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3rd International Conference on Machine Learning and computer Application, ICMLCA 2022

作者： Tong, Xin Tan, Xiaobo Sun, Xianlang College of Information Science and Engineering Ligong University Liaoning Shenyang China Department of Computer Science and Technology Shenyang Ligong University Liaoning Shenyang China

ISBN: (纸本)9781510664814

With the rapid development of the Internet, the world is gradually moving toward the information age, and the current cyber security situation is getting more and more serious. Traditional methods based on statistical analysis, feature proximity, tensor decomposition, etc. have detection limitations and low detection accuracy when addressing user network security issues. In this paper, we propose an anomaly behavior detection method based on GCN-BiLSTM. First, a graph convolutional neural network is used as a feature extractor to extract the useful graph structure information in the network as a representation vector for the entire graph. Then, a bidirectional long short-term memory network method with an integrated attention mechanism is used for training, and the abnormal behavior detection is completed by combining the extracted feature information. Experiments were conducted on the IDS2017 dataset and compared with the current typical abnormal behavior detection methods, showing that the accuracy of the GCN-BiLSTM based anomaly detection method is further improved and the overall performance is better, which verifies the effectiveness of the proposed method. © 2023 SPIE.

关键词： Network security

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Provably Efficient Multi-Cancer Image Segmentation Based on Multi-Class Fuzzy Entropy

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Informatica (Slovenia) 2023年第8期47卷 77-88页

作者： Jasim, Hend Muslim Ghrabat, Mudhafar Jalil Jassim Abdulrahman, Luqman Qader Nyangaresi, Vincent Omollo Ma, Junchao Abduljabbar, Zaid Ameen Abduljaleel, Iman Qays Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Iraqi Commission for Computers and Informatics The Informatics Institute for Postgraduate Studies Baghdad Iraq Computer Science Department Al-Turath University College Baghdad10013 Iraq College of Health Science Hawler Medical University Erbil Iraq Jaramogi Oginga Odinga University of Science and Technology Bondo40601 Kenya College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Technical Computer Engineering Department AL-Kunooze University College Basrah6100 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518000 China Department of Computer Science College of Computer Science and Information Technology University of Basrah Basrah61004 Iraq

One of the segmentation techniques with the greatest degree of success used in numerous recent applications is multi-level thresholding. The selection of appropriate threshold values presents difficulties for traditional methods, however, and, as a result, techniques have been developed to address these difficulties multidimensionally. Such approaches have been shown to be an efficient way of identifying the areas affected in multi-cancer cases in order to define the treatment area. Multi-cancer methods that facilitate a certain degree of competence are thus required. This study tested storing MRI brain scans in a multidimensional image database, which is a significant departure from past studies, as a way to improve the efficacy, efficiency, and sensitivity of cancer detection. The evaluation findings offered success rates for cancer diagnoses of 99.08%, 99.87%, 94%;97.08%, 98.3%, and 93.38% sensitivity;the success rates of in particular were 99.99%;98.23%, 99.53%, and 99.98%. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Image segmentation

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Kalman Filter for Radon Based Earthquake Early Detection

Kalman Filter for Radon Based Earthquake Early Detection

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2024 International Conference on Informatics Electrical and Electronics, ICIEE 2024

作者： Putra, Wahyu Sukestyastama Sunarno Mustika, I Wayan Universitas Gadjah Mada Department of Electrical Engineering and Information Technology Yogyakarta Indonesia Universitas AMIKOM Yogyakarta Faculty of Computer Science Yogyakarta Indonesia Universitas Gadjah Mada Department of Nuclear Engineering and Physics Engineering Yogyakarta Indonesia

ISBN: (纸本)9798331519216

Radon measurement for earthquake prediction is challenging due to environmental noise and sensor limitations. Factors such as temperature, humidity, and soil moisture cause fluctuations in radon levels, making distinguishing seismic-related changes from normal variations difficult. In addition, sensor limitations, such as sensor resolution, can reduce the ability of early warning systems to detect precursor signals. In this study, we designed an early earthquake detection method using the Kalman Filter on radon gas concentration data collected using an ion chamber sensor at a sampling interval of 1 minute. The Kalman Filter was applied to overcome the uncertainties in sensor measurements and the radon-earthquake phenomenon. The differences between the filter predictions and the raw sensor data indicate the presence of external factors, possibly earthquake precursors. In all six analyzed events, radon anomalies were effectively captured through the difference of squares, indicating the potential of the method as an early warning system for seismic activity. The proposed method detected radon anomalies 1 to 19 days before an earthquake. © 2024 IEEE.

关键词： Kalman filters

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Implementing a Support Vector Machine Algorithm on Social Media Flat Forms to Detect and Restrict Cyberbullying Conversations 5th

Implementing a Support Vector Machine Algorithm on Social Me...

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Rao, M. Jayanthi Prasanthi, P. Ramakrishna, B. Prasad, K. G. Durga Ramanaiah, M. Department of Computer Science and Engineering Aditya Institute of Technology and Management Tekkali532201 India Department of Information Technology Aditya Institute of Technology and Management Tekkali532201 India Department of Mechanical Engineering Aditya Institute of Technology and Management Tekkali532201 India Visakhapatnam530049 India Department of Chemistry Aditya Institute of Technology and Management Tekkali532201 India

ISBN: (纸本)9789819767311

In contemporary times, there has been a significant prevalence of communication that is subject to abuse inside the realm of social media. According to a recent survey research, it has been substantiated that over 80% of online social networks exhibit instances of abusive or obscene communication within their user accounts. These forms of communication are mostly disseminated on the personal profiles of users with the intention of subjecting adolescents, preadolescents, and other minors to harassment through the dissemination of offensive content. To date, no existing program has successfully addressed the issue of preventing the dissemination of offensive cybercontent on social media platforms. This lack of a viable solution has served as a catalyst for my motivation to develop a novel application aimed at curbing indecent communication within online social networks. This suggested application primarily aims to introduce a novel representation learning approach for addressing the issue of identifying and preventing the dissemination of abusive communications in online chat platforms. In this study, we employ widely recognized machine learning algorithms, such as support vector machine, to categorize communications into two categories: abused messages and regular messages. Additionally, we utilize the Porter Stemming algorithm to preprocess the text messages. The Porter Stemming algorithm is a widely recognized component of the Natural Language Toolkit (NLTK) package. It functions by segmenting the entire text into distinct units and subsequently assigning tokens to each individual word. In this study, the cyberbullying discourse is categorized into five distinct classifications, drawing upon existing research on hate speech, vulgarity, offensiveness, sexual content, and violence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector machines

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Assessing the Impact of AI-Driven Systems on Automated Data science

Assessing the Impact of AI-Driven Systems on Automated Data ...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Sharma, Bhuvnesh JayasudhaS, S. Dhabliya, Dharmesh Acharjya, Kalyan Zaidi, Taskeen Dev, Sudhanshu Vivekananda Global University Department of Mechanical Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350329773

This paper gives an evaluation of current literature assessing the impact of synthetic intelligence (AI) on automatic information science approaches. AI-driven structures have been, and continue to be, integral for numerous aspects of facts science, from statistics preprocessing to the detection of styles. The paper opinions current studies on the effect of AI-driven systems on facts technology, consisting of the usage of automated device learning gear, in addition to paintings on synthetic intelligence mixed with herbal language processing. Additionally, the paper examines the potential implications that the use of automated data technology ought to have on society as a whole. The paper concludes by using presenting counseled directions for future research. © 2023 IEEE.

关键词： Flexible AC transmission systems

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Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

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computer Systems science & engineering 2023年第3期44卷 2395-2409页

作者： Ashit Kumar Dutta Basit Qureshi Yasser Albagory Majed Alsanea Manal Al Faraj Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Science Prince Sultan UniversityRiyadh11586Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determine fake *** recently developed machine learning(ML)models can be employed for the detection and classification of fake *** study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine(CAS-WELM)for Cybersecurity Fake News Detection and *** goal of the CAS-WELM technique is to discriminate news into fake and *** CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embed-ding ***,N-gram based feature extraction technique is derived to gen-erate feature ***,WELM model is applied for the detection and classification of fake news,in which the weight value of the WELM model can be optimally adjusted by the use of CAS *** performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several *** experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.

关键词： Cybersecurity cybercrime fake news data classification machine learning metaheuristics

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A comprehensive analysis of IoT security towards providing a cost-effective solution: a layered approach

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International Journal of information technology (Singapore) 2023年第7期15卷 3813-3826页

作者： Gopinath, Velivela Rao, K. Venkata Rao, S. Krishna Department of Information Technology and Computer Applications Andhra University College of Engineering Andhra University Andhra Pradesh Visakhapatnam 530003 India Department of Computer Science and Systems Engineering Andhra University College of Engineering Andhra University Andhra Pradesh Visakhapatnam 530003 India Department of Information Technology Sir C R Reddy College of Engineering Andhra Pradesh Eluru 534007 India

Abstract: The Internet of Things (IoT) greatly influences people’s lives. Traditional privacy and security mechanisms may not suit for Internet of Things due to its resource constraint environments. So there is a need for privacy and security mechanisms, especially for IoT. A good number of security mechanisms are available in the network and application layers. The study reveals that there should be much more scope for providing security and privacy in the sensing layer itself. The paper focuses on IoT security goals and the security framework at the sensing layer. The sensing layer privacy and security are implemented through Blockchain technology. The security is transmitted to the top layers, such as the network and application layers, by safeguarding the sensing layer. The cost of the highest tiers of security is automatically reduced. Then we analysed the proposed framework concerning security attacks and security goals. Finally, we analysed the cost-effectiveness of the system. Also, with the proposed work, 57.41% of IoT security goals impacts are reduced. At the physical layer, 71.43%;at the network layer, 70.59% and at the application layer, 38.46% of security goal impacts are prevented. Also, we formulated the thrust vector for the proposed framework. The findings indicate that the suggested approach increases trust by 61.36% while reducing costs by 60.16%. Graphical abstract: [Figure not available: see fulltext.]. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Internet of Things Privacy Security Security attacks Security goals Sensing layer

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Research on Abnormal Network Traffic Detection Method based on Machine learning 3

Research on Abnormal Network Traffic Detection Method based ...

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3rd International Conference on Machine Learning and computer Application, ICMLCA 2022

作者： Tan, Xiaobo Jia, Yumei College of Information Science and Engineering Ligong University Liaoning Shenyang China Department of Computer Science and Technology Shenyang Ligong University Liaoning Shenyang China

ISBN: (纸本)9781510664814

Aiming at the problem of high false detection rate and low detection rate of abnormal network traffic detection methods due to uneven data distribution of current network traffic datasets, a network abnormal traffic detection method based on CNN-GRU is proposed. The method firstly performs local feature learning on the data according to the local learning ability unique to the convolutional neural network (CNN), and replaces the fully connected layer with a global average pooling layer to reduce the feature dimension and parameters;secondly, it uses a gated recurrent unit (GRU) learns the time series features of the data to improve the nonlinear representation ability of the method;finally, for the imbalance between the data, the cross entropy loss function is optimized, and the penalty for abnormal class sample detection and normal class sample detection is passed. The adjustment of the weight increases the attention of the model to the abnormal samples, thereby improving the accuracy of the model detection. The simulation results show that the method can effectively improve the classification and detection performance. © 2023 SPIE.

关键词： Convolutional neural networks

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University department Recommendations Using Subject-Score-Based Skyline Queries

University Department Recommendations Using Subject-Score-Ba...

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2024 International Conference on information technology and Computing, ICITCOM 2024

作者： Damarjati, Cahya Wicaksana, Galang Suripto, Slamet Wijayanto, Heri Setyawan, Haris Chen, Hsing-Chung Universitas Muhammadiyah Yogyakarta Department of Information Technology Yogyakarta Indonesia Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Yogyakarta Indonesia Universitas Mataram Department of Informatics Engineering Mataram Indonesia Asia University China Medical University Department of Computer Science and Information Engineering Department of Medical Research Taichung Taiwan

ISBN: (纸本)9798350379839

Selecting the right university department is a critical decision-making problem for high school graduates, significantly influenced by limited guidance and personal preferences. Traditional counseling methods often do not fully utilize available academic data, resulting in less effective recommendations. This study proposed a Subject-Score-Based Skyline Queries Algorithm to enhance decision-making by recommending university departments based on candidates' high school subject scores. It used the score differences and minimum score rule in addition to the native Skyline Queries. The Algorithm identifies departments where a candidate's grades meet or exceed minimum requirements across multiple academic subjects, thereby suggesting optimal matches. This approach aims to mitigate uncertainty and enhance decision confidence among prospective students, potentially reducing dropout rates and improving academic outcomes. Experimental results demonstrate the method's effectiveness in providing personalized recommendations, underscoring its applicability beyond university admissions to various decision-making contexts. Future research could extend this approach by incorporating non-academic factors for more comprehensive guidance. © 2024 IEEE.

关键词： Students

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Average Performance Analysis of Multi-Class Classification Based on Error-Correcting Output Codes

Average Performance Analysis of Multi-Class Classification B...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Kobayashi, Manabu Kumoi, Gendo Yagi, Hideki Hirasawa, Shigeichi Center for Data Science Waseda University Tokyo Japan Nagaoka University of Technology Department of Information and Management Systems Engineering Niigata Japan University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan

ISBN: (纸本)9781665410205

In machine learning, one of the methods to solve multiclass classification problems is a framework called Error-Correcting Output Codes (ECOC), which constructs a multiclass classifier by combining a lot of binary classifiers. ECOC assigns binary codewords to each category, and the multiclass classification performance varies depending on the code. In this study, we treat each element of the codeword as a random variable and evaluate the average performance of ECOC. As a result, for M class classification if the number of binary classifiers is O(M), then the average error probability of various codes approaches that of MAP estimation. We show that the important points are the ratio between the number of binary classifiers and M and the difference between the maximum posterior probability and the second highest posterior probability for the categories. © 2024 IEEE.

关键词： Coding errors

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