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ADNet: An Advanced Artificial Neural Network for Early Diagnosis of Alzheimer's Disease

ADNet: An Advanced Artificial Neural Network for Early Diagn...

引用

Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md. Arif Hasan Rifat Nusrat Jahan Tanha Taranum Muhammad Nazrul Islam Faiz Al Faisal Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Alzheimer's disease is a progressive neurological disorder, often referred to as dementia, that requires early and accurate diagnosis for effective treatment and management. It's a complex disease type that originates in the brain and only gets worse with timestamps, which is why it should be diagnosed early and accurately. Traditional diagnostic methods often fall short, posing challenges to healthcare professionals. This paper introduces a novel deep learning technique, ADNet, a specialized convolutional neural network (CNN) developed to classify Alzheimer's disease into four stages. For MRI image processing, we applied preprocessing techniques, including Green Fire Blue conversion and contrast enhancement using CLAHE, to improve image clarity. To address data imbalance, various data augmentation methods were implemented. During experimentation, pre-trained models like VGG16, MobileNet-V2, InceptionV3, and ResNet-50 achieved accuracies of 85%, 85%, 79 %, and 67 %, respectively, but fell short of our goals. Our ADNet model, however, attained an impressive 97.26% accuracy. This work offers significant contributions to Alzheimer's research and advances deep learning applications in medical imaging and neurological disease diagnosis.

关键词： Neurological diseases Deep learning Training Technological innovation Accuracy Magnetic resonance imaging Computational modeling Brain modeling Convolutional neural networks Alzheimer's disease

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FireSense: AI-Based Wildfire and Smoke Detection in Forests

FireSense: AI-Based Wildfire and Smoke Detection in Forests

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2025 International Conference on New Trends in Computing sciences, ICTCS 2025

作者： Mughal, Abdul Basit Haider, Syed Mujtaba Khan, Rafi Ullah Ali Shah, Syed Mohsin Arain, Javiriya Hameed Department of Computer Science Bahria University Islamabad Pakistan Department of Mathmatics University of Pavia Pavia Italy Dept. of Electrical & Computer Engineering PAF-IAST Haripur Pakistan Faculty of Engineering University of Deusto Bilbao Spain Department of Computer Science COMSATS University Islamabad Pakistan

ISBN: (纸本)9798331523411

Forests are an essential part of the environment which plays a vital role in sustaining ecological balance and providing habitat to different wildlife. Wildfires are one of the serious threats to forests which create a devastating impact on the natural ecosystem. Early identification of smoke and wildfires will help to reduce damage and support a quick reaction. Earlier, wildfire and smoke detection in forests were very difficult due to the challenging forest terrain, low light conditions, and difficult weather *** the development in technology, several computer vision methods are combined with satellite and Unmanned Aerial Vehicle (UAV) data to correctly identify wildfires, but accuracy is still a concern with these models. This paper introduced a novel method to detect wildfire and smoke in forests utilizing fine-tuned YOLOv11n using an aerial vehicle dataset. The mAP of the fine-tuned YOLOv11n is 88.6%, with a precision of 100% and an F1-score of 85%. The number of parameters has decreased by 30% compared to its previous models, and the model size is only 5.5MB. ©2025 IEEE.

关键词： Object Detection smoke detection Unmanned Aerial Vehicle (UAV) Wildfire detection

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LLM-Based Framework for Administrative Task Automation in Healthcare 12

LLM-Based Framework for Administrative Task Automation in He...

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

作者： Gebreab, Senay A. Salah, Khaled Jayaraman, Raja Rehman, Muhammad Habib Ur Ellaham, Samer Khalifa University Dept. of Computer & Communication Engineering Abu Dhabi United Arab Emirates Khalifa University Dept. of Management Science & Engineering Abu Dhabi United Arab Emirates King's College London Dept. of Biomedical Engineering & Imaging Sciences London United Kingdom Heart & Vascular Institute & Continuous Cleveland Clinic Improvement Dept. Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350330366

Artificial Intelligence (AI) has been transformative in the healthcare sector, leading to enhanced precision in medical diagnosis, more effective treatment options, and a significant improvement in patient safety. However, computer-based administrative tasks, such as retrieval of medical and health records, patient registration, medical billing, filing and documentation, and appointment scheduling, still impose a heavy burden on healthcare professionals, causing a reduced quality of care and efficiency. In light of these challenges, this paper proposes a large language model (LLM)-based multi-agent framework designed to automate some of the administrative work in clinical settings. In our proposed solution, these LLM agents coordinate to parse instructions, breakdown tasks, and execute a sequence of actions in a workflow. They are equipped to not only execute documentation process at the database level but also operate directly on web-based electronic medical record (EMR) platforms. Moreover, the framework integrates data sources through a retrieval-augmented generation (RAG) system to allow streamlined interaction with patient information and medical records, mediated through an agent interface. The framework is designed with security in mind to defend against malicious prompts. We demonstrate the practicality of our solution by testing on various complex tasks that require the use of multiple tools and an EMR website. The result show the framework's effectiveness in handling diverse healthcare administrative tasks. © 2024 IEEE.

关键词： Patient treatment

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Application of Tensor Completion for Reducing the Beamforming Time in Ultrafast Ultrasound Imaging: A Doppler Ultrasound Assessment

Application of Tensor Completion for Reducing the Beamformin...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Sajjad Afrakhteh Federico Mento Libertario Demi Dept. of Information Engineering and Computer Science University of Trento Italy

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Doppler ultrasound is a non-invasive technique used to measure blood flow in blood vessels. However, this application requires a large number of temporal frames for precise blood flow measurements, posing challenges for beamforming time, even with a simple delay and sum (DAS) beamformer. Therefore, in this study, we aim to address this issue by proposing a post-processing solution and evaluating its effectiveness for Doppler ultrasound. In this study, We introduce sparse beamforming as a method to reduce beamforming time in preprocessing followed by applying tensor completion (TC) to reconstruct the missing information of the 3-dimensional (3D) beamforming data. We applied the proposed technique to the carotid artery data, demonstrating the feasiblity of improving the beamforming computational complexity by 4 to 5 times, while achieving comparable doppler velocity estimates to the original case (i.e., without sparse beamforming).

关键词： Ultrasonic imaging Tensors Array signal processing Ultrasonic variables measurement Time measurement Doppler effect Computational complexity Image reconstruction Carotid arteries Blood flow

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Enhancing Software Effort Estimation with Ant Colony Optimization Algorithm and Fuzzy-Neural Networks

Enhancing Software Effort Estimation with Ant Colony Optimiz...

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Distributed Computing and High Performance Computing (DCHPC), International Conference on

作者： Mahnaz Afshari Taghi Javdani Gandomani Data Science Research Group Shahrekord University Shahrekord Iran Dept. computer science shahrekord university Shahrekord Iran

Software effort estimation remains a persistent challenge and requires serious attention in the early stages of software project management. Inherent uncertainties arising from incomplete and inaccurate requirements pose a significant barrier to reliable estimations. Despite numerous efforts and various techniques proposed for cost estimation, the pursuit of improving estimation accuracy remains essential. In response to this challenge, this study introduces a novel model that integrates an Adaptive Neuro-Fuzzy Inference System with the Ant Colony Optimization algorithm. The model is further compared with well-known evolutionary algorithms such as Differential Evolution, Genetic Algorithm, Artificial Neural Network, and Particle Swarm Optimization. Applying the proposed model to popular software effort estimation datasets, including Albrecht, Desharnais, and Kemerer, demonstrates its superiority over the mentioned algorithms. The improved estimation provided by this model can assist software project managers in better project cost estimation.

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Improving Image Classification Using Attention Mechanisms in Deep Learning Models 4

Improving Image Classification Using Attention Mechanisms in...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Sharma, Vinod Mishra, Atul Kumar Kumar, Anil Shakti, Nanda Sharma, Madhuri Srinju, M. Slp University Dept of Cse Gwalior India Madhav University Dept. of Commerce & Management Pindwara India Mangalayatan University Dept. of Eee Aligarh India Usha Martin University Dept. of Engineering and Applied Sciences Ranchi India Chri Campus University Dept. of Life Sciences Gwalior India Dhanalakshmi Srinivasan University Dept. of Ai and Data Science Coimbatore India

ISBN: (纸本)9798350364729

In this study, we explore the application of attention mechanisms to enhance deep learning models in the context of image classification. We assess several types of attention mechanisms across diverse image classification datasets. Our findings indicate substantial enhancements in model accuracy facilitated by these attention mechanisms. Additionally, we delve into implementation challenges such as computational overhead and the necessity for extensive datasets. Furthermore, we investigate the potential benefits of transfer learning and fine-tuning pretrained models integrated with attention mechanisms to optimize performance and streamline training efforts. © 2024 IEEE.

关键词： Adversarial machine learning

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Semantic-Aware Dynamic Parameter for Video Inpainting Transformer

Semantic-Aware Dynamic Parameter for Video Inpainting Transf...

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International Conference on computer Vision (ICCV)

作者： Eunhye Lee Jinsu Yoo Yunjeong Yang Sungyong Baik Tae Hyun Kim Dept. of Computer Science Hanyang University Dept. of Artificial Intelligence Hanyang University Dept. of Data Science Hanyang University

Recent learning-based video inpainting approaches have achieved considerable progress. However, they still cannot fully utilize semantic information within the video frames and predict improper scene layout, failing to restore clear object boundaries for mixed scenes. To mitigate this problem, we introduce a new transformer-based video inpainting technique that can exploit semantic information within the input and considerably improve reconstruction quality. In this study, we use the mixture-of-experts scheme and train multiple experts to handle mixed scenes, including various semantics. We leverage these multiple experts and produce locally (token-wise) different network parameters to achieve semantic-aware inpainting results. Extensive experiments on YouTube-VOS and DAVIS benchmark datasets demonstrate that, compared with existing conventional video inpainting approaches, the proposed method has superior performance in synthesizing visually pleasing videos with much clearer semantic structures and textures.

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ICT: Iterative Clustering with Training: Preliminary Results

ICT: Iterative Clustering with Training: Preliminary Results

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2025 International Conference on New Trends in Computing sciences, ICTCS 2025

作者： Hassanat, Ahmad B. Tarawneh, Ahmad S. Alhasanat, Ahmad S. Alghamdi, Mansoor Almohammadi, Khalid Tarawneh, Zakaria Dept. Computer Science Faculty of Information Technology Mutah University Karak Jordan Dept. Data Science and Artificial Intelligence Mutah University Karak Jordan School of Foundation Studies and Mathematics Oryx Universal College Doha Qatar Dept of Mathematics Al-Hussein Bin Talal University Maan Jordan Dept. of Computer Science Applied College University of Tabuk Tabuk Saudi Arabia CiTIUS Universidade de Santiago de Compostela(USC) Spain

ISBN: (纸本)9798331523411

We introduce Iterative Clustering with Training (ICT), a hybrid approach that combines clustering with iterative classifier-based refinement. Unlike traditional clustering methods, ICT leverages supervised learning to reassign ambiguous boundary points, improving cluster quality. The proposed approach starts with an initial clustering phase, followed by the computation of silhouette scores to separate high-confidence core points from uncertain boundary points. A classifier (e.g., Random Forest) is then trained on core points to predict labels for boundary points, refining clusters in an iterative process until convergence is achieved. To avoid trivial clusters, minimum cluster ratio constraints are implemented. Experimental results on multiple benchmark datasets show that ICT outperforms traditional clustering methods such as k-means, HDBSCAN, and spectral clustering across several evaluation metrics, such as Adjusted Rand Index, Normalized Mutual Information, and Accuracy. ICT particularly excels at handling clusters that overlap, have noise, and complex geometries, where standard methods often fail. Key advantages include adaptive core selection, iterative refinement, and classifier flexibility, making ICT suitable for applications in customer segmentation, anomaly detection, and biological data analysis. ©2025 IEEE.

关键词： Agglomerative clustering Clustering Machine learning Unsupervised learning

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Report on the 28th Foundation of Software engineering Workshop (FOSE2021)

Report on the 28th Foundation of Software Engineering Worksh...

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作者： Nagura, Masataka Sawada, Atsushi Sekizawa, Toshifusa Dept. of Software Engineering Fuculty of Sciense and Technology Nanzan University Japan Dept. of Computer Science College of Engineering Nihon University Japan

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Stacking Ensemble Framework for Hate Speech Detection in Bangla Videos

Stacking Ensemble Framework for Hate Speech Detection in Ban...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Shamrin Jahan Mim Tanjim Mahmud Md Hasan Ali Mohammad Tarek Aziz Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

The proliferation of internet usage and social media platforms has significantly enhanced the ability of individuals to express their opinions on various topics. However, this freedom of expression sometimes morphs into a vehicle for disseminating hate speech, leading to increased incidents of cyberbullying, violations, and conflicts. Particularly on video-sharing websites, which have become a prominent stage for such activities due to their widespread use and the dynamic nature of video content. This study aims to address the issue of hate speech in video content by developing a robust method for detecting hate speech in Bangla language videos. The focus is on the spoken content within these videos, which is a primary vector for the transmission of harmful messages. We constructed a comprehensive dataset by extracting and converting audio from a collection of videos into text. Utilizing this dataset, we applied machine learning techniques and deep learning models to analyze and classify the content. Specifically, our approach involves a stacking ensemble model that combines the strengths of Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BiGRU) with the analytical capabilities of customized feature extraction applied to Multinomial Naive Bayes and Random Forest classifiers serving as a meta-model. The proposed stacking ensemble model demonstrates a high level of efficacy, achieving an accuracy rate of 96% in detecting hate speech within the tested video content. This performance indicates a significant advancement over existing methods, underlining the effectiveness of our hybrid, multi-model approach.

关键词： Analytical models Computational modeling Hate speech Stacking Logic gates Vectors Bayes methods Convolutional neural networks Vehicle dynamics Random forests

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