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18th International Computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2021

作者： Atandoh, Peter Feng, Zhang Adu-Gyamfi, Daniel Leka, Habte Lejebo Atandoh, Paul Hakeem University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China University of Technology and Applied Sciences School of Computing Information Sciences Department of Cyber Security and Computer Engineering CK Tedam Navrongo Ghana Western Michigan University Department of Statistics KalamazooMI49008-5278 United States

ISBN: (纸本)9781665413640

Reviewing products online has become an increasingly popular way for consumers to voice their opinions and feelings about a product or service. Analyzing this Big data of online reviews would help to discern and extract useful facts and information that could provide a competitive and economic advantage to merchants and other organizations that are interested. Text classification organizes documents according to a variety of predefined categories. In other to solve the aforementioned problems, we employed Glove embeddings for our review sentiment analysis. We further integrate this embedding layer into a deep convolutional neural network (CNN)-bidirectional LSTM model. We further train our model on the IMDB and movie review dataset to extract the polarity as positive or negative and subsequently compare our model with other state-of- the-art models. The aforementioned experiments validate the efficacy and superiority of our proposed approach. © 2021 IEEE.

关键词： Sentiment analysis

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Two-Area Power System with Automatic Generation Control Utilizing PID Control, FOPID, Particle Swarm Optimization, and Genetic Algorithms

Two-Area Power System with Automatic Generation Control Util...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Madan Mohan Sati Dinesh Kumar Akhilesh Singh Mohan Raparthi Faisal Yousef Alghayadh Mukesh Soni Deptt. of Mathematics Graphic Era Hill University Bhimtal India Dept. of Computer Science & Engg. SRM Institute of Science & Technology Ghaziabad UP India Deptt. of Electrical Engg. Uttarakhand Technical University Campus Institute Pithoragarh Software Engineer Alphabet Life Science Dallas Texas USA Computer Science & IT Department College of Applied Sci Almaarefa Univ Riyadh Saudi Arabia School of Science & Technology Dr. D. Y. Patil Vidyapeeth Pune India

This study aims to present a feasible LFC (Load Frequency Control) model that integrates physical security limitations, namely GRC, GDB, and TD. The presence of this characteristic guarantees that the design is not only accurate but also capable of being materialized in a physical form. It is suggested that the traditional PI controller be replaced with a PID controller in order to handle the issues that are posed by large-scale power systems that are interconnected. Developing extremely efficient soft computing approaches that are capable of producing the necessary results for both individual and extensive power systems while taking into consideration flaws in modeling is the goal of this endeavor. It is important to get the frequency deviation to zero while still following the rules set by real issues and different working conditions. In order to maintain the electrical flow between the inter-area tie lines that are located within the various control zones at the schedule values that have been specified. In order to achieve optimal levels of performance in both steady state and transient conditions. The purpose of this is to obtain valuable multiple solutions with the intention of future planning and design. It is important to simplify the robust load frequency control design problem by changing it into a low-order proportional integral derivative (PID) control synthesis problem in order to address the practical benefits. This approach is commonly employed in real-world power systems applications.

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Personalized tourism recommendation algorithm integrating tag and emotional polarity analysis

Personalized tourism recommendation algorithm integrating ta...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Peng Yuan Qiuxia Chen Zhifeng Wang Jinfeng Yang School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan Liaoning China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen Guangdong China

ISBN: (纸本)9798350309720

The matrix factorization algorithm has achieved good results in the field of scenic spot recommendation, but there are still many problems. Most of the studies only refer to the user’ s score of the tourist attraction to represent the user ’s overall evaluation of the attraction, thus ignoring the correlation between the online review text information of the attraction and the attraction, which eventually leads to the low accuracy of the entire recommendation system. This paper proposes a personalized tourism recommendation algorithm that integrates scenic spot labels and sentiment polarity analysis. On the basis of constructing feature labels for various scenic spots, the sentiment polarity analysis of user comments is carried out through natural language text information processing, and implicit emotions are given to the labels contained in scenic spots. Secondly, it is introduced into the factor vector of matrix decomposition model algorithm, and finally the potential relationship between users and feature labels and scenic spots is deeply explored. Through the user ’s preference for emotional feature labels, the user ’s approximate rating value for attractions is predicted. Experiments show that compared with the user-based collaborative filtering scenic spot recommendation algorithm, the average absolute error (MAE) and root mean square error (RMSE) are reduced by 70.28 % and 65.23 % respectively. Compared with the tag-based collaborative filtering attraction recommendation algorithm, MAE and RMSE are reduced by 65.02 % and 63.93 %, respectively. Compared with the matrix factorization recommendation algorithm of scenic spot labels, MAE and RMSE are reduced by 34.02% and 29.93 % respectively. To sum up the performance of the algorithm is significantly higher than the existing attractions recommended algorithm can be more accurate to provide users with recommended options.

关键词： Correlation Collaborative filtering Natural languages Information processing Prediction algorithms Approximation algorithms Matrix decomposition

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DPM-OT: A New Diffusion Probabilistic Model Based on Optimal Transport

arXiv

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arXiv 2023年

作者： Li, Zezeng Li, ShengHao Wang, Zhanpeng Lei, Na Luo, Zhongxuan Gu, Xianfeng School of Software Dalian University of Technology China Beijing Key Laboratory of Light-field Imaging and Digital Geometry Capital Normal University China School of Mathematical Sciences University of the Chinese Academy of Sciences China Computer Science and Applied Mathematics State University of New York Stony Brook United States

Sampling from diffusion probabilistic models (DPMs) can be viewed as a piecewise distribution transformation, which generally requires hundreds or thousands of steps of the inverse diffusion trajectory to get a high-quality image. Recent progress in designing fast samplers for DPMs achieves a trade-off between sampling speed and sample quality by knowledge distillation or adjusting the variance schedule or the denoising equation. However, it can’t be optimal in both aspects and often suffer from mode mixture in short steps. To tackle this problem, we innovatively regard inverse diffusion as an optimal transport (OT) problem between latents at different stages and propose the DPM-OT, a unified learning framework for fast DPMs with a direct expressway represented by OT map, which can generate high-quality samples within around 10 function evaluations. By calculating the semi-discrete optimal transport map between the data latents and the white noise, we obtain an expressway from the prior distribution to the data distribution, while significantly alleviating the problem of mode mixture. In addition, we give the error bound of the proposed method, which theoretically guarantees the stability of the algorithm. Extensive experiments validate the effectiveness and advantages of DPM-OT in terms of speed and quality (FID and mode mixture), thus representing an efficient solution for generative modeling. Source codes are available at https://***/cognaclee/DPM-OT Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

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Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma

arXiv

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arXiv 2024年

作者： Bhattacharjya, Abanti Islam, Md Manowarul Uddin, Md Ashraf Talukder, Md. Alamin Azad, A.K.M. Aryal, Sunil Paul, Bikash Kumar Tasnim, Wahia Abdulllah Almoyad, Muhammad Ali Moni, Mohammad Ali School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Department of Software Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Riyadh13318 Saudi Arabia Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Bangladesh Department of Basic Medical Sciences College of Applied Medical Sciences King Khalid University Saudi Arabia Artificial Intelligence & Data Science Faculty of Health and Behavioural Sciences The University of Queensland Australia

With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitated the rapid processing and analysis of vast quantities of biological data for human perception. Most studies focus on locating two connected diseases and making some observations to construct diverse gene regulatory interaction networks, a forerunner to general drug design for curing illness. For instance, Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. In this study, we select EGFR-mutated lung adenocarcinoma and Hypopharyngeal cancer by finding the Lung metastases in hypopharyngeal cancer. To conduct this study, we collect Mircorarray datasets from GEO (Gene Expression Omnibus), an online database controlled by NCBI. Differentially expressed genes, common genes, and hub genes between the selected two diseases are detected for the succeeding move. Our research findings have suggested common therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). Our suggested therapeutic molecules will be fruitful for patients with those two diseases simultaneously. © 2024, CC BY.

关键词： Topology

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A new lightweight data security system for data security in the cloud computing

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Measurement: Sensors 2023年 29卷

作者： Mohammed, Shameer Nanthini, S. Bala Krishna, N. Srinivas, Inumarthi V. Rajagopal, Manikandan Ashok Kumar, M. University of Technology and Applied Sciences Sur Oman Department of AI & DS Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Thandalam Chennai India Department of CSE Mohan Babu University &SreeVidyanikethan Engineering College Andhrapradesh Tirupati517102 India Mumbai400077 India School of Business and Management Christ University Bangalore560029 India Faculty of Computer Science Department of Computer Science and Software Engineering Skyline University Nigeria Kano Nigeria

In recent decades, data has proved indispensable to all facets of human existence. The development of several applications has resulted in the exponential expansion of data. This information can be encrypted and stored in secure areas. Cloud computing is the technology that can be used to store these massive data sets. The article suggests a Cloud-based Data Security System (C-DSS) that employs a five-tiered trust model for cloud-edge data-sharing architectures. The data owner can select an appropriate trust level and Cyber Threat Information (CTI) sanitization procedure before releasing CTI for analytic strategy. In addition, this cleansing method is conducted either by an end device or by the cloud service supplier, based on the organization's degree of confidence in latter. Research presents the trust architecture, cloud architecture, and installation methodology, all of which are intended to meet widest variety of needs for exchanging secret CTI information. The testing findings high degree of data security and an evident improvement in terms of cypher processing time and security services when compared to the encryption systems that are most often employed in cloud technology. In conclusion, research briefly outlines development and evaluation performed so far by pilot applications confirming the architecture. © 2023 The Authors

关键词： Cryptography

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A Feature Subset Selection Method Based on the Combination of PCA and Improved GA 2

A Feature Subset Selection Method Based on the Combination o...

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2nd International Conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2020

作者： Li, Yinghao Shi, Kai Qiao, Fuqiang Luo, Hongyu Tianjin University of Technology School of Computer Science and Engineering China Tianjin Sino-German University of Applied Sciences College of Software and Communication China

ISBN: (纸本)9781728196381

Nowadays, intrusion detection data has redundant features, which leads to unsatisfactory detection results. This paper proposes a feature selection method based on the combination of principal component analysis (PCA) and improved Genetic algorithm(GA). First, PCA is used to reduce the dimensionality of the intrusion data, and the cosine similarity is used to calculate angles between the principal component and the original feature;the feature with high cosine similarity to the principal component is selected and the genetic algorithm is input, and the improved inter-group crossover strategy is adopted to reduce the probability of entering the local optimum. The fitness function of the genetic algorithm is the detection rate, and the optimal feature subset is selected as the intrusion detection data feature. Experimental results show that this method can improve the efficiency of intrusion detection and improve the classification ability of the classifier. © 2020 IEEE.

关键词： Principal component analysis

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A Novel Framework for Learning and Classifying the Imbalanced Multi-Label Data

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Computer Systems science & Engineering 2024年第5期48卷 1367-1385页

作者： P.K.A.Chitra S.Appavu alias Balamurugan S.Geetha Seifedine Kadry Jungeun Kim Keejun Han Department of Computer Science and Engineering SRM Institute of Science and TechnologyTiruchirappalliTamil Nadu603203India Department of Computer Science and Engineering Periyar Maniammai Institute of Science&Technology(Deemed to be University)ThanjavurTamil Nadu613403India School of Computer Science and Engineering ChennaiTamil Nadu600048India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346AjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos10150Lebanon Department of Software Kongju National UniversityCheonan31080Republic of Korea Division of Computer Engineering Hansung UniversitySeoul02876Republic of Korea

A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label *** main objective of this work is to create a novel framework for learning and classifying imbalancedmulti-label *** work proposes a framework of two *** imbalanced distribution of themulti-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase ***,an adaptive weighted l21 norm regularized(Elastic-net)multilabel logistic regression is used to predict unseen samples in phase *** proposed Borderline MLSMOTE resampling method focuses on samples with concurrent high labels in contrast to conventional *** minority labels in these samples are called difficult minority labels and are more prone to penalize classification *** concurrentmeasure is considered borderline,and labels associated with samples are regarded as borderline labels in the decision *** phase II,a novel adaptive l21 norm regularized weighted multi-label logistic regression is used to handle balanced data with different weighted synthetic *** on various benchmark datasets shows the outperformance of the proposed method and its powerful predictive performances over existing conventional state-of-the-art multi-label methods.

关键词： Multi-label imbalanced data multi-label learning Borderline MLSMOTE concurrent multi-label adaptive weighted multi-label elastic net difficult minority label

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PCA: Semi-Supervised Segmentation with Patch Confidence Adversarial Training

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IEEE Transactions on Network science and Engineering 2025年

作者： Xu, Zhenghua Yang, Runhe Xu, Zihang Zhang, Shuo Yang, Yuchen Liu, Weipeng Xu, Weichao Chen, Junyang Lukasiewicz, Thomas Leung, Victor C. M. Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Tianjin China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The Johns Hopkins University Department of Applied Mathematics and Statistic Baltimore United States Hebei University of Technology School of Artificial Intelligence Tianjin China Hebei Provincial Hospital of Traditional Chinese Medicine Digestive Disease Center Shijiazhuang China Shenzhen University College of Computer Science and Software Engineering Shenzhen China University of Oxford Department of Computer Science University of Oxford United Kingdom Department of Computer Science United Kingdom Vienna University of Technology institute of Logic and Computation nstitute of Logic and Computation Vienna Austria

Deep-learning-based semi-supervised learning (SSL) methods have achieved a strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions. We argue that the current performance restrictions for such approaches are the problems of feature extraction and learning preferences. In this paper, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. The PCA method's discriminator penalizes patch-level structures, guiding the generator to optimize different patch areas, by leveraging pixel context, the generator is driven to focus on high-frequency features, making it harder to deceive the discriminator and easy to converge to an ideal state, which more effectively guides the segmenter to generate high-quality pseudo-labels. Furthermore, at the discriminator's input, we supplement image information constraints, making it simpler to fit the expected data distribution. Extensive experiments on the Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019 challenge dataset show that our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation. © 2025 IEEE. All rights reserved.

关键词： Adversarial machine learning

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FEDCSD: A FEDERATED LEARNING BASED APPROACH FOR CODE-SMELL DETECTION

arXiv

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arXiv 2023年

作者： Alawadi, Sadi Alkharabsheh, Khalid Alkhabbas, Fahed Kebande, Victor Awaysheh, Feras M. Palomba, Fabio Awad, Mohammed Center for Applied Intelligent Systems Research School of Information Technology Halmstad University Halmstad Sweden Department of Software Engineering Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Jordan Internet of Things and People Research Center Malmö University Sweden Department of Computer Science Blekinge Institute of Technology Sweden Institute of Computer Science Delta Research Centre University of Tartu Estonia SeSa Lab University of Salerno Italy Arab American University Palestine Palestine

software quality is critical, as low quality, or "Code smell," increases technical debt and maintenance costs. There is a timely need for a collaborative model that detects and manages code smells by learning from diverse and distributed data sources while respecting privacy and providing a scalable solution for continuously integrating new patterns and practices in code quality management. However, the current literature is still missing such capabilities. This paper addresses the previous challenges by proposing a Federated Learning Code Smell Detection (FedCSD) approach, specifically targeting "God Class," to enable organizations to train distributed ML models while safeguarding data privacy collaboratively. We conduct experiments using manually validated datasets to detect and analyze code smell scenarios to validate our approach. Experiment 1, a centralized training experiment, revealed varying accuracies across datasets, with dataset two achieving the lowest accuracy (92.30%) and datasets one and three achieving the highest (98.90% and 99.5%, respectively). Experiment 2, focusing on cross-evaluation, showed a significant drop in accuracy (lowest: 63.80%) when fewer smells were present in the training dataset, reflecting technical debt. Experiment 3 involved splitting the dataset across 10 companies, resulting in a global model accuracy of 98.34%, comparable to the centralized model’s highest accuracy. The application of federated ML techniques demonstrates promising performance improvements in code-smell detection, benefiting both software developers and researchers. Copyright © 2023, The Authors. All rights reserved.

关键词： Computer software selection and evaluation

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