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RTAD-HIS: Regulated transformer architecture based anomaly detection framework towards security in healthcare IoT systems

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Applied Soft Computing 2025年 177卷

作者： Rai, Arun Kumar Verma, Deepak Kumar Dwivedi, Rajendra Kumar Department of Computer Science and Engineering University Institute of Engineering & Technology Chhatrapati Shahu Ji Maharaj University Kanpur India Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Gorakhpur India

The rapid expansion of Healthcare Internet of Things (HIS) devices has transformed modern medical systems by enabling real-time patient monitoring, seamless data exchange, and automated diagnostics. However, existing methods struggle with accurate anomaly detection, often facing overfitting and false positives/negatives due to vast and complex datasets. Reliable solutions are essential to ensure precise and timely anomaly identification of security threats and system vulnerabilities. This paper introduces the Regulated Transformer-Based Anomaly Detection (RTAD) model, which employs a Self-Regulated Transformer-Convolutional Residual Neural Network (SRTrans-ConvRNN) for anomaly detection in HIS data streams. The RTAD model utilizes temporal pooling layers to enable feature extraction, Stateful Long Short-Term Memory (SLSTM) layers to enable sequential learning, Multi-Head Self-Attention (MHSA) to enable parallel processing, and position-wise feed-forward neural networks to boost data processing and feature extraction. Additionally, an Anomaly Scoring Module (ASM) evaluates and ranks deviations from normal behavior patterns, thus ensuring efficient and effective anomaly detection. Experimental findings show that the RTAD realizes precision of 98.8 %, recall of 99.2 %, F1-score of 99.3 %, and overall accuracy of 99.6 % tested on three publicly available datasets such as Edith Cowan University - Internet of Health Things (ECU-IoHT) dataset, the Washington University in St. Louis - Enhanced Healthcare Monitoring System 2020 (WUSTL-EHMS 2020), and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017) dataset. From these fact, it is a reliable real-time solution for anomaly detection in HIS environments. © 2025 Elsevier B.V.

关键词： Electrotherapeutics

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Icon-to-Icon Translation using Pixel-Level Weight Adversarial Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第1期7卷 147-160页

作者： Pan, Yao-Ming Lin, Kai-Biao Chen, Chin-Ling School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Engineering Research Center of Big Data Application in Private Health Medicine Fujian Provincial University Putian351100 China School of Information Engineering Changchun Sci-Tech University Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

We investigate image-to-image translation as a solution to icon-to-icon translation problems. Icon-to-icon translation aims to translate the input icon to appear like the target domain’s icons and simultaneously preserve the original icon’s information (e.g., application logo, text and background color). We present a novel approach to trans-lating icons to the version with the target style intelligently. By observing many icons with various styles, we propose a pixel-level weight matrix to calculating the weighted losses. These losses guide the generator to focus on the critical regions of the icon. People can clearly get the original icon’s information from the proposed method’s results, while the existing state-of-the-art image-to-image translation methods may fail to do this. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Pixels

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Analogous Particle Swarm Optimization for Choosing Optimal Selection of Virtual Machine in Cloud 12

Analogous Particle Swarm Optimization for Choosing Optimal S...

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12th International Conference on computer Communication and Informatics, ICCCI 2022

作者： Kumar, R. Prasanna Vignesh, T. Venkateswarulu, Balaji Vijayan Sivakumar, R. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Vidyapeetham Chennai India Srm Institute of Science and Technology Department of Master of Computer Application Chennai Ramapuram India Vignan's Foundation for Science Technology and Research Department of Computer Science and Engineering Vadlamudi AP Guntur India Easwari Engineering College Dept of Physics Chennai Ramapuram India

ISBN: (纸本)9781665480352

Cloud computing is one of the chief elements in Medical Care Administrations. It tends to be utilized to recover data about patients, their records identified with diagnosing, therapy taken and other clinical data in less measure of time. In this field picking of an ideal VM is a difficult and more intriguing in medical care administrations. The way to deal with pick the best ideal VM helps in expanding the exhibition of medical care benefits consequently decreasing the running opportunity to execute the solicitation. This paper proposes a novel and upgraded rendition of PSO called Similar to Molecule Multitude Enhancement (APSO). The presentation is contrasted and Hereditary Calculation and PSO. The assessment is finished by considering three boundaries like holding up time, use of computer chip and turnaround time. The outcomes unmistakably express that the APSO approach performs well when contrasted with different models. The general execution time for every molecule is registered as 1s and effectiveness apparently is improved by 5.6%. © 2022 IEEE.

关键词： Genetic algorithms

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Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on Retentive Network 7

Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on ...

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7th International Conference on computer Information science and application Technology, CISAT 2024

作者： Su, Ruilin Zhang, Wanshan Yu, Dunhui Hubei University Wuhan China Wuhan430062 China Hubei University School of Computer Science and Information Engineering Wuhan430062 China Hubei University Hubei Key Laboratory of Big Data Intelligent Analysis and Application Wuhan430062 China Engineering and Technical Research Center of Hubei Province in Software Engineering Wuhan430062 China

ISBN: (纸本)9798350375107

Multi-hop Knowledge Reasoning is a task that involves generating an answer given a query and a knowledge graph. Existing sequence-to-sequence reasoning models use the Transformer to encode and decode sequences, but these models have some flaws, such as the inability to effectively handle long-sequence reasoning and susceptibility to exposure bias. To address these issues, we provides a sequence-to-sequence reasoning model named STSR, which is based on Retentive Network. Aiming at improving training efficiency through parallel training, this model leverages the advantages in Retentive Network, reduce time overhead, and enhance reasoning efficiency through iterative reasoning. It effectively mitigates the problems of high spatial overhead and low efficiency in long-sequence reasoning faced by the Transformer. Moreover, it retains and updates historical information during the encoding and decoding process, enhancing the model's memory and generalization capabilities. Additionally, the Scheduled Sampling method is adopted to alleviate the exposure bias introduced during reasoning, which involves using the model's own output as the input for the next moment with a certain probability during training, instead of using the true label. We conduct the experiment on six public datasets, with the results showing that the proposed model outperforms existing baseline models in terms of precision and generation quality. This paper provides a new solution for the task of sequence-to-sequence multihop knowledge reasoning and also demonstrates the potential application of the Retentive Network in natural language processing. © 2024 IEEE.

关键词： Iterative decoding

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The Reliability of Information Sources: Student Perceptions and Educational Opportunities 7

The Reliability of Information Sources: Student Perceptions ...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the University of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources;social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and AI-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using AI-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape. © 2024 IEEE.

关键词： Data reliability

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Fighting Grape Black Rot with Deep Learning: A CNN-LSTM Hybrid Model for Disease Severity Classification 4

Fighting Grape Black Rot with Deep Learning: A CNN-LSTM Hybr...

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4th International Conference for Emerging Technology, INCET 2023

作者： Gaur, Poonam Sharma, Rohit Kumar, Ravi Gupta, Amit Sharma, Rishabh Kukreja, Vinay CGC Landran Department of Computer Application Mohali India College of Engineering CGC Chandigarh Group of Technology Landran India Manav Rachna International Institute of Research and Studies India Computer Science and Engineering Graphic Era Hill University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350335750

Grape black rot is a devastating disease that affects grape crops globally. Detecting and preventing the disease as early as possible is crucial for minimizing crop loss and increasing yield and quality. In this study, we propose a CNN-LSTM hybrid model for multi-classification of the severity of grape black rot based on six distinct disease degrees. The model obtained an accuracy of 93.06% after being trained on a dataset of 10,000 grape leaf images collected from an Indian vineyard, outperforming all other deep learning (DL) and traditional machine learning models. The capacity of the proposed model to capture both spatial and temporal characteristics of grape leaf images, as well as the application of data augmentation techniques and early halting, contributed to its superior performance. The model can be utilized as an efficient instrument for the early detection and prevention of grape black rot disease, thereby contributing to the enhancement of the yield and quality of grape crops. However, the model's performance varied depending on the degree of the disease, with reduced classification accuracy for grape leaves with severe black rot degree. To enhance the model's ability to precisely classify leaves with severe black rot, additional research is required. Overall, the CNN-LSTM hybrid model proposed for multi-classification of grape black rot severity is a promising approach for the early detection and prevention of grape black rot disease, with potential applications to other plant disease detection tasks. © 2023 IEEE.

关键词： Image classification

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Sarcasm Detection in X Data Using Node Embedding and Graph Convolutional Networks

Sarcasm Detection in X Data Using Node Embedding and Graph C...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Aditya Dayal Tyagi Setu Garg Swati Sharma Vivek Tomar Kimmi Verma Computer Science & Application Sharda University Greater Noida JIIT(Deemed to be University) Noida India Electronics & Communication Engineering ITS Engineering College Noida India Computer Science & Engineering KIET Group of Institutions Ghaziabad India Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun India Computer Science & Engineering ABES Engineering College Ghaziabad India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

An extensive amount of attention has been paid to the implications that the detection of sarcasm on online social networks such as Facebook, X etc. could have for the reason of sarcasm detection, sentiment analysis, content regulation, and public opinion. In light of the fact that traditional methods frequently struggle to comprehend the nuances of sarcasm, there is a growing interest in more advanced methodology that make uses of network architectures and deep learning. For the purpose of identifying instances of sarcasm in X data, this study presents a novel method that combines Graph Convolutional Networks (GCNs) and node embeddings. Our solution makes use of a generalized convolutional neural network (GCN), constructs a user-word interaction network, and incorporates word2vec for initial text embedding in order to capture both textual and contextual characteristics. Using our methodology, we demonstrate that it is superior to traditional methods in terms of both accuracy and robustness.

关键词： Deep learning Sentiment analysis Accuracy Social networking (online) Graph convolutional networks Semantics Natural languages Network architecture Robustness Regulation

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PISketch: Finding Persistent and Infrequent Flows 3

PISketch: Finding Persistent and Infrequent Flows

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3rd ACM SIGCOMM 2022 Workshop on Formal Foundations and Security of Programmable Network Infrastructures, FFSPIN 2022

作者： Fan, Zhuochen Hu, Zhoujing Wu, Yuhan Guo, Jiarui Liu, Wenrui Yang, Tong Wang, Hengrui Xu, Yifei Uhlig, Steve Tu, Yaofeng School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Peking University China Computer Science Department University of California Los Angeles United States School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom ZTE Corporation China

ISBN: (纸本)9781450393294

Finding persistent and inactive activity periods is very helpful in practice, for example to detect intrusion activities. Most of the literature focuses on finding persistent flows or frequent flows. No previous work is able to find persistent and infrequent flows. In this paper, we propose a novel sketch data structure, PISketch, to find persistent and infrequent flows in real time. The key idea of PISketch is to define a weight and its Reward and Penalty System for each flow to combine and balance the information of both persistency and infrequency, and to keep high-weighted flows in a limited space through a strategy. We implement PISketch on P4 and CPU platforms, and compare the performance of PISketch with two strawman solutions (On-Off + CM sketch, and PIE + CM sketch), in terms of finding persistent and infrequent flows. Our experimental results demonstrate the advantage of PISketch, by comparing it to two strawman solutions: 1) The F1 Score of PISketch is around 22.1% and 57.6% higher than two strawman solutions, respectively;2) The Average Relative Error (ARE) of PISketch is around 820.9 (up to 1188.8) and 126.2 (up to 265.6) times lower than two strawman solutions, respectively;3) The insertion throughput of PISketch is around 1.23 and 16.5 times higher than two strawman solutions, respectively. Moreover, we implement two concrete cases of PISketch through end-to-end experiments. All of our codes are available at GitHub. © 2022 ACM.

关键词： infrequent items persistent items sketch weight fusion

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Identification of Age, Plumage, and Sex of Bird Species Using Pre-Trained Deep Convolutional Neural Network

Identification of Age, Plumage, and Sex of Bird Species Usin...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Sourik Banerjee Richa Gupta J. Arun Pandian S. Saranya L. Thanga Mariappan Department of Computer Application Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Tiruchirappalli India

The research aims to identify the age, plumage, and sex of bird species using standard Pre-trained Deep Convolutional Neural Networks (Pre-DCNNs). The proposed work involves collecting various bird images, which are then used for training and validating the pre-trained DCNNs. The dataset contains 200 images of four bird species. The dataset was split into twelve labeled classes of images based on age, plumage, and sex. The standard image manipulation techniques such as cropping, flipping, mirroring, rotating, shearing, and skewing were used as image augmentation techniques to increase the size of the proposed dataset. The proposed data is preprocessed, including feature extraction and data cleaning, to ensure the performance of the Pre-Trained DCNNs. The Pre-Trained DCNNs were trained on 90% of the dataset to recognize patterns in bird images. The accuracy of the pre-trained DCNNs was evaluated using metrics such as classification accuracy. The research acknowledged the advantages and limitations of the standard Pre-Trained DCNNs on the age, plumage, and sex of birds using the images. Also, it compares the performance of the Pre-Trained DCNNs on the proposed bird species image dataset.

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Exploring OSINT for Modern Day Reconnaissance

Exploring OSINT for Modern Day Reconnaissance

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Ram, Ajay S. Thalakkat, Arya Udayan, Devika Khader, Aiby A. Hari, K. George, Vipin Government Engineering College Dept. of Computer Science and Engg. Thrissur India School of Technology and Applied Science Dept. of Cyber Forensics Kottayam India Kerala Police Academy Dept. of Computer Application Thrissur India Kerala Police Academy Police Computer Lab Thrissur India Kerala Police Academy Thrissur India

ISBN: (纸本)9798350303452

The Internet's growth has connected users globally, forming a vast network of data. As new social media platforms with diverse features emerge, individuals are drawn to sharing their personal data online. This inclination stems from a desire to display social status. However, this openness poses risks, as personal data uploaded to the internet can easily unveil one's online identity. Extracting such data is a straightforward process, as a simple correlation of evidence with an online profile can summarize a user's identity. By combining various sources of information linked to a specific individual, a comprehensive profile can be created. As the digital landscape expands, Open Source Intelligence (OSINT) has become an invaluable tool for gathering information. OSINT involves searching and accessing publicly available data to extract relevant information, analyze patterns, and draw conclusions. It encompasses activities like web scraping, data mining, social media monitoring, and online research. OSINT is crucial for understanding public sentiment, identifying potential threats, conducting background checks, investigating crimes, and performing competitive analysis. This paper investigates the impact of OSINT as a modern day reconnaissance tool. Through case studies and real-world examples, the research demonstrates the effectiveness of OSINT in enhancing situational awareness, supporting decision-making for law enforcement agencies, and using OSINT as a modern-day reconnaissance tool. © 2023 IEEE.

关键词： Data mining

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