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RETRACTED ARTICLE: An Intelligent and Secure Air Quality Monitoring System Using Neural Network Algorithm and Blockchain

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IETE Journal of Research 2023年第11期69卷 CVII-CXX页

作者： Abu Buker Siddique Rafaqat Kazmi Habib Ullah Khan Sikandar Ali Ali Samad Gulraiz Javaid 1 Department of Software Engineering The Islamia University of Bahawalpur Bahawalpur63100 Pakistan 2 Department of Accounting and Information Systems College of Business and Economics Qatar University Doha 2713 Qatar 3 Department of Information Technology The University of Haripur Haripur22620 Khyber Pakhtunkhwa Pakistan 4 Faculty of Computing Department of Computer Sciences The Islamia University of Bahawalpur Bahawalpur 63100 Pakistan

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain systems and Industrial Internet of Things”:Abu Buker Siddique, Rafaqat Kazmi, Habib Ullah Khan, Sikandar Ali, Ali Samad & Gulraiz Javaid (2022) An Intelligent and Secure Air Quality Monitoring System Using Neural Network Algorithm and Blockchain, IETE Journal of Research, DOI:10.1080/03772063.2022.2052984Following publication, concerns were raised about the peer review and decision-making processes for this special *** an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal’s peer review standards and *** the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special *** journal has not confirmed if the authors were aware of this compromised peer review *** journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this *** have been informed in our decision-making by our editorial policies and the COPE *** retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as “Retracted”.

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[Retracted] Cyber Security against Intrusion Detection Using Ensemble-Based Approaches

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Security and Communication Networks 2023年第1期2023卷

作者： Mohammed Naif Alatawi Najah Alsubaie Habib Ullah Khan Tariq Sadad Hathal Salamah Alwageed Shaukat Ali Islam Zada Information Technology Department Faculty of Computing and Information Technology University of Tabuk Tabuk Saudi Arabiaut.edu.sa Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabiapnu.edu.sa Department of Accounting and Information Systems College of Business and Economics Qatar University Doha Qatarqu.edu.qa Department of Computer Science University Engineering and Technology Mardan Khyber Pakhtunkhwa Pakistan College of Computer and Information Sciences Jouf University Sakakah Saudi Arabiaju.edu.sa Department of Computer Science University of Peshawar Peshawar Khyber Pakhtunkhwa Pakistanuop.edu.pk Department of Software Engineering Faculty of Computing & Information Technology International Islamic University Islamabad Pakistaniiu.edu.pk

The attacks of cyber are rapidly increasing due to advanced techniques applied by hackers. Furthermore, cyber security is demanding day by day, as cybercriminals are performing cyberattacks in this digital world. So, designing privacy and security measurements for IoT-based systems is necessary for secure network. Although various techniques of machine learning are applied to achieve the goal of cyber security, but still a lot of work is needed against intrusion detection. Recently, the concept of hybrid learning gives more attention to information security specialists for further improvement against cyber threats. In the proposed framework, a hybrid method of swarm intelligence and evolutionary for feature selection, namely, PSO-GA (PSO-based GA) is applied on dataset named CICIDS-2017 before training the model. The model is evaluated using ELM-BA based on bootstrap resampling to increase the reliability of ELM. This work achieved highest accuracy of 100% on PortScan, Sql injection, and brute force attack, which shows that the proposed model can be employed effectively in cybersecurity applications.

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A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis

arXiv

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arXiv 2025年

作者： Zubair, Muhammad Hussain, Muzammil Al-Bashrawi, Mousa Ahmad Bendechache, Malika Owais, Muhammad Interdisciplinary Research Center for Finance and Digital Economy King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Department of Software Engineering Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Information Systems and Operations Management King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia ADAPT Research Centre School of Computer Science University of Galway GalwayH91 TK33 Ireland Department of Mechanical and Nuclear Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

Multi-modal medical image fusion (MMIF) is increasingly recognized as an essential technique for enhancing diagnostic precision and facilitating effective clinical decision-making within computer-aided diagnosis systems. MMIF combines data from X-ray, MRI, CT, PET, SPECT, and ultrasound to create detailed, clinically useful images of patient anatomy and pathology. These integrated representations significantly advance diagnostic accuracy, lesion detection, and segmentation. This comprehensive review meticulously surveys the evolution, methodologies, algorithms, current advancements, and clinical applications of MMIF. We present a critical comparative analysis of traditional fusion approaches, including pixel-, feature-, and decision-level methods, and delves into recent advancements driven by deep learning, generative models, and transformer-based architectures. A critical comparative analysis is presented between these conventional methods and contemporary techniques, highlighting differences in robustness, computational efficiency, and interpretability. The article addresses extensive clinical applications across oncology, neurology, and cardiology, demonstrating MMIF’s vital role in precision medicine through improved patient-specific therapeutic outcomes. Moreover, the review thoroughly investigates the persistent challenges affecting MMIF’s broad adoption, including issues related to data privacy, heterogeneity, computational complexity, interpretability of AI-driven algorithms, and integration within clinical workflows. It also identifies significant future research avenues, such as the integration of explainable AI, adoption of privacy-preserving federated learning frameworks, development of real-time fusion systems, and standardization efforts for regulatory compliance. This review organizes key knowledge, outlines challenges, and highlights opportunities, guiding researchers, clinicians, and developers in advancing MMIF for routine clinical use and promotin

关键词： Neurophysiology

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A comparative evaluation of visual and natural language question answering over linked data

arXiv

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arXiv 2019年

作者： Wohlgenannt, Gerhard Mouromtsev, Dmitry Pavlov, Dmitry Emelyanov, Yury Morozov, Alexey Faculty of Software Engineering and Computer Systems ITMO University St. Petersburg Russia Vismart Ltd. St. Petersburg Russia

With the growing number and size of Linked Data datasets, it is crucial to make the data accessible and useful for users without knowledge of formal query languages. Two approaches towards this goal are knowledge graph visualization and natural language interfaces. Here, we investigate specifically question answering (QA) over Linked Data by comparing a diagrammatic visual approach with existing natural language-based systems. Given a QA benchmark (QALD7), we evaluate a visual method which is based on iteratively creating diagrams until the answer is found, against four QA systems that have natural language queries as input. Besides other benefits, the visual approach provides higher performance, but also requires more manual input. The results indicate that the methods can be used complementary, and that such a combination has a large positive impact on QA performance, and also facilitates additional features such as data exploration. Copyright © 2019, The Authors. All rights reserved.

关键词： Data handling

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Hybrid Filter for Attributes Reduction in Soft Set 2nd

Hybrid Filter for Attributes Reduction in Soft Set

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2nd International Conference on Advanced Data and Information engineering, DaEng 2015

作者： Mohammed, Mohammed Adam Taheir Mohd, Wan Maseri Wan Arshah, Ruzaini Abdullah Mungad, M. Sutoyo, Edi Chiroma, Haruna Faculty of Computer System & Software Engineering Universiti Malaysia Pahang Lebuhraya TunRazak KuantanPahang Malaysia Department of Information Systems University of Malaya Pantai Valley Kuala Lumpur50603 Malaysia Department of Artificial Intelligence University of Malaya Pantai Valley Kuala Lumpur50603 Malaysia Gombe Nigeria

ISBN: (纸本)9789811317972

The purpose of this research is to overcome parameterization reduction limitation that focuses only on individual parameter reduction, whereas in some cases the individual parameter reduction is not sufficient even implies reduction. It was found that the dimensions sometimes are not able to reduce the number of data in the case of big data;hence, for this reason it became necessary to look for an alternative technique that can significantly reduce the parameters. This paper proposed an alternative decision partition order method based on rough set indiscernibility to select attributes reductions in soft set using decompositions. For significant candidates, the method decomposition partition order used R supp checking to confirm the correctness of the reduction. Comparison of the reduction methods shows that the proposed method provides better result than the parameterization reduction in enhancing reduction. The false candidates were filtered in the huge candidate reduction by the Min supp. The proposed method can be used to maintain object before attribute reduction as well as to reduce parameter size drastically while maintaining consistency in decision making. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Decision making

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Energy efficient algorithms based on VM consolidation for cloud computing: Comparisons and evaluations

arXiv

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arXiv 2020年

作者： Zhou, Qiheng Xu, Minxian Gill, Sukhpal Singh Gao, Chengxi Tian, Wenhong Xu, Chengzhong Buyya, Rajkumar Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom School of Software and Information Engineering University of Electronic Science and Technology of China China Faculty of Science and Technology Macao University China School of Computing and Information Systems University of Melbourne Australia

Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fifth utility. With the pay-as-you-go model, cloud computing enables to offer the resources dynamically for customers anytime. Drawing the attention from both academia and industry, cloud computing is viewed as one of the backbones of the modern economy. However, the high energy consumption of cloud data centers contributes to high operational costs and carbon emission to the environment. Therefore, Green cloud computing is required to ensure energy efficiency and sustainability, which can be achieved via energy efficient techniques. One of the dominant approaches is to apply energy efficient algorithms to optimize resource usage and energy consumption. Currently, various virtual machine consolidation-based energy efficient algorithms have been proposed to reduce the energy of cloud computing environment. However, most of them are not compared comprehensively under the same scenario, and their performance is not evaluated with the same experimental settings. This makes users hard to select the appropriate algorithm for their objectives. To provide insights for existing energy efficient algorithms and help researchers to choose the most suitable algorithm, in this paper, we compare several state-of-the-art energy efficient algorithms in depth from multiple perspectives, including architecture, modelling and metrics. In addition, we also implement and evaluate these algorithms with the same experimental settings in CloudSim toolkit. The experimental results show the performance comparison of these algorithms with comprehensive results. Finally, detailed discussions of these algorithms are provided. Copyright © 2020, The Authors. All rights reserved.

关键词： Cloud computing

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AMOGA: A Static-Dynamic Model Generation Strategy for Mobile Apps Testing

arXiv

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arXiv 2019年

作者： Salihu, Ibrahim-Anka Ibrahim, Rosziati Ahmed, Bestoun S. Zamli, Kamal Z. Usman, Asmau Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor86400 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor86400 Malaysia Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang GambangPahang26300 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor86400 Malaysia

In the past few years, mobile devices have been increasingly replacing traditional computers as their capabilities such as CPU computation, memory, RAM size, and many more, are being enhanced almost to the level of conventional computers. These capabilities are being exploited by mobile apps developers to produce apps that offer more functionalities and optimized performance. To ensure acceptable quality and to meet their specifications (e.g., design), mobile apps need to be tested thoroughly. As the testing process is often tedious, test automation can be the key to alleviating such laborious activities. In the context of the Android-based mobile apps, researchers and practitioners have proposed many approaches to automate the testing process mainly on the creation of the test suite. Although useful, most existing approaches rely on reverse engineering a model of the application under test for test case creation. Often, such approaches exhibit a lack of comprehensiveness as the application model does not capture the dynamic behavior of the applications extensively due to the incompleteness of reverse engineering approaches. To address this issue, this paper proposes AMOGA, a strategy that uses a hybrid, static-dynamic approach for generating user interface model from mobile apps for model-based testing. AMOGA implements a novel crawling technique that uses the event list of UI element associated with each event to dynamically exercise the events ordering at the run-time to explore the applications’ behavior. An experimental evaluation was performed to assess the effectiveness of our strategy by measuring the code coverage and the fault detection capability through the use of mutation testing concept. Results of the experimental assessment showed that AMOGA represents an alternative approach for model-based testing of mobile apps by generating comprehensive models to improve the coverage of the applications. The strategy proved its effectiveness by achieving high co

关键词： Model checking

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A Preliminary Component Model for IoT

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IOP Conference Series: Materials Science and engineering 2020年第1期769卷

作者： Arbaayah Kamarudin Ng Keng Yap Mohamed Abdullahi Ali Lim Swee Phang Department of Software Engineering and Information Systems Faculty Computer Science and Information Technology Universiti Putra Malaysia

The enthusiasm of IT entrepreneurs in producing Internet of Things (IoT) systems is undeniable as currently, the number of connected devices is enormously increasing. Many research has been done to efficiently develop IoT systems. IoT systems are usually engineered from scratch. IoT component models have been introduced but lack of generic development framework or model that supports high reusability and loose coupling in dealing with the heterogeneous devices that can hinder its development. Thus, an IoT component model is proposed. Meta-modelling has been used to define the component model where the specific interaction and composition standard in a component are abstracted. IoT component model is intended to develop a prototype for IoT development. With this IoT prototype, IoT system developers will not need to develop everything from scratch every time, as generic components can be reused even when it is applied in different domains or during system enhancement is required. Smart home IoT system has been selected as a case study to evaluate our prototype tool. In this study, we provide an alternative way to develop IoT software in component-based software engineering method. A prototype has also been developed to assist reusability and reduce coupling between modules.

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Creation and evaluation of datasets for distributional semantics tasks in the digital humanities domain

arXiv

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arXiv 2019年

作者： Wohlgenannt, Gerhard Barinova, Ariadna Ilvovsky, Dmitry Chernyak, Ekaterina Faculty of Software Engineering and Computer Systems ITMO University St. Petersburg Russia National Research University Higher School of Economics Moscow Russia

Word embeddings are already well studied in the general domain, usually trained on large text corpora, and have been evaluated for example on word similarity and analogy tasks, but also as an input to downstream NLP processes. In contrast, in this work we explore the suitability of word embedding technologies in the specialized digital humanities domain. After training embedding models of various types on two popular fantasy novel book series, we evaluate their performance on two task types: term analogies, and word intrusion. To this end, we manually construct test datasets with domain experts. Among the contributions are the evaluation of various word embedding techniques on the different task types, with the findings that even embeddings trained on small corpora perform well for example on the word intrusion task. Furthermore, we provide extensive and high-quality datasets in digital humanities for further investigation, as well as the implementation to easily reproduce or extend the experiments. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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A New Support Value Method Filtering Based on Object Support Partition for Soft Reduction 2nd

A New Support Value Method Filtering Based on Object Support...

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2nd International Conference on Advanced Data and Information engineering, DaEng 2015

作者： Mohammed, Mohammed Adam Taheir Mohd, Wan Maseri Wan Arshah, Ruzaini Abdullah Mungad, M. Sutoyo, Edi Chiroma, Haruna Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak KuantanPahang Malaysia Department of Information System University of Malaya Pantai Valley Kuala Lumpur50603 Malaysia Department of Artificial Intelligence University of Malaya Pantai Valley Kuala Lumpur50603 Malaysia Gombe Nigeria

ISBN: (纸本)9789811317972

Soft set time complexity is become really a problem when the numbers of parameters are increased. In order to solve time complexity problem, it necessary to reduce the boundary of optimal soft set growth and due to this the time cost can be enhanced. Several soft set methods are determining the soft set reduction but in performing the reduction it spends more time to produce the result and this happens because the false candidate sets are a part of solution. So, if the boundary of candidate reduction is narrowed then the reduction process will speed up. In this paper, we proposed a new method which reducing the boundary of candidate reduction using Lipschitz constant and wavelet discrete transform to eliminate large false sets from the solution. In Lipschitz constant function the value of candidate implies are determined, where based on wavelet WDT the false sets which is not in the form of implies also can be determined. The proposed method remove an inconsistency noise from the soft set in a pre-processing filtering based on if then method which help to classify further reduction in short time. It found that by using Lipschitz constant function and wavelet discrete transform the reduction time can be enhanced several times comparing to previous reduction methods. The result indicates that Lipschitz constant function and wavelet WDT algorithm. It complements each other to determine candidate soft set reduction. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Set theory

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