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An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of computer Science Shaanxi Normal University faculty of computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Proteins RNA Neural networks Encoding Robustness Decoding Convolutional neural networks

Relational Turkish Text Classification Using Distant Supervised Entities and Relations

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computers, Materials & Continua 2024年第5期79卷 2209-2228页

作者： Halil Ibrahim Okur Kadir Tohma Ahmet Sertbas Department of computer Engineering Faculty of Engineering and Natural SciencesIskenderun Technical UniversityHatay31200Turkey Department of computer Engineering Faculty of EngineeringIstanbul University-CerrahpasaIstanbul34310Turkey

Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing *** study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)*** on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from *** adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational *** conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural *** results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP *** of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational *** demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)***,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

关键词： Text classification relation extraction NER distant supervision deep learning machine learning

Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of computer Science Shaanxi Normal University faculty of computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drugs Training Proteins Measurement Message passing Prediction methods Graph neural networks

Molecular Generation and Optimization of Molecular Properties Using a Transformer Model

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Big Data Mining and Analytics 2024年第1期7卷 142-155页

作者： Zhongyin Xu Xiujuan Lei Mei Ma Yi Pan School of computer Science Shaanxi Normal UniversityXi'an 710119China faculty of computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical ***,we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated *** Matched Molecular Pairs(MMPs),which contain the source and target molecules,are used herein,and logD and solubility are selected as the optimization *** main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix *** intervals and state changes are then used to encode logD and solubility for subsequent *** the experiments,we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365,1503,and 1570 MMPs as the training,validation,and test sets,*** models are compared with the baseline models with respect to their abilities to generate molecules with specific *** show that the transformer model can accurately optimize the source molecules to satisfy specific properties.

关键词： molecular optimization transformer Matched Molecular Pairs(MMPs) logD solubility

Gender-Based Analysis of User Reactions to Facebook Posts

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Big Data Mining and Analytics 2024年第1期7卷 75-86页

作者： Yassine El Moudene Jaafar Idrais Rida El Abassi Abderrahim Sabour mathematical Engineering and computer Science Laboratory Faculty of SciencesIbn Zohr UniversityAgadir 8706Morocco Department of computer Science mathematical−High School of Technology Ibn Zohr UniversityAgadir 8706Morocco

Online Social Networks(OSNs)are based on the sharing of different types of information and on various interactions(comments,reactions,and sharing).One of these important actions is the emotional reaction to the *** diversity of reaction types available on Facebook(namely FB)enables users to express their feelings,and its traceability creates and enriches the users’emotional identity in the virtual *** paper is based on the analysis of 119875012 FB reactions(Like,Love,Haha,Wow,Sad,Angry,Thankful,and Pride)made at multiple levels(publications,comments,and sub-comments)to study and classify the users’emotional behavior,visualize the distribution of different types of reactions,and analyze the gender impact on emotion *** of these can be achieved by addressing these research questions:who reacts the most?Which emotion is the most expressed?

关键词： profiling knowledge extraction data mining emotion mining social media data crawling Facebook reactions gender

An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

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computers, Materials & Continua 2024年第1期78卷 589-616页

作者： Syeda Wajiha Zahra Muhammad Nadeem Ali Arshad Saman Riaz Muhammad Abu Bakr Ashit Kumar Dutta Zaid Alzaid Badr Almutairi Sultan Almotairi Department of computer Science Alhamd Islamic UniversityIslamabad44000Pakistan Department of computer Science and Technology University of Science and Technology BeijingBeijing100083China Department of computer Science National University of TechnologyIslamabad44000Pakistan Department of Electrical Engineering National University of TechnologyIslamabad44000Pakistan Department of computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of computer Science Faculty of Computer and Information SystemsIslamic University of MadinahMedinah42351Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Various organizations store data online rather than on physical *** the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also *** researchers worked on different algorithms to protect cloud data from replay *** of the papers used a technique that simultaneously detects a full-message and partial-message replay *** study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay *** program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original *** the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the *** mechanism has the benefit of enhancing the detectability of replay ***,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy *** the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

关键词： Replay attack malware message attack file encryption cryptology data security

Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

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computers, Materials & Continua 2024年第6期79卷 3855-3875页

作者： Maie Aboghazalah Passent El-kafrawy Abdelmoty M.Ahmed Rasha Elnemr Belgacem Bouallegue Ayman El-sayed math and computer Science Department Faculty of ScienceMenoufia UniversityShebin El-komEgypt College of Engineering Computer Science DepartmentEffat UniversityJeddahKingdom of Saudi Arabia Department of computer Engineering College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityShebin El-komEgypt Climate Change Information Center and Expert Systems Agriculture Research CenterGizaEgypt

Heart monitoring improves life ***(ECGs or EKGs)detect heart *** learning algorithms can create a few ECG diagnosis processing *** first method uses raw ECG and time-series *** second method classifies the ECG by patient *** third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer *** ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and *** using all three approaches have not been examined till *** researchers found that Machine Learning(ML)techniques can improve ECG *** study will compare popular machine learning techniques to evaluate ECG *** algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization *** plus prior knowledge has the highest accuracy(99%)of the four ML *** characteristics failed to identify signals without chaos *** 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

关键词： ECG extraction ECG leads time series prior knowledge and arrhythmia chaos theory QRS complex analysis machine learning ECG classification

RUSAS: Roman Urdu Sentiment Analysis System

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computers, Materials & Continua 2024年第4期79卷 1463-1480页

作者： Kazim Jawad Muhammad Ahmad Majdah Alvi Muhammad Bux Alvi faculty of computer Science and Engineering Frankfurt University of Applied SciencesFrankfurt am Main60318Germany Technical Writer and Researcher Proteus Technologies LLCIslamabad04405Pakistan faculty of Engineering The Islamia University of BahawalpurBahawalpur63100Pakistan

Sentiment analysis, the meta field of Natural Language Processing (NLP), attempts to analyze and identify thesentiments in the opinionated text data. People share their judgments, reactions, and feedback on the internetusing various languages. Urdu is one of them, and it is frequently used worldwide. Urdu-speaking people prefer tocommunicate on social media in Roman Urdu (RU), an English scripting style with the Urdu language *** have developed versatile lexical resources for features-rich comprehensive languages, but limitedlinguistic resources are available to facilitate the sentiment classification of Roman Urdu. This effort encompassesextracting subjective expressions in Roman Urdu and determining the implied opinionated text polarity. Theprimary sources of the dataset are Daraz (an e-commerce platform), Google Maps, and the manual effort. Thecontributions of this study include a Bilingual Roman Urdu Language Detector (BRULD) and a Roman UrduSpelling Checker (RUSC). These integrated modules accept the user input, detect the text language, correct thespellings, categorize the sentiments, and return the input sentence’s orientation with a sentiment intensity *** developed system gains strength with each input experience gradually. The results show that the languagedetector gives an accuracy of 97.1% on a close domain dataset, with an overall sentiment classification accuracy of94.3%.

关键词： Roman Urdu sentiment analysis Roman Urdu language detector Roman Urdu spelling checker flask

Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

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computers, Materials & Continua 2024年第7期80卷 1163-1177页

作者： Amel Ali Alhussan Doaa Sami Khafaga El-Sayed M.El-kenawy Marwa M.Eid Abdelhameed Ibrahim Department of computer sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and TechnologyMansoura35511Egypt faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric *** demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of *** establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this ***,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of *** the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging *** is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic *** vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering ***,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature ***,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic *** on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is *** results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algori

关键词： Electric vehicle graylag goose optimization metaheuristics optimization machine learning