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Information Dynamics and Applications
Information Dynamics and Applications (IDA)
ISSN (print): 2958-1486
ISSN (online): 2958-1494
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2023: Vol. 2
IDA Flyer

Information Dynamics and Applications (IDA) stands out in the realm of academic publishing as a distinct peer-reviewed, open-access journal, primarily focusing on the dynamic nature and diverse applications of information technology and its related fields. Distinguishing itself from other journals in the domain, IDA dedicates itself to exploring both the underlying principles and the practical impacts of information technology, thereby bridging theoretical research with real-world applications. IDA not only covers the traditional aspects of information technology but also delves into emerging trends and innovations that set it apart in the scholarly community. Published quarterly by Acadlore, the journal typically releases its four issues in March, June, September, and December each year.

  • Professional Service - Every article submitted undergoes an intensive yet swift peer review and editing process, adhering to the highest publication standards.

  • Prompt Publication - Thanks to our proficiency in orchestrating the peer-review, editing, and production processes, all accepted articles see rapid publication.

  • Open Access - Every published article is instantly accessible to a global readership, allowing for uninhibited sharing across various platforms at any time.

balamurugan balusamy
Shiv Nadar University, India | website
Research interests: Big Data; Network Security; Cloud Computing; Block Chain; Data Sciences; Engineering Education
kuo-yi lin
Tongji University, China | website
Research interests: Machine Learning; Artificial Intelligence; Semiconductor; Manufacturing Systems; Big Data Analysis; User Experience; Manufacturing; Production; UX Design

Aims & Scope


Information Dynamics and Applications (IDA), as an international open-access journal, stands at the forefront of exploring the dynamics and expansive applications of information technology. This fully refereed journal delves into the heart of interdisciplinary research, focusing on critical aspects of information processing, storage, and transmission. With a commitment to advancing the field, IDA serves as a crucible for original research, encompassing reviews, research papers, short communications, and special issues on emerging topics. The journal particularly emphasizes innovative analytical and application techniques in various scientific and engineering disciplines.

IDA aims to provide a platform where detailed theoretical and experimental results can be published without constraints on length, encouraging comprehensive disclosure for reproducibility. The journal prides itself on the following attributes:

  • Every publication benefits from prominent indexing, ensuring widespread recognition.

  • A distinguished editorial team upholds unparalleled quality and broad appeal.

  • Seamless online discoverability of each article maximizes its global reach.

  • An author-centric and transparent publication process enhances submission experience.


The scope of IDA is diverse and expansive, encompassing a wide range of topics within the realm of information technology:

  • Artificial Intelligence (AI) and Machine Learning (ML): Investigating the latest developments in AI and ML, and their applications across various industries.

  • Digitalization and Data Science: Exploring the transformation brought about by digital technologies and the analytical power of data science.

  • Signal Processing and Simulation Optimization: Advancements in the field of signal processing, including audio, video, and communication signal processing, and the development of optimization techniques for simulations.

  • Social Networking and Ubiquitous Computing: Research on the impact of social media on society and the pervasiveness of computing in everyday life.

  • Industrial Engineering and Information Architecture: Studies on the integration of information technology in industrial engineering and the structuring of information systems.

  • Internet of Things (IoT): Delving into the connected world of IoT and its implications for smart cities, healthcare, and more.

  • Data Mining, Storage, and Manipulation: Techniques and innovations in extracting valuable insights from large data sets, and the management of data storage and manipulation.

  • Database Management and Decision Support Systems: Exploring advanced database management systems and the development of decision support systems.

  • Enterprise Systems and E-Commerce: The evolution and future of enterprise resource planning systems and the impact of e-commerce on global markets.

  • Knowledge-Based Systems and Robotics: The intersection of knowledge-based systems with robotics and automation.

  • Cybersecurity and Software as a Service (SaaS): Cutting-edge research in cybersecurity and the growing trend of SaaS in business and consumer applications.

  • Supply Chain Management and Systems Analysis: Innovations in supply chain management driven by information technology, and systems analysis in complex IT environments.

  • Quantum Computing and Optimization: The role of quantum computing in solving complex problems and its future potential.

  • Virtual and Augmented Reality: Exploring the implications of virtual and augmented reality technologies in education, training, entertainment, and more.

Recent Articles
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Open Access
Research article
Enhancing Healthcare Data Security in IoT Environments Using Blockchain and DCGRU with Twofish Encryption
kumar raja depa ramachandraiah ,
naga jagadesh bommagani ,
praveen kumar jayapal
Available online: 11-30-2023


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In the rapidly evolving landscape of digital healthcare, the integration of cloud computing, Internet of Things (IoT), and advanced computational methodologies such as machine learning and artificial intelligence (AI) has significantly enhanced early disease detection, accessibility, and diagnostic scope. However, this progression has concurrently elevated concerns regarding the safeguarding of sensitive patient data. Addressing this challenge, a novel secure healthcare system employing a blockchain-based IoT framework, augmented by deep learning and biomimetic algorithms, is presented. The initial phase encompasses a blockchain-facilitated mechanism for secure data storage, authentication of users, and prognostication of health status. Subsequently, the modified Jellyfish Search Optimization (JSO) algorithm is employed for optimal feature selection from datasets. A unique health status prediction model is introduced, leveraging a Deep Convolutional Gated Recurrent Unit (DCGRU) approach. This model ingeniously combines Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) processes, where the GRU network extracts pivotal directional characteristics, and the CNN architecture discerns complex interrelationships within the data. Security of the data management system is fortified through the implementation of the twofish encryption algorithm. The efficacy of the proposed model is rigorously evaluated using standard medical datasets, including Diabetes and EEG Eyestate, employing diverse performance metrics. Experimental results demonstrate the model's superiority over existing best practices, achieving a notable accuracy of 0.884. Furthermore, comparative analyses with the Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) models reveal enhanced performance metrics, with the proposed model achieving a processing time and throughput of 40 and 45.42, respectively.


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The swift global spread of Corona Virus Disease 2019 (COVID-19), identified merely four months prior, necessitates rapid and precise diagnostic methods. Currently, the diagnosis largely depends on computed tomography (CT) image interpretation by medical professionals, a process susceptible to human error. This research delves into the utility of Convolutional Neural Networks (CNNs) in automating the classification of COVID-19 from medical images. An exhaustive evaluation and comparison of prominent CNN architectures, namely Visual Geometry Group (VGG), Residual Network (ResNet), MobileNet, Inception, and Xception, are conducted. Furthermore, the study investigates ensemble approaches to harness the combined strengths of these models. Findings demonstrate the distinct advantage of ensemble models, with the novel deep learning (DL)+ ensemble technique notably surpassing the accuracy, precision, recall, and F-score of individual CNNs, achieving an exceptional rate of 99.5%. This remarkable performance accentuates the transformative potential of CNNs in COVID-19 diagnostics. The significance of this advancement lies not only in its reliability and automated nature, surpassing traditional, subjective human interpretation but also in its contribution to accelerating the diagnostic process. This acceleration is pivotal for the effective implementation of containment and mitigation strategies against the pandemic. The abstract delineates the methodological choices, highlights the unparalleled efficacy of the DL+ ensemble technique, and underscores the far-reaching implications of employing CNNs for COVID-19 detection.

Open Access
Rapid communication
Comparative Analysis of Seizure Manifestations in Alzheimer’s and Glioma Patients via Magnetic Resonance Imaging
jayanthi vajiram ,
sivakumar shanmugasundaram ,
rajeswaran rangasami ,
utkarsh maurya
Available online: 10-24-2023


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A notable association between Alzheimer's Disease and Epilepsy, two divergent neurological conditions, has been established through previous research, illustrating an elevated seizure development risk in individuals diagnosed with Alzheimer’s Disease (AD). The hippocampus, fundamental in both seizure and tumour pathology, is intricately investigated herein. The subsequent aberrant electrical activity within this brain region, frequently implicated in seizure onset and propagation, underpins a complex relationship between degenerative cerebral changes and seizure incidence. Symptomatic manifestations in hippocampal glioma include, but are not limited to, seizures, memory deficits, and language difficulties, contingent upon the tumour's location and size. Thus, the cruciality of proficient seizure detection and analysis is underscored. Employing canny edge detection and thresholding to delineate contours and boundaries within images, an analysis was conducted by transmuting grayscale or colour images into a binary format. The input dataset, utilised for the training and testing of machine and deep-learning models, comprised images of seizures. These models were subsequently trained to discern patterns and features within the images, facilitating the differentiation between two predefined classes. Resultantly, the models predicted, with a defined accuracy level, the presence or absence of a seizure within a new image. The Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models demonstrated classification accuracies of 96% and 95%, respectively. By analysing performance metrics on a per-slice basis, the localization of seizure activity within the brain could be visualised, offering valuable insights into regions affected by this activity. The amalgamation of edge detection, feature extraction, and classification models proficiently discriminated between seizure and non-seizure activities, providing pivotal insights for the diagnosis and therapeutic strategies for epilepsy. Further, studying these neurological alterations can illuminate the progression and severity of cognitive and emotional deficits within affected individuals, whilst advancements in diagnostic methodologies, such as Magnetic Resonance Imaging (MRI), facilitate an enriched comparative analysis.


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Cyclins, commonly referred to as co-enzymes, are a pivotal family of proteins that modulate cellular growth by activating cell-cycle mediators, proving essential for the cell cycle. Due to the marked dissimilarity in their sequences, effective differentiation among cyclins remains a challenging endeavour. In this study, an innovative methodology was proposed, wherein the amino acid composition was utilized to inform an SVM-based classification approach. SVMs, being supervised machine learning algorithms, are typically employed for classification and regression tasks. From the data analyzed, eighteen (18) feature labels were extracted, culminating in an extensive set of thirteen thousand one hundred and fifty-one (13,151) discernible features. Employing the jackknife cross-validation technique revealed that this SVM-informed approach facilitated the identification of cyclins with an accuracy rate of 91.9%, a notable improvement from prior studies. Such advancements underscore the potential for more accurate and efficient differentiation of cyclins in future endeavours.

Open Access
Research article
MR Image Feature Analysis for Alzheimer’s Disease Detection Using Machine Learning Approaches
d. s. a. aashiqur reza ,
sadia afrin ,
md. ahsan ullah ,
sourav kumar kha ,
sadia chowdhury toma ,
raju roy ,
lasker ershad ali
Available online: 09-26-2023


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Alzheimer’s disease (AD), a progressive neurological disorder, predominantly impacts cognitive functions, manifesting as memory loss and deteriorating thinking abilities. Recognized as the primary form of dementia, this affliction subtly commences within brain cells and gradually aggravates over time. In 2023, dementia's financial burden for elderly adults aged 65 and older was projected to reach \$345 billion, encompassing health care, long-term care, and hospice services. Alarmingly, Alzheimer's disease claims one in three seniors, outnumbering combined fatalities from breast and prostate cancer. Currently, the diagnostic landscape for Alzheimer's lacks definitive tests, and diagnoses based purely on biological definitions have been observed to possess low predictive accuracy. In the presented study, a diagnostic methodology has been proposed using machine learning models that harness image features derived from brain MRI scans. Specifically, nine salient image features, grounded in color, texture, shape, and orientation, were extracted for the study. Four classifiers — Naïve-Bayes, Logistic regression, XGBoost, and AdaBoost — were employed, as the challenge presented a binary classification scenario. A grid search parameter optimization technique was employed to fine-tune model configurations, ensuring optimal predictive outcomes. Conducted experiments utilizing the Kaggle dataset, and for each model, parameters were rigorously optimized. The XGBoost classifier demonstrated superior performance, achieving a test accuracy of 92%, while Naïve Bayes, Logistic Regression, and AdaBoost registered accuracies of 63%, 70%, and 72%, respectively. Relative to contemporary methods, the proposed diagnostic approach exhibits commendable accuracy in predicting AD. If AI-based predictive diagnostics for AD are realized using the strategies delineated in this study, significant benefits may be anticipated for healthcare practitioners.

Open Access
Research article
Enhanced Channel Estimation in Multiple-Input Multiple-Output Systems: A Dual Quadratic Decomposition Algorithm Approach for Interference Cancellation
sakkaravarthi ramanathan ,
tirupathaiah kanaparthi2 ,
ravi sekhar yarrabothu ,
ramesh sundar
Available online: 09-20-2023


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In Multiple-Input Multiple-Output (MIMO) systems, a considerable number of antennas are deployed at each base station, utilizing Time-shifted pilot contamination strategies. It was observed that Time-shifted pilot contamination can mitigate the adverse effects of pilot contamination, subsequently reducing Inter-group interference. However, constraints are introduced in the channel estimation process when pilots are time-shifted. To address the challenge of increasing mean square channel estimation errors in finite antenna massive MIMO systems, a novel approach using a Dual Quadratic Decomposition Algorithm for Interference Cancellation (DQDA-IC) is introduced. Through this methodology, data interference gets effectively canceled when base stations collaborate. Furthermore, compressive sensing techniques are employed, resulting in enhanced channel compensation and reduced pilot contamination in massive MIMO systems. Comparative experimental analysis, conducted using the MATLAB tool, pitted this method against two conventional techniques: Integer Linear Programming (ILP) and Q-Learning based Interference Control (QLIC). Results indicated that the DQDA-IC model surpassed its counterparts by achieving a 63% improvement in Signal to Noise Ratio (SNR), a 56% reduction in Bit Error Rate (BER), and a 92% enhancement in spectral efficiency, all within a 40 ms computational timeframe.


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Digital forensics, a crucial subset of cybersecurity, encompasses sophisticated tools and methodologies for the interpretation, analysis, and investigation of digital evidence, facilitating the identification and mitigation of cybercrimes and security breaches. With the advent of cryptocurrencies, an array of unique challenges has emerged in the domain of digital forensic investigations. This review elucidates the prevailing state of digital forensic practices vis-à-vis cryptocurrencies, emphasizing the obstacles and limitations inherent in probing decentralized and intricate technologies. Notable deficiencies in extant investigative practices were observed. Solutions proffered encompass the formulation of novel software applications tailored for cryptocurrency analyses, the integration of machine learning and artificial intelligence capabilities, and the employment of advanced analytics to discern patterns and irregularities within blockchain transactions. Furthermore, a pioneering methodology, merging traditional digital forensic strategies with blockchain-specific techniques, is posited for efficacious cryptocurrency inquiries. The analysis underscores the imperative for a renewed paradigm in digital forensic examinations to surmount the challenges integral to cryptocurrency probes. By forging novel methodologies and standardizing investigative procedures, support for legal enforcement endeavors can be enhanced, facilitating the efficacious detection and prosecution of cryptocurrency-associated misdemeanors.

Open Access
Research article
An Optimized Algorithm for Peak to Average Power Ratio Reduction in Orthogonal Frequency Division Multiplexing Communication Systems: An Integrated Approach
rathod shivaji ,
nataraj kanathur ramaswamy ,
mallikarjunaswamy srikantaswamy ,
rekha kanathur ramaswamy
Available online: 09-05-2023


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The impact of the peak to Average Power Ratio (PAPR) on the efficiency of an Orthogonal Frequency Division Multiplexing (OFDM) communication system is significantly mitigated through an innovative Reconfigurable Integrated Algorithm (RIA). In this study, the RIA combines the advantages of Partial Transmit Sequence (PTS) and Companding Transformation (CT) techniques, enhancing the overall efficiency while reducing the signal distortion inherent in linear transformation methods. A unique reconfiguration process enables integration of PTS and CT to minimize PAPR. This process considers key parameters including multi-channel inputs and delay attenuation factors. Comparison of the RIA with conventional methods such as PTS, CT, selective mapping (SLM), and Tone Reservation (TR) reveals superior performance, as evidenced by the Complementary Cumulative Distribution Function (CCDFs) curve. Implementations of the algorithm using MATLAB R2022a demonstrate significant improvements in PAPR performance, showing gains of 0.55dB and 0.656dB compared to the PTS and CT methods respectively. Moreover, the novel RIA methodology exhibits enhanced transmission rates and lower Bit Error Rates (BER) relative to conventional methods. In conclusion, the proposed RIA offers a promising approach for optimizing OFDM system performance through efficient PAPR reduction. Its implementation can drive the advancement of telecommunications technologies and further understanding of OFDM communication systems.


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Social media, particularly Twitter, has emerged as a vital platform for understanding public opinion on contemporary issues. This study investigates public attitudes towards UK rail strikes by analyzing Twitter data and provides a framework to assist policymakers in the RMT Union and the government in managing social media information. A dataset comprising tweets related to rail strikes from 25 June 2022 to 7 October 2022 was collected and multidimensional scaling and sentiment analysis techniques were employed to examine public opinions and sentiments. The analysis revealed that the predominant trends in tweets were dissatisfaction and negativity, with users expressing inconvenience caused by the rail strikes. Interestingly, the public also questioned the government's capabilities, with some suggesting that rail strikes were politically motivated events orchestrated by the government. Sentiment analysis results indicated that approximately 85% of tweets displayed negative sentiment towards the rail strikes. This research contributes to the understanding of public attitudes derived from tweet mining and offers valuable insights for academics and policymakers in interpreting public reactions to current events. Based on the findings, recommendations for the RMT Union are proposed through the lenses of stakeholder orientation theory and signaling theory. For instance, fostering public engagement can help reduce information asymmetry between the RMT Union and the public, enabling the union to better comprehend public sentiment towards rail strikes. The approach amalgamates these two theories, presenting a novel theoretical perspective for such investigations and extending their applicability, while also providing clear and in-depth recommendations for the RMT Union.

Open Access
Research article
An IoT-Based Multimodal Real-Time Home Control System for the Physically Challenged: Design and Implementation
kennedy okokpujie ,
david jacinth ,
gabriel ameh james ,
imhade p. okokpujie ,
akingunsoye adenugba vincent
Available online: 06-15-2023


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Physical impairments affect a significant proportion of the global populace, emphasizing the need for assistive technologies to increase the ability of these individuals to perform daily activities autonomously. This study discusses the development and implementation of a multimodal home control system, designed to afford physically challenged individuals greater control over their home environments. This system utilizes the Internet of Things (IoT) for its functionality. The system is primarily based on the utilization of the Amazon Alexa Echo Dot, which facilitates speech-based control, and a sequential clap recognition system, both made possible through an internet connection. These methods are further supplemented by an additional manual switching option, thereby ensuring a diverse range of control methods. The processing core of this system consists of an Arduino Uno and an ESP32 Devkit module. In conjunction with these, a sound detector is employed to discern and process a variety of clap patterns, which is set to function at a predefined threshold. The Amazon Alexa Echo Dot serves as the primary interface for voice commands and real-time information retrieval. Furthermore, an Android smartphone, equipped with the Alexa application, provides alternate interfaces for appliance control, through both soft buttons and voice commands. Based on an analysis of this system, it is suggested that it is not only viable but also effective. Key attributes of the system include rapid response times, aesthetic appeal, secure operation, low energy consumption, and most importantly, increased accessibility for physically disabled individuals.
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
Available online: 05-24-2023


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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.
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