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13th International Symposium on Computational Intelligence and Design, ISCID 2020

作者： Jin, Chunxia Bai, Qiuchan Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China Faculty of Automation Huaiyin Institute of Technology Huaian China

ISBN: (纸本)9781728184463

According to the needs of the aquaculture industry, this article proposes to design an aquaculture environment monitoring system for collecting temperature, PH, and dissolved oxygen parameters that affect fishery water quality environment, real-time tracking and monitoring, remote transmission and abnormal handling, etc. The system has played an important role in the application of fish breeding industry, providing technical support for the development of the breeding industry, and bringing greater economic profits to farmers. ©2020 IEEE.

关键词： Aquaculture

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Modelling IoT Application Requirements for Benchmarking IoT Middleware Platforms 2021

Modelling IoT Application Requirements for Benchmarking IoT ...

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23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021

作者： Mondal, Shalmoly Hassani, Alireza Jayaraman, Prem Prakash Haghighi, Pari Delir Georgakopoulos, Dimitrios Faculty of Science Engineering and Technology Swinburne University of Technology Australia School of Info Technology Deakin University Australia Department of Computer Science and Software Engineering Swinburne University of Technology Australia Department of Human Centred Computing Monash University Australia

ISBN: (纸本)9781450395564

The significant advances in the Internet of Things (IoT) have led to IoT applications being widely used in various scenarios ranging from smart city, smart farming, to Industrial IoT (IIoT) solutions. With the explosion of IoT application development, IoT middleware platforms are increasingly being used for hosting such IoT applications. This has given rise to the need for developing benchmarking solutions to analyze and test the performance of different middleware platforms that host these IoT applications. To develop such benchmarks, there are a number of key components that are needed. One of these components is an IoT dataset. To generate such datasets, representing IoT application requirements in a general and formal way is important. In this paper, we propose a framework to model the IoT Applications Requirements and enable Data Generation(ARDG-IoT). The framework supports a formal way to capture IoT application requirements and use these requirements to generate IoT data that can be used to create benchmarks for different IoT middleware platforms. ARDG-IoT consists of our proposed model, IoTSySML, which captures the application requirements, and an IoT data simulator tool, which is used to generate IoT data. We present an evaluation of the framework using a real world Industrial IoT application case study. © 2021 ACM.

关键词： Benchmarking

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Mining Student Information System Records to Predict Students’ Academic Performance 4th

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4th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2019

作者： Saa, Amjad Abu Al-Emran, Mostafa Shaalan, Khaled Faculty of Engineering and IT The British University in Dubai Dubai United Arab Emirates Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Gambang Malaysia

ISBN: (纸本)9783030141172

Educational Data Mining (EDM) is an emerging field that is concerned with mining and exploring the useful patterns in educational data. The main objective of this study is to predict the students’ academic performance based on a new dataset extracted from a student information system. The dataset was extracted from a private university in the United Arab of Emirates (UAE). The dataset includes 34 attributes and 56,000 records related to students’ information. The empirical results indicated that the Random Forest (RF) algorithm was the most appropriate data mining technique used to predict the students’ academic performance. It is also revealed that the most important attributes that have a direct effect on the students’ academic performance are belonged to four main categories, namely students’ demographics, student previous performance information, course and instructor information, and student general information. The evidence from this study would assist the higher educational institutions by allowing the instructors and students to identify the weaknesses and factors affecting the students’ performance, and act as an early warning system for predicting the students’ failures and low academic performance. © 2020, Springer Nature Switzerland AG.

关键词： Students

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Metaheuristic Based IDS Using Multi-objective Wrapper Feature Selection and Neural Network Classification 2nd

Metaheuristic Based IDS Using Multi-objective Wrapper Featur...

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2nd International Conference on Advances in Cyber Security, ACeS 2020

作者： Ghanem, Waheed Ali H. M. El-Ebiary, Yousef A. Baker Abdulnab, Mohamed Tubishat, Mohammad Alduais, Nayef A. M. Nasser, Abdullah B. Abdullah, Nibras Al-wesabi, Ola A. Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Terengganu Malaysia Faculty of Engineering and Faculty of Education-Aden and Saber Aden University Aden Yemen Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Kuala Terengganu Malaysia School of Computing and Technology Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Pekan Malaysia National Advanced IPv6 Center Universiti Sains Malaysia Gelugor Malaysia Faculty of Computer Science and Engineering Hodeidah University Al Hudaydah Yemen

ISBN: (纸本)9789813368347

Due to the significant ongoing expansion of computer networks in our lives nowadays, the demand for network security and protection from cyber-attacks has never been more imperative to either clients or businesses alike, which signifies the key role of cyber intrusion detection systems in network security. This article proposes a cyber-intrusion detecting system classification with MLP trained by a hybrid metaheuristic algorithm and feature selection based on multi-objective wrapper method. The classifier, named as HADMLP is trained using a hybridization of the artificial bee colony along with the dragonfly algorithm. A multi-objective artificial bee colony model which is wrapper-based is used for selection of feature. Hence, collective name of the proposed technique referred as MO-HADMLP. For performance evaluation, the proposed method was assessed using ISCX 2012 and KDD CUP 99 datasets. The results of our experiments indicate a significant enhancement to the efficacy of network intrusion detection when compared to other approaches. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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MSKI-Net: Towards modality-specific knowledge interaction for glioma survival prediction

MSKI-Net: Towards modality-specific knowledge interaction fo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ran Su Jin Xiao Hui Cui Ping Xuan Xikang Feng Leyi Wei Qiangguo Jin School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China Department of Computer Science and Information Technology La Trobe University Melbourne Australia Department of Computer Science School of Engineering Shantou University China School of Software Northwestern Polytechnical University China AIDD Faculty of Applied Science Macao Polytechnic University Macao SAR China School of Information Xiamen University Xiamen China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Gliomas hold a prominent position in neurooncology due to their high malignancy and poor survival rates. Accurately predicting the prognosis and survival risk of glioma patients is crucial for clinical treatment. Recent advances in survival prediction methods emphasize the importance of integrating complementary information from diverse modalities while neglecting the significant modality gap between pathological images and genomic data. To address this issue, we propose a modality-specific knowledge interaction network (MSKI-Net), which integrates whole slide images (WSI), RNA-Seq gene expression data, and copy number variation (CNV) data for glioma survival analysis. The MSKI-Net consists of a modality-specific feature enhancement (MSFE) module, a modality-interactive cross-attention (MICA) module, and a modality-specific knowledge-guided representation learning (MSKR) module. The three modules collaborate by complementing modality-specific features with modality-agnostic knowledge to improve the learning capability of MSKI-Net. Furthermore, we construct a dataset named TCGA mm , which combines WSI, RNA-Seq, and CNV data from The Cancer Genome Atlas (TCGA) to address the issue of data scarcity. Extensive experiments demonstrate that MSKI-Net achieves superior performance in predicting the survival risk of glioma cancer.

关键词： Knowledge engineering Representation learning Pathology Genomics Prediction methods Data models Gene expression Bioinformatics Prognostics and health management Cancer

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Containerization in Multi-Cloud Environment: Roles, Strategies, Challenges, and Solutions for Effective Implementation

arXiv

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arXiv 2024年

作者： Waseem, Muhammad Ahmad, Aakash Liang, Peng Akbar, Muhammad Azeem Khan, Arif Ali Ahmad, Iftikhar Setälä, Manu Mikkonen, Tommi Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland School of Computing and Communications Lancaster University Leipzig Leipzig Germany School of Computer Science Wuhan University Wuhan China Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland TietoEVRY Oy Tampere Finland Solita Oy Tampere Finland Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

Containerization in a multi-cloud environment facilitates workload portability and optimized resource utilization. Containerization in multi-cloud environments has received significant attention in recent years both from academic research and industrial development perspectives. However, there exists no effort to systematically investigate the state of research on this topic. The aim of this research is to systematically identify and categorize the multiple aspects of containerization in multi-cloud environment. We conducted the Systematic Mapping Study (SMS) on the literature published between January 2013 and July 2024. One hundred twenty one studies were selected and the key results are: (1) Four leading themes on containerization in multi-cloud environment are identified: ‘Scalability and High Availability’, ‘Performance and Optimization’, ‘Security and Privacy’, and ‘Multi-Cloud Container Monitoring and Adaptation’. (2) Ninety-eight patterns and strategies for containerization in multi-cloud environment were classified across 10 subcategories and 4 categories. (3) Ten quality attributes considered were identified with 47 associated tactics. (4) Four catalogs consisting of challenges and solutions related to security, automation, deployment, and monitoring were introduced. The results of this SMS will assist researchers and practitioners in pursuing further studies on containerization in multi-cloud environment and developing specialized solutions for containerization applications in multi-cloud environment. © 2024, CC BY.

关键词： Cloud platforms

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VR REAL RUN: An immersive Oculus Quest 2-Based Virtual Reality Exergaming

VR REAL RUN: An immersive Oculus Quest 2-Based Virtual Reali...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Joe Y. Mambu Rismayani Jay Idoan Sihotang Vivi Peggy Rantung Computer Science Faculty Universitas Klabat Airmadidi Indonesia Dept. of Software Engineering Dipa Makassar University Makassar Indonesia Faculty of Information Technology Universitas Advent Indonesia Bandung Indonesia

ISBN: (纸本)9781665453967

There are many different types of exergaming, but one of the most engaging one is the one with virtual reality. This type of gaming allows you to immerse yourself in a completely different world, and it can be a lot of fun. Due to Covid-19 pandemic many people resort to their usual exercise activities and opt to do it at home yet only to find a monotonous and boring fitness routine. VR Real Run is aimed to give a solution on how to have fun and to get fit at the same time. By using the latest and front runner in the VR headsets products, the Oculus Quest 2, we developed an exergame that require user to run in place and jump to play. It also features high score to motivate users in perform better in the next session. A black box test has been well performed and a usability test was came out with a 79 SUS score which translate the application as “good” therefore its usability is acceptable.

关键词： Headphones COVID-19 Pandemics Optimized production technology Virtual reality Games Usability

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Metric Spaces in Which Many Triangles Are Degenerate

arXiv

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arXiv 2024年

作者： Chvátal, Vašek de Rancourt, Noé Quintero, Guillermo Gamboa Kantor, Ida Szabó, Péter G.N. Department of Computer Science and Software Engineering Concordia University Montreal Canada Department of Applied Mathematics Charles University Prague Czech Republic Univ. Lille CNRS UMR 8524 Laboratoire Paul Painlevé LilleF-59000 France Computer Science Institute of Charles University Prague Czech Republic Department of Engineering Mathematics Faculty of Engineering and Information Technology University of Pécs Pécs Hungary Department of Computer Science and Information Theory Budapest University of Technology and Economics Hungary

Richmond and Richmond (American Mathematical Monthly 104 (1997), 713–719) proved the following theorem: If, in a metric space with at least five points, all triangles are degenerate, then the space is isometric to a subset of the real line. We prove that the hypothesis is unnecessarily strong: In a metric space on n points, fewer than 7n2/6 suitably placed degenerate triangles suffice. However, fewer than n(n − 1)/2 degenerate triangles, no matter how cleverly placed, never *** Codes 30L99, 51F99, 54E99 © 2024, CC BY.

关键词： Topology

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A Robust Methodology for Fruit Quality Prediction and Estimation Using Intelligent Learning Based Image Processing Logic

A Robust Methodology for Fruit Quality Prediction and Estima...

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

作者： Malathi K Vunnava Dinesh Babu M. Ramkumar M. Ayyadurai Vivek K Hamzeh J Aljawawdeh Department of CSE Saveetha School of Engineering Saveetha Institute of Science and Technology (SIMATS) Saveetha University Chennai Department of CSE RV Institute of Technology Guntur Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Department of software engineering Faculty of information technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Fruit quality prediction plays a vital role in agricultural and food industries, ensuring that only high-quality produce reaches the consumer. Traditional methods of assessing fruit quality rely on manual inspection, which is both time-consuming and prone to human error. This paper presents a robust methodology for fruit quality prediction and estimation, utilizing a hybrid deep learning model that integrates DenseN et for feature extraction with LightGBM for efficient classification. The proposed model is designed to automate the process of fruit quality assessment by analyzing key attributes such as color, texture, and shape from high-resolution fruit images. The Fruit360 dataset was used for training and testing, with images classified into categories such as fresh, ripe, overripe, and damaged. A comprehensive comparison with nine existing models, including SVM, Random Forest, and XGBoost, was conducted using performance metrics such as accuracy, precision, recall, and Fl-score. The proposed model achieved an accuracy of 96.78%, significantly outperforming all other models. Furthermore, the model's precision and recall reached 95.33% and 95.67%, respectively, demonstrating its reliability in distinguishing high-quality fruits from low-quality ones.

关键词： Training Support vector machines Accuracy Shape Computational modeling Estimation Predictive models Feature extraction Reliability Testing

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EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events

EventTrojan: Manipulating Non-Intrusive Speech Quality Asses...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Ying Ren Kailai Shen Zhe Ye Diqun Yan Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Juphoon System Software Co. Ltd Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Non-Intrusive speech quality assessment (NISQA) has gained significant attention for predicting speech’s mean opinion score (MOS) without requiring the reference speech. Researchers have gradually started to apply NISQA to various practical scenarios. However, little attention has been paid to the security of NISQA models. Backdoor attacks represent the most serious threat to deep neural networks (DNNs) due to the fact that backdoors possess a very high attack success rate once embedded. However, existing backdoor attacks assume that the attacker actively feeds samples containing triggers into the model during the inference phase. This is not adapted to the specific scenario of NISQA. And current backdoor attacks on regression tasks lack an objective metric to measure the attack performance. To address these issues, we propose a novel backdoor triggering approach (EventTrojan) that utilizes an event during the usage of the NISQA model as a trigger. Moreover, we innovatively provide an objective metric for backdoor attacks on regression tasks. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the EventTrojan attack. Besides, it also has good resistance to several defense methods.

关键词： Measurement Resistance Adaptation models Multimedia systems Current measurement Artificial neural networks Benchmark testing

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