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9th IEEE Information Technology International Seminar, ITIS 2023

作者： Fahrudin, Tresna Maulana Illah, Ibnu Zahy'Atha Universitas Pembangunan Nasional 'Veteran' Jawa Timur Faculty of Computer Science Department of Data Science Surabaya Indonesia

ISBN: (纸本)9798350306835

The skin has a very significant role in humans. But unfortunately, the skin is one of the organs vulnerable to disease. Skin diseases can have an impact on the annoying itching, pain, emotional, and social feelings of the sufferer. The causes of skin diseases include bacteria, fungi, viruses, dust, and allergies. Delay in treatment can exacerbate the condition of the affected skin. Therefore, the research proposed a skin disease detection mobile application for early diagnosis. The total of skin disease images is 2.747 data, which is divided into five classes and separated into training and validation data. The category of the dataset includes Acne Vulgaris, Actinic Keratosis, Nail Fungus, Psoriasis, and Seborrheic Keratoses. The development of this application uses several augmentation techniques so that the model gets input training data from various image forms, and then uses pre-trained MobileNetV2, which has low latency and low power as needed on mobile devices. The development of this application also uses the TensorFlow Lite model for inference with ML Kit on Android. The model performance gave the best accuracy of 90.62%, precision of 89%, recall of 90%, and F1- score of 89%. These results indicate that the classifying of skin diseases using the pre-trained MobileNetV2 on CNN architecture is feasible. © 2023 IEEE.

关键词： Convolutional neural networks

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Using Decentralized Identifiers and InterPlanetary File System to Create a Recoverable Rare Disease Patient Identity Framework 23

Using Decentralized Identifiers and InterPlanetary File Syst...

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7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： McHale, Sean Murr, Lincoln Zhang, Peng Department of Computer Science Vanderbilt University United States Data Science Institute Vanderbilt University United States

ISBN: (纸本)9798400700712

This paper presents a novel framework for creating a recoverable rare disease patient identity system using blockchain and smart contracts, decentralized identifiers (DIDs), and the InterPlanetary File System (IPFS). Smart contracts are executable code that can be written into decentralized storage such as blockchains to enable tamper-proof data transactions. DIDs provide a secure, decentralized, and extensible way to create, store, and manage digital identities, while IPFS provides a distributed, immutable, and secure storage system for patient identities. Utilizing these technologies with smart contracts, we created a framework to store persistent medical records of patients. Smart contracts additionally allow account recovery without the use of any centralized authority. The framework provides a standardized identity layer that ensures healthcare providers have correct and secure access to a patient's data. The paper explores the advantages of using a decentralized identity system and highlights the potential of this approach to improve the security and universality of medical records for patients with rare diseases. © 2023 ACM.

关键词： Smart contract

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Analyzing Jordanian Higher Education Instructors' Perception Towards Metaverse Based Education 2

Analyzing Jordanian Higher Education Instructors' Perception...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Omar, Khalil Abu-Aishah, Saif Matar, Izzeddin Al Smadi, Duha Fakhouri, Hussam Abuarqoub, Mohammad University Of Petra Computer Science Department Amman Jordan University Of Petra Information Security Department Amman Jordan University Of Petra Data Science And Ai Department Amman Jordan

ISBN: (纸本)9798350394962

In recent years, many researchers have become interested in how the Metaverse can be applied in higher education. Although the use of Metaverse in education is still in its early stages, ongoing research on virtual worlds in education provides an opportunity to consider potential challenges in exploring the integration of Metaverse in education. Furthermore, there is a lack of empirical studies exploring the factors that impact the acceptance and use of immersive technologies, such as Augmented Reality, Virtual Reality, and Metaverse in higher education, particularly in developing countries such as Jordan. Therefore, the present study aims to create a comprehensive understanding of the current situation, perceptions, needs, and concerns related to integrating the Metaverse into Jordanian higher education institutes. © 2024 IEEE.

关键词： Augmented reality

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3D E-commerce Using AI Depth Algorithm Augmented Reality 8th

3D E-commerce Using AI Depth Algorithm Augmented Reality

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Silviya, S. H. Annie Devi, S. Renuka Sriman, B. Aswinrani, M. Raja, S. Sunitha, T. Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9789819784219

In today's dynamic world of online shopping, augmented reality (AR) integration has emerged as a game-changing innovation. It transcends the limitations of traditional online shopping by harnessing AR technologies, such as Google's ARCore, to create immersive 3D product models. These models empower customers to virtually interact with and visualize products in their own environment, effectively bridging the experiential gap with in-store shopping. Employing AR software development kits and game engines, these 3D models seamlessly come to life on customers’ devices, offering a more engaging and informative shopping experience. This not only bolsters customer satisfaction but also helps online retailers manage operational costs more efficiently. With AR’s ability to let customers virtually try on clothes, visualize furniture placements, and inspect products from the comfort of their homes, it's poised to reshape the e-commerce landscape. The ongoing technological revolution is characterized by increasingly immersive, personalized online shopping, setting new benchmarks for customer expectations and retail success. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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Trajectory-Aware Task Coalition Assignment in Spatial Crowdsourcing (Extended Abstract) 40

Trajectory-Aware Task Coalition Assignment in Spatial Crowds...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Xie, Yuan Wu, Fan Zhou, Xu Luo, Wensheng Yin, Yifang Zimmermann, Roger Li, Keqin Li, Kenli College of Computer Science and Electronic Engineering Hunan University Hunan China Institute of Data Science National University of Singapore Singapore State University of New York Department of Computer Science New Paltz United States School of Data Science Chinese University of Hong Kong Hong Kong A STAR Singapore

ISBN: (纸本)9798350317152

With the popularity of GPS-equipped smart devices, spatial crowdsourcing (SC) techniques have attracted growing attention in both academia and industry. In existing trajectory-aware task assignment approaches, tasks assigned to a worker may be far apart from each other, resulting in a higher detour cost as the worker needs to deviate from the original trajectory more often than necessary. Motivated by the above observations, we investigate a trajectory-aware task coalition assignment (TCA) problem and prove it to be NP-hard. The goal is to maximize the number of assigned tasks by assigning task coalitions to workers based on their preferred trajectories. To tackle the TCA problem, we develop a batch-based three-stage framework consisting of task grouping, planning, and assignment. Extensive experiments on real and synthetic datasets demonstrate the effectiveness and efficiency of the proposed algorithms. © 2024 IEEE.

关键词： Trajectories

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Multi-View Multi-Modal Head-Gaze Estimation for Advanced Indoor User Interaction

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computers, Materials & Continua 2022年第3期70卷 5107-5132页

作者： Jung-Hwa Kim Jin-Woo Jeong Department of Computer Engineering Kumoh National Institute of TechnologyGumi39177Korea Department of Data Science Seoul National University of Science and TechnologySeoul01811Kore

Gaze estimation is one of the most promising technologies for supporting indoor monitoring and interaction ***,previous gaze estimation techniques generally work only in a controlled laboratory environment because they require a number of high-resolution eye *** makes them unsuitable for welfare and healthcare facilities with the following challenging characteristics:1)users’continuous movements,2)various lighting conditions,and 3)a limited amount of available *** address these issues,we introduce a multi-view multi-modal head-gaze estimation system that translates the user’s head orientation into the gaze *** proposed system captures the user using multiple cameras with depth and infrared modalities to train more robust gaze estimators under the aforementioned *** this end,we implemented a deep learning pipeline that can handle different types and combinations of *** proposed system was evaluated using the data collected from 10 volunteer participants to analyze how the use of single/multiple cameras and modalities affect the performance of head-gaze *** various experiments,we found that 1)an infrared-modality provides more useful features than a depth-modality,2)multi-view multi-modal approaches provide better accuracy than singleview single-modal approaches,and 3)the proposed estimators achieve a high inference efficiency that can be used in real-time applications.

关键词： Human-computer interaction deep learning head-gaze estimation indoor monitoring

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Advancements in AI-Assisted Plant Disease Diagnosis 4

Advancements in AI-Assisted Plant Disease Diagnosis

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Chhabra, Anmol Saroch, Tarun Gupta, Ruchika Shukla, Aditya Raghav Chandigarh University Centre of Excellence-Data Science and Cognitive Computing Computer Science & Engineering Department Mohali India

ISBN: (纸本)9798350306118

Plant disease detection is a crucial task in agriculture to ensure healthy crop production. It is vital to identify plant diseases early in order to avert economic and environmental damages. A Machine learning-based approach including Deep Learning techniques for identifying plant disease was developed using Grape leaf images in this work. Inception v3, Squeezenet, VGG-16, and VGG-19 are some embedders that are used in the suggested method to convert raw image data into a fixed-length vector representation, also known as a 'embedding' or 'feature vector.' Our research concludes that CNN stands out with exceptional accuracy above 99.70% compared to classifiers like Naive Bayes, Logistic Regression, and SVM. Additionally, Inception V3 proved to be an efficient feature extractor, enhancing feature representation and reducing processing time. © 2023 IEEE.

关键词： Embeddings

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Driver Snoozing System with Infrared Technology 3

Driver Snoozing System with Infrared Technology

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Rani, Rupa Bisht, Deepanshu Mittal, Divyansh Abes Institute of Technology Department of Computer Science & Engineering-Data Science U.P. Ghaziabad India

ISBN: (纸本)9798350342338

This project aims to mature a Driver Snoozing System (DSS) that provide alertness to drivers when they are fatigue, both in day time and night time. A regular camera is used to monitor the driver's face and eyes to check their alertness level. The system sounds an alarm when it detects signs of tiredness. This system's main goals are to improve traffic flow, avoid accidents caused by tired drivers, warn drivers in advance, and reduce the financial burden of traffic accidents. Additionally, it will lessen the strain on healthcare systems, aid in preventing traffic-related injuries and fatalities, and enhance the quality of life for people and communities. The purpose of this study was to create a technological solution for the problem of drowsy driving, which is a major risk for road safety and a common cause of accidents. In this article, the proposed Driver Snoozing System comprises of Infrared sensor and camera which can potentially reduce the accidents caused by driver fatigue because it can identify sleepy driving both in the daytime and at nighttime. Despite some limitations, it can potentially improve traffic safety and reduce the financial expense of accidents. More research and development are needed to make it more accurate and effective. © 2023 IEEE.

关键词： Alertness vs. Drowsiness Detection System Driver Snoozing System Infrared Sensor Integration Infrared Technology Machine Learning

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Categorisation by Leveraging CNNs and Remote Sensing Satellite Imagery for Crop Analysis in Arid Environments

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Remote Sensing in Earth Systems sciences 2024年第2期7卷 66-79页

作者： Malhan, Anu Chavan, Yogita Rahulsing Swamikan, Brinthakumari Gupta, Megha Varun Bobade, Sunil Department of Artificial Intelligence and Data Science New Horizon Institute of Technology and Management Department of Computer Engineering New Horizon Institute of Technology and Management

The classification of walnuts in the dry areas of Ganquan Township, Awati County, Xinjiang, is investigated in this research using traditional approaches, remote sensing photos taken by satellite, and convolutional neural networks (CNNs). Thorough field sampling and the collecting of Landsat-8 Level-2 data were necessary for correct analysis due to the different environmental features of the research region, which presented major hurdles. Despite some early difficulties, CNN design revisions led to significant gains in classification accuracy. These modifications included AlexNet, VGG, GoogleNet, ResNet, and EfficientNetV2. When compared to more traditional approaches, ResNet and EfficientNetV2 stood up as the most successful models. Nevertheless, challenges including computational complexity and low-resolution imaging highlight the necessity for more research to improve detection efficiency without sacrificing accuracy. The study emphasises the need to keep looking for new ways to analyse and monitor crops, and it suggests that combining remote sensing with advanced machine learning methods could help improve precision agriculture management and increase crop yields in dry areas. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Categorisation of crop Machine learning Precision agriculture Remote sensing Walnut

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Microprocessor Instruction Design Tool for RISC-V Architecture 22

Microprocessor Instruction Design Tool for RISC-V Architectu...

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22nd International Symposium on Communications and Information Technologies, ISCIT 2023

作者： Jiahui, Luo Morimoto, Tomoyuki Ogita, Tadahiro Kawamata, Ryota Wang, Ziming Tsutsumi, Toshiyuki Meiji University Computer Science Program Graduate School of Science and Technology Kanagawa Japan Meiji University School of Science and Technology Department of Computer Science Kanagawa Japan

ISBN: (纸本)9781665457316

We have been developing MEIMAT (MEiji Microprocessor Architecture Design Tools). The MEIMAT is essentially designed to be able to represent any instruction of various processors by the MEIMAT meta-instruction in two ways of semantic and functional expressions. In this paper, we have verified the RISC-V support to the MEIMAT, and have improved so that MEIMAT further supports RISC-V. First, we have verified whether the RISC-V instructions can be expressed by the MEIMAT semantic expression. It is also verified that the functional expression is automatically generated from the semantic instruction, and the corresponding correct circuit diagrams are generated in the MEIMAT instruction visualization tool. Then, we have newly developed a stage configuration mechanism to implement the RISC-V instructions. Because the RISC-V architecture is implemented by 4 or 5 processing stages in most actual processors. To develop further an easy-to-understand design tool, we have also introduced illustration windows for RISC-V specified hardware modules such as a register file, especially a status register in the instruction visualization tool. Finally, through these verifications and improvements, we have successfully confirmed that the 43 instructions in the RISCV (RV32I) instructions set of the RISC-V architecture can be converted into MEIMAT meta-instructions, and then they can be represented as circuit diagrams with the RISC-V stage configuration in the MEIMAT instruction visualization tool. © 2023 IEEE.

关键词： Integrated circuit design

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