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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Walelign Sewunetie Assefa Beza Hailemariam Abebe Tesfamariam M Abuhay Wasyihun Admass Hayat Hassen Tsion Haile Hana Hailemariam Lydia Debebe Nurlign Moges Nathnael Bekele Surafel L Tilahun Mahlet Berta Mahlet Mammo Seid Muhie Yimam Kovacs Laszlo School of Computing Debre Markos University Ethiopia Faculty of Computing Bahir Dar University Ethiopia University of Gondar Ethiopia Queen's University Canada School of Computing Mekdela Amba University Mekdela Ethiopia College of Medicine and Health Sciences University of Gondar Ethiopia Saint Paul's Hospital Millennium Medical College AA Ethiopia College of Medicine and Health Sciences Bahir Dar University Ethiopia College of Medicine and Health Sciences Debre Markos University Ethiopia Addis Ababa Science and Technology University Ethiopia HPC & Big Data Analytics CoE Addis Ababa Science and Technology University Ethiopia House of Computing and Data Science University of Hamburg Hamburg Germany Institute of Informatics Univerisity of Miskolc Miskolc Hungary

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

This research explores the potential of Large Language Models in the context of healthcare solutions, with a specific focus on Sexual, Reproduc-tive, and Maternal Health Rights (SRMHR) Question Answering (QA) in the low-resource language, Amharic (““'Ie;;:). To construct the dataset, we first collected data from medical textbooks and guidelines authored by reputable medical institutions and organizations. U ti-lizing automatic question-and-answer generation techniques, we then generated pairs for the dataset. Sub-sequently, the dataset underwent annotation, translation, and evaluation processes, resulting in a refined collection of 2.8k Amharic datasets. We use the dataset to fine-tune the LLaMA-2-Amharic model, with test results assessed using BLEU scores and human-level evaluations, demonstrating promising outcomes. The curated Amharic SRMHRQA dataset serves as a foun- dational resource for future research. However, further enhancements are necessary to optimize its efficacy, particularly within the realm of SRMHR for low- resource languages like Amharic. Future research could involve scaling up the dataset in terms of size, quality, and domain coverage.

关键词： Annotations Large language models Medical services Organizations Question answering (information retrieval) Informatics Guidelines

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Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

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Multimedia Tools and Applications 2024年第26期83卷 68415-68420页

作者： Ahammad, Shaik Hasane Jayaraj, R. Shibu, S. Sujatha, V. Prathima, Ch Leo, L. Megalan Prabu, R. Thandaiah Hossain, Md.Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of Data Science School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Deptartment of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Chennai India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt

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An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering

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Array 2025年 26卷

作者： Abualigah, Laith Alomari, Saleh Ali Almomani, Mohammad H. Zitar, Raed Abu Migdady, Hazem Saleem, Kashif Smerat, Aseel Snasel, Vaclav Ezugwu, Absalom E. Computer Science Department Al al-Bayt University Mafraq25113 Jordan Faculty of Science and Information Technology Jadara University Irbid21110 Jordan Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates CSMIS Department Oman College of Management and Technology Barka320 Oman Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh11362 Saudi Arabia Faculty of Educational Sciences Al-Ahliyya Amman University Amman19328 Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava 70800 Czech Republic Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom2520 South Africa

data clustering plays a crucial role in various domains, such as image processing, pattern recognition, and data mining. Traditional clustering techniques often suffer from limitations like sensitivity to initialization, poor convergence, and entrapment in local optima. To address these challenges, this paper proposes an Enhanced Walrus Optimizer (IWO) tailored for clustering tasks. The proposed IWO integrates two powerful strategies–Opposition-Based Learning (OBL) and Mutation Search Strategy (MSS)–to improve population diversity and prevent premature convergence, thereby enhancing both exploration and exploitation capabilities. These enhancements enable more accurate and stable identification of cluster centers. The effectiveness of IWO is validated through extensive experiments on multiple benchmark clustering datasets and compared against several state-of-the-art metaheuristic algorithms, including PSO, GWO, AOA, and others. The results demonstrate that IWO achieves better results, indicating improved compactness and separation of clusters. Statistical validation using p-values and ranking scores further confirms the superiority of the proposed method. These findings suggest that IWO offers a robust and flexible framework for solving complex clustering problems. Future work will explore hybrid deep learning-integrated models and parallel implementations to enhance scalability. © 2025

关键词： Swarm intelligence

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Timeseries Anomaly Detection Using SAX and Matrix Profiles Based Longest Common Subsequence 23rd

Timeseries Anomaly Detection Using SAX and Matrix Profiles B...

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23rd International Conference on Computational science, ICCS 2023

作者： Nguyen, Thi Phuong Quyen Tran, Trung Nghia Giang, Hoang Ton Nu Huong Nguyen, Thanh Tung Faculty of Project Management The University of Danang- University of Science and Technology 54 Nguyen Luong Bang Danang Viet Nam 268 Ly Thuong Kiet Street District 10 Ho Chi Minh City Viet Nam Department of Information Systems and Data Analytics School of Computing National University of Singapore Singapore Singapore 268 Ly Thuong Kiet Street District 10 Ho Chi Minh City Viet Nam

ISBN: (纸本)9783031360206

Similarity search is one of the most popular techniques for time series anomaly detection. This study proposes SAX-MP, a novel similarity search approach that combines Symbolic Aggregate ApproXimation (SAX) and matrix profile (MP). The proposed SAX-MP method consists of two phases. The SAX method is used in the first phase to extract all of the subsequences of a time series, convert them to symbolic strings, and store these strings in an array. In the second phase, the proposed method calculates the MP based on the symbolic strings that are represented for all subsequences extracted in the first phase. Since a subsequence is represented by a symbolic string, the MP is calculated using a distance-based longest common subsequence rather than the z-normalized Euclidean distance. Top-k discords are detected based on the similarity MP. The proposed SAX-MP is implemented on several time series datasets. Experimental results reveal that the SAX-MP method is particularly effective at detecting anomalies when compared to HOT SAX and MP-based methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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Development of Advanced Noise Filtering Techniques for Medical Image Enhancement

Development of Advanced Noise Filtering Techniques for Medic...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Sumit Kushwaha K. Amuthachenthiru Geetha. K Jonnadula Narasimharao Dileep Kumar M Sai Sudha Gadde Department of Computer Applications University Institute of Computing Chandigarh University Punjab India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Faculty of Management Sciences Hensard University Bayelsa Nigeria Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Telangana India

Medical imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), X-rays, and ultrasound, are extensively employed in the healthcare industry for diagnostic purposes. Noise, on the other hand, can disturb these approaches and lead to inaccurate diagnosis. Due to this problem, the significance of noise removal technologies has grown in the field of medical imaging, specifically for the examination and understanding of medical images and anatomical structures. To tackle these challenges, a variety of denoising techniques have been developed, such as the Weiner filter (WF), Gaussian filter, and median filter. This work employs a Deep Learning (DL) method known as Convolutional Autoencoder (CAE) to remove noise from medical images. We gather chest X-ray images for analysis and focus on common noise types that affect medical images, such as Poisson and Gaussian noise. We evaluate the effectiveness of the suggested denoising method CAE by comparing it to the commonly employed WF methodology. The Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are employed to assess and compare the performance of the WF and CAE in reducing Poisson and Gaussian noise. The study's findings indicate that the suggested denoising method based on CAE shows promising results in terms of improving quality.

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Paddy Diseases Multi-Class Classification using CNN Variants

Paddy Diseases Multi-Class Classification using CNN Variants

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Information Technology (ACIT)

作者： Danial Izzat Anwar Bin Abdul Aziz Marina Yusoff Nurain Ibrahim Raed Alazaidah College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan

Agriculture is critical to ensuring and securing food supply for human consumption. Paddy crops are an agricultural practice that produces one of the highest food yields in agriculture. Paddy diseases must now be detected and diagnosed as soon as possible rather than later. Farmers currently use the naked eye to observe suspected infected paddies. The suspected infected paddy will then be sampled and sent to the laboratory for further examination. These methods are highly inefficient, time-consuming, and error prone. Most paddy farmers lack the knowledge to make an informed and accurate diagnosis of which paddies are infected by which diseases. This paper explores using deep learning algorithms to detect and classify various paddy diseases automatically. This method will take an image of paddy leaves and classify it based on the disease category indicated by the idea. The PaddyDoctor dataset from the Kaggle website was used to train and test the deep-learning models. Convolutional Neural Network (CNN) and Transfer Learning models, including MobileNet, MobileNetv2, and InceptionV3, have been constructed. The results of an experiment involving different dataset split ratios, learning rates, and batch sizes. CNN with MobileNetV2 outperformed CNN with MobileNetV1 and CNN with InceptionV3 by 98.05 percent, 80.08 percent, and 83.98 percent, respectively.

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Meta-Learning Based Adaptive Crop Price Prediction for Agriculture Application 5

Meta-Learning Based Adaptive Crop Price Prediction for Agric...

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5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Dhanasekaran, K. Ramprasath, M. Sathiyamoorthi, V. Poornima, N. Jayaraj, Irrai Anbu SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems TN Chennai Chengalpattu603 203 India Sona College of Technology TN Salem India CSL Australia Pty Ltd Australia

ISBN: (纸本)9781665435246

Dealing with unexpected changes in agricultural product prices that affect the farmers and the economic growth of a country is inevitable in the current day context. A unique approach is proposed for time-series prediction, which utilizes the power of crop price prediction and crop yield prediction of select crops to identify the relevant information with respect to the market prices, and crop yields. Many uncertain conditions such as climate changes, fluctuations in market, flooding, etc., cause problems to the agricultural process. In this work, the prices of selected essential crops analyzed for time-series prediction using meta-learning. The Self-Organized Map (S OM), LSTM (Long-Short Term Model), and this proposed Meta-Learning Based Adaptive Crop Price Prediction (MLACPP) was trained using crop price dataset, and crop yield dataset. Meta-learning function used in the approach utilizes the input, optimization-output, and task-related estimators for calculating meta-loss over multiple meta-network. The meta-learning with self-organizing capability learns a meta-network observing additional information from specialized support set. Moreover, this method deals with a task-oriented loss. Interestingly, it compares the current optimal-output of the optimization function g with the target-specific information. Experimental results show significant improvement in terms of prediction accuracy and cross-correlation entropy over the existing crop price prediction approaches. This research work provides the basis for making better decisions for price-fixing and crop yield maximization based on insights obtained from the dataset. © 2021 IEEE.

关键词： Crops

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Real-Time Traffic Flow Optimization using Adaptive IoT and data Analytics: A Novel DeepStreamNet Model

Real-Time Traffic Flow Optimization using Adaptive IoT and D...

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Sustainable Expert Systems (ICSES), International Conference on

作者： V.S. Saranya Garimidi Subbarao Dega Balakotaiah Maloth Bhavsingh K. Suresh Babu Srinivasa Rao Dhanikonda Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Department o Computer Science and Engineering (AIDS) Pace Institute of Technology and Sciences Department of Computer Science and Engineering Vignan's Founadation for Science Technology & Research (Deemed to be University) Department of Computer Science and Engineering Ashok: Womens Engineering College Kurnool Andhra Pradesh Department of Computer Science and Engineering Narasaraopeta Engineering College(Autonomous) Narasaraopet A.P Department of Artificial Intelligence and Data Science Faculty of Science and Technology (IcfaiTech) ICFAI Foundation for Higher Education (Deemed to Be University) Hyderabad Telangana India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Thisstudy introduces the DeepStreamNet model, an advanced framework for enhancing real-time traffic management in urban environments using adaptive IoT and sophisticated big data analytics. Central to our approach is the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) units, which synergize with IoT devices to facilitate dynamic adaptation to fluctuating traffic conditions. The model excels in operational performance, achieving a notable accuracy of 94.7% in congestion prediction with an exceptionally low latency of only 45 ms per decision cycle and the capability to process up to 50,000 data points per second. These technical achievements have marked significant improvements over traditional traffic management systems, enhancing traffic flow efficiency and reducing congestion effectively. The results underscore the transformative potential of the DeepStreamNet model in urban traffic management and public safety, offering actionable insights for intelligent transportation systems and setting the stage for more informed urban planning. Looking forward, we suggest extending the application of our model to areas such as logistics and broader smart city initiatives, highlighting its adaptability and broad utility in various urban contexts

关键词： Adaptation models Analytical models Accuracy Smart cities Urban planning data models Real-time systems Internet of Things Optimization Context modeling

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Competitive Facility Location under Random Utilities and Routing Constraints

arXiv

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arXiv 2024年

作者： Pham, Hoang Giang Dam, Tien Thanh Duong, Ngan Ha Mai, Tien Hà, Minh Hoàng ORLab Faculty of Computer Science Phenikaa University Ha Dong Hanoi Viet Nam ORLab-SLSCM Faculty of Data Science and Artificial Intelligence National Economics University Hai Ba Trung Hanoi Viet Nam School of Computing and Information Systems Singapore Management University 80 Stamford Rd 178902 Singapore Department of Engineering Computer Science and Mathematics University of L'Aquila AQ L'Aquila67100 Italy

In this paper, we study a facility location problem within a competitive market context, where customer demand is predicted by a random utility choice model. Unlike prior research, which primarily focuses on simple constraints such as a cardinality constraint on the number of selected locations, we introduce routing constraints that necessitate the selection of locations in a manner that guarantees the existence of a tour visiting all chosen locations while adhering to a specified tour length upper bound. Such routing constraints find crucial applications in various real-world scenarios. The problem at hand features a non-linear objective function, resulting from the utilization of random utilities, together with complex routing constraints, making it computationally challenging. To tackle this problem, we explore three types of valid cuts, namely, outer-approximation and submodular cuts to handle the nonlinear objective function, as well as sub-tour elimination cuts to address the complex routing constraints. These lead to the development of two exact solution methods: a nested cutting plane and nested branch-and-cut algorithms, where these valid cuts are iteratively added to a master problem through two nested loops. We also prove that our nested cutting plane method always converges to optimality after a finite number of iterations. Furthermore, we develop a local search-based metaheuristic tailored for solving large-scale instances and show its pros and cons compared to exact methods. Extensive experiments are conducted on problem instances of varying sizes, demonstrating that our approach excels in terms of solution quality and computation time when compared to other baseline approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Location

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Dendritic Neural Regression Model Trained by Chicken Swarm Optimization Algorithm for Bank Customer Churn Prediction 30th

Dendritic Neural Regression Model Trained by Chicken Swarm O...

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30th International Conference on Neural Information Processing, ICONIP 2023

作者： Wang, Qi Zhang, Haiyan Ji, Junkai Tang, Cheng Tang, Yajiao College of Economics Central South University of Forestry and Technology Changsha410004 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka819-0395 Japan

ISBN: (纸本)9789819981830

Recently, banks are constantly facing the problem of customers churning. Customer churn not only leads to a decline in bank funds and profits but also reduces its credit capacity and affects the bank’s operational management. As an important component of Customer Relationship Management, predicting customer churn has been increasingly urgent. Inspired by biological neurons, we build up a dendritic neural regression model (DNRM) with four layers, namely the synaptic layer, the dendritic layer, the membrane layer, and the soma layer for bank customer churn prediction. To pursue better prediction performance in this experiment, the Chicken Swarm Optimization (CSO) algorithm is defined as the training algorithm of DNRM. With the ability to balance exploration and exploitation, CSO is implemented to optimize and improve the accuracy of the DNRM. In this paper, we propose a novel dendritic neural regression model called CSO-DNRM for churn prediction, and the experimental results are based on a benchmark dataset from Kaggle. Compared with other algorithms and models, our proposed model obtains the highest accuracy of 92.27% and convergence speed in customer churn prediction. Due to the novel bionic algorithms and the pruning function of the model, it is evident that our proposed model has advantages in accuracy and computational speed in the field of customer churn prediction and can be widely applied in commercial bank customer relationship management. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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