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7th International Conference of computer and Informatics engineering, IC2IE 2024

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798331505530

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. Technology also plays an essential role in supporting runners and running races. Technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential. © 2024 IEEE.

关键词： artificial intelligence aspects marathon performance

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Using Formal Knowledge to Support Episodic Evidence-Based Nursing Care

Using Formal Knowledge to Support Episodic Evidence-Based Nu...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Jaffe, Shai Ben Shahar, Bruria Shahar, Yuval Goldstein, Ayelet Shalom, Erez Selivanova, Maya Rimon, Ephraim Cohen, Odeya Department of Nursing Faculty of Health Sciences Ben-Gurion University of the Negev Israel Nursing Informatics lab Faculty of Health Sciences Ben-Gurion University of the Negev Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Computer Science Department Hadassah Academic College Israel Herzfeld Geriatric Rehabilitation Medical Center Israel

ISBN: (纸本)9781643685335

Applying evidence-based medicine prevents medical errors highlighting the need for applying Clinical Guidelines (CGs) to improve patient care by nurses. However, nurses often face challenges in utilizing CGs due to patient-specific needs. Developing a Clinical Decision Support System (CDSS) can provide real-time context-sensitive CG-based recommendations. Therefore, there is a need to acquire and represent CGs in a machine-applicable manner. Also, there is a need to be able to provide recommendations episodically, only when requested, and not continuously, and to assess previous partial performance of evidence-based actions on a continuous scale. This study evaluated the feasibility of acquiring and representing major nursing CGs, in a machine-applicable manner for episodic use. Using data from an Israeli geriatric center, the results suggest that an episodic CDSS effectively supports the application of formalized nursing knowledge. © 2024 The Authors.

关键词： Nursing

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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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Utilizing Machine Learning Algorithms for Effectively Detection IoT DDoS Attacks 1st

Utilizing Machine Learning Algorithms for Effectively Detect...

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1st International Conference on Advances in Computing Research, ACR’23

作者： Mughaid, Ala Alqahtani, Ali AlZu’bi, Shadi Obaidat, Ibrahim Alqura’n, Rabee AlJamal, Mahmoud AL-Marayah, Raid Department of Information Technology Faculty of Prince Al-Hussien Bin Abdullah for IT The Hashemite University Zarqa Jordan Department of Networks and Communication Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia Computer Science Department Faculty of Science and Information Technology Al Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9783031337420

One of the most critical features of the Internet of Things (IoT) is that it allows a massive number of devices to communicate with each other at the same time, for example (cars, smoke alarms, watches, glasses, web cameras, etc.). Given that they can connect to the Internet, the huge demand to convert systems into smart systems that can be controlled remotely increases the chances of cyber-attacks that threaten security and availability becoming more frequent, severe, and in a different form. Distributed Denial of Service (DDoS) attacks have become the most common threats in IoT. These attacks make IoT systems unreliable because complex DDoS attacks cannot be detected by traditional security countermeasures such as firewalls and intrusion detection systems (IDS). In this research, we proposed a model dedicated to work on prediction and detection of DDoS attacks on networks IoT, where we run this model on a NF-BoT-IoT-v2 dataset, which contains 37,763,497 records divided into five categories (Benign, Reconnaissance, DDoS, DoS, and Theft), and then we process our dataset to be more business friendly so it only has two categories (100,000 records broken down into 23,906 benign attacks and 76,094 DDoS attacks). This study used WEKA and MATLAB tools to perform the classification efficiently and find the most important features through feature selection. We used a decision tree (J48) machine learning classifier, a Naive Bayes (NB) classifier, and a random forest (RF) classifier. However, the results were as expected and improved accuracy and efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Denial-of-service attack

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MABPD: Mobile Agent-Based Prevention and Black Hole Attack Detection in Wireless Sensor Networks 2

MABPD: Mobile Agent-Based Prevention and Black Hole Attack D...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ashraf, Humaira Khan, Fizza Ihsan, Uswa Al-Quayed, Fatima Jhanjhi, N.Z. Humayun, Mamoona International Islamic University Islamabad Department of Computer Science and Software Engineering Islamabad Pakistan Jouf University College of Computer and Information Sciences Department of Computer Science Sakakah Saudi Arabia Malaysia Applied Science Private University Applied Science Research Center Amman11937 Jordan Jouf University College of Computer and Information Sciences Department of Information Systems Sakakah Saudi Arabia

ISBN: (纸本)9798350335644

With the advancement and availability of the internet in the present age, everything is being wireless. Be it our home appliances or high defined monitoring systems, Wireless sensor networks play an important role. But with advancement comes a high probability of getting attacked. One of these attacks is the Blackhole attack (BHA), in which a node drains all the traffic towards itself and discards the data traffic without sending it towards the receiver. There exist multiple approaches that help to protect a network against BHA, but existing approaches have some drawbacks. Many proposed schemes make sure that the given technique uses less energy but failed to reduce the packet drop ratio. In the detection of BHA, the proposed scheme should not only detect defected nodes but also manages the efficiency. The proposed methodology used mobile agents with authentication of nodes and trust values to detect black hole nodes. The methodology is tested using different evaluation measures such as energy consumption, latency, network life, and packet delivery ratio. The proposed method uses a detection algorithm that increases energy consumption and hence networks lifetime. Packet delivery rate is increased by 19.51%, the energy consumption is reduced by 53.3%, and network life is increased by 43.3% as compared to the previous technique. © 2023 IEEE.

关键词： Energy utilization

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks

Unified Approach Integrating Machine Learning Algorithms for...

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Computing, Sciences and Communications (ICCSC), International Conference on

作者： Abeer Mathur Abhinav Sudhakar Dubey Pranav Singh Mahara Usha Devi Gandhi Department of Software Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India Department of Information Technology School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment.

关键词： Support vector machines Radio frequency Phishing Scalability Forestry Natural language processing Vectors Robustness Electronic mail Random forests

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An Architectural Approach for Enhanced Data Interoperability Across Building systems 7

An Architectural Approach for Enhanced Data Interoperability...

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7th Conference on Cloud and Internet of Things, CIoT 2024

作者： Yefi, Peter Ejaz, Sikandar Menon, Ramanunni Parakkal Eicker, Ursula Guéhéneuc, Yann-Gaël Concordia University Computer Science and Software Engineering Dept. Montreal Canada Concordia University Institute for Information Systems Engineering Montreal Canada Institute for Solar Research German Aerospace Center Jülich Germany Concorida University NextGen Cities Institute Montreal Canada

ISBN: (纸本)9798350356717

Access to building data is crucial for creating portable applications and improving building operation and energy efficiency. Ensuring data transmutability is essential for facilitating research and overcoming the diverse data representation, management, and collection methods across various building management systems (BMSs). Different BMSs use multiple interfaces for data exchange. Some BMSs provide Application Programming Interfaces (APIs) for data exchanges, while others have gateways connecting to cloud services for application subscription and data access. This study introduces a five-layered architecture that describes how client applications, researchers, and other interested stakeholders can exchange data with different BMSs. Our objective is to help researchers and practitioners understand the various ways of accessing transmutable (capable of being transformed into a compatible format) data from BMSs, regardless of the building management system a building uses. Through transmutable access to building systems' data, we aim to enable transferable energy-efficient-related applications for buildings. We evaluate our work with a building model of the Varennes Library in Varennes, QC, Canada. The building model includes information about the building, floors, rooms, electric meter and historical time series data of the meter, weather station and its historical time series data. The model also has carbon dioxide concentration sensors, temperature sensors, humidity sensors, and historical time series data of the sensors. We include two test client programs to show how they access the transmutable data of the building through a model. We conclude that the five-layered architecture facilitates the exchange of transmutable data across diverse BMSs, making it a valuable tool for researchers and practitioners. © 2024 IEEE.

关键词： Middleware

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Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

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computer systems Science & engineering 2023年第9期46卷 3121-3139页

作者： Khaled M.Alalayah Fatma S.Alrayes Jaber S.Alzahrani Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial *** Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production *** industry 4.0,powerful IntrusionDetection systems(IDS)play a significant role in ensuring network *** various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of *** is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT ***,the existing Deep Learning(DL)can be employed to identify anonymous ***,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT *** presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful *** HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of ***,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the ***,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN *** proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

关键词： Industrial IoT deep learning network security intrusion detection system attribute selection smart factory

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TELEX: Two-Level Learned Index for Rich Queries on Enclave-based Blockchain systems

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4299-4313页

作者： Wu, Haotian Tang, Yuzhe Shen, Zhaoyan Tao, Jun Lin, Chenhao Peng, Zhe The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Syracuse University Department of Electrical Engineering and Computer Science United States Shandong University School of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Xi'an Jiaotong University Faculty of Electronic and Information Engineering China The Hong Kong Polytechnic University Shenzhen Research Institute Hong Kong

Blockchain has become a popular paradigm for secure and immutable data storage. Despite its numerous applications across various fields, concerns regarding the user privacy and result integrity during data queries persist. Additionally, the need for rich query functionalities to harness the full potential of blockchain data remains an area ripe for exploration. In order to address these challenges, our paper first utilizes a framework based on the Trusted Execution Environment (TEE) and oblivious RAM technique to achieve both privacy and data integrity. To enhance the query efficiency over the entire blockchain, we then devise a two-level learned indexing methodology named TELEX within the TEE for both integer and string keys. We also propose different query processing algorithms for versatile query types, including exact queries, aggregate queries, Boolean queries, and range queries. By implementing the prototype and conducting extensive evaluation, we demonstrate the feasibility and remarkable improvement in efficiency compared to existing solutions. © 1989-2012 IEEE.

关键词： Query languages

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Optimal Method Combining Blockchain and Multi-Agent System to Ensure Data Integrity and Deduplication in the Cloud Environment

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第10期18卷 90-105页

作者： El Ghazouani, Mohamed Ikidid, Abdelouafi Zaouiat, Charafeddine Ait Aziz, Layla Ichahane, Moulay Youssef Er-Rajy, Latifa ESIM Polydisciplinary Faculty of Sidi Bennour Chouaîb Doukkali University El Jadida Morocco Laboratory of System Analysis Information Processing and Industrial Management EST Salé Mohammed V University Salé Morocco Information Technology Laboratory National School of Applied Sciences Chouaîb Doukkali University El Jadida Morocco Computer Science Department Laboratory of Information Systems Engineering Cadi Ayyad University Marrakesh Morocco

Cloud computing (CC) refers to the transmission, storage, and processing of any type of information at a location that is not owned or controlled by the information owner. This information can be stored and accessed anytime, from anywhere, and using any device. However, several challenges threaten the deployment of cloud computing technology within organizations, including user privacy, data integrity, and managing the retention of the vast amount of data that will be stored and analyzed in this environment. In recent years, various data integrity checking systems and data storage mechanisms have been proposed to address these diverse challenges. Nevertheless, some protocols do not guarantee data confidentiality against auditors during the auditing process, while others do not provide batch auditing. Additionally, the majority of these protocols do not support the deduplication technique. Therefore, in this research work, the security features of blockchain technology and those of multi-agent systems (MAS) have prompted us to propose models that enable both data deduplication, saving storage space, and data integrity auditing, efficiently verifying the accuracy of data outsourced to CC environments. Implementation findings indicate that the suggested system is highly feasible and applicable in real-world scenarios. © 2024 by the authors of this article.

关键词： Multi agent systems

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