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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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Fully-Decoupled and Second-Order Time-Accurate Scheme for the Cahn–Hilliard Ohta–Kawaski Phase-Field Model of Diblock Copolymer Melt Confined in Hele–Shaw Cell

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Communications in Mathematics and Statistics 2024年第3期12卷 479-504页

作者： Junying Cao Jun Zhang Xiaofeng Yang School of Data Science and Information Engineering Guizhou Minzu UniversityGuiyang 550025People’s Republic of China Guizhou Key Laboratory of Big Data Statistics Analysis Computational Mathematics Research CenterGuizhou University of Finance and EconomicsGuiyang 550025People’s Republic of China Department of Mathematics University of South CarolinaColumbiaSCUSA

In this work,we consider numerical approximations of the phase-field model of diblock copolymer melt confined in Hele–Shaw cell,which is a very complicated coupled nonlinear system consisting of the Darcy equations and the Cahn–Hilliard type equations with the Ohta–Kawaski *** the combination of a novel explicit-Invariant Energy Quadratization approach and the projection method,we develop the first full decoupling,energy stable,and second-order time-accurate numerical *** introduction of two auxiliary variables and the design of two auxiliary ODEs play a vital role in obtaining the full decoupling structure while maintaining energy *** scheme is also linear and unconditional energy stable,and the practical implementation efficiency is also very high because it only needs to solve a few elliptic equations with constant coefficients at each time *** strictly prove that the scheme satisfies the unconditional energy stability and give a detailed implementation *** experiments further verify the convergence rate,energy stability,and effectiveness of the developed algorithm.

关键词： Phase-field Explicit-IEQ Darcy Decoupled Energy Stability Diblock copolymer melt

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Hybrid Graph Neural Networks with LSTM Attention Mechanism for Recommendation Systems in MOOCs 20

Hybrid Graph Neural Networks with LSTM Attention Mechanism f...

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Luo, Hao Abdipoor, Sina Li, Qing Xu, Litao BaoShan University Department Of Big Data BaoShan67800 China Universiti Putra Malaysia Upm Department Of Computer Science Serdang43400 Malaysia

ISBN: (纸本)9798350382310

Massive Open Online Courses (MOOC) represent a relatively recent development in the educational landscape, rapidly gaining popularity and drawing research attention. Transforming the traditional approach to education, MOOCs provide learners with flexible and accessible avenues for acquiring knowledge. However, the sheer abundance of courses available can overwhelm users. Recommending relevant courses to learners remains a complicated challenge, impacting engagement and completion rates. Conventional recommendation systems often struggle to capture MOOCs' dynamic and interconnected nature. This paper examines the application of Hybrid Graph Neural Networks with a Long Short-Term Memory attention mechanism (HGNN-LSTM) to enhance recommendation systems for MOOCs. By leveraging learner behavior data, these approaches examine and predict learning activities, discern temporal relationships among courses through Adam optimization algorithms, and ultimately enhance the accuracy of recommendations. We illustrate that HGNN-LSTM adeptly captures hidden linkages between courses, resulting in improved automatic course classification and a reduction in the burden of course maintenance. The paper concludes with an analysis of challenges, identified gaps, and suggestions for potential future research directions. © 2024 IEEE.

关键词： Recommender systems

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Low-temperature Thermogravimetry Study of Post-Infrared-Exposure PMMA and PMMA/zircon Composites 7

Low-temperature Thermogravimetry Study of Post-Infrared-Expo...

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7th International Conference on Advanced Materials for Better Future, ICAMBF 2022

作者： Sholicha, S.P. Hilmi, A.R. Pratapa, S. Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Sukolilo Surabaya60111 Indonesia

A thermogravimetruic study of PMMA/zircon composites was performed at temperatures between 30 and 200°C, instead of above degradation temperature of PMMA at around 300°C. The focus of the study was to observe mass changes between the designated temperatures due to zircon filler and infrared (IR) exposure. Four samples were prepared, i.e. pure PMMA without IR exposure and with IR exposure, each without and with 1 wt.% zircon filler. The success of the synthesis of the composites was examined using x-ray diffraction (XRD). Thermal gravimetry (TG) data of samples at the designated temperatures were plotted to explain several thermal phenomena. Results showed that all samples exhibited a drop of 1.5-2% weight between 30 and 75°C with only a slight increase after IR exposure. The slight drop maybe due to water loss or evaporation of broken weak hydrocarbon bonds. There is a further slight increase in mass for the unexposed samples between 75 and 105°C, i.e. just before the glass transition temperature of PMMA. The mass decrease continued up to 135 °C for the unexposed pure PMMA and up to 135°C for the other samples, indicating that filler and IR exposure favored PMMA mass loss. After that, up to 200 °C, all samples gained mass which was presumably due to oxidation. The presence of filler and the IR exposure promoted a larger oxidation as a result of some breaking PMMA bonds. © Published under licence by IOP Publishing Ltd.

关键词： Fillers

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A Reduced State Space Generation Method for Concurrent Systems Based on CPN-PR Model

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri Nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model checking method traverses the full state space, which suffers from the state space explosion problem. A reduced state space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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A dynamic exploratory hybrid modelling framework for simulating complex and uncertain system

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Journal of Safety science and Resilience 2024年第2期5卷 167-178页

作者： Gangqiao Wang Han Xing Yongqiang Chen Yi Liu Institute of Big Data Inteligent Management and Decision-making College of ManagementShenzhen UniversityShenzhen 518060China School of Safety Science Tsinghua UniversityBejing 100084China Department of Mechanics and Engineering Science College of EngineeringPeking UniversityBejing 100871China

Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time,leading to significant deep *** to deep uncertainty,decision-makers have severe inadequacy of knowledge and often encounter unpredictable surprises that may emerge in the future,thus making it difficult to specify appropriate models and parameters to describe the system of *** this paper,we propose a dynamic exploratory hybrid modeling framework that fits data,models,and computational ex-periments together to simulate complex systems with deep *** the framework,one needs to develop multiple plausible models from a hybrid modeling perspective and perform enormous computational experi-ments to explore the diversity of future ***-time data is then incorporated into diverse forecasts to dynamically adjust the simulation *** ultimately enables an ongoing modeling and analysis process in which deep uncertainty would be gradually *** approach has been applied to a human-involved car-following system simulation under complex traffic *** results show that the proposed approach can improve the prediction accuracy while enhancing the sensitivity of the simulation system to uncertain changes in the system of interest.

关键词： Disaster modeling and analysis Deepuncertainty Hybrid modeling Adaptivesimulation

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A Transmission and Transformation Fault Detection Algorithm Based on Improved YOLOv5

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Computers, Materials & Continua 2023年第9期76卷 2997-3011页

作者： Xinliang Tang Xiaotong Ru Jingfang Su Gabriel Adonis School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Department of Computer Science and Information Systems Birkbeck Institute for Data AnalyticsLondonB100ABWC1E 7HXUK

On the transmission line,the invasion of foreign objects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the safe operation of power ***,a YOLOv5 target detection method based on a deep convolution neural network is *** this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the *** structure uses depth-wise separable convolution to reduce the amount of calculation and parameters;improve the detection *** the same time,to compensate for the detection accuracy,the Squeeze-and-Excitation Networks(SENet)attention model is fused into the algorithm framework and a new detection scale suitable for small targets is added to improve the significance of the fault target area in the *** pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data *** experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,*** the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.

关键词： Transmission line YOLOv5 multi-scale integration SENet

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Thalaseemia Prediction Using Deep Neural Networks: A Step Towards Personalized Medicine 2

Thalaseemia Prediction Using Deep Neural Networks: A Step To...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Pream Sudha, V. Karpagavalli, S. Sathiyakumari, K. Sountharraj, A.C. Dr.N. G.P Arts and Science College Department of Computer Science with Data Analytics Coimbatore India PSGR Krishnammal College for Women Department of Computer Science Coimbatore India

ISBN: (纸本)9798331505745

Thalaseemia is a series of genetic hemolytic diseases brought on by defective hemoglobin synthesis. It is prevalent in many Asian, African, and Mediterranean nations. Four million Indians are Thalaseemia carriers, and more than 100,000 are actively combating the condition, according to the World Health Organization. It is specifically taken under investigation because cases of Thalassemia that have clinical, hematologic characteristics cannot be effectively separated from cases of sickle cell anemia and are misdiagnosed as such. The precise profiles of the genetic and pathophysiological mechanisms of patients with Thalassemia are not yet well defined, despite the fact that they are clinically treated identically. Even among people with the same genotype, there are differences in the severity of the clinical phenotype. The frequency of complications changes over time, both within and between individuals. Patients display a striking variation in severity, from a milder clinical course to severe transfusion dependency and progressive organ destruction. This study focuses on the creation of a discriminative model for Thalaseemia classification. In this study, the prediction of Thalassemia is modeled as a pattern classification issue, and the models are constructed using deep learning approaches. The hybrid CNN-Long Short Term Memory (LSTM) based Thalaseemia prediction model has demonstrated improved performance, according to the performance evaluation of these models. © 2025 IEEE.

关键词： Theranostics

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An Enhanced BERTopic Framework and Algorithm for Improving Topic Coherence and Diversity 24

An Enhanced BERTopic Framework and Algorithm for Improving T...

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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： Sawant, Sahil Yu, Jinhong Pandya, Kirtikumar Ngan, Chun-Kit Bardeli, Rolf Data Science Program Worcester Polytechnic Institute WorcesterMA United States Big Data and Consumer Analytics Vodafone GmbH NRW Duesseldorf Germany

ISBN: (纸本)9798350319934

In this paper, we enhance and customize the existing BERTopic framework to develop and implement an automated pipeline that delivers a more coherent and diverse set of topics with an even moderate dataset. More specifically, the contributions of this work are threefold: (1) integrate a dynamic and advanced optimizer into the existing BERTopic framework to learn the optimal number of dimensions of different document embeddings, (2) develop a k-means-based algorithm in the optimizer to support the dimension-embedding learning, and (3) conduct an extensive experimental study on three distinct types of datasets, including DBPedia, AG News, and Reuters, to evaluate the performance of our approach in terms of the topic quality (TQ) score computed by the topic coherence and the topic diversity. From the results, we can conclude that our enhanced, automated BERTopic framework with its dimension-embedding learning algorithm on documents outperforms the TQ score of the existing framework by 4.49% (before removing the stop words) and 16.52% (after removing the stop words) among all the four representable document-embedding approaches, including the BERTopic's Default Sentence Transformer, Google's Universal Sentence Encoder, OpenAI GPT-2, and our investigators' developed Context-aware Embedding Model, on all the three datasets. © 2022 IEEE.

关键词： Embeddings

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Towards Graph-Based Semi-Supervised Learning on Audio Embeddings for Label Classification 22

Towards Graph-Based Semi-Supervised Learning on Audio Embedd...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Chandaliya, Rishabh Timilsina, Mohan Breslin, John Serrano, Martin University of Galway Ireland Insight Sfi Research Centre for Data Analytics Department of Computer Science Galway Ireland

ISBN: (纸本)9798350345346

The increasing importance of audio-based healthcare diagnostics, particularly in chronic respiratory problems, has prompted the development of novel approaches for disease classification using the full spectrum analysis of cough sounds. This paper presents an innovative study exploring the potential of employing supervised and semi-supervised learning methodologies for disease categorization based on cough audio samples. Specifically, this study focuses on scenarios characterized by a shortage of annotated data related to chronic diseases. The objectives of our study involved the utilization of standard machine learning algorithms for direct classification based on embeddings, as well as the integration of Graph Neural Networks (GNNs) on the KNN graph. Preliminary results indicate that GNN models consistently outperformed traditional classifiers. For instance, with just 1 % of the data, GAT and GCN achieved AUC PR values of 0.84 and 0.87, respectively, surpassing all traditional methods. The superiority mentioned above was sustained even when the data fraction was augmented to 3% and 5%. The usefulness of graph neural networks (GNNs) was further supported by comprehensive performance measures, wherein the graph convolutional network (GCN) exhibited exceptional preci-sion and PR (precision-recall). In summary, the AudioVecDiagnosis framework presents a promising opportunity for further investigation in audio-based healthcare diagnostics. It provides an optimum approach for situations with a limited availability of labeled data. © 2023 IEEE.

关键词： Audio-based medical diagnostics AudioVec-Diagnosis Cough noises Disease classification Semi-supervised learning Wav2Vec2.0 model

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