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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Performance Investigation of a New Chaotic Search Method for Vehicle Routing Problem with Drones 16th

Performance Investigation of a New Chaotic Search Method for...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Nakajima, Kazuma Matsuura, Takafumi Kimura, Takayuki Graduate School of Electronics Information and Media Engineering Nippon Institute of Technology 4-1 Gakuendai Miyashiro Saitama Minami-Saitama345-8501 Japan Department of Data Science Faculty of Advanced Engineering Nippon Institute of Technology 4-1 Gakuendai Miyashiro Saitama Minami-Saitama345-8501 Japan Department of Electrical Electronics and Communication Engineering Faculty of Fundamental Engineering Nippon Institute of Technology 4-1 Gakuendai Miyashiro Saitama Minami-Saitama345-8501 Japan

ISBN: (纸本)9789819615346

Transport and logistics operated by unmanned aerial vehicles, commonly known as drones, have attracted attention in recent years for their potential to revolutionize the transport industries. For example, Amazon was the first to use drones to deliver goods. Several distribution companies have since been working on similar services. To find effective delivery routes by using vehicles and drones, a flying sidekick traveling salesman problem has firstly been formulated. This problem constructs a delivery route by using a single drone and a single vehicle. In further formulation of this problem, vehicle routing problem with drones (VRPD), in which several numbers of drones and vehicles deliver goods to customers, has been formulated. We have already proposed a solution search method based on chaotic neurodynamics for VRPD. In this article, to further achieve effective search capability for VRPD, we change neural codings of chaotic neural networks according to the combinations of neighborhood operations. Experimental results show that our proposed method exhibits better objective function values than those by the conventional chaotic search method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drones

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A Learning Support Method for Multi-threaded Programs Using Trace Tables

arXiv

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arXiv 2024年

作者： Murata, Takumi Hashiura, Hiroaki Electronics Information and Media Engineering Major Graduate School of Engineering Nippon Institute of Technology Saitama Japan Dept. of Data Science Faculty of Advanced Engineering Nippon Institute of Technology Saitama Japan

Multi-threaded programs are expected to improve responsiveness and conserve resources by dividing an application process into multiple threads for concurrent processing. However, due to scheduling and the interaction of multiple threads, their runtime behavior is more complex than that of single-threaded programs, making which makes debugging difficult unless the concepts specific to multi-threaded programs and the execution order of instructions can be understood. In this paper, we propose a learning tool for multi-threaded programs using trace tables. Copyright © 2024, The Authors. All rights reserved.

关键词： Application programs

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Proposal of a Support System for Visualization of Acquired Skills Using iCD in Project-Based Learning for Software Development and How to Acquire its data 35

Proposal of a Support System for Visualization of Acquired S...

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35th IEEE International Conference on Software engineering Education and Training, CSEE and T 2023

作者： Kondo, Hane Hashiura, Hiroaki Hazeyama, Atsuo Tokyo Gakugei University Graduate School of Education Tokyo Japan Faculty of Advanced Engineering Nippon Institute of Technology Saitama Japan Tokyo Gakugei University Dept. Information Science Tokyo Japan

ISBN: (纸本)9798350322026

There have been many practices using software development as a subject for Project-Based Learning (PBL). While PBL for software development has high educational effects, it is difficult for learners to realize many of its educational effects because they overly focus on the activities themselves. Therefore, this paper proposes a support system for visualization of acquired skills in PBL for software development using i-Competency Dictionary. We also describe the data acquisition method for the realization of the system. © 2023 IEEE.

关键词： Visualization

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INVESTIGATION OF CONCRETE IN THE FORMER OGIMI VILLAGE OFFICE BUILDING CONSTRUCTED WITH BLAST FURNACE CEMENT CLASS C

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AIJ Journal of technology and Design 2025年第77期31卷 36-39页

作者： Nerome, Yasufumi Imamoto, Kei-Ichi Odajima, Yuri Ridengaoqier, E. Kiyohara, Chizuru Sakihara, Kohei Kinose, Toru Chen, Junye Dept. of Architecture Graduate School of Engineering Tokyo Univ. of Science Japan Research Institute for Science & Technology Tokyo Univ. of ScienceP Japan Faculty of Eng. Univ. of the Ryukyus Japan General Building Research Corporation of Japan Japan Nippon Steel Blast Furnace Slag Cement Japan

A 99-year-old reinforced concrete building, formerly serving as the Ogimi Village Office and constructed with concrete equivalent to blast furnace cement Class C, was investigated. Despite being located in a salt-affected environment with high levels of airborne salt, EMPA analysis confirmed that the permeation of chloride ions into the building was suppressed. © 2025 Architectural institute of Japan. All rights reserved.

关键词： Blast furnaces

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Detection of Pre Shoplifting Suspicious Behavior Using Deep Learning 16

Detection of Pre Shoplifting Suspicious Behavior Using Deep ...

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16th IIAI International Congress on advanced Applied Informatics, IIAI-AAI 2024

作者： Shrestha, Sujan Taniguchi, Yoji Tanaka, Tetsuo Graduate school of Engineering Kanagawa Institute of Tehnology Course of Information and computer sciences Atsugi Japan Daiichi Institute of Technology Artificial Intelligence and Data Science Department of Information Tokyo Japan Kanagawa Institute of Technology Faculty of Information Technology Dept. of Information and Computer Sciences Atsugi Japan

ISBN: (纸本)9798350377903

Shoplifting presents a significant challenge for retailers, leading to substantial financial losses and security concerns. Current surveillance systems effectively identify shoplifting incidents postoccurrence;however, the capability to detect and prevent such activities preemptively remains limited. This research focuses on identifying behaviors that likely precede shoplifting events. To this end, we introduce a novel benchmark dataset, specifically designed for the early detection of suspicious behavior that may lead to shoplifting. This dataset is curated by selecting and segmenting relevant sequences from the UCF101-Crime dataset, encompassing suspicious behavior, actual shoplifting incidents, and normal activities. Our approach addresses the limitations of existing datasets where the brief duration of shoplifting events may skew neural network performance. The dataset includes clips of only a few seconds each, for which we propose a robust neural network architecture combining the strengths of convolutional neural networks (CNN) for efficient feature extraction and Bidirectional long short-term memory (BiLSTM) networks for capturing temporal dependencies within action sequences. Our model achieves an impressive classification accuracy of 86.21 % on the newly created dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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A routing method using chaotic neurodynamics for the transportation networks with the next generation vehicles

A routing method using chaotic neurodynamics for the transpo...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Takatoshi Inaba Konosuke Hiraki Takafumi Matsuura Takayuki Kimura Graduate School of Electronics Information and Media Engineering Nippon Institute of Technology Saitama Japan Graduate School of Electronics Information and Media Engineering Nippon Institute of Technology Saitama Japan Department of Data Science Faculty of Advanced Engineering Nippon Institute of Technology Saitama Japan Department of Electrical Electronics and Communication Engineering Nippon Institute of Technology Saitama Japan

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Traffic congestion in the transportation networks, which increases vehicle travel times, energy consumption, and environmental pollution, is one of the major social problems occurring in most urban areas. One of the ways to reduce traffic congestion is to use effective routing methods to guide vehicles to their destinations that avoid or reduce traffic congestion. However, routing for vehicles, especially in urban areas, is difficult due to the dynamic and unpredictable nature of the transportation networks. To deal with these characteristics, the use of connected vehicles, which successfully obtain real-time data by using Internet communication, is attracting as the vehicles in the transportation networks. The ability to communicate over the Internet allows connected vehicles to communicate with other connected vehicles and infrastructure to monitor road congestion. In this work, we propose a routing method using chaotic neurodynamics for the transportation networks in which connected vehicle move and evaluate its effectiveness. In addition, we use Simulation of Urban MObility (SUMO) traffic simulator to construct realistic traffic environments. Numerical experiments using SUMO show that the routing method using chaotic neuro-dynamics shows effective routing performance compared to other routing methods in both the number of arriving vehicles and the average travel time.

关键词： Neurodynamics Connected vehicles Chaotic communication Roads Urban areas Transportation Routing

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Effects of material separation of polye-modified cement waterproofing membranes on fatigue resistance of joint at substrate

Journal of Structural and Construction Engineering

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Journal of Structural and Construction engineering 2021年第781期86卷 353-360页

作者： Tsukagoshi, Masayuki Nakamori, Masaki Ueda, Takao Tanaka, Kyoji Dept. of Architecture Faculty of Engineering Fukuoka University West Nippon Expressway Company Limited Civil and Environmental Engineering Division of Science and Technology Graduate School of Technology Tokushima University Japan Tokyo Institute of Technology Japan

In this study, the relationship between the microstructure and fatigue resistance of joint at substrate of polymer modified cement waterproofing membrane (PCWM) were investigated. PCWM was applied on the substrate slate plate. And cracks were generated in the substrate, which were then expanded and contracted to produce movement. The higher the elongation rate of PCWM, tended to increase the fatigue resistance. In the case where the non-uniformity PCWM, the fatigue resistance was reduced. On the other hand, even in the case of large P/C and deterioration by weathering, if the material was homogeneous, it retained good fatigue resistance. © 2021 Architectural institute of Japan. All rights reserved.

关键词： Waterproofing

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Development of an Intelligent System for Predictive Analysis of Student Academic Performance Using Machine Learning Techniques

Development of an Intelligent System for Predictive Analysis...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Anand Tripathi KTV Reddy Dept. of Artificial Intelligence and Data Science Datta Meghe Institute Of Higher Education and Research Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Wardha

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

This research proposes an intelligent system utilizing machine learning techniques to predict student academic performance. A Random Forest classification model was developed based on a dataset consisting of key attributes such as attendance, previous grades, hours spent weekly on studying, medical emergencies, and assignment scores. The model classifies students into three categories: High-Performing, Average, and At-Risk, achieving an impressive accuracy of 99 %. Attendance and previous grades emerged as the key determinants of academic success. To facilitate real-world application, this project also includes an interactive Streamlit-based web application, enabling educators to make instant data-driven predictions by inputting students' academic details. This tool empowers educators with actionable insights, allowing them to implement timely interventions, optimize resource allocation, and adapt learning experiences for improved student outcomes. The study underscores the rapid advancements achieved through predictive analytics in education. By integrating artificial intelligence into pedagogic practice, institutions can proactively address learning difficulties, enhance student engagement, and foster academic excellence.

关键词： Education data visualization Learning (artificial intelligence) Alarm systems Real-time systems Resource management data mining Predictive analytics Intelligent systems Random forests

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Malicious Traffic Classification in WSN using Deep Learning Approaches 2

Malicious Traffic Classification in WSN using Deep Learning ...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Gupta, Chhote Lal Prasad Rajassekharan, Dinesh Sharma, Dilip Kumar Elangovan, Mohanraj Myilsamy, Varatharaj Upreti, Kamal Bansal Institute of Engineering and Technology Dept. of Computer Science and Engineering Lucknow India Peninsula College Faculty of Computer Science Peninsula Malaysia Jaypee University of Engineering and Technology Department of Mathematics Madhya Pradesh Guna473226 India K. S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruchengode637215 India V.S.B College of Engineering Technical Campus Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore642109 India Department of Computer Science Delhi-NCR Ghaziabad India

ISBN: (纸本)9798350369960

Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data. © 2023 IEEE.

关键词： data routing deep learning LSTM malicious transmission neural network Traffic data transmission WSN

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