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2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies, 3ICT 2020

作者： Nandy, Tarak Idris, Mohd Yamani Idna Bin Noor, Rafidah Md Bhattacharyya, Sananda Ghani, Norjihan Binti Abdul University of Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur Malaysia Maldives Business School Department of Information Technology Male Maldives University of Malaya Faculty of Computer Science and Information Technology Department of Information Systems Kuala Lumpur Malaysia

ISBN: (纸本)9781728196732

Optimization of data usability and privacy protection is a challenging goal. Although the released data are sanitized with the upgraded model of data semantic changer, always face privacy and scalability issues. On the other hand, local anonymization may not be suitable in a multi-nodal environment such as a vehicular network. Alternatively, hiding sensitive attributes are essential alongside discreet data privacy. In this paper, we proposed a collaborative privacy-preserving data anonymization technique for the vehicular network. Moreover, the method is capable of achieving a desirable level of data sanitization in a group. The use of k-anonymity, l-diversity and t-closeness make the proposed technique more strong to protect data privacy in different stages of communication. Furthermore, the performed analysis of the proposed scheme ensures usability and practicality. Lastly, the future direction of the stipulated field shows the research path in collaborative data anonymization in the vehicular environment. © 2020 IEEE.

关键词： Vehicular ad hoc networks

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Towards an Intelligent Industrial Environment: Blockchain-Empowering Human-Centered Artificial Intelligence for Risk Assessment and Strategic Evaluation

SSRN

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SSRN 2023年

作者： Khan, Abdullah Ayub Chen, Yen-Lin Hajjej, Fahima Shaikh, Aftab Ahmed Yang, Jing Ku, Chin Soon Por, Lip Yee Department of Computer Science and Information Technology Benazir Bhutto Shaheed University Lyari Sindh Karachi75660 Pakistan Department of Computer Science Sindh Madressatul Islam University Sindh Karachi74000 Pakistan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei106344 Taiwan Department of Software Engineering College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O.Box 84428 Riyadh11671 Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Department of Computer Science Universiti Tunku Abdul Rahman Kampar31900 Malaysia

With the recent introduction of high-level human control and high-level IoT control in intelligent transportation systems, their association with blockchain increases the performance of the Internet of Vehicles (IoVs) and E-Healthcare, which leads to wider adaptation in the future as well. In this paper, the contributions of this systematic article are divided into three different folds: first, this paper evaluates more than a hundred previously published articles and highlights the gaps in futuristic concerns. Second, the paper presents a novel and secure human-centered AI-enabled risk assessment framework for the IoVs with blockchain distributed ledger technology (DLT). Third, a distributed public permissionless network with two operational channels of inter-communication for vehicle-to-vehicle (V2V) is proposed. It is considered an extension in the Vehicle-to-Everything (V2X) environment. Along with that, the pseudo-development of chaincodes to automate human-centered IoV node transactions. In addition, a Practical Byzantine Fault Tolerance (PBFT) consensus protocol and related controlled executions are programmed for fast scheduling, offloading, managing, and optimizing with reliability. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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A new design of a digital filter for an efficient field programmable gate array using quantum dot technology

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Materials Science and Engineering: B 2024年 300卷

作者： Taghavirashidizadeh, Ali Ahmadpour, Seyed-Sajad Ahmed, Suhaib Jafari Navimipour, Nima Ramkrishna Kassa, Sankit Yalcin, Senay Tehran Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Chitkara University Institute of Engineering and Technology Punjab India Department of Electronics and Communication Engineering Model Institute of Engineering and Technology Jammu India Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Pune India Department of Energy System Engineering School of Engineering and Natural Sciences Bahçeşehir University Istanbul Turkey

Digital filtering algorithms are most frequently used to implement generic-based Field-programmable gate arrays (FPGAs) chips, which are used for higher sampling rates. In the filtering structure, delay and occupied areas play a vital role. Since the existing structures suffered from shortcomings such as high delay and high occupied area, implementing a high-performance digital filter circuit with high speed and low occupied area based on unique technology can significantly improve the performance of whole FPGA structures. One of the best technologies to implement this vital structure to solve these shortcomings is quantum-dot cellular automata (QCA) technology. This paper presents several new efficient full adders for digital filter applications based on quantum technology, including a multiplier, AND gate, and accumulator. The QCADesigner 2.0.3 tool is used to create and validate the suggested designs. According to the results, all designed circuits have simple structures with few quantum cells, low area, and low latency. © 2023

关键词： Nanocrystals

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XoT (Xducation of Things): Harnessing AI and edge computing to educate all things

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IEEE Access 2024年

作者： Nurtantyana, Rio Hwang, Wu-Yuin Hariyanti, Uun Research Center for Data and Information Sciences Bandung Indonesia Telkom University School of Computing Indonesia Telkom University Center of Excellence Artificial Intelligence for Learning and Optimization Bandung Indonesia National Dong Hwa University College of Science and Engineering Department of Computer Science and Information Engineering Taiwan National Central University Graduate Institute of Network Learning Technology Taiwan Universitas Brawijaya Faculty of Computer Science Department of Information System Indonesia

Most English foreign language (EFL) studies focus solely on human beings. This research explores how edge computing can facilitate learning for all things. The XoT (Xducation of Things) framework was proposed to educate both human and all things. All things encompass two terms: AI-Agent and smartthings (covering physical and digital smart objects). At the core of this framework is Smart Question Answer Forwarding Mechanism (SQA-Forwarding), specifically designed to assist all things in building knowledge. To demonstrate this, the smartXoT environment was developed based on XoT framework, and its impacts on EFL learners was assessed. A quasi-experimental study involving 26 EFL learners, divided into an experimental group (EG) and a control group (CG), examined the differences in learning achievement of smartthings and EFL learners when using the smartXoT environment with/without SQA-Forwarding. Findings, on one hand, indicated that smartthings in the EG developed knowledge bases greater than those in the CG. On the other hand, the interaction between EFL learners and smartthings with SQA-Forwarding significantly improved learners' writing skills, with revisions playing a crucial role in enhancing writing quality. Thus, the XoT framework offers a novel and promising approach to educating both humans and all things. © 2013 IEEE.

关键词： Contrastive Learning

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Investigation the Performance Effect of QOS in MPLS-TE Network 2

Investigation the Performance Effect of QOS in MPLS-TE Netwo...

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2nd IEEE International Conference on Signal, Control and Communication, SCC 2021

作者： Bozed, Kenz A. Elbeskri, Ahlam Mohammed Zreg, Jazia R. Ali Zerek, Amer R. Benghazi University Faculty of Information Technology Department of Computer System Design Benghazi Libya Higher Institute of Technical Water Affairs Sabratha Libya Sabratha University College of Science Sabratha Libya Zawia University Faculty of Engineering Electrical and Electronic Eng. Department Zawia Libya

ISBN: (纸本)9781665402705

Due to the need for different communication types (Voice, video, and data) over the network, vendors improving enterprise networks for this purpose to minimize delays in order to support the better result, some applications like voice considered real-time applications which suffer from delay, jitter and packet losses, This paper discusses comparison of QoS performance between three QoS algorithms (PQ, FIFO, and WFQ), when using MPLS-TE technology considering the VoIP QoS parameters (end to end delay, jitter, and packet loss, as simulation done by OPNET modeler 14.5. © 2021 IEEE.

关键词： Quality of service

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Gaussian Optimized Deep Learning-based Belief Classification Model for Breast Cancer Detection

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computers, Materials & Continua 2022年第11期73卷 4123-4138页

作者： Areej A.Malibari Marwa Obayya Mohamed K.Nour Amal S.Mehanna Manar Ahmed Hamza Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

With the rapid increase of new cases with an increased mortality rate,cancer is considered the second and most deadly disease *** cancer is the most widely affected cancer worldwide,with an increased death rate *** to radiologists’processing of mammogram images,many computer-aided diagnoses have been developed to detect breast *** detection of breast cancer will reduce the death rate *** early diagnosis of breast cancer using the developed computer-aided diagnosis(CAD)systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with a reduced false positive *** paper proposed an efficient and optimized deep learning-based feature selection approach with this *** model selects the relevant features from the mammogram images that can improve the accuracy of malignant detection and reduce the false alarm *** learning is used in the extraction of features *** ext,a convolution neural network,is used to extract the *** two feature vectors are fused and optimized with enhanced Butterfly Optimization with Gaussian function(TL-CNN-EBOG)to select the final most relevant *** optimized features are applied to the classifier called Deep belief network(DBN)to classify the benign and malignant *** feature extraction and classification process used two datasets,breast,and *** to the existing methods,the optimized deep learning-based model secured 98.6%of improved accuracy on the breast dataset and 98.85%of improved accuracy on the MIAS dataset.

关键词： Breast cancer detection computer-aided diagnosis(CAD) deep learning CNN entropy butterfly optimization

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Deployment of polar codes for mission-critical machine-type communication over wireless networks

arXiv

引用

arXiv 2021年

作者： Mohammed, Najib Ahmed Mansoor, Ali Mohammed Ahmad, Rodina Binti Azzuhri, Saaidal Razalli Bin Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Department of Computer System & Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Mission critical Machine-type Communication (mcMTC), also referred to as Ultra-reliable Low Latency Communication (URLLC), has become a research hotspot. It is primarily characterized by communication that provides ultra-high reliability and very low latency to concurrently transmit short commands to a massive number of connected devices. While the reduction in physical (PHY) layer overhead and improvement in channel coding techniques are pivotal in reducing latency and improving reliability, the current wireless standards dedicated to support mcMTC rely heavily on adopting the bottom layers of general-purpose wireless standards and customizing only the upper layers. The mcMTC has a significant technical impact on the design of all layers of the communication protocol stack. In this paper, an innovative bottom-up approach has been proposed for mcMTC applications through PHY layer targeted at improving the transmission reliability by implementing ultra-reliable channel coding scheme in the PHY layer of IEEE 802.11a standard bearing in mind short packet transmission system. To achieve this aim, we analyzed and compared the channel coding performance of convolutional codes (CCs), low-density parity-check (LDPC) codes, and polar codes in wireless network on the condition of short data packet transmission. The Viterbi decoding algorithm (VA), logarithmic belief propagation (Log-BP) algorithm, and cyclic redundancy check (CRC) successive cancellation list (SCL) (CRC-SCL) decoding algorithm were adopted to CC, LDPC codes, and polar codes, respectively. Consequently, a new PHY layer for mcMTC has been proposed. The reliability of the proposed approach has been validated by simulation in terms of Bit error rate (BER) and packet error rate (PER) ***-to-noise ratio (SNR). The simulation results demonstrate that the reliability of IEEE 802.11a standard has been significantly improved to be at PER = 10-5or even better with the implementation of polar codes. The results als

关键词： Wireless networks

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Analysis of Deep Learning Algorithms for Prawn Aquaculture in a Challenging Environment 6

Analysis of Deep Learning Algorithms for Prawn Aquaculture i...

引用

6th International Conference on Applied Computational Intelligence in Information systems, ACIIS 2023

作者： Zariful, Wafiq Tashim, Najeebah Az-Zahra Lim, Tiong Hoo Basri, Aida Maryam Chuprat, Suriayati Putra, Seno Adi Liu, Pengcheng Fakhrurroja, Hanif Universiti Teknologi Brunei Electrical and Electronic Engineering Bandar Seri Begawan Brunei Universiti Teknologi Brunei School of Applied Sciences and Mathematics Bandar Seri Begawan Brunei Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Johor Bahru Malaysia Telkom Universiti School of Industrial and System Engineering Bandung Indonesia University of York Department of Computer Science York United Kingdom Telkom University Research Center for Smart Mechatronics National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798350317428

Deep Learning plays a vital role in various domains, spanning surveillance, agriculture, and healthcare. However, the current landscape lacks comprehensive comparative studies, underscoring the needs to explore various convolutional neural networks (CNNs) for aquaculture industries. This study conducts a comprehensive comparative analysis on state-of-the-art object detection models to identify the most suitable approach for detecting freshwater prawns, specifically in challenging turbid and murky water conditions as there is a lack of comparison studies for underwater images of freshwater prawns. The evaluated models, including YOLOv8, Faster R-CNN (FRCNN), Single Shot Detector (SSD), and EfficientDet, are assessed based on the following metrics namely inference time, accuracy, image cropping size, and model memory footprint. Using a dataset comprising 2976 manually collected images of freshwater prawns, augmented to a total of 7148 images, our results demonstrate that YOLOv8 outperforms its counterparts, achieving an impressive mean Average Precision (mAP) accuracy of 83.71% with an inference time of 32.9 milliseconds. This positions YOLOv8 as a dependable choice for real-time object detection in challenging underwater environments and contributes to addressing the limited research comparing YOLOv8 with other popular object detection algorithms, thereby aiding model selection for similar object detection applications. © 2023 IEEE.

关键词： Object detection

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Inline cure monitoring of epoxy resin by dielectric analysis 23

Inline cure monitoring of epoxy resin by dielectric analysis

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23rd European Microelectronics and Packaging Conference and Exhibition, EMPC 2021

作者： Franieck, Erick Fleischmann, Martin Hölck, Ole Schneider-Ramelow, Martin Automotive Electronics Engineering Technology Polymer & Packaging Robert Bosch GmbH Reutlingen Germany System Integration and Interconnections Technologies Fraunhofer IZM Berlin Germany Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany

ISBN: (纸本)9780956808677

In the past years, epoxy resin molding compounds (EMCs) have gained in importance as packaging material in the field of electrification due to their very good mechanical and chemical resistance and electrical insulation properties. Forthcoming trends, such as the use of silicon carbide and ceramics, are accelerating developments and are leading to new requirements for encapsulation materials in terms of high temperature resistance and stability. A focus here is on epoxy resin molding compounds with high glass transition temperatures, which as a consequence of the limited manufacturing conditions of electronic components must be cured below their maximum glass transition temperature. As a result, setting-up the ideal curing process to achieve the desired material properties poses quite a challenge, making therefore the study of the cure behavior under process-related conditions a key factor to achieve the desired material properties. The dielectric analysis (DEA) is a powerful measurement technique well suited for in-situ monitoring of EMC curing in the field of direct packaging of electronic parts. However, it possesses one major drawback. The DEA has a systematic temperature dependence that hinders the determination of the cure state as it is usually known from standard offline measurement techniques such as thermomechanical analysis (TMA), or dynamic-scanning-calorimetry (DSC). In this work we present an empirical approach how to compensate the temperature influence of the dielectric analysis (DEA) for a commercially available high glass transition temperature (Tg) (higher 200 °C) EMC. This proposed normalization method allows to gain information about the cure state of an EMC under near process conditions what would already be a great advantage considering the future trends in electronic packaging technology. In addition, it opens new possibilities on how to expand the application of the DEA, e.g. kinetic cure characterization. The detailed material characterizat

关键词： Epoxy resins

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TOWARDS NONLINEARITY. THE p-REGULARITY THEORY. APPLICATIONS AND DEVELOPMENTS.

arXiv

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arXiv 2025年

作者： Bednarczuk, E. Brezhneva, O. Leśniewski, K. Prusińska, A. Tret’Yakov, A. Warsaw University of Technology Poland Department of Mathematics Miami University OxfordOH United States System Research Institute Polish Academy of Sciences Poland Faculty of Science University of Siedlce Poland Dorodnicyn Computing Center Federal Research Center "Computer Science and Control " Russian Academy of Sciences Moscow119333 Russia

We present recent advances in the analysis of nonlinear equations with singular operators and nonlinear optimization problems with constraints given by singular mappings. The results are obtained within the framework of p-regularity theory, which has developed successfully over the last forty years. We illustrate the theory with its applications to degenerate problems in various areas of mathematics. In particular, we address the problem of describing the tangent cone to the solution set of nonlinear equations in a singular case. The structure of p-factor operators is used to propose optimality conditions and construct numerical methods for solving degenerate nonlinear equations and optimization problems. The methods presented in the paper can be considered as the first numerical approaches targeting solutions of degenerate problems, such as the Van der Pol differential equation, boundary-value problems with a small parameter, partial differential equations where Poincaré’s method of small parameter fails, nonlinear degenerate dynamical systems, and others. There are various practical applications for the theory of p-regularity, including structural engineering, composite materials, and material design. For instance, the theory can be applied to analyze the behavior of materials with irregular or complex properties. By considering higher-order derivatives, it becomes possible to model and predict the response of materials to external forces, such as stress or temperature variations. In geophysics, the p-regularity theory can be utilized to analyze and interpret complex data obtained from seismic surveys, gravity measurements, or electromagnetic surveys. The theory also finds applications in the analysis of nonlinear differential equations arising in control systems, geometric and topological analysis, biomechanics, and many other fields. The theory offers a valuable approach to analyzing and understanding nonlinear phenomena in situations where classical regularity

关键词： Boundary value problems

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