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3rd International Conference on Artificial Intelligence and Data Sciences, AiDAS 2022

作者： Tuh, Moi Hua Kon, Cynthia Mui Lian Chua, Hong Siang Lau, Man Fai Hui Chang, Robin Yee Swinburne University of Technology Faculty of Engineering Computing and Science Sarawak Kuching Malaysia Universiti Teknologi Mara Faculty of Computer and Mathematical Sciences Sarawak Kota Samarahan Malaysia School of Software and Electrical Engineering Swinburne University of Technology Melbourne Australia Universiti Teknologi Mara Faculty of Applied Sciences Sarawak Kota Samarahan Malaysia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

The Markov chain method has been adopted in this work to evaluate the performance of the synthetic double sampling (SynDS) np chart, which combines the double sampling (DS) np sub-chart and the conforming run length (CRL) sub-chart. In the literature, the performance of the existing SynDS np chart is investigated using average run length (ARL). It is found that the skewness of the run length (RL) distribution varies depending on the size of shift in the process fraction nonconforming, from being significantly right-skewed when the process is in-control (IC) or small shift to being nearly symmetric when the process shift is large. As a result, the true performance of the chart may differ from what the ARL displays. Thus, median run length (MRL) is recommended to represent the central tendency associated with the varying shapes of the RL distribution in accordance to the process shift sizes. This work suggests an optimal design approach for SynDS np chart based on MRL to monitor the process fraction nonconforming, under zero-state and steady-state modes. © 2022 IEEE.

关键词： Control charts

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Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits

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Multimedia Tools and Applications 2024年第33期83卷 80201-80223页

作者： Dutta, Ashit Kumar Raparthi, Mohan Alsaadi, Mahmood Bhatt, Mohammed Wasim Dodda, Sarath Babu Prashant, G.C. Sandhu, Mukta Patni, Jagdish Chandra Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Alphabet Life Science DallasTX7506 United States Department of Computer Science Al-Maarif University College Al Anbar31001 Iraq Model Institute of Engineering and Technology J&K Jammu India Software Engineer Central Michigan University DallasTX75009 United States Department of Computer Science Texas Tech University Lubbock79409 United States Skill Faculty of Engineering & amp Technology Shri Vishwakarma Skill University Haryana Palwal India CSE School of advance Computing Alliance University Bengaluru India

The neurological condition epilepsy is demanding and even fatal. Electroencephalogram (EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and different patients have various seizure activity patterns. EEG signal detection is time-consuming and labour-intensive, which puts a strain on medical staff and raises the possibility of erroneous detections. Usually, electrodes are inserted into the scalp or inside the brain for a brief period of time in order to obtain EEG data. It is essential to research efficient cross-patient automatic epilepsy detection techniques. The multi-head self-attention mechanism recognises long-distance dependencies with the same proficiency as it does temporal dynamic correlations between short-term temporal pattern characteristics and sequential relationships. The contextual representations are inputted into a bidirectional long short-term memory network (BiLSTM) so that information can be extracted in both directions. Classification and training are carried out utilising the log SoftMax algorithm. The experiments utilised scalp EEG data from the CHB-MIT database. Sensitivity, specificity, F1-score, and accuracy were computed to be 96.5 percent, 97.04 percent, 96.6 percent, and 96.2 percent, respectively. The results of the experiment show how well the technique works for detecting seizures in several patients utilising multi-channel EEG recordings. The results also demonstrate the method's improved generalisation capabilities and resilience and consistency in collecting seizure patterns. This is particularly critical for the tertiary diagnosis of epilepsy, and the findings indicate that the proposed method significantly improves the accuracy of detection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electroencephalography

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“User AR” — Leveraging AI Based Augmented Reality for User Manuals

“User AR” — Leveraging AI Based Augmented Reality for Use...

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International Conference on computing and Networking technology (ICCNT)

作者： Thirumurugan Shanmugam Chirag Chandrashekar Vipin Bhati Maheswari Subburaj Arun Kumar Sivaraman Ajmery Sultana College of Computing and Information Sciences University of Technology and Applied Sciences Suhar Oman School of Computer Science and Engineering Vellore Institute of Technology Chennai India Digital Engineering Solution Center Photon Interactive Inc Toronto Ontario Canada School of Computer Science and Technology Algoma University Brampton Ontario Canada

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The objective of this research is to propose an AI based novel augmented reality (AR) software to assist users in operating a wide range of technical and non-technical devices. The project’s aim is to make this AR application, which provides detailed explanations of all the device’s functions, more widely used than traditional user manuals. Augmented reality, which overlays digital information onto the real world, can be employed to develop an AR-based user manual for products or devices. Mobile applications that use a camera to recognize a product or device and display dynamic graphics, such as 3 D models, animations, or text, are one example of AR user manuals. These augmented reality user manuals enable consumers to quickly and easily learn how to operate, assemble, repair, or troubleshoot their purchased products. Experiments show that the proposed algorithm was able to recognize the object accurately with a percentage of 95.2%.

关键词： Three-dimensional displays Biological system modeling Manuals Transforms Animation Software User experience Artificial intelligence Usability Augmented reality

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MOAC: Multi-Objective Adaptive Clustering Optimization Algorithm Based on Cluster Adaptation

MOAC: Multi-Objective Adaptive Clustering Optimization Algor...

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

作者： Hossam M. J. Mustafa Dheeb Albashish Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper presents a novel Multi-Objective Adaptive Clustering (MOAC) algorithm that addresses current limitations in existing multi-objective optimization methods. While current algorithms often struggle with premature convergence and diversity preservation, the MOAC algorithm proposes a novel clustering-based approach to search space by partitioning. The algorithm combines cluster evolution with an adaptive parameter control mechanism to achieve significant improvements in balancing exploration-exploitation and diversity maintenance. MOAC employs k-means clustering for population partitioning, combined with cluster-based genetic operators, and an efficient diversity preservation scheme. Extensive experiments across different ZDT, MMF, and UF test suites demonstrate that MOAC's performance is superior, which achieves outstanding hypervolume and IGD values in complex multi-modal problems. The algorithm shows strength in handling complex Pareto fronts, and comprehensive statistical analysis through the Friedman rankings test with a 2.21 rank. Moreover. The results reveal that MOAC is a robust approach for complex multi-objective optimization problems, particularly maintaining solution diversity and achieving consistent convergence across different problems.

关键词： Ranking (statistics) Statistical analysis Metaheuristics Clustering algorithms Optimization methods Evolutionary computation Partitioning algorithms Maintenance Convergence Genetic operators

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Advancing Brain Image Segmentation: A Comprehensive Exploration of Enhanced VGG16 Architecture for Precise Neuroanatomical Mapping

Advancing Brain Image Segmentation: A Comprehensive Explorat...

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computing and Data Analytics (ICCDA), International Conference on

作者： Sheeja Kumari V Wilfred Blessing N. R Suresh Palarimath Hemalatha Gunasekaran Poorana Senthilkumar S Sutherlin Subitha G Department of Computational Intelligence Saveetha School of Engineering SIMATS University Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Dr. N. G. P Arts and Science College Coimbatore Tamil Nadu India Department of Computer Science and Engineering Stella Mary's College of Engineering Kanyakumari Dt Tamil Nadu India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This study introduces a novel approach to brain image segmentation by enhancing the VGG16 architecture. Neuroanatomical mapping is a critical aspect of understanding brain structures, and our research focuses on refining the VGG16 model to achieve more accurate segmentation results. We address the intricacies of brain image analysis, optimizing the architecture for the nuanced structures inherent in neuroimaging data. Our enhanced VGG16 model is thoroughly evaluated across diverse datasets, demonstrating superior performance compared to state-of-the-art segmentation methods. The framework proves robust and efficient in accurately delineating complex brain regions. We also delve into the interpretability of the segmentation results, shedding light on the learned features that contribute to the model's efficacy. The significance of our work extends beyond improved segmentation, offering valuable insights into the interpretability of deep learning models in neuroimaging. This research contributes to the broader field of biomedical image analysis and holds promise for applications in neuroscience and medical diagnostics. The proposed enhanced VGG16 framework emerges as a potent tool for advancing our understanding of brain structures and enhancing the capabilities of neuroimaging technologies.

关键词： Neuroimaging Deep learning Measurement Image segmentation Neuroscience Biological system modeling Refining Computer architecture Brain modeling Medical diagnosis

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Salvinia-like slippery surface with stable and mobile water/air contact line

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National Science Review 2021年第5期8卷 119-129页

作者： Xiaomei Li Jinlong Yang Kaixuan Lv Periklis Papadopoulos Jing Sun Dehui Wang Yanhua Zhao Longquan Chen Dapeng Wang Zuankai Wang Xu Deng Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China State Key Laboratory of Polymer Physics and Chemistry Institute of Applied ChemistryChinese Academy of Sciences Physics Department University of Ioannina University Research Center of Ioannina (URCI) Institute of Materials Science and Computing Department of Mechanical Engineering City University of Hong Kong School of Physics University of Electronic Science and Technology of China

Superhydrophobic surfaces are widely used in many industrial settings, and mainly consist of rough solid protrusions that entrap air to minimize the liquid/solid area. The stability of the superhydrophobic state favors relatively small spacing between protrusions. However, this in turn increases the lateral adhesion force that retards the mobility of drops. Here we propose a novel approach that optimizes both properties simultaneously. Inspired by the hydrophobic leaves of Salvinia molesta and the slippery Nepenthes pitcher plants, we designed a Salvinia-like slippery surface(SSS) consisting of protrusions with slippery heads. We demonstrate that compared to a control surface, the SSS exhibits increased stability against pressure and impact, and enhanced lateral mobility of water drops as well as reduced hydrodynamic drag. We also systematically investigate the wetting dynamics on the SSS. With its easy fabrication and enhanced performance, we envision that SSS will be useful in a variety of fields in industry.

关键词： slippery Cassie state Salvinia molesta stability low adhesion drag reduction

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Timing Jitter Remover for Optical Non-Gaussian State Preparation

Timing Jitter Remover for Optical Non-Gaussian State Prepara...

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Frontiers in Optics, FiO 2022

作者： Sonoyama, Tatsuki Takahashi, Kazuma Takasu, Sachiko Hattori, Kaori Fukuda, Daiji Asavanant, Warit Takase, Kan Fukui, Kosuke Yoshikawa, Jun-Ichi Endo, Mamoru Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan National Institute of Advanced Industrial Science and Technology Ibaraki Tsukuba305-8563 Japan AIST-UTokyo Advanced Operando-Measurement Technology Open Innovation Laboratory Ibaraki Tsukuba305-8563 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan

In this work, we remove the timing jitter of the photon-number-resolving detector which limited non-Gaussian state preparation using a continuous-wave light source, and generate a Schrödinger cat state with Wigne...

ISBN: (纸本)9781557528209

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Simulation vignette generation from answer set specifications 17

Simulation vignette generation from answer set specification...

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17th Annual International Conference on Information Systems for Crisis Response and Management, ISCRAM 2020

作者： Rouwendal van Schijndel, Dashley K. Hannay, Jo E. Stolpe, Audun University of Oslo Department of Technology Systems Norway Norwegian Computing Center Department of Applied Research in Information Technology Norway University of Oslo Department of Technology Systems Norwegian Computing Center Department of Applied Research in Information Technology Norway

ISBN: (纸本)9781949373271

We investigate an approach that allows exercise managers to design simulations with an explicit focus on building skills, rather than having to focus on all the objects and interactions that a simulation must have. Exercise managers may design exercises at various levels of abstraction and always independently of how those sessions are implemented in simulations, while simulation components that implement the design are assembled and to some extent, automatically, behind the scenes. We outline (1) how Answer Set Programming can assist exercise managers in exercise planning and (2) how automated stage and content generation may be used to invoke appropriate simulation components to realize the design. For deliberate and recurrent training of decision-making skills, stages and content must vary to avoid familiarity (testing effects). We conclude by distilling a main research hypothesis that stipulates how (1) and (2) represent two modes of automated reasoning (so-called deductive versus abductive) and how that distinction clarifies the planning task. © 2020 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.

关键词： Mixed reality

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Two-Tier Clustering with Routing Protocol for IoT Assisted WSN

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Computers, Materials & Continua 2022年第5期71卷 3375-3392页

作者： AArokiaraj Jovith Mahantesh Mathapati M.Sundarrajan N.Gnanasankaran Seifedine Kadry Maytham N.Meqdad Shabnam Mohamed Aslam Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Rajarajeswari College of EngineeringBengaluru560074India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science Thiagarajar CollegeMadurai625019India Department of Applied Data Science Noroff University CollegeKristiansandNorway Al-Mustaqbal University College HillahIraq Department of Information Technology College of Computing and Information SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia

In recent times,Internet of Things(IoT)has become a hot research topic and it aims at interlinking several sensor-enabled devices mainly for data gathering and tracking *** Sensor Network(WSN)is an important component in IoT paradigm since its inception and has become the most preferred platform to deploy several smart city application areas like home automation,smart buildings,intelligent transportation,disaster management,and other such IoT-based *** methods are widely-employed energy efficient techniques with a primary purpose i.e.,to balance the energy among sensor *** and routing processes are considered as Non-Polynomial(NP)hard problems whereas bio-inspired techniques have been employed for a known time to resolve such *** current research paper designs an Energy Efficient Two-Tier Clustering with Multi-hop Routing Protocol(EETTC-MRP)for IoT *** presented EETTC-MRP technique operates on different stages namely,tentative Cluster Head(CH)selection,final CH selection,and *** first stage of the proposed EETTC-MRP technique,a type II fuzzy logic-based tentative CH(T2FL-TCH)selection is ***,Quantum Group Teaching Optimization Algorithm-based Final CH selection(QGTOA-FCH)technique is deployed to derive an optimum group of CHs in the ***,Political Optimizer based Multihop Routing(PO-MHR)technique is also employed to derive an optimal selection of routes between CHs in the *** order to validate the efficacy of EETTC-MRP method,a series of experiments was conducted and the outcomes were examined under distinct *** experimental analysis infers that the proposed EETTC-MRP technique is superior to other methods under different measures.

关键词： Wireless networks internet of things energy efficiency clustering multi-hop routing metaheuristics

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Attention-Guided Deep Learning Framework For Movement Quality Assessment

Attention-Guided Deep Learning Framework For Movement Qualit...

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

作者： Aditya Kanade Mansi Sharma Manivannan Muniyandi Department of Electrical Engineering Indian Institute of Technology Madras India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India Department of Applied Mechanics Indian Institute of Technology Madras India

Physical rehabilitation programs frequently begin with a brief stay in the hospital and continue with home-based rehabilitation. Lack of feedback on exercise correctness is a significant issue in home-based rehabilitation. Deep learning-based movement quality assessment (MQA) can assist with home-based rehabilitation by providing the necessary quantitative feedback. However, systems developed for home-based settings must be fast and offer interpretability of the generated assessment. In this paper, we explore an attention-guided transformer-based architecture for MQA. A comparative analysis against the current state-of-the-art methods is undertaken to establish the validity of the proposed model. Further, we show that the proposed model offers significant performance improvement in training and inference time, which is pivotal for any real-time system. Finally, we show that analysis of the attention maps of the proposed model can give critical insights into the decision-making process of the deep-learning model, thus improving the overall interpretability of predicted assessment scores.

关键词： Training Deep learning Analytical models Predictive models Signal processing Transformers Feature extraction

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