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A Comparison of Controller Architectures and Learning Mechanisms for Arbitrary Robot Morphologies

A Comparison of Controller Architectures and Learning Mechan...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Luo, Jie Tomczak, Jakub Miras, Karine Eiben, Agoston E. Vrije Universiteit Amsterdam Computer Science Dept. Netherlands Eindhoven University of Technology Computer Science Dept. Netherlands

ISBN: (纸本)9781665430654

The main question this paper addresses is: What combination of a robot controller and a learning method should be used, if the morphology of the learning robot is not known in advance? Our interest is rooted in the context of morphologically evolving modular robots, but the question is also relevant in general, for system designers interested in widely applicable solutions. We perform an experimental comparison of three controller-and-learner combinations: one approach where controllers are based on modelling animal locomotion (Central Pattern Generators, CPG) and the learner is an evolutionary algorithm, a completely different method using Reinforcement Learning (RL) with a neural network controller architecture, and a combination 'in-between' where controllers are neural networks and the learner is an evolutionary algorithm. We apply these three combinations to a test suite of modular robots and compare their efficacy, efficiency, and robustness. Surprisingly, the usual CPG-based and RL-based options are outperformed by the in-between combination that is more robust and efficient than the other two setups. © 2023 IEEE.

关键词： controller CPG evolutionary robotics learning algorithm Reinforcement learning

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Performance Analysis of Comparison Operations on Homomorphically Encrypted Sensitive data 13

Performance Analysis of Comparison Operations on Homomorphic...

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13th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2023

作者： Naito, Hana Matsumoto, Marin Nakano, Miyuki Oguchi, Masato Ochanomizu University Dept. of Computer Science Tokyo Japan Tsudu University Dept. of Computer Science Tokyo Japan

ISBN: (纸本)9798350324150

Homomorphic encryption (HE) is one of the solutions to securely analyze and utilize sensitive client data such as purchase history and sensor data accumulated on cloud servers. HE permits the computation of encrypted data, such as addition and multiplication, without decryption. However, not all applications can run with HE because of restrictions on certain types of calculations. To perform matching or comparison operations in encrypted form, the plaintext must be encoded before encryption to enhance data usability. Depending on the encoding method, the execution time of the HE operations and ciphertext size may increase. Therefore, it is important to select an encoding method based on the purpose of the computations and data features. In this study, we performed comparison op-erations on homomorphically encrypted data using two different encoding methods: binary encoding and bitmask encoding. We demonstrate how computations can be executed using the TFHE scheme, which supports logical operations over encrypted data, and we evaluate the performance of the comparison operations using two different encoding methods. © 2023 IEEE.

关键词： Sensitive data

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Classification of Left/Right Hand Movement from EEG Signal by Empirical Mode Decomposition with Ensemble of classifiers 14

Classification of Left/Right Hand Movement from EEG Signal b...

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14th International Conference on Cloud Computing, data science and Engineering, Confluence 2024

作者： Choudhary, Neeraj Kumar, Kodidasu Murali Mathew, Jimson Acchugatla, Sravanthi Mahindra University Dept. of Computer Science and Engg. Hyderabad500050 India Indian Institute of Technology Patna Dept. of Computer Science and Engg. Patna801103 India Mahindra University Dept. of Computer Science and Engg. Hyderabad500090 India

ISBN: (纸本)9798350344837

Electroencephalography (EEG) is a brain wave signal that plays a significant role in the neural control interface. EEG signals have many nonlinear dynamic properties;because of these properties, handling EEG signals for modeling with standard linear approaches will lead to inconsistent results. EEG has a significant importance in the brain-computer interface (BCI). BCI has demonstrated a great deal of potential to benefit the human way of life. Additionally, its application targets people with disabilities. BCI extracts the informative featuresfrom acquired brain signals and generates signals knowing these features, which can control external devices. EEG signals can show improvement in classifying motor imagery tasks. In this paper, we introduce a new method for extracting time-frequency-related properties on different scales using empirical mode decomposition (EMD) and classifying the left and right hand movements of the subject. EMD is a signal processing technique that decomposes a signal into different frequency bands of the same length. We use an ensemble of classifiers with bootstrapped data for classification. The proposed method perform better than the previous method [1] in terms of accuracy and F1-score. The empirical results show that the proposed method has an average accuracy of 97.30% and an average F1 score of 96.35%. © 2024 IEEE.

关键词： Empirical mode decomposition

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Quantum-Enhanced Machine Learning for Precision Breast Cancer Diagnosis 17

Quantum-Enhanced Machine Learning for Precision Breast Cance...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Banerjee, Agniva Indian Institute of Science Education and Research Bhopal Dept. Electrical Engineering and Computer Science Bhopal India

ISBN: (纸本)9798331531195

Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. This research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes. © 2025 IEEE.

关键词： Diseases

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Identification of Affective States from Malayalam Audios Based on Valence Arousal Prediction

Identification of Affective States from Malayalam Audios Bas...

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2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： George, Talit Sara George, Jini Pournami, P.N. National Institute of Technology Dept. of Computer Science and Engg. Calicut India Providence College of Engineering Dept. of Computer Science and Engg. Chengannur India

ISBN: (纸本)9798331540067

Affective state plays a key role in emotion recognition, influencing various applications from mental health evaluation to human-machine interaction. These states can be described using two primary aspects: valence, which indicates the pleasantness of the speech, and arousal, which relates to the intensity of the emotion. This study aims to automate the prediction of valence and arousal values from speech data, addressing the limitations of manual annotation and human error. The IIITSAINT-EMOMDB dataset, created from Malayalam films, uses the Circumplex Model of Affect to map emotions such as happy, sad, angry, and calm. The approach leverages machine learning models, including Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), for prediction. Experimental results demonstrate that the traditional models like SVR and ANN are outperformed by the CNN model, which achieved a mean squared error (MSE) of 0.3 for valence and 0.1 for arousal. These findings highlight the power of automated emotion recognition in large-scale audio datasets, offering promising applications in affective computing and psychological analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Artificial Neural Network for Human Object Interaction System over Aerial Images 4

Artificial Neural Network for Human Object Interaction Syste...

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4th International Conference on Advancements in Computational sciences, ICACS 2023

作者： Pervaiz, Mahwish Jalal, Ahmad Bahria University Dept. of Computer Science Islamabad Pakistan Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9781665463775

Recognition of human and object interaction is an important milestone in image understanding and event analysis. Recognizing interactions between humans and objects is the most promising way in visual classification to analyze activities or events happening at any place. Many researchers have invested their efforts in the field of activity recognition between humans and objects. However, some challenges are still open due to incorrect interaction inferences, occlusion between a person and target objects, unrelated target objects, or unclear activities. The major goal of this research project is to provide a useful system for categorising event classification and human-object interaction. Preprocessing, feature extraction, feature optimization, and classification using an artificial neural network are the four main processes of the proposed method. The Games Action dataset, which contains aerial photos, has been used to test the proposed technique. Results demonstrate the effectiveness of the suggested system. © 2023 IEEE.

关键词： Object recognition

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A Novel Deep Ensemble Learning Approach to Predict Eye Diseases from OCT Images 5

A Novel Deep Ensemble Learning Approach to Predict Eye Disea...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Uddin, Nazim Mia, Md Sajib Uddin, Mohammed Nasir Mahmud, Prince Chandpur Science and Technology University Dept. of Information and Communication Technology Bangladesh Sylhet Engineering College Dept. of Computer Science and Engineering Bangladesh Jagannath University Dept. of Computer Science and Engineering Bangladesh Chandpur Science and Technology University Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

As the world advances in technology, the classification of eye diseases is thought to have clinical applications since it evaluates and analyzes the results. Optometrists research and diagnose age-related eye problems such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen using optical coherence tomography (OCT) of the human eye. Because their symptoms are similar, identifying these diseases is challenging and requires hours of expert analysis. The requirements for eye illness classification should be straightforward in nature, structure, and evaluation. Deep Learning Convolutional Neural Network Classification can create a hierarchical representation of images to differentiate between normal and diseased eye patterns for diagnostic purposes. Therefore, this paper aims to implement and define deep learning to classify and identify eye diseases. Using a four-class classification problem, this work investigates the automatic detection of Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal in Optical Coherence Tomography (OCT) pictures. Multi-transfer learning architecture was used in the suggested deep learning Ensemble classification model. The most incredible accuracy of our proposed Ensemble model for multi- class classification was 83%. © 2023 IEEE.

关键词： Optical tomography

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Homogenous Ensemble Learning for Air Quality Index Prediction 15

Homogenous Ensemble Learning for Air Quality Index Predictio...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Medhi, Shrabani Boruah, Rituraj Baruah, Sagar Pratim Das, Hiranya Kumar Girijananda Chowdhury University Dept. of Computer Science and Engineering Guwahati India Girijananda Chowdhury Institute of Management and Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331300579

Air pollution is a global issue of concern. Accurate prediction of the air quality index (AQI) plays a crucial role in understanding pollution patterns and implementing effective mitigation strategies. This research paper presents a comprehensive study on the application of ensemble learning techniques for AQI prediction, aiming to enhance prediction accuracy and provide insights into the driving factors behind pollution levels. The primary objective is to conduct a comparative analysis of homogenous ensemble method, namely, bagging in conjunction with individual base models, such as multiple linear regression, support vector regression, decision trees, and artificial neural networks. In total we have created 4 non-ensembled models and 4 ensembled models. Additionally, the research investigates the importance of different features in AQI prediction models, aiming to identify the key factors influencing air pollution. Hyperparameter tuning is done to optimize the models. © Grenze Scientific Society, 2024.

关键词： Prediction models

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Analytical Approach Towards Radiological Image-based Fracture Assessment Using Artificial Intelligence 1

Analytical Approach Towards Radiological Image-based Fractur...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Sylvia, Takhellambam Dubey, Sanjay Kumar Thongam, Khelchandra Amity University Computer Science & Engineering Dept. Uttar Pradesh Noida India National Institute of Technology Manipur Computer Science & Engineering Dept. Imphal India

ISBN: (纸本)9798350353648

The utilization of Artificial Intelligence (AI) techniques has been prevalent in various applications and the gradual increase in implementation towards healthcare sectors has scaled up. computer aided diagnosis has emerged as a major research area directed toward efficient use of resources. Fracture of bone has been a popular health problem associated with risk for mortality. Fractures are frequent in the long bones or the weight-bearing bones. AI techniques to accurately detect and classify fractures are still a challenging task beyond the conventional methods. Our objective is to assess the effectiveness of multiple algorithms in identifying and categorizing ankle fractures using radiological images. This review paper focuses on several available approaches of Deep-Learning (DL) and Machine-Learning (ML) regarding Ankle Fracture Classification and Identification based on Radiological Images for Medical Applications. The goal is to assist physicians and orthopaedics with simple and realistic decision support systems. In addition to a comprehensive analysis of the research, this study looked at several real-world examples of AI applications in the healthcare industry. Consequently, it is critical to have a fundamental grasp of these ML and DL algorithms to be ready for upcoming advancements in AI. We have reviewed and classified scientific conference and journal articles used for various AI techniques and offer future utility as a comparative analysis for further research in healthcare sectors. © 2024 IEEE.

关键词： Deep learning

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The Effect of Using Tangible User Interfaces Compared to Traditional Learning for Teaching Programming in Higher Education: An Experimental Study 1

The Effect of Using Tangible User Interfaces Compared to Tra...

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1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Hany, Ashraf Ramadan, Enjy Akl, Amr Atia, Ayman Dept. of Computer Science Giza Egypt Faculty of Computers and AI HCI-LAB Dept. of Computer Science Egypt

ISBN: (纸本)9798350335569

There is a lot of interest in employing tangible user interfaces (TUIs) to enhance teaching programming, but there is conflicting evidence for their advantages since implementing them in educational settings offers more difficulties than traditional learning. This paper examines the effect of using TUIs in learning and compares it to traditional learning methods. The study measures the difference in learning rate on university students majoring in computer science. The study was conducted on 20 university students aged 17 to 22 who participated in a 45-minute learning activity. The activity used a Tangible User Interface (TUI) and paper notes from a textbook. Pre-test and post-test results were collected as part of the study's between-groups design, and the TUI group outperformed the traditional learning group in terms of learning gains. The TUI group had an increase of 25.4% in their scores following the three learning sessions and a 1.5% decrease when the retention was measured two weeks later. The traditional paper learning group had increase of 9.1% in scores and a decrease of 7.2% in retention. © 2023 IEEE.

关键词： User interfaces

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