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FishNet Freshwater Fish Disease Detection using Deep Learning Techniques

FishNet Freshwater Fish Disease Detection using Deep Learnin...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： A Sanjay Kumaar A Vishnu Vignesh K Deepak Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

Fish diseases pose significant challenges to aquaculture, causing economic losses and environmental issues. Early and accurate detection is important for effective management and prevention of diseases. This study proposes using three deep convolutional neural network architectures: Inception, VGG16, and a new CNN model called Fishnet. The efficacy of the models is assessed in two scenarios: using transfer learning and without it. The new FishNet model outperforms the pre-trained VGG16 network, demonstrating a 2% improvement in accuracy for disease detection in freshwater fishes.

关键词： Training Analytical models Computational modeling Transfer learning Fish Convolutional neural networks Freshwater

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Comparative Study on Classification of Images in Compressed and Uncompressed Domains

Comparative Study on Classification of Images in Compressed ...

引用

International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Ashwini K Perla Sree Neha S Jaiadityan Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita school of computing Chennai India

ISBN: (数字)9798350350845

ISBN: (纸本)9798350350852

Image Classification is the basis of computer vision. Classification with image data finds a variety of applications in various fields. A comparative study of Classifying the images in the compressed and uncompressed domain is exploited in this research. A garbage classification dataset with 6 different categories is employed for this purpose. Naitve Convolutional neural network is utilized as the classifier. Compressed classification is achieved by compressing the images using discrete wavelet transform of three levels and by feeding the compressed data to the model for classification. The accuracy achieved by classification in the uncompressed domain is about 82.1% with a loss of 0.892. The accuracy achieved in the compressed domain by the three levels of the discrete wavelet transform is 81.7%,81.2%, and 74.6% with losses of 0.793, 0.891, and 1.071 respectively. There is minimal difference in accuracy achieved between uncompressed and compressed domains. Image compression by discrete wavelet transform overperforms the uncompressed domain by several factors like size, elapsed time and memory space.

关键词： Wireless communication Image coding Wavelet domain Memory management Transforms Signal processing Data models

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Enhancing Early Detection and Classification of Alzheimer’s Disease Stages Using Deep Learning and LIME for Interpretability

Enhancing Early Detection and Classification of Alzheimer’s...

引用

Communication, Control and Intelligent Systems (CCIS)

作者： P Syam Prasad P Mokshit N Sumanth Reddy Rimjhim Padam Singh Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

The issue of transparency in diagnostic methods has been a crucial challenge in Alzheimer’s Disease detection. This paper focuses on analyzing state-of-art deep learning algorithms namely, customized convolutional network, InceptionV3, DenseNet121, AlexNet, VGG16, and ResNet50 for detecting Alzheimer’s disease. The work also focuses on analyzing the transparency and decision-making capabilities of these models by using the Explainable AI tool, Local Interpretable Model-agnostic Explanations, for clinicians and patients. The learnings and decision outputs obtained from these convolutional models are related and explained using LIME, where ResNet and DenseNet lead to efficient solutions with appropriate feature extraction and importance provided in LIME visualization. This explication of the model’s decision-making process affected by LIME can improve diagnostic accuracy and foster trust that can offer a great chance for the medical experts to offer better care for patients, who are suffering from Alzheimer’s Disease.

关键词： Deep learning Analytical models Visualization Accuracy Decision making Brain modeling Convolutional neural networks Alzheimer's disease Medical diagnostic imaging Residual neural networks

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An Investigation into YOLO-v8 Model Optimization for Small Object Detection in UAVs using Attention Mechanism

An Investigation into YOLO-v8 Model Optimization for Small O...

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International Conference on computing and Networking Technology (ICCNT)

作者： N S Rupak N. Rayvanth Pulipati Kushank Rimjhim Padam Singh Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The growth of UAV technology has led to advancements in aerial surveillance, resulting in the development of specialized algorithms for object detection in complex environments like university campuses, beaches etc. This study proposes to leverage the recent YOLOv8 model with various sophisticated attention mechanism like Spatial attention, channel attention, squeeze and excitation block attention, Efficient channel attention network, etc. to efficiently detect the tiny person objects in images captured using Unmanned aerial vehicles (UAVs) like drones. For the purpose of this analysis, the work utilizes a recent dataset, Manipal-UAV dataset, that focuses on detecting tiny objects in the form of person. The work also provides a comprehensive comparison of recent YOLO models for model selection and provides a detailed analysis of state-of-art attention mechanism when incorporated into the best baseline YOLOv8x model. The findings from this study will suggest that tailored YOLO models are effective for UAV-based person detection tasks, potentially driving advancements in aerial surveillance technology, particularly in scenarios requiring accurate identification of small objects like individuals. The research over-all aims to highlight the importance of dataset-specific optimizations to enhance the performance of object detection models in UAV imagery with mAP50 of 50.2%, thereby, promoting more reliable surveillance applications in various real-world settings.

关键词： YOLO Analytical models Attention mechanisms Surveillance Computational modeling Transfer learning Autonomous aerial vehicles Transformers Reliability Optimization

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A Novel approach for detecting Fibroids in Uterus using Deep Learning models

A Novel approach for detecting Fibroids in Uterus using Deep...

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International Conference on computing and Networking Technology (ICCNT)

作者： Chellu Vyshnavi Chukka Mishal Raj Gokul Gopakumar Guddeti Bindu Prasanna B Sreevidya M Rajesh Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Uterine fibroids are non-cancerous growths in a woman’s uterus and are estimated to occur in 70-80% of women by age 50. Symptoms include heavy menstrual bleeding, pelvic pain, and frequent urination, with complications of anemia, infertility, and reduced quality of life. Deep learning models are being used in many fields like health care, education and so on. Our project, therefore, will focus on the detection of uterine fibroids using deep learning models: CNN, ResNet50, VGG16, InceptionV3, and DCGAN. Although imbalance in the dataset is a problem, we handle it using balancing via SMOTE. They are heterogeneous in appearance, which challenges their detection. Uterine fibroids are thus among the most common complaints for women. We thus employ a variety of deep learning architectures and dataset balancing techniques in order to increase fibroid detection accuracy. Looking at the results, there is promise with increased accuracy and sensitivity-an encouraging sign of our approach in improving diagnostic outcomes for uterine fibroids. Based on the results the most accurate model after balancing the dataset is CNN with an accuracy of $94.843 \%$.

关键词： Deep learning Accuracy Pain Uterus Computational modeling Education Medical services Hemorrhaging Residual neural networks

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Segmentation and Classification of Gastric Cancer from Endoscopic Image Dataset with the Aid of Artificial Intelligence 7

Segmentation and Classification of Gastric Cancer from Endos...

引用

7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Anitha, Cuddapah Srinivas Rao, Vemu Ramyadevi, R. Maya, R. Debroy, Subhasish Mahaveerakannan, R. School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram India Saveetha Engineering College Department of Computer Science & Engineering India Chennai Institute of Technology & Applied Research Department of Electrical & Electronics Engineering India Dr. B.C. Roy Engineering College West Bengal Durgapur India Saveetha School of Engineering Department of Computer Science and Engineering SIMETs Tamil Nadu Chennai India

ISBN: (纸本)9798350340600

In order to detect early stomach malignancies, upper GI endoscopy is commonly used. An object detection model, a form of deep learning, was projected as a means of automating the diagnosis of early stomach cancer using endoscopic pictures. Yet there were difficulties in reducing false positives in the results that were detected. Thus, this research proposes an automatic classification method for stomach cancer classification using a (CNN) that has been pre-trained. To classify cancer in endoscopic pictures automatically, our method is superior to those that rely on traditional, manual features. Thirteen convolutional layers with small 33 size kernels and three fully linked layers make up the suggested model. We use the learning approach, which involves pre-training the weight of fine-tuning the weight of layers with the Slime Mould Algorithm, to deal with the data scarcity (SMA). Experiments employing 1208 photos from healthy subjects and 533 photographs from patients with stomach cancer examined detection performance using the 5-fold cross validation approach. These findings suggest the projected strategy will be effective for automating the diagnosis of early stomach cancer in endoscopic pictures. © 2023 IEEE.

关键词： Endoscopy

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Code Confabulator: Harnessing LLMs to Compile Code for Visualization

Code Confabulator: Harnessing LLMs to Compile Code for Visua...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Nannapaneni Rayvanth Pasupuleti Pranavi E Suryaa Venkatahemant Kumar Reddy Challa Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Code visualization tools play a core role for visualization of the code and helping novices in understanding, analyzing, and optimization of their programs. Tools such as Algorithm Visualizer and PythonTutor are developed to make algorithm visualization possible across different programming languages like Python, Java, C, and $\mathrm{C++}$. Despite their advantages, these tools suffer from unnecessary data collection, increased time complexity, and limitation in visualizing a few data structures. In our research, we want to introduce a more humanlike view of understanding and solve the shortcomings mentioned above by proposing a framework, which will make use of LLMs for extracting data from the code in Python, Java, C, or C++ to be visualized. In our framework, we have used LLMs instead of the traditional compiler for feasibility and adaptability. Our objective is the appropriateness evaluation of LLMs for this task, starting from freeform prompts. The LLM is asked to generate outputs in a given format; hence, these outputs are taken and converted into visualizations with a rule-based approach. The task will be done to enhance quality visualization and reduce time complexity while allowing the user to view the code at a human level view. We have also publicly released the framework so that people are able to provide further contributions and improvements.

关键词： Java Codes Computational modeling Data visualization Data collection Data structures Data mining Time complexity Optimization Python

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Cross Language Compiler Construction From Matlab To Python

Cross Language Compiler Construction From Matlab To Python

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Abhishek v Kulkarni Addanki Ranga Ravindra Maheswarla Sai Sangeethi G Tejdeep Reddy Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The domain of cross compiler development pertains to the process wherein code written in one programming language is translated to another so that it may be compatible with another environment and may take advantage of the benefits of the other platform. This work aims at constructing a cross-language compiler to convert code of MATLAB to Python. Therefore, higher performance language and a powerful matrix operation library NumPy and a robust visualization library matplotlib is used in this research. The system is named as Mat2py, and it works efficiently in translation. Difficulties are to resolve issues related to the syntactic and semantic differences in the languages, overcome numeric imprecisions, and ensure the coherence of data structures and libraries. These complications are responded in this work by using NumPy and matplotlib of Python to transplant all the functionality accessible in MATLAB. Mat2py, therefore, ensure a correct and optimal conversion process through an analysis of the MATLAB function to its equivalent Python version and testing individual functions.

关键词： Codes Semantics Syntactics Libraries Vectors Software MATLAB Matrix converters Python Testing

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FretMate: ChatGPT-Powered Adaptive Guitar Learning Assistant 25

FretMate: ChatGPT-Powered Adaptive Guitar Learning Assistant

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Shu, Xin Shi, Lei Cheng, Jiacheng Ouyang, Lingling Chu, Mengdi Shu, Xinhuan Open Lab Newcastle University Newcastle Upon Tyne United Kingdom Open Lab School of Computing Newcastle University Newcastle upon Tyne United Kingdom Computer Science and Engineering The Ohio State University ColumbusOH United States School of Computing Newcastle University Newcastle Upon Tyne United Kingdom

ISBN: (纸本)9798400713064

Learning to play the guitar poses significant challenges for beginners, who often choose to practice alone to avoid the embarrassment of making mistakes in front of others. This isolation leads to a lack of timely feedback and encouragement, resulting in frustration and decreased motivation. Traditional learning methods fail to provide personalized and immediate support. To address these issues, we propose a GPT-powered guitar learning assistant, FretMate, that provides immediate error correction, personalized learning paths, and emotional support. The design was informed by formative interviews with six guitar instructors and six learners. We evaluated our assistant against the traditional self-guided practice in a controlled two-week study with 16 participants. Results showed that participants using FretMate improved in skill acquisition, engagement, and motivation compared to the control group. We discuss the potential of integrating conversational AI into instrument learning to provide personalized instruction and emotional engagement. © 2025 Copyright held by the owner/author(s).

关键词： Federated learning

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Enhanced Art Style Classification with VGG16 and Machine Learning Techniques

Enhanced Art Style Classification with VGG16 and Machine Lea...

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Global Conference for Advancement in Technology (GCAT)

作者： Garikipati Karthik Tejaswi Muppala Vinitha Chowdary A Peeta Basa Pati Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Art is an expression of human creativity and culture that has greatly impacted history. Classifying art styles is an important process in art and culture to preserve cultural heritage and facilitate educational and research purposes. The proposed work presents a comprehensive study that focuses on classifying 13 different art styles using deep learning techniques. The VGG16 convolutional neural network was employed for feature extraction using its convolutional and pooling layers up to the ’fc1’ layer, generating 512 and 4096-dimensional feature vectors. To evaluate the classification performance, seven machine learning classifiers were utilized. The classification accuracy is evaluated before and after applying Principal Component Analysis (PCA), to reduce the dimensionality of the feature vectors. Additionally, the paper explored the direct classification by the VGG16 model. Results prove that deep feature learning with the combination of traditional classifiers of machine learning is efficient.

关键词： Dimensionality reduction Deep learning Art Accuracy Computational modeling Machine learning Feature extraction Vectors Cultural differences Principal component analysis

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