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Exploiting the sequential nature of genomic data for improved analysis and identification

computers in Biology and Medicine 2024年 183卷 109307-109307页

作者： Nawaz, M. Saqib Nawaz, M. Zohaib Junyi, Zhang Fournier-Viger, Philippe Qu, Jun-Feng College of Computer Science and Software Engineering Shenzhen University China Faculty of Computing and Information Technology Department of Computer Science University of Sargodha Pakistan School of Computer Engineering Hubei University of Arts and Science Hubei Xiangyang China

Genomic data is growing exponentially, posing new challenges for sequence analysis and classification, particularly for managing and understanding harmful new viruses that may later cause pandemics. Recent genome sequence classification models yield promising performance. However, the majority of them do not consider the sequential arrangement of nucleotides and amino acids, a critical aspect for uncovering their inherent structure and function. To overcome this, we introduce GenoAnaCla, a novel approach for analyzing and classifying genome sequences, based on sequential pattern mining (SPM). The proposed approach first constructs and preprocesses datasets comprising RNA virus genome sequences in three formats: nucleotide, coding region, and protein. Then, to capture sequential features for the analysis and classification of viruses, GenoAnaCla extracts frequent sequential patterns and rules in three forms and in codons. Eight classifiers are utilized, and their effectiveness is assessed by employing a variety of evaluation metrics. A performance comparison demonstrates that the suggested approach surpasses the current state-of-the-art genome sequence classification and detection techniques with a 3.18% performance increase in accuracy on average. © 2024 Elsevier Ltd

关键词： Genome

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ENCL: Empirical Evaluation of Consumer Behaviour Analysis Model Using Internet of Things (IoT) Based Enhanced Neural Classification Logic

ENCL: Empirical Evaluation of Consumer Behaviour Analysis Mo...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Merlin, S. Sudha Malathy, K. Ravi, S. Arun, M. Raut, S.P. Fawareh, H. Dmi College of Engineering Department of Electrical and Electronics Engineering Palanchur Chennai India S.A. Engineering College Department of Mca Chennai India Department of Vlsi Microelectronics Chennai India Kings College of Engineering Dept of Computer Science and Engineering Punalkulam Thanjavur India Yeshwantrao Chavan College of Engineering Department of Civil Engineering Nagpur India Zarqa University Faculty of Information Technology Software Engineering Dept Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The proliferation of consumer data can be attributed to 'the Internet of Things (IoT),' which makes it easier to access and more detailed than before. In this research our purpose is to evaluate the effectiveness of a consumer behavior analysis model using data from IoT. The quality of the model is evaluated on multiple parameters - accuracy, predictability and business sense for marketing. It uses the analytics of huge amounts for data from IoT devices to predict patterns, customer preferences and trends. The proposed method called as Enhanced Neural Classification Logic (ENCL) is cross-validated with the already existing method, which is termed as Consumer behavior Forecasting Strategy (CBFS), and helps users to share gather or process information in a fast and cost-effective way through the proposed method. The results are useful to grasp the promise of IoT in more effective consumer behavior analysis. The research portrays the strengths, weaknesses and possible areas of improvement in this model which will be beneficial for both academic and industry practitioners. As such, the objective of this study is to show how an IoT-based consumer behavior analysis can increase business growth and customer satisfaction. The experimental evaluations demonstrate significant improvements in predictive performance over traditional methods, providing actionable insights for targeted marketing and strategic decision-making. The results highlight ENCL's potential to transform consumer behavior analysis by delivering more precise and nuanced understandings of market dynamics. © 2024 IEEE.

关键词： CBFS Consumer Behavior Data Analysis ENCL Internet of Things IoT Marketing Strategy Model Evaluation

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A Prediction Method of Consumer Buying Behavior Based on Attention Mechanism and CNN-BiLSTM

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第2期7卷 375-385页

作者： Wang, Jian-Nan Cui, Jian-Feng Chen, Chin-Ling College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China College of Software Engineering Xiamen University of Technology Xiamen361024 China School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China School of Information Engineering Changchun Sci-Tech University Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

A prediction method of consumer buying behavior based on attention mechanism and CNN-BiLSTM is proposed, to fully extract the effective features of the behavior and make its prediction more accurate and stable. In this paper, the CNN model is used to automatically extract local features of the consumer buying behavior to select and optimize important information. The BiLSTM model is used to collect sequence patterns of buying behavior to find the correlation among features. The attention mechanism is used to assign different weights to the state values of the hidden layers of each neural network so that the hidden layers can better express important information to obtain more accurate and stable predictions. The experiment results demonstrate that the proposed prediction method has advantages in not only accuracy but stability over other models. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Forecasting

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Difficulty-Aware Data Augmentor for Scene Text Recognition 48

Difficulty-Aware Data Augmentor for Scene Text Recognition

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Meng, Guanghao Dai, Tao Chen, Bin Li, Naiqi Jiang, Yong Xia, Shu-Tao Tsinghua University Tsinghua Shenzhen International Graduate School China Peng Cheng Laboratory Shenzhen China Shenzhen University College of Computer Science and Software Engineering China Harbin Institute of Technology Department of Computer Science and Technology Shenzhen China

ISBN: (纸本)9781728163277

Deep neural network (DNN) based scene text recognition (STR) methods usually require a large amount of annotated data for training, which is time-consuming and cost-expensive in practice. To address this issue, many data augmentation methods have been developed to train recognizers by improving the diversity of training samples. However, most existing methods neglect the difficulty inherent in samples, and easily suffer from the problem of over-diversity, i.e., the distribution of the augmented data significantly deviates from that of clean data. In this paper, we propose a novel difficulty-aware data augmentation framework for scene text recognition, which jointly considers the difficulty of samples and the strength of augmentations. Specifically, our framework first predicts the sample difficulty, followed by an adaptive data augmentation strategy. Furthermore, we build a more diverse set of augmentation methods for STR and integrate it into our augmentation framework. Extensive experiments on scene text recognition benchmarks show that our augmentation framework significantly improves the performance of recognizers. © 2023 IEEE.

关键词： data augmentation deep neural network difficulty-aware Scene text recognition

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A-REA: Relation-Aware Graph Convolutional Network Entity Alignment Via a Differential Adjacency Matrix

A-REA: Relation-Aware Graph Convolutional Network Entity Ali...

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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Dang, Yuemei Qiu, Chen Gao, Feng Gu, Jinguang Zhang, Lihua Arman, Ghazaleh School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Software College East China Jiaotong University Jiangxi Nanchang China Urmia University of Technology Faculty of Information Technology and Computer Engineering Urmia Iran

ISBN: (纸本)9781665457033

Entity Alignment is the task of seeking entities with the same semantics from distinct knowledge graphs (KGs). Researchers have recently proposed Graph Convolutional Network (GCN) based approaches to achieve this task. Most existing GCN-based methods embed all the relations into an identical value, making the underlying loss on the diversity of the relationship. Relation semantics of entities and relation structure have benefitted the entity alignment task in the monolingual scenarios. This paper proposes a Relation-Aware Graph Convolutional Network with a Differential Adjacency Matrix (A-REA), which exploits the relation-specific semantics and structural information of entities in the KGs to promote the entity representations. The experiments on DBP-15K and DWY-100K datasets, including three cross-lingual subsets and two cross-knowledge graph sub-datasets, reveal that our approach leads to advance in entity alignment task in both the cross-lingual and cross-knowledge KGs. Discriminating the relation shows the effective signal for entity alignment, especially for the schema heterogeneity of different KGs. © 2023 IEEE.

关键词： Knowledge graph

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Unlocking Antenna Performance: Harnessing the Power of the Hahn-Banach Theorem in Wireless Communication Systems 14

Unlocking Antenna Performance: Harnessing the Power of the H...

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19th International Conference on software Technologies, ICSOFT 2024

作者： Uzair, Muhammad Ali, Sijjad Ali, Asad Amir, Hamza Bari, Rana Zaki Abdul Sharif, Hamid Jamil, Maryam Hunza, M. Akram, Nabel Allaberdiev, Sharofiddin College of Electronics and Information Engineering Shenzhen University Guangdong 518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong 518060 China School of Media and Communication Shenzhen University Guangdong 518060 China College of Physics and Optoelectronic Engineering Shenzhen University Guangdong 518060 China School of Government Shenzhen University Guangdong 518060 China

ISBN: (纸本)9789897587061

This paper presents a novel approach for improving the performance of the antenna in wireless communication systems through the utilization of the Hahn-Banach Theorem. With advancements of such standards as 5G and B5G in design-related issues, traditional methods are typically inadequate. Applying the Hahn-Banach Theorem results in a strong mathematical framework that augments several vital antenna parameters including gain, efficiency, bandwidth, and radiation patterns. The approach takes solutions from limited to large design spaces, allowing the search for new configurations within tight constraints. Results show substantial advancement in antenna performance, creating better, more reliable progressions. This interdisciplinary method connects theoretical mathematics with the engineering solutions making a great step forward for practical communication technologies. This guarantees excellent linearity and overall performance which is very important for wireless communication signal integrity, impacting future antenna technology and wireless communication research and development. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Banach spaces

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Brain Tumor Augmentation using the U-NET Architecture

Brain Tumor Augmentation using the U-NET Architecture

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2022 International Conference on Frontiers of Information Technology, FIT 2022

作者： Jabbar, Mohsin Siddiqui, Maria Hussain, Farhan Daud, Sultan Nust College of E&me Department of Computer and Software Engineering Islamabad Pakistan Namal University Department of Computer Science Mianwali Pakistan

ISBN: (纸本)9798350345933

Studies have found out that tumors in brain are one of the fiercest diseases which can ultimately lead to death. Gliomas are the most commonly found primary tumors that are very hard to predict and can be found anywhere in the brain. It is prime objective to differentiate the different tumor tissues such as enhancing tissues, edema, from healthy ones. To do this task, two types of segmentation techniques come into existent i.e. manual and automatic. The automation methods of brain tumor segmentation have gained ground over manual segmentation algorithms and further its estimation is very closer to clinical results. In this paper we propose a comprehensive U-NET architecture with modification in their layers for 2D slices segmentation as a major contribution to BRATS 2015 challenge. Then we enlisted different dataset that are available publicly i.e. BRATS and DICOM. Further, we present a robust frame- work inspired from U-NET model with addition and modification of layers and image pre-processing methodology such as contrast enhancement for visible input and output details. In this way our approach achieves highest dice score 0.92 on the publicly available BRATS 2015 dataset and with better time constraint i.e. training time decreases to 80-90 minute instead of previously 2 to 3 days. © 2022 IEEE.

关键词： Tumors

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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

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IEEE Transactions on Automation science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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Class-wise Adaptive Logits Distillation with Meta-Learning

Class-wise Adaptive Logits Distillation with Meta-Learning

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiao Huang Wu Chen Wei Zhou College of Computer and Information Science College of Software Southwest University Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Knowledge distillation has become a crucial technique for transferring intricate knowledge from a teacher model to a smaller student model. While logit-based knowledge distillation has shown promise, existing methods often overlook the efficient distillation of logits. In this paper, we introduce a novel approach called Class-wise Adaptive Logits Distillation (CALD) based on meta-learning. Our method leverages a meta-network to generate class-adaptive weights, delivering both explicit and implicit knowledge adaptively. By training the meta-network to assign higher weights to specific classes crucial for the student model’s learning from the teacher model, our approach enhances the knowledge transfer process. Experimental results on CIFAR-100 and ImageNet datasets demonstrate that CALD surpasses state-of-the-art knowledge distillation methods, achieving enhanced accuracy and efficiency in transferring knowledge from teacher to student models.

关键词： Metalearning Training Adaptation models Accuracy Signal processing Acoustics Speech processing Knowledge transfer

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KGCODE-Tab Results for SemTab 2022

KGCODE-Tab Results for SemTab 2022

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2022 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, SemTab 2022

作者： Li, Xinhe Wang, Shuxin Zhou, Wei Zhang, Gongrui Jiang, Chenghuan Hong, Tianyu Wang, Peng School of Computer Science and Engineering Southeast University China College of Software Engineering Southeast University China Chien-Shiung Wu College Southeast University China

This paper presents the results of KGCODE-Tab in the tabular data to knowledge graph matching contest SemTab 2022. As an efficient tabular data linking system, KGCODE-Tab is intended to participate in three tasks of the content: Column Type Annotation (CTA), Cell Entity Annotation (CEA), and Columns Property Annotation (CPA). The specific techniques used by KGCODE-Tab will be introduced briefly. The strengths and weaknesses of KGCODE-Tab will also be discussed. © 2022 Copyright for this paper by its authors.

关键词： Knowledge graph

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