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5th International Conference on Smart Sensors and Application, ICSSA 2024

作者： Mekki, Amr Emad Dawood, Aly Khaled Khairuddin, Uswah Eissa, Noureldin S. Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia Department of Computer Engineering Arab Academy for Science Technology and Maritime Transport Cairo2033 Egypt

ISBN: (纸本)9798350354294

Hydrotherapy provides health advantages, such as reducing pain, enhancing blood circulation and easing symptoms. Making use of cutting edge motion capture technology, the detection of poses without markers has emerged as a tool for examining exercise patterns for hydrotherapy patients. This research assesses the effectiveness of four pose estimation models—OpenPose, MediaPipe Pose, AlphaPose and YOLOv7—in extracting and analyzing human gait pose specifically under challenging underwater conditions (i.e: camera positioning, complex backgrounds, underwater imaging issues, and the absence of head connections). A dedicated dataset of hydrotherapy movements was developed to evaluate model accuracy. Findings revealed that YOLOv7 and AlphaPose outperformed the other techniques. YOLOv7 achieved the highest accuracy in scenarios involving multiple participants, delivering near-perfect results for single-person exercises both on land and underwater. For four-person exercises underwater, AlphaPose had an average accuracy of 90.6%, while YOLOv7 slightly outperformed it with an accuracy of 93%. © 2024 IEEE.

关键词： Underwater imaging

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Enhancing students' learning achievements, self-efficacy, and motivation using mobile augmented reality

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Journal of computer Assisted Learning 2024年第4期40卷 1823-1837页

作者： Aldeeb, Fatimah H. Sallabi, Omar M. Elaish, Monther M. Hwang, Gwo-Jen Computer Network College of Computer Technology Benghazi Libya Computer Science The Libyan Academy Benghazi Libya Software Engineering Information Technology Faculty University of Benghazi Benghazi Libya Computer Science Information Technology Faculty University of Benghazi Benghazi Libya Graduate Institute of Educational Information and Measurement National Taichung University of Education Taichung City Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taipei City Taiwan Yuan Ze University Taoyuan City Taiwan

Background: This paper examines the use of augmented reality (AR) as a concept-association tool in schools, with the aim of enhancing primary school students' learning outcomes and engagement. Conflicting findings exist in previous studies regarding the cognitive load of AR-enriched learning, with some reporting reduced load and others indicating increased demand and poorer performance. Understanding these implications is essential for effectively leveraging AR in education. The study offers a fresh perspective on the potential of AR technology in improving educational experiences. Objectives: The primary goal of this research was to evaluate the effectiveness of an AR-assisted concept-association strategy for improving essential knowledge acquisition and skills outcomes, increasing cognitive load, and increasing self-efficacy and learning motivation among primary school students. Methods: A quasi-experimental design with a control group was employed to investigate the effectiveness of the intervention. Results and Conclusions: The results demonstrate that the implementation of the AR-assisted concept-association strategy effectively enhanced essential knowledge acquisition and skills outcomes, increased cognitive load, and increased self-efficacy and learning motivation among primary school students. These findings highlight the potential of AR technology to improve the learning experience and engagement of primary school students. The study contributes to the existing literature on the effectiveness of AR technology in education, suggesting future research directions. Ultimately, it offers a practical solution for improving the learning experience by presenting a new approach for teaching using AR technology. © 2024 John Wiley & Sons Ltd.

关键词： augmented reality biology education cognitive load learning motivation mobile learning self-efficacy

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The connection between meridians and physiological functions:A quantum principle

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Nano Research 2023年第11期16卷 12817-12820页

作者： Xiang-Yu Kong Min Xiang Jiancheng Fang Dong Han Bo Song Lei Jiang CAS Key Laboratory of Bio-inspired Materials and Interfacial Science Technical Institute of Physics and ChemistryChinese Academy of SciencesBeijing 100190China Science and Technology Center for Quantum Biology National Institute of Extremely-Weak Magnetic Field InfrastructureHangzhou 310051China CAS Center for Excellence in Nanoscience National Center for Nanoscience and TechnologyBeijing 100190China School of Optical-Electrical Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Future Technology University of Chinese Academy of SciencesBeijing 100049China

In the long history of traditional Chinese medicine(TCM),meridians play essential roles as the critical network to regulate the normal physiological functions of the human *** are regarded to be the channels connecting the internal organs with the body surface and various parts of the *** there are many studies and doctrines trying to reveal the nature of meridians for their validation in TCM,the mechanism underlying the meridians remains ***,based on our macroscopic quantum state concept of ion channels(i.e.,sub-nanometer scale channels),we propose a quantum principle of *** acupoints and organ symptom are in a macroscopic coherence state of the ion channels in *** applying TCM treatments(e.g.,TCM massage,acupuncture,moxibustion,and electroacupuncture)on the acupoint,the corresponding organ symptom could be well regulated with help of quantum meridian state.

关键词： traditional Chinese medicine meridians macroscopic quantum state external stimulation

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MediClass-RF: An Optimized Random Forest Framework for Medicinal Plant Classification with Feature Importance and Comprehensive Performance Evaluation

MediClass-RF: An Optimized Random Forest Framework for Medic...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Sapna, R. Sheshappa, S.N. Raja, S Pravinth Preethi Devadas, Raghavendra M Hiremani, Vani Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India Sir Mvit Department of Information Science and Engineering Bangalore India Presidency University Department of Computer Science and Engineering Bangalore India University Pune India

ISBN: (纸本)9798331533311

Proper categorization of medicinal and non-medicinal herbs is essential for further application in healthcare and the herbal industries. In our work, we proposed MediClass-RF, the Random Forest-based optimized framework aimed at medicinal plant classification, providing feature importance analysis and in-depth performance metrics evaluation. MediClass-RF was trained on a dataset that contains instances of real-world medicinal plants and non-medicinal plants. The popular term Frequency-Inverse Document Frequency approach fed these plants' properties into the modules, and synthetic ones with labels of different quality were added artificially to control noise levels and, therefore, simulate practical usage more accurately. These plants' properties were fed into the model using a popular approach. The Random Forest model was trained, tested, and validated with the normal 70:30 separation of training and testing sets, and it performed well, scoring 89% overall accuracy. In terminologies of performance, the model had a recall of 0.71, indicating its capability to capture a significant portion of actual medicinal plants, and an F 1-score of 0.83, signifying stability among precision with recall. According to the feature importance study, the most important features leading to classification are terms with anti-inflammatory, antioxidant, and antibacterial activities. Furthermore, an Area Under the Curve (AUC) suggests the model's great power in recognizing medicinal properties against non-medicinal plants. Cross-validation was done five times to validate the model further, obtaining a mean accuracy score of 0.88, calculated out of 0.875,0.91666667, 0.875,0.83333333,0.91666667, demonstrating the model's robustness across diverse subsets of the data. This work shows the possibility of machine learning, specifically Random Forest, in medicinal plant classification, making it a valuable tool for medicinal plant specialists and investigators. © 2024 IEEE.

关键词： Medicinal chemistry

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A Machine Learning Based Automated Model for Managing Student Dropout

A Machine Learning Based Automated Model for Managing Studen...

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International Conference on Software engineering Research, Management and Applications (SERA)

作者： Partha Ghosh Arnab Charit Hindol Banerjee Debanwesa Bandhu Agniv Ghosh Ankita Pal Takaaki Goto Soumya Sen Department of Computer Science and Engineering Academy of Technology Adisaptagram India Faculty of Information Sciences and Arts Toyo University Saitama Japan A.K.Choudhury School of Information Technology University of Calcutta Kolkata India

ISBN: (数字)9798350391343

ISBN: (纸本)9798350391350

Addressing the persistent challenge of student dropout, particularly prevalent in developing countries like India, Bangladesh, etc. are of paramount importance. Factors such as poverty, natural calamities, and early marriages exacerbate this issue. High student dropout rates can negatively impact a country by diminishing its economic productivity, increasing social inequalities, and perpetuating a cycle of poverty. Addressing dropout issues requires comprehensive strategies to ensure a skilled and educated workforce, fostering societal well-being and global competitiveness. This research focuses on analysing comprehensive data on students who have dropped out. Thereafter, a machine learning based methodology is used to discern the underlying causes of student attrition in various schools. Furthermore, it allows for efficient monitoring of the state's educational landscape, with the ability to drill down to granular levels when necessary to identify specific regional challenges. The effectiveness of this approach is validated through the utilization of real-world datasets.

关键词： Productivity Disasters Machine learning Developing countries Monitoring Software engineering

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DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

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CAAI Transactions on Intelligence technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina Machine Vision Technology Innovation Center of Hebei Province BaodingChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

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A Modified 2:1 Multiplexer-Based Low Power Ternary ALU for IoT Applications

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Journal of engineering science and technology Review 2024年第5期17卷 104-109页

作者： Allwin Devaraj, S. Magdalin Mary, D. Kannan, P. Esakki Rajavel, S. Thangaraj, Cynthia Anbuselvi Gurumoorthy, K.B. William, Blanie Scrimshaw Department of Electronics and Communication Engineering Francis Xavier Engineering College Tamilnadu Tirunelveli627003 India Department of Electrical and Electronics Engineering Sri Krishna College of Technology Tamilnadu Coimbatore641042 India Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Tamilnadu Coimbatore641021 India Department of Electronics and Communication Engineering SEA College of Engineering and Technology Karnataka Bengaluru560049 India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering Rohini College of Engineering and Technology Tamilnadu Kanyakumari629401 India

The ternary logic has a benefit over the binary logic which provides a secured solution to achieve a trade-off between the area and power of the design. However, from the structure of the ternary Aritmetic Logic Unit (ALU), it is clear that its architecture increases the area, propagation delay, and power consumption. To overcome this drawback, a loopback algorithm is proposed to achieve low power and high throughput Internet of Things (IoT) processors. The loopback algorithm reduces the number of processing stages in multipliers and adders which can significantly reduce area and power dissipation. The proposed 2:1 multiplexer-based approach reduces the need for a decoder and results in low power consumption. The proposed design will be implemented in Xilinx ISE 13.0 and simulation will be done in Modelsim. The modified Ternary ALU (TALU) performs finer than the previous TALU method. The number of registers used in this architecture is reduced by up to 25% than the existing system therefore there is a reduction in power dissipation. © (2024), (International Hellenic University - School of science). All rights reserved.

关键词： Logic design

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Application of Ensemble Machine Learning Techniques in Yield Predictions of Major and Commercial Crops 4th

Application of Ensemble Machine Learning Techniques in Yield...

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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Jayashree, T.R. Subba Reddy, N.V. Dinesh Acharya, U. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789819920990

Farming activities and crop productivity has been greatly affected by climate change, soil fertility, and the availability of planting areas in recent decades. Early estimation of yields and their quality are the prime requirements in the global food market which depend on several input parameters. Predicting crop yields gives the farmers an insight to decide on the crop production rate and cultivate suitable crops for the given climatic conditions. The present work aims at estimating yields of major and cash crops of Karnataka using climate and crop-related data through four ensemble regression approaches. The feature importance concept is incorporated that describes which features contribute most to the prediction results in all the models that help in better data interpretation. The performance metrics such as MSE, MAE, and R2 were adapted to measure the accuracy. The extreme gradient boosting regressor was found to deliver the best performance (R2 of 0.999) while incurring the lowest computational cost among the four ensemble regressor models. The results showed that ensemble regression methods can be effectively used for yield predictions when there is a medium-sized dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crops

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Growing Greener: Exploring the Creative Potential of Deep Learning in Agricultural Disease Control

Growing Greener: Exploring the Creative Potential of Deep Le...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Kiran, Ajmeera Rahman, Syed Ziaur Sreelatha, P. Dhanasekaran, S. Ayyasamy, Ramesh Kumar Swetha, Devulapally MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Majan University College Faculty of Information Technology Oman A.P Tirupati India Kalasalingam Academy of Research and Education Deemed to be University Department of Information Technology Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Vaagdevi Engineering College Department of Computer Science and Engineering Telangana Bolli Kunta India

ISBN: (纸本)9798331540685

According to estimates, by mid-2030, there will be 8.6 billion people on the planet. The human population has been growing exponentially. There will be a rise in food production and consumption due to the massive population growth. Crop disease is the primary danger to food security, which damages agricultural goods while growing. Due to the limited availability of infrastructure, it remains challenging to recognize crop-damaging diseases in various parts of the world. Deep learning methods and computer vision technologies are used in this work to help identify agricultural illnesses. The train set is used to train the model, and the data split is used for validation. The farmer can use the app (Identifying and Resolving Crop Diseases using Deep Learning) to take pictures of the crops and examine whether or not there are any infections, proving the solution's viability. © 2024 IEEE.

关键词： Plant diseases

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PPA: Preference Profiling Attack Against Federated Learning 30

PPA: Preference Profiling Attack Against Federated Learning

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30th Annual Network and Distributed System Security Symposium, NDSS 2023

作者： Zhou, Chunyi Gao, Yansong Fu, Anmin Chen, Kai Dai, Zhiyang Zhang, Zhi Xue, Minhui Zhang, Yuqing School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Science China Data61 CSIRO Syndey Australia National Computer Network Intrusion Protection Center University of Chinese Academy of Science China

ISBN: (纸本)1891562835

—Federated learning (FL) trains a global model across a number of decentralized users, each with a local dataset. Compared to traditional centralized learning, FL does not require direct access to local datasets and thus aims to mitigate data privacy concerns. However, data privacy leakage in FL still exists due to inference attacks, including membership inference, property inference, and data inversion. In this work, we propose a new type of privacy inference attack, coined Preference Profiling A ttack (PPA), t hat accurately profiles t he p rivate p references o f a l ocal u ser, e .g., m ost liked (disliked) items from the client’s online shopping and most common expressions from the user’s selfies. I n g eneral, P PA can profile t op-k (i.e., k = 1, 2, 3 a nd k = 1 i n p articular) preferences contingent on the local client (user)’s characteristics. Our key insight is that the gradient variation of a local user’s model has a distinguishable sensitivity to the sample proportion of a given class, especially the majority (minority) class. By observing a user model’s gradient sensitivity to a class, PPA can profile the sample proportion of the class in the user’s local dataset, and thus the user’s preference of the class is exposed. The inherent statistical heterogeneity of FL further facilitates PPA. We have extensively evaluated the PPA’s effectiveness using four datasets (MNIST, CIFAR10, RAF-DB and Products-10K). Our results show that PPA achieves 90% and 98% top-1 attack accuracy to the MNIST and CIFAR10, respectively. More importantly, in real-world commercial scenarios of shopping (i.e., Products-10K) and social network (i.e., RAF-DB), PPA gains a top-1 attack accuracy of 78% in the former case to infer the most ordered items (i.e., as a commercial competitor), and 88% in the latter case to infer a victim user’s most often facial expressions, e.g., disgusted. The top-3 attack accuracy and top-2 accuracy is up to 88% and 100% for the Products-10K and RAF-DB, re

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