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Integrating Structural information: Comparing Classification-Based and Segmentation-Based Predictors in Bioinformatics (case study: protein-peptide region residue-level interaction) using structural information 29

Integrating Structural Information: Comparing Classification...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Shafiee, Shima Fathi, Abdolhossein Safari, Faezeh Department of Computer Engineering and Information Technology Razi University Kermanshah Iran School of Electrical Eng. and Computer Science Faculty of Eng. Architecture and Info. Tech University of Queensland BrisbaneQLD Australia

ISBN: (纸本)9798331523114

Understanding the regions where proteins bind to peptides is vital for studying diseases like cancer, as it sheds light on various cellular processes and aids in drug discovery. Although researchers can experimentally investigate protein-peptide interactions at the region level by analyzing the structures of their complexes, only a limited number of these structures are known. Additionally, experiments to determine these interactions can be both costly and time-consuming. Computational methods provide an alternative and complementary approach to experimental techniques. As a result, using computational methods to predict peptide-binding regions can significantly enhance the efficiency and cost-effectiveness of experimental research. To leverage these advantages, this study introduces a new method named Structure-based Prediction of Residue-level Interaction of Peptides (SPRINT-Pep), designed to pinpoint the binding regions between proteins and peptides. This approach leverages two distinct predictive models: a classification-based model combining DeepResNet with a Support Vector Machine (SVM) and a segmentation-based model called DeepU-Net. Our predictions outperform other state-of-the-art methods in terms of F-measures (by at least 23%), precision (by at least 3.7%), and the balance between sensitivity and specificity (by about 7.7%). These findings demonstrate the proficiency of the proposed method. © 2025 IEEE.

关键词： Bioinformatics

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SwinGALE: fusion of swin transformer and attention mechanism for GAN-augmented liver tumor classification with enhanced deep learning

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International Journal of information technology (Singapore) 2024年第8期16卷 5351-5369页

作者： Bandaru, Sumash Chandra Mohan, G. Bharathi Kumar, R. Prasanna Altalbe, Ali Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Engineering Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

Liver diseases represent a significant challenge to global healthcare systems, necessitating accurate and timely diagnosis for effective intervention. However, the intricate nature of liver tumor multi-classification remains a daunting obstacle. In this work, we provide a novel framework that integrates state-of-the-art technologies, including Generative Adversarial Networks (GANs), Convolutional Block Attention Module (CBAM), and Enhanced Channel Attention (ECA), within a deep learning architecture. Leveraging the comprehensive Duke Liver dataset, our approach synthesizes GAN-generated data to augment the training dataset and employs attention mechanisms to discern crucial details within medical images. Our ensemble model, incorporating CBAM with VGG19, achieves a remarkable accuracy of 99.29% in liver tumor classification. This research heralds a significant advancement in liver disease diagnosis, offering a promising avenue to improve patient outcomes. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Attention mechanisms DCGAN ECA Liver tumour Swin transformer and feature fusion VGG19

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Innovative fusion of VGG16, MobileNet, EfficientNet, AlexNet, and ResNet50 for MRI-based brain tumor identification

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Iran Journal of computer science 2024年第1期8卷 185-215页

作者： Kia, Marjan Sadeghi, Soroush Safarpour, Homayoun Kamsari, Mohammadreza Jafarzadeh Ghoushchi, Saeid Ranjbarzadeh, Ramin Department of Business Information System Business Analysis (ISBA) Park University Parkville United States School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Software Engineering University of Szeged Szeged Hungary Faculty of Electrical Engineering Malek-Ashtar University of Technology(MUT) Esfahan Iran Faculty of Industrial Engineering Urmia University of Technology Urmia Iran School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland

This study presents a novel approach for brain MRI classification by integrating multiple state-of-the-art deep learning (DL) architectures, including VGG16, EfficientNet, MobileNet, AlexNet, and ResNet50, with an attention mechanism. The primary objective was to accurately detect the presence or absence of brain tumors—a critical challenge in medical diagnosis. The methodology was validated on a comprehensive dataset comprising 3264 brain MRI scans, systematically divided into training, validation, and testing subsets. Preprocessing techniques tailored for MRI data were employed to enhance input quality. Each model was initially developed and evaluated individually to establish a performance baseline before their integration. The proposed ensemble technique capitalized on the complementary strengths of these models to enhance classification accuracy and robustness. The inclusion of an attention mechanism further refined the models by emphasizing critical regions in the MRI scans, thereby boosting predictive performance. In addition, data augmentation techniques were applied to mitigate overfitting and increase dataset resilience. Performance metrics, including precision, accuracy, and recall, demonstrated significant improvements across all evaluations, particularly for the models incorporating the attention mechanism. These findings highlight the potential of combining advanced DL architectures with attention mechanisms as a promising strategy for precise and reliable brain tumor classification in MRI scans. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： AlexNet Attention mechanism Brain tumor classification MobileNet ResNet50

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Accurate Prediction of Workloads and Resources with Multi-Head Attention and Hybrid LSTM for Cloud Data Centers

Accurate Prediction of Workloads and Resources with Multi-He...

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作者： Bi, Jing Ma, Haisen Yuan, Haitao Zhang, Jia Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science Lyle School of Engineering DallasTX75205 United States

Currently, cloud computing service providers face big challenges in predicting large-scale workload and resource usage time series. Due to the difficulty in capturing nonlinear features, traditional forecasting methods usually fail to achieve high prediction performance for resource usage and workload sequences. Besides, there is much noise in original time series of resources and workloads. If these time series are not de-noised by smoothing algorithms, the prediction results can fail to meet the providers' requirements. To do so, this work proposes a hybrid prediction model named VAMBiG that integrates Variational mode decomposition, an Adaptive Savitzky-Golay (SG) filter, a Multi-head attention mechanism, Bidirectional and Grid versions of Long and Short Term Memory (LSTM) networks. VAMBiG adopts a signal decomposition method named variational mode decomposition to decompose complex and non-linear original time series into low-frequency intrinsic mode functions. Then, it adopts an adaptive SG filter as a data pre-processing tool to eliminate noise and extreme points in such functions. Afterwards, it adopts bidirectional and grid LSTM networks to capture bidirectional features and dimension ones, respectively. Finally, it adopts a multi-head attention mechanism to explore importance of different data dimensions. VAMBiG aims to predict resource usage and workloads in highly variable traces in clouds. Extensive experimental results demonstrate that it achieves higher-Accuracy prediction than several advanced prediction approaches with datasets from Google and Alibaba cluster traces. © 2016 IEEE.

关键词： Time series analysis

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Evaluating collaborative rationality-based decisions: A literature review

Evaluating collaborative rationality-based decisions: A lite...

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2022 International Conference on ENTERprise information Systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care information Systems and Technologies, HCist 2022

作者： Elgendy, Nada Elragal, Ahmed Päivärinta, Tero M3S Faculty of Information Technology and Electrical Engineering University of Oulu Finland Department of Computer Science Electrical and Space Engineering Lulea University of Technology Sweden

Decision making has evolved throughout the years, nowadays harnessing massive amounts and types of data through the unprecedented capabilities of data science, analytics, machine learning, and artificial intelligence. This has potentially led to higher quality and more informed decisions based on the collaborative rationality between humans and machines, no longer bounded by the cognitive capacity and limited rationality of each on their own. However, the multiplicity of modes of collaboration and interaction between humans and machines has also increased the complexity of decision making, consequentially complicating ex-Ante and ex-post decision evaluation. Nevertheless, evaluation remains crucial to enable human and machine learning, rationalization, and sensemaking. This paper addresses the need for more research on why and how to evaluate collaborative rationality-based decisions, setting the stage for future studies in developing holistic evaluation solutions. By analyzing four relevant streams of literature: 1) classical decision theory and organizational management, 2) cognitive and neuroscience, 3) AI and ML, and 4) data-driven decision making, we highlight the limitations of current literature in considering a holistic evaluation perspective. Finally, we elaborate the theoretical underpinnings from the knowledge base on how humans and machines evaluate decisions, and the considerations for evaluating collaborative rationality-based decisions. © 2023 Elsevier B.V.. All rights reserved.

关键词： Decision making

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AVIG: A Real-Time Visual Inspection for Guava Grading System Using computer Vision and XGBoost 23

AVIG: A Real-Time Visual Inspection for Guava Grading System...

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8th International Conference on Sustainable information engineering and technology, SIET 2023

作者： Setyawan, Raden Arief Perdana, Rizal Setya Harilegawa, Made Wena Balantimuhe, Alan Stevrie Basuki, Achmad Department of Electrical Engineering Faculty of Engineering Brawijaya University Malang Indonesia Department of Informatics Engineering Faculty of Computer Science Brawijaya University Malang Indonesia Directorate of Information Technology Brawijaya University Malang Indonesia

ISBN: (纸本)9798400708503

The conventional approach to manual inspection can be described by its considerable time and labor demands and the unavoidable occurrence of human error when faced with the inspection of a large number of products. Therefore, the application of an automated visual inspection system is frequently employed. The camera(s) can easily capture visual features for automatic visual inspection. However, it is essential to note that there are limitations when using this method to estimate the size-based grading system. The volume measurement is an essential feature in the field of automatic image analysis for fruit and vegetable grading, specifically guava. In this study, we propose a novel image analysis approach that utilizes a dual-view camera system to enhance the accuracy of volume approximation, particularly in scenarios involving non-perfect spherical objects. Hence, this research presents the training process of a comprehensive model for estimating the volume of objects with irregular shapes, named AVIG (Automatic Visual Inspection for Grading). The model utilizes the ellipsoid approximation technique, a dual-view camera, and XGBoost algorithm. The results from the accuracy comparison demonstrate that XGBoost shows the highest degree of accuracy, specifically 86%, thereby outperforming the performance of other algorithms. The comprehensive experiment conducted in this study has shown that the proposed architecture, AVIG, exhibits superior performance compared to previous research in terms of automatic evaluation of moving objects. © 2023 ACM.

关键词： computer vision

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Simulating a Finite-State Markov Chain for Rayleigh Block-Fading Channel based on its Mathematical Model, and Practical Representation 25

Simulating a Finite-State Markov Chain for Rayleigh Block-Fa...

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25th International Arab Conference on information technology, ACIT 2024

作者： Attar, Hani Ababneh, Jafar Al-Hihi, Mohammad Rachini, Ali Solyman, Ahmed Zarqa University Faculty of Engineering Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan Erlangen Germany Department of Computer Science and Information Technology Jounieh Lebanon Glasgow Caledonian University School of Computing Engineering and Built Environment Glasgow United Kingdom

ISBN: (纸本)9798331540012

In the proposed paper, simulating the Finite State Markov Chain (FSMC) to be implemented as Rayleigh Block-fading channel. Indeed, FSMC is regarded as a practical fading channel because of its random choice for future transmission probability. Accordingly, this paper introduces the FSMC technology with the theoretical limits and equations;moreover, the uplink and downlink Rayleigh Block-fading channel scenarios are also investigated and supported by the simulation set-up. The results for the Rayleigh fading channel for the Long-Term Evaluation-Advanced network were obtained as an example of the channel's attitude. The findings demonstrate that FSMC provides such reliable results and is suitable for use as a practical fading channel, particularly in terms of burst error behavior. © 2024 IEEE.

关键词： Markov chains

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A Deep Graph Convolutional Network based Web API Classification Approach for Mashup Creation 25

A Deep Graph Convolutional Network based Web API Classificat...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Qi, Lianyong Yan, Boyuan Zheng, Jiaqi Xu, Xiaolong Dou, Wanchun Zhou, Xiaokang Zhang, Jie School of Computer Science Qufu Normal University China China Nanjing University Department of Computer Science and Technology China School of Computer and Software Nanjing University of Information Science and Technology China Shiga University Faculty of Data Science Japan Riken Center for Advanced Intelligence Project Japan

ISBN: (纸本)9798350330014

software developers create mashups of their interest by searching, selecting, and combining web Application Programming Interfaces (APIs) with diverse functionalities. However, achieving the selection of APIs relies on a crucial prerequisite: accurate classification of APIs. However, the traditional web API classification methods are often based on a single piece of API function's information such as manual annotations and description documents which decreases the accuracy of API classification results significantly, especially for the web API sharing communities with sparse data (e.g., ***). To tackle this issue, we propose BDGCN which improves some existing web API classification approaches by adding isomorphic graph of strong correlation among different web APIs on the basis of API description documents. Concretely, at the first, we integrate the information of multiple dimensions of web APIs as embedded inputs in which we then build an API-API strongly correlated isomorphic network to strengthen the association among API individuals, and to initialize the embedding vector from the description of each API by tuning the pre-trained Bidi-rectional Encoder Representation from Transformers (BERT) model. Besides, in order to smooth the impact of high-order features between neighboring nodes, a deep graph convolutional network is introduced in this paper that is consistent with the problem. In fact, we set up a categorization layer and utilize cross-entropy function to achieve accurate classification of APIs. Experimental results demonstrate the superiority of our method compared to the existing methods. © 2023 IEEE.

关键词： Classification (of information)

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EDUZONE - A Educational Video Summarizer and Digital Human Assistant for Effective Learning 7

EDUZONE - A Educational Video Summarizer and Digital Human A...

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7th International Conference on information technology Research, ICITR 2022

作者： Wangchen, Tandin Tharindi, P. Navodya Chaveena De Silva, K.C.C. Thushan Sandeepa, W.D. Kodagoda, Nuwan Suriyawansa, Kushnara Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Sri Lanka

ISBN: (数字)9798350332032

ISBN: (纸本)9798350332032

The availability of technology and the expansive nature of the internet have created a surge in the demand for online learning. Despite so many advantages, there are some existing drawbacks related to online learning. The lengthy recorded video lectures of different subjects and modules in a static manner, are extremely tedious for the learner to understand the contents available. and lack of assistance for academic-related problems of students is also stated as a major issue that comes with online education. EDUZONE provides a reliable solution to mitigate and overcome these challenges. This tool is educational assistance that generates a summarized version of the video lectures which depicts the overall idea of the whole video with the capability of a lecture notes generator along with a digital human which helps to clarify students' problems and build an efficient conversational flow. The summarized video content can be used by the learners for revisions and as a quick reference before any examinations. In addition to generating short and precise content, EDUZONE also indexes any specific topics to make it easier to find content and generate class notes, highlighting all the important content. Overall EDUZONE can be considered a time-efficient educational assistant which helps students with their studies. © 2022 IEEE.

关键词： Students

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Foreign Embassies Internationalization Support for Small and Medium-Sized Enterprises: Need to Balance Innovation Strategy and Innovation Support Policies

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IEEE engineering Management Review 2023年第2期51卷 63-74页

作者： Gupta, Varun Gupta, Chetna Rubalcaba, Luis Nguyen-Duc, Anh Wang, Xiaofeng Butlewski, Marcin University of Alcala Department of Economics and Business Administration Madrid28802 Spain Potsdam14469 Germany University of South-Eastern Norway School of Business Department of Business and It Notodden3679 Norway Free University of Bozen-Bolzano Faculty of Computer Science Bolzano39100 Italy Poznań University of Technology Faculty of Engineering Management Poznań60-965 Poland Jaypee Institute of Information Technology Department of Computer Science and Engineering Noida201305 India

For American businesses trying to expand internationally, the embassies serve as a crucial component of the innovation ecosystem. By utilizing their close professional relationships with stakeholders like governments, market experts, local consumers, and local public institutions, they boost access to information about international markets. Since foreign markets differ from domestic markets, expanding internationally necessitates adaptations and/or innovations to current business models. An accurate international market study should be the foundation for successful adaptations or innovations. The cost of the assistance provided by embassies appears to be minimal to the firms. From the perspective of small and medium-sized enterprises (SMEs), support policy innovations appear to be required. SMEs do not appear to be in a position to decide which embassy resources they need and take the best advantage to internationalize, given their liability of newness and smallness. This makes it clear that support policies should provide these SMEs with more holistic, organized, and thorough help that is customized to their specific needs, business size, product category, and industry of operation. Additionally, SMEs must align their innovation strategies with the innovation support policies of a nation, which are pushed out through by different institutions, for instance, embassies abroad. As they must take part in SMEs' innovation management processes in global marketplaces, embassies play a role more akin to boundary spanners or coinnovators. Strategic alliances with university libraries may also aid embassies in improving their commercial diplomacy because they can cocreate with SMEs to innovate their services and SMEs benefit from improved globalization assistance. © 1973-2011 IEEE.

关键词： International trade

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