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BIDS: Blockchain and Intrusion Detection System Coalition for Securing Internet of Medical Things Networks

IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-9页

作者： Gupta, Karan Gupta, Koyel Datta Kumar, Devender Srivastava, Gautam Sharma, Deepak Kumar Department of Information Technology Netaji Subhas University of Technology New Delhi India Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India Dept. of Math and Computer Science Brandon University Canada Department of Information Technology Indira Gandhi Delhi Technical University for Women Delhi India

The benefits of the Internet of Medical Things (IoMT) in providing seamless healthcare to the world are at the forefront of technological advancement. However, security concerns of any IoMT systems are high since they threaten to compromise personal information of patients and can even cause health hazards. Researchers are exploring the use of various techniques to ensure a high level of security of IoMT systems. One key concern is that the computing power of any Internet of Things (IoT) device is relatively low, hence mechanisms that require low computational power are appropriate for designing Intrusion Detection Systems (IDS). In this research work, a blockchain IDS coalition is proposed for securing IoMT networks and devices. The blockchain ledger is compact and uses less processing resources. Additionally, the ledger requires less communication overhead. The cryptographic hashes in the suggested architecture ensure complete data secrecy and integrity between parties who are trusted and those who are untrustworthy. Peer-to-peer networks in both central and cluster networks are also included in this work for complete decentralization. The proposed model can counter various attacks, including Denial of Service (DoS), anonymity attacks, impersonation attacks, Man-In-The-Middle (MITM), and Cross-Site Scripting (XSS). The proposed method achieved an F1- score as high as 100% and reported an AUC value of over 99%. IEEE

关键词： Blockchain

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An Enhancement of Object Detection Using YOLO V8 and Mobile Net in Challenging Conditions

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SN computer science 2025年第4期6卷 1-20页

作者： Pasupuleti, Shailaja Ramalakshmi, K. Gunasekaran, Hemalatha Arokiaraj, Rex Macedo Debnath, Saswati Jebaseeli, T. Jemima Department of Computer Science and Engineering Alliance University Karnataka Anekal Bangalore India AU-Centre of Excellence Department of Computer Vision Alliance University Karnataka Anekal Bangalore India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Alliance University Karnataka Bangalore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

Autonomous drones and deep learning neural networks are becoming popular tools to revolutionize aircraft operations by partially automating visual inspection processes in aircraft maintenance. The research aims to provide a trustworthy method for locating and classifying surface flaws on aircraft, such as dents, fractures, corrosion, and a few other anomalies. To assess the model performance with and without a lightweight network integration, the implementation merges YOLOv8 and Mobile Net architectures, using dropout regularization and data augmentation approaches. To guarantee the model's performance in low-light situations, the dataset has been carefully selected to contain images replicating such settings. Annotation, environment setup, model training, and online dataset scraping are all covered by the thorough technique. A web-scraped dataset is used to train and validate two popular semantic segmentation algorithms, YOLOv8 and Mobile Net, which show enough performance for segmenting and grading aircraft surface problems in practical settings. The results show that precision and recall values are improved using a hybrid technique that ensembles trained Mobile Net and YOLOv8 models. Using COCO metrics, in the proposed approach an assessment of Ensemble WBF, Mobile Net-YOLO, and YOLOv8 outcomes on test and validation sets, guaranteed reliable object recognition and classification abilities. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aircraft Classification Mobile-Net Object detection Weighted Box Fusion YOLOv8

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A Physical Framework for Testing and Evaluating of a Mechatronic Shunting System

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International Journal Bioautomation 2023年第4期27卷 181-192页

作者： Alkharabsheh, Abdelrahman Momani, Lina Qawaqzeh, Yousef Alsakarneh, Amjad Computer Science Department British Applied College Umm Al Quwain United Arab Emirates Mechanical Engineering Department Higher Colleges of Technology Fujairah United Arab Emirates Information Technology Department Fujairah University Fujairah United Arab Emirates Department of Mechanical Engineering Yarmouk University Irbid Jordan

Shunt is a flexible tube called a catheter implanted inside the brain where cerebrospinal fluid (CSF) is produced. Existing treatments rely on passive implantable shunts with differential pressure valves. The authors defined, designed, and programmed an intelligent wireless hydrocephalus shunting system. The shunting system was designed, simulated, and tested to carry out many tasks such as regulating the mechatronic valve, collecting Injection Control Pressure (ICP) readings, analyzing these readings, responding to all emergency cases, and diagnosing the whole shunting system. Nowadays, many challenges face clinical trials for new medical devices. Clinical trials are complex and require following several rules and regulations to ensure compliance with different standards. Due to the difficulty of using medical trials, an urgent need for a physical framework for implementing a virtual model of intracranial pressure and cerebrospinal fluid dynamics in hydrocephalus mechatronic shunt testing. Such a physical framework will play a vital role in assessing the functioning of the whole mechatronic shunting system. A framework of a mechatronic shunting system (implanted and external) is illustrated and integrated with embedded management and diagnosis software. Such a framework will help assess, test, and evaluate the main functions of the proposed shunting system. © 2023 by the authors. Licensee Institute of Biophysics and Biomedical engineering, Bulgarian Academy of sciences. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://***/licenses/by/4.0/). All Rights Reserved.

关键词： Cerebrospinal fluid

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Guest Editorial: Special Issue of Selected Extended Papers From the IEEE International Conference on Flexible, Printable Sensors and Systems (FLEPS) 2024

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第1期4卷 2-3页

作者： Mantysalo, Matti Marrocco, Gaetano Agarwale, Shweta Grau, Gerd Gao, Wei Tampere University Faculty of Information Technology and Communication Sciences Tampere33720 Finland University of Roma Tor Vergata Dipartimento di Ingegneria Civile ed ed Ingegneria Informatica Rome00133 Italy Aarhus University Department of Electrical and Computer Engineering Aarhus8200 Denmark York University Department of Electrical Engineering and Computer Science TorontoONM3J 1P3 Canada Caltech Department of Engineering and Applied Science PasadenaCA91125 United States

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Data-Driven Analysis of EV Energy Prediction and Planning of EV Charging Infrastructure 9

Data-Driven Analysis of EV Energy Prediction and Planning of...

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9th IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2023

作者： Palaniappan, Arul Bhukya, Purnima Chitti, Sai Kiran Gao, Jerry San Jose State University Department of Applied Data Science San Jose United States San Jose State University Department of Applied Data Science Department of Computer Engineering San Jose United States

ISBN: (纸本)9798350333794

To reduce the current crisis on energy and environmental problems, the adoption of electric vehicles is an effective way. But limited access and availability of charging infrastructure are restricting the EV market development. This paper presents data-driven analysis of current EV charging infrastructure to plan future EV infrastructure in the city. This paper tackles the problem using three approaches. The first approach that is covered in this paper is to predict the number of EV charging stations that need to be implemented in a geographic location. This is done using the SARIMAX model on the California EV charging station dataset that is preprocessed to be split into zip codes. The second approach that is tackled by this paper is the energy consumption prediction by an EV charging station. This information is useful to allow planners to understand how much energy is consumed by an EV station across multiple different time periods. An optimized Meta-Fusion regression model based on a stacked model is proposed for this approach and is applied to the Palo Alto charging station dataset. The third approach utilized the K-means clustering algorithm on the Palo Alto charging station dataset to predict the optimal location for EV charging stations in that city. The SARIMAX mode had an R2 score of 0.97, a MAE of 6.50, and RMSE of 17.43 when predicting EV station counts in the zip code 92101. The stacked model had an R2 score of 0.98, a MAE of 0.91 and an RMSE of 2.26 when predicting the daily energy consumption by an EV station. © 2023 IEEE.

关键词： Forecasting

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Development of a Novel Mechanical Layer Jamming Cable-driven Soft Actuator for Soft Gripper System

Development of a Novel Mechanical Layer Jamming Cable-driven...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Uahchinkul, Chinvudh Muramatsu, Satoshi Inagaki, Katsuhiko Tokai University Course of Science and Technology Kanagawa259-1292 Japan Tokai University Department of Applied Computer Engineering Kanagawa259-1292 Japan

ISBN: (纸本)9798331531614

Soft actuators have many advantages, such as flexibility and safe interaction with the environment. Despite these advantages, they still lack the stiffness to carry the high load. The layer jamming mechanism can be applied to the actuator to increase stiffness, and a vacuum can control the stiffness of the mechanism. However, the typical jamming mechanism has a risk of air leakage, and vacuums are bulky to some systems. In order to solve this problem, we have developed a novel mechanical layer jamming for the cable-driven soft actuator in this paper. Our design uses the actuator body to generate the compression force to the layer at the middle of the actuator's body to increase the actuator's stiffness. The result shows the effectiveness of our mechanical layer jamming in increasing the stiffness of the actuator when adding the load to the tip of the actuator. In addition, we have applied our actuator to the soft gripper system, which successfully grasped various objects and changed the stiffness of the actuator. © 2025 IEEE.

关键词： Grippers

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Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles

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science China Chemistry 2025年第5期68卷 2027-2034页

作者： Jie Zhang Yi Chen Guijun Li Zelin Sun Zhengong Meng Wai-Yeung Wong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute Division of Integrative Systems and Design and Department of Electronic and Computer EngineeringThe Hong Kong University of Science and Technology School of Flexible Electronics (Future Technology) and Institute of Advanced Materials (IAM) Nanjing Tech University

L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.

关键词： organometallic precursor magnetic nanoparticles block copolymer self-assembly nanoimprint lithography

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A supervised machine learning-based solution for efficient network intrusion detection using ensemble learning based on hyperparameter optimization

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International Journal of Information Technology (Singapore) 2023年第1期15卷 423-434页

作者： Sarkar, Arindam Sharma, Hanjabam Saratchandra Singh, Moirangthem Marjit Department of Computer Science and Electronics Ramakrishna Mission Vidyamandira Belur Math West Bengal Howrah 711202 India Department of Computer Science and Engineering North Eastern Regional Institute of Science and Technology Arunachal Pradesh Nirjuli 791109 India

An efficient machine learning (ML) ensemble technique for categorizing Intrusion Detection (ID) is proposed in this study. The tuning of the ML model’s parameters is a critical topic since it can improve detection quality. Another area where quality might be enhanced is pre-processing. Corrections to the training dataset can help with class identification, especially for unusual attacks like R2L (Root to Local attacks), U2R (User to Root attack). When compared to existing methodologies, the proposed methodology has a number of advantages, such as (1) it proposes two methods for classifying intrusions on the two most widely used datasets using ML models. (2) The KDD Cup99 and NSL-KDD datasets are rebalanced through data augmentation. (3) Provides a 3 steps approach for improving detection of intrusion utilizing Multi Layer Perceptron (MLP) in a cascaded structure. (4) To classify each class using a specialized one, a cascaded meta-specialized classifier architecture has been developed. (5) All meta-specialists assess the dataset’s non-flagged connections. With a classification accuracy of 89.32% and an FPR of 1.95%, this approach has been shown to considerably increase detection quality. (6) Finally, to enhance detection capability, the best algorithms’ predictions are integrated by increasing their weights. On the NSL-KDD dataset, this approach has a high accuracy of 87.63% and a low FPR of 1.68%. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Intrusion Detection (ID) Neural Network R2L (Root to Local attacks) U2R (User to Root attack)

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Cloud Security Service for Identifying Unauthorized User Behaviour

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computers, Materials & Continua 2022年第2期70卷 2581-2600页

作者： D.Stalin David Mamoona Anam Chandraprabha Kaliappan S.Arun Mozhi Selvi Dilip Kumar Sharma Pankaj Dadheech Sudhakar Sengan Department of Computer Science and Engineering IFET College of EngineeringVillupuram605108Tamil NaduIndia Department of Computer Sciences and Software Engineering Faculty of Basic and Applied SciencesInternational Islamic UniversityIslamabadPakistan Department of Information Technology Bannari Amman Institute of TechnologySathyamangalam638401Tamil NaduIndia Department of Computer Science Engineering DMI St.John the Baptist UniversityMangochiLilongweMalawi Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226Madhya PradeshIndia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement and Gramothan(SKIT)Jaipur302017RajasthanIndia Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Recently,an innovative trend like cloud computing has progressed quickly in *** a background of distributed networks,the extensive sprawl of internet resources on the Web and the increasing number of service providers helped cloud computing technologies grow into a substantial scaled Information Technology service *** cloud computing environment extracts the execution details of services and systems from end-users and ***,through the system’s virtualization accomplished using resource pooling,cloud computing resources become more *** attempt to design and develop a solution that assures reliable and protected authentication and authorization service in such cloud environments is described in this *** the help of multi-agents,we attempt to represent Open-Identity(ID)design to find a solution that would offer trustworthy and secured authentication and authorization services to software services based on the *** research aims to determine how authentication and authorization services were provided in an agreeable and preventive *** on attack-oriented threat model security,the evaluation *** considering security for both authentication and authorization systems,possible security threats are analyzed by the proposed security systems.

关键词： Cloud computing user behaviour access control security model

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A Novel Spark-Based Algorithm for Mining Frequent Utility Patterns 6

A Novel Spark-Based Algorithm for Mining Frequent Utility Pa...

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6th International Conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Wu, Jimmy Ming-Tai Zhou, Huiying Lin, Jerry Chun-Wei Wang, Ke Liu, Shuo Li, Ranran Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9781665491259

We are now living in the era of big data thanks to technological innovation. It is very important to discover valuable information in massive data. High utility itemset mining and frequent itemset mining are commonly used techniques in data mining, but they only consider a single factor, ignoring some frequent and efficient itemsets. So as to mine more valuable information, a method for mining skyline frequent utility patterns is proposed. Facing multi-dimensional and large-scale datasets, how to quickly find skyline points is a new challenge. In this article, a parallel algorithm is proposed within the framework of Spark, using Transformations and Actions operators to mine skyline patterns. Through the analysis of experimental data, it is proved that the proposed method has high computing efficiency and high search efficiency, and can effectively mine skyline frequent utility itemsets. © 2023 IEEE.

关键词： Large dataset

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