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Brain Tumour Prediction Using MRI Images with MobileNet and Transfer learning: A Deep learning Approach 7

Brain Tumour Prediction Using MRI Images with MobileNet and ...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Rastogi, Deependra Singh, Anand Dhanda, Sumit Singh Bindewari, Shantanu Yadav, Suman Avdhesh Iilm University School of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

Brain tumour detection and classification are crucial for early diagnosis, improving patient survival rates. Magnetic Resonance Imaging (MRI) offers non-invasive, high-resolution brain images, but manual interpretation is time-consuming and error prone. This research proposes a deep learning approach for brain tumour prediction using MRI images, leveraging the MobileNet architecture with transfer learning, incorporating Flatten, Dense, and Dropout layers to enhance model generalization. MobileNet, a lightweight Convolutional Neural Network (CNN), efficiently processes large-scale MRI datasets. The pre-trained MobileNet is integrated with a Flatten layer to reduce feature map dimensions, followed by Dense layers for learning complex patterns, and a Dropout layer to prevent overfitting. Transfer learning allows leveraging pre-trained weights, reducing the need for large datasets. The model was finetuned on an MRI brain tumour dataset, achieving an accuracy of 0.9845 with a loss of 0.0015, outperforming traditional deep learning methods in tumour classification. This architecture is computationally efficient, making it suitable for real-time clinical applications, providing medical professionals with a reliable, automated tool for brain tumour diagnosis. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Identification of Thyroid Disorder using Deep learning Techniques- A Survey 10

Identification of Thyroid Disorder using Deep Learning Techn...

引用

10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Dency Flora, G. Shanmathi, S. Gowtham, G. Nikil, S. Rajarajan, M. Mahindhar, K.S. Sri Eshwar College of Engineering Department of Cce Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

Thyroid diseases are a global health concern that require precise diagnosis in order to initiate and maintain appropriate treatment. Conventional diagnostic techniques frequently depend on laboratory testing and clinical evaluations, which can be error-prone and time-consuming. In recent years, deep learning techniques have shown promise in the identification of thyroid disorders among other medical uses. An detailed review of the most recent advancements in the use of deep learning techniques to thyroid problem detection is given in this study. We go over the use of convolutional, recurrent, and hybrid neural networks to the processing of thyroid-related text, images, and clinical records.. Then we compared various deep learning methods and we are using the Hybrid Deep Convolutional neural networks in which it has the accuracy value as 93.8%. We emphasize how crucial extensive, annotated datasets are to the efficient training of deep learning models. We go over the importance of feature extraction approaches, model optimization tactics, and data preprocessing methods in enhancing the robustness and effectiveness of thyroid problem diagnostic systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Transforming Agriculture with AI: Automated Crop Insect Identification Using InceptionV3 Transfer learning 1

Transforming Agriculture with AI: Automated Crop Insect Iden...

引用

1st international conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Thyagharajan, K.K. Kalaiarasi, G. Nirmala, G. Saravanan, P. Vignesh, T. Makashkumar, V. R.M.D Engineering College Department of Electronics and Communication Engineering Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India R.M.D Engineering College Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350342796

Identifying crop insects and classifying them is a challenging task for agriculturalists. A transfer learning method is suggested in this paper to address this issue. The proposed approach uses InceptionV3 as a base deep learning model and it is given additional custom layers. The model is fine-tuned using a custom dataset made up of five classes with 1000 insects each. The insect and pest images available from the National Bureau of Agricultural Insect Resources datasets and the Xie1 dataset are used for testing the model. Preprocessing techniques are applied to resize the images, and data augmentation is employed to prevent overfitting. The model was analyzed on the validation dataset with Adam, Adagrad, and Nadam optimizers, and obtained recognition accuracies of 86.6%, 77.8%, and 88.56% respectively. The outcomes confirms that the model performs well with Nadam optimizer for insect identification. © 2023 IEEE.

关键词： Deep learning InceptionV3 Insect Detection Smart Agriculture Transfer learning

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Analyzing and researching the intermediate layer of Alliance medical data combined with edge computing

Analyzing and researching the intermediate layer of Alliance...

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1st IEEE international conference on Software Services engineering (IEEE SSE)

作者： Hsu, Tse Chuan Chu, William Cheng-Chung Tseng, Yao-Hsien Lee, Shou-Yu Soochow Univ Dept Comp Sci & Informat Management Taipei Taiwan Tunghai Univ Dept Comp Sci Taichung Taiwan Tungs Taichung MetroHarbor Hosp Div Endocrinol & Metab Dept Internal Med Taichung Taiwan

ISBN: (纸本)9798350340754

Through big data analytics and deep learning, users can uncover unseen hidden information from personal data in the cloud and derive health improvements from it - including genomic, microbial, medical history, comprehensive blood analysis chemistry, proteins and metabolites, and daily data from exercise devices and scales. The analysis and integration of this data has the potential to improve health and prevent disease. In the research, a set of tools to collect, compute and analyze data through the middle layer, combined with alliance learning and deep learning technologies to conduct sampling comparison training on medical data, while combining edge computing nodes and data hidden features of alliance learning for data construction and model verification. The outcomes can provide early warning of disease agents through data analysis, early symptom detection, and long-term monitoring before the disease becomes widespread. Daily behavior and health can be improved by supporting the P4 medical model, and by integrating these capabilities, many chronic diseases can be prevented from further devastating patients' health. In the experiments, we propose a framework for designing proxies through system construction, data quantification, description and analysis of patient-specific chronic disease risk, and data learning to validate the model construction.

关键词： Data Analysis Healthcare Edge computing Alliance learning Federated learning Service Agent

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Six-Link teaching Methods for Improving the Creative Practical Ability -Taking "Mechanical Principle" as an Example 7

Six-Link Teaching Methods for Improving the Creative Practic...

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7th international conference of Artificial Intelligence, Medical engineering, Education, AIMEE 2023

作者： Li, Wenhui Meng, Chaoying Lian, Xun He, Qin Kong, Min Gao, Yang Department of Mechanical and Electrical Engineering Wenhua College Wuhan China

ISBN: (纸本)9781643684901

In the construction of 'Emerging engineering Education' and 'First-Class Course', 'Mechanical Principle' is oriented to the requirements of talent training, the application of innovative thinking and creative practical ability in teaching. Firstly, the teaching system of 'Mechanical Principle' is reconstructed. Secondly, based on TRIZ Theory in teaching methods is improved in the 'teaching, learning, Practice, Creation, Research, Competition' six links. Thirdly, closed-loop process evaluation is adopted in teaching evaluation to reflect the situation of teachers and students. The effect of teaching reform shows that teacher evaluation, student achievement, student work and the achievement of curriculum objectives have been improved after the curriculum improvement. Especially in the second classroom, such as subject competition, innovation, entrepreneurship. The students' innovative practical ability is effectively improved. © 2024 The Authors.

关键词： Students

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Online learning Motivation and Dilemma of Secondary Vocational Students 13th

Online Learning Motivation and Dilemma of Secondary Vocation...

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13th international conference on Frontier computing, FC 2023

作者： Wu, Jun Liu, Hsiao-Fen National Taipei University of Technology No. 81 Section 1 Jianguo South Road Daan District Taipei106 Taiwan

ISBN: (纸本)9789819994151

Online learning has been widely adopted in different levels of education. In the field of secondary vocational education, the application of online learning in education and teaching is also becoming more popular. However, previous studies on online learning among secondary vocational students mainly focused on the technical aspects and often centered on the domain of mandatory online learning led by schools, meanwhile also neglecting the voluntary learning through various online resources among secondary vocational students. Therefore, based on cyberethnography and a voluntary online learning community formed by secondary vocational students, this study conducted in-depth interviews with some members of the community to gain insights into their motivations for participating in online learning voluntarily and the difficulties they faced in the process. Through the analysis of the interview data, the results showed that the main motivations for secondary vocational students to participate in online learning voluntarily included improving learning efficiency, enriching learning methods, and expanding learning content. However, during the voluntary online learning process, students also encountered many difficulties, such as insufficient learning resources, lack of learning motivation, and time management issues. Therefore, this study suggests that secondary vocational education should actively explore the application of online learning and provide more support and guidance from both policy and practical perspectives to cultivate students' ability to discern learning resources and plan their self-learning, so that they can better demonstrate autonomy and initiative in online learning and achieve better learning outcomes. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Deep Reinforcement learning-Based Task Offloading for Multi-User Distributed Edge computing 30

Deep Reinforcement Learning-Based Task Offloading for Multi-...

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30th international conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Shi, Zhengtian Zhang, Zhisheng Dai, Min Xia, Zhijie Wen, Haiying Huang, Fan Southeast University School of Mechanical Engineering Nanjing China JiangSu Nan Gao Intelligent Equipment Innovation Center Nanjing China Southeast University Nanjing China

ISBN: (纸本)9798350391916

Edge computing (EC) deploys multidimensional resources, including computation, storage, and communication, at the network edge to fulfill certain applications with stringent latency requirements. In the edge environment, computing tasks can be offloaded to mobile edge computing servers (MECS) for reduced task processing latency. However, traditional methods of task offloading in edge computing are inadequate for complex environments demanding high real-time performance. This paper focuses on studying the computational task processing problem in a multi-user distributed edge environment and propose a deep reinforcement learning-based multi-user distributed edge computing task offloading method aimed at minimizing task processing latency. The proposed method effectively addresses the computational task processing challenge in dynamic and complex edge environments with high real-time demands. Simulation results show that the proposed method has superior performance in terms of both task processing delay and algorithm execution time. © 2024 IEEE.

关键词： Computation offloading

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Classroom teaching Management and Resource Optimization in Colleges and Universities Based on Big Data Technology 3

Classroom Teaching Management and Resource Optimization in C...

引用

3rd IEEE international conference on Social Sciences and Intelligence Management, SSIM 2023

作者： Wang, Gengyan Sheng, Cao Wenhua College Wuhan China Wuhan Wanzhongzhilian Intelligent Technology Co. Ltd Wuhan China

ISBN: (纸本)9798350327786

With the rapid development of information technology, the importance of introducing advanced big data technology for classroom teaching management and resource optimization in university education was investigated. Through the extensive application of modern data science, artificial intelligence (AI) and cloud computing technologies, we collected data on the integration and efficient utilization of student information, teaching resources, and teaching environment. We used data mining technology for the in-depth analysis of students' learning behaviors and performance to support personalized teaching. In cloud computing and big data storage, we explored how to establish a powerful educational information platform for safe storage and efficient management of large data. In addition, in decision support, we introduced industry-leading business intelligence and decision analysis tools to help decision-makers optimize the management of classroom teaching and resources in colleges and universities and maintain their flexibility and adaptability. By integrating various advanced technologies into classroom teaching higher education, scientific and technological support can be provided for innovation and progress in higher education. © 2023 IEEE.

关键词： Big data

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Custom Transformer-Based Approach for Enhanced Bengali Automatic Speech Recognition 1

Custom Transformer-Based Approach for Enhanced Bengali Autom...

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1st international conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Surendran, R. SRM Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Anna University St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

Automated speech recognition (ASR) systems struggle with Bengali, which is the fifth most spoken language. Bengali has a varied morphology, many dialects, and limited high-quality annotated voice data. Traditional voice recognition techniques work well however struggle with Bengali phonetics and syntax. This method eliminates the need for large amounts of tagged data and the difficulties of differentiating sounds in different languages, both issues are addressed by this procedure. The research in this paper offers an Intellectual Bengali Speech Recognition System using Deep learning Techniques (IBSRS-DLT). The model in this system uses transformer architecture and attention processes to manage Bengali speech pattern long-range dependencies. The IBSRS-DLT outperforms standard models in recognition accuracy using a pre-trained transformer model modified for phonetic and lexical correctness and fine-tuned on Bengali datasets. After training on a vast Bengali speech corpus, real-world datasets and rigorous simulations confirm the system. IBSRS-DLT can be used for automatic transcription, voice-controlled devices, and Bengali virtual assistants, such applications are efficient and accessible. The simulation showed a considerable boost in recognition speed and a decrease in keyword mistakes compared to current methods. This paper shows that deep learning can solve linguistic issues in ASR systems, enabling scalable regional language detection. © 2024 IEEE.

关键词： Deep learning

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Application of Machine learning Techniques to Characterize Floating Point Benchmarks using Hardware Events 20

Application of Machine Learning Techniques to Characterize F...

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20th international conference on Electrical engineering, computing Science and Automatic Control, CCE 2023

作者： Victor, Rodriguez-Bahena Luis, Pizano-Escalante Omar, Longoria-Gandara Electronics Systems and It Iteso Mexico

ISBN: (纸本)9798350306767

The pursuit of improved computing architecture drives continuous innovation to enhance user application performance. One such advancement involves the implementation of floating-point accelerators. However, identifying applications heavily reliant on floating-point operations proves complex due to application intricacies. Selecting optimal workloads for execution on floating-point accelerators can yield significant benefits, including up to a 30% reduction in execution time. In this article, the authors propose a novel approach that leverages machine learning algorithms and meticulous analysis to identify the minimum hardware performance counters necessary for detecting applications with significant utilization of floating-point operations. Our methodology achieves an accuracy of 69% using unsupervised machine learning and 100% using supervised machine learning. By focusing on workloads that stress new CPU designs, this approach streamlines the development and validation of novel computing architectures. It effectively pinpoints ideal candidates for benchmarking new floating-point co-processors, optimizes test execution time, and minimizes power consumption during the validation phase. This research promises to drive advancements in computing architecture, leading to improved application performance and efficiency. © 2023 IEEE.

关键词： Benchmarking

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