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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Eelanila Thayalan Ayingaran Arumugavel Sharujan Ganeshan Makenthiran Dalaxshan Harinda Fernando Kaushalya Rajapakse Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system’s proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife.

关键词： Transfer learning Medical services Skin Pattern recognition Medical diagnostic imaging Standards Robots

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Accident Severity Prediction using Machine Learning: A case study on the US Accidents Dataset

Accident Severity Prediction using Machine Learning: A case ...

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Ennaji Fatima Zohra Knouzi Maryam El Kabtane Hamada Elalaoui Elabdallaoui Hasna Laboratory of Process Engineering Computer Science and Mathematics (LIPIM) National School of Applied Science USMS Khouribga Morocco SMARTE Systems and Applications (SSA) National School of Applied Science UCA Marrakech Morocco Computer Science Engineering Laboratory Faculty of Sciences Semlalia UCA Marrakech Morocco

In recent years, road accidents have caused numerous victims, injuries, and deaths every minute, not to mention economic damages. Due to the enormous number of traffic accidents, understanding and predicting their severity becomes a necessity. In this work, the “US Accidents (2016 - 2023)” dataset has been used. The data exploration task was applied before the training phase. Subsequently, and after splitting data into training and testing subsets, experiments were conducted using ANN(Artificial Neural Network), Random Forest Classifier, and Logistic Regression. We achieved an accuracy of 81.1 %, 90.7 %; and 87.03 % respectively.

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Development of measurement system for subsurface scattering light of skin and analysis of its age-related changes

Development of measurement system for subsurface scattering ...

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Biomedical Imaging and Sensing Conference 2023

作者： Kikuchi, K. Katsuyama, M. Shibata, T. Hardeberg, J.Y. Yuasa, T. Aizu, Y. Shiseido Co. Ltd. MIRAI Technology Institute 1-2-11 Takashima Nishiku Yokohama220-0011 Japan Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Teknologivegen 22 Gjøvik2815 Norway Division of Production Systems Engineering Muroran Institute of Technology 27-1 Mizumoto Muroran Hokkaido050-8585 Japan

ISBN: (纸本)9781510663435

We have developed a system that separates and measures the optical properties of skin, i.e., the surface reflection, diffuse reflection, and sub-surface scattering components of the skin. This system includes two polarization filters that separate light from the skin into a surface reflection component image and a diffuse reflection component image. Furthermore, by using a projector as a light source and irradiating the skin using a high-frequency binary illumination pattern, the subsurface scattering component image alone can be separated and generated. Using the proposed system, we performed a survey of 154 Japanese women aged from their 20s to their 70s and analyzed age-related changes in the optical properties of their skin. The results revealed the following. First, the luminance value Y of the surface reflection from the cheek and its standard deviation within the analysis area increase with age. Second, the Y value of diffuse reflection from the skin decreases with age. Third, the amount of light in the sub-surface scattering components also decreases with age. The proposed system is expected to have a wide range of applications in the medical and cosmetic fields. © 2023 SPIE.

关键词： Surface scattering

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Detecting Human Embryo Cleavage Stages Using YOLO V5 Object Detection Algorithm 4th

Detecting Human Embryo Cleavage Stages Using YOLO V5 Object ...

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4th Symposium of the Norwegian AI Society, NAIS 2022

作者： Sharma, Akriti Stensen, Mette H. Delbarre, Erwan Siddiqui, Momin Haugen, Trine B. Riegler, Michael A. Hammer, Hugo L. Department of Computer Science Faculty of Technology Art and Design Oslo Metropolitan University Oslo Norway Department of Life Sciences and Health Faculty of Health Sciences Oslo Metropolitan University Oslo Norway Department of Holistic Systems Simula Metropolitan Center for Digital Engineering Oslo Norway Department of Computer Science Jamia Millia Islamia New Delhi India Fertilitetssenteret Embryology Oslo Norway

ISBN: (纸本)9783031170294

Assisted reproductive technology (ART) refers to treatments of infertility which include the handling of eggs, sperm and embryos. The success of ART procedures depends on several factors, including the quality of the embryo transferred to the woman. The assessment of embryos is mostly based on the morphokinetic parameters of their development, which include the number of cells at a given time point indicating the cell stage and the duration of each cell stage. In many clinics, time-lapse imaging systems are used for continuous visual inspection of the embryo development. However, the analysis of time-lapse data still requires the evaluation, by embryologists, of the morphokinetic parameters and cleavage patterns, making the assessment subjective. Recently the application of object detection in the field of medical imaging enabled the accurate detection of lesion or object of interest. Motivated by this research direction, we proposed a methodology to detect and track cells present inside embryos in time-lapse image series. The methodology employed an object detection technique called YOLO v5 and annotated the start of observed cell stages based on the cell count. Our approach could identify cell division to detect cell cleavage or start of next cell stage accurately up to the 5-cell stage. The methodology also highlighted instances of embryos development with abnormal cell cleavage patterns. On an average the methodology used 8 s to annotate a video frame (20 frames per second), which will not pose any delay for the embryologists while assessing embryo quality. The results were validated by embryologists, and they considered the methodology as a useful tool for their clinical practice. © 2022, The Author(s).

关键词： Object detection

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A CNN Based Approach to Classify the Folk Dances of Odisha

A CNN Based Approach to Classify the Folk Dances of Odisha

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2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022

作者： Samantaray, Swati Patra, Sudhansu Shekhar Mohanty, Amlan Barik, Lalbihari Barik, Akash Brahma, Aditya Narayan Kiit Deemed to Be University School of Humanities Bhubaneswar India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India Iiit Department of Computer Science Bhubaneswar India King Abdulaziz University Faculty of Computing & Information Technology in Rabigh Department of Information Systems Saudi Arabia New Horizon College of Engineering Dept. of Computer Science Bengaluru India

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

Folk dances are a primordial expression of ecstasy and bliss and serve as a mode of communication. They also facilitate in keeping the people connected to their traditions as well as ancestry. Furthermore, these dances help in preserving cultural unity and their tales can often reveal a lot about the periods these dances have developed through. Mahari, Gotipua and Odissi are such folk dance forms of the eastern state of Odisha. These folk dances are on the verge of extinction owing to the reducing number of practitioners, audience, absence of documentation, and digitalization. The absence of documentation has presented a barrier to the legitimacy of folk dance as an academic field of study. This research paper intends to focus on the classification of the folk dances of Odisha such as Mahari, Gotipua and Odissi which will help them to digitize and preserve. We propose deep learning-based methods for the classification and then it can be preserved. To validate the poses and hand motions, we offer a convolutional neural network model (CNN). © 2022 IEEE.

关键词： Neural network models

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On Understanding Sports–HAR: Hierarchical, Mobile, Multi-Sensor based Classification of Table–Tennis Strokes

On Understanding Sports–HAR: Hierarchical, Mobile, Multi-Se...

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Intelligent Methods, systems, and Applications (IMSA)

作者： Mustafa Ezzeldin Amr S. Ghoneim Laila Abdelhamid Ayman Atia Faculty of Computers and AI Software Engineering Helwan University Cairo Egypt Faculty of Computers and AI Computer Science Helwan University Cairo Egypt Faculty of Computers and AI Information Systems Helwan University Cairo Egypt HCI-LAB Faculty of Computers and AI Computer Science Helwan Univeristy Cairo Egypt Faculty of Computer Science October University for Modern Sciences and Arts (MSA) Giza Egypt

Table Tennis is a renowned competitive and recreational sport. An Olympic sport since 1988, table tennis in-cludes several movements, shots (i.e., strokes), and positions. Consequently, many factors can affect the strokes’ accuracy and strength. This paper aims twofold: to classify the type of shot and to enhance the performance of the classification by improving the recognition accuracy and reducing the computational time. To track the movement of the body parts and extract table tennis stroke information, sensors (both Accelerometers and Gyroscopes) were utilized. The extracted data are processed and passed to a P–Dollar classifier to recognize six different table tennis strokes. This work utilizes two classification approaches to evaluate the performance, both linear and hierarchical. The presented results demonstrate that the hierarchical classification yields a better accuracy of 88%, achieved in 4 seconds, while traditional linear classification achieved 83% within 6 seconds.

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Enhancing Password Security: Analyzing Password Management Practices Among IT Students

Enhancing Password Security: Analyzing Password Management P...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Anita Mihalovicsne Kollar Jozsef Katona Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kando Kalman Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

Today, with the digital world in its full swing, strong password management is key to protecting sensitive information from both internal and external threats. The current research explores the passwords managing behavior of the IT students at the University of Dunaujvaros. A survey made in May 2024 questioned 68 students, 59 males and 9 females, about behaviors such as using passwords with complexity, not repeating them for multiple accounts or machines, changing them regularly, and which applications they use for password management. The findings have depicted that, as a whole, students appear to be moderately sensitive about the password security instructions. Nonetheless, most continue to indulge in unsafe practices, including easily remembered or weak passwords and infrequent password changes. Furthermore, approximately half of the responders use password management applications, indicating room for significant improvement in the future. The results suggest that intervention and special education measures should be taken to enhance the practice of password security among future IT experts. This research has good potential to contribute toward strategies designed for people behaving safely in password management, and hence promote a secure digital environment.

关键词： Surveys Continuing education Power engineering Correlation engineering profession Education Passwords Complexity theory Security Protection

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Thermal Gait Dataset for Deep Learning-Oriented Gait Recognition

Thermal Gait Dataset for Deep Learning-Oriented Gait Recogni...

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International Joint Conference on Neural Networks (IJCNN)

作者： Fatma Youssef Ahmed El-Mahdy Tetsuji Ogawa Walid Gomaa Department of Computer Science and Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Alexandria University Alexandria Egypt Department of Communications and Computer Engineering Waseda University Tokyo Japan Cyber-Physical Systems Lab Egypt-Japan University of Science and Technology Alexandria Egypt

This study attempted to construct a thermal dataset of human gait in diverse environments suitable for building and evaluating sophisticated deep learning models (e.g., vision transformers) for gait recognition. Gait is a behavioral biometric to identify a person and requires no cooperation from the person, making it suitable for security and surveillance applications. For security purposes, it is desirable to be able to recognize a person in darkness or other inadequate lighting conditions, in which thermal imagery is advantageous over visible light imagery. Despite the importance of such nighttime person identification, available thermal gait datasets captured in the dark are scarce. This study, therefore, collected a relatively large set of thermal gait data in both indoor and outdoor environments with several walking styles, e.g., walking normally, walking while carrying a bag, and walking fast. This dataset was utilized in multiple gait recognition tasks, such as gender classification and person verification, using legacy convolutional neural networks (CNNs) and modern vision transformers (ViTs). Experiments using this dataset revealed the effective training method for person ver-ification, the effectiveness of ViT on gait recognition, and the robustness of the models against the difference in walking styles; it suggests that the developed dataset enables various studies on gait recognition using state-of-the-art deep learning models.

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HeX: Encrypted Rich Queries with Forward and Backward Privacy Using Trusted Hardware

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wu, Haotian Peng, Zhe Xiao, Jiang Xue, Lei Lin, Chenhao Chung, Sai-Ho The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hung Hom Hong Kong The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Hong Kong Polytechnic University Shenzhen Research Institute Hong Kong Huazhong University of Science and Technology School of Computer Science and Technology China Sun Yat-sen University School of Cyber Science and Technology China Xi'an Jiaotong University Faculty of Electronic and Information Engineering China

Dynamic searchable symmetric encryption (DSSE) schemes empower data owners to outsource their encrypted data to clouds while retaining the ability to update or search on it. Despite a lot of efforts devoted in recent years, there are still several challenges that have not been well addressed. Firstly, the confidentiality of data might be compromised if forward privacy and backward privacy cannot be ensured. Secondly, only the traditional single keyword-file search has attracted tremendous attention, while other popular queries like Boolean queries and range queries are not fully investigated. Lastly, how to solve these problems on untrusted servers that may deviate from pre-defined protocols is also challenging. In this paper, aiming to tackle the above problems, we propose a novel DSSE scheme named HeX based on Trusted Execution Environment (TEE) that supports rich queries on untrusted servers while guaranteeing forward and backward privacy. We achieve strong forward and backward security by designing a deferred obfuscating read-write technique atop the bitmap index. We further extend the basic scheme to realize Boolean queries and range queries by reducing them to basic keyword queries. Strict theoretical analysis is conducted to prove the security of HeX, and extensive evaluations illustrate its efficiency and practicality. © 2004-2012 IEEE.

关键词： Differential privacy

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Vehicle type recognition based on audio data 58

Vehicle type recognition based on audio data

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58th Hawaii International Conference on System sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michal Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Pawel Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE computer Society. All rights reserved.

关键词： Image coding

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