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检索条件"机构=Computer Science and Computer Engineering Dept."

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An Interpretable Framework for Predicting Type 2 Diabetes using ML and Explainable AI 26

An Interpretable Framework for Predicting Type 2 Diabetes us...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ahmed, Kazi Fardeen Uz Zaman, Md Shahid Peyal, Hasibul Islam Hossain, Anika Rahman Ratul, Md. Toyaha Abdal, Md. Nazmul Islam, Md. Injamul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Bangladesh Uttara University Dept. of Computer Science & Engineering Bangladesh Khulna University Dept. of Computer Science & Engineering Discipline Bangladesh

ISBN: (纸本)9798350359015

Diabetes Mellitus is an incurable disease and stands as a major universe cause of destruction. With the universal prevalence of diabetes increasing rapidly, accurate detection and identification of the disease have become crucial. In this paper, we present an explainable AI-based diabetes indicative methodology that is carefully constructed, effective, and, most importantly, interpretable. Using two real-world diabetes datasets, the three most well-known machine learning classifiers in the literature - Random Forest (RF), Decision Tree (DT), and XGBoost - were subjected with quantitative assessments. After training and assessment of all classification models, the suggested method achieved the best results in the XGBoost classifier for the Sylhet dataset with 99.4% accuracy and the Pima Indian dataset with 92.59% accuracy. The datasets were normalized across observations to provide standardized values for improved analytical applicability and comparability. Explainable AI has been implemented for the XGBoost machine learning model by generating both global and local explanations using Shapley additive explanations (SHAP) and Local interpretable model-agnostic explanations (LIME). The elements that contribute to diabetes are explained and shown in graphs to help medical professionals make decisions about clinical diagnosis and treatment options. © 2023 IEEE.

关键词： Decision Tree (DT) Diabetes diabetes diagnosis explainable AI interpretable LIME Pima Indian dataset Random Forest (RF) SHAP Sylhet dataset XGBoost

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Detection of Primary Open Angle Glaucoma Based on Deep CNN Using Fundus Images 26

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

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26th International Conference on computer and Information Technology, ICCIT 2023

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

ISBN: (纸本)9798350359015

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. © 2023 IEEE.

关键词： CNN deep learning fundus image glaucoma image classification

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Privacy-Enhanced Image Restoration in Remote Sensing via Federated Learning 12

Privacy-Enhanced Image Restoration in Remote Sensing via Fed...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Jahanzeb Khan, M. Rath, Suman Zaib, Muhammad Hassan University of Nevada Reno Dept. of Computer Science and Engineering Reno United States Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350330366

This research addresses the dual challenges of image restoration quality and data privacy in optical remote sensing. Traditional restoration methods often fall short due to the complex nature of remote sensing images, and data privacy concerns further complicate the use of advanced techniques. By integrating the Deep Memory Connected Neural Network (DMCN) with the Data-Decoupled Federated Learning (DDFL) framework, our approach enables significant improvements in image restoration without requiring direct access to sensitive raw data. This method not only enhances data privacy by leveraging federated learning principles but also incorporates advanced techniques like Gaussian image denoising to maintain high restoration quality despite potential noise introduced by the federated process. The performance of the federated DMCN, particularly on the UCMERCED dataset, demonstrates minimal accuracy degradation, even in the presence of noise, while the strategic use of Downsampling Units within DMCN optimizes computational efficiency. Our comprehensive evaluations reveal the effectiveness of this approach in balancing data privacy with the need for high-quality image restoration, suggesting a promising direction for future advancements in remote sensing applications. © 2024 IEEE.

关键词： Image reconstruction

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An Intelligent Forecasting Classification Model for Traffic Analysis using Machine Learning Techniques 4

An Intelligent Forecasting Classification Model for Traffic ...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kishor, Bodla Tiwari, Rajesh Patra, Raj Kumar Pinjakar, Latika Singh, Mahesh Cmr Engineering College Dept. of Computer Science and Engineering Hyderabad India Cmr Technical Campus Dept. of Computer Science and Engineering Hyderabad India Symbiosis Institute of Technology Dept. of Computer Science and Engineering Nagpur India Shri Shankaracharya Technical Campus Dept. of Eee Durg Bhilai India

ISBN: (纸本)9798350343670

In addition to taking care of the normal citizens, Road Traffic protection is also one of the main issue for corporations governing the transportation. We use data mining techniques to get the hidden matter from the database. The applications of Data mining is there in various fields such as fraud detection, marketing and many more. Many countries were affected due to many decisions that were taken by the governments one such issue is globalization. This has brought some drastically changes in the intake level and the financial related activities and this has given a boost to transportation department. Understanding the importance of the street protection the authorities are putting effort to know the reasons behind the street accidents. The increase in the street injuries made it difficult to determine the limitations of the street injuries. This paper explains the street injuries frequency from analysing the database. This work is about 95% efficient. © 2024 IEEE.

关键词： classification data mining machine learning traffic analysis Transportation models

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ARNAV: computer vision and Machine Learning-Based Augmented Reality-Based Indoor Navigation System 5

ARNAV: Computer vision and Machine Learning-Based Augmented ...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sai Suryaa, K. Saravana Kumar, C. Sudharsan, S. Anandkumar, K.M. Easwari Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Introducing an advanced Augmented Reality (AR) indoor navigation system that uses computer vision for precise positioning and obstacle detection which eliminates the need for GPS. The system is implemented on Android devices using Unity. The system works based on a 3D model of the navigational space that provides the user with unique interior Point of Interests with AR guided overlays for navigation. It uses an advanced mapping and machine learning techniques for real time positioning and obstacle detection. The ARNAV system is independent of GPS making it reliable in complex indoor spaces where GPS fails. Additionally, in emergency situations where there is low visibility the system provides an advanced AR walls feature to guide users to reach the nearest exit by using AR generated walls and overlays. The retrace path feature which is designed especially for patients with cognitive impairments like Alzheimer's which allows the user to retrace the path that they have already traversed using the AR overlay. Additionally, it provides AR based reminders that helps them complete day to day tasks. In conclusion the ARNAV revolutionizes the AR technology to create a safe, dynamic and user centric system that handles emergency evacuation and needs of patients with special cognitive needs. © 2024 IEEE.

关键词： Augmented Reality computer Vision Indoor Navigation System Mobile Application

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Gaze Detection and Text Extraction from Images using CNN with DBSCAN-based Filter Design 11

Gaze Detection and Text Extraction from Images using CNN wit...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Jose, Jithin Ramanujan, Ajeesh College Of Engineering Trivandrum Dept. Of Computer Science And Engineering Trivandrum India

ISBN: (纸本)9798350379136

Gaze detection and text extraction are pivotal technologies in the domain of human-computer interaction and computer vision, enabling applications such as assistive technologies, user interface optimization, and automated content analysis. This paper presents a novel method that identifies the user's gaze direction and extracts the necessary portion of text, which constitutes the user's region of interest, using a Convolutional Neural Network (CNN) integrated with a spatial attention mechanism. A Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based filter is incorporated into the system to remove textual noise from the extracted image, thereby improving the model's accuracy and reducing computation time. The proposed method demonstrates reduced dependency on the CNN compared to previous approaches, resulting in enhanced real-time operation capability and lower latency. Computational optimization and real-time performance were thoroughly evaluated, showing that our system not only achieves high accuracy in gaze detection and text extraction but also maintains efficient processing speeds suitable for dynamic applications. This advancement offers significant improvements for applications in assistive technology, educational tools, and interactive media, where precise and efficient gaze detection and text extraction are critical. © 2024 IEEE.

关键词： Convolutional neural networks

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Utilization of Decentralized Finance (DeFi) and Distributed Ledger Technology (DLT) in Banking operations

Utilization of Decentralized Finance (DeFi) and Distributed ...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Kumar, Ch Sree Singh, Akhilendra Pratap Reddy, K Hemant K National Institute of Technology Meghalaya Dept. of Computer Science Meghalaya India VIT-AP Dept. of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350369175

Distributed Ledger Technology (DLT) is a decentralized database system where transactions are recorded and verified across multiple nodes. Its key features include immutability, time-stamping, and consensus-based validation. Numerous DLT applications are in supply chain management, intellectual property, cross border payments, energy trading, real estate, and online donations. DeFi, a combination of cryptocurrency and blockchain technology, offers financial services without intermediaries. Hence transactions with various digital assets based on cryptocurrency price feeds needs an automated framework. Smart contracts, self-executing contracts with terms directly written into code, automate processes and reduce manual intervention. This paper proposes a decentralized financial trading model using the AAVE protocol. AAVE is a decentralized platform that allows for transactions with various digital assets based on cryptocurrency price feeds. Proposed model uses Chainlink, a decentralized oracle network, to provide accurate and reliable price feeds. IPFS is used for data storage, while Graph is employed for indexing and querying blockchain data. The paper presents an example of a DeFi protocol to simulate banking operations, showcasing the potential of DeFi and DLT in revolutionizing traditional banking processes. © 2024 IEEE.

关键词： Distributed ledger

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Single Object Tracking in Satellite Videos Through Deep Learning and Motion Detection Techniques 6

Single Object Tracking in Satellite Videos Through Deep Lear...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Hayder, Israa Alizadeh, Fattah Dept. of Computer Science and Engineering University of Kurdistan Hawlere Erbil Iraq Dept. of computer science Toronto Metropolitan university Toronto Canada

ISBN: (纸本)9798350394634

Single object tracking in satellite videos has recently gained a lot of attention in the field of computer vision. Although the satellite images are very informative, the small size of the objects and limited spatial and temporal resolution might make object tracking difficult and inaccurate. In this paper, we propose a method for accurately tracking small objects in satellite videos. In the initial phase, we applied the Fast Super-Resolution Convolutional Neural Network (FSRCNN), a deep learning-based super-resolution technique, to enhance the resolution of the frames. This procedure serves to sharpen the details and features of the objects, resulting in a clearer representation. The main contribution, however, lies in our incorporation of a robust motion detection technique into our framework, marking a significant step forward in this study. The motion detection approach combines various methods and operations with the primary goal of making moving objects distinctly visible and easily distinguishable from the complex image background. This enhancement significantly contributes to the precision and accuracy of the tracking system. We combined both approaches with Fully Convolutional Siamese Network (SiamFC) due to its robustness in tracking processes across frames in videos, capacity to handle changes in target appearance, and ability to deal with small datasets. The Satellite Video Single Object Tracking (SatSOT) and SV248A10-SOT (SV248S), two satellite video single object tracking datasets, were used to evaluate the proposed approach. When the techniques were applied, the SatSOT dataset showed an 11% precision score increase and a 10% success rate improvement. The SV248S dataset exhibited even more significant improvements, with precision scores increasing by 18% and success scores rising by 9%. © 2024 IEEE.

关键词： Satellites

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A Multistage Cascaded Neural Network Model to Identify the Severity of Epileptic Attack

A Multistage Cascaded Neural Network Model to Identify the S...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Simon, Martha Thampi, Lidiya Lilly Dept. of Computer Science College of Engineering Trivandrum India Indian Institute of Information Technology Kottayam Dept. of Computer Science Kottayam India

ISBN: (纸本)9798350376135

Epilepsy is a neurological disorder characterized by recurrent seizures, impacting the quality of life for millions of individuals worldwide. Timely and accurate detection of epileptic seizures is crucial for proper diagnosis and effective treatment. Electroencephalogram (EEG) signals provide vital information about brain activity and serve as a valuable tool for epilepsy detection. The methodology opted for this study was an enhanced multilayer feed forward neural network for automatic detection of epilepsy from EEG signals and hence predicting the severity. The authors build a multistage cascaded neural network model for achieving the same. Initially a Simple Artificial Neural Network (SimpleANN) is designed to classify EEG segments into seizure, healthy and interictal. Later a Multilayer Feed Forward Neural Network (MFFNN) is employed for detecting the severity of epileptic patients and hence can be classified into primary and secondary stages. The experimental results showed that the proposed SimpleANN classifier had provided noticeable results, and outperforms all tested classifiers. The study involves the collection of EEG data from a diverse group of patients, including those with epilepsy and healthy individuals. This automated epilepsy identifying system passes through different stages including decomposition, feature extraction for achieving a better performance model. The proposed system was tested and compared with SVM, KNN, MLP and Decision Tree under different measurement metrics. ANN has managed to give an accuracy of 80 - 90% which is significantly higher as compared to other machine learning models. The proposed work can automatically learn complex patterns from EEG data provides an advantage in identifying subtle seizure related features that may not be evident through manual analysis. Furthermore, the model offers real-time capabilities, making it suitable for continuous monitoring of patients at risk of seizures. © 2024 IEEE.

关键词： Electroencephalography

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Big Mart Sales Analysis 6

Big Mart Sales Analysis

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

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

ISBN: (纸本)9798350374629

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. ©2024 IEEE.

关键词： Sales

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