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16th International Conference on Social Robotics, ICSR + BioMed 2024

作者： Samani, Hooman Zaraki, Abolfazl Davies, Daisy-Rose Fernando, Owen Noel Newton Creative Computing Institute University of the Arts London London United Kingdom and Robotics Research Group University of Hertfordshire Hatfield United Kingdom School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819789627

This paper introduces Robotic Cognitive Behavioural Therapy (rCBT) as a novel approach to overcoming the limitations of traditional Cognitive Behavioural Therapy (CBT) in treating emotional and psychological issues. After a brief overview of CBT and its inherent challenges, this paper explores the innovative application of physical robots in therapy, building on the success of technology-based CBT methods that have effectively treated various mental health conditions. The proposed rCBT solution is a compact, portable, and cost-effective 3D-printed robot designed to aid individuals with anxiety or depression. This robot includes interactive features such as a diary, health questionnaires, daily mood monitoring, to-do lists, mindfulness exercises, and a crisis helpline, enabling users to track their emotional states and activities. These functions support healthcare professionals and users in identifying emotional patterns and triggers. The robot can monitor physiological signals like heart rate. This research aims to enhance the accessibility and effectiveness of CBT, suggesting that rCBT could significantly broaden the therapy’s reach and improve mental health outcomes for a larger population. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Social robots

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Optimization of Interval Type-2 Fuzzy Logic System Using Grasshopper Optimization Algorithm

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Computers, Materials & Continua 2022年第5期71卷 3513-3531页

作者： Saima Hassan Mojtaba Ahmadieh Khanesar Nazar Kalaf Hussein Samir Brahim Belhaouari Usman Amjad Wali Khan Mashwani Institute of Computing Kohat University of Science and TechnologyKohatPakistan Department of Mechanical Materials and Manufacturing EngineeringFaculty of EngineeringUniversity of NottinghamUK College of Computer Sciences and Mathematics Tikrit UniversityIraq Division of Information and Computing Technology College of Science and EngineeringHamad Bin Khalifa UniversityQatar Department of Computer Science Sir Syed University of Engineering and TechnologyPakistan Institute of Numerical Sciences Kohat University of Science and TechnologyKohatPakistan

The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system(IT2-FLS)is a challenging task in the presence of uncertainty and *** optimization algorithm(GOA)is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature,which has good convergence ability towards *** main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an *** antecedent part parameters(Gaussian membership function parameters)are encoded as a population of artificial swarm of grasshoppers and optimized using its *** of the consequent part parameters are accomplished using extreme learning *** optimized IT2-FLS(GOAIT2FELM)obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and *** forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization *** of the performance,on the same data-sets,reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2-FLS.

关键词： Parameter optimization grasshopper optimization algorithm interval type-2 fuzzy logic system extreme learning machine electricity market forecasting

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A Dynamical Variable-separation Method for Parameter-dependent Dynamical Systems

arXiv

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arXiv 2025年

作者： Chen, Liang Chen, Yaru Li, Qiuqi Zhou, Tao School of Mathematics Hunan University Changsha410082 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

This paper proposes a dynamical Variable-separation method for solving parameter-dependent dynamical systems. To achieve this, we establish a dynamical low-rank approximation for the solutions of these dynamical systems by successively enriching each term in the reduced basis functions via a greedy algorithm. This enables us to reformulate the problem to two decoupled evolution equations at each enrichment step, of which one is a parameter-independent partial differential equation and the other one is a parameter-dependent ordinary differential equation. These equations are directly derived from the original dynamical system and the previously separate representation terms. Moreover, the computational process of the proposed method can be split into an offline stage, in which the reduced basis functions are constructed, and an online stage, in which the efficient low-rank representation of the solution is employed. The proposed dynamical Variable-separation method is capable of offering reduced computational complexity and enhanced efficiency compared to many existing low-rank separation techniques. Finally, we present various numerical results for linear/nonlinear parameter-dependent dynamical systems to demonstrate the effectiveness of the proposed *** Codes 35R60, 37M99, 65P99 Copyright © 2025, The Authors. All rights reserved.

关键词： Ordinary differential equations

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IoT-Based Water Quality Monitoring and Detection System 1

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2nd International Conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

作者： Kanchana, M. Gopirajan, P.V. Sureshkumar, K. Sudharsanan, R. Suganthi, N. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai603203 India Department of Information Technology Saveetha Engineering College Chennai602105 India Department of Information Technology SRM Institute of Science and Technology Ramapuram Chennai600089 India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai600089 India

ISBN: (数字)9789819919093

ISBN: (纸本)9789819919086

This paper suggests Internet of things (IoT) and Arduino-based tele-water quality monitoring systems integrated with the web application. The most critical component of the Wireless Sensor Network includes a microcontroller for processing the circuit's sensor data, a communication system between and within node communication, and a few other sensors such as pH, Microbial Fuel Cell (MFC), humidity, and temperature. All data is collected at the Arduino (data which is sensed by the sensors) by using remote monitoring and Internet of things technology (ESP8622MOD). The real-time data is transmitted, and that data is displayed by sending through the cloud in a human interface format on a server device (pc/mobile) by the web or mobile applications. The main objective is to get a water monitoring system with high mobility (the ability to check water more freely, the ability to check wherever required), high frequency, and low power (as it is compatible, we can take and check quickly). Therefore, our main objective is that our system will immensely help people with small water reservoirs, water mines, and villagers/ framers with fields to become conscious/aware of polluted water and stop contaminating the water. The deciding parameters of water, whether toxic or drinkable, are decided by the potential level of hydrogen value. If the hydrogen value is less than five, it is acidic, and if the hydrogen value is above 7.5, then it is primary. The MFC sensor, if the analogy sensor, senses any electricity from the cathode and anode ends (0–200 indicates acidic water, 200–300 is normal water, and above 300 is essential to water);by this water, we decide whether water toxic in nature and temperature and humidity also plays a role in determining the quality, it can result in an effect on the level of O2. Also, the capacity of organisms to resist certain pollutants, so different conditions should have different water temperatures. © 2023, The Author(s), under exclusive license to Spr

关键词： Internet of things

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Streaming Algorithms for Non-Submodular Maximizationon the Integer Lattice

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Tsinghua science and Technology 2023年第5期28卷 888-895页

作者： Jingjing Tan Yue Sun Yicheng Xu Juan Zou School of Mathematics and Information Science Weifang UniversityWeifang 261061China Beijing Institute for Scientific and Engineering Computing Beijing University of TechnologyBeijing 100124China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China School of Mathematical Sciences Qufu Normal UniversityQufu 273165China

Many practical problems emphasize the importance of not only knowing whether an element is selectedbut also deciding to what extent it is selected,which imposes a challenge on submodule *** this study,we consider the monotone,nondecreasing,and non-submodular maximization on the integer lattice with a *** first design a two-pass streaming algorithm by refining the estimation interval of the optimal *** element,the algorithm not only decides whether to save the element but also gives the number of ***,we introduce the binary search as a subroutine to reduce the time ***,we obtain a one-passstreaming algorithm by dynamically updating the estimation interval of optimal ***,we improve the memorycomplexity of this algorithm.

关键词： integer lattice non-submodular streaming algorithm cardinality constraint

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Deep learning:Applications,architectures,models,tools,and frameworks:A comprehensive survey

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 581-606页

作者： Mehdi Gheisari Fereshteh Ebrahimzadeh Mohamadtaghi Rahimi Mahdieh Moazzamigodarzi Yang Liu Pijush Kanti Dutta Pramanik Mohammad Ali Heravi Abolfazl Mehbodniya Mustafa Ghaderzadeh Mohammad Reza Feylizadeh Saeed Kosari School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)ShenzhenChina Department of Cognitive Computing Institute of Computer Science and EngineeringSaveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiIndia Department of Computer Science Islamic Azad UniversityTehranIran Computer Engineering Department Bu‐Ali Sina UniversityHamedanIran Department of Mathematics and Statistics Iran University of Science and TechnologyTehranIran Department of Mathematics and Statistics University of Northern British ColumbiaBCCanada Peng Cheng Laboratory ShenzhenChina Deprtment of Technology Jodhpur Institute of Engineering&TechnologyJodhpurIndia Department of Computer Engineering Sadjad University of TechnologyMashhadIran Department of Electronics and Communications Engineering Kuwait College of Science and TechnologyDoha DistrictKuwait Department of Artificial Intelligence Smart University of Medical SciencesTehranIran Department of Industrial Engineering Shiraz BranchIslamic Azad UniversityShirazIran Institute of Computing Science and Technology Guangzhou UniversityGuangzhouChina

Deep Learning(DL)is a subfield of machine learning that significantly impacts extracting new *** using DL,the extraction of advanced data representations and knowledge can be made *** effective DL techniques help to find more hidden *** learning has a promising future due to its great performance and *** need to understand the fundamentals and the state‐of‐the‐art of DL to leverage it effectively.A survey on DL ways,advantages,drawbacks,architectures,and methods to have a straightforward and clear understanding of it from different views is explained in the ***,the existing related methods are compared with each other,and the application of DL is described in some applications,such as medical image analysis,handwriting recognition,and so on.

关键词： data mining data privacy deep learning

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Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

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Journal of Rock Mechanics and Geotechnical engineering 2024年第10期16卷 3982-4003页

作者： Libing Yang Trung Nguyen-Thoi Trung-Tin Tran College of Civil Engineering and Architecture Hunan Institute of Science and TechnologyYueyang414006China Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityViet Nam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh CityViet Nam Department of Information Technology Greenwich VietnamFPT UniversityDa Nang550000Viet Nam

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering *** study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal *** technique is expected to improve the accuracy of friction angle prediction *** friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,***-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,***-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering *** improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.

关键词： Natural hazards Slope stability Friction angle Clay Soft computing models Geotechnical engineering

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Autonomous Robot Navigation in Unknown Terrains using Generative Adversarial Networks 4

Autonomous Robot Navigation in Unknown Terrains using Genera...

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4th International Conference on Soft computing for Security Applications, ICSCSA 2024

作者： Chitra, P. Sridar, K. Govindan, Sudha Britto Raj, S. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. St Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Veerammal Engineering College Department of Computer Science and Engineering Dindigul India Sona College of Technology Department of Computer Science and Engineering Salem India RRASE College of Engineering Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computational Intelligence Chennai India SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798331515720

Autonomous Robot Navigation in unknown and dynamic environments is considered one of the grand challenges in robotics. These motions that guide it span from rescue operations to planetary exploration. Classical methods for path planning, including A*, Dijkstra, and RRT, were unable to balance the efficiency and adaptability of motion planning over complex terrain. This may have a powerful solution using Generative Adversarial Networks (GANs) with advanced machine learning techniques to improve the ability of robotic navigation. This study proposes a GAN-based approach for designing an effective self-adaptive path planning and obstacle avoidance system for autonomous robots. Despite performing well in structured environments, traditional techniques normally fail in analysing the unstructured terrains with real-time adjustments and dynamic changes due to the high computational costs and lack of flexibility involved. In this regard, the proposed GAN-based system resolves these issues by rating navigation paths concerning safety and efficiency. Quantitative analysis demonstrates that within a forest, desert, urban, or off-road environment, the computational times by using the developed strategy turn out to be 0.12 seconds, 0.10 seconds, 0.14 seconds, and 0.13 seconds, respectively, behaving much lower than what can be achieved with other algorithms. This approach proves much superior in terms of computational efficiency path smoothness and adaptability under different environmental conditions in applications related to autonomous robot navigation systems. © 2024 IEEE.

关键词： Generative adversarial networks

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Permissioned Blockchain Security Pqfabric from Quantum and Classical Attacks 1

Permissioned Blockchain Security Pqfabric from Quantum and C...

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1st International Conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Tamilarasi, C. Bhavadrarani, K. Kittappa, Thiagarajan Subashini, N. Anitha, V. Vimalraj, E. B.S.Abdur Rahman Crescent Institute of Science & Technology Department of Computer Science and Engineering Chennai India V.S.B. Engineering College Department of Computer Science and Engineering Karur India Rajalakshmi Institute of Technology Department of Mathematics Chennai600124 India Sathyabama Institute of Science and Technology Department of Mathematics Chennai600119 India Panimalar Engineering College Department of CSE Poonamallee Chennai600123 India School of Computing Mohan Babu University Andhra Pradesh Tirupati India

ISBN: (纸本)9798331518578

Old style cryptographic techniques are genuinely compromised by the development of quantum computing, which requires the formation of safety ideal models that are impervious to quantum mistakes. A new permissioned blockchain security system called PqFabric is intended to oppose both traditional and quantum attacks. PqFabric ensures the validness, mystery, and respectability of exchanges in a quantum-empowered future by including post-quantum cryptographic techniques like hash-based and grid based marks. As opposed to ordinary blockchains, PqFabric utilizes a half breed agreement process that gets information trades and members characters by melding Byzantine Fault Tolerance (BFT) with quantum-safe encryption. The system gives protection against calculation based attacks by Shor and Grover, tending to huge imperfections in existing blockchain structures. Besides, when quantum-safe guidelines advance, its secluded plan empowers smooth enhancements. With this procedure, PqFabric is situated as a ground breaking answer for organizations and areas wishing to safeguard their blockchain networks from new cryptographic risks. © 2025 IEEE.

关键词： Quantum computers

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Cervical Cancer Risk Prediction using SMOTE and Explainable AI

Cervical Cancer Risk Prediction using SMOTE and Explainable ...

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Pervasive computational Technologies (ICPCT), International Conference on

作者： Priyansh Agarwal Annanya Chaudhary T. R. Sarvanan Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Department of Computational Intelligence Faculty of Engineering & Tech. SRM Institute of Science and Technology

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Cervical cancer, accounting for 7.9% of cancers in women globally, is a critical health challenge, particularly due to its asymptomatic nature in early stages. This study proposes a machine learning-driven framework to predict cervical cancer risk using clinical data, addressing class imbalance through SMOTE and optimizing model performance via GridSearchCV. By evaluating five classifiers—K-Nearest Neighbors, Random Forest, Support Vector Classifier, Logistic Regression, and Decision Trees—this research identifies the Decision Tree model as the most robust, achieving superior precision, recall, and accuracy metrics. Additionally, Explainable AI techniques such as SHAP and LIME were employed to enhance interpretability, offering clinicians actionable insights into key risk factors driving predictions. The results demonstrate the effectiveness of machine learning in facilitating early detection and personalized treatment strategies for cervical cancer, thereby advancing its integration into clinical applications.

关键词： Measurement Accuracy Explainable AI Pipelines Predictive models Data models Decision trees Cervical cancer Random forests Overfitting

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