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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Bhargava, Misha Mahi, Samarjit Rao, Vidya Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

ISBN: (纸本)9781032911571

With its vast population and extensive healthcare requirements, India is moving toward digitalizing its health records. Safeguarding Electronic Health Records (EHRs) is crucial given the increase in security breaches and attacks, with India’s healthcare sector at the forefront. This study provides an overview of EHR security in India by exploring the EHR adoption rate in India and the barriers to its use. It focuses on the significant security challenges they bring, and the initiatives currently being implemented in India, such as the Ayushman Bharat Digital Mission (ABDM) and the recently released National Accreditation Board for Hospitals (NABH) Digital Health Standards. Integrating Blockchain in current EHR systems to resolve the observed challenges is then explored. The findings of this study show the urgency of integrating emerging technologies such as blockchain which have the potential for patient-controlled data sharing and data immutability possibly within the framework of the ABDM. This study aims to serve as a valuable resource for healthcare professionals, researchers and other regions seeking to understand and improve EHR security. © 2025 the Author(s).

关键词： Electronic health record

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Targeted Universal Adversarial Attack Based on Fourier Transform 2nd

Targeted Universal Adversarial Attack Based on Fourier Tran...

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2nd International Conference on data Security and Privacy Protection, DSPP 2024

作者： Sun, Yancheng Chen, Yuling Liu, Ziyang Dou, Hui Tan, Chaoyue The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9789819785391

The existence of adversarial example reveals the fragility in neural networks, and the exploration of their theoretical origins increases the interpretability of deep learning, enhances researchers’ understanding of neural networks, and contributes to the development of next-generation artificial intelligence, which has attracted widespread research in various fields. The targeted adversarial attack problem based on sample features faces two problems: on the one hand, the difference in the model’s attention to different features in the example;On the other hand, the bias that occurs in adversarial attacks can have an impact on targeted attacks. The mechanism of the human eye relies more on the shape information of the image. However, in the past, artificial intelligence models based on convolutional neural networks often relied on the texture features of image examples to make decisions. At present, general optimize adversarial attack algorithms do not distinguish different types of features based on different parts of the image, but only process the entire example in a general manner, making it difficult to effectively utilize the effective features in the example, resulting in poor algorithm performance and interpretability. This article optimizes the adversarial attack algorithm based on optimization iteration, as follows: Firstly, different types of information in adversarial examples are studied, and fourier transform technology is used to process the attacked original image and obtain its low-frequency information. The obtained low-frequency examples are randomly cropped to obtain some feature examples. Then, the clustering effect was studied when the examples were attacked without targets, and an inter-class smoothing loss was designed to improve the success rate of target attacks. This Rebalance Universal Feature Method (RFM) is based on fourier low pass filtering and inter-class smoothing, which effectively improves the ability of optimization iteration bas

关键词： Adversarial machine learning

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Deep ensemble optimal classifier-based software defect prediction for early detection and quality assurance

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Journal of Umm Al-Qura University for Engineering and Architecture 2025年 1-12页

作者： Srinivas, Kalyanapu Janapati, Krishna Chaithanya Sai Kiran, Mungalched Sudheer Reddy, Davu Vinay, Thota Vamshi Yashwanth, Choula Department of Computer Science and Engineering Dhanekula Institute of Engineering and Technology Andhra Pradesh Vijayawada India Department of Electronics and Communication Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering (Data Science) Malla Reddy Engineering College and Management Sciences Medchal Telangana Kistapur India

Software Defect Prediction (SDP) is critical in identifying fault-prone modules during the software development life cycle, enhancing software quality, and reducing maintenance costs. However, existing SDP models face significant challenges, including high false positive rates and severe class imbalance between defect-prone and non-defect modules, which hinder practical model training and accurate defect detection. To overcome these challenges, this study proposes a robust and efficient SDP architecture that integrates several advanced techniques to enhance predictive accuracy and model reliability. The methodology begins with comprehensive data preprocessing, incorporating normalization to standardize feature scales and outlier detection to eliminate noise and improve data quality. To address the inherent class imbalance in the dataset, the minority oversampling by synthetic data (MOSD) technique is employed to generate synthetic samples for the minority class, thus achieving a more balanced distribution. Following this, an Adaptive sequential K-best (ASKB) feature selection strategy is implemented to identify and retain the most relevant features, effectively reducing dimensionality while preserving critical information. Finally, the weighted random forest (WRF) classifier is utilized, which assigns appropriate weights to training instances, enabling improved classification performance, especially for minority class instances. The proposed methodology achieved an accuracy of 99.119%, a precision of 99.431%, a recall of 99.122%, and an F1-score of 99.333%. © The Author(s) 2025.

关键词： Adaptive sequential K-best Class imbalance data preprocessing Feature selection Minority oversampling Predictive modeling Software defect prediction Synthetic data Weighted random forest

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kProtoClust:Towards Adaptive k-Prototype Clustering without Known k

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computers, Materials & Continua 2025年第3期82卷 4949-4976页

作者： Yuan Ping Huina Li Chun Guo Bin Hao School of Information Engineering Xuchang UniversityXuchang461000China Henan Province Engineering Technology Research Center of Big Data Security andApplications Xuchang461000China College of Computer Science and Technology Guizhou UniversityGuiyang550025China Here Data Technology Shenzhen518000China

Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are still challenges,particularly for non-predetermined data *** propose an adaptive k-prototype clustering method(kProtoClust)which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and *** behalf of a group of data samples,support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes,while inner samples become the first candidates for instability reduction of *** from the representation of samples in traditional,we extend sample selection by encouraging fictitious samples to emphasize the representativeness of *** get out of the circle-like pattern limitation,we introduce a convex decomposition-based strategy of one-cluster-multiple-prototypes in which convex hulls of varying sizes are prototypes,and accurate connection analysis makes the support of arbitrary cluster shapes *** by geometry,the three presented strategies make kProtoClust bypassing the K dependence well with the global and local position relationship analysis for data *** results on twelve datasets of irregular cluster shape or high dimension suggest that kProtoClust handles arbitrary cluster shapes with prominent accuracy even without the prior knowledge K.

关键词： Prototype finding convex hull support vector data description geometrical information

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Underwater target recognition based on adaptive multi-feature fusion network

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Multimedia Tools and Applications 2025年第10期84卷 7297-7317页

作者： Pan, Xiaoying Sun, Jia Feng, TianHao Lei, MingZhu Wang, Hao Zhang, WuXia Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an710121 China School of Computer Science and Technology Xi’an University of Post & Telecommunications Xi’an710121 China

Due to the complexity of the underwater environment, underwater acoustic target recognition is more challenging than ordinary target recognition, and has become a hot topic in the field of underwater acoustics research. In recent years, deep learning has been widely used in underwater recognition due to its powerful feature learning capabilities. However, most deep learning-based underwater target recognition methods use only the time-frequency domain features of signals, ignoring the frequency domain image features of signals. Additionally, they only use simple summation or splicing strategies to fuse and discriminate the extracted features, which leads to limited improvement in the accuracy of underwater target recognition. To address these issues, this paper proposes an underwater target recognition method based on an adaptive multi-feature fusion network. The method consists of a data pre-processing module, a multi-dimensional feature extraction module, and an adaptive multi-feature fusion module. In the multi-dimensional feature extraction module, the long short time memory network (LSTM) and the one-dimensional convolutional neural network (1DCNN) are used to extract the time-frequency features of the underwater signal. Furthermore, the two-dimensional convolutional neural network (2DCNN) is used to extract the image features in the frequency domain. The adaptive multi-feature fusion module uses an attention mechanism for adaptive weighted fusion to make full use of the learned features. The effectiveness of the proposed method is validated on the ShipEar dataset, and the recognition accuracy reaches 94.92%, which is higher than other existing methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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FHO-based Hybrid Neural Networks for Short-Term Load Forecasting in Economic Dispatch of Power Systems

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 262-284页

作者： Wei, Yi Wang, Chia-Hung Suo, Yi-Fan Zhao, Qi-Gen Yuan, Jin-Chen Chen, Shu-Meng College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China

With the development of the smart grid era, the data volume of the power system on the user side is growing rapidly, and the dependence of citizens on electricity is increasing significantly. In this paper, we aim to maximize the benefits of both supply and demand in the power system, from two aspects: improving the accuracy of short-term load forecasting and optimizing the economic dispatch of the power system. Firstly, based on the analysis of electricity consumption data, an improved load forecasting method via hybrid neural network is proposed. After determining the prediction results of the proposed model, we solved a short-term economic dispatch problem with the objective function of minimizing the generation cost of the power system and constraints such as unit start-up and shutdown, to achieve optimization of economic dispatch. In this work, an improved hybrid neural network with Firehawk Optimization (FHO) algorithm is developed to improve the accuracy of the prediction model by optimizing hyperparameters such as input data, number of hidden layer neurons, and batch size. In a series of numerical experiments, the proposed method is compared with other well-known optimization algorithms, such as Particle Swarm Optimization (PSO), Grey Wolf Optimiser (GWO) and Wild Horse Optimisation (WHO). From the comparison results, it shows that our method acheives better performance evaluation, and also improves the prediction accuracy of neural network models. Based on the load forecasting results, we determine the optimal scheduling scheme for the combination of power supply units, which minimizes the load and achieves higher applicability. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： economic dispatch problem electric load forecasting electric power system firehawk optimization algorithm smart power grids supply and demand sides

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Enhanced Clickbait Detection through Ensemble Machine Learning Techniques

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Procedia computer science 2025年 258卷 599-608页

作者： Neetu Sardana Deepika Varshney Seema Luthra Department of Computer Science & Engineering-Data Science ABES Engineering College Ghaziabad India

Nowadays online news websites are one of the quickest ways to get information. However, the credibility of news from these sources is sometimes questioned. One common problem with online news is the prevalence of clickbait. Clickbait uses exaggerated headlines to lure people to click the suspected link, but the content often disappoints the reader and degrades user experience it may also hamper public emotions. The proposed work aims to examine diverse set of models for clickbait detection. The models are formed by integration of Machine learning (ML) and Ensemble learning methods (EL) with Term Frequency and Inverse Document Frequency (TF-IDF) & Embedding technique. Five ML and three EL are analysed &compared. Random Forest along with TF-IDF gave the best results of 85%. The resultant model shows significant improvements with a minimal false-positives.

关键词： Clickbait Detection Machine Learning text classification NLP Ensemble learning Stacking classifir

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Revocable Hierarchical Identity-based Inner Product Function Encryption in Smart Healthcare

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IEEE Internet of Things Journal 2025年第11期12卷 15319-15332页

作者： Zhu, Yasi Zhou, Yanwei Wang, Jing Yang, Bo Zhang, Mingwu Shaanxi Normal University School of Computer Science Xi'an710119 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Hubei University of Technology School of Computer Wuhan430068 China

With the development of cloud computing and the digital transformation of the medical industry, the application scenarios and effects of smart healthcare are constantly expanding and improving. Smart healthcare plays an important role in improving service quality and medical efficiency and reducing medical costs. However, in the process of data collection, storage, and transmission, the risk of patients' privacy being illegally accessed or leaked has become increasingly prominent. These medical data contain sensitive information about patients, and once leaked, it will violate the privacy of patients, cause medical accidents, and seriously damage the legitimate rights and interests of patients. An efficient and reliable privacy protection mechanism is the foundation and key to establishing a harmonious doctor-patient relationship and improving medical quality. Although traditional encryption methods can provide certain information security protection, they cannot balance data protection and data analysis and processing and are not suitable for intelligent medical environments that require more precise medical decisions. Therefore, we propose a hierarchical identity-based inner product functional encryption scheme with a malicious user revocation mechanism aimed at addressing the privacy and security issues of patients in smart healthcare. This solution allows specific access and analysis of data while ensuring that patients' sensitive information is protected from infringement, enabling doctors to diagnose and treat more accurately and optimize the allocation of medical resources to the greatest extent possible. Furthermore, we have formally demonstrated the security of the scheme and conducted a security analysis and performance comparison. The results show that our solution has better performance while ensuring security and is suitable for the efficient and secure data processing requirements of smart healthcare. © 2014 IEEE.

关键词： Differential privacy

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NOMA Attack Prediction in Large-Scale Networks using Non-Linear Chaotic Belief Mechanism 3

NOMA Attack Prediction in Large-Scale Networks using Non-Lin...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sangeetha, K. Suguna, M. Jebakumar Immanuel, D. Suje, S.A. Department of Computer Science and Engineering SNS College of Technology Tamil Nadu Coimbatore India Department of Artificial Intelligence and Data Science Karpagam Institute of Technology Tamil Nadu Coimbatore India Department of Artificial Intelligence and Data Science Nandha Engineering College Tamil Nadu Erode India

ISBN: (纸本)9798331527549

Non-Orthogonal Multiple Access (NOMA) systems are becoming relevant in the fast-expanding terrain of large-scale networks because of their efficiency in concurrently managing many users. This is true since NOMA systems let numerous users concurrently be managed. On the other hand, the intricacy of these networks leaves them vulnerable to a wide spectrum of attacks, including the more advanced and erratic NOMA attacks on the network. These strikes could produce major disturbances that would compromise the quality of service and cast questions regarding the general network security. It has been demonstrated that the effective projection of these hazards is limited by standard linear and probabilistic techniques. This is true as contemporary methods fail to adequately capture the basic non-linear dynamics of these large-scale networks. This article offers a novel method for NOMA attack prediction by means of a non-linear chaotic belief process. The results are shown here. To recreate the uncertainty and intricate interactions inside the network, the proposed method which is the logistic map which in turn generates the sequences for ensuring the accurate iterative updates which in turn provides better scalability and precision. This integrates belief networks with chaos theory. More exactly, we capture the random and nonlinear aspect of network dynamics by building belief values indicating the likelihood of an attack by use of a chaotic map. After that, the belief values proliferate across the network in search of defects and project the probability of NOMA attacks. Effectiveness of the proposed method is demonstrated by test results on a simulated large-scale network simulation. With a prediction accuracy of 92.7%, the chaotic belief mechanism obtained much above the average accuracy of 78.4% of traditional linear prediction systems. Moreover, the proposed approach lowered the false positive rate to 5.3%, substantially below the rate of 12.8% applied in the standard ap

关键词： Large scale systems

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Blockchain-Based Revocable Key-Aggregate Searchable Encryption for Group data Sharing in Cloud-Assisted Industrial IoT

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IEEE Internet of Things Journal 2025年第11期12卷 16899-16911页

作者： Zhang, Kai Hu, Xinyi Zhao, Jian Wei, Lifei Ning, Jianting Shanghai University of Electric Power College of Computer Science and Technology Shanghai China Shanghai University of Electric Power College of Electrical Engineering Shanghai China Shanghai Maritime University College of Information Engineering Shanghai China Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Macau Faculty of Data Science China

In the cloud-assisted Industrial Internet of Things (IIoT), flexible and secure data sharing promotes industry processes optimization and new products-making. To enable selective data retrieval over categorized data collected by IoT devices in the encryption domain, key-aggregate searchable encryption (KASE) is adopted for group data sharing in Industrial IoT due to efficient management of encryption keys. Nonetheless, existing solutions rarely consider user revocation property and suffered from the following limitations: (i) the user revocation needs interaction process between data owner and cloud;(ii) the non-revoked users may be accidentally revoked. Therefore, we propose a blockchain-based revocable KASE system (BC-RKASE) that achieves non-interactive user revocation and trusted revoked user management. Technically, the result is non-trivially obtained from revisiting the generation of aggregate keys and key updates via secret sharing, along with re-designing trapdoor adjustment and providing public verifiable proof for key updates by public blockchain. Besides formal security analysis, we conduct extensive experiments in a real cloud environment and thin IoT devices (Raspberry Pi) using a public IIoT dataset to confirm the practical performance of BC-RKASE. In particular, BC-RKASE outperforms state-of-the-art schemes with 13.4×~15× time efficiency accelerating for data retrieval and runs more than 16× faster for user revocation. © 2025 IEEE.

关键词： Industrial Internet of Things Cloud computing Aggregates Encryption Blockchains Permission Cryptography data retrieval Production facilities Costs

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