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Navigating Breakthroughs in Pediatric Brain Stroke Detection: A Comprehensive Survey

Navigating Breakthroughs in Pediatric Brain Stroke Detection...

引用

Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： N R Deepak Santosh Kumar Paital Saritha Verma Tenzin Choeyang Udit Kumar Singh Bhavya dept. of Computer Science and Engineering HKBK College of Engineering Bengaluru India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Brain strokes, characterized by sudden interruptions in cerebral blood flow, pose a significant health concern, especially in children, where detection is intrinsically challenging due to limitations in existing methodologies. Recent attempts leveraging Machine Learning with Deep Learning techniques have grappled with accuracy and efficiency shortcomings in addressing this critical issue. In response, our innovative model integrates the Extreme Learning Machine (ELM), Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs) algorithms. Rigorously evaluated using a comprehensive dataset, our ELM + CNN + GAN model demonstrates unparalleled accuracy, precision, and recall rates, surpassing benchmarks set by conventional approaches like the Random Forest algorithm and the AlexNet + SVM hybrid model. Notably, our model excels in diagnostic speed, offering a promising avenue for swift and efficient early detection of pediatric brain strokes. The strategic incorporation of ELM, recognized for its efficiency in single layer neural networks, along with the analytical power of CNN for image analysis and GAN for synthetic data generation, establishes a robust and comprehensive model. By outperforming existing methods, our approach signifies a substantial leap forward in addressing this critical health concern, revolutionizing stroke detection with enhanced accuracy and speed.

关键词： Surveys Support vector machines Technological innovation Accuracy Stroke (medical condition) Brain modeling Generative adversarial networks

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Dynamic Blockchain Reconfiguration: Balancing the Trilemma Trade-off Using Digital Twins 27

Dynamic Blockchain Reconfiguration: Balancing the Trilemma T...

引用

27th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2023

作者： Diamantopoulos, Georgios Tziritas, Nikos Bahsoon, Rami Theodoropoulos, Georgios School of Computer Science University of Birmingham Birmingham United Kingdom University of Thessaly Dept. of Informatics and Telecommunications Lamia Greece Southern University of Science and Technology Dept. of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350337846

The trilemma trade-off problem between decentralisation, scalability, and security states that in blockchain systems the above properties are negatively correlated. Infrastructure, node configuration, choice of Consensus Protocol, and complexity of the underlying application are cited among the factors that affect the balance of the trade-off. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This work proposes the use of Digital Twins as the means of optimising the trilemma trade-off of blockchain i.e., re-configuring system parameters such as to maximise scalability, decentralisation and security. Specifically, through a bi-directional feedback loop between the system and the digital twin, simulation, what-if analysis and machine learning techniques will be employed for the computation of an optimal configuration given the current system state. Furthermore, a dynamic update mechanism is proposed to allow for blockchain reconfiguration without violating the decentralisation of the system. © 2023 IEEE.

关键词： Blockchain

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Face Mask Detection under the Threat of Covid-19 Virus 1

Face Mask Detection under the Threat of Covid-19 Virus

引用

1st IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2022

作者： Dange, B.J. Khalate, S.S. Kshirsagar, D.B. Gunjal, S.N. Khodke, H.E. Bhaskar, T. Sanjivani College of Engineering Kopargaon Savitribai Phule Pune University Dept. of Computer Engineering Pune India

ISBN: (数字)9781665428323

ISBN: (纸本)9781665428323

For wearing a mask, Covid19 has a new identity. Detecting masked faces with accuracy and efficiency is becoming increasingly important. Face mask recognition is a tough task because of the high occlusions that cause the loss of face in-formation. Furthermore, There aren't many large-scale, masked face with a well-label datasets out there, which makes face mask recognition more difficult. CNN-based deep learning algorithms have achieved significant advances in a variety, one aspect of computer vision is face detection disciplines. We introduce a new cascade structure based on CNN for recognising face masks in this paper, This is made up of three convolutional neural networks that have been successfully built. To finetune our CNN models, we propose a new dataset named 'facial mask dataset.' due to a paucity of face masked training instances. Face masks were tested on a set of faces, we assess our suggested face mask recognition algorithm, and it performs admirably. A new cascade framework based on CNN is proposed for detecting face masks and their types, which comprises of three properly built convolutional neural networks. © 2022 IEEE.

关键词： Feature extraction

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Big Mart Sales Analysis

Big Mart Sales Analysis

引用

Computational Intelligence and Communication Technologies (CCICT), International Conference on

作者： Kunal Muskaan Singh Harshit Sharma Shweta Roy Kadambri Agarwal dept. of computer science and engineering ABES engineering college Ghaziabad India

ISBN: (数字)9798350374629

ISBN: (纸本)9798350374636

Machine learning is a powerful technology that enables computer systems to recognize patterns and make prediction and choices without performing explicit programming. The basic notion behind this technology is to create models and algorithms that can take in input information, predict an outcome using statistical computation, and adjust outputs in response to fresh information. In this research paper, we examine the Big Mart Shopping Centre scenarios in order to forecast sales of various product categories and comprehend the impact of various factors on sales of various product categories. This paper focuses on leveraging machine learning techniques to predict sales for Big Mart with the aim of optimizing inventory management and maximizing revenue. Using a variety of characteristics from a dataset that Big Mart gathered, along with the methodology used to construct a prediction model, very accurate results are produced and these findings can be utilized to make decisions aimed at increasing sales and increase operational efficiency.

关键词： Machine learning algorithms Computational modeling Machine learning Forestry Predictive models Programming Inventory management

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Analyzing and Visualizing Causes of Poverty in India: A Data-Driven Approach Using HPCC Systems

Analyzing and Visualizing Causes of Poverty in India: A Data...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Skanda P R Shobha G Jyoti Shetty Dept. of Computer Science and Engineering RV College of Engineering Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Poverty in India is a complex issue with multiple causes. This paper mainly focuses on three of the main causes for poverty: Education, Unemployment and Consumer Price Index. This study makes use of data received from the National Informatics Center (NIC) of India to predict poverty across the various states of India. Through applying preprocessing techniques and employing the HPCC Systems Visualization bundle, the research shows that Bihar is facing a higher level of poverty, whereas Mizoram exhibits converse trend. Subsequently, the HPCC ML_Core bundle is applied to conduct linear regression analysis for predicting poverty scores. This predictive model demonstrated an accuracy of 97%. Lastly, the difference between a single-node and 8-node cluster is presented for runtime performance which clearly shows that the 8-node cluster has an advantage for similar computational tasks and thus makes it suitable.

关键词： Accuracy Runtime Linear regression Data visualization Predictive models Market research Unemployment Information technology Informatics Non-governmental organizations

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Enhanced Text-Summarization and Language Simplification:End-to-End Approach for Content Comprehension

Enhanced Text-Summarization and Language Simplification:End-...

引用

Trends in engineering Systems and Technologies (ICTEST), International Conference on

作者： Riza Ayisha P George Mathew Dept. of Computer Science and Engineering Government Engineering College Thrissur India

ISBN: (数字)9798350307795

ISBN: (纸本)9798350307801

Text summarization is the process of shortening a large body of text in a document to form a shorter while preserving its most important information. With the exponential growth of digital data, there is a need for automated text summarization to help users quickly comprehend the content. The use of machine learning techniques, particularly deep learning models, has shown great promise in generating high-quality summaries. Among these models, BERT (Bidirectional Encoder Representations from Transformers) has emerged as a state-of-the-art method for various natural language processing (NLP) tasks, including text summarization. In this paper, we propose to implement a text summarization system using the BERT model. Apart from the normal text summarization, we will be implementing a Language Simplification model that simplifies complex sentence structures or use simpler vocabulary while preserving the original meaning. This model transforms convoluted or verbose sentences into shorter and more straight-forward constructions The paper aims to develop an end-to- end summarization system that takes a long document as input and generates a summary of the most important information contained in the document. For example, like taking the entire news from an online portal and generates it summary.

关键词： Vocabulary Bidirectional control Transforms Linguistics Transformers Market research Encoding

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Leveraging AI and NLP in Chatbot Development: An Experimental Study 20

Leveraging AI and NLP in Chatbot Development: An Experimenta...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Paracha, Abdul Wahab Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Ali Khan, Talha Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

In the current era of chatbots, this research delves into the advancements in AI chatbots, drawing on artificial intelligence (AI) and natural language processing (NLP) techniques to mimic human-like conversations. A particular focus is given to the potential of chatbots in facilitating multitasking dialogues, offering emotional support, and addressing complex subject matter, all the while respecting user privacy and trust. The implemented chatbot model is trained on a neural network, using Keras and TensorFlow libraries. This model's performance indicates a considerable dependence on the dataset's size, with larger datasets leading to better outcomes by providing more extensive language usage and context examples. Additionally, we also analyze the effect of varying architectures and hyperparameters on chatbot performance. The significance of localizing chatbots to adapt to different languages and cultures is also highlighted. While promising, the study identifies areas of improvement, suggesting future research directions in enhancing language capture techniques, expanding training datasets, and integrating emotional intelligence within chatbot systems. © 2023 IEEE.

关键词： Natural language processing systems

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Raw Coffee Bean Classification for Roasting Suitability Assessment Using Transfer Learning 11

Raw Coffee Bean Classification for Roasting Suitability Asse...

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11th IEEE Conference on Systems, Process and Control, ICSPC 2023

作者： Pratondo, Agus Zani, Tafta Novianty, Astri Pudjoatmodjo, Bambang School of Applied Sciences Telkom University Department of Multimedia Engineering Bandung Indonesia School of Electrical Engineering Telkom University Department of Computer Engineering Bandung Indonesia Dept. of Interactive Media Melaka Malaysia

ISBN: (纸本)9798350340860

The classification of raw robusta coffee beans is a pivotal process with profound implications for the coffee industry. Recognizing the critical significance of this classification, this research endeavors to establish an effective and automated method for discerning coffee bean quality. The primary objective of this study is to employ advanced machine learning techniques to classify raw robusta coffee beans accurately. Specifically, we utilize deep learning models, VGG-16 and Inception V3, for this purpose. These models are trained to classify coffee beans based on their quality attributes, offering a systematic approach to segregating beans into high and low-quality categories. The research methodology encompasses comprehensive data preprocessing, model construction, and rigorous experimentation. Notably, the Inception V3 model, initialized with pre-trained weights from ImageNet, emerged as the top performer, achieving an outstanding accuracy rate of 99%. The implications of this research extend to various stakeholders within the coffee industry. By enhancing the quality assessment of raw robusta coffee beans, this research contributes to improved quality control processes. It ensures that only the finest beans proceed through the production chain, bolstering economic viability and market competitiveness for coffee-producing regions. © 2023 IEEE.

关键词： Deep learning

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Detection of Primary Open Angle Glaucoma Based on Deep CNN Using Fundus Images

Detection of Primary Open Angle Glaucoma Based on Deep CNN U...

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Toyaha Rahman Ratul Shyla Afroge Hasibul Islam Peyal Dia Zafrin Kazi Fardeen Ahmed Md. Nazmul Abdal Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Uttara University Dept. of Computer Science & Engineering Discipline Khulna University

The early and accurate diagnosis of glaucoma, a primary cause of permanent blindness, is critical for efficient treatment and prevention of vision loss. Although the exact causes of glaucoma are not yet fully understood, it is thought to be a result of several factors, including raised pressure inside the eye and decreased blood supply to the optic nerve. We have developed a convolutional neural network model for accurate detection of glaucoma. Methods based on deep learning have been effective at classifying diseases in retinal fundus images, facilitating in the evaluation of the growing number of images. The goal of this work is to create and train a unique deep CNN model that makes use of the connections between related eye-fundus tasks and metrics used to identify glaucoma. We have meticulously selected two distinct datasets to underpin this research endeavor: the ACRIMA dataset and the LAG dataset. Notably, our model attains a remarkable accuracy score of 99.29% on the ACRIMA dataset and an equally commendable accuracy score of 97.22% on the LAG dataset. This performance eclipses that of the majority of contemporary deep CNN models, underscoring the prowess and sophistication of our approach.

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A Study of Various Optimization Algorithms and Entropy Measures for Image Segmentation

A Study of Various Optimization Algorithms and Entropy Measu...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Nagamani Gonthina L V Narasimha Prasad Dept. of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India

One of the most challenging jobs in image processing techniques is image segmentation. To identify the objects of interest in an image, we segment the image into different parts and extract the interesting objects. Previous studies have addressed various techniques for image segmentation, like clustering, thresholding, watershed, neural networks, etc. However, a segmentation technique alone cannot achieve better-segmented results. Much research is being carried out by combining the segmentation technique with either an optimization algorithm or entropy measures to enhance the efficiency of segmented results in the segmentation process. So, there is a need to study various segmentation techniques combined with optimization algorithms or entropy measures. Recent studies focused only on segmentation techniques, entropy measures, or optimization algorithms rather than on these combinations. This paper presents various works based on optimization techniques and entropy measures combined with clustering and thresholding techniques of image segmentation. From the study it is observed that most of the work is carried out in combining thresholding-based image segmentation techniques with optimization algorithms and entropy measures.

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