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IT and Industrial Technologies (ICIT), International Conference on

作者： Muhammad Huzaifa Shah Muhammad Abu Bakar Raja Hashim Ali Zain Ul Abideen Usama Arshad Ali Zeeshan Ijaz Nisar Ali Muhammad Imad Said Nabi Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) with different kernels (1st, 2nd, and 3rd), are implemented for classification purposes. The proposed DT model in this study shows higher accuracy than the DT model proposed in the original paper by Ingre et al. for Intrusion Detection System (IDS). Additionally, a multi-class classification model for NSL-KDD datasets is developed, considering both normalized and non-normalized features. Interestingly, it is observed that the models trained without normalized features achieve higher accuracies compared to those trained with normalized features. Moreover, the study enhances the classification performance of the DT-based IDS using the Correlation based Feature Selection (CFS) technique for feature selection. The proposed IDS is evaluated both before and after feature selection for multi-class classification (normal and various attack types) and binary classification (normal and abnormal data).

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Deep learning based classification of papaya disease recognition 3

Deep learning based classification of papaya disease recogni...

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3rd International Conference on Intelligent Sustainable Systems, ICISS 2020

作者： Hossen, Md. Sagar Haque, Imdadul Islam, Md. Saif Ahmed, Md. Tanvir Nime, Md. Jannati Islam, Md. Ashiqul Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9781728170893

Every year many of farmers had to face a huge amount of loss due to the papaya disease. From the last several years, thousands of researchers pay attention to the papaya disease recognition system to reduce the losses of farmers. Farmers have no prior knowledge about the detection technique to mitigate the disease. They found the diseases when the papaya already affected and wasted. Due to the loss of cultivation, many of them are now frightening to take steps about planting papaya. On this matter, a research has been performed with the advancement of deep learning technology to detect and classify the papaya disease. CNN model is used according to Keras API. This model becomes reliable with fully connected, where classification is completed and all the process is a deep learning based. It took the fixed size of image 200x200 RGB image as input. © 2020 IEEE.

关键词： Deep learning

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Towards Eco-Sustainability and Green Analytics Model to Measure the Performance of Big Data Systems

Towards Eco-Sustainability and Green Analytics Model to Meas...

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International Conference on Information Management (ICIM)

作者： Yusmadi Yah Jusoh Rusli Abdullah Rozi Nor Haizan Nor Jamaiah Hj. Yahaya Shireen Muhammad Aishwharya Raani Arunachalam Dept. of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Malaysia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Faculty of Communication Visual Art and Computing Universiti Selangor (UNISEL) Selangor Malaysia

Many BDS is doomed to failure because of the missing knowledge on measuring the performance of BDS. The failure to identify the performance measurement and correct will make the problems worsen. This will complicate the efforts of fixing such problems and more importantly, will affect the quality of knowledge, insights, and results expected by the users in the future. In achieving durable performance, the organization needsto understand the factors that contribute to the performance of the eco-sustainability and green analytics metrics for BDS. Although the existence of its importance and awareness was largely neglected. Therefore, the eco-sustainability and green analytic measurement of the BDS is examined in this research. Measuring the BDS performance has the benefit of identifying problems and launching corrective actions before these problems happen and become worsen. The research will investigate the eco-sustainability and green analytic measures which will be proposed and used to capture performance for each process of BDS. Then, based on such measures and metrics, as well as existing performance concepts, frameworks, and models, a measurement model for BDS will be created. The model will be validated through expert evaluations and confirmatory studies with users and practitioners of BDS users. The research has a greater significance in leveraging existing performance concepts in BDS settings and the research proposes users' participation in the sustainability and continuous performance evaluation of theirBDS.

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Technical Debt Management: The Road Ahead for Successful software Delivery

arXiv

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arXiv 2024年

作者： Avgeriou, Paris Ozkaya, Ipek Chatzigeorgiou, Alexander Ciolkowski, Marcus Ernst, Neil A. Koontz, Ronald J. Poort, Eltjo Shull, Forrest Dept. of Computing Science University of Groningen Groningen Netherlands Software Engineering Institute Carnegie Mellon University PittsburghPA United States Dept. of Applied Informatics University of Macedonia Thessaloniki Greece QAware GmbH München Germany Department of Computer Science University of Victoria Victoria Canada The Boeing Company MesaAZ United States CGI Rotterdam Netherlands

Technical Debt, considered by many to be the 'silent killer' of software projects, has undeniably become part of the everyday vocabulary of software engineers. We know it compromises the internal quality of a system, either deliberately or inadvertently. We understand Technical Debt is not all derogatory, often serving the purpose of expediency. But, it is associated with a clear risk, especially for large and complex systems with extended service life: if we do not properly manage Technical Debt, it threatens to "bankrupt" those systems. software engineers and organizations that develop softwareintensive systems are facing an increasingly more dire future state of those systems if they do not start incorporating Technical Debt management into their day to day practice. But how? What have the wins and losses of the past decade of research and practice in managing Technical Debt taught us and where should we focus next? In this paper, we examine the state of the art in both industry and research communities in managing Technical Debt;we subsequently distill the gaps in industrial practice and the research shortcomings, and synthesize them to define and articulate a vision for what Technical Debt management looks like five years hence. © 2024, CC BY-NC-ND.

关键词： Industrial research

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Malware Detection and Classification for Internet of Things Using Self-Attention Based Long Short-Term Memory

Malware Detection and Classification for Internet of Things ...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Sudhakar K Saif O. Husain D. Raja Babu M. Ashok Kumar R. Dineshkumar Dept. of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering SR University Warangal India Department of Computer Science Software Engineering and Cyber Security Skyline University Nigeria Kano State Nigeria Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

The Internet of Things (IoT) connects numerous intelligent devices providing security features that interact with default settings accessed through applications. Additionally, Deep Learning (DL)-based mechanisms was involved for malware detection to identify and mitigate attacks. The Adaptive Synthetic Sampling (ADASYN) technique addresses class imbalances, especially when splitter class instances are distributed data in complex patterns. The proposed Self-Attention based Long Short-Term Memory (LSTM) classification method facilitates the handling of long-range dependencies in transferred data sequences offering high flexibility and adaptability to varying input lengths. For feature selection Particle Swarm Optimization (PSO) is utilized iteratively optimizes a solution in a high-dimensional space although it may have a low convergence rate due to its iterative process. The obtained results demonstrate the proposed Self-Attention based LSTM model achieves better accuracy reaching 98.05% on the NSL-KDD dataset and 99.01 % on the UNSW-NB15 dataset. These results ensure accurate classification compared to other existing methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN).

关键词： Adaptation models Recurrent neural networks Training data Malware computer networks Internet of Things Convolutional neural networks

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MOOC student success prediction using knowledge distillation

MOOC student success prediction using knowledge distillation

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2020 International Conference on computer Information and Big Data Applications, CIBDA 2020

作者： Kang, Tingting Wei, Zhengang Huang, Jianxiong Yao, Zhaoliang Dept. of Computer Science and Technology Ocean University of China Qingdao Shandong China Dept. of Software Engineering Zhejiang University Hangzhou Zhejiang China Dept. of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu China

ISBN: (纸本)9781728198378

Massive Open Online Courses (MOOCs) have boomed in recent years because learners can arrange learning at their own pace. Due to the low success rate of MOOCs, student success prediction has received much attention recently. Yet many utilize post-hoc prediction architectures, where model fitting requires features which are not knowable until a course completes. These works do not match the actual environment in which the prediction model is used, which may lead to unsatisfactory results. In order to build a reliable model that can be used in the actual environment, we propose a new student success prediction algorithm based on knowledge distillation. This algorithm can make the model obtain good prediction results with only a few basic features, and solve the problem of using features which are not knowable until a course completes. © 2020 IEEE.

关键词： Forecasting

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Turbo-GTS: Scaling Mobile Crowdsourcing using Workload-Balancing Bisection Tree 20

Turbo-GTS: Scaling Mobile Crowdsourcing using Workload-Balan...

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28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020

作者： Li, Wei Chen, Haiquan Ku, Wei-Shinn Qin, Xiao Dept. of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Computer Science California State University SacramentoCA United States

ISBN: (纸本)9781450380195

In mobile crowdsourcing, workers are financially motivated to perform self-selected tasks to maximize their revenue. Unfortunately, the existing task scheduling approaches in mobile crowdsourcing fail to scale for massive tasks and large geographic areas. We present Turbo-GTS, a system that assigns tasks to each worker to maximize the total number of the tasks that can be completed for an entire worker group while taking into account various spatial and temporal constraints, such as task execution duration, task expiration time, and worker/task geographic locations. The core of Turbo-GTS is WBT-NNH and WBT-NUD, our two newly developed scheduling algorithms, which build on the algorithms, QT-NNH and QT-NUD, proposed in our prior work [5]. The key idea is that Turbo-GTS performs dynamic workload balancing among all workers using the proposed Workload-balancing Bisection Tree (WBT) in support of large-scale Geo-Task Scheduling (GTS). Turbo-GTS includes an interactive interface for users to load the current task/worker distributions and compare the task assignment of each worker returned by different algorithms in a real-time fashion. Using the Foursquare mobile user check-in data in New York City and Tokyo, we show the superiority of Turbo-GTS over the state of the art in terms of the total number of the tasks that can be accomplished by the entire worker group and the corresponding running time. We also demonstrate the front-end interface of Turbo-GTS with two exploratory use cases in New York City. © 2020 Owner/Author.

关键词： Scheduling algorithms

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Teaching DevOps Security Education with Hands-on Labware: Automated Detection of Security Weakness in Python

arXiv

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arXiv 2023年

作者： Akter, Mst Shapna Rodriguez-Cardenas, Juanjose Rahman, Md Mostafizur Shahriar, Hossain Rahman, Akond Wu, Fan Dept. of Intelligence and Robotics Systems University of West Florida United States Dept. of Information Technology Kennesaw State University United States Dept. of Information Technology University of West Florida United States Center of Cyber Security University of West Florida United States Dept. of Computer Science and Software Engineering Auburn University Auburn United States Dept of Computer Science Tuskegee University Tuskegee United States

The field of DevOps security education necessitates innovative approaches to effectively address the ever-evolving challenges of cybersecurity. In adopting a student-centered approach, there is the need for the design and development of a comprehensive set of hands-on learning modules. In this paper, we introduce hands-on learning modules that enable learners to be familiar with identifying known security weaknesses, based on taint tracking to accurately pinpoint vulnerable code. To cultivate an engaging and motivating learning environment, our hands-on approach includes a pre-lab, hands-on and post lab sections. They all provide introduction to specific DevOps topics and software security problems at hand, followed by practicing with real world code examples having security issues to detect them using tools. The initial evaluation results from a number of courses across multiple schools show that the hands-on modules are enhancing the interests among students on software security and cybersecurity, while preparing them to address DevOps security vulnerabilities. © 2023, CC BY.

关键词： computer aided instruction

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Metaverse and Healthcare: An Empirical Investigation

Metaverse and Healthcare: An Empirical Investigation

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Hussain Falih Mahdi Bhumika Sharma Roohi Sille Tanupriya Choudhury Department of Computer and Software Engineering University of Diyala Baquba Iraq School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand India SoCS University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand India CSE Dept. Graphic Era Hill University Dehradun Uttarakhand India

A virtual setting that provides experiences in real-time is introduced through a new field of research called metaverse. Combining the metaverse with healthcare results in a unique real-space experience that enables both patients and physicians to evaluate the health of their respective patients and engage in in-person interactions. The metaverse is a collection of technologies that includes elements of artificial intelligence, virtual reality, augmented reality, the Internet of Things, and more. The integration of healthcare and the metaverse has made it possible to focus on the patient's health in a more accurate, effective, and precise manner. Using the many imaging technologies in the metaverse, researchers are working to speed up the usage of the metaverse for patient diagnosis in addition to clinical diagnostics. It is no longer necessary for a doctor to be there in person while making a diagnosis. In order to diagnose patients in a highly effective and exact manner, there is a lot of research being done to improve the metaverse environment. This study's main focus will be on how the metaverse has streamlined the healthcare industry and the various healthcare amenities it offers. This article offers a thorough examination of clinical and diagnostic developments achieved in the metaverse in an effort to enhance the field of healthcare in the metaverse. Also, the focus is on the cutting-edge developments that will transform the metaverse healthcare industry.

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Cybersecurity and Privacy Attacks Detection in IoT Networks with Improved Data engineering and Machine Learning Methods

Cybersecurity and Privacy Attacks Detection in IoT Networks ...

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IEEE International Conference on Big Data Computing Service and Applications (BigDataService)

作者： Donald Elmazi Dimitrios A. Karras Abdulsalam Alkholidi Klea Çapari Dept. of Electronics and Telecommunications Faculty of Engineering Canadian Institute of Technology (CIT) Tirana Albania Dept. S/W Engineering Engineering Faculty Canadian Institute of Technology (CIT) Tirana Albania Dept. General School of Science National and Kapodistrian University of Athens (NKUA) Athens Greece Canadian Institute of Technology (CIT) Tirana Albania Faculty of Engineering - Sana’a University Yemen Dept. of Computer Engineering Faculty of Engineering University of Rijeka Croatia Dept. of Software Engineering Faculty of Engineering CIT Tirana Albania

Presently, the global proliferation of Internet of Things (IoT) devices and applications has accelerated owing to their advantages in enhancing the business and industrial environment, as well as the daily lives of individuals. Nevertheless, IoT devices are vulnerable to malevolent network traffic, which can lead to undesirable outcomes and disrupt the operation of IoT devices. Consequently, it is imperative to devise a screening method for network traffic to identify and categorize malicious activity and reduce its harmful effects. In this regard, the present study proposes a framework and thoroughly compares single and ensemble machine learning models that are using predictive analytics to detect and classify network activity in an IoT network. Specifically, our framework and models distinguish between normal and anomalous network activity. Furthermore, network traffic is classified into eight categories, normal, denial of service (DoS) attack, Scan attack, Data Type Probing, Malicious Operation, Malicious Control, Spying attack and Wrong Setup based attack. The special focus of this paper is in identifying privacy related attacks in comparison with main cybersecurity related ones. Several supervised machine learning models have been herein implemented to characterize their performance in detecting and classifying network activities for IoT Networks based on the ensemble learning framework as well as on the single learning machines framework. Moreover, classifiers based on the statistical learning theory framework are herein involved too, including hybrid Bayesian theory models, decision trees (DT) and the ordinal Learning Modelling approach. The statistical pattern recognition based models as well as all supervised learning machines models have been evaluated on a broad benchmark dataset for IoT attacks. Besides, an improved data engineering feature processing model has been applied to the dataset under consideration to improve the accuracy of all models under co

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