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arXiv 2023年

作者： Chakravarthi, Bharatesh Prasad, Prabhu B.M. Kumar, Pavan B.N. School of Computing and Augmented Intelligence Arizona State University TempeAZ United States Dept. of Computer Science and Engineering Indian Institute of Information Technology Dharwad India Dept. of Computer Science and Engineering Indian Institute of Information Technology Andhra Pradesh Sri City India

This paper comprehensively reviews hand gesture datasets based on Ultraleap’s leap motion controller, a popular device for capturing and tracking hand gestures in real-time. The aim is to offer researchers and practitioners a valuable resource for developing and evaluating gesture recognition algorithms. The review compares various datasets found in the literature, considering factors such as target domain, dataset size, gesture diversity, subject numbers, and data modality. The strengths and limitations of each dataset are discussed, along with the applications and research areas in which they have been utilized. An experimental evaluation of the leap motion controller 2 device is conducted to assess its capabilities in generating gesture data for various applications, specifically focusing on touchless interactive systems and virtual reality. This review serves as a roadmap for researchers, aiding them in selecting appropriate datasets for their specific gesture recognition tasks and advancing the field of hand gesture recognition using leap motion controller technology. © 2023, CC BY.

关键词： Palmprint recognition

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Effect of Picture Prime on the Response Time and Error Rate Among Bilingual Children

Effect of Picture Prime on the Response Time and Error Rate ...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Java Banerjee Hrishikesh Roy Baisakhi Chakraborty Anupam Basu Dept. of Computer Science and Engineering National Institute of Technology Durgapur India

The Priming phenomena establishes a positive result in response to various word or picture naming tasks due to preceding manifestation in the form of a word or picture. The response time in identifying the words is less in the context of words appearing after a semantically connected word or picture compared to the words in segregation or words anteceded by a semantically unconnected word or picture. In order to investigate this phenomenon in the context of Bengali-speaking children, fifteen primary school children with ages ranging between 7 to 10 years were involved in the picture priming experiment. The experiment comprised of 25 words to be read by the children under two conditions consisting of without prime and with picture prime. Error rates of the participants are measured based on their performance during the experiment. The reaction time of the participants is also computed in seconds with reference to the two conditions that is the availability of prime and no prime. Analysis of the student's performance during the above experiment showed that the response time and error rate are comparatively reduced while identifying the target word in the condition of the presence of picture prime. To the best of our knowledge, this is the first-ever investigation regarding the effect of the presence of picture prime among Bengali-speaking children.

关键词： Visualization Technological innovation Error analysis Atmospheric measurements Semantics Measurement uncertainty Particle measurements

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Investigation of Musical Factors Affecting Walking Speed with the Same Tempo

Investigation of Musical Factors Affecting Walking Speed wit...

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Summer Virtual Conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Summer), ACIS International

作者： Makoto Fukumoto Sakura Ichikawa Dept. of Computer Science and Engineering Fukuoka Institute of Technology Fukuoka Japan

ISBN: (纸本)9798350396386

One of the applications of listening to music pieces is walking. Previous studies suggested that musical tempo is an important factor in a listener’s walking. Leman et al. found the difference in walking distance in the fixed time by employing many music pieces with the same tempo. However, the other musical factors that affect walking have not been revealed. This study aims to investigate the effects of music factors on walking by employing musical stimuli having different musical factors with the same musical tempo. Three walking experiments were conducted with fourteen participants. They walked the same distance while listening to the musical stimuli composed of different factors: musical scale, beats, and key were focused in each experiment. The musical melody and other factors of these musical stimuli were composed to be similar to each other to conduct the experiments with the independence of the focused factor. The elapsed time, the number of footsteps, and three subjective evaluations related to impressions of musical stimuli and feelings evoked by the musical stimuli were employed as the experimental indices. The experimental results showed that the difference in the musical beats affects the elapsed time, the number of steps, and the subjective evaluations.

关键词： Legged locomotion Music Artificial intelligence Software engineering

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Stage Wise Classification and prediction of Alzheimer disease using Hybrid Appraoch

Stage Wise Classification and prediction of Alzheimer diseas...

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Intelligent Technologies (CONIT), International Conference on

作者： A Prabagaran Vinay Dwivedi Neelam Jyoti Rani J Angelin Blessy P Padmanabhan Gupshup Technology India Pvt Ltd Bengaluru India School of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Dept of Computer Application School of Computing Science and Engineering Galgotias Univeristy India Department of Computer Science and Engineering Greater Noida Institute of Technology Greater Noida Uttar Pradesh India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Alzheimer’s, the most prevalent cause of dementia, leads to a continuous decline in cognitive functions, social skills, and behavior, affecting individuals’ independent functioning. As per survey 2010 in India around 3.7 million affected by dementia, and this data is expected to twofold by 2030. The associated cost of care is projected to triple, necessitating ongoing support. This issue is a significant societal concern that warrants careful consideration. Prediction and classification of Alzheimer’s disease (AD) are crucial for identifying and addressing it at earlier stages. The disease is typically classified into three stages: early-stage dementia, middle-stage, and late-stage. However, several challenges exist, including limited awareness, diagnostic difficulties, insufficient technologies and treatments, unpredictable progression across stages, and a lack of comprehensive stage-by-stage predictions. This work proposes a hybrid model for predicting, classifying, and stage-wise classifying Alzheimer’s disease. The hybrid model aims to address the challenges associated with the disease, utilizing different features for prediction and classification. The hybrid model used a Random forest, convolutional neural network (CNN), kernel density algorithm (KDA) with hyperparameter tuning for accurate prediction and classification. The proposed hybrid method used Random forest used for basic classification, CNN and kernel density for stage wise classification. The proposed hybrid method achieved an impressive 95.6% accuracy in stage wise classification and prediction of AD.

关键词： Surveys Accuracy Magnetic resonance imaging Predictive models Convolutional neural networks Alzheimer's disease Kernel

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African Vultures Optimization Algorithm for Optimal Damping Controllers Design in the Electrical Power Grid System 13

African Vultures Optimization Algorithm for Optimal Damping ...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Sabo, Aliyu Odoh, Theophilus Ebuka Habu, Samuel Shahinzadeh, Hossien Ebrahimi, Farshad Department of Electrical and Electronic Engineering Serdang Malaysia Water Resource Institute Department of Electrical Engineering Kaduna Nigeria Amikabir University of Technology Department of Electrical Engineering Tehran Iran University of Houston Dept. of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9798350330151

Electromechanical oscillation affects power system stability and hinders the improvement of the generating capacity of the electrical power grid system. This is because if electromechanical oscillation is not controlled, it leads to the loss of synchronism in generators and total power disruption in the power grid system. To solve this problem, this study introduces Power System Stabilizer (PSS) damping controllers in the IEEE standard power test systems of Single Machine Infinite Bus (SMIB), Western System Coordinating Council (WSCC), and the New England Power Test Systems for electromechanical oscillation damping and control. The African Vultures Optimization Algorithm (AVOA) was used to obtain the PSS damping controller parameters to ensure optimal performance. Results showed that the introduced PSS damping controllers can dampen and control electromechanical oscillation. © 2023 IEEE.

关键词： IEEE Standards

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Machine Learning based Enhanced Autonomous Driving for Autonomous Vehicles 6

Machine Learning based Enhanced Autonomous Driving for Auton...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Lavanya, M.C. Akshatha, M. Radhakrishnan, P. Bai, Kshatriya Vinaya Sree Alakshmi, S. Vijay Ranga, Jarabala Dept of CSE Vidyavardhaka College of Engineering Mysuru India Dept. of Electronics& communication Engineering Tagore Engineering College India Artificial Intelligence & Data Science Lakireddy Bali Reddy College of Engineering India Department of Computer Science and Engineering Park College of Engineering and Technology Tamilnadu Coimbatore641659 India Department of EEE Ramachandra college of engineering Eluru AP India

ISBN: (纸本)9798350398496

In particular, the emphasis of our study is on the most important difficulties associated with finding data-driven representations of nonlinear systems that allow for linear analysis is a key step towards deriving dynamics from data. Many issues plaguing modern dynamical systems are explored and the innovative methods being created in data science and machine learning to address them. Nonlinear dynamics, on the one hand, and unknown or only partially understood dynamics, on the other, are the two greatest challenges. Machine learning is providing cutting-edge, effective answers to all of these issues. Methods of reducing the number of dimensions are used to visualize dynamical processes in a more compact format. It is computationally efficient to use these methods on real -world data. Finding the fundamental equations fundamental principles of physics requires models that are themselves driven by evidence. By using deep learning techniques, dynamical systems are identified correctly, and that in turn enables accurate inference about physical systems. S everal cutting-edge and very successful new approaches to nonlinear dynamics have emerged from the area of machine learning. Autoencoders, recurrent neural networks, convolutional neural networks, and reinforcement learning are only some of the cutting-edge deep learning approaches used to simulate dynamical systems. © 2023 IEEE.

关键词： Autonomous vehicles

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An Efficient Electrode Ranking Method for Single Trial Detection of EEG Error-Related Potentials 4

An Efficient Electrode Ranking Method for Single Trial Detec...

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4th International Conference on Bio-engineering for Smart Technologies, BioSMART 2021

作者： Parashiva, Praveen K. Vinod, A.P. Indian Institute of Technology Palakkad Dept. of Electrical Engineering Palakkad India The Hong Kong University of Science and Technology Dept. of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9781665408103

The human brain's response to mistakes or erroneous events is termed as Error-Related Potential (ErrP). The ErrP can be recorded non-invasively using Electroencephalogram (EEG). The ErrP activity is localized and gets reflected in a few EEG electrodes only. Further, EEG offers a poor signal-to-noise ratio. Therefore, single-trial detection of ErrP from EEG data is challenging. The objective of this work is to propose an efficient method for selecting electrodes that carry ErrP related information to enhance single-trial detection accuracy. In this work, the cosine similarity and Euclidian distance measures are used to rank the EEG electrodes. The selected top-ranked electrodes are used to extract electrode-average features followed by a classifier. This work is implemented on a public dataset containing 6 subjects' datasets each having 2 sessions of EEG data. The two proposed electrode ranking methods - cosine similarity measure and Euclidian distance measure are implemented separately. Both electrode ranking methods aided in achieving equally good ErrP detection rates. The cross-validated average detection rates achieved using the proposed electrode ranking methods are 73.5% and 80% for error and correct trials respectively. Further, the results are compared with three existing methods including Convolutional Neural Network (CNN) implemented on the same dataset used in this work to show the efficiency of the proposed method. The significance of this work is that the single-trial detection of ErrP can aid in improving the classification accuracy of decoding EEG tasks in Brain-computer Interface systems. © 2021 IEEE.

关键词： Electrodes

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A Comparison of Feature Selection Approaches for Liver Disease Data

A Comparison of Feature Selection Approaches for Liver Disea...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Ayushi Pillay Monika Arya Sumit Kumar Sar Bhupesh Kumar Dewangan Dept. of Computer Science & Engg. Bhilai Institute of Technology Durg CG INDIA Dept. of Computer Science & Engg OP Jindal University Raigarh Chhatisgarh India

Efficient feature selection (FS) methods are often needed to correctly classify multiple types of diseases, recognize disease symptoms, and enhance treatment modalities. The aim of this study is to compare the filter and wrapper FS approaches and to show how they enhance classification. First, on disease data, the FS approach has been employed with different classification models. We compared the effectiveness of two FS techniques: recursive feature elimination (RFE) and chi-square feature (CSF) selection. Logistic Regression (LR), Support Vector Machine (SVM), and Decision Tree are three different models (DT) that are used. Performance in classification is evaluated using K-fold crossvalidation. The methods are evaluated on datasets of diseases that are openly accessible. The findings showed that FS is important for accurate disease classification. A subset of the most prevalent characteristics is obtained after examining the relationship between particular symptoms and disease. Thus, by using FS that selects only a few disease predictors, the classification accuracy can be significantly improved.

关键词： Support vector machines Liver diseases Predictive models Feature extraction Medical diagnosis Decision trees Sustainable development

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BERTops: Studying BERT Representations under a Topological Lens

arXiv

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arXiv 2022年

作者： Chauhan, Jatin Kaul, Manohar Dept. of Computer Science & Engineering Indian Institute of Technology Hyderabad India

Proposing scoring functions to effectively understand, analyze and learn various properties of high dimensional hidden representations of large-scale transformer models like BERT can be a challenging task. In this work, we explore a new direction by studying the topological features of BERT hidden representations using persistent homology (PH). We propose a novel scoring function named "persistence scoring function (PSF)" which: (i) accurately captures the homology of the high-dimensional hidden representations and correlates well with the test set accuracy of a wide range of datasets and outperforms existing scoring metrics, (ii) captures interesting post fine-tuning "per-class"level properties from both qualitative and quantitative viewpoints, (iii) is more stable to perturbations as compared to the baseline functions, which makes it a very robust proxy, and (iv) finally, also serves as a predictor of the attack success rates for a wide category of black-box and white-box adversarial attack methods. Our extensive correlation experiments demonstrate the practical utility of PSF on various NLP tasks relevant to BERT. © 2022, CC BY.

关键词： Machine learning

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Enhancing Power Grid Resilience: Advanced Strategies for Detecting and Analyzing Faults in Three-Phase Transmission Lines

Enhancing Power Grid Resilience: Advanced Strategies for Det...

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Information technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Khandakar Rabbi Ahmed Khandokar Shadman Shayer Jahidul Islam Shuvo F. M. Mahmudul Haque Towfika Salam Shafiun Miraz Abdullah Al Noman Md. Shohanur Islam Sobuj Md. Yaqub Arafat MD Istiak Hasan Rial Adjunct Researcher Miyan Research Institute International University of Business Agriculture and Technology Uttara Model Town Dhaka Bangladesh Dept. of Mechatronics Engineering Rajshahi University of Engineering and Technology (RUET) Kazla Rajshahi Bangladesh Dept. of Electrical & Electronic Engineering American International University-Bangladesh Dhaka Bangladesh Dept. of Electrical & Electronic Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology (RUET) Rajshahi Bangladesh Dept. of Institute of Information Technology Jahangirnagar University Saver Dhaka Bangladesh Dept. of Electrical and Electronic Engineering Hajee Mohammad Danesh Science and Technology University Dhaka Bangladesh Dept. of Mechanical Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Dept. of Computer Science and Engineering Aditya College of Engineering (AOCE) Surampelam kakinada Andhra Pradesh India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Power system fault analysis is a process that establishes the bus voltages and line currents when various fault types occur. Three-phase balanced faults and unbalanced faults are two categories of power system faults. Single line-to-ground, line-to-line, and double line-to-ground faults are three different types of unbalanced faults that can occur on transmission lines for power systems. Fault analysis is used to choose and set the proper protective devices and switchgear. Determining the bus voltages and line currents is critical while investigating power system issues. Numerous mathematical calculation techniques are shown in the process, which is challenging to do by hand. The calculation can be performed quickly on a computer using a Simulink created using MATLAB. However, an error occurs when inverting an extensive bus system's Y-bus to Z-bus in the conventional short circuit study; this paper offers a solution that is solved using “MATLAB SIMULINK” to reduce error.

关键词： Fault diagnosis Power transmission lines Software packages Fault detection Electrical fault detection Power systems Relays

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