's success by displaying how well it compares to other cutting-edge and current industry efforts. Experiments will be conducted to compare the efficacy of the pr

关键词： Supervised learning

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Towards fracture risk assessment by deep-learning-based classification of prevalent vertebral fractures

Towards fracture risk assessment by deep-learning-based clas...

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Medical Imaging 2023: computer-Aided Diagnosis

作者： Yilmaz, Eren B. Fricke, Tobias Laue, Julian Polzer, Constanze Sedaghat, Sam Hövener, Jan-Bernd Glüer, Claus-Christian Meyer, Carsten Campus Kiel Germany Dept. of Radiology and Neuroradiology UKSH Campus Kiel Germany Dept. of Computer Science Ostfalia University of Applied Sciences Wolfenbüttel Germany Dept. of Computer Science Faculty of Engineering Kiel University Germany

ISBN: (纸本)9781510660359

Osteoporosis is a complex multifactorial skeletal disease and has become a major socioeconomic issue, causing tremendous hospitalization and rehabilitation costs. In addition to age, premature menopause or the use of oral corticosteroids, vertebral fractures are regarded as main risk factors for developing osteoporosis and associated fractures. In this work, we adapt an existing automated pipeline to classify fracture grades of individual vertebrae and the spine as a whole: First, vertebral body centers were identified on CT images by a hierarchical neural network. Next, the fracture grades of individual vertebrae were processed by a multi-head, feed-forward convolutional neural network. The sum of the classified grades was then evaluated according to the German radiology guidelines. A hyperparameter search on validation data showed the most promising results for an output configuration based on three sequentially applied binary classification outputs trained using binary cross-entropy: Grade 0-1 vs 2-3, and 0 vs 1-3, and 0-2 vs 3. In a cross-validation setting on 159 low-dose CT images, our pipeline accurately classified patients to have sum-scores ≥ 2 with sensitivities and specificities of 90 % ± 5.0 % and 87 % ± 2.7 %, respectively. As our method was based on classifying individual vertebrae, we were able to provide both the fracture position and severity to enhance transparency, interpretability and usability. © 2023 SPIE.

关键词： Fracture

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Security in Social Networks and Media 1

Security in Social Networks and Media

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Jyothi, A.P. Badiger, Vinayak N Shivakumar, Vinyas Ramaiah University of Applied Sciences Faculty of Engineering and Technology Dept. of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350348057

In today's data-driven world, data privacy and security is of utmost importance. Social media platforms have become the main source of data breaches due to the sheer number of users who are exposed to these risks. It is essential to understand the viable risks associated with such breaches in order to ensure that personal data remains secure. In this paper, we have discussed and reviewed the different forms of digital privacy breaches, their risks, possible preventive strategies and responsibilities of Institutions and Governments to handle digital security. © 2023 IEEE.

关键词： Data privacy

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Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach

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Neutrosophic Sets and Systems 2025年 81卷 479-502页

作者： Abdo, Doaa A. Salama, A.A. Abdelmegaly, Alaa A. Mahmoud, Hanan Khadari Mahdi Applied statistics and insurance department faculty of commerce Mansoura university Mansoura Egypt Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Higher Institute of Advanced Management Sciences and Computers Al-Buhayrah Egypt Depatrment of applied statistics at the Nile Higher Institute of commercial sciences and Computer Technology in Mansoura Egypt

This study tackles the problem of missing data in migrant datasets by introducing a new framework that combines machine learning techniques with neutrosophic sets. These sets, which can represent uncertainty and ambiguity, are well-suited for managing the complex nature of missing information in sensitive fields like migration research. We test the effectiveness of KNN, SVM, decision tree, random forest, and Ada Boost algorithms on a migrant dataset, comparing their results using different imputation methods (mean/mode, model-based imputer (simple tree), and random values). Our research showed that our proposed approach, which used neutrosophic sets, improved imputation accuracy and strengthened model reliability. Our results underscored the potential of neutrosophic set-based machine learning for addressing missing data issues across various fields. © 2025, University of New Mexico. All rights reserved.

关键词： Adversarial machine learning

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A Novel Flow Priority and Continuity Control Mechanism in SDN Network 1

A Novel Flow Priority and Continuity Control Mechanism in SD...

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1st International Conference on Innovative Engineering sciences and Technological Research, ICIESTR 2024

作者： Yaseen, Fouad A. Alkhalidi, Nahlah Abdulrahman Al Wahaibi, Fawziya Nasser University of Baghdad College of Engineering Electronic and Communications Dept Iraq University of Baghdad College of Science Computer Science Dept. Iraq University of Technology and Applied Science College of Engineering Elecrical and Electronics Dept Oman

ISBN: (纸本)9798350348637

In recent days, the escalating need to seamlessly transfer data traffic without discontinuities across the Internet network has exerted immense pressure on the capacity of these networks. Consequently, this surge in demand has resulted in the disruption of traffic flow continuity. Despite the emergence of intelligent networking technologies such as software-defined networking, network cloudification, and network function virtualization, they still need to improve their performance. Our proposal provides a novel solution to tackle traffic flow continuity by controlling the selected packet header bits (Differentiated Services Code Point (DSCP)) that govern the traffic flow priority. By setting the DSCP bits, we can determine the appropriate precedence level for the data traffic flow. The control and modification of the DSCP bits directly impact the priority assigned to data packets, thereby shaping the traffic flow continuity accordingly. The evaluation and performance analysis of the proposed network were conducted using the Mininet simulator and the MATLAB platform. The outcomes of the comprehensive testing demonstrate that the implementation of our novel priority management technique has successfully reduced queuing delay and minimized packet loss. As a result, the overall data traffic continuity has been significantly enhanced. The obtained results demonstrate that the traffic flow continuity, handled by the Software-Defined Networking (SDN) controller with the support of DSCP modification, has increased by approximately 65% when implementing the proposed priority management based on DSCP bits modification in the SDN network. © 2024 IEEE.

关键词： MATLAB

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Using a Design Pattern to Build Microservices for Instant Messaging Software to Access IoT Devices 13

Using a Design Pattern to Build Microservices for Instant Me...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Huang, Hsin-Yu Fanjiang, Yong-Yi Tsai, Hsing-Yu Huang, Yung-Shiang Yu, Yi-Shiang Lee-Ming Institute of Technology Dept. of Digital Multimedia New Taipei City Taiwan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering Dept. of Computer Science and Information Engineering New Taipei City Taiwan Fu Jen Catholic University Dept. of Computer Science and Information Engineering New Taipei City Taiwan

ISBN: (纸本)9798350355079

This study introduces a design pattern to build microservices, which provides an opportunity for instant messaging software developers, Internet of Things(IoT) device manufacturers and third parties to deploy themselves belonging to the APIs in microservices. Users can use instant messaging software to access IoT devices through the APIs deployed by the system. Instant messaging software developers, IoT device manufacturers and third parties can develop and deploy APIs in Instant Messaging (IM) microservice and IoT microservice of this system based on the OpenAPI specification document using a design pattern. The goal of this system is for users to use the installed and usually used instant messaging software without having to install and learn new app to access various IoT devices. © 2024 IEEE.

关键词： Instant messaging applications

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Neural Network for Musical Data Mining for Phrase Boundary Detection

Neural Network for Musical Data Mining for Phrase Boundary D...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Henel, Daniel Mazur, Aleksander Retajczyk, Marcin Adrian, Weronika T. Kluza, Krzysztof Horzyk, Adrian Agh University of Krakow Dept. of Applied Computer Science Krakow Poland Agh University of Krakow Dept. of Biocybernetics and Biomedical Engineering Krakow Poland

ISBN: (纸本)9781665430654

The surge of interest in artificial intelligence systems has sparked new directions of research in the realm of music data analysis. At the same time, the exploration and exploitation of intrinsic musical structures and sequences, a timeless endeavor, continue to captivate scholars and practitioners alike. In this context, the fusion of computational techniques with music analysis emerges as a natural progression. One of the pivotal crossroads in this convergence is the identification of musical phrase boundaries, pivotal demarcations that underpin the organizational fabric of a musical composition. This article pioneers an inventive approach to address the challenge of detecting these musical phrase boundaries, harnessing the power of artificial neural networks. However, the innovation does not stop there;the pinpointed phrase boundaries undergo a comprehensive dissection utilizing pattern mining techniques. The focus of this analysis is on unveiling recurrent motifs and classifying phrases into coherent clusters, predicated on the repetitions and similarities exposed through these neural network-driven techniques. This exploration was conducted using an extensive repository of folk songs, a treasure trove of foundational musical expressions that indelibly shape the stylistic contours of musical compositions. We claim that the presented approach not only opens avenues for penetrating and nuanced analysis, but also enriches our comprehension of the intricate interplay of musical components and their manifestations inspired by neural networks. © 2023 IEEE.

关键词： computational intelligence data mining music analysis musical phrases detection neural networks

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