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Innovative Healthcare Advancements: Harnessing Artificial and Human Intelligence for Bionic Solutions

Innovative Healthcare Advancements: Harnessing Artificial an...

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Sapkal, Samruddhi Jadhav, Sulaxan Mallikarjun, P. Shamim, Rejuwan Ul Islam, Atowar Bamane, Kalyan D y Patil International University Artificial Intelligence and Machine Learning Pune Akurdi411044 India D y Patil International University Pune Akurdi411044 India Malla Reddy Engineering College Jntuh Department of Electrical and Electronic Engineering Hyderabad India Maharishi University of Information Technology School of Data Science Noida India University of Science and Technology Meghalaya Department of Computer Science and Electronics Ri-Bhoi Techno city Kiling Road Meghalaya Baridua793101 India D y Patil College of Engineering Department of Information Technology Pune Akurdi India

ISBN: (纸本)9798350373783

According to initial data, individuals who have been diagnosed with type 2 diabetes (T2DM) appear to be at a more chances of evolving breast cancer compared to those who have not received a T2DM diagnosis. The primary goal of the research was to estimate the efficacy of three dissimilar methods in forecasting the probability of breast cancer in T2DM patients with diverse attributes. To achieve this objective, a danger expectation model was created for breast cancer in individuals with T2DM using the primary *** ensure a comprehensive analysis, we also gathered information on potential factors that may predict the growth of breast cancer. As the population sample size was restricted, we utilized Synthetic Minority Oversampling Technology to amplify the quantity of accessible data. Random assignment of data points to training or test sets was conducted at a ratio of roughly 39 to 1. Three distinct models, specifically Artificial Neural Network (ANN), Logistic Regression (LR), and Random Forest (RF), were assessed for effectiveness using a range of performance criteria, including as F1 score, area under the receiver operation characteristic curves, recalled, and correctness. (AUC). AUC values for these models were as follows: LR had an AUC of 0.834, ANN had an AUC of 0.865, and RF had an AUC of 0.959, with RF having the greatest AUC. According to our study, the Random Forest model outperformed the LR and ANN models in correctly estimating the incidence of breast cancer in people with T2DM. © 2024 IEEE.

关键词： Diseases

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Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning

arXiv

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arXiv 2023年

作者： Cho, Wonguk Park, Jinha Kim, Taesup Graduate School of Data Science Department of Electrical and Computer Engineering Seoul National University Korea Republic of

Continual domain shift poses a significant challenge in real-world applications, particularly in situations where labeled data is not available for new domains. The challenge of acquiring knowledge in this problem setting is referred to as unsupervised continual domain shift learning. Existing methods for domain adaptation and generalization have limitations in addressing this issue, as they focus either on adapting to a specific domain or generalizing to unseen domains, but not both. In this paper, we propose Complementary Domain Adaptation and Generalization (CoDAG), a simple yet effective learning framework that combines domain adaptation and generalization in a complementary manner to achieve three major goals of unsupervised continual domain shift learning: adapting to a current domain, generalizing to unseen domains, and preventing forgetting of previously seen domains. Our approach is model-agnostic, meaning that it is compatible with any existing domain adaptation and generalization algorithms. We evaluate CoDAG on several benchmark datasets and demonstrate that our model outperforms state-of-the-art models in all datasets and evaluation metrics, highlighting its effectiveness and robustness in handling unsupervised continual domain shift learning. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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An Entropy Based Unsupervised Visual Vocabulary Optimization for Visual Loop Closure Detection

An Entropy Based Unsupervised Visual Vocabulary Optimization...

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2023 International Conference on computer, Artificial Intelligence, and Control engineering, CAICE 2023

作者： Li, Bo Yu, Xiangdong Feng, Xin He, Ying Zhu, Zhaohan Ge, Yongxin Geng, Li Department of Criminal Science Technology Chongqing Police College Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China School of Electrical Engineering Southwest Jiaotong University Sichuan China School of Big Data & Software Engineering Chongqing University Chongqing China New York City College of Technology City University of New York New York United States

ISBN: (纸本)9781510665040

In the task of simultaneous localization and mapping (SLAM), a mobile robot should be able to recognize the location that it has previously visited. This is termed as the loop closure detection. Current loop closure detection is implemented by matching of image features extracted from present image and previous images, where good feature representation is highly required. Bag-of-visual words (BoW) is known to be an effect feature representation for image and video matching task. However, to build the visual feature vocabulary in the unsupervised manner is still challenging. In this paper, we are aiming at evaluating the representability of the visual words and generating the discriminative vocabulary representation. We first present an entropy ranking based scheme to improve the spectral clustering algorithm by leveraging eigenvector selection and feature embedding simultaneously. We then propose to evaluate the representative ability of the visual vocabulary quantitatively based on the complexity of the decomposition of similarity matrix in the unsupervised manner. We perform experiments on the loop closure detection datasets. which contains strong perceptual aliasing in both indoor and outdoor scenarios. Results demonstrate that the proposed BoW based feature representation has strong representation ability in measuring the similarity of loop closure events and differentiating new visited places. © 2023 SPIE.

关键词： Entropy

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SELF-CLUSTERING GRAPH TRANSFORMER APPROACH TO MODEL RESTING STATE FUNCTIONAL BRAIN ACTIVITY

arXiv

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arXiv 2025年

作者： Thapaliya, Bishal Akbas, Esra Sapkota, Ram Ray, Bhaskar Calhoun, Vince Liu, Jingyu Department of Computer Science Georgia State University Atlanta United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain’s functional organization and is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this study, we introduce a novel attention mechanism for graphs with subnetworks, named Self Clustering Graph Transformer (SCGT), designed to handle the issue of uniform node updates in graph transformers. By using static functional connectivity (FC) correlation features as input to the transformer model, SCGT effectively captures the sub-network structure of the brain by performing cluster-specific updates to the nodes unlike uniform node updates like vanilla graph transformers, further allowing us to learn and interpret the subclusters. We validate our approach on the Adolescent Brain Cognitive Development (ABCD) dataset, comprising 7,957 participants, for the prediction of total cognitive score and gender classification. Our results demonstrate that SCGT outperforms the vanilla graph transformer method, and other recent models, offering a promising tool for modeling brain functional connectivity and interpreting the underlying subnetwork structures. © 2025, CC BY.

关键词： Distribution transformers

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MEAT: Median-Ensemble Adversarial Training for Improving Robustness and Generalization

MEAT: Median-Ensemble Adversarial Training for Improving Rob...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhaozhe Hu Jia-Li Yin Bin Chen Luojun Lin Bo-Hao Chen Ximeng Liu Fujian Province Key Laboratory of Information Security and Network Systems Fuzhou China College of Computer Science and Big Data Fuzhou University Fuzhou China Department of Computer Science and Engineering Yuan Ze University Taiwan

Self-ensemble adversarial training methods improve model robustness by ensembling models at different training epochs, such as model weight averaging (WA). However, previous research has shown that self-ensemble defense methods in adversarial training (AT) still suffer from robust overfitting, which severely affects the generalization performance. Empirically, in the late phases of training, the AT becomes more overfitting to the extent that the individuals for weight averaging also suffer from overfitting and produce anomalous weight values, which causes the self-ensemble model to continue to undergo robust overfitting due to the failure in removing the weight anomalies. To solve this problem, we aim to tackle the influence of outliers in the weight space in this work and propose an easy-to-operate and effective Median-Ensemble Adversarial Training (MEAT) method to solve the robust overfitting phenomenon existing in self-ensemble defense from the source by searching for the median of the historical model weights. Experimental results show that MEAT achieves the best robustness against the powerful AutoAttack and can effectively allievate the robust overfitting. We further demonstrate that most defense methods can improve robust generalization and robustness by combining with MEAT.

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Siamese-Transformer Network for Offline Handwritten Signature Verification using Few-shot

Siamese-Transformer Network for Offline Handwritten Signatur...

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International Conference on computer and Information Technology (ICCIT)

作者： Prattoy Majumder AFM Mohimenul Joaa Ehsanur Rahman Rhythm Md Humaion Kabir Mehedi Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bangladesh

Handwritten signature verification is a crucial task with applications spanning authentication, financial transactions, and legal documents. In scenarios where only a single reference signature is available, the challenge of accurate verification becomes pronounced due to variations in writing styles, distortions, and limited labeled data. In this paper, we propose a novel Siamese-Transformer network tailored for handwritten signature verification using few-shot learning. By synergizing Siamese neural networks and Transformer architectures, our model excels in capturing contextual relationships and discerning genuine from forged signatures. A triplet loss function facilitates discriminative feature learning. Convolution layers extract local features from an image, while the transformer component utilizes these local features to capture global dependencies within signatures. Experimental results on benchmark datasets showcase the model’s superior performance in few-shot verification scenarios, marking it as a promising advancement in signature verification and few-shot learning techniques.

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Analysis of Trie-Based Lan File Search Engine

Analysis of Trie-Based Lan File Search Engine

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： C Shanmugasundaram C Umamaheswari V Suriyaa Kartikeyan TR Department of Electrical and Electronics Engineering Manakula Vinayagar Institute of Technology Department of Computer Science with Data Science St. Thomas College of Arts and Science Department of Computer Technology Anna University Chennai

Searching and Accessing files in a computer device is one of the most important and basic task performed by the computer today. Each Operating System has a dedicated search bar for this, But however, In a network encompassing a multitude of systems, retrieving a specific file from various systems can pose a formidable task. Put differently, users frequently find it challenging to recollect the precise location where they've stored a particular file. This undertaking offers a user-friendly application interface wherein the user inputs the file's name, subsequently receiving both the precise file path and the IP address of the computer device within the network as output. We can access the file by clicking on the path generated. Accessing Files from multiple systems in a network is quite difficult and a Slow process and there are not many Applications which serve this purpose. We aim to overcome this problem by providing an efficient Trie data Structure based Searching Algorithm to search and access files in a network from any node in the Local Area Network.

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ReSkipNet: Skip Connected Convolutional Autoencoder for Original Document Denoising

ReSkipNet: Skip Connected Convolutional Autoencoder for Orig...

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International Conference on computer and Information Technology (ICCIT)

作者： Mohammad Muhibur Rahman Anushua Ahmed Mohammad Rakibul Hasan Mahin Fahmid Bin Kibria Waheed Moonwar Ehsanur Rahman Rhythm Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bangladesh

data pre-processing, data analysis, and Optical Character Recognition need a huge amount of clean data, and document images are usually a good source for this. However, document images frequently exhibit blurring and various other forms of noise, which can pose challenges in their manipulation and analysis. To denoise and deblur such document images, autoencoders have been used for a long time. For this task, we propose a novel Convolutional Autoencoder Network which is composed of multiple skip-connected residual blocks and other layers for supporting the encoder and decoder parts. This model not only uses less computational power to denoise existing document image datasets but also performs well. While prior research primarily concentrates on optimizing evaluation metrics, our approach additionally prioritizes larger resolution input sizes. This characteristic of using larger image sizes enhances its practicality and usability as real-world documents are typically characterized by a higher word density. Moreover, in order to further advance the development of our model, we produced an original dataset and proceeded to train our model on this dataset, resulting in satisfactory outcomes.

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SmartTrap: An On-Field Insect Monitoring System Empowered by Edge Computing Capabilities

SmartTrap: An On-Field Insect Monitoring System Empowered by...

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2023 RIVF International Conference on Computing and Communication Technologies, RIVF 2023

作者： Nguyen, Quan M. Pham, Duy A. Pham, Dong T. Le, An D. Vo, Nguyen Q. H. Vo, Hien B. School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam Universität Osnabrück Joint Lab Artificial Intelligence and Data Science Osnabrück Germany Graduate School of Bioresource and Bioenvironmental Sciences Kyushu University Fukuoka Japan University of California San Diego Department of Electrical and Computer Engineering San DiegoCA United States Vietnamese-German University Department of Electrical and Computer Engineering Binh Duong Viet Nam

ISBN: (纸本)9798350315844

Fruit flies pose a significant threat to fruit yields, necessitating immediate detection solutions for effective pest management. In this study, we present our approach using YOLOv7 and the Jetson Nano 4GB for rapid and accurate fruit fly detection. Our methodology attains an Average Precision (AP) of 0.993 at an Intersection over Union threshold (IoU) of 0.75, facilitating almost instantaneous enumeration of ensnared fruit flies in field conditions, thereby augmenting the efficacy of pest management practices. Concurrently, the system transmits this data to either a local or cloud-based server, assisting end-users in accurately identifying regions with elevated fruit fly densities to strategically administer pesticides. This targeted approach has significant implications for sustainable agriculture as it reduces the need for widespread spraying. Moreover, our versatile method adapts for detecting other pests like aphids or beetles, serving as a valuable tool in agriculture and beyond. © 2023 IEEE.

关键词： Edge computing

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Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation

arXiv

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arXiv 2025年

作者： Zhang, Shengzhe Chen, Liyi Shen, Dazhong Wang, Chao Xiong, Hui University of Science and Technology of China Hefei China Nanjing University of Aeronautics and Astronautics Nanjing China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei China Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Multi-modal sequential recommendation (SR) leverages multi-modal data to learn more comprehensive item features and user preferences than traditional SR methods, which has become a critical topic in both academia and industry. Existing methods typically focus on enhancing multi-modal information utility through adaptive modality fusion to capture the evolving of user preference from user-item interaction sequences. However, most of them overlook the interference caused by redundant interest-irrelevant information contained in rich multi-modal data. Additionally, they primarily rely on implicit temporal information based solely on chronological ordering, neglecting explicit temporal signals that could more effectively represent dynamic user interest over time. To address these limitations, we propose a Hierarchical time-aware Mixture of experts for multi-modal Sequential Recommendation (HM4SR) with a two-level Mixture of Experts (MoE) and a multi-task learning strategy. Specifically, the first MoE, named Interactive MoE, extracts essential user interest-related information from the multi-modal data of each item. Then, the second MoE, termed Temporal MoE, captures user dynamic interests by introducing explicit temporal embeddings from timestamps in modality encoding. To further address data sparsity, we propose three auxiliary supervision tasks: sequence-level category prediction (CP) for item feature understanding, contrastive learning on ID (IDCL) to align sequence context with user interests, Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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