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作者： Seyyedi, Seyyed Hossein Minaei-Bidgoli, Behrouz Faculty of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin34199-15195 Iran School of Computer Engineering Iran University of Science and Technology Tehran16846-13114 Iran

Nowadays, text is one prevalent forms of data and text classification is a widely used data mining task, which has various application fields. One mass-produced instance of text is email. As a communication medium, despite having a lot of advantages, email suffers from a serious problem. The number of spam emails has steadily increased in the recent years, leading to considerable irritation. Therefore, spam detection has emerged as a separate field of text classification. A primary challenge of text classification, which is more severe in spam detection and impedes the process, is high-dimensionality of feature space. Various dimension reduction methods have been proposed that produce a lower dimensional space compared to the original. These methods are divided mainly into two groups: feature selection and feature extraction. This research deals with dimension reduction in the text classification task and especially performs experiments in the spam detection field. We employ Information Gain (IG) and Chi-square Statistic (CHI) as well-known feature selection methods. Also, we propose a new feature extraction method called Sprinkled Semantic Feature Space (SSFS). Furthermore, this paper presents a new hybrid method called IG-SSFS. In IG-SSFS, we combine the selection and extraction processes to reap the benefits from both. To evaluate the mentioned methods in the spam detection field, experiments are conducted on some well-known email datasets. According to the results, SSFS demonstrated superior effectiveness over the basic selection methods in terms of improving classifiers' performance, and IG-SSFS further enhanced the performance despite consuming less processing time. © 2017 World Scientific Publishing Company.

关键词： Classification (of information)

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High performance computing for detecting complex diseases using deep learning

High performance computing for detecting complex diseases us...

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2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019

作者： Ghanem, Sahar I. Ghoneim, Ahmed A. Ghanem, Nagia M. Ismail, Mohamed A. Alexandria University Dept. of Computer and Systems Engineering Alexandria Egypt Faculty of Engineering Arab Academy for Science Technology and Maritime Transport Dept. of Computer Engineering Alexandria Egypt

ISBN: (纸本)9781728144528

The study of the Genome-wide association study (GWAS) and the complex diseases is of high importance nowadays. The epistasis describes the analysis of the single nucleotide polymorphisms (SNPs) interactions and their effects on the complex diseases. However, enormous number of SNPs interactions should be tested against the disease that is highly computational expensive. In this paper, High Performance Computing (HPC) is being applied on a supercomputer to reduce the processing time. Parallel Deep Learning (PDL) is applied and tested using different datasets. Simulated datasets of 12 different models and the real WTCCC Rheumatoid arthritis (RA) dataset are being tested. Results show the high accuracy, specificity and true positive rate values. Moreover, they show low values of the false discovery rate and the robustness of power through the different simulated models. When tested on the real RA dataset, our model shows the ability to detect the 2-way interaction SNPs with their promising related genes with high accuracy due to the parallel deep learning architecture. © 2020 IEEE.

关键词： Supercomputers

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A Hypergraph Matching-Based Subchannel Allocation for Multi-Platoon's Communications

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IEEE Access 2023年 11卷 139345-139365页

作者： Huang, Chung-Ming Lam, Dan-Nguyen Dao, Duy-Tuan National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan The University of Danang-University of Science and Technology Faculty of Electronics and Telecommunications Engineering Da Nang50000 Viet Nam

In a platoon communication scenario, the Platoon Leader (PL) vehicle interacts with the gNodeB (gNB) to obtain some assigned radio resources and then the obtained radio resources are allocated to the PL vehicle and its Platoon Member (PM) vehicles. The high transmission delay in the platoon network affects the quality of vehicles' connectivity. Therefore, the issues of improving radio resource's spectral efficiency and stability of the vehicle platooning with limited radio resources need to be tackled. This work proposes the 2-STage Resource Allocation (2-STRA) method for multi-platoon communication to reduce the transmission latency of platoon vehicles, i.e., it can improve the data transmission rates of platoon vehicles for the conditions of various speeds and various numbers of subchannels. In the $1^{st}$ stage, a greedy algorithm that jointly considers subchannel allocation and power control for PL vehicles' resource allocation is devised. In the $2^{nd}$ stage, the resource allocation for PM vehicles, which is based on the subchannels that are not allocated to PL vehicles, is resolved. A tripartite hypergraph, in which a tripartite hyperedge links:I 1) cluster of PM vehicles that use the same subchannel;2) individual cellular user;and 3) subchannel, is adopted to devise the PM vehicles' resource allocation algorithm. The simulation results show that the proposed 2-STRA method can have better performance in terms of spectral efficiency and the sum of data transmission rate comparing with the other methods. © 2013 IEEE.

关键词： Quality of service

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Is Group Means Imputation Any Better Than Mean Imputation: A Study Using C5.0 Classifier 2

Is Group Means Imputation Any Better Than Mean Imputation: A...

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2018 2nd International Conference on Data Mining, Communications and Information technology, DMCIT 2018

作者： Khan, Faizan U. F. Khan, Kashan U. Z. Singh, S.K. Varanasi221005 India Department of Computer Engineering Faculty of Engineering and Technology Jamia Millia Islamia New Delhi110025 India

Since most data-driven systems including classifiers require large amounts of complete data, the task of handling missing data has garnered much attention. If one of the variables under study in a dataset has some incomplete values, it is treated as a missing data problem. Various methods in the literature exist for dealing with missing data including complete case analysis, listwise deletion, single imputation and multiple imputations. Out of these, mean imputation remains a favourite for researchers due to its simplicity and ease of use, despite some glaring flaws. In this paper, we compare Mean imputation with a similar single imputation method - Group Means imputation - and present our results on nine real-world datasets with respect to classifier accuracy of the C5.0 classifier on the imputed dataset. We show that while Group Means imputation fares better on training data, the test set accuracies fall in favour of Mean Imputation, which deals with novel data in a much better fashion. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

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Dual mode controlled water surface garbage collecting robot by using embedded deep learning 18

Dual mode controlled water surface garbage collecting robot ...

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18th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2021

作者： Panyavaraporn, Jantana Chaimongkol, Natthawat Limsomnuek, Nattasit Wasayangkul, Wichai Charoenwattana, Nathamon Horkaew, Paramate Burapha University Faculty of Engineering Department of Electrical Engineering Chonburi Thailand School of Computer Engineering Suranaree University of Technology Nakhon Ratchasima Thailand

ISBN: (纸本)9780738111278

Plastic waste has become one of the most prevailing environmental concerns in modern society. Without proper environmental control policies and regulations in place, we have witnessed plastic garbage being irresponsibly disposed of into rivers and other water reservoirs, causing blockage of water passage as well as toxic contamination. Since plastic materials are unable to degrade in nature so quickly as biomaterials, human workers are typically required to collect plastic garbage, either manually or mechanically assisted. In some conditions, the maneuvers could cause personal injuries and infections. To resolve this issue, this paper proposes the design and development of a prototype water surface garbage collecting robot. The prototype was implemented on a Raspberry Pi board and controllable from an Android device via wireless (WiFi™) channel. Controlling robot movement, speed, and conveyor belt could be done either manually or automatically. The former was enabled by intuitive user interface, while the latter relied on deep learning of local scene acquired within active range. Experimental results of automatic operation, driven by deep learning models demonstrated garbage collecting performance at 83.25% accuracy. © 2021 IEEE.

关键词： Machine design

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Utilizing Machine Learning in Medical Diagnosis: Systematic Review and Empirical Analysis 24

Utilizing Machine Learning in Medical Diagnosis: Systematic ...

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24th International Arab Conference on Information technology, ACIT 2023

作者： Alazaidah, Raed Hassan, Mohammad Al-Rbabah, Lara Samara, Ghassan Yusof, Marina Al-Sherideh, Ala'a Saeb Zarqa University Ai Faculty of Information Technology Department of Data Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan The Hashemite University Faculty of Engineering Department of Computer Engineering Zarqa Jordan Selanger Malaysia Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798350384307

During the last few years, several real-life applications have attempted to utilize the proven high capabilities of artificial intelligence in general and machine learning in particular. Machine learning has been utilized in several domains, such as spam detection, image recognition, recommendation systems, self-driving cars, and medical diagnosis. This paper aims to survey the most related work of utilizing machine learning in the domain of medical diagnosis. Moreover, the paper proposes a comparative analysis for identifying and determining the best classification model and feature selection method in mind of handling medical datasets. Hence, four different medical datasets have been used to train twenty-three classification models and four well-known feature selection methods with respect to several evaluation metrics such as Accuracy, True Positive ratio, False Positive ratio, Precision, and Recall. The results reveal that RandomForest, J48, and SMO classifiers are the best classifiers when it comes to handling medical datasets respectively. Furthermore, the Gain Ratio method is the best choice for handling the step of feature selection. © 2023 IEEE.

关键词： Classification Feature Selection Machine Learning Medical Diagnosis Prediction

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Using Ensemble Learning, A Cosine Similarity-Based Model for Detecting Security Anomalies in Software-Defined Networks 20

Using Ensemble Learning, A Cosine Similarity-Based Model for...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Samadzadeh, Mohammadreza Zahedi, Mohammad Hadi Farahani, Elham University of Tehran Department of Electrical and Computer Engineering Tehran Iran K. N. Toosi University of Technology Faculty of Industrial Engineering Information Technology Department Tehran Iran Sharif University of Technology Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350383942

Currently, Software-Defined Network (SDN) is as one of the most commonly used network architectures. It requires using a software-based, centralized controller to communicate with the underlying network hardware to control the flow of the traffic through the network. This helps achieve easy, flexible, and integrated control and management of the entire network. In SDNs, the controller is the sole entity that monitors the entire network and is accountable for traffic management based on its comprehensive understanding of the network. Software-Defined Networks (SDNs) rely on a centralized controller for network management, yet this centralized control exposes vulnerabilities, leaving networks susceptible to disruptive attacks. To fortify SDN security, this article presents an innovative approach that merges machine learning and deep learning techniques through ensemble learning. Our method introduces several key innovations. Firstly, it employs ensemble learning, combining multiple models to enhance predictive capability. Secondly, the utilization of cosine similarity-based classification enables the grouping of attacks with varying degrees of similarity, refining the distinction between attack types. Thirdly, the approach includes customized classifiers, trained specifically for distinct attack classes, optimizing detection accuracy for each type. Finally, the K-Nearest Neighbor (KNN) algorithm is applied for final classification of new data samples, improving precision in identifying attack types. With an exceptional accuracy rate of 99.91%, our method surpasses the performance of analogous studies. By amalgamating these innovations, our approach establishes a robust framework for detecting security anomalies in SDNs, reinforcing network integrity and reliability. © 2024 IEEE.

关键词： Learning systems

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Data Secure Storage Mechanism of Sensor Networks Based on Blockchain

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computers, Materials & Continua 2020年第12期65卷 2365-2384页

作者： Jin Wang Wencheng Chen Lei Wang R.Simon Sherratt Osama Alfarraj Amr Tolba School of Information Science and Engineering Fujian University of TechnologyFuzhou350118China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Civil Engineering Changsha University of Science&TechnologyChangsha410000China Department Biomedical Engineering University of ReadingReadingRG66AYUK Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Mathematics and Computer Science Department Faculty of ScienceMenoufia UniversityShebin-El-Kom32511Egypt

As the number of sensor network application scenarios continues to grow,the security problems inherent in this approach have become obstacles that hinder its wide ***,it has attracted increasing attention from industry and *** blockchain is based on a distributed network and has the characteristics of non-tampering and traceability of block *** is thus naturally able to solve the security problems of the sensor ***,this paper first analyzes the security risks associated with data storage in the sensor networks,then proposes using blockchain technology to ensure that data storage in the sensor networks is *** the traditional blockchain,the data layer uses a Merkle hash tree to store data;however,the Merkle hash tree cannot provide non-member proof,which makes it unable to resist the attacks of malicious nodes in *** solve this problem,this paper utilizes a cryptographic accumulator rather than a Merkle hash tree to provide both member proof and non-member ***,the number of elements in the existing accumulator is limited and unable to meet the blockchain’s expansion *** paper therefore proposes a new type of unbounded accumulator and provides its definition and security ***,this paper constructs an unbounded accumulator scheme using bilinear pairs and analyzes its performance.

关键词： Sensor networks blockchain unbounded accumulator storage mechanism

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Unsupervised Anomaly Detection for IoT Data based on Robust Adversarial Learning 24

Unsupervised Anomaly Detection for IoT Data based on Robust ...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Qiao, Yan Zhang, Benchu Zhang, Zeyu School of Computer Science and Information Engineering Hefei University of Technology Hefei China Macau University of Science and Technology Faculty of Information Technology China

ISBN: (纸本)9798350319934

Anomaly detection based on Generative Adversarial Networks (GANs) has been demonstrated its superiority on in imaging area. However, most existing GAN-based methods require noise-free datasets to accurately train the network, whereas neither collecting an absolute clean dataset or labeling the anomalies in the dataset are quite difficult for Internet of Things (IoT) applications due to the harsh environment and the high dimensionality of the data. In this paper, we propose a robust GAN-based model for anomaly detection in IoT, which can accurately learn the data pattern unsupervisedly even with polluted training data. The new model adopts the framework of Bidirectional GAN (BiGAN) to enable an efficient representation mapping, and integrates the mechanism of Robust Principal Component Analysis (RPCA) to rule out the noises of data from the training set. To improve its performance, a new objective function and scoring function are elaborately designed, while a proximal method and Alternating Direction Method of Multiplier (ADMM) are incorporated into the training process. Comparing our method with four state-of-the-art anomaly detection methods, the experimental results strongly confirm the superiority of our method with polluted IoT datasets. © 2022 IEEE.

关键词： Anomaly detection

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The effect of AMC for each medium on QoE of video and audio transmission over wireless LANs 9

The effect of AMC for each medium on QoE of video and audio ...

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2019 9th International Workshop on computer Science and engineering, WCSE 2019 SPRING

作者： Nunome, Toshiro Murase, Naruhiro Department of Computer Science Graduate School of Engineering Nagoya Institute of Technology Nagoya466-8555 Japan Department of Computer Science Faculty of Engineering Nagoya Institute of Technology Nagoya466-8555 Japan

ISBN: (纸本)9789811414558

In this paper, we assess QoE of video and audio transmission over wireless LANs. This study tries to employ different AMC mechanis m for each medium. We consider four AMC methods: ARF, AARF, AARF-CD, and CARA. Through computer simulation and subjective experiment, we notice that the appropriate AMC for the required quality can enhance QoE. © Proceedings of 2019 the 9th International Workshop on computer Science and engineering, WCSE 2019 SPRING. All rights reserved.

关键词： Wireless local area networks (WLAN)

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