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Information Dynamics and Applications
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Information Dynamics and Applications (IDA)
IJKIS
ISSN (print): 2958-1486
ISSN (online): 2958-1494
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2023: Vol. 2
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Information Dynamics and Applications (IDA) is a peer-reviewed, scholarly open access journal on the dynamics and applications of information technology, and the related fields. It is published quarterly by Acadlore. The publication dates of the four issues usually fall in March, June, September, and December each year.

  • Professional service - All articles submitted go through rigorous yet rapid peer review and editing, following the strictest publication standards.

  • Fast publication - All articles accepted are quickly published, thanks to our expertise in organizing peer-review, editing, and production.

  • Open access - All articles published are immediately available to global audience, and freely sharable anywhere, anytime.

  • Additional benefits - All articles accepted enjoy free English editing, and face no length limit or color charges.

Editor(s)-in-chief(1)
kuo-yi lin
Tongji University, China
kylink1008@hotmail.com | website
Research interests: Machine learning, artificial intelligence, semiconductor, manufacturing systems, big data analysis, user experience, manufacturing, production, UX design

Aims & Scope

Aims

Information Dynamics and Applications (IDA) (ISSN 2958-1486) is a fully refereed international open access journal, which publishes original research results in all subjects related to the dynamics and applications of information technology. The mission of the journal is to promote interdisciplinary research centered around the key issues of information processing, storage, and transmission. We welcome original submissions in various forms, including reviews, regular research papers, and short communications as well as Special Issues on particular topics. Our special interest lies in new analytical and application techniques of information technology in different fields of science and engineering.

The aim of IDA is to encourage scientists to publish their theoretical and experimental results in as much detail as possible. Therefore, the journal has no restrictions regarding the length of papers. Full details should be provided so that the results can be reproduced. In addition, the journal has the following features:

  • Journal editors behave in a professional and courteous manner toward authors, and offer specific suggestions for improving a paper.
  • Authors from non-English speaking countries will receive language support.
  • The published papers have maximum exposure under our open access policy.

Scope

The scope of the journal covers, but is not limited to the following topics:

  • Artificial intelligence
  • Digitalization
  • Signal processing
  • Simulation optimization
  • Social networking
  • Ubiquitous computing
  • Industrial engineering
  • Information architecture
  • Internet of things
  • Data mining and manipulation
  • Data storage, retrieval, and transmission
  • Database management
  • Decision support systems
  • Enterprise systems
  • Management information systems
  • Electronic commerce
  • Joint application development, knowledge-based systems
  • Local area networks
  • Robotics
  • Security
  • Software as a service
  • Supply chain management
  • Systems analysis
  • Quantum optimization
Articles
Recent Articles
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Open Access
Research article
A Cervical Lesion Recognition Method Based on ShuffleNetV2-CA
chunhui liu ,
jiahui yang ,
ying liu ,
ying zhang ,
shuang liu ,
tetiana chaikovska ,
chan liu
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Available online: 05-24-2023

Abstract

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Cervical cancer is the second most common cancer among women globally. Colposcopy plays a vital role in assessing cervical intraepithelial neoplasia (CIN) and screening for cervical cancer. However, existing colposcopy methods mainly rely on physician experience, leading to misdiagnosis and limited medical resources. This study proposes a cervical lesion recognition method based on ShuffleNetV2-CA. A dataset of 6,996 cervical images was created from Hebei University Affiliated Hospital, including normal, cervical cancer, low-grade squamous intraepithelial lesions (LSIL, CIN 1), high-grade squamous intraepithelial lesions (HSIL, CIN 2/CIN 3), and cervical tumor data. Images were preprocessed using data augmentation, and the dataset was divided into training and validation sets at a 9:1 ratio during the training phase. This study introduces a coordinate attention mechanism (CA) to the original ShuffleNetV2 model, enabling the model to focus on larger areas during the image feature extraction process. Experimental results show that compared to other classic networks, the ShuffleNetV2-CA network achieves higher recognition accuracy with smaller model parameters and computation, making it suitable for resource-limited embedded devices such as mobile terminals and offering high clinical applicability.
Open Access
Research article
Enhancing Data Storage and Access in CSN Labs with Raspberry Pi 3B+ and Open Media Vault NAS
ritzkal ritzkal ,
kodarsyah kodarsyah ,
asep ramdan sopyan nudin ,
ibnu hanafi setiadi ,
freza riana ,
berlina wulandari
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Available online: 05-23-2023

Abstract

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The purpose of this study was to devise a more efficient system for data storage and exchange in the Computer System and Network (CSN) Laboratory at Ibn Khaldun Bogor University. Open Media Vault (OMV) software and Raspberry Pi 3B+ were employed to establish a Network Attached Storage (NAS) system. The performance and file transfer speeds of the Raspberry Pi were evaluated in the context of this implementation. The implementation of the NAS system was intended to offer students of the CSN laboratory swifter and more efficient access to data, thereby reducing dependence on USB media. The findings of this study could hold substantial implications for enhancing the efficiency and effectiveness of data storage and exchange in educational environments.
Open Access
Research article
An Enhanced QoS-Aware Multipath Routing Protocol for Real-Time IoT Applications in MANETs
venkata reddy pathapalli srinivasappa ,
nandini prasad kanakapura shivaprasad ,
puttamadappa chaluvegowda
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Available online: 05-16-2023

Abstract

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Recent transmission of large volumes of data through mobile ad hoc networks (MANETs) has resulted in degraded Quality of Service (QoS) due to factors such as packet loss, delay, and packet drop in multipath routing. To address this issue, a traffic-aware Enhanced QoS-Aware Multipath Routing Protocol (EQMRP) has been proposed for real-time IoT applications in MANETs. EQMRP efficiently switches between multiple paths and monitors traffic conditions to maintain an optimal data transmission rate. The proposed method considers different delay sensitivity levels and link expiration time (LTE) to maintain QoS in each path. Through IoT application data analysis, EQMRP maintains QoS in each path more efficiently than conventional methods. The proposed method has been simulated and validated using MATLAB, and the performance analysis shows that EQMRP achieves a higher packet delivery ratio, lower delay, and reduced packet drop compared to conventional methods. In conclusion, the traffic-aware EQMRP protocol offers a significant improvement in QoS for real-time IoT applications in MANETs.

Abstract

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This paper proposes a novel architecture based on blockchain technology to enhance the dependability and safety of wireless sensor networks (WSN) by authenticating WSN nodes. In a WSN, sensor nodes collect and transmit data to cluster heads (CHs) for further processing. The proposed model employs the distance and residual energy-based low-energy adaptive clustering hierarchy (ECO-LEACH) protocol to replace CHs with ordinary nodes and the Interplanetary File System (IPFS) for storing data. In addition, consensus based on proof of authority (PoA) is used to validate transactions, reducing the computational cost associated with proof of work. The proposed system was evaluated using simulations with 300 sensor nodes and compared with other protocols, including LEACH, DDR-LEACH, PEGASIS, and LEACH-PSO. The simulation results showed that the proposed ECO-LEACH outperformed the other protocols in terms of energy consumption, throughput achieved, and network lifetime improvement. Specifically, the proposed system consumed 23.5J for 300 sensor nodes, achieved 687.5 kbps, and improved the network's lifetime by 4.12 seconds for 50 rounds. Overall, this paper provides a reliable and secure solution for authenticating WSN nodes, enhancing data transfer safety, and dependability. The proposed architecture offers a promising approach for addressing the challenges of WSN design using blockchain technology and PoA consensus. The comparative analysis shows that the proposed ECO-LEACH protocol outperforms other protocols in terms of energy consumption, throughput achieved, and network lifetime improvement for 300 sensor nodes

Abstract

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Grape leaf diseases can significantly reduce grape yield and quality, making accurate and efficient identification of these diseases crucial for improving grape production. This study proposes a novel classification method for grape leaf disease images using an improved convolutional neural network. The Xception network serves as the base model, with the original ReLU activation function replaced by Mish to improve classification accuracy. An improved channel attention mechanism is integrated into the network, enabling it to automatically learn important information from each channel, and the fully connected layer is redesigned for optimal classification performance. Experimental results demonstrate that the proposed model (MS-Xception) achieves high accuracy with fewer parameters, achieving a recognition accuracy of 98.61% for grape leaf disease images. Compared to other state-of-the-art models such as ResNet50 and Swim-Transformer, the proposed model shows superior classification performance, providing an efficient method for intelligent diagnosis of grape leaf diseases. The proposed method significantly improves the accuracy and efficiency of grape leaf disease diagnosis and has potential for practical application in the field of grape production.
Open Access
Research article
The Need to Improve DNS Security Architecture: An Adaptive Security Approach
daniel o. alao ,
folasade y. ayankoya ,
oluwabukola f. ajayi ,
onome b. ohwo
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Available online: 03-30-2023

Abstract

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The Domain Name System (DNS) is an essential component of the internet infrastructure. Due to its importance, securing DNS becomes a necessity for current and future networks. Various DNS security architecture have been developed in order to provide security services; such as DNS over HTTPS (DoH), DNS over TLS (DoT), and DNS over QUIC (DoQ). Unfortunately, these security architectures, especially DoT, are limited and are open to a number of performance issues. In this paper, we evaluate the present state of DNS security architecture, and we would see clearly that existing DNS security architectures are insufficient to secure DNS data transiting over the network; considering the growing cybersecurity landscape. On this note, we propose the need and adoption of a security architecture named Adaptive Security Architecture. Adaptive Security Architecture is devised to guard against identified threats, and anticipate unidentified threats in a manner similar to the immune-response system of human. Basically, mimicking nature’s biodiversity as the fundamental means of effective attack responses. Finally, we conclude by an analysis to prove the need to improve DNS security architecture.

Open Access
Research article
Routing Attack Detection Using Ensemble Deep Learning Model for IIoT
ramesh vatambeti ,
gowtham mamidisetti
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Available online: 03-30-2023

Abstract

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Smart cities, ITS, supply chains, and smart industries may all be developed with minimal human interaction thanks to the increasing prevalence of automation enabled by machine-type communication (MTC). Yet, MTC has substantial security difficulties because of diverse data, public network access, and an insufficient security mechanism. In this study, we develop a novel IIOT attack detection basis by joining the following four main steps: (a) data collection, (b) pre-processing, (c) attack detection, and (d) optimisation for high classification accuracy. At the initial stage of processing, known as "pre-processing," the collected raw data (input) is normalised. Attack detection requires the creation of an intelligent security architecture for IIoT networks. In this work, we present a learning model that can recognise previously unrecognised attacks on an IIoT network without the use of a labelled training set. An IoT network intrusion detection system-generated labelled dataset. The study also introduces a hybrid optimisation algorithm for pinpointing the optimal LSTM weight when it comes to intrusion detection. When trained on the labelled dataset provided by the proposed method, the improved LSTM outperforms the other models with a finding accuracy of 95%, as exposed in the research.

Open Access
Research article
A Deep Convolutional Neural Network Framework for Enhancing Brain Tumor Diagnosis on MRI Scans
jyostna devi bodapati ,
shaik feroz ahmed ,
yarra yashwant chowdary ,
konda raja sekhar
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Available online: 03-30-2023

Abstract

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Brain tumors are a critical public health concern, often resulting in limited life expectancy for patients. Accurate diagnosis of brain tumors is crucial to develop effective treatment strategies and improve patients' quality of life. Computer-aided diagnosis (CAD) systems that accurately classify tumor images have been challenging to develop. Deep convolutional neural network (DCNN) models have shown significant potential for tumor detection, and outperform traditional deep neural network models. In this study, a novel framework based on two pre-trained deep convolutional architectures (VGG16 and EfficientNetB0) is proposed for classifying different types of brain tumors, including meningioma, glioma, and pituitary tumors. Features are extracted from MR images using each architecture and merged before feeding them into machine learning algorithms for tumor classification. The proposed approach achieves a training accuracy of 98% and a test accuracy of 99% on the brain-tumor-classification-mri dataset available on Kaggle and btc_navoneel. The model shows promise to improve the accuracy and generalizability of medical image classification for better clinical decision support, ultimately leading to improved patient outcomes.

Open Access
Research article
Integration of Ontology Transformation into Hidden Markov Model
lazarre warda ,
guidedi kaladzavi ,
amaria samdalle ,
kolyang
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Available online: 12-26-2022

Abstract

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The goal of this study is to suggest a method for turning an ontology into a hidden Markov model (HMM). Ontology properties (relationships between classes) and ontology classes are taken as HMM symbols and states, respectively. Knowledge is represented in many different fields using the central element of the Semantic Web dubbed ontology. The authors employed machine learning technologies like HMM to add knowledge to these ontologies or to extract knowledge from within them. The meaning obtained from ontologies is not described during this task. The ontology triples that were extracted using SPARQL queries are used in this paper to transform the ontology into an HMM in order to handle this semantic. The Pizza ontology has been used to implement this method, which is based on lightweight ontologies.

Abstract

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Digital learning is the use of telecommunication technology to deliver information for education and training. As the increased acceleration of the propagation speed of the web, a lot of data collected by automated or semi-automated way. The 4s (Volume, Velocity, Variety and Veracity) of big data increase the challenge to extract useful value via systemic framework. This study aims to construct the data model of big data digital learning. Based on the digital learning data, data-driven innovation framework was proposed to identify data form and collect data. Bayesian network was proposed to capture learning model to extract user experience of students to enhance learning efficiency. Empirical study was conducted on a university to validate the proposed approach. The results have been implemented to support the strategies to improve student learning outcomes and competitiveness.

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This study proposes a systematic review of the application of Ensemble learning (EL) in multiple industries. This study aims to review prevailing application in multiple industries to guide for the future landing application. This study also proposes a research method based on Systematic Literature Review (SLR) to address EL literature and help advance our understanding of EL for future optimization. The literature is divided three categories by the National Bureau of Statistics of China (NBSC): the primary industry, the secondary industry and the tertiary industry. Among existing problems in industrial management systems, the frequently discussed are quality control, prediction, detection, efficiency and satisfaction. In addition, given the huge potential in various fields, the gap and further directions are also suggested. This study is essential to industry managers and cross-disciplinary scholars to lead a guideline to solve the issues in practical work, as it provided a panorama of application domains and current problems. This is the first review of the application of EL in multiple industries in the literature. The paper has potential values to broaden the application area of EL, and to proposed a novel research method based SLR to sort out literature.

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