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Ensemble-Based Explainable Approach for Rare Medicinal Plant Recognition and Conservation

Ensemble-Based Explainable Approach for Rare Medicinal Plant...

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information and Network Technologies (ICINT), International Conference on

作者： Shakil Khan Hamdadur Rahman Md. Ismail Hossain Siddiqui Zishad Hossain Limon Mahbub Alam Khan Rezaul Haque Md. Redwan Ahmed Ahmed Wasif Reza Shamim Hasnat Ripon Dept. of Management of Information System International American University CA USA Dept. of Engineering/Industrial Management Westcliff University Irvine CA USA Dept. of Business Analytics International American University CA USA Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Management Information System Pacific State University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798331536756

ISBN: (纸本)9798331536763

Accurate classification of medicinal plants, particularly those that are threatened, rare, or endangered, is crucial for biodiversity conservation and sustainable use. This study introduces a robust ensemble-based classification model that integrates various architectures, including EfficientNet-B7, ConvNeXt, NasNet, and XceptionNet. Data augmentation with normalization enabled a training process using 4,500 high-resolution leaf images from a balanced dataset while helping achieve improved performance metrics. Transparent insight into model decisions became possible through our integration of Explainable AI (XAI) techniques which provided interpretable information about how the model reached its output. This transparency enables researchers and conservationists to better understand the system’s outputs. The ensemble model achieved an impressive accuracy of 99.29%, surpassing the performance of individual models and demonstrating strong generalization capabilities. We also built a real-time web application that delivers useful biodiversity monitoring solutions and helps assess species for pharmaceutical research and conservation initiatives. The study demonstrates AI-driven potentials through its analysis but it reveals two main challenges including dataset scope boundaries and high computational costs of modeling. Developments in future research will concentrate on enhancing database size along with computational strategies to enable mobile applications as field identification tools. This research has developed a flexible and dependable system which unites artificial intelligence investigation with practical environmental protection necessities.

关键词： Medicinal plants Accuracy Biological system modeling Computational modeling Real-time systems Mobile applications Biodiversity Environmental management Monitoring Pharmaceuticals

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Secure and Privacy-Preserving IoT-Based Arrhythmia Detection with Federated Learning 15

Secure and Privacy-Preserving IoT-Based Arrhythmia Detection...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Goranthala, Vishnu Prasad Sankar, K. Siva Malar, C. Brintha Noorul Islam Centre for Higher Education Kumaracoil Thuckalay Dept. of Computer Science & Engineering TamilNadu Thuckalay Kanyakumari District629180 India Noorul Islam Centre for Higher Education Kumaracoil Department of Information Technology TamilNadu Thuckalay Kanyakumari District629180 India

ISBN: (纸本)9798350370249

Arrhythmias pose a significant challenge for early diagnosis and treatment, necessitating advanced detection techniques. This paper introduces the Secure and PrivacyPreserving IoT-Based Arrhythmia Detection (SPIoT-AD) model, a federated learning (FL) based approach designed to enhance arrhythmia detection from ECG signals within IoT healthcare frameworks. Utilizing a secure multi-layered architecture that processes data at the edge, the methodology ensures data privacy and real-time monitoring, adapting to individual patient profiles. The model showcases exceptional performance metrics, demonstrating an accuracy of 99. 85 %, sensitivity of 98. 84 %, specificity of 99. 93 % and with a precision of 98.63% in the classification of multiple arrhythmia cases. Further, it also demonstrates a highest classification accuracy of 99.97% in the detection of normal cases. These results demonstrate the model's potential as a reliable tool for arrhythmia detection and classification, setting a foundation for future integration into clinical workflows, ensuring security and privacy of patient data. © 2024 IEEE.

关键词： Differential privacy

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Seeing Without Alarming Thief: Passive WiFi Sensing for Indoor Security Monitoring

Seeing Without Alarming Thief: Passive WiFi Sensing for Indo...

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MILCOM, Military Communications Conference

作者： Shu-Ying Chang Hung-Wen Liang Van-Linh Nguyen Po-Ching Lin Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan

WiFi chipset is critical to every smartphone, tablet, laptop, and smart home device. So far, authentication and encryption matters such as WPA2/WPA3 attacks have gotten much attention from the security research community. However, due to the ubiquity of WiFi in our life, the surveillance concerns in WiFi, such as tracking, are substantial. This paper explains how thieves might utilize passive WiFi sensing to conduct surveillance and determine whether the owner of a house is at home (in order to get into the house and take goods). Unlike previous research, we conduct this analysis using the latest commercial WiFi generation, IEEE 802.11ax. The assessment findings demonstrate that our system can reliably detect when a person enters or exits a room, even when the signal receiver is at a sibling room or non-line-of-sight location. This capability indicates that WiFi surveillance/privacy issues are genuine and significant. Additionally, this is beneficial for monitoring activity and supplementing a camera-based system in inaccessible regions.

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Channel-Aware Domain-Adaptive Generative Adversarial Network for Robust Speech Recognition

Channel-Aware Domain-Adaptive Generative Adversarial Network...

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

作者： Chien-Chun Wang Li-Wei Chen Cheng-Kang Chou Hung-Shin Lee Berlin Chen Hsin-Min Wang Dept. Computer Science and Information Engineering National Taiwan Normal University Taiwan United-Link Co. Ltd Taiwan Institute of Computer Science Academia Sinica Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

While pre-trained automatic speech recognition (ASR) systems demonstrate impressive performance on matched domains, their performance often degrades when confronted with channel mismatch stemming from unseen recording environments and conditions. To mitigate this issue, we propose a novel channel-aware data simulation method for robust ASR training. Our method harnesses the synergistic power of channel-extractive techniques and generative adversarial networks (GANs). We first train a channel encoder capable of extracting embeddings from arbitrary audio. On top of this, channel embeddings are extracted using a minimal amount of target-domain data and used to guide a GAN-based speech synthesizer. This synthesizer generates speech that faithfully preserves the phonetic content of the input while mimicking the channel characteristics of the target domain. We evaluate our method on the challenging Hakka Across Taiwan (HAT) and Taiwanese Across Taiwan (TAT) corpora, achieving relative character error rate (CER) reductions of 20.02% and 9.64%, respectively, compared to the baselines. These results highlight the efficacy of our channel-aware data simulation method for bridging the gap between source- and target-domain acoustics.

关键词： Training Error analysis Synthesizers Phonetics Generative adversarial networks Acoustics Recording Data mining Speech processing Automatic speech recognition

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Unsupervised Training Framework for 3D Point Cloud Object Detection Model 25

Unsupervised Training Framework for 3D Point Cloud Object De...

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40th Annual ACM Symposium on Applied Computing, SAC 2025

作者： Wu, Min-Jyun Lin, Hsiang-Jui Hung, Shih-Hao Shih, Chi-Sheng Dept. of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Center National Taiwan University Taipei Taiwan Institute of Networking and Multimedia High Performance and Scientific Computing Center Center of Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400706295

Point cloud data frames are critical, if not indispensable, for precise robot navigation and localization, but training the object detection models for them remains challenging. Many models require labeled 3D objects to train the model. However, the sparse and occluded 3D point cloud data make it difficult, if not impossible, to automate the labeling process. This work proposes a training framework to generate 3D labels on point clouds to tackle the aforementioned challenges. The proposed method takes advantage of the consecutive presence of the same object on different frames to automate the labeling process. The experimental results show that the unsupervised framework trains a robust model for 3D object detection. On the roadside data, the model archives 90.27% AP for scooters and 91.33% AP for cars. On nuScenes dataset, the framework demonstrates the detection precision doubles on IoU 0.25 and IoU 0.5 when the recalls remain similar, compared to the model trained by the MODEST framework. Copyright © 2025 held by the owner/author(s).

关键词： Object detection

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Energy Consumption for Cognitive Radio Network Enabled Multi-Access Edge Computing 3

Energy Consumption for Cognitive Radio Network Enabled Multi...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Elnaim, Areeg Ali Babeker, Amin Barakat, Mohammed Gaid, Abdulguddoos S.A. Saeed, Mamoon M. Ahmed, Zainab. E. Saeed, Rashid A. College of Engineering and Communication Alneelain University Khartoum Sudan School of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan Taiz University Dept. of Communication & Computer Engineering Taiz Yemen Faculty of Engineering Department of Communications and Electronics Engineering Yemen Gezira University Department of Electrical and Computer Engineering Khartoum Sudan College of Computers and Information Technology Taif University Department of Computer Engineering Taif21944 Saudi Arabia

ISBN: (纸本)9798350305333

This article discusses optimizing energy Consumption in CRN-enabled Multi-access Edge Computing (MEC) systems, presenting a novel approach to optimize the energy Consumption of a cognitive radio network-enabled MEC system using iterative optimization techniques. The proposed model aims to minimize the system's energy consumption while maintaining its performance and quality of service requirements. The iterative optimization algorithm is used to find the optimal allocation of resources, such as computing, communication, and power, among different nodes in the MEC system. As a result, the cognitive radio network enables efficient spectrum utilization by dynamically allocating available spectrum to different users based on their requirements. The performance evaluation approach is evaluated through simulations, and the results show that it can significantly improve the energy consumption of the MEC system while maintaining its performance and QoS requirements. This article contributes to developing sustainable and energy-efficient communication systems for future wireless networks. © 2023 IEEE.

关键词： Cognitive radio

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Utilizing Logistic Map to Enhance the Population Diversity of PSO

Utilizing Logistic Map to Enhance the Population Diversity o...

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2022 Malaysia-Japan Workshop on Radio Technology, MJWRT 2022

作者： Ajibade, S.M. Chweya, R. Ogunbolu, M.O. Fadipe, S.R. Dept. of Computer Engineering Istanbul Ticaret Universitesi Istanbul Turkey School of Information Science and Technology Kisii University Kisii Kenya Dept. of Computer Science Lead City University Oyo State Ibadan Nigeria Lagos State University Ojo Lagos Nigeria

Particle swarm optimization (PSO) is a high-quality, nature-inspired global optimization algorithm that can be applied to a variety of real-world optimization problems. PSO, on the other hand, has some flaws, such as slow convergence, premature convergence, and the ability to become stuck at local optimum solutions. This research aims to address the issue of population diversity in the PSO search process, which leads to premature convergence. As a result, in this study, a method is introduced to PSO in order to avoid early stagnation, which leads to premature convergence. A chaotic dynamic weight particle swarm optimization (CTPSOA) is proposed, in which a chaotic logistic map is delivered to increase the population range within the PSO search technique by editing the inertia weight of PSO to avoid premature convergence. This study also looks into the overall performance and viability of the proposed CTPSOA as a set of function selection rules for solving optimization issues. There are eight traditional benchmark functions that are used to assess the overall results and obtain the accuracy of the proposed (CTPSOA) algorithms when compared to a few other meta-heuristics optimization rules. The test results reveal that the CTPSOA outperforms other meta-heuristics algorithms in solving optimization problems by 85% and has established itself as a reliable and superior metaheuristics algorithm for feature selection. © Published under licence by IOP Publishing Ltd.

关键词： Particle swarm optimization (PSO)

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Stop Reinventing the Wheel! Promoting Community Software in Computing Education 22

Stop Reinventing the Wheel! Promoting Community Software in ...

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27th Annual Conference on Innovation and Technology in computer science Education, ITiCSE-WGR 2022

作者： Blanchard, Jeremiah Hott, John R. Berry, Vincent Carroll, Rebecca Edmison, Bob Glassey, Richard Karnalim, Oscar Plancher, Brian Russell, Seán Dept. of Engineering Education University of Florida GainesvilleFL United States Dept. of Computer Science University of Virginia CharlottesvilleVA United States Polytech Engineering School LIRMM-Univ Montpellier CNRS Montpellier France Dept. of Computer Science Full Sail University Winter ParkFL United States Dept. of Computer Science Virginia Tech BlacksburgVA United States School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden School of Information and Physical Sciences University of Newcastle CallaghanNSW Australia Dept. of Computer Science Barnard College Columbia University New York CityNY United States School of Computer Science University College Dublin Dublin Ireland

ISBN: (纸本)9798400700101

Historically, computing instructors and researchers have developed a wide variety of tools to support teaching and educational research, including exam and code testing suites and data collection solutions. However, these tools often find limited adoption beyond their creators. As a result, it is common for many of the same functionalities to be re-implemented by different instructional groups within the Computing Education community. We hypothesise that this is due in part to discoverability, availability, and adaptability challenges. Further, instructors often face institutional barriers to deployment, which can include hesitance of institutions to rely on community developed solutions that often lack a centralised authority and may be community or individually maintained. To this end, our working group explored what solutions are currently available, what instructors needed, and the reasons behind the above-mentioned phenomenon. To do so, we reviewed existing literature and surveyed the community to identify the tools that have been developed by the community;the solutions that are currently available and in use by instructors;what features are needed moving forward for classroom and research use;what support for extensions is needed to support further Computing Education research;and what institutional challenges instructors and researchers are currently facing or have faced in using community software solutions. Finally, the working group identified factors that limited adoption of solutions. This work proposes ways to integrate and improve the availability, discoverability, and dissemination of existing community projects, as well as ways to manage and overcome institutional challenges. © 2022 Owner/Author.

关键词： Open source software

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Blockchain Anchored Federated Learning and Tokenized Traceability for Sustainable Food Supply Chains 4

Blockchain Anchored Federated Learning and Tokenized Traceab...

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4th International Conference on Ubiquitous Computing and Intelligent information Systems, ICUIS 2024

作者： Chaganti, Koushik Reddy Kumar, B. Naveen Gutta, Pruthvi Krishna Elicherla, Sivananda Lahari Reddy Nagesh, C. Raghavendar, K. University of the Cumberlands Dept. of Computer and Information Systems KY United States Vignan Institute of Technology and Science Department of Information Technology Hyderabad India Tesla Dept of It AustinTX United States Dayananda Sagar University Dept of Cse Bangalore India Srinivasa Ramanujan Institute of Technology Dept of Cse Anantapur India Teegala Krishna Reddy Engineering College Dept of Cse Hyderabad India

ISBN: (纸本)9798331529635

The management of food supply chains relates to just about the most imperative issues concerning sustainability, quality, and traceability, where existing methodologies usually fail to meet the demands caused by data privacy, lack of transparency, and inefficiencies concerning collaboration among stakeholders. This compromises data security, and thus there arises single points of failure. Conversely, traditional traceability frameworks fail to incite wide-scale participation and lack real-time accuracy. Having these limitations in mind, we introduce a hybrid framework consisting of three advanced methodologies: Decentralized Federated Learning with Blockchain Anchoring (DFLBA), Dynamic Tokenized Traceability Framework (DTTF), and Adaptive Supply Chain Graph Neural Networks (ASCGNN) process. DFLBA would support privacy-preserving machine learning as such that collaborative training of yield prediction and crop monitoring models by stakeholders without revealing raw data will be feasible. Blockchain anchoring ensures logs of training updates are tamper-proof, thus increasing trust and transparency. DTTF introduces tokenized incentives that enhance traceability in real-time: every product has a digital twin represented by blockchain-anchored tokens updated by stakeholders. Smart contracts enforce the compliance that will improve participation and lifecycle data accuracy. ASCGNN models the food supply chain as a dynamic graph, leveraging graph neural networks to capture stakeholder interdependencies, optimize operations, and detect fraud. Blockchain integration ensures reliability and transparency by securing edge weights. The suggested framework has demonstrated good impact, like 95% model accuracy for yield prediction, 99% traceability coverage, and 92% prediction accuracy for bottlenecks of the supply chain. It provides a scalable, privacy-preserving, and transparent solution that enhances the efficiency of food supply chains, and positively impacts stakeholders in a

关键词： Federated learning

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Leveraging Community Software in CS Education to Avoid Reinventing the Wheel 22

Leveraging Community Software in CS Education to Avoid Reinv...

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27th ACM Conference on Innovation and Technology in computer science Education, ITiCSE 2022

作者： Blanchard, Jeremiah Hott, John R. Berry, Vincent Carroll, Rebecca Edmison, Bob Glassey, Richard Karnalim, Oscar Plancher, Brian Russell, Seán University of Florida Dept. of Engineering Education GainesvilleFL United States University of Virginia Dept. of Computer Science CharlottesvilleVA United States LIRMM - Univ Montpellier CNRS Polytech Engineering School Montpellier France Full Sail University Dept. of Computer Science Winter ParkFL United States Virginia Tech Dept. of Computer Science BlacksburgVA United States KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden University of Newcastle School of Information and Physical Sciences CallaghanNSW Australia Barnard College Columbia University Dept. of Computer Science New York CityNY United States University College Dublin School of Computer Science Dublin Ireland

ISBN: (纸本)9781450392006

Historically, computing instructors and researchers have developed a wide variety of tools to support teaching and educational research, including exam and code testing suites and data collection solutions. Many are then community or individually maintained. However, these tools often find limited adoption beyond their creators. As a result, it is common for many of the same functionalities to be re-implemented by different instructional groups within the CS Education community. We hypothesize that this is due in part to accessibility, discoverability, and adaptability challenges, among others. Further, instructors often face institutional barriers to deployment, which can include hesitance of institutions to utilize community developed solutions that often lack a centralized authority. This working group will explore what solutions are currently available, what instructors need, and reasons behind the above-mentioned phenomenon. This will be accomplished via a literature review and survey to identify the tools that have been developed by the community;the solutions that are currently available and in use by instructors;what features are needed moving forward for classroom and research use;what support for extensions is needed to support further CS Education research;and what institutional challenges instructors and researchers are currently facing or have faced in the past in developing, deploying or otherwise using community software solutions. Finally, the working group will identify factors that limit adoption of solutions and ways to integrate and improve the accessibility, discoverability, and dissemination of existing community projects, as well as manage and overcome institutional challenges. © 2022 Owner/Author.

关键词： community software educational tools

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