International Journal of Intelligent Systems最新文献

{"title":"K-Means Centroids Initialization Based on Differentiation Between Instances Attributes","authors":"Ali Akbar Khan,&nbsp;Muhammad Salman Bashir,&nbsp;Asma Batool,&nbsp;Muhammad Summair Raza,&nbsp;Muhammad Adnan Bashir","doi":"10.1155/2024/7086878","DOIUrl":"https://doi.org/10.1155/2024/7086878","url":null,"abstract":"<div>\u0000 <p>The conventional K-Means clustering algorithm is widely used for grouping similar data points by initially selecting random centroids. However, the accuracy of clustering results is significantly influenced by the initial centroid selection. Despite different approaches, including various K-Means versions, suboptimal outcomes persist due to inadequate initial centroid choices and reliance on common normalization techniques like min-max normalization. In this study, we propose an improved algorithm that selects initial centroids more effectively by utilizing a novel formula to differentiate between instance attributes, creating a single weight for differentiation. We introduce a preprocessing phase for dataset normalization without forcing values into a specific range, yielding significantly improved results compared to unnormalized datasets and those normalized using min-max techniques. For our experiments, we used five real datasets and five simulated datasets. The proposed algorithm is evaluated using various metrics and an external benchmark measure, such as the Adjusted Rand Index (ARI), and compared with the traditional K-Means algorithm and 11 other modified K-Means algorithms. Experimental evaluations on these datasets demonstrate the superiority of our proposed methodologies, achieving an impressive average accuracy rate of up to 95.47% and an average ARI score of 0.95. Additionally, the number of iterations required is reduced compared to the conventional K-Means algorithm. By introducing innovative techniques, this research provides significant contributions to the field of data clustering, particularly in addressing modern data-driven clustering challenges.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7086878","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142666133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ViT-AMD: A New Deep Learning Model for Age-Related Macular Degeneration Diagnosis From Fundus Images","authors":"Ngoc Thien Le,&nbsp;Thanh Le Truong,&nbsp;Sunchai Deelertpaiboon,&nbsp;Wattanasak Srisiri,&nbsp;Pear Ferreira Pongsachareonnont,&nbsp;Disorn Suwajanakorn,&nbsp;Apivat Mavichak,&nbsp;Rath Itthipanichpong,&nbsp;Widhyakorn Asdornwised,&nbsp;Watit Benjapolakul,&nbsp;Surachai Chaitusaney,&nbsp;Pasu Kaewplung","doi":"10.1155/2024/3026500","DOIUrl":"https://doi.org/10.1155/2024/3026500","url":null,"abstract":"<div>\u0000 <p>Age-related macular degeneration (AMD) diagnosis using fundus images is one of the critical missions of the eye-care screening program in many countries. Various proposed deep learning models have been studied for this research interest, which aim to achieve the mission and outperform human-based approaches. However, research efforts are still required for the improvement of model classification accuracy, sensitivity, and specificity values. In this study, we proposed the model named as ViT-AMD, which is based on the latest Vision Transformer (ViT) structure, to diagnosis a fundus image as normal, dry AMD, or wet AMD types. Unlike convolution neural network models, ViT consists of the attention map layers, which show more effective performance for image classification task. Our training process is based on the 5-fold cross-validation and transfer learning techniques using Chula-AMD dataset at the Department of Ophthalmology, the King Chulalongkorn Memorial Hospital, Bangkok. Furthermore, we also test the performance of trained model using an independent image datasets. The results showed that for the 3-classes AMD classification (normal vs. dry AMD vs. wet AMD) on the Chula-AMD dataset, the averaged accuracy, precision, sensitivity, and specificity of our trained model are about 93.40%, 92.15%, 91.27%, and 96.57%, respectively. For result testing on independent datasets, the averaged accuracy, precision, sensitivity, and specificity of trained model are about 74, 20%, 75.35%, 74.13%, and 87.07%, respectively. Compared with the results from the baseline CNN-based model (DenseNet201), the trained ViT-AMD model has outperformed significantly. In conclusion, the ViT-AMD model have proved their usefulness to assist the ophthalmologist to diagnosis the AMD disease.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/3026500","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Switched Observer-Based Event-Triggered Safety Control for Delayed Networked Control Systems Under Aperiodic Cyber attacks","authors":"Shuqi Li,&nbsp;Yiren Chen,&nbsp;Wenli Shang,&nbsp;Feiqi Deng,&nbsp;Xiaobin Gao","doi":"10.1155/2024/6971338","DOIUrl":"https://doi.org/10.1155/2024/6971338","url":null,"abstract":"<div>\u0000 <p>The networked control systems (NCSs) under cyberattacks have received much attention in both industrial and academic fields, with rare attention on the delayed networked control systems (DNCSs). In order to well address the control problem of DNCSs, in this study, we consider the resilient event-triggered safety control problem of the NCSs with time-varying delays based on the switched observer subject to aperiodic denial-of-service (DoS) attacks. The observer-based switched event-triggered control (ETC) strategy is devised to cope with the DNCSs under aperiodic cyberattacks for the first time so as to decrease the transmission of control input under limited network channel resources. A new piecewise Lyapunov functional is proposed to analyze and synthesize the DNCSs with exponential stability. The quantitative relationship among the attack activated/sleeping period, exponential decay rate, event-triggered parameters, sampling period, and maximum time-delay are explored. Finally, we use both a numerical example and a practical example of offshore platform to show the effectiveness of our results.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/6971338","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Innovative Application of Swarm-Based Algorithms for Peer Clustering","authors":"Vesna Šešum-Čavić,&nbsp;Eva Kühn,&nbsp;Laura Toifl","doi":"10.1155/2024/5571499","DOIUrl":"https://doi.org/10.1155/2024/5571499","url":null,"abstract":"<div>\u0000 <p>In most peer-to-peer (P2P) networks, peers are placed randomly or based on their geographical position, which can lead to a performance bottleneck. This problem can be solved by using peer clustering algorithms. In this paper, the significant results of the paper can be described in the following sentences. We propose two innovative swarm-based metaheuristics for peer clustering, slime mold and slime mold K-means. They are competitively benchmarked, evaluated, and compared to nine well-known conventional and swarm-based algorithms: artificial bee colony (ABC), ABC combined with K-means, ant-based clustering, ant K-means, fuzzy C-means, genetic K-means, hierarchical clustering, K-means, and particle swarm optimization (PSO). The benchmarks cover parameter sensitivity analysis and comparative analysis made by using 5 different metrics: execution time, Davies–Bouldin index (DBI), Dunn index (DI), silhouette coefficient (SC), and averaged dissimilarity coefficient (ADC). Furthermore, a statistical analysis is performed in order to validate the obtained results. Slime mold and slime mold K-means outperform all other swarm-inspired algorithms in terms of execution time and quality of the clustering solution.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5571499","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deepfake Detection Based on the Adaptive Fusion of Spatial-Frequency Features","authors":"Fei Wang,&nbsp;Qile Chen,&nbsp;Botao Jing,&nbsp;Yeling Tang,&nbsp;Zengren Song,&nbsp;Bo Wang","doi":"10.1155/2024/7578036","DOIUrl":"https://doi.org/10.1155/2024/7578036","url":null,"abstract":"<div>\u0000 <p>Detecting deepfake media remains an ongoing challenge, particularly as forgery techniques rapidly evolve and become increasingly diverse. Existing face forgery detection models typically attempt to discriminate fake images by identifying either spatial artifacts (e.g., generative distortions and blending inconsistencies) or predominantly frequency-based artifacts (e.g., GAN fingerprints). However, a singular focus on a single type of forgery cue can lead to limited model performance. In this work, we propose a novel cross-domain approach that leverages a combination of both spatial and frequency-aware cues to enhance deepfake detection. First, we extract wavelet features using wavelet transformation and residual features using a specialized frequency domain filter. These complementary feature representations are then concatenated to obtain a composite frequency domain feature set. Furthermore, we introduce an adaptive feature fusion module that integrates the RGB color features of the image with the composite frequency domain features, resulting in a rich, multifaceted set of classification features. Extensive experiments conducted on benchmark deepfake detection datasets demonstrate the effectiveness of our method. Notably, the accuracy of our method on the challenging FF++ dataset is mostly above 98%, showcasing its strong performance in reliably identifying deepfake images across diverse forgery techniques.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7578036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic differential entropy and brain connectivity features based EEG emotion recognition","authors":"Fa Zheng,&nbsp;Bin Hu,&nbsp;Xiangwei Zheng,&nbsp;Cun Ji,&nbsp;Ji Bian,&nbsp;Xiaomei Yu","doi":"10.1002/int.23096","DOIUrl":"10.1002/int.23096","url":null,"abstract":"<p>Emotion recognition has become a research focus in the brain–computer interface and cognitive neuroscience. Electroencephalogram (EEG) is employed for its advantages as accurate, objective, and noninvasive nature. However, many existing research only focus on extracting the time and frequency domain features of the EEG signals while failing to utilize the dynamic temporal changes and the positional relationships between different electrode channels. To fill this gap, we develop the dynamic differential entropy and brain connectivity features based EEG emotion recognition using linear graph convolutional network named DDELGCN. First, the dynamic differential entropy feature which represents the frequency domain feature as well as time domain feature is extracted based on the traditional differential entropy feature. Second, brain connectivity matrices are constructed by calculating the Pearson correlation coefficient, phase-locked value and transfer entropy, and then are used to denote the connectivity features of all electrode combinations. Finally, a linear graph convolutional network is customized and applied to aggregate the features from total electrode combinations and then classifies the emotional states, which consists of five layers, namely, an input layer, two linear graph convolutional layers, a fully connected layer, and a softmax layer. Extensive experiments show that the accuracies in the valence and arousal dimensions reach 90.88% and 91.13%, and the precision reaches 96.66% and 97.02% on the DEAP dataset, respectively. On the SEED dataset, the accuracy and precision reach 91.56% and 97.38%, respectively.</p>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"37 12","pages":"12511-12533"},"PeriodicalIF":7.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42719499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CNN- and GAN-based classification of malicious code families: A code visualization approach","authors":"Ziyue Wang,&nbsp;Weizheng Wang,&nbsp;Yaoqi Yang,&nbsp;Zhaoyang Han,&nbsp;Dequan Xu,&nbsp;Chunhua Su","doi":"10.1002/int.23094","DOIUrl":"10.1002/int.23094","url":null,"abstract":"<p>Malicious code attacks have severely hindered the current development of the Internet technologies. Once the devices are infected with virus, the damages to companies and users are unpredictable. Although researchers have developed malware detection methods, the analysis result still cannot achieve the desired accuracy due to complicated malicious code families and fast-growing variants. In this paper, to solve this problem, we combine Convolutional Neural Networks (CNNs) with Generative Adversarial Networks (GANs) to design an efficient and accurate malware detection method. First, we implement a code visualization method and utilize GAN to generate more samples of malicious code variants in the role of data augmentation. Then, the lightweight AlexNet originated from CNN to classify malware families. Finally, simulation experiments are conducted to evaluate that our CNN plus GAN model can achieve a higher classification accuracy (i.e., 97.78%) compared with some related work.</p>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"37 12","pages":"12472-12489"},"PeriodicalIF":7.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42633135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning algorithms for smart and intelligent healthcare system in Society 5.0","authors":"Ikhlas Fuad Zamzami,&nbsp;Kuldeep Pathoee,&nbsp;Brij B. Gupta,&nbsp;Anupama Mishra,&nbsp;Deepesh Rawat,&nbsp;Wadee Alhalabi","doi":"10.1002/int.23061","DOIUrl":"10.1002/int.23061","url":null,"abstract":"<p>The pandemic has shown us that it is quite important to keep track record our health digitally. And at the same time, it also showed us the great potential of Instruments like wearable observing gadgets, video conferences, and even talk bots driven by artificial intelligence (AI) can provide good care from remotely. Real time data collected from different health care devices of cases across globe played an important role in combatting the virus and also help in tracking its progress. The evolution of biomedical imaging techniques, incorporated sensors, and machine learning (ML) in recent years has led in various health benefits. Medical care and biomedical sciences have become information science fields, with a solid requirement for refined information mining techniques to remove the information from the accessible data. Biomedical information contains a few difficulties in information investigation, including high dimensionality, class irregularity, and low quantities of tests. AI is a subfield of AI and computer science which centric the utilization of information and calculations to impersonate the way that people learn, steadily further developing its accuracy. ML is an essential element of the rapidly growing area of information science. Calculations are created using measurable procedures to make characterizations or forecasts, exposing vital experiences inside information mining operations. In this chapter, we explain and compare the different algorithms of ML which could be helpful in detecting different disease at earlier stage. We summarize the algorithms and different steps involved in ML to extract information for betterment of the society which is already exposed to the world of data.</p>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"37 12","pages":"11742-11763"},"PeriodicalIF":7.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41782322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An adaptive multiobjective evolutionary algorithm for dynamic multiobjective flexible scheduling problem","authors":"Weiwei Yu,&nbsp;Li Zhang,&nbsp;Ning Ge","doi":"10.1002/int.23090","DOIUrl":"10.1002/int.23090","url":null,"abstract":"<p>There are various uncertain disturbances in the actual manufacturing environment, which makes dynamic multiobjective flexible scheduling problem of flexible job shop (MDFJSP) become the research focus in the field of optimal scheduling. In this paper, MDFJSP in the environment of temporary order insertion uncertainty is studied, and a multiobjective dynamic scheduling scheme based on rescheduling index and adaptive nondominated sorting genetic algorithm (NSGA-II) is proposed. First, based on the actual manufacturing environment, the mathematical model of the traditional flexible job shop scheduling problem is improved, and the multiobjective dynamic rescheduling model of flexible work center is established. Then, the existing rescheduling mechanisms are summarized, and a rescheduling hybrid driving mechanism based on the rescheduling index is proposed to enable it to reschedule and drive according to the actual situation. Finally, the shortcomings of the traditional multiobjective scheduling algorithm NSGA-II are analyzed, the adaptive cross mutation strategy and the simplified harmonic normalized distance measure method are proposed to improve it, and an adaptive multiobjective dynamic scheduling algorithm NSGA-II (MDSA-NSGA-II) is formed. To analyze the performance of this algorithm, the performance of this algorithm is compared with five classical flexible job shop multiobjective scheduling algorithms in international general examples, and the effectiveness is verified by real aircraft production examples. The experimental results fully show that MDSA-NSGA-II has good performance in solving MDFJSP.</p>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"37 12","pages":"12335-12366"},"PeriodicalIF":7.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42579507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Verifiable dynamic search over encrypted data in cloud-assisted intelligent systems","authors":"Yunling Wang,&nbsp;Pei Wei,&nbsp;Meixia Miao,&nbsp;Xuefeng Zhang","doi":"10.1002/int.23065","DOIUrl":"10.1002/int.23065","url":null,"abstract":"<p>The cloud-assisted intelligent systems have attracted extensive attention due to their powerful data analysis and computation capabilities. However, how to handle encrypted data remains a challenging problem in intelligent systems. A promising solution is searchable symmetric encryption (SSE), which enables a client to privately outsource their data to the cloud while preserving keyword search functionality. In practice, dynamic SSE is more practical and supports efficient data addition and deletion. Unfortunately, data update will leak some additional information which can be exploited to break data privacy. To address this issue, forward and backward secure SSE schemes are proposed to reduce the leakage of data update. That is, forward security guarantees that the newly updated documents cannot reveal the previously searched keywords, while backward security guarantees that the server cannot recover the deleted documents. However, the existing forward and backward secure SSE schemes mainly consider curious-but-honest server. How to verify the soundness and completeness of search results is still a challenge. In this paper, we propose a noninteractive verifiable dynamic SSE scheme with forward and backward security from two universal accumulators. Specifically, the server in our scheme only needs one roundtrip to return the nondeleted search results to the client, which saves the communication overhead dramatically. Besides, our scheme can achieve public verification that anyone can verify the search results but not only the client who has the private key. Finally, we give a formal security analysis and compare the proposed scheme with other related work, the results show that our scheme can achieve the desired security properties with practical efficiency.</p>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"37 12","pages":"11830-11852"},"PeriodicalIF":7.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42364502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}