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International Conference on Green engineering Technology and Applied Computing 2019, IConGETech2 019 and International Conference on Applied Computing 2019, ICAC 2019

作者： Adam Ibrahim Fakherldin, Mohammed Adam, Khalid Akma Abu Bakar, Noor Abdul Majid, Mazlina Faculty of Computer System and Software Engineering Universiti Malaysia Pahang Malaysia Faculty of Electric and Electronic Engineering Universiti Malaysia Pahang Malaysia Faculty of Computer Science and Information Systems Jazan University Malaysia

Weather data is very crucial in every aspect of human daily life. It plays an important role in many sectors such as agriculture, tourism, government planning, industry and so on. Weather has a variety of parameters like temperature, pressure, humidity and wind speed. The meteorological department deployed sensors for each weather parameter at different geographical locations to collect data. This data is stored mostly in the unstructured format. Thus, a big amount of data has been collected and archived. Therefore, storage and processing of this big data for accurate weather prediction is a huge challenge. Hadoop an apache product it used to support big data sets in a distributed environment. Hadoop has greatest advantages over scalable and fault-tolerant distributed processing technologies. This paper explains a system that uses the historical weather data of a region and apply the MapReduce and Hadoop techniques to analysis these historical data. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Digital storage

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Preface

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical engineering 2025年 1304卷 v页

作者： Prajapati, Arvind Kumar Tripathy, Manoj Singh, Asheesh K. Sood, Vijay K. Malik, Om P. Department of Electrical Engineering Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj India Department of Electrical Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee India Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University OshawaON Canada Department of Electrical and Computer Engineering University of Calgary CalgaryAB Canada

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A Systematic Literature Review of Studies Related to Mental Activities of software Developers

A Systematic Literature Review of Studies Related to Mental ...

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Nonlinearity, Information and Robotics, (NIR) International Conference

作者： Ilyas Sirazitdinov Giancarlo Succi Vladimir Ivanov Philips Research Philips Moscow Russia Faculty of Computer Science and Software Engineering Innopolis University Innopolis Russia

ISBN: (数字)9781728187631

ISBN: (纸本)9781728187648

In this paper we have performed a systematic review of studies related to the mental states of people engaged in software development, particularly programmers. This review underscores consensus relating to some more thoroughly studied areas, such as the relationship between emotional state and productivity. It also highlights emerging trends such as the use of biosensors, and many areas of limited understanding that might be fruitful topics for the application of new emerging techniques.

关键词： Systematics Bibliographies Market research software Electroencephalography Biosensors software engineering

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DRCNN-IDS Approach for Intelligent Intrusion Detection System

DRCNN-IDS Approach for Intelligent Intrusion Detection Syste...

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2020 International Conference on Computing and Information Technology, ICCIT 2020

作者： Manikandan, V. Gowsic, K. Prince, T. Umamaheswari, R. Ibrahim, Banar Fareed Sampathkumar, A. Lebanese French University Department of Computer Networking Erbil Kurdistan Region Iraq KSR Institute for Engineering and Technology Department of Information Technology Namakkal India Arba Minch University Arba Minch Institute of Technology \Faculty of Computing and Software Engineering Ethiopia Gnanamani College of Technology Department of CSE Namakkal India Cyprus International University Computer Engineering Department Cyprus VIT Bhopal University Department of Computer Science Engineering Bhopal India

ISBN: (纸本)9781728126807

In recent times, deep learning technique turns to be an interesting topic, which is extensively utilized to construct an Intrusion Detection System (IDS) for identifying and classifying web attacks at network level in a periodic manner. Moreover, numerous challenges as malicious attack change its characteristics constantly. There are diverse malware datasets that are available publicly. In this investigation, Convolutional Neural Network (CNN), which a kind of Deep learning is explored along with dimensionality reduction for Intrusion Detection (DRCNN-IDS). This approach is used to identify and classify unpredictable attacks in networks. Due to the constantly changing nature of attacks, evaluation of benchmark dataset is also essential. A conceptual evaluation of this investigation with DRCNN-IDS is compared with Machine learning approaches. The irrelevant features of attacks are eliminated for reducing the network dimensionality for the computation of accuracy. The reduced features are extracted automatically by the anticipated CNN model, which is a preliminary advantage of using deep learning concepts. This work utilizes KDD cup 99' dataset for accuracy computation. Simulation was carried out in MATLAB environment and the performance is compared with prevailing Machine Learning approaches. The anticipated model shows enhanced performance in terms of accuracy. Comparison is made with DNN, RNN, RF, SVM, Naive Bayes and so on. Accuracy attained with simulation is 96%. © 2020 IEEE.

关键词： Intrusion detection

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Quantum-inspired algorithm for general minimum conical hull problems

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Physical Review Research 2020年第3期2卷 033199-033199页

作者： Yuxuan Du Min-Hsiu Hsieh Tongliang Liu Dacheng Tao UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering The University of Sydney Australia Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Australia

A wide range of fundamental machine learning tasks that are addressed by the maximum a posteriori estimation can be reduced to a general minimum conical hull problem. The best-known solution to tackle general minimum conical hull problems is the divide-and-conquer anchoring learning scheme (DCA), whose runtime complexity is polynomial in size. However, big data are pushing these polynomial algorithms to their performance limits. In this paper, we propose a sublinear classical algorithm to tackle general minimum conical hull problems when the input has stored in a sample-based low-overhead data structure. The algorithm's runtime complexity is polynomial in the rank and polylogarithmic in size. The proposed algorithm achieves the exponential speedup over DCA and, therefore, provides advantages for high-dimensional problems.

关键词： Machine learning

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1st International Workshop on Essence in Education Training (WEET 2020) 32

1st International Workshop on Essence in Education Training ...

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32nd IEEE Conference on software engineering Education and Training, CSEE and T 2020

作者： Castro, Luis Fernando Cooper, Kendra M. L. Duc, Anh Nguyen Pieper, Joran Striewe, Michael Faculty of Engineering University of Quindío Quindío Colombia Independent Scholar Kelowna Canada USN School of Business Department of Business and It University of South Eastern Norway Norway Faculty of Electrical Engineering and Computer Science Iacs University of Applied Sciences Stralsund Germany Paluno-The Ruhr Institute for Software Technology University of Duisburg-Essen Essen Germany

ISBN: (纸本)9781728168074

The workshop connects to the central theme 'Educating for the Future' of CSEET 2020 and thus explores opportunities to improve software engineering education and training by using Essence. Essence, an OMG Standard, delivers essential, universal elements found in all software engineering (SE) endeavours and a language to describe and extend these elements and their use in concrete practices to tailor SE methods to teams' needs. Its Kernel separates the stable What and Why from the more adaptable How. That way, it provides an essential thinking framework facilitating the adoption and customization of practices and methods. As such, it supports multiple ways of educating SE students as well as training practitioners for the future. The first deployments of Essence in industry and academic courses around the globe show promising results. But since it is an entirely new way to think about SE practices, methods, and their building blocks-it requires some effort and thought to dive into the standard. This workshop facilitates the adoption by sharing experiences, best practices, and lessons learned. © 2020 IEEE.

关键词： software engineering

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EDSDF: An Efficient Deep Supervised Distillation Framework for medical image segmentation

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Neurocomputing 2025年 648卷

作者： Xuefang Chen Zhining Li Suigu Tang Jiayao Li Huazhu Liu Jian Nong School of Computer Science and Technology Dongguan University of Technology Dongguan 523808 China International School of Microelectronics Dongguan University of Technology Dongguan 523808 China Guangxi Colleges and Universities Key Laboratory of Intelligent Software Wuzhou University Wuzhou 543003 China Faculty of Innovation Engineering-School of Computer Science and Engineering Macau University of Science and Technology 999078 Macao Special Administrative Region of China

Significant progress has been made in medical image segmentation, but it still faces persistent challenges: inefficient multi-scale feature fusion that neglects fine-grained local details; attention to irrelevant regions causing computational waste; and boundary ambiguity exacerbated by upsampling artifacts. We, therefore, propose the EDSDF to address these issues through three key innovations: (1) Multi-Scale Grouped Attention Gate (MSGAG), which leverages multi-scale gating mechanisms to suppress redundant features and enhance critical feature representation; A hierarchical feature fusion architecture comprising: Efficient Multi-Scale Fusion Block (EMSFB), lightweight multi-scale fusion using depthwise separable convolutions; Efficient Frequency Boundary Extraction Block (EFBEB), which utilizes frequency domain information to improve boundary prediction. (2) Knowledge-Aligned Multi-Scale Distillation (KAMSD): Multi-stage alignment of encoder–decoder features between teacher-student models. (3) Universal lightweight architecture: Compatible with both CNN and Transformer encoders achieving 88% parameter reduction(compared to MADGNet). When tested on four publicly available medical image segmentation datasets, the proposed EDSDF achieves state-of-the-art performance, with a mean DSC of 84.65%. Furthermore, ablation studies confirm the contributions of each component, with KAMSD playing a pivotal role in enhancing segmentation performance for the lightweight student model. Consequently, the compatibility of the proposed framework with diverse encoders enables deployment across clinical scenarios without sacrificing accuracy. Our EDSDF code is available in https://***/lzn-ai/EDSDF .

关键词： Frequency domain information Knowledge distillation Medical image segmentation Multi-scale features

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PepHarmony: A Multi-View Contrastive Learning Framework for Integrated Sequence and Structure-Based Peptide Encoding

arXiv

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arXiv 2024年

作者： Zhang, Ruochi Wu, Haoran Liu, Chang Li, Huaping Wu, Yuqian Li, Kewei Wang, Yifan Deng, Yifan Chen, Jiahui Zhou, Fengfeng Gao, Xin Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin Changchun130012 China School of Artificial Intelligence Jilin University Jilin Changchun130012 China Syneron Technology Guangzhou510700 China Beijing Life Science Academy Beijing102209 China School of Biomedical Sciences LKS Faculty of Medicine The University of Hong Kong Hong Kong Thuwal23955 Saudi Arabia Thuwal23955 Saudi Arabia College of Software Jilin University Jilin Changchun130012 China College of Computer Science and Technology Jilin University Jilin Changchun130012 China

Recent advances in protein language models have catalyzed significant progress in peptide sequence representation. Despite extensive exploration in this field, pre-trained models tailored for peptide-specific needs remain largely unaddressed due to the difficulty in capturing the complex and sometimes unstable structures of peptides. This study introduces a novel multi-view contrastive learning framework PepHarmony for the sequence-based peptide encoding task. PepHarmony innovatively combines both sequence- and structure-level information into a sequence-level encoding module through contrastive learning. We carefully select datasets from the Protein Data Bank (PDB) and AlphaFold database to encompass a broad spectrum of peptide sequences and structures. The experimental data highlights PepHarmony's exceptional capability in capturing the intricate relationship between peptide sequences and structures compared with the baseline and fine-tuned models. The robustness of our model is confirmed through extensive ablation studies, which emphasize the crucial roles of contrastive loss and strategic data sorting in enhancing predictive performance. The proposed PepHarmony framework serves as a notable contribution to peptide representations, and offers valuable insights for future applications in peptide drug discovery and peptide engineering. We have made all the source code utilized in this study publicly accessible via GitHub at https://***/zhangruochi/PepHarmony or http://***/supp/. © 2024, CC BY-NC-ND.

关键词： Peptides

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T

arXiv

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arXiv 2020年

作者： Aloufi, Khalid S. Alsewari, Abdulrahman A. Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Mala sia26300 Department of Computer Engineering College of Computer Science and Engineering Taibah University Medina Saudi Arabia

This research presents a methodology for trusting the provenance of data on the web. The implication is that data does not change after publication and the source of the data is stable. There are different data that should not change over time, such as published information in books and similar documents as well as news or events reported on the web. If the data change after publication on the web, the web pages that reference the unstable data will lose points of interest or link to different resources. With the current move to linked data and the semantic web, this is becoming a greater obstacle to be solved. This research presents a methodology for establishing trusted information using an encoded reference of the data embedded in its URI, which creates a stable reference of the data and a method for ensuring its provenance stability. After applying the methodology, the results showed that the methodology is highly applicable and has no overhead cost over the loading time. The novel solution can be applied directly to any data portals or web content management systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Websites

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A Self-adaptive Mechanism Embedded Gravitational Search Algorithm 12

A Self-adaptive Mechanism Embedded Gravitational Search Algo...

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12th International Symposium on Computational Intelligence and Design, ISCID 2019

作者： Song, Zhenyu Tang, Cheng Chen, Xingqian Song, Shuangyu Ji, Junkai Taizhou University College of Computer Science and Technology Taizhou China University of Toyama Faculty of Engineering Toyama Japan Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9781728146522

Gravitational search algorithm (GSA) has attracted more and more attention in dealing with complex optimization problems. However, it still suffers from some major drawbacks, such as poor exploitation. This paper has proposed a novel self-adaptive chaotic search mechanism embedded GSA (SA-GSA), in which different chaotic maps are utilized to guide the local search in a self-adaptive way. Specifically, instead of defining the search range randomly and arbitrarily, the distance between distinct individuals has been utilized in the current population as the search range. Thus the search range will decrease synchronously according to the convergence speed of the population, which is thought to improve the exploitation ability of GSA effectively. To evaluate the performance of SA-GSA, we compare it with classic GSA and the classic chaotic GSA (CGSA) on 23 benchmark optimization problems. The experimental results and statistic analysis verify that SA-GSA is superior to its competitors in terms of convergence speed and solution accuracy rate. © 2019 IEEE.

关键词： Benchmarking

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