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A Novel Image Alignment Technique Leveraging Teaching Learning-Based Optimization for Medical Images

A Novel Image Alignment Technique Leveraging Teaching Learni...

引用

International Conference on Data Analytics and Management, ICDAM 2023

作者： Arora, Paluck Mehta, Rajesh Ahuja, Rohit Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India

ISBN: (纸本)9789819965434

In image registration, traditional optimization techniques are incapable of detecting the optimum value of geometric transformation parameters. To resolve this issue, a novel scheme of monomodal (isomodal) biomedical image registration employing teaching learning-based optimization (TLBO) is proposed. In pre-processing, reference image undergoes gaussian filtering to eliminate noise followed through normalization. During de-noising, contrast between anatomical features of an image is degraded. In order to create the floating image, rigid transformation is employed. These images are aligned by detecting optimum value of rigid transformation parameters (RTP) using TLBO with mutual information (MI) maximization as an objective function. MI and structural similarity index measure (SSIM) are used to evaluate visual quality of registered image. The proposed scheme is tested on several isomodal medical images such as magnetic resonance imaging (MRI) and computed tomography (CT). The value of MI, SSIM increases by 8% and the value of RMSE is significantly reduced from 0.4953 to 0.1306 [1] and 3.7858 to 0.1809 [2] which clearly reveals that proposed scheme is robust and effective as compared with the state-of-the-art methods. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image registration

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Neptune - An Integrated Social Media Platform 2

Neptune - An Integrated Social Media Platform

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Saidalavi, Sadiya Shajahan, Milaana Nourin Gopal, Nivitha Gopalakrishnan, Athira Divya, R. Joseph, Rehna Baby Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350328202

This conference paper looks at the pervasive influence of social media on different aspects of our lives and examines four different areas. The first area focuses on realtime detection of events in social media and addresses the challenges posed by informal language and acronyms. The second area, web development, looks at the transformative power of frameworks such as Django in creating feature-rich web applications. The third section looks at analyzing mental health in social media and highlights the need for comprehensive research on English-speaking users. Finally, privacy in social media is critically examined, highlighting the importance of raising aware-ness and implementing best practice. This comprehensive study provides valuable insights for navigating the evolving landscape of online interactions. © 2024 IEEE.

关键词： Social networking (online)

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A Chatbot Assistant for Early Detection and Management of Sugarcane Diseases 2

A Chatbot Assistant for Early Detection and Management of Su...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Sarala, R. Vishnu Varthini, A. Swetha Raji, T. Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

The Poaceae family's sugarcane, which has a high sucrose content, is an important resource used to make jaggery, white sugar, and byproducts like bagasse and molasses. Meanwhile, these Sugarcanes are getting largely affected and unproductive due to diseases making early detection crucial. This paper has bring forward a deep-learning framework which identifies the vitality of sugarcane plants by examining their physical traits. The market has foreseen a huge demand for Sugarcane plants therefore examining sugarcane diseases is crucial and the main hurdle for this is posed by varying monsoon seasons and other climatic changes. To tackle this hurdle, the authors put forward a solution which includes examining the staging of some pre-trained deep learning models, specifically VGG19 and ResNet50 to detect disorder in Sugarcane plants. Error rates and Performance metrics are checked for measuring overall efficiency. A graphical analysis comparing the models based on their sensitivity is also included. © 2024 IEEE.

关键词： Plant diseases

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TrackBot: Building an Intelligent Multipurpose Line Follower Robot 3

TrackBot: Building an Intelligent Multipurpose Line Follower...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Selva Lakshmi, C.B. Sandheep, S. Mani Ashwin Raaj, M. Harish Kumar, P. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350396157

Robots are the solutions for replacing humans from dangerous and manual labour. Repetitive, non-creative manual tasks can be automated by using robots. One of the principal approaches to automate robots is by line sensing principle. We create a robot that acquires the front view using a camera mounted on the vehicle then applying few processes in order to detect the lines thereby steering the vehicle autonomously. © 2024 IEEE.

关键词： Intelligent robots

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An Ensemble Approach for Bangla Handwritten Character Recognition 2nd

An Ensemble Approach for Bangla Handwritten Character Recog...

引用

2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Mormo, Samver Jahan RakibulHaque, Md. Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh

ISBN: (纸本)9789819989362

Bangla is the sixth most widely spoken language worldwide, making the recognition of handwritten characters in this language an important task. However, recognizing Bangla characters is challenging due to the large number of characters in this language, which is around 400. Furthermore, with the increase in number of characters to recognize, the accuracy decreases. In this research, we propose a transfer learning architecture that branches pretrained convolutional neural network models VGG-16 and DenseNet-121, along with Nadam optimizer. The model is evaluated on Ekush dataset which contains 3,68,776 images over 122 characters or classes. Our proposed model achieved 96.94% accuracy on training data and 96.14% accuracy on test data. A comparison of the various state-of-the-art benchmarks for classifying Bangla Handwritten Characters demonstrates that the proposed model has outperformed previous studies. Our efforts will contribute to the development of an effective tool for automatically recognizing handwritten characters in Bangla. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Machine Utilization Prediction in Cloud Using Informer Model 4

Machine Utilization Prediction in Cloud Using Informer Model

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Naik, Archana Sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331529635

This study investigates CPU utilization prediction in cloud environments using the Informer model, a deep learning framework optimized for long-sequence forecasting. By leveraging its ability to capture complex temporal patterns, the Informer model is benchmarked against GRU, LSTM, Prophet, and XGBoost across diverse CPU utilization datasets. Performance is evaluated using RMSE, MSE, MAE, and MAPE metrics. Experimental results demonstrate that the Informer model significantly outperforms the comparison models, excelling in capturing both gradual trends and abrupt fluctuations. Specifically, the Informer achieved a 47.51% reduction in RMSE over GRU, 49.90% over LSTM, 67.36% over Prophet, and 50.46% over XGBoost. These findings underscore the Informer model's superior adaptability and precision, making it a robust solution for dynamic resource utilization prediction in cloud environments. Its predictive accuracy is pivotal for enhancing workload management and optimizing resource allocation. © 2024 IEEE.

关键词： Prediction models

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Medical Plant Detection using Transfer learning Coupled with K-Nearest Neighbour Algorithm 3

Medical Plant Detection using Transfer learning Coupled with...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Srambickal, Sai Subramanian Srivastava, Aman Somasundaram, S.K. School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science and Engineering Department of Information Security Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

Medical Plants are being used for more than a thousand year, with evidences dating before the Mauryain Era (around 322 BCE), they are widely used in the Ayurvedic school of medicine for therapeutic as well as medicinal purposes, it requires an expert to identify the species as well as the medical property associated with a given plant. This paper aims to use deep learning in order to identify a given plant using the leaves of the plant by using a pretrained Convolutional Neural Network (CNN) model namely ResNet50 and MobileNet to extract the latent vector which will then be passed to K-Nearest Neighbour (KNN) algorithm in order to identify the plant class, the best accuracy of the proposed models comes out to be 97.65%. © 2024 IEEE.

关键词： Neural network models

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Combining Extreme Learning Machine and Linear Discriminant Analysis for Optimized Apple Leaf Disease Classification 4

Combining Extreme Learning Machine and Linear Discriminant A...

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4th International Conference on Electronic and electrical engineering and Intelligent System, ICE3IS 2024

作者： Parjito Ahmad, Imam Borman, Rohmat Indra Alexander, Allan D. Jusman, Yessi Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Universitas Bhayangkara Jakarta Raya Faculty of Computer Science South Jakarta Indonesia Universitas Muhammadiyah Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350378368

Apple trees are an agricultural commodity with high economic value that often face serious challenges due to various leaf diseases. Early detection and proper treatment are crucial to reducing economic losses and ensuring healthy plant growth. Manual methods for detecting apple leaf diseases tend to be time-consuming and require specialized expertise, making them difficult to apply on a large scale. This study aims to develop an automatic classification system based on image processing to detect apple leaf diseases by combining Extreme Learning Machine (ELM) and Linear Discriminant Analysis (LDA) algorithms. ELM offers high training speed with randomly assigned weights, while LDA performs dimensionality reduction to retain the most relevant features, thereby improving model performance. The study results indicate that the combination of ELM and LDA produces a model capable of classifying apple leaf diseases with an accuracy of 92.50%, an improvement of 6.25% compared to using ELM alone. This demonstrates that the model is highly effective in classifying apple leaf diseases. © 2024 IEEE.

关键词： Fruits

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Exploring the Usage of Mobile Phones in the Assessment Process: A Case Study 47

Exploring the Usage of Mobile Phones in the Assessment Proce...

引用

47th ICT and Electronics Convention, MIPRO 2024

作者： Kovačević, Željko Bubnic, Bostjan Kosar, Tomaž Zagreb University Of Applied Sciences Department Of Computer Science And Information Technology Zagreb Croatia University Of Maribor Faculty Of Electrical Engineering And Computer Science Maribor Slovenia

ISBN: (纸本)9798350382495

Technology is changing how students learn in the 21st century significantly. Integrating mobile devices in teaching, learning, and assessment processes has emerged as an important strategy for improving teaching methodologies and students' learning. However, adopting mobile devices presents several challenges, including understanding the students' performance, impact on time, and efficiency in the assessment process. This study explores this area with an empirical study of using mobile devices and compares the results to those using traditional desktop computers. In the educational environment, we investigated students' effective use while interacting with mobile and desktop versions of the Second Introductory programming summative assessment. Multinational/multi-institutional research involved a comparative analysis between two student groups undertaking identical tasks on mobile phones and desktop computers. Our results suggest that the students' results are not affected significantly by using the mobile version of the assessment application. These results highlight the usability of mobile devices as a flexible and effective tool for various educational applications, strengthening their role in the modern learning environment. © 2024 IEEE.

关键词： Object oriented programming

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DeepCerviNet: Enhanced Cervical Cancer Classification Using Supervised Learning Model 3

DeepCerviNet: Enhanced Cervical Cancer Classification Using ...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Rahaman, Md Anikur Binte Rabbani Nuha, Maksura Tahsin, Md Hossain, Al Islam, Raihan Ul Rifat Ahmmad Rashid, Mohammad Reza, Ahmed Wasif Ripon, Shamim H. East West University Department of Computer Science and Engineering Dhaka Bangladesh International Islamic University Chittagong Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331534400

Cervical cancer is a female-specific malignancy that poses a substantial threat worldwide, especially in less privileged and underdeveloped regions. Despite continuous progress and advances in medical science research, the rate of death from cervical cancer remains strikingly high among women. Therefore, accurate early-stage cervical cancer identification is imperative for effective diagnosis. It facilitates cost-effective and timely treatment to increase the survival rate of patients, leading to an optimal diagnosis improvement in disease classification. Deep learning approaches demonstrate superior accuracy in medical data classification, motivating us to consider these techniques in this research. In our study, we propose DeepCerviNet, a custom CNN model that achieves 99% precision, which exceeds the accuracy of other deep learning models, MobileNetV2 (97%), ResNET34 (95%), and GoogleNet (94%). Furthermore, to assess our model's generalizability, we use another Multicancer dataset where the proposed model classifies various cancers with 99% accuracy. The DeepCerviNet model's strong performance and adaptability can assist in identifying more aggressive or recurrent cervical cancer types, aiding the development of targeted therapeutic strategies. © 2024 IEEE.

关键词： Self-supervised learning

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