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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Kavipriya, E. Sasikala, S. Department of Computer Science With Cognitive Systems and AIML Hindusthan College of Arts & Science Tamil Nadu Coimbatore India

ISBN: (纸本)9789819795062

Machine Learning techniques on different sectors plays a vital role for prediction analysis. In healthcare sector, diagnosis support and prognostic evaluation has been done using various algorithms of machine learning. The Multiple Sclerosis (MS) is one of the chronic diseases which affects the central nervous system of human. There are multiple symptoms and disabilities indicate the issues of Multiple Sclerosis. This neurological disorder has various patterns and disease progression rate among worldwide. This causes a variety of symptoms including fatigue, numbness, visual difficulties, urinary incontinence, and difficulty moving and cognitive deficiencies. This paper analyzes the risk factors of Multiple Sclerosis and early prediction using Machine learning methods. This work proposed hybrid Recurrent Neural Network with mini-batch gradient descent method for Multiple Sclerosis prediction. The performance of this proposed method is evaluated and the achieved accuracy rate is 92%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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Robust Human Interaction Recognition Using Extended Kalman Filter

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computers, Materials & Continua 2024年第11期81卷 2987-3002页

作者： Tanvir Fatima Naik Bukht Abdulwahab Alazeb Naif Al Mudawi Bayan Alabdullah Khaled Alnowaiser Ahmad Jalal Hui Liu Faculty of Computing&Artificial Intelligence Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

In the field of computer vision and pattern recognition,knowledge based on images of human activity has gained popularity as a research *** recognition is the process of determining human behavior based on an *** implemented an Extended Kalman filter to create an activity recognition system *** proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the *** minimize noise,we use Gaussian *** of silhouette using the statistical *** use Binary Robust Invariant Scalable Keypoints(BRISK)and SIFT for feature *** next step is to perform feature discrimination using Gray *** that,the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive *** experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88%and 0.86%recognition rate.

关键词： Pattern recognition geometric features activity recognition full-body texture

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CSCW – past, present and future

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i-com 2024年第1期23卷 1-5页

作者： Richter, Alexander Koch, Michael Prilla, Michael Computer Science Department Universität der Bundeswehr München München Germany Wellington School of Business and Government Victoria University of Wellington Wellington New Zealand Interactive Systems Department for Human Centered Computing and Cognitive Science Faculty for Computer Science University of Duisburg-Essen Duisburg Germany

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Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

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computers, Materials & Continua 2024年第8期80卷 3315-3332页

作者： Asifa Mehmood Qureshi Abdul Haleem Butt Abdulwahab Alazeb Naif Al Mudawi Mohammad Alonazi Nouf Abdullah Almujally Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

关键词： Semantic segmentation YOLOv5 vehicle detection and tracking Kalman filter SURF

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Generalized Two-Point Visual Control Model of Human Steering for Accurate State Estimation1

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ASME Letters in Dynamic systems and Control 2025年第1期5卷 011004页

作者： Mai, Rene E. Sears, Katherine Roessling, Grace Julius, Agung Mishra, Sandipan Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Cognitive Science Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.15 m error across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle's lateral state. © 2024 by ASME.

关键词： Man machine systems

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AI-Powered Privacy Preservation: A Novel Framework for Adaptive Data Protection 2

AI-Powered Privacy Preservation: A Novel Framework for Adapt...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Kumar, R. Senthil Lokeshwari, J. Shanmugam, Selvanayaki Kolandapalayam Dr. N.G.P. Arts and Science College Department of Computer Science with Cognitive Systems Tamil Nadu Coimbatore India Ashland University Dept. of Mathematics and Computer Science AshlandOH United States

ISBN: (纸本)9798331542573

The enlargement of Artificial Intelligence (AI)-powered privacy preservation techniques leads to new research avenues and innovations. Moreover, in today's data driven world the privacy and data protection are increased. In order to balance both data utility and individual privacy rights. This research paper provides a unique AI-powered privacy preservation framework by using machine learning algorithm that adapts to diverse data scenarios. The proposed framework uses Adaptive Privacy-Preserving Ensemble Learning (APPEL) machine learning algorithms to ensure the optimal data protection while maintaining both data utility and also privacy settings dynamically. Additionally, this paper evaluates the efficacy of data driven applications like Healthcare, social media and Online Platforms, Internet of Things (IoT) and smart devices. This study collected and pre-processed the data from data driven applications to determine trends, insights, and highlighting key challenges and opportunities for improvement. This framework's efficacy in real-world applications demonstrate the experimental results and provides an accurate and promising solution for privacy-preserving data analysis. This research contributes to the development of privacy-preserving AI solutions to safeguard sensitive data in data driven environment. © 2024 IEEE.

关键词： Adversarial machine learning

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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Using Deep Learning Models for COVID-19 Related Sentiment Analysis on Twitter Data

Using Deep Learning Models for COVID-19 Related Sentiment An...

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2023 Human-Centered cognitive systems, HCCS 2023

作者： Bangyal, Waqas Haider Amina, Saeeda Shakir, Rabia Ubakanma, George Iqbal, Muddesar Department of Computer Science Murree Pakistan Department of Computer Science Sialkot Pakistan Fuuast Department of Computer Science Islamabad Pakistan London South Bank University School of Engineering School of Engineering Cognitive Systems Research Centre United Kingdom

ISBN: (纸本)9798350359183

The significance of sentiment analysis has increased in modern times due to the extensive use of social media platforms as a medium for individuals to express their opinions. Twitter is widely acknowledged as a popular social media site mostly used for microblogging. People often voice their opinions on current events, making it challenging for researchers to accurately classify the mood expressed in these opinions. This research study presents a novel and efficient method for identifying and detecting false or misleading information pertaining to the COVID-19 pandemic. The dataset including artificially created news stories is obtained from a collection of texts and processed via the cycle of natural language processing (NLP). For this research study, three advanced deep learning models were used to predict the emotion of news items, accurately differentiating between genuine and fraudulent ones. This study utilizes Convolutional Neural Networks, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) as deep learning classifiers. Subsequently, it compares the outcomes achieved from these classifiers. The findings suggest that the BiGRU deep learning classifier has exceptional accuracy and efficiency, achieving a remarkable accuracy rate of 91%. © 2023 IEEE.

关键词： Sentiment analysis

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Energy-Aware Reliable Routing with Blockchain Security for Heterogeneous Wireless Sensor Networks 5th

Energy-Aware Reliable Routing with Blockchain Security for H...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Swapna, M.P. Ramkumar, J. Karthikeyan, R. Center for Cyber Security Systems and Networks Amrita Vishwa Vidyapeetham Kollam Amritapuri India Department of Information Technology & Cognitive Systems Sri Krishna Arts & Science College Coimbatore India Department of Computer Technology Sri Krishna Adithya College of Arts and Science Coimbatore India

ISBN: (纸本)9789819761050

Wireless sensor networks, the dynamic networks with spatially dispersed sensors are used indispensably in a wide range of applications. Heterogeneous wireless sensor networks represent a collection of sensor nodes of varying capabilities. However, if the energy in the network is not handled effectively, the resource-constrained sensors may reach an early death, affecting the network lifetime. Security and routing are the two vital phenomena in wireless sensor networks that need careful and effective algorithmic execution. The proposed approach is an energy-aware routing algorithm combined with secure data transmission. TDDSRP is an innovative protocol to ensure security while reducing energy consumption. The residual energy and the direct trust value of nodes are crucial parameters to evaluate the energy trust value. The effectiveness of the protocol is tested against the existing counterparts using MATLAB simulations. The experimental results illustrate the performance of the protocol in energy consumption rate, throughput, and security measures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： MATLAB

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ROBUST ADVERSARIAL REINFORCEMENT LEARNING VIA BOUNDED RATIONALITY CURRICULA 12

ROBUST ADVERSARIAL REINFORCEMENT LEARNING VIA BOUNDED RATION...

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12th International Conference on Learning Representations, ICLR 2024

作者： Reddi, Aryaman Tölle, Maximilian Peters, Jan Chalvatzaki, Georgia D'Eramo, Carlo Department of Computer Science TU Darmstadt Germany Germany Systems AI for Robot Learning Germany Center for Cognitive Science TU Darmstadt Germany Center for Artificial Intelligence and Data Science University of Würzburg Germany

Robustness against adversarial attacks and distribution shifts is a long-standing goal of Reinforcement Learning (RL). To this end, Robust Adversarial Reinforcement Learning (RARL) trains a protagonist against destabilizing forces exercised by an adversary in a competitive zero-sum Markov game, whose optimal solution, i.e., rational strategy, corresponds to a Nash equilibrium. However, finding Nash equilibria requires facing complex saddle point optimization problems, which can be prohibitive to solve, especially for high-dimensional control. In this paper, we propose a novel approach for adversarial RL based on entropy regularization to ease the complexity of the saddle point optimization problem. We show that the solution of this entropy-regularized problem corresponds to a Quantal Response Equilibrium (QRE), a generalization of Nash equilibria that accounts for bounded rationality, i.e., agents sometimes play random actions instead of optimal ones. Crucially, the connection between the entropy-regularized objective and QRE enables free modulation of the rationality of the agents by simply tuning the temperature coefficient. We leverage this insight to propose our novel algorithm, Quantal Adversarial RL (QARL), which gradually increases the rationality of the adversary in a curriculum fashion until it is fully rational, easing the complexity of the optimization problem while retaining robustness. We provide extensive evidence of QARL outperforming RARL and recent baselines across several MuJoCo locomotion and navigation problems in overall performance and robustness. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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