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ACO-GA-Based Optimization to Enhance Global Path Planning for Autonomous Navigation in Grid Environments

IEEE Transactions on Evolutionary Computation 2025年

作者： Heng, Han Rahiman, Wan Universiti Sains Malaysia Engineering Campus School of Electrical and Electronic Engineering Penang14300 Malaysia Universiti Sains Malaysia Engineering Campus Nibong Tebal Penang14300 Malaysia Daffodil Robotics Laboratory Department of Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh

Exploring path planning techniques for autonomous vehicles to find the safest and quickest routes is fascinating. Among these techniques, generating grid-based maps is notable for simplifying the path planning process, especially in complex environments. This study introduces a novel optimization approach, the modified ant colony optimization and genetic algorithm (MACOGA), which is tailored for path planning in grid environments. MACOGA combines ant colony optimization (ACO) with genetic algorithm (GA) to efficiently navigate grid spaces. First, ACO identifies potential routes within a grid map, and GA optimizes them to find the optimal route. A key feature of MACOGA is its probabilistic prediction mechanism, which improves node selection by integrating the heuristic factors and probabilistic calculations of feasible potential paths, which significantly increases the likelihood of generating feasible initial paths and attains higher success rates in various environmental tests. The enhanced GA in the MACOGA incorporates ACO's pheromones and heuristic factors, and introduces adaptive probability crossover and mutation operations. Additionally, a path reconnection operation ensures favorable length and smoothness. To evaluate MACOGA's effectiveness, six planar models of varying sizes and complexities were studied. Results show MACOGA's superiority in path length and execution time, achieving the shortest path and planning time in several environments. The shortest path can be optimized to yield an improvement of up to 6%. Regarding runtime, even in the complex scenarios, it executes in just 0.3 seconds, outperforming other comparative algorithms. Furthermore, the success rate can reach 100% in relatively complex environments. © 2025 IEEE.

关键词： Ant colony optimization

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A Mamdani Fuzzy Inference System for Comprehensive Diabetes Risk Evaluation

A Mamdani Fuzzy Inference System for Comprehensive Diabetes ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Tanjina Ahmed Tuly Tanjid Ahammed Shafin Md. Zahid Hasan Md. Hasan Imam Bijoy Mohammad Shamsul Arefin Mohammad Shorif Uddin Department of Computer Science and Engineering Health Informatics Research Lab Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh Department of Computer Science and Engineering Jahangirnagar University Savar Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Diabetes mellitus is a chronic condition characterized by insufficient insulin production and the inability to effectively utilize the insulin hormone, leading to elevated blood glucose levels. As diabetes is incurable and persists throughout a patient's lifetime, effective management requires early identification of risk factors to ensure continuous and feasible treatment. This study proposes a unique decision support system based on the Mamdani Fuzzy Inference System (MFIS) to predict diabetes risk. This study utilizes a publicly available diabetes dataset containing 442 data that includes nine key health metrics such as BMI, blood glucose, cholesterol levels, and blood pressure to predict the risk of diabetes. The dataset aims to support early diagnosis and better diabetes management by providing insights into individual risk factors. This resource contributes to the development of more accurate decision-support systems in healthcare. We identify key variables and establish a membership function to assess individual risk. The Fuzzy Inference System (FIS) is well-suited for handling complex and uncertain conditions, making it an effective tool for this application. The advantages of the MFIS include its ability to incorporate expert knowledge through linguistic variables and fuzzy rules. This system demonstrates the practical benefits of MFIS in developing intelligent decision-making tools, particularly in the domain of diabetes risk prediction.

关键词： Fuzzy logic Decision support systems Accuracy Uncertainty Predictive models Blood pressure Diabetes Glucose Insulin Blood

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Predicting Heat Stroke Risk: A Clinical Decision Support System using Fuzzy Association Mining Approach

Predicting Heat Stroke Risk: A Clinical Decision Support Sys...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Abdullah Al Noman Rinvi Jaman Riti Md. Zahid Hasan Md. Hasan Imam Bijoy Mohammad Shorif Uddin Mohammad Shamsul Arefin Department of Computer Science and Engineering Health Informatics Research Lab Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Savar Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Heat stroke is a serious medical condition that requires immediate treatment and is exacerbated by intense heat and climate change. This paper introduces a Clinical Decision Support System (CDSS) for heat stroke risk assessment based on Fuzzy Association Rule Mining. The system evaluates key attributes extracted from the Stanford Bioengineering Senior Capstone Project dataset, including daily water intake, cardiovascular history, heat index, environmental and rectal temperatures, blood pressure, pulse rate, humidity, age, and sex. These attributes help identify detailed patterns associated with heat stroke risk. Using fuzzy logic, the CDSS addresses the inherent vagueness of medical information through a set of "if- then" rules designed for healthcare practitioners. The system is tested with historical data, demonstrating its effectiveness in recognizing critical parameters to provide personalized, timely attention to potential heat stroke cases. It aims to reduce heat- related illnesses and fatalities by enabling rapid data-driven decision-making in healthcare. Despite certain limitations, this study highlights the necessity of intelligent systems for proactive health management.

关键词： Decision support systems Climate change Fuzzy systems Risk management Predictive models Medical services Medical conditions

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A Hybrid EEG-EMG Framework for Humanoid Control using Deep Learning Transformers

A Hybrid EEG-EMG Framework for Humanoid Control using Deep L...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Olikkal, Parthan Pillai, Branesh M. Suthakorn, Jackrit Ali, Habib Vinjamuri, Ramana University of Maryland Baltimore County Vinjamuri lab Department of Computer Science and Electrical Engineering BaltimoreMD21220 United States Mahidol University Center for Biomedical and Robotics Technology Nakorm Pathom Salaya Thailand

ISBN: (纸本)9781665481090

Brain-computer interfaces (BCIs) offer promising solutions for upper limb rehabilitation. Despite advancements in deep learning, traditional models for motor rehabilitation using electroencephalography (EEG) or electromyography (EMG) to control assistive devices require enhancement. This study aims to enhance motor control capabilities by integrating EEG and EMG signals using a Transformer-based deep learning model. Ten able-bodies subjects performed center-out tasks on a low-cost upper limb rehabilitation table, capturing 2D kinematic data, EEG, and EMG signals simultaneously. The tasks varied in complexity across four levels. Preprocessed EEG and EMG signals were fused and given as input to the proposed model, which was evaluated using three performance metrics. Results showed that the EEG-EMG combined model achieved 87.27% accuracy across all the four levels. Furthermore, the model's output successfully controlled a humanoid robot to replicate similar movements. These findings highlight the efficacy of combined EEG-EMG data in improving accuracy and performance in BCI applications, advancing assistive technologies and neurorehabilitation interventions. © 2024 IEEE.

关键词： Anthropomorphic robots

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Progress Estimation of an Excavation Pit 39

Progress Estimation of an Excavation Pit

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39th International Symposium on Automation and robotics in Construction, ISARC 2022

作者： Vierling, A. Groll, T. Meckel, D. Heim, K. Walter, D. Scheidhauer, L. Körkemeyer, K. Berns, K. Department of Computer Science Robotics Research Lab University of Kaiserslautern Germany Department of Civil Engineering Construction Management University of Kaiserslautern Germany

ISBN: (纸本)9789526952420

This paper presents a method for automated excavation speed and progress estimation. First, a measure for the progress speed of an excavation pit is taken from the literature and evaluated regarding the possibility for automation. For each possible parameter, an automated extraction algorithm is presented. The used system is an autonomous excavator arm of a backhoe loader where the used hardware and software system is described. Experimental evaluation of the presented approach has been done with the autonomous system for a small trench, including multiple digging cycles. The resulting measurements seem to include some systematic errors which could be identified and suitable sanity checks could be implemented, removing the erroneous measurements. The remaining measurements were used to determine the excavation speed of the autonomous excavator arm and compared to the values of experienced and amateur operators. © 2022 International Association on Automation and robotics in Construction.

关键词： Excavation

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Maize EfficientNet Fusion: Advancing Maize Disease Detection with MF-NET 25

Maize EfficientNet Fusion: Advancing Maize Disease Detection...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Khalid, Fatima Hanif, Muhammad Ul Ain, Qurat Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Faculty of Computer Science and Engineering KPK Topi Pakistan Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Aerial Robotics and Vision Lab KPK Topi Pakistan Shifa Tameer-E-Millat University Department of Computing Islamabad Pakistan

ISBN: (纸本)9798350379037

Maize is a vital global crop, essential for food security but highly susceptible to diseases that threaten yield and quality. Traditional methods for detecting these diseases are computationally intensive and rely on high-quality images, limiting their practical use in diverse field conditions. This research addresses these challenges by proposing MF-Net, a novel model that leverages two EfficientNet-b0 architectures with ReLU and Swish activation functions. Our approach involves partially training Model R with frozen early layers using ReLU, and fully training Model S with Swish activation for enhanced optimization and robustness. We employ model concatenation and feature fusion techniques to create a lightweight yet powerful model, complemented by additional layers for improved feature extraction and regularization. Experimental results on the Corn Disease and Severity (CD&S) dataset are compelling: MF-Net achieves a remarkable 95.7% accuracy and 96% precision in disease classification, and 88.3% accuracy and 88.46% precision in severity level detection. These results highlight the model's effectiveness under challenging conditions such as cluttered backgrounds and variable lighting, significantly reducing computation time without compromising accuracy. Hence, MF-Net presents an innovative and efficient solution for maize disease detection and severity assessment, offering significant advancements for agricultural practices and crop management. © 2024 IEEE.

关键词： CD&S Dataset EffiecientNet-b0 Fused Truncated EfficientNet Maize Disease Maize Severity Level

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PAKE - PoS Tagger Augmented Keyword Extraction

PAKE - PoS Tagger Augmented Keyword Extraction

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Kohinoor, Md Saidur Rahman Miah, Ayesha Loylus Ali, Shah Fayez Hossain, Md Sabir Bijoy, Md. Hasan Imam Sakib, Shadman King Fahd U. of Petroleum and Minerals Information and Computer Science Dhahran31261 Saudi Arabia InteX Research Lab Ai and Nlp Discipline Sylhet3100 Bangladesh Daffodil International University Computer Science and Engineering Dhaka1216 Bangladesh University of Maryland Baltimore County Department of Information Systems MD21250 United States

ISBN: (纸本)9798331529765

The state-of-the-art techniques for automatic keyword extraction majorly deal with the collection of long documents. However, for several reasons, these do not provide satisfactory results for shorter lengths of documents. Moreover, with the ever-increasing amounts of information available, a keyword extraction system that automatically deals with varying lengths of text can lessen the workload and make the entire process of manually assigning the keywords less time-consuming. For this, the widely used Natural Language Processing (NLP) techniques are examined in the context of extensive data. Therefore, this research introduces PAKE - PoStagger Augmented Keyword Extraction system as a practical amalgamation of statistical and textual-based features based on an unsupervised key phrase extracting algorithm to stand out as a suitable alternative to the existing solutions. The effectiveness is demonstrated by comparing it with six state-of-the-art unsupervised methods, and the results are illustrated using four datasets. © 2024 IEEE.

关键词： Natural language processing systems

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Preference detection of the humanoid robot face based on EEG and eye movement

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Neural Computing and Applications 2024年第19期36卷 11603-11621页

作者： Wang, Pengchao Mu, Wei Zhan, Gege Wang, Aiping Song, Zuoting Fang, Tao Zhang, Xueze Wang, Junkongshuai Niu, Lan Bin, Jianxiong Zhang, Lihua Jia, Jie Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI and Robotics Ministry of Education Shanghai Engineering Research Center of AI and Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

The face of a humanoid robot can affect the user experience, and the detection of face preference is particularly important. Preference detection belongs to a branch of emotion recognition that has received much attention from researchers. Most of the previous preference detection studies have been conducted based on a single modality. In this paper, we detect face preferences of humanoid robots based on electroencephalogram (EEG) signals and eye movement signals for single modality, canonical correlation analysis fusion modality, and bimodal deep autoencoder (BDAE) fusion modality, respectively. We validated the theory of frontal asymmetry by analyzing the preference patterns of EEG and found that participants had higher alpha wave energy for preference faces. In addition, hidden preferences extracted by EEG signals were better classified than preferences from participants' subjective feedback, and also, the classification performance of eye movement data was improved. Finally, experimental results showed that BDAE multimodal fusion using frontal alpha and beta power spectral densities and eye movement information as features performed best, with the highest average accuracy of 83.13% for the SVM and 71.09% for the KNN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electroencephalography

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A comparative study of machine learning models with LASSO and SHAP feature selection for breast cancer prediction

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Healthcare Analytics 2024年 6卷 100353-100353页

作者： Shaon, Md. Shazzad Hossain Karim, Tasmin Shakil, Md. Shahriar Hasan, Md. Zahid Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Dhaka Birulia 1216 Bangladesh

In recent decades, breast cancer has become the most prevalent type of cancer that impacts women in the world, which shows a significant risk to the death rates of women. Early identification of breast cancer might drastically decrease patient mortality and greatly improve the chance of an effective treatment. In modern times, machine learning models have become crucial for classifying cancer and strengthening both the accuracy and efficiency of diagnostic and medical treatment strategies. Therefore, this study is focused on early detection of breast cancer using a variety of machine learning algorithms and desires to identify the most effective feature selection process with an amalgamated dataset. Initially, we evaluated five traditional models and two meta-models on separate datasets. To find the most valuable features, the study used the Least Absolute Shrinkage and Selection Operator (LASSO) as well as SHapley Additive exPlanations (SHAP) selection methods and analyzed them through a wide range of performance regulations. Additionally, we applied these models to the combined dataset and observed that the mergeddataset was significantly beneficial for breast cancer diagnosis. After analyzing the feature selection strategies, it was demonstrated that the majority of models performed more accurately when utilizing SHAP methodologies. Notably, three traditional models and two meta-classifiers obtained an accuracy of 99.82%, demonstrating superior performance compared to state-of-the-art methods. This advancement holds a crucial role as it lays the foundation for refining diagnostic tools and enhancing the progression of medical science in this field. © 2024

关键词： Breast cancer Diagnostic tools Feature selection Machine learning Meta-models SHapley additive exPlanations (SHAP)

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EfficientNet-SAM: A Novel EffecientNet with Spatial Attention Mechanism for COVID-19 Detection in Pulmonary CT Scans

EfficientNet-SAM: A Novel EffecientNet with Spatial Attentio...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Ramy Farag Parth Upadhay Jacket Demby’s Yixiang Gao Katherin Garces Montoya Seyed Mohamad Ali Tousi Gbenga Omotara Guilherme DeSouza Department of Electrical Engineering and Computer Science Vision-Guided and Intelligent Robotics Lab - ViGIR Lab University of Missouri-Columbia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Manual analysis and diagnosis of COVID-19 through the examination of Computed Tomography (CT) images of the lungs can be time-consuming and result in errors, especially given high volume of patients and numerous images per patient. So, we address the need for automation of this task by developing a new deep learning-based pipeline. Our motivation was sparked by the CVPR Workshop on "Domain Adaptation, Explainability and Fairness in AI for Medical Image Analysis", more specifically, the "COVID-19 Diagnosis Competition (DEF-AI-MIA COV19D)" under the same Workshop. This challenge provides an opportunity to assess our proposed pipeline for COVID-19 detection from CT scan images. The same pipeline incorporates one of the architectures in the EfficientNet "family", but with an added Spatial Attention Mechanism: EfficientNet-SAM. Also, unlike the traditional/past pipelines, which relied on a preprocessing step, our pipeline takes the raw selected input images without any such step, except for an image-selection step to simply reduce the number of CT images required for training and/or testing. Moreover, our pipeline is computationally efficient, as, for example, it does not incorporate a decoder for segmenting the lungs. It also does not combine different models nor combine RNN with a backbone, as other pipelines in the past did. Nevertheless, our pipeline outperformed all approaches presented by other teams in last year’s instance of the same challenge using the validation subset. It also placed 5th in this year’s competition, ranking less than 1.3% below the 1st place and close to 3.5% above the 6th place based on the macro-F1 score.

关键词： COVID-19 Training Visualization Attention mechanisms Computed tomography Conferences Pipelines

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