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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Khan, Asif Siddiqui, Mohammad Atif Integral University Integral Robotics Lab Department of Computer Application Lucknow India Integral University Integral Robotics Lab Department of Electrical Engineering Lucknow India

ISBN: (纸本)9798350376425

The research study on the "Design and Implementation of an AI-Enhanced Mental Health Tool for Academic Stress"addresses the growing mental health crisis among students, particularly concerning academic pressures. The study highlights alarming statistics, such as over 13,000 student suicides in India in 2021 - a 4.5% increase from the previous year and a 70% rise since 2011 - indicating an urgent need for targeted interventions. The academic environment, characterized by high expectations, intense pressure, and fierce competition, places considerable strain on students' mental well-being. Recognizing that one-size-fits-all solutions are inadequate, this research advocates for a personalized approach to address these unique challenges. Artificial Intelligence (AI) is positioned as a key solution in this effort. The proposed AI-enhanced mental health tool aims to provide students with real-time, personalized support. Through proactive interventions and individualized assistance, it seeks to revolutionize mental health care in academic settings. This study underscores the importance of innovative approaches to tackling the student mental health crisis. By fostering resilience and well-being, the AI tool aims to effectively address academic stress and improve mental health outcomes. © 2024 IEEE.

关键词： Emotional intelligence

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Fusion of Feature Ranking Methods for an Effective Intrusion Detection System

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computers, Materials & Continua 2023年第8期76卷 1721-1744页

作者： Seshu Bhavani Mallampati Seetha Hari School of Computer Science and Engineering VIT-AP UniversityAndhra PradeshIndia Center of Excellence AIand RoboticsVIT-AP UniversityAndhra PradeshIndia

Expanding internet-connected services has increased cyberattacks,many of which have grave and disastrous *** Intrusion Detection System(IDS)plays an essential role in network security since it helps to protect the network from vulnerabilities and *** extensive research was reported in IDS,detecting novel intrusions with optimal features and reducing false alarm rates are still ***,we developed a novel fusion-based feature importance method to reduce the high dimensional feature space,which helps to identify attacks accurately with less false alarm ***,to improve training data quality,various preprocessing techniques are *** Adaptive Synthetic oversampling technique generates synthetic samples for minority *** the proposed fusion-based feature importance,we use different approaches from the filter,wrapper,and embedded methods like mutual information,random forest importance,permutation importance,Shapley Additive exPlanations(SHAP)-based feature importance,and statistical feature importance methods like the difference of mean and median and standard deviation to rank each feature according to its *** by simple plurality voting,the most optimal features are *** the optimal features are fed to various models like Extra Tree(ET),Logistic Regression(LR),Support vector Machine(SVM),Decision Tree(DT),and Extreme Gradient Boosting Machine(XGBM).Then the hyperparameters of classification models are tuned with Halving Random Search cross-validation to enhance the *** experiments were carried out on the original imbalanced data and balanced *** outcomes demonstrate that the balanced data scenario knocked out the imbalanced ***,the experimental analysis proved that our proposed fusionbased feature importance performed well with XGBM giving an accuracy of 99.86%,99.68%,and 92.4%,with 9,7 and 8 features by training time of 1.5,4.5 and 5.5 s on Network Security Laborat

关键词： Cyber security feature ranking imbalance preprocessing IDS SHAP

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Nonlinear Dynamic Inversion-based Motion Planning of a Floating Satellite Platform

Nonlinear Dynamic Inversion-based Motion Planning of a Float...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Banerjee, Avijit Tevetzidis, Ilias Satpute, Sumeet Gajanan Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents a nonlinear dynamic inversion-based motion plan for a levitating robotic satellite emulation platform. The frictionless motion of such a levitating platform has dynamic equivalency with a satellite’s realistic movement in space, thus evolving as an efficient demonstration tool as a hardware-in-loop satellite motion simulator. A brief design description of the emulation platform unified with various sensor and actuator units is presented, and an associated nonlinear mathematical model representing its coupled dynamic motion is described. The proposed control design architecture manipulates the associated nonlinear dynamics, which appear due to the coupling of translational and rotational movement of the robotic platform, thereby establishing an efficient trajectory tracking capability for various operating regions without the additional requirement of gain scheduling. In order to realize the control requirement through a set of ON-OFF thruster-based actuation units, which closely resembles the reaction control system of realistic satellite design, an optimal thrust allocation logic is proposed that accounts for various physical limitations of thrusters such as thrusters as its magnitude constraint and minimum on time. The efficacy of the proposed modelling and control scheme is experimentally validated in different realistic motion planning scenarios, such as point-to-point manoeuvres with terminal objectives and a superior path-following capability. The experimental studies are supported with related results. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Motion planning

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Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning models

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Multimedia Tools and Applications 2024年 1-25页

作者： Polo-Rodríguez, A. Valera, J.C. Peral, J. Gil, D. Medina-Quero, J. Department of Computer Engineering Automation and Robotics University of Granada Granada18071 Spain Alicante03690 Spain Department of Software and Computing Systems University of Alicante Alicante03690 Spain

The field of human activity recognition has evolved significantly, driven largely by advancements in Internet of Things (IoT) device technology, particularly in personal devices. This study investigates the use of ultra-wideband (UWB) technology for tracking inhabitant paths in home environments using deep learning models. UWB technology estimates user locations via time-of-flight and time-difference-of-arrival methods, which are significantly affected by the presence of walls and obstacles in real environments, reducing their precision. To address these challenges, we propose a fingerprinting-based approach utilizing received signal strength indicator (RSSI) data collected from inhabitants in two flats (60 m2 and 100 m2) while performing daily activities. We compare the performance of convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN+LSTM models, as well as the use of Bluetooth technology. Additionally, we evaluate the impact of the type and duration of the temporal window (future, past, or a combination of both). Our results demonstrate a mean absolute error close to 50 cm, highlighting the superiority of the hybrid model in providing accurate location estimates, thus facilitating its application in daily human activity recognition in residential settings. © The Author(s) 2024.

关键词： Ultra-wideband (UWB)

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Existing Approaches & A Proposal to Content Analysis of Pilots' Speech Activities

Existing Approaches & A Proposal to Content Analysis of Pilo...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Bogdanova, Diana Giniiatullin, Azamat Batth, Jaspreet Singh Ufa University of Science and Technology Institute of Computer Science Mathematics and Robotics Ufa Russia Chandigarh University Computer Science and Engineering Chandigarh India

ISBN: (纸本)9798350395044

Content analysis of pilot speech is a key tool for improving flight safety as it allows to identify potential problems in communication between pilots, dispatchers and crew. The development of efficient algorithms for analyzing pilot speech contributes to improving communication skills, preventing incidents and improving overall airline safety. The research focuses on the possibility of applying machine learning techniques and deep neural networks to analyze speech data, which opens new perspectives for information processing and interpretation. The results of the study emphasize the interdisciplinary nature of the topic and suggest new methodological approaches, including the use of fuzzy set theory and fuzzy logic systems to handle uncertainty in speech data, which in turn helps to improve communication performance in complex and stressful flight conditions. The finalization of the work includes the evaluation of the developed methods and approaches in terms of their applicability and impact on improving flight safety, incorporating model training results and addressing testing errors. © 2024 IEEE.

关键词： Deep neural networks

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Biomimetics in Photonic Systems 16

Biomimetics in Photonic Systems

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Fok, Mable P. School of Electrical and Computer Engineering College of Engineering University of Georgia Photonics and Soft Robotics Lab AthensGA30602 United States

By learning and mimicking strategies of living organisms, effective photonics systems can be designed to tackle technology challenges. Biomimetics in microwave photonic systems and fiber-optic sensing systems for soft... 详细信息

ISBN: (纸本)9798350372076

关键词： Microwave photonics

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Real-Time Foot Pressure Mapping Using a Smart Insole with Sinusoidally Embedded Fiber Bragg Gratings 16

Real-Time Foot Pressure Mapping Using a Smart Insole with Si...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Binder, Steven Anton, Thomas Fok, Mable School of Electrical and Computer Engineering College of Engineering University of Georgia Photonics and Soft Robotics Laboratory AthensGA30602 United States

A flexible smart insole with sinusoidally embedded fiber Bragg grating sensors is designed and fabricated for delamination-free real time foot pressure mapping. The unique sensor configuration allows for accurate and ... 详细信息

ISBN: (纸本)9798350372076

关键词： Fiber Bragg gratings

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Classification of Jackfruit Species Using Deep Learning Model 2

Classification of Jackfruit Species Using Deep Learning Mode...

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2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Veeresha, R.K. Karegoudra, Shilpa Kumar, Bhavin Pavan, Pavan Department of Robotics and Artificial Intelligence Udupi574110 India Department of Computer Science and Engineering Udupi574110 India

ISBN: (纸本)9798350354461

Identifying Jackfruit spices using a deep learning model involves leveraging advanced neural network architectures to classify spices accurately. The model is trained on a dataset containing images of various spices commonly used with Jackfruit, such as deng surya, manmohan, prakashchandra, vietnamearly, and others. Through multiple layers of convolutional neural networks (CNNs) and possibly recurrent neural networks (RNNs), the model learns intricate patterns and features within the images to differentiate between different species. Transfer learning techniques might also be employed to adapt pre-trained models to the specific task of spice identification. The model's performance is evaluated on a separate test dataset to measure its accuracy, precision, recall, and F1 score. The paper provides a comprehensive overview of the deep learning approach to identifying Jackfruit spices, highlighting its potential applications in culinary research, food industry automation, and dietary analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Quadtree-based Free-space Cell-decomposition for Pathplanning with RRT* Implementation

Quadtree-based Free-space Cell-decomposition for Pathplannin...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Otte, Noah Satpute, Sumeet Gajanan Banerjee, Avijit Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper introduces a novel, computationally efficient, random-search based path-planning algorithm specifically designed to enhance the dexterous accessibility of autonomous lunar rovers operating on highly cluttered obstacle-dense planetary surfaces. The proposed pathplanning framework employs a Quadtree-based cell decomposition to enhance the performance of sampling-based path-planning algorithms, specifically those in the category of Rapidlyexploring Random Trees (RRT) and its optimized variant, RRT*. In the process of generating the path through an occupancy grid map, the obstacle-free cells are utilized as discrete sampling points for the RRT* algorithm. A unique non-uniform sampling approach is employed to favour larger cells with a higher degree of safety, thereby enabling seamless navigation through the obstacle-dense map. Additionally, the non-uniform sampling approach enhances the overall computational efficiency of the framework, making it suitable for fast, lightweight onboard implementation. The efficacy of the proposed planning framework is demonstrated through numerous simulation scenarios. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Decision trees

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Immuno-inspired Selective Aggregation for Decentralized Federated Deep Reinforcement Learning

Immuno-inspired Selective Aggregation for Decentralized Fede...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Rangu, Gayathri Nair, Shivashankar Robotics Lab. Department of Computer Science and Engineering Indian Institute of Technology Guwahati Assam Guwahati India

ISBN: (纸本)9798400704956

Conventional approaches to Federated Deep Reinforcement Learning (FDRL) often mandate the participation of all the associated devices and perform indiscriminate aggregation of the models. This can, at times, culminate in a low-performance global model being pumped back to the devices causing a setback in the performances of all the local models. Aggregation should thus, be performed judiciously based on the model performance. Unlike offline Reinforcement Learning (RL) setups, when datasets and ground truths are unavailable and data needs to be gathered in real-time, defining a suitable metric for the model performance, can be challenging. In this paper, we propose a novel Immuno-inspired approach for Selective Aggregation suitable for decentralized FDRL (dFDRL) that can act as a metric for the performance of a model based on which the decision to aggregate could be made. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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