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BIT Conference (BITCON), International

作者： Ajmeera Kiran Prithiviraj Ananth P Veera Prakash N. Kumareshan Ramesh Kumar Ayyasamy P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of CSE Sona College of Technology Salem Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Ananthapuramu Andhra Pradesh Department of ECE Professor/Centre for Advances in Signal Processing and Sensor Networks Sri Eshwar College of Engineering Coimbatore Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

Ebuddy is a ground breaking platform dedicated to rescuing and rehabilitating child laboures. By leveraging advanced technology, this initiative effectively connects with at-risk children, providing them with crucial education and support services. With a mission to break the vicious cycle of poverty and exploitation, Ebuddy collaborates with local communities and organizations to raise awareness about child labor and advocate for policies that safeguard children's rights. This platform offers comprehensive support services aimed at addressing the children's physical, emotional, and psychological well-being. By providing counseling, rehabilitation, and necessary medical assistance, Ebuddy empowers children to leave their past behind and embrace a better future.

关键词： Employee welfare Pediatrics Education Psychology Morphology Organizations

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Enhanced Electrochemical Performance by Structural Design of Electrolyte Surface Combining 3d Printing technology with Multi-Physical Modeling

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SSRN 2022年

作者： Zheng, Lina Xu, Ruixue Zhang, Jinjin Yu, Fangyong Li, Claudia Sunarso, Jaka Meng, Xiuxia Yang, Naitao School of Chemistry and Chemical Engineering Shandong University of Technology Zibo255000 China Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia

Patterned electrolyte surfaces are considered an effective strategy to enhance the cell performance of solid oxide fuel cells (SOFCs) by increasing the contact area between the electrode and the electrolyte, and subsequently reduce the area specific resistance. This study tailored the patterning of an 8 mol% yttria stabilized zirconia (8YSZ) electrolyte surface using the stereolithography (SLA) 3D printing technology, and the detailed effects of the electrolyte surface geometry on the cell performance was further explored using multi-physical field simulation and quantitative analysis. The defect-free densified planar and concavo-convex electrolytes were successfully prepared by SLA, and the concavo-convex cell presented the maximum output power densities (MPDs) of 288.9 mW cm -2 at 850°C, which was 46.2% higher than that of the planar cell. The simulation results revealed large consumption of reactants and strong electrochemical reactions at the concave surface. Moreover, the hydrogen and oxygen consumptions at the edges were greater than those at the center of both the concave and convex surfaces, whereas the edges of the concavo-convex structure were more conducive to the electrochemical reaction. Finally, the quantitative correlation of the cell performance with the influencing factors was obtained by stepwise linear regression analysis. Reducing the ion transfer path length and providing sufficiently large effective contact area proved to be productive strategies to improve the cell output performance. © 2022, The Authors. All rights reserved.

关键词： Structural design

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One-Step Preparation of Omniphobic Membrane with Concurrent Anti-Scaling and Anti-Wetting Properties for Membrane Distillation

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SSRN 2022年

作者： Xie, Songchen Pang, Zhiguang Hou, Chunguang Wong, Ngie Hing Sunarso, Jaka Peng, Yuelian Beijing Key Laboratory for Green Catalysis and Separation Faculty of Environment and Life Beijing University of Technology Beijing100124 China Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia

Membrane scaling represents one of the significant challenges for membrane distillation (MD), especially when treating hypersaline brines. This work presents a one-step dip-coating method to develop an omniphobic membrane. The commercial polyvinylidene fluoride (PVDF) hydrophobic porous membranes were immersed into the epoxy acrylic (EA) resin and fluorosilane (1H,1H,2H,2H Perfluorodecyltrimethoxysilane, PFTS) mixture followed by a heating cross-linking process. EA/PFTS ratio of 1:1 (v/v) gave the best performance in terms of surface morphology, surface chemical composition, surface energy, and mechanical property. The resultant PVDF-(EA/PFTS)-1:1 membrane demonstrated excellent wetting resistance and omniphobicity during the penetration of deionized water, 30% and 60% ethanol in water, and kerosene, giving 141º, 124º, 106º, and 116º contact angles, respectively. It also exhibited excellent scaling resistance in concentrating 14.7 mM gypsum solution and during reverse osmosis of brine in the MD process. A series of capillary experiments additionally confirmed that the membrane's surface pores achieved the gas-wetting state. The increase in the membrane surface hydrophobicity, the reduction in the surface pore size, and the toughening of the surface pores contributed to the formation of a stable gas-liquid interface on the modified membrane surface that can enhance the anti-scaling and anti-wetting performance. © 2022, The Authors. All rights reserved.

关键词： Wetting

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Optical Anisotropy in van der Waals materials: Impact on Direct Excitation of Plasmons and Photons by Quantum Tunneling

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Light(science & Applications) 2021年第12期10卷 2387-2398页

作者： Zhe Wang Vijith Kalathingal Thanh Xuan Hoang Hong-Son Chu Christian A.Nijhuis Department of Chemistry National University of Singapore3 Science Drive 3Singapore 117543Singapore Centre for Advanced 2D Materials National University of Singapore6 Science Drive 2Singapore 117564Singapore Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Singapore Department of Electronics and Photonics Institute of High Performance ComputingA*STAR(Agency for ScienceTechnology and Research)1 Fusionopolis WaySingapore 138632Singapore Hybrid Materials for Opto-Electronics Group Department of Molecules and MaterialsMESA+Institute for Nanotechnology and Center for Brain-Inspired Nano SystemsFaculty of Science and TechnologyUniversity of Twente7500 AE EnschedeThe Netherlands

Inelastic quantum mechanical tunneling of electrons across plasmonic tunnel junctions can lead to surface plasmon polariton(SPP)and photon *** far,the optical properties of such junctions have been controlled by changing the shape,or the type of the material,of the electrodes,primarily with the aim to improve SPP or photon emission *** we show that by tuning the tunneling barrier itself,the efficiency of the inelastic tunneling rates can be improved by a factor of *** exploit the anisotropic nature of hexagonal boron nitride(hBN)as the tunneling barrier material in Au//hBN//graphene tunnel junctions where the Au electrode also serves as a plasmonic strip *** this junction constitutes an optically transparent hBN–graphene heterostructure on a glass substrate,it forms an open plasmonic system where the SPPs are directly coupled to the dedicated strip waveguide and photons outcouple to the far *** experimentally and analytically show that the photon emission rate per tunneling electron is significantly improved(~×3)in Au//hBN//graphene tunnel junction due to the enhancement in the local density of optical states(LDOS)arising from the hBN *** the dedicated strip waveguide,SPP outcoupling efficiency is quantified and is found to be∼80%stronger than the radiative outcoupling in Au//hBN//graphene due to the high LDOS of the SPP decay channel associated with the inelastic *** new insights elucidated here deepen our understanding of plasmonic tunnel junctions beyond the isotropic models with enhanced LDOS.

关键词： tunneling strip coupling

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Cost-Sensitive Distribution Alignment for Improving Automated Medical Diagnosis Using Retinal Fundus Photography

Cost-Sensitive Distribution Alignment for Improving Automate...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Yangqin Feng Xinxing Xu Zizhou Wang Yan Wang Huazhu Fu Shaohua Li Liangli Zhen Tien-En Tan Mukharram M. Bikbov Jost B. Jonas Chee Wai Wong Ching-Yu Cheng Daniel Shu Wei Ting Rick Siow Mong Goh Yong Liu Agency for Science Technology and Research Institute of High Performance Computing Singapore Singapore Eye Research Institute Singapore Singapore National Eye Centre Singapore Duke-National University of Singapore Medical School Singapore Ufa Eye Research Institute Ufa Russia Department of Ophthalmology Medical Faculty Mannheim Heidelberg University Germany

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Domain adaptation (DA) has emerged as a promising approach to address the domain shift problem in deep learning for automated medical diagnosis. However, current approaches often overlook the imbalanced nature of different categories and primarily focus on aligning the distributions of the source and target domains globally. This oversight leads to suboptimal performance on imbalanced target datasets, as the alignment process becomes dominated by the majority class during adaptation. To tackle this limitation, this paper proposes a novel domain adaptation method called cost-sensitive distribution alignment (CSDA), which aims to enhance deep learning performance on target data. The proposed approach involves collecting a limited-sized dataset from the target domain and utilising CSDA to bridge the domain gap between the source and target domains. Specifically, to address class imbalance, cost-sensitive learning is incorporated into the distribution alignment process, giving more emphasis to the misalignment cost associated with minority class samples. More importantly, CSDA aligns the cross-domain projection distribution in the feature space with the ideal geometric distribution derived from the ground-truth labels. Unlike existing methods that directly minimise or employ adversarial learning to reduce the distribution discrepancy between the source and target domains, our proposed CSDA method focuses on minimising the semantic relationship misalignment among cross-domain samples. Experimental results on the detection of high myopia and myopic macular degeneration (MMD) demonstrate the superiority of CSDA over state-of-the-art methods. The results provide empirical evidence of CSDA’s efficacy in enhancing automated medical diagnosis.

关键词： Photography Macular degeneration Deep learning Bridges Costs Semantics Bidirectional control

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Watermarking and Encryption for Electronic Medical Records (EMR) Security Based on mCloud 2

Watermarking and Encryption for Electronic Medical Records (...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Abdulshaheed, Haider Rasheed Abbas, Yaseen Saadi Tawfeq, Jamal F. Ateeq, Karamath Alnawayseh, Saif. E. A. Ghazal, Taher M. Baghdad College Of Economic Sciences University Computer Engineering Techniques Department Baghdad Iraq College Of Engineering Al-Nahrain University Electronic And Communications Engineering Department Baghdad Iraq Medical Technical College Al-Farahidi University Department Of Medical Instrumentation Technical Engineering Baghdad10070 Iraq School Of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates College Of Engineering Mutah University Department Of Electrical Engineering Al-Karak Jordan Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

Providing quality healthcare services and promoting collaborative clinical research are easier and more efficient with electronic medical record (EMR) systems. All aspects of care are included in electronic health records. Data characterized by volume, variety, and velocity is what IBM defines as big data. Therefore, we evidently have a significant problem managing 'big EMR data' from a volume, accuracy, and velocity perspective. Data misuse and patient safety are significant risks associated with poorly implemented EMR-based systems. Storage, access, and retrieval of big data are all aspects of security to consider. Location is another important aspect of secure data use, in addition to strong access control mechanisms. Researchers are investigating secure mobile data usage based on location after recent reports of illegal trade and theft of patient data over mobile devices. To improve work flow dynamics and efficiency, healthcare facilities are increasingly using mobile devices. In spite of this, mobile devices are becoming more and more common. A new technique for preventing EMR infiltration and manipulation by hackers using the cloud is presented in this article. © 2024 IEEE.

关键词： Authentication

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Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms

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Green Chemical Engineering 2025年第2期 193-199页

作者： Ao Yang Shirui Sun Lu Qi Zong Yang Kong Jaka Sunarso Weifeng Shen College of Safety Science and Engineering Chongqing University of Science and Technology Beijing Institute of Technology Chongqing Innovation Center School of Chemistry and Chemical Engineering Chongqing University School of Chemistry and Chemical Engineering Yangtze Normal University Jiangsu Key Laboratory of Advanced Catalytic Materials and Technology School of Petrochemical EngineeringChangzhou University School of Engineering and Technology Sunway University Research Centre for Sustainable Technologies Faculty of EngineeringComputing and ScienceSwinburne University of Technology

This study aims to significantly improve existing quantitative structure-property relationship（QSPR） models for predicting the octanol-water partition coefficient(KOW). This is because accurate predictions of KOWare crucial for assessing the environmental behavior and bioaccumulation potential of chemicals. Previous models have reported determination coefficient（R2） values between 0.9451 and 0.9681, and this research seeks to exceed these benchmarks. Three machine learning（ML） models are explored, r.e., feed-forward neural networks（FNN）,extreme gradient boosting（XGBoost）, and random forest（RF）. Using a dataset of 14,610 solvents（14,580 after data cleaning） and 21 molecular descriptors derived from SMILES representations, we rigorously evaluate these models based on R2, mean absolute error（MAE）, root mean squared error（RMSE）, and mean relative error（MRE）.Notably, the best model developed, the XGBoost-based QSPR, demonstrated exceptional performance, exhibiting an impressive R2value of 0.9772, surpassing benchmarks set by prior research models. Additionally, shapley additive explanation（SHAP） analysis is also employed for model interpretation, and it is revealed that the top five influential input features include SMR＿VSA8, SMR＿VSA3, Kappa2, Heavy Atom Count, and fr＿furan. This study not only sets a new benchmark for KOWprediction accuracy but also enhances the interpretability of QSPR models.

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Distributed optimization for IoT attack detection using federated learning and Siberian Tiger optimizer

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ICT Express 2025年第3期11卷 542-546页

作者： Gupta, Brij B. Gaurav, Akshat Alhalabi, Wadee Arya, Varsha Alharbi, Eman Chui, Kwok Tai Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India School of Cybersecurity Korea University Seoul South Korea Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul South Korea Ronin Institute Montclair NJ United States International Center for AI and Cyber Security Research and Innovations (CCRI) Asia University Taichung 413 Taiwan Department of Computer Science Immersive Virtual Reality Research Group King Abdulaziz University Jeddah Saudi Arabia Hong Kong Metropolitan University Hong Kong SAR China Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

The rapid growth of IoT devices has heightened the risk of botnet attacks, calling for scalable and distributed detection solutions. In this context, this study proposes a distributed optimization system for IoT attack detection using CNN model utilizing federated learning. After optimizing the hyperparameters of the model at the server, the Siberian Tiger Optimization (STO) method distributes these values to clients for dispersed training. Our model achieves accuracy, recall, and precision of 0.89978, 0.94355, and 0.94455, respectively, using the N-BaIoT dataset. These findings show, in spite of latency issues, the efficiency of federated learning in distributed IoT security systems. © 2025

关键词： CNN model Distributed optimization Federated learning IoT attack detection Siberian Tiger Optimization (STO)

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Fuzzy-Based Weighted Federated Machine Learning Approach for Sustainable Energy Management with IoE Integration

Fuzzy-Based Weighted Federated Machine Learning Approach for...

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IEEE Symposium on Systems and Information Engineering Design, SIEDS

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Sagheer Abbas Areej Fatima Muhammad Saleem Muhammad Adnan Khan Abdulrahman Alqarafi Computer Science Department Centre for Cyber Physical Systems Khalifa University Abu Dhabi UAE Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Malaysia School of Computer Sciences National College of Business Administration & Economics Lahore Pakistan Al-Khawarizmi Institute of Computer Science University of Engineering & Technology (UET) Lahore Pakistan Department of Computer Science Bahria University Lahore Pakistan Department of Computer Science LGU Lahore Pakistan School of Computer Science Minhaj University Lahore Pakistan School of Computing Skyline University College Sharjah UAE Riphah School of Computing and Innovation Riphah International University Lahore Pakistan College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798350385144

ISBN: (纸本)9798350385151

The considerable surge in energy consumption due to population growth and the use of new technology has posed significant challenges to energy security and the environment. Managing energy consumption on the consumer side is crucial as the demand for energy continues to increase. Consumers need to monitor their daily usage and understand consumption standards to prepare themselves for energy efficiency and cost savings. To address these challenges, a Smart Energy Management System (SEMS) based on the Internet of Energy (IoE) has been developed. This system uses day-ahead planning and accurate energy availability forecasting to manage energy demand in a smart environment with high sustainable energy penetration. The Fuzzy based Weighted Federated Learning (FWFML) approach is integrated into the system to optimize energy management. The SEMS system monitors energy usage at the user level, and a review of management plans has been introduced to improve energy efficiency in terms of 93.5% accuracy, and 6.5% miss-rate. This research work also highlights future challenges in developing SEMS to increase energy efficiency in a more robust manner.

关键词： Energy consumption Costs Smart buildings Sociology Energy efficiency Energy management systems Surges

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Electrocatalytic Ammonia Synthesis Using a Fe@Mxene Catalyst as Cathode of High-Temperature Proton-Conducting Solid Oxide Cell

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SSRN 2022年

作者： Yang, Naitao Li, Linzhe Xue, Zhiwei Meng, Xiuxia Zhang, Jinjin Li, Claudia Yu, Fangyong Sunarso, Jaka Shandong University of Technology China School of Chemistry and Chemical Engineering Shandong University of Technology Zibo255049 China Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia

As the only carbon-free energy carrier without CO2 emission upon decomposition, ammonia is an ideal storage medium for H2. However, the current low efficiency of ammonia synthesis using high-temperature proton-conducting electrochemical cells due to low catalyst selectively is a main challenge. Herein, we develop a novel non-precious cathode catalyst consisting of Fe nanoparticles loaded on two-dimensional (2D) MXene nanosheets (Fe@MXene) that can achieve a high Faradaic efficiency of 8.4% and an NH3 yield of 8.24×10-9 mol•s-1•cm-2 under an applied voltage of 1.6 V at 650 °C using an anode-supported Ba0.95Ce0.6Tb0.1Y0.2Zr0.1O3-δ-based electrolyte. The resultant catalyst with high specific surface area and catalytic active sites is beneficial to N2 reduction, resulting from the effective activation of N2 molecules imposed by the transported protons. These show a promising catalyst of Fe@MXene for N2 reduction reaction using a high-temperature ceramic proton-conducting electrochemical solid oxide cell. © 2022, The Authors. All rights reserved.

关键词： Electrolytes

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