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computer Software 2023年第3期40卷 97-112页

作者： Kawatani, Tomohiro Tsukada, Koji Kurihara, Kazutaka Dept. of Media Architecture Future University Hakodate Japan Dept. of Computer Science College of Liberal Arts Tsuda University Japan

In recent years, as the IoT has become popular, there are more requirements for sharing operation/mechanisms of IoT devices, such as Arduino and M5Stack. In addition, because of the corona disaster, many educational institutions adopted online lectures, such as on-demand tutorial videos and live lectures using video conference systems. For IoT programming education, these methods have problems such as lack of linkage with real-world devices and source codes. In this study, we propose a system called "IoTeach", which supports learning of IoT programming by attaching a script language on sequential contents such as videos and slides shared on the Web. Thus, the IoTeach can link videos and slides with real-world IoT devices and source code. In this paper, we describe the concept and implementation of the system. We also perform a basic evaluation and discuss challenges and prospects. © 2023 Japan Society for Software science and Technology. All rights reserved.

关键词： Online systems

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Sentiment Analysis of Social Support on Treads of Social Media Using Deep Learning Approach

Sentiment Analysis of Social Support on Treads of Social Med...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Hsu, Hsin-Yun Yu, Kai-Chi Wu, Jheng-Long Hsu, Kai-Shyang Dept. of Data Science Soochow University Taipei Taiwan Dept. of Social Work Soochow University Taipei Taiwan

ISBN: (纸本)9798350350548

This research aims to automatically classify social support threads shared by users on public social network platforms. Recognizing the growing role of social platforms in providing emotional support, this study focuses on identifying different support types using sentiment analysis. By incorporating data augmentation, three datasets were created for this analysis. We used existing models to compare classification performance. The results indicate that while data augmentation can sometimes improve model performance, the quality and context of the original data are crucial for accurately identifying emotional support. The study also found that current models struggle to distinguish subtle differences between support categories, especially when the data is imbalanced. These findings highlight the limitations of current AI models and the need for more balanced datasets and better classification techniques. This research contributes to the development of automated social support classification and provides a foundation for future studies to improve model accuracy using data augmentation and advanced methods. © 2024 IEEE.

关键词： data argumentation sentiment analysis social support

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Automatically Suggesting Suitable Crop from Soil Samples Using Optimized Neural Networks 3

Automatically Suggesting Suitable Crop from Soil Samples Usi...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Pasha, Syed Nawaz Ramesh, D. Mohmmad, Sallauddin Deepak, Gujjula School of Computer Science and Artificial Intelligence Sr University Warangal India Kamala Institute of Technology and Science Dept. of Computer Science and Engineering Huzurabad India

ISBN: (纸本)9798350381931

A soil-centric crop recommendation system recommends suitable crops utilizing vital soil attributes as input factors. This sophisticated system incorporates parameters like Nitrogen (N), Phosphorus (P), Potassium (K) levels, temperature, humidity, pH, and rainfall to suggest ideal crops for cultivation. The dataset comprises diverse crops and their specific requirements for each input parameter. We implemented an optimized neural network to provide precise crop recommendations based on the provided input values. By analyzing this data, the system provides farmers with the information necessary to make well-informed decisions regarding their choice of crops, enhancing the potential for optimal yields. Our model performed well on training and testing data with an accuracy of 0.94. These results demonstrate its effectiveness and potential as a valuable tool for agricultural decision-making. This system offers farmers tailored recommendations based on soil properties, climatic conditions, and other environmental factors. © 2024 IEEE.

关键词： Crops

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A Hybrid Approach Using Cryptography and K-Least Significant Bit Steganography Algorithm in Cloud Computing 1

A Hybrid Approach Using Cryptography and K-Least Significant...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Goyal, Anshika Gupta, Bhumika VMSB Uttarakhand Technical University Dept. of Computer Science & Engineering Dehradun India G.B. Pant Institute of Engineering & Technology Dept. of Computer Science & Engineering Pauri India

ISBN: (纸本)9798331518578

The exponential growth of communication technologies has broadened the cloud computing ecosystem horizon to meet main communication needs. However, in-parallel upsurge in online attacks has alarmed industries to ensure optimal data security. The combination of steganography and cryptosystems has been recognized as an enhanced security model for data transfer among the main security systems now in use. In this paper, a hybrid approach using cryptography and K- Least Significant Bit Steganography algorithm in Cloud Computing (HYBC-KLSBS-CC) are proposed. Initially, input text data is retrieved from cloud storage and compressed using Huffman Compression Coding (HCC) to reduce data size. The compressed data is then encrypted with a Hybrid Cryptography Algorithm (Hyb-MRFC-ALC), combining Modified Random Fibonacci Cryptography and Asymmetric Lightweight Cryptography to ensure robust encryption. Finally, encrypted data is embedded within an image using the K-Least Significant Bit (KLSB) steganography technique, providing additional layer of security. The proposed method enhances data confidentiality, integrity, and storage efficiency in cloud environments. The proposed HYBC-KLSBS-CC approach attains 26.89%, 29.49% and 18.64% lower PSNR and 26.89%, 29.49% and 18.64% lower MSE when analyzed through existing techniques like data security dependent on cryptography steganography related with OTP procedure and Huffman coding in the cloud environment (CS-OTP-CE), a novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and Time-Limited access control (AES-OTP-RSA-CC) and Crypto-Stegno dependent model for securing medical information on IOMT platform (CS-SMI-IOMT). © 2025 IEEE.

关键词： Cloud platforms

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dataset Creation for Segmentation to Enhance Visual Navigation in a Targeted Indoor Environment 17

Dataset Creation for Segmentation to Enhance Visual Navigati...

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17th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2023

作者： Ueda, Yuriko Wada, Marin Adachi, Miho Miyamoto, Ryusuke Meiji University Graduate School of Science and Technology Dept. of Computer Science Kawasaki Japan Meiji University School of Science and Technology Dept. of Computer Science Kawasaki Japan

ISBN: (纸本)9798350370911

To show the effectiveness of visual navigation based on the results of semantic segmentation, the authors participating in the Tsukuba Challenge, a well-known autonomous robot motion competition in Japan. The Tsukuba Challenge, which normally includes events only in outdoor environments, included in 2023 a new indoor environmental challenge, in which a robot must navigate a crowded shopping mall. In order to achieve visual navigation in an indoor environment, based on the results of segmentation, we needed to train a classifier suitable to navigate many simultaneously moving obstacles such as crowded people. For this purpose, this paper proposes a method for creating an appropriate dataset using a public dataset and modifying an existing dataset for an outdoor scenario: class labels are re-assigned considering the target environment. Experimental results using images captured at the course of the Extra Challenge in the Tsukuba Challenge 2023 showed that the proposed dataset enables the construction of a better classifier for semantic segmentation than only a public dataset. © 2023 IEEE.

关键词： Semantics

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Crowd Digital Twins: High-Level Requirements and Architecture 22

Crowd Digital Twins: High-Level Requirements and Architectur...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Casadei, Roberto Delnevo, Giovanni Girau, Roberto Mirri, Silvia University of Bologna Dept. of Computer Science and Engineering Italy

ISBN: (纸本)9798331508050

Crowd management is the problem of dealing with physical crowds of humans, e.g., for safety reasons or to improve services through crowd-awareness. In previous work, the notion of a Crowd Digital Twin (CDT), namely the digital twin associated to physical gatherings of humans, was motivated and proposed as a fundamental component of crowd management systems. In this work, we better characterise the CDT notion, by investigating the functional and non-functional requirements that such a technical solution should satisfy. Then, we consider what are the fundamental components of CDT systems, and accordingly delineate a general architecture aimed at flexibly supporting the corresponding physical-digital thread. © 2025 IEEE.

关键词： Crowdsourcing

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An efficient conference key agreement protocol suited for resource constrained devices

An efficient conference key agreement protocol suited for re...

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作者： Pundir, Manmohan Kumar, Abhimanyu Dept. of Computer Science and Engg. NIT Uttarakhand Uttarakhand Srinagar Garhwal246174 India Dept. of Computer Science and Engg. NIT Patna Bihar Patna800005 India

Conference key agreement (CKA) is essential for securing communication in group-oriented scenarios like multi-party messaging and collaborative environments. While elliptic curve cryptography (ECC) offers efficiency and strong security, ECC-based CKA protocols often rely on expensive pairings, making them computationally impractical for deployment over the resource limited devices. This paper introduces a novel CKA approach using ECC without requiring pairing computations, thus addressing scalability and efficiency challenges. The proposed protocol employs scalar point multiplications over a prime field elliptic curve group, enabling secure and efficient CKA operations with reduced computational overhead. Compared to existing ECC-based key agreement protocols, it minimizes user-level computation and enhances performance in computational efficiency, communication overhead, and security strength. Particularly suitable for resource-constrained environments like IoT and edge computing, where computational resources are limited yet secure group communication is crucial. © 2024 Elsevier Inc.

关键词： Authentication Protocol

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Exploring Machine Learning Techniques for Accurate Heart Disease Detection: A Comprehensive Study

Exploring Machine Learning Techniques for Accurate Heart Dis...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Singh, Pardeep Chaudhary, Vipin Kumar Bhatia, Arvind Kumar Sharma, Gaurav Kumbhawat, Amit Singh, Suchita Lovely Professional University Dept. of Computer Science Engineering Punjab Phagwara India Buddha Institute of Technology Dept. of Computer Science Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350385199

Globally, heart disease poses a significant challenge to public health. Early identification is crucial for effectively managing and treating heart conditions. Machine learning has shown promising results in predicting medical problems for the past few years. In this study, A system was developed that is capable of detecting cardiovascular diseases using machine learning methods. The project consists of data loading, Exploratory data analysis, preprocessing, training, evaluation and storage. A dataset was introduced that contained an array of characteristics related to the cardiovascular health status (such as age, gender, cholesterol level.). However, training measured the accuracy of 3 algorithms: K Nearest Neighbours (KNN), Decision Tree, and Random Forest in identifying heart ailment. The article also returns an ensemble graph that considers the forecasts of both these models together, saving the best GNB model for future use. Results showed that the machine learning methods are proficient in heart problem identification, as in this hybrid model, with a 97.32% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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Improving QoE in IoV: a review of solutions and challenges for MPEG-DASH 6

Improving QoE in IoV: a review of solutions and challenges f...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Abdelghani, Guerrad Abdelatif, Sahraoui Derdour, Makhlouf Dept. of Computer Science Tebessa12000 Algeria University of Larbi Ben m'Hidi Liaoa Laboratory Dept. of Mathematics And Computer Science Oum El Bouaghi Algeria

ISBN: (纸本)9798350350265

The deployment of video streaming in an Internet of Vehicles (IoV) context enhances both driving comfort and road safety. Video transmission provides clear and informative perspectives, offering a wide range of entertainment and infotainment, including details on restaurants, parking, and tourist attractions. However, maintaining a satisfactory quality of experience (QoE) in an IoV environment, which is characterized by fluctuating bandwidth and changing topology, presents a significant challenge. Furthermore, traffic parameters such as vehicle speed negatively impact video quality. This paper reviews various solutions proposed in the literature to optimize the MPEG-DASH (Dynamic Adaptive Streaming over HTTP) protocol in an IoV environment. The objective is to dynamically adjust video quality based on available resources to enhance QoE. Additionally, this review identifies key areas that researchers should focus on to improve this protocol. We highlight also the important challenges for our future contributions. © 2024 IEEE.

关键词： HTTP

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A Comparative Study on Keyword Extraction and Generation of Synonyms in Natural Language Processing

A Comparative Study on Keyword Extraction and Generation of ...

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2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023

作者： Dhala, Rasmi Rani Pavan Kumar, A.V.S. Panda, Soumya Priyadarsini Giet University Dept. of Computer Science and Engineering Gunupur India Sit Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350339369

Natural Language Processing (NLP) is an emerging field that aims to enable machines to understand and interpret human language. Keyword extraction and synonym generation are essential tasks in natural language processing. They play a significant role in information retrieval, text classification, and sentiment analysis. In this paper, we explore three different approaches to keyword extraction and synonym generation: rule-based model, statistical model, and extreme learning machine (ELM) model. We compare the performance of each method on a corpus of text and analyze the strengths and weaknesses of each approach. Our results show that the ELM model outperforms the other two methods in terms of accuracy and efficiency. © 2023 IEEE.

关键词： Neural network models

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