Digital Library

Vol. 20, No. 2, Apr. 2024  Cover

1. Crop Leaf Disease Identification Using Deep Transfer Learning
   Changjian Zhou, Yutong Zhang, Wenzhong Zhao
   Vol. 20, No. 2, pp. 149-158, Apr. 2024  
   https://doi.org/10.3745/JIPS.04.0305
   Details Full Text PubReader
   Keywords: Agricultural Artificial Intelligence, Crop Leaf Disease Identification, Plant Protection, Transfer Learning
   Show / Hide Abstract

   Traditional manual identification of crop leaf diseases is challenging. Owing to the limitations in manpower and resources, it is challenging to explore crop diseases on a large scale. The emergence of artificial intelligence technologies, particularly the extensive application of deep learning technologies, is expected to overcome these challenges and greatly improve the accuracy and efficiency of crop disease identification. Crop leaf disease identification models have been designed and trained using large-scale training data, enabling them to predict different categories of diseases from unlabeled crop leaves. However, these models, which possess strong feature representation capabilities, require substantial training data, and there is often a shortage of such datasets in practical farming scenarios. To address this issue and improve the feature learning abilities of models, this study proposes a deep transfer learning adaptation strategy. The novel proposed method aims to transfer the weights and parameters from pre-trained models in similar large-scale training datasets, such as ImageNet. ImageNet pre-trained weights are adopted and fine-tuned with the features of crop leaf diseases to improve prediction ability. In this study, we collected 16,060 crop leaf disease images, spanning 12 categories, for training. The experimental results demonstrate that an impressive accuracy of 98% is achieved using the proposed method on the transferred ResNet-50 model, thereby confirming the effectiveness of our transfer learning approach.


2. 1D-CNN-LSTM Hybrid-Model-Based Pet Behavior Recognition through Wearable Sensor Data Augmentation
   Hyungju Kim, Nammee Moon
   Vol. 20, No. 2, pp. 159-172, Apr. 2024  
   https://doi.org/10.3745/JIPS.02.0211
   Details Full Text PubReader
   Keywords: Behavior Recognition, CNN-LSTM, data augmentation, Deep Learning, Sensor data, wearable device
   Show / Hide Abstract

   The number of healthcare products available for pets has increased in recent times, which has prompted active research into wearable devices for pets. However, the data collected through such devices are limited by outliers and missing values owing to the anomalous and irregular characteristics of pets. Hence, we propose pet behavior recognition based on a hybrid one-dimensional convolutional neural network (CNN) and long shortterm memory (LSTM) model using pet wearable devices. An Arduino-based pet wearable device was first fabricated to collect data for behavior recognition, where gyroscope and accelerometer values were collected using the device. Then, data augmentation was performed after replacing any missing values and outliers via preprocessing. At this time, the behaviors were classified into five types. To prevent bias from specific actions in the data augmentation, the number of datasets was compared and balanced, and CNN-LSTM-based deep learning was performed. The five subdivided behaviors and overall performance were then evaluated, and the overall accuracy of behavior recognition was found to be about 88.76%.


3. Security Threat Analysis for Remote Monitoring and Control Functions of Connected Car Services
   Jin Kim, Jinho Yoo
   Vol. 20, No. 2, pp. 173-184, Apr. 2024  
   https://doi.org/10.3745/JIPS.03.0195
   Details Full Text PubReader
   Keywords: Connected car, Security, Threat modeling, vulnerability Analysis
   Show / Hide Abstract

   The connected car services are one of the most widely used services in the Internet of Things environment, and they provide numerous services to existing vehicles by connecting them through networks inside and outside the vehicle. However, although vehicle manufacturers are developing services considering the means to secure the connected car services, concerns about the security of the connected car services are growing due to the increasing number of attack cases. In this study, we reviewed the research related to the connected car services that have been announced so far, and we identified the threats that may exist in the connected car services through security threat modeling to improve the fundamental security level of the connected car services. As a result of performing the test to the applications for connected car services developed by four manufacturers, we found that all four companies' applications excessively requested unnecessary permissions for application operation, and the apps did not obfuscate the source code. Additionally, we found that there were still vulnerabilities in application items such as exposing error messages and debugging information.


4. An Improved Automated Spectral Clustering Algorithm
   Xiaodan Lv
   Vol. 20, No. 2, pp. 185-199, Apr. 2024  
   https://doi.org/10.3745/JIPS.04.0307
   Details Full Text PubReader
   Keywords: Cosine Angle Classification Method, Cluster Number Evaluation Factor, Density-Sensitive Distance, spectral clustering, UCI
   Show / Hide Abstract

   In this paper, an improved automated spectral clustering (IASC) algorithm is proposed to address the limitations of the traditional spectral clustering (TSC) algorithm, particularly its inability to automatically determine the number of clusters. Firstly, a cluster number evaluation factor based on the optimal clustering principle is proposed. By iterating through different k values, the value corresponding to the largest evaluation factor was selected as the first-rank number of clusters. Secondly, the IASC algorithm adopts a density-sensitive distance to measure the similarity between the sample points. This rendered a high similarity to the data distributed in the same high-density area. Thirdly, to improve clustering accuracy, the IASC algorithm uses the cosine angle classification method instead of K-means to classify the eigenvectors. Six algorithms—K-means, fuzzy Cmeans, TSC, EIGENGAP, DBSCAN, and density peak—were compared with the proposed algorithm on six datasets. The results show that the IASC algorithm not only automatically determines the number of clusters but also obtains better clustering accuracy on both synthetic and UCI datasets.


5. An Empirical Study of Absolute-Fairness Maximal Balanced Cliques Detection Based on Signed Attribute Social Networks: Considering Fairness and Balance
   Yixuan Yang, Sony Peng, Doo-Soon Park, Hye-Jung Lee, Phonexay Vilakone
   Vol. 20, No. 2, pp. 200-214, Apr. 2024  
   https://doi.org/10.3745/JIPS.04.0306
   Details Full Text PubReader
   Keywords: Absolute-Fairness Maximal Balanced Cliques, Attributed Social Network, Fairness of Nodes, Signed Social Network
   Show / Hide Abstract

   Amid the flood of data, social network analysis is beneficial in searching for its hidden context and verifying several pieces of information. This can be used for detecting the spread model of infectious diseases, methods of preventing infectious diseases, mining of small groups and so forth. In addition, community detection is the most studied topic in social network analysis using graph analysis methods. The objective of this study is to examine signed attributed social networks and identify the maximal balanced cliques that are both absolute and fair. In the same vein, the purpose is to ensure fairness in complex networks, overcome the “information cocoon” bottleneck, and reduce the occurrence of “group polarization” in social networks. Meanwhile, an empirical study is presented in the experimental section, which uses the personal information of 77 employees of a research company and the trust relationships at the professional level between employees to mine some small groups with the possibility of "group polarization." Finally, the study provides suggestions for managers of the company to align and group new work teams in an organization.


6. Construction of an Internet of Things Industry Chain Classification Model Based on IRFA and Text Analysis
   Zhimin Wang
   Vol. 20, No. 2, pp. 215-225, Apr. 2024  
   https://doi.org/10.3745/JIPS.01.0100
   Details Full Text PubReader
   Keywords: Industrial Chain, IoT, Random Forest algorithm, Text Analysis, Visualization
   Show / Hide Abstract

   With the rapid development of Internet of Things (IoT) and big data technology, a large amount of data will be generated during the operation of related industries. How to classify the generated data accurately has become the core of research on data mining and processing in IoT industry chain. This study constructs a classification model of IoT industry chain based on improved random forest algorithm and text analysis, aiming to achieve efficient and accurate classification of IoT industry chain big data by improving traditional algorithms. The accuracy, precision, recall, and AUC value size of the traditional Random Forest algorithm and the algorithm used in the paper are compared on different datasets. The experimental results show that the algorithm model used in this paper has better performance on different datasets, and the accuracy and recall performance on four datasets are better than the traditional algorithm, and the accuracy performance on two datasets, P-I Diabetes and Loan Default, is better than the random forest model, and its final data classification results are better. Through the construction of this model, we can accurately classify the massive data generated in the IoT industry chain, thus providing more research value for the data mining and processing technology of the IoT industry chain.


7. Computation Offloading with Resource Allocation Based on DDPG in MEC
   Sungwon Moon, Yujin Lim
   Vol. 20, No. 2, pp. 226-238, Apr. 2024  
   https://doi.org/10.3745/JIPS.03.0194
   Details Full Text PubReader
   Keywords: computation offloading, DDPG, MEC, Resource Allocation
   Show / Hide Abstract

   Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.


8. Post-COVID-19 Syndrome: The Effect of Regret on Travelers' Dynamic Carpooling Decisions
   Li Wang, Boya Wang, Qiang Xiao
   Vol. 20, No. 2, pp. 239-251, Apr. 2024  
   https://doi.org/10.3745/JIPS.04.0308
   Details Full Text PubReader
   Keywords: Carpooling Psychology, COVID-19 pandemic, Psychological Distance
   Show / Hide Abstract

   Coronavirus disease 2019 (COVID-19) has severely curtailed travelers' willingness to carpool and complicated the psychological processing system of travelers' carpooling decisions. In the post-COVID-19 era, a two-stage decision model under dynamic decision scenarios is constructed by tracking the psychological states of subjects in the face of multi-scenario carpooling decisions. Through a scenario experiment method, this paper investigates how three psychological variables, travelers' psychological distance to COVID-19, anticipated regret, and experienced regret about carpooling decisions, affect their willingness to carpool and re-carpool. The results show that in the initial carpooling decision, travelers' perception gap of anticipated regret positively predicts carpooling willingness and partially mediates between psychological distance to COVID-19 and carpooling willingness; in the re-carpooling decision, travelers' perception gap of anticipated regret mediates in the process of experienced regret influencing re-carpooling willingness; the inhibitory effect of experienced regret on carpooling in the context of COVID-19 is stronger than its facilitative effect on carpooling willingness. This paper tries to offer a fact-based decision-processing system for travelers.


9. Confidential Convergecast Based on Random Linear Network Coding for the Multi-hop Wireless Sensor Network
   Davaabayar Ganchimeg, Sanghyun Ahn, Minyeong Gong
   Vol. 20, No. 2, pp. 252-262, Apr. 2024  
   https://doi.org/10.3745/JIPS.03.0197
   Details Full Text PubReader
   Keywords: Confidentiality, Convergecast, Network coding, Security, Wireless Sensor Network
   Show / Hide Abstract

   The multi-hop wireless sensor network (WSN) suffers from energy limitation and eavesdropping attacks. We propose a simple and energy-efficient convergecast mechanism using inter-flow random linear network coding that can provide confidentiality to the multi-hop WSN. Our scheme consists of two steps, constructing a logical tree of sensor nodes rooted at the sink node, with using the Bloom filter, and transmitting sensory data encoded by sensor nodes along the logical tree upward to the sink where the encoded data are decoded according to our proposed multi-hop network coding (MHNC) mechanism. We conducted simulations using OMNET++ CASTALIA-3.3 framework and validated that MHNC outperforms the conventional mechanism in terms of packet delivery ratio, data delivery time and energy efficiency.


10. An Early Warning Model for Student Status Based on Genetic Algorithm-Optimized Radial Basis Kernel Support Vector Machine
    Hui Li, Qixuan Huang, Chao Wang
    Vol. 20, No. 2, pp. 263-272, Apr. 2024  
    https://doi.org/10.3745/JIPS.02.0213
    Details Full Text PubReader
    Keywords: Early Warning Model, Genetic Algorithm Optimization, Radial Basis Kernel, Support Vector Machine
    Show / Hide Abstract

    A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of their status. This model improves the predictive effect of support vector machines. The genetic optimization algorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, and gamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain the optimal solution. The experimental model was trained on open-source datasets and validated through comparisons with random forest, backpropagation neural network, and GA-SVM models. The test results show that the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtain higher accuracy rates when used for early warning in university learning.


11. QNFT: A Post-Quantum Non-fungible Tokens for Secure Metaverse Environment
    Abir El Azzaoui, JaeSoo Kim
    Vol. 20, No. 2, pp. 273-283, Apr. 2024  
    https://doi.org/10.3745/JIPS.03.0196
    Details Full Text PubReader
    Keywords: Copyrights Protection, Metaverse, NFT, Quantum NTF
    Show / Hide Abstract

    The digital domain has witnessed unprecedented growth, reshaping the way we interact, work, and even perceive reality. The internet has evolved into a vast ecosystem of interconnected virtual worlds, giving birth to the concept of the Metaverse. The Metaverse, often envisioned as a collective virtual shared space, is created by the convergence of virtually enhanced physical reality and interactive digital spaces. Within this Metaverse space, the concept of ownership, identity, and authenticity takes on new dimensions, necessitating innovative solutions to safeguard individual rights. The digital transformation through Metaverse has also brought forth challenges, especially in copyright protection. As the lines between the virtual and physical blur, the traditional notions of ownership and rights are being tested. The Metaverse, with its multitude of user-generated content, poses unique challenges. The primary objective of this research is multifaceted. Firstly, there's a pressing need to understand the strategies employed by non-fungible token (NFT) marketplaces within the Metaverse to strengthen security and prevent copyright violations. As these platforms become centers for digital transactions, ensuring the authenticity and security of each trade becomes paramount. Secondly, the study aims to delve deep into the foundational technologies underpinning NFTs, from the workings of blockchain to the mechanics of smart contracts, to understand how they collectively ensure copyright protection. Thus, in this paper, we propose a quantum based NFT solution that can secure Metaverse and copyright contents in an advanced manner.