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A Deep Learning Approach for Prediction of Protein Secondary Structure

computers, Materials & Continua 2022年第8期72卷 3705-3718页

作者： Muhammad Zubair Muhammad Kashif Hanif Eatedal Alabdulkreem Yazeed Ghadi Muhammad Irfan Khan Muhammad Umer Sarwar Ayesha Hanif Department of Computer Science Government College UniversityFaisalabadPakistan Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Software Engineering/Computer Science Al Ain UniversityAbu DhabiUnited Arab Emirates

The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary *** this reason,it is important to design methods for accurate protein secondary structure *** of the existing computational techniques for protein structural and functional prediction are based onmachine learning with *** deep learning architectures have already been applied to tackle protein secondary structure prediction *** this study,deep learning based models,i.e.,convolutional neural network and long short-term memory for protein secondary structure prediction were *** input to proposed models is amino acid sequences which were derived from CulledPDB *** tuning with cross validation was employed to attain best parameters for the proposed *** proposed models enables effective processing of amino acids and attain approximately 87.05%and 87.47%Q3 accuracy of protein secondary structure prediction for convolutional neural network and long short-term memory models,respectively.

关键词： Convolutional neural network machine learning protein secondary structure deep learning long short-term memory protein secondary structure prediction

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Development of Area-Based Explosive Trace Detection Using Deep Transfer Learning 1

Development of Area-Based Explosive Trace Detection Using De...

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Ohemu, Monday F. Azeta, Ambrose A. Adeyanju, Ibrahim A. Obasi, Chukwuemeka C. Airforce Institute of Technology Department of Electrical/Electronics Engineering Kaduna Nigeria Namibia University of Science and Technology Department of Software Engineering Namibia Federal University Department of Computer Engineering Oye-Ekiti Nigeria Edo State University Department of Computer Engineering Uzairue Nigeria

ISBN: (纸本)9798350348637

Terrorism poses a significant global threat, necessitating comprehensive security measures to protect people and critical infrastructure. With the increasing frequency of explosive-based attacks, there is a pressing need for advanced detection systems to mitigate potential devastation. This paper presents the development of an area-based explosive trace detection system using deep transfer learning. Leveraging deep learning technology trained on a substantial dataset collected from sensor networks, the proposed model, named Deep Transfer Learning for Explosive Trace Detection (DTLETD), demonstrates remarkable capabilities in classifying explosive gases based on concentrations of Carbon (C), Hydrogen (H), Oxygen (O), and Nitrogen (N). The methodology involves converting the dataset into 2D data using a serial data to image generator, followed by the adaptation of a pre-trained model for transfer learning. The DTLETD model outperformed conventional Convolutional Neural Network (CNN) models, achieving a training time reduction of about 92 seconds compared to 1287 seconds for the CNN model. Additionally, the transfer learning model exhibits faster convergence with nearly zero losses during training and validation, yielding an impressive accuracy of 99.7% and an average AUC value of approximately 0.89. This research significantly advances the field of explosive trace detection by integrating machine learning-based approaches with deep transfer learning techniques, thereby enhancing security protocols and mitigating potential threats. © 2024 IEEE.

关键词： Convolutional neural networks

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Question-Aware Deep Learning Model for Arabic Machine Reading Comprehension 3rd

Question-Aware Deep Learning Model for Arabic Machine Readi...

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3rd International Conference on Intelligent Systems and Pattern Recognition, ISPR 2023

作者： Al-Harbi, Marwa Obeidat, Rasha Al-Ayyoub, Mahmoud Alawneh, Luay Department of Computer Science Jordan University of Science Irbid Jordan Department of Information Technology Ajman University Ajman United Arab Emirates Department of Software Engineering Jordan University of Science Irbid Jordan

ISBN: (纸本)9783031463372

Machine reading comprehension is one of the most long-standing challenges in artificial intelligence. It is a subtask question-answering system that aims to find a text span in a reading context that answers a question. It is the core of several applications, such as customer service systems and chatbots. However, However, few studies have targeted machine reading comprehension of Arabic text. Besides, The existing models generate a context representation independently from the question. As an analogy to some people’s comprehension styles who believe that by reading the question, they gain a better understanding of the reading passage, we present a question-aware neural machine reading comprehension model for Arabic. Our model extracts a representation of the context by incorporating the question using several bidirectional attention units to achieve various levels of question-centered context understanding. Our experimental evaluation shows that our model outperforms strong machine reading comprehension baselines, including DrQA and QaNET models applied to Arabic, by a significant margin with an F1 score of 59.6%. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Community Smell Occurrence Prediction on Multi-Granularity by Developer-Oriented Features and Process Metrics

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Journal of computer science & Technology 2022年第1期37卷 182-206页

作者： Zi-Jie Huang Zhi-Qing Shao Gui-Sheng Fan Hui-Qun Yu Xing-Guang Yang Kang Yang Department of Computer Science and Engineering East China University of Science and TechnologyShanghai 200237China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai 200237China Shanghai Engineering Research Center of Smart Energy Shanghai 200237China

Community smells are sub-optimal developer community structures that hinder *** studies performed smell prediction and provided refactoring guidelines from a top-down aspect to help community ***,refactoring smells also requires bottom-up effort from every ***,supportive measures and guidelines for them are not available at a fine-grained *** recent work revealed developers'personalities and working states could influence community smells'emergence and variation,we build prediction models with experience,sentiment,and development process features of developers considering three smells including Organizational Silo,Lone Wolf,and Bottleneck,as well as two related classes including smelly developer and smelly *** predict the five classes in the individual granularity,and we also generate forecasts for the number of smelly developers in the community *** proposed models achieve F-measures ranging from 0.73 to 0.92 in individual-wide within-project,time-wise,and cross-project prediction,and mean R2 performance of 0.68 in community-wide Smelly Developer *** also exploit SHAP(SHapley Additive exPlanations)to assess feature importance to explain our *** conclusion,we suggest developers with heavy workload should foster more frequent communication in a straightforward and polite way to build healthier communities,and we recommend community shepherds to use the forecasting model for refactoring planning.

关键词： community smell developer sentiment socio-technical analysis empirical software engineering

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Service Level Agreement Monitoring as a Service: An Independent Monitoring Service for Service Level Agreements in Clouds

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Big Data 2023年第5期11卷 339-354页

作者： Badshah, Afzal Jalal, Ateeqa Farooq, Umar Rehman, Ghani-Ur Band, Shahab S. Iwendi, Celestine Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan Department of Computer Science University of Science and Technology Bannu Pakistan Department of Computer Science and Bioinformatics Khushal Khan Khattak University Karak Pakistan Future Technology Research Center College of Future National Yunlin University of Science and Technology Douliou Taiwan School of Creative Technologies University of Bolton Bolton United Kingdom

The cloud network is rapidly growing due to a massive increase in interconnected devices and the emergence of different technologies such as the Internet of things, fog computing, and artificial intelligence. In response, cloud computing needs reliable dealings among the service providers, brokers, and consumers. The existing cloud monitoring frameworks such as Amazon Cloud Watch, Paraleap Azure Watch, and Rack Space Cloud Kick work under the control of service providers. They work fine;however, this may create dissatisfaction among customers over Service Level Agreement (SLA) violations. Customers’ dissatisfaction may drastically reduce the businesses of service providers. To cope with the earlier mentioned issue and get in line with cloud philosophy, Monitoring as a Service (MaaS), completely independent in nature, is needed for observing and regulating the cloud businesses. However, the existing MaaS frameworks do not address the comprehensive SLA for customer satisfaction and penalties management. This article proposes a reliable framework for monitoring the provider’s services by adopting third-party monitoring services with clearcut SLA and penalties management. Since this framework monitors SLA as a cloud monitoring service, it is named as SLA-MaaS. On violations, it penalizes those who are found in breach of terms and condition enlisted in SLA. Simulation results confirmed that the proposed framework adequately satisfies the customers (as well as service providers). This helps in developing a trustworthy relationship among cloud partners and increases customer attention and retention. © Mary Ann Liebert, Inc.

关键词： Sales

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Deep domain adaptation by joint distribution neural matching

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Neural Computing and Applications 2025年 1-21页

作者： Hong, Zijie Chen, Sentao Wen, Lisheng Yang, Xiaowei School of Software Engineering South China University of Technology Panyu District Guangdong Guangzhou510006 China Department of Computer Science Shantou University Daxue Road Guangdong Shantou515063 China

Domain adaptation aims to learn a prediction model that performs well in a target domain with unlabeled data by harnessing the distributionally different labeled data from a source domain. In this paper, we design a simple yet powerful neural network approach named joint distribution neural matching (JOINT) to address the challenge of joint distribution difference/mismatch in domain adaptation. Our JOINT approach matches the source joint distribution and the target one in the activation space of a neural network by minimizing an estimate of the Jensen–Shannon (JS) divergence between the two joint distributions. The estimated JS divergence is derived in an explicit form, which frees our approach from solving a minimax adversarial problem when matching the distributions. Experiments on four representative large-scale visual recognition datasets show that our JOINT approach outperforms the state-of-the-art under both unsupervised and semi-supervised domain adaptation settings. Our code will be available at https://***/TitanRisen/JOINT-CODE. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep neural networks

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CylE: Cylinder Embeddings for Multi-hop Reasoning over Knowledge Graphs 17

CylE: Cylinder Embeddings for Multi-hop Reasoning over Knowl...

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17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023

作者： Nguyen, Chau Duc Minh French, Tim Liu, Wei Stewart, Michael ARC Centre for Transforming Maintenance through Data Science Department of Computer Science and Software Engineering The University of Western Australia

ISBN: (纸本)9781959429449

Recent geometric-based approaches have been shown to efficiently model complex logical queries (including the intersection operation) over Knowledge Graphs based on the natural representation of Venn diagram. Existing geometric-based models (using points, boxes embeddings), however, cannot handle the logical negation operation. Further, those using cones embeddings are limited to representing queries by two-dimensional shapes, which reduced their effectiveness in capturing entities query relations for correct answers. To overcome this challenge, we propose unbounded cylinder embeddings (namely CylE), which is a novel geometric-based model based on three-dimensional shapes. Our approach can handle a complete set of basic first-order logic operations (conjunctions, disjunctions and negations). CylE considers queries as Cartesian products of unbounded sector-cylinders and consider a set of nearest boxes corresponds to the set of answer entities. Precisely, the conjunctions can be represented via the intersections of unbounded sector-cylinders. Transforming queries to Disjunctive Normal Form can handle queries with disjunctions. The negations can be represented by considering the closure of complement for an arbitrary unbounded sector-cylinder. Empirical results show that the performance of multi-hop reasoning task using CylE significantly increases over state-of-the-art geometric-based query embedding models for queries without negation. For queries with negation operations, though the performance is on a par with the best performing geometric-based model, CylE significantly outperforms a recent distribution-based model. © 2023 Association for Computational Linguistics.

关键词： Knowledge graph

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A Hybrid Search Method for Accelerating Convolutional Neural Architecture Search 23

A Hybrid Search Method for Accelerating Convolutional Neural...

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15th International Conference on Machine Learning and Computing, ICMLC 2023

作者： Xun, Zhou Songbai, Liu Ka-Chun, Wong Qiuzhen, Lin Kaychen, Tan The Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University KA-CHun Wong China College of Computer Science and Software Engineering Shenzhen University China The Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781450398411

The performance evaluation of candidate architectures is a key step in evolution-based neural architecture search (ENAS). Generally, high-fidelity evaluation is desired for finding the optimal architecture but suffers from expensive computational cost while low-fidelity evaluation can reduce search cost but may influence the search performance. In this work, a hybrid search method, HybridNAS, is proposed by synergizing these two evaluation strategies to improve the search effectiveness of ENAS. This method consists of two stages: global exploration and local exploitation. The former applies low-fidelity evaluation to explore the promising architectures efficiently. As for the latter, it exploits the most promising ones identified by the former to generate the better-performancing architectures. Those two stages are iteratively repeated toward the optimal architecture. Extensive experiments on NAS-Bench-101 and NAS-Bench-201 reflect that HybridNAS can achieve comparable accuracy compared to existing popular NAS but at the expense of only 60%-80% computational cost. © 2023 ACM.

关键词： Iterative methods

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TOST: School Bus Monitoring and Management System for Schools and Parents

TOST: School Bus Monitoring and Management System for School...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Thakur, Fahad Aqeel Hafiz, Muhammad Alani, Maath Saadoon, Muntadher Kian Chiew, Chiew Thiam University of Malaya Faculty of Computer Science & Information Technology Kuala Lumpur Malaysia Hillool Software Engineering Department Queensland Australia Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia

ISBN: (纸本)9798350302684

School bus contributes positively to reducing the number of cars on the road, thereby reducing the environmental impact of cars. It also dramatically relieves working parents, who do not have to pick up and drop off their children separately in their private vehicles. However, the irregularity of the bus arrival times often leads to an extended waiting time for students at the bus stops, potentially compromising the students' safety. The communication barrier between schools, parents and bus drivers is also a significant challenge, mainly when a delay occurs during the transit of the students. This paper presents TOST (to-school-together), a school bus monitoring and management system for schools and parents. The school administrators, parents, and bus drivers are connected in one comprehensive school bus system. The proposed system provides realtime updates on the bus status, allowing peace of mind and easing the burden of parents, school administrators, and bus drivers. The design of TOST is based on feedback from the system stakeholders collected via a survey. We draw a complete understanding of the existing issues by analyzing the feedback. Finally, we have implemented the system using state-of-the-art technologies to address the identified issues and fulfil the system requirements. © 2023 IEEE.

关键词： Buses

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