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Towards Explainability of Dimension Reduction Plots of Unsupervised Learning Model Outcomes 21

Towards Explainability of Dimension Reduction Plots of Unsup...

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21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Davila, Tony E. Astuhuaman Hier, Daniel B. Obafemi-Ajayi, Tayo Missouri State University Computer Science Dept. SpringfieldMO United States Missouri University of Science & Technology Kummer Institute RollaMO United States Missouri State University Engineering Program SpringfieldMO United States

ISBN: (纸本)9798350356632

Dimension reduction methods are used to visualize the output of unsupervised learning models when applied to complex data. These techniques improve interpretability by transforming a high-dimension space to a lower-dimension space (usually 2D or 3D). The results are typically viewed as 2D scatter plots, and class centroids may be added to increase interpretability. Although useful, the relationship of these class centroids to the underlying feature space remains opaque. The innovative aspect of this work is to create a strong link between the dimension-reduced space and the underlying high-dimension feature space by adding selected feature centroids to the 2D scatter plots. This approach simultaneously visualizes the centers for the classes and the features on the same 2D scatter plot. Since classes are often imbalanced, we provide a method to balance class sizes. We present an automated framework that performs a grid search to find the optimal dimension reduction parameters, balances the class sizes, uses an ensemble approach to find the most important features, and adds class centroids and selected feature centroids to 2D dimension-reduced plots. This is especially useful when applied to complex, feature-rich biomedical data, as addition of feature centroids to 2D scatter plots serve as landmarks for the previously featureless dimension-reduced space. The utility of this approach is demonstrated by its application to seven classes of neurogenetic diseases with 31 defining phenotypic features. © 2024 IEEE.

关键词： Unsupervised learning

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Demonstration of Effectiveness of Nativeness in Stepwise Learning by Performing Sentiment Analysis

Demonstration of Effectiveness of Nativeness in Stepwise Lea...

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Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Al-Mahmud Kazutaka Shimada Dept. of Computer Science and Systems Engineering Kyushu Institute of Technology Japan

Nowadays, more people and entities express opinions on online platforms. With this increasing trend of online opinion mass information, it is impossible to analyze them manually. For this reason, an automatic approach to opinion and sentiment analysis is essential and useful in the real world. Several approaches based on machine learning techniques in sentiment analysis have been proposed. However, machine learning essentially needs massive data to generate a robust model. On the other hand, collecting the training data is costly, especially in low-resource language. To solve this problem, we introduce stepwise learning. Stepwise learning is a technique that improves the model capacity for a target task (i.e., main task) by exploiting auxiliary information from source tasks. It is effective when the data in the target tasks are inadequate. The target task of this paper is sentiment analysis for reviews in the Bangla language. Our proposed method consists of two stages: source and target tasks. For the source task, we train a transformers-based model, such as BanglaBERT, by a large dataset translated into the Bangla language; the original dataset is written in English. For the target task, we retrain and evaluate the model with a relatively small target dataset. We further investigate the effect of opinion expression style of writers between source and target data in stepwise learning. We call it “nativeness.” The experimental result shows that nativeness has an important role in improving performance.

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Artificial Intelligence for Green Agriculture and Sustainable Resource Management: A Comprehensive Overview

Artificial Intelligence for Green Agriculture and Sustainabl...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Eman Alduweib Waseem Alromema Essa Hezzam Dept. of Computer Science University of Petra Amman Jordan Dept. of Computer Science and Information Applied College Taibah University Medina Saudi Arabia Dept. of Information Systems College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Artificial Intelligence (AI) is a revolutionary technology disrupting the process of finding solutions for various sustainability challenges. Specifically, AI is having a significant influence on many sectors, especially agriculture, energy, waste management, and biodiversity conservation. This paper offers a comprehensive overview of AI's applications in sustainable resource management, with a special focus on green agriculture as a case study, in which the application of AI greatly enhances the efficiency of resource use, crop yield, and environmental conservation efforts. This overview describes the potential application of AI to address performance concerns (ethical value, energy use, and data quality deficiency), and proposes the future research directions such as the explainable AI, circular economies, and the integration of AI with the Internet of Things (IoT) to guide researchers and policymakers in exploiting AI to enhance sustainability in key domains.

关键词： Waste management Ethics Explainable AI Data integrity Transforms Resource management Internet of Things Biodiversity Artificial intelligence Sustainable development

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An Implementation of Answer Code Validation program for Code Writing Problem in Java programming Learning Assistant System

An Implementation of Answer Code Validation Program for Code...

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11th International Conference on Information and Education Technology, ICIET 2023

作者： Wai, Khaing Hsu Funabiki, Nobuo Aung, Soe Thandar Mon, Khin Thet Kyaw, Htoo Htoo Sandi Kao, Wen-Chung Okayama University Dept. of Information and Communication Systems Okayama Japan Tokyo University of Agriculture and Technology Dept. of Computer and Information Science Tokyo Japan National Taiwan Normal University Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9781665465489

To assist Java programming learning of novice students, we have developed the web-based Java programming Learning Assistant System (JPLAS). It provides various exercise problems to cultivate code reading and code writing skills to support studies at various levels. The code writing problem (CWP) asks a student to write a source code to pass the given test code where the correctness is verified by running them on JUnit. Previously, we implemented the answer platform for students to solve CWP assignments with the automatic runs. However, the teacher needs to manually run them one by one to verify a lot of source codes from students. In this paper, we implement the answer code validation program that automatically verifies all the source codes for one test code and reports the number of tests that each code passed with the CSV file. For evaluations, we applied the program to a total of 999 source codes from 12-43 students for a total of 45 test codes that were assigned in the Java programming course in Okayama university, including for final examinations. The application results confirm the validity and effectiveness of the proposal. © 2023 IEEE.

关键词： Students

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Optimizing 3D Flight Paths for Multiple UAVs with Connectivity Management in Urban Delivery systems

Optimizing 3D Flight Paths for Multiple UAVs with Connectivi...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Yeongmin Lee Raihan Muhammad Syahran Mael Spangenberger Huigyu Yang Jiyong Oh Hyunseung Choo Dept. of Artificial Intelligence Dept. of Electrical and Computer Engineering Dept. of Informatics Télécom SudParis Évry France Convergence Research Institute Dept. of AI Systems Engineering Sungkyunkwan University Suwon S. Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

The dynamic deployment of aerial vehicles in urban delivery scenarios demands precise route planning, reliable data links, and efficient use of network infrastructure. Although prior efforts have explored various aspects of unmanned navigation or communication optimization, existing approaches often overlook the combined impact of three-dimensional path selection and robust connectivity maintenance. To address this gap, this paper proposes a deep reinforcement learning framework employing Independent Proximal Policy Optimization (IPPO) to steer the flight paths of unmanned vehicles while curtailing needless interactions with base stations (BSs). By incorporating communication-specific metrics, Reference Signal Received Power (RSRP), the proposed system adapts to urban conditions and upholds resilient network links. Comprehensive performance evaluations reveal the potential of the framework to reduce excessive BS connections by up to 82.24%, preserve RSRP levels above -80 dBm, and balance handover frequencies across flight trajectories. These findings affirm the scalability and effectiveness of the method for achieving efficient aerial navigation and consistent communication in urban delivery services.

关键词： Three-dimensional displays Navigation Scalability Surveillance Handover Autonomous aerial vehicles Trajectory Resource management Vehicle dynamics Optimization

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Fully Geometric Panoramic Localization

Fully Geometric Panoramic Localization

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Junho Kim Jiwon Jeong Young Min Kim Dept. of Electrical and Computer Engineering Seoul National University Dept. of Electrical Engineering Stanford University Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce a lightweight and accurate localization method that only utilizes the geometry of 2D-3D lines. Given a pre-captured 3D map, our approach localizes a panorama image, taking advantage of the holistic 360° view. The system mitigates potential privacy breaches or domain discrepancies by avoiding trained or hand-crafted visual descriptors. However, as lines alone can be ambiguous, we express distinctive yet compact spatial contexts from relationships between lines, namely the dominant directions of parallel lines and the intersection between non-parallel lines. The resulting representations are efficient in processing time and memory compared to conventional visual descriptor-based methods. Given the groups of dominant line directions and their intersections, we accelerate the search process to test thousands of pose candidates in less than a millisecond without sacrificing accuracy. We empirically show that the proposed 2D-3D matching can localize panoramas for challenging scenes with similar structures, dramatic domain shifts or illumination changes. Our fully geometric approach does not involve extensive parameter tuning or neural network training, making it a practical algorithm that can be readily deployed in the real world. Project page including the code is available through this link: https://***/fgpl/.

关键词： Location awareness Geometry Training Visualization Accuracy Three-dimensional displays Pipelines

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Joint Design of Spirograph Method and Reconfigurable Intelligent Surface for Future Communication systems

Joint Design of Spirograph Method and Reconfigurable Intelli...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yafis, Balqis Huang, Jane-Hwa Yu, Chih-Min Wang, Li-Chun National Yang Ming Chiao Tung University Eecs Int'l Graduate Program Hsinchu Taiwan National Chi Nan University Dept. of Electrical Engineering Nantou County Taiwan Chung Yuan Christian University Department of Information and Computer Engineering Taoyuan City Taiwan

ISBN: (纸本)9798350351255

Reconfigurable Intelligent Surface (RIS) is a transformative technology using the passive beamforming capabilities to create the programmable wireless environments. However, the control overhead is a challenging issue. The system needs to deliver the high-precision reflection coefficients to the RIS controller, for adjusting the phase shifts of the numerous RIS elements. Therefore, we propose a Spirograph-based discrete beamdirection strategy, which selects a set of non-uniformly distributed beam directions based on the generic patterns generated by the Spirograph (SG). We evaluate the achievable data rate and the number of controlling bits of SG-based method. We compare the SG-based method, the ideal continuous phase-shift method, and the SISO method. Simulation results show that with the properly-selected discrete beam directions, the SG-based method can achieve a comparable data rate as the ideal method, while having a lower control overhead. © 2024 IEEE.

关键词： Beamforming

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Valorization of Agricultural and Marine Waste for Fabrication of Bio-Adsorbent Sheets

Valorization of Agricultural and Marine Waste for Fabricatio...

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World Environmental and Water Resources Congress 2023: Adaptive Planning and Design in an Age of Risk and Uncertainty

作者： Elshabrawy, Salma O. Mansour, Mohammad Elhussieny, Amal Husien, Shimaa Abdel-Aziz, A.B. Fahim, Irene S. Said, Lobna A. Radwan, Ahmed G. Smart Engineering Systems Research Center Nile Univ Giza Egypt Dept. of Chemical Engineering Faculty of Engineering Alexandria Univ Alexandria Egypt Nanoelectronics Integrated Systems Center Nile Univ Giza Egypt School of Engineering and Applied Sciences Nile Univ Giza Egypt October High Institute for Engineering and Technology Giza Egypt Industrial Engineering Program Smart Engineering Systems Research Center Nile Univ Giza Egypt

ISBN: (数字)9780784484852

ISBN: (纸本)9780784484852

Industrial wastewater often contains considerable amounts of toxic pollutants that would endanger public health and the environment. In developing countries, these toxins are often discharged into natural ecosystems without pretreatment as it requires costly treatment processes, which causes long-term harmful socioeconomic impacts. Employing wastewater treatment plants using physical, biological, and chemical methods to clean the wastewater is considered by many nations the answer to the environmental crises. The treated water could be used for targeting the irrigation systems in its majority, as it is biologically acceptable for that specific use, which economizes the use of freshwater sources for municipal use specifically. This study presents a novel method for fabricating an efficient adsorbent sheet for wastewater treatment. The sheets are fabricated by combining sugarcane bagasse pulp as a scaffold with commercial, naturally activated carbon and bimetallic-prepared adsorbents. Fava beans and algae biomass are utilized in the production of activated carbon because of their high carbon contents, availability, and low cost. The prepared composite sheets are synthesized and investigated for several pollutants' removal such as methyl orange, crystal violet dyes, and chromium heavy metals. These pollutants are selected due to the high discharge amount and toxic effect on aquatic life. FT-IR and SEM analyses are used to characterize the samples. To determine the mechanism of adsorption, the intra-particle diffuse, pseudo-first-order, and pseudo-second-order kinetic models are used to test the experimental data. All the prepared sheets can retain the pollutants, with the best removal efficiency of 96.24% for methyl orange adsorption onto the bio-composite mixed sheet. For methyl orange, the error values and correlation coefficient R2of 0.971 and 0.951 shows that the Temkin isotherm and pseudo-first-order kinetic model, respectively, are capable of providing the highes

关键词： Adsorption

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BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework for IoMT Applications

BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework f...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Panchal, Barkha Bhatia, Jitendra Kumhar, Malaram Tanwar, Sudeep Dutta, Ashit Kumar Rodrigues, Joel J.P.C. Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Dept. of Comp. Science and Information Systems College of Applied Sciences AlMaarefa University Saudi Arabia Amazonas State University AM Manaus Brazil

ISBN: (纸本)9798350350548

The Internet of Medical Things (IoMT) revolutionizes healthcare by integrating medical devices and systems with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant security and privacy concerns. Traditional security measures often fall short in identifying the malicious data attacks within the IoMT ecosystems. This paper introduces an AI-based non-malicious data classification scheme based on blockchain. We applied and evaluated the machine learning (ML) classifiers, such as support vector machine (SVM), random forest (RF), and K-Nearest neighbor (KNN). We evaluated the proposed framework based on various performance metrics that includes accuracy, precision, recall, and F1 score. The accuracy using SVM obtained 75.3%, RF is 75.9%, and KNN is 84.3%. The results shows that KNN performs better than other models by the factor of 9%. © 2024 IEEE.

关键词： Adversarial machine learning

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Sensing Together: Cooperative Task Adaptation and Scheduling for IoT-Nets Using Renewable Energy

Sensing Together: Cooperative Task Adaptation and Scheduling...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor systems (MASS)

作者： Elizabeth Liri K. K. Ramakrishnan Koushik Kar Flavio Esposito Dept. of Computer Science and Engineering University of California Riverside Riverside CA USA Computer Science Dept. Saint Louis University St. Louis MO USA Electrical Computer and Systems Engineering Dept. Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

IoT devices used in various applications, such as monitoring agricultural soil moisture, or urban air quality assessment, are typically battery-operated and energy-constrained. We develop a lightweight and distributed cooperative sensing scheme that provides energy-efficient sensing of an area by reducing spatio-temporal overlaps in the coverage using a multi-sensor IoT network. Our “Sensing Together” solution includes two algorithms: Distributed Task Adaptation (DTA) and Distributed Block Scheduler (DBS), which coordinate the sensing operations of the IoT network through information shared using a distributed “token passing” protocol. DTA adapts the sensing rates from their “raw” values (optimized for each IoT device independently) to minimize spatial redundancy in coverage, while ensuring that a desired coverage threshold is met at all points in the covered area. DBS then schedules task execution times across all IoT devices in a distributed manner to minimize temporal overlap. On-device evaluation shows a small token size and execution times of less than 0.6s on average while simulations show average energy savings of 5% per IoT device under various weather conditions. Moreover, when devices had more significant coverage overlaps, energy savings exceeded 30% thanks to cooperative sensing. In simulations of larger networks, energy savings range on average between 3.34% and 38.53%, depending on weather conditions. Our solutions consistently demonstrate near-optimal performance under various scenarios, showcasing their capability to efficiently reduce temporal overlap during sensing task scheduling.

关键词： Performance evaluation Schedules Renewable energy sources Simulation Redundancy Soil moisture Smart systems Sensors Internet of Things Meteorology

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