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2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

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Data Poisoning: Issues, Challenges, and Needs 7

Data Poisoning: Issues, Challenges, and Needs

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7th IET Smart Cities Symposium, SCS 2023

作者： Aljanabi, Mohammad Omran, Alaa Hamza Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Mohammed, Sahar Yousif Ibrahim, Abdelhameed Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science &Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence. © The Institution of Engineering & technology 2023.

关键词： Machine learning

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Machine Learning for Intrusion Detection in Vehicular Ad-hoc Networks (VANETs): A Survey

Machine Learning for Intrusion Detection in Vehicular Ad-hoc...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Nesmah A. Al-Khulaidi Ammar T. Zahary Asma A. Al-Shargabi Muneer A. S Hazaa Department of Computer Networks Yemen Academy for Graduate Studies Sana'a Yemen Department of Computer Science Faculty of Computing and IT University of Science and Technology Sana'a Yemen Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Science Faculty of Computer and Information System Thamar University Thamar Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Intrusion Detection systems (IDSs) have become a key security problem due to the increasing number of connected automobiles and the sensitive nature of the data transferred in Vehicular Ad-hoc Networks (VANETs). By keeping an eye on network traffic, spotting questionable activity, and putting countermeasures in place to lessen risks, IDSs protect the integrity and security of VANETs. For VANETs, this study explores the state-of-the-art in machine learning-based IDSs, with a particular emphasis on work released in 2020–2022. We provide a thorough analysis of developments in widely used machine learning methods used for VANET intrusion detection throughout this time. This investigation explores certain machine learning methods that have been recently applied to VANET IDSs. The survey ends with a summary of the current issues and an exploration of potential directions for further investigation.

关键词： Surveys Machine learning algorithms Intrusion detection Vehicular ad hoc networks Machine learning Telecommunication traffic Safety

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Unveiling Ethical Challenges and Malpractice in Academic Research and Integrity

Unveiling Ethical Challenges and Malpractice in Academic Res...

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Creative Communication and Innovative technology (ICCIT), IEEE

作者： Ankur Singh Bist Bhupesh Rawat Untung Rahardja Marviola Hardini Mustofa Kamal Faculty of Computer Science & Engineering Graphic Era Hill University Uttarakhand India Faculty of Computing Graphic Era Hill University Uttarakhand India Faculty of Information Technology University of Raharja Tangerang Indonesia Faculty of Computer System University of Raharja Tangerang Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

In the pursuit of knowledge and innovation, academic research stands as a cornerstone of intellectual progress. However, the integrity of this process is susceptible to malpractice, raising profound ethical concerns. This research paper delves into the nuanced landscape of malpractice and ethical considerations within the realm of academic research, aiming to shed light on the challenges that undermine the credibility and reliability of scholarly endeavours. The study explores various forms of malpractice, including plagiarism, data fabrication, and authorship disputes, which have the potential to erode the foundations of academic integrity. Through a combination of literature reviews, case studies, and expert interviews, the research seeks to identify patterns and motivations behind such malpractices. The findings reveal that systematic solutions are necessary to address these issues, and propose the integration of advanced technological tools to enhance ethical oversight. This study contributes to the ongoing discourse on ethics in academic research by providing comprehensive insights and recommendations to uphold ethical standards.

关键词： Fabrication Ethics Technological innovation systematics Reviews Plagiarism Data integrity Blockchains Reliability Standards

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Deep Learning Enabled Intelligent Healthcare Management system in Smart Cities Environment

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computers, Materials & Continua 2023年第2期74卷 4483-4500页

作者： Hanan Abdullah Mengash Lubna A.Alharbi Saud S.Alotaibi Sarab AlMuhaideb Nadhem Nemri Mrim M.Alnfiai Radwa Marzouk Ahmed S.Salama Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk71491Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Science and Arts at MuhayelKing Khalid UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

In recent times,cities are getting smart and can be managed effectively through diverse architectures and *** cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic *** latest developments in digital data collection,AI techniques can be employed for clinical decision making *** the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the ***,wearables can be employed in healthcare systems that instigate the development of CVD detection and *** this motivation,the current study develops an Artificial Intelligence Enabled Decision Support system for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC *** proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data ***,the collected data is saved in cloud server for *** by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health *** accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)*** addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of *** experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

关键词： Smart cities e-healthcare decision support system cardiovascular disease deep learning feature selection

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Model-Based Testing of Access Control Requirement in Multi-tenant Application: An Extensive Life Cycle 1

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3rd International Conference on computer Science, Electrical and Electronic Engineering, ICCEE 2021

作者： Duraisamy, Gunavathi Ghani, Abdul Azim Abd Zulzalil, Hazura Abdullah, Azizol Faculty of Computer Science and Information Technology Department of Software Engineering and Information System Universiti Putra Malaysia Selangor Serdang Malaysia Faculty of Computer Science and Information Technology Department of Communication Technology and Networking Universiti Putra Malaysia Selangor Serdang Malaysia Lee Kong Chian Faculty of Engineering and Science Department of Internet Engineering and Computer Science Universiti Tunku Abdul Rahman Sungai Long Campus Selangor Kajang Malaysia

ISBN: (数字)9789811697814

ISBN: (纸本)9789811697807

Model-based testing is a substantial approach that is based on and involving models. It is well known for achieving test coverage and for generating and executing test cases automatically. The main and core activity of model-based testing is the modelling activity. Models are used to explain and represent the behaviour of the product. The requirement will be translated into models and those models will be used in generating the test cases automatically in model-based testing. The modelling activity clarifies test requirement and contribute to automatic generation and execution of large test suites with tool support. Model-based testing provides numerous advantages in testing;mainly on achieving appropriate test coverage and reduce manual effort and time spent since it can execute the test cases automatically. Despite all the benefit it can provide, there are some drawbacks too. However, the challenges faced can overcome by implementing proper test modelling activity and by adopting a well-versed test management activity. Thus, in this paper, we have studied the existing testing process, the taxonomy of model-based testing and model-based testing steps and proposed an extensive lifecycle for model-based testing with detail activities and steps to be carried out in order to test and validate access control requirement for multi-tenant application. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Access control

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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

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SN computer Science 2024年第5期5卷 549页

作者： Hamza, Manar Ahmed Hashim, Aisha Hassan Abdalla Motwakel, Abdelwahed Elhameed, Elmouez Samir Abd Osman, Mohammed Kumar, Arun Singla, Chinu Munjal, Muskaan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Management Information Systems College of Business Administration in Hawtat bani Tamim Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science College of Post-Graduated Studies University of Science and Technology Omdurman Sudan Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Machine learning Natural language processing Sentiment analysis Social media

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Smart Happy Device with IoT : For those who suffer anxiety, stress, and depression 12

Smart Happy Device with IoT : For those who suffer anxiety, ...

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12th IEEE Region 10 Humanitarian technology Conference, R10-HTC 2024

作者： Yakub, Fitri Azizan, Azizul Dhamanti, Inge Khudzari, Ahmad Zahran Md. Govindan, Niranjana Muljono, Muljono UTM Malaysia-Japan International Institute of Technology Electronic System Engineering Department Kuala Lumpur Malaysia Universiti Teknologi Malaysia Cyberphysical Research Group Embedded AI & ML Science Lab Faculty of Artificial Intelligence Kuala Lumpur Malaysia Universitas Airlangga Faculty of Public Health Department of Health Policy and Administration Surabaya Indonesia UTM Faculty of Engineering Department of Biosciences & Medical Engineering Johor Bahru Malaysia SRMIST Faculty of Engineering & Technology Department of Computing Technologies Chennai India Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350340204

Due to the rising cases of Covid-19 in the world especially in Malaysia, the impact had been hit hard for many people. Due to this all people must be isolated in their home. It has affected the well-being and working life of millions of people across the world. Due to this disease, the mental health among people has increased significantly for the past 2 years. According to MoH, out of 983 Malaysian students, about 20.4%, 6.6%, and 2.8% are experienced minimal to moderate, marked to severe, and most extreme levels of anxiety, respectively. The main stressors are the remote online teaching, and uncertainty about the future regarding study and career which can affect mental health. Thus, the main objective of this project is to develop a product that may provide different medication methods to its users by helping people who suffer anxiety, stress, and depression. We used aromatherapy, positive quotes, and music therapy in order to release stress. The user can also check his or her pulse rate by only touching the fingerprint through the sensor. The microcontrollers used in this project are Arduino Uno and Arduino Mega to build the project circuit. The experimental results can improve body health, mind and spirit which enhances both physical and emotional health. © 2024 IEEE.

关键词： COVID-19

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Manufacturing A Low-Cost Telegram and Optical Character Recognition-Based Indoor Air Quality Monitoring Data Logger 8

Manufacturing A Low-Cost Telegram and Optical Character Reco...

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8th International Conference on Information Management and technology, ICIMTech 2023

作者： Pramono, Andi Budiman, Satrio Arif Farchan, Muchammad Mappaturi, Andi Baso School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia Brawijaya University Information System Computer Science Faculty Malang65145 Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Electrical Engineering Surabaya60111 Indonesia Islamic State University of Maulana Malik Ibrahim Faculty of Science and Technology Architecture Department Malang Indonesia

ISBN: (纸本)9798350326093

Urban air is frequently contaminated with CO, CO2, VOC, HCHO, PM 2.5, and PM 10. Rural regions are at a lower risk than those near roads and industrial areas that produce emissions. Air pollutants negatively affect its inhabitants' health, particularly the respiratory system. Most people will spend more time indoors, requiring a method for monitoring indoor air quality (IAQ). Urban communities need IAQ monitoring instruments as an early warning system for their health. This research seeks to produce inexpensive IAQ that can be utilized by all levels of society, particularly urban communities. This research utilizes a qualitative, design-thinking-based methodology. The research diverged from Empatize, which asserts that urban communities require a method for determining the local air quality. Moreover, it is determined at the define stage that IAQ monitoring, which includes a data log feature, is still comparatively expensive, so a solution is developed at the ideate stage. Creating a prototype is the next step in realizing the specified solution. In this investigation, they decided to use IAQ monitoring, which is relatively inexpensive but lacks log data storage. The author uses the ESP32-cam microcontroller to capture the screen display device, and the resulting image is sent to a telegram and processed with optical character recognition (OCR) to generate a data log. The results of prototype testing are utilized for prototype development. © 2023 IEEE.

关键词： Industrial emissions

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Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning system

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computers, Materials & Continua 2023年第2期74卷 4435-4451页

作者： Radwa Marzouk Eatedal Alabdulkreem Mohamed KNour Mesfer Al Duhayyim Mahmoud Othman Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart *** plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so *** an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be *** aim of the image captioning process is to generate a caption for *** next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically *** this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the *** current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning system(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input *** attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding ***,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)*** model represents the input data appropriately by inserting it into a predefined length ***,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the *** application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models *** proposed NLPODL-IICS model was experimentally validated with the help of two benchmark *** comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.

关键词： Natural language processing information retrieval image captioning deep learning metaheuristics

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