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Performance evaluation of transdermal optical wireless communication using spatial diversity techniques

10.11591/ijeecs.v38.i2.pp865-875
Rawan Almajdoubah , Omar Hasan
Active medical implants and other contemporary medical applications need a dependable, high-speed communication channel between external and internal transceivers. Optical wireless communications have demonstrated advantages over widely used radio frequency technology in terms of data speeds, bandwidth abundance, and immunity to interference. Regretfully, this advantage implies strict alignment requirements for both sending and receiving ends. This study focuses on the effects of using multiple transmitters or receivers under the influence of pointing error on the transcutaneous link's overall performance measured by the outage probability and outage rate. Spatial diversity techniques have demonstrated their viability in increasing the link's reliability in free space optical communications. This drives the investigation of improvement transdermal communication system by adding numerous transmitters or receivers. Various misalignment severities are used to represent different operating circumstances, and these analyses result in explicit closed-form formulas for the relevant metrics. The findings clearly show the benefits of employing multiple transmitters and receivers on the link's outage performances. A notable improvement in the average signal-to-noise ratio values for the outage probability and outage rate compared to the single input single output setup was achieved. Furthermore, the theoretical conclusions are subsequently confirmed by MATLAB-based Monte-Carlo simulation for several instructive cases.
Volume: 38
Issue: 2
Page: 865-875
Publish at: 2025-05-01

Emerging approaches of artificial intelligence tools for distance learning: a review

10.11591/ijeecs.v38.i2.pp1219-1230
Ghita Faouzi , Naila Amrous , Nour-Eddine El Faddouli , Mostafa Khabouze
Learning management system (LMS) is the best way to deliver educational content in the context of higher education, by settings students worldwide with high-quality educational material. This paper principally seeks to examine the use of e-learning platforms in the last years from 2019 to 2023, which has coincided with the pandemic period, by elucidating the benefits and limitations of e-learning platforms, analyzing the real-world artificial intelligence (AI) algorithms used and their operating context. A comprehensive literature search was conducted on different electronic databases to identify relevant studies related to e-learning and AI tools used during this period by applying inclusion, exclusion criteria and preferred reporting items for systematic reviews and meta-analysis (PRISMA) process. Based on this review the tools were necessary social media and free communication platforms that offer the flexibility and build autonomy to students. On the other hand, many challenges are arisen due to the lack of experience in the term of using those tools or due to technical problems, for this reason, the use of AI tools to enhance learning experience still one of the approved solutions.
Volume: 38
Issue: 2
Page: 1219-1230
Publish at: 2025-05-01

An embedded system for the classification of sleep disorders using ECG signals

10.11591/ijeecs.v38.i2.pp767-773
Lavu Venkata Rajani Kumari , Babishamili Daravath , Yarlagadda Padma Sai
Sleep apnea (SA) is a well-known sleep disorder. It predominantly appears due to lack of oxygen in humans. Identifying SA at an early stage can help early diagnosis. The primary motto of our research is to identify SA using electrocardiogram (ECG) signals. Here, three classes are considered for classification. One is normal (N), and the other two are SA classes obstructive sleep apnea (OA) and central sleep apnea (CA). ECG signals are accumulated for MIT-BIH polysomnographic dataset. The ECG data divided into ECG segments and labelled using annotation file. The proposed deep long short-term memory (LSTM) model is then trained using ECG segments and further tested. The model is then finetuned and optimized to obtain the best accuracy. An accuracy of 98.51% is obtained. In addition, performance measures like precision, sensitivity, specificity, F-score are also evaluated. The model is then deployed on NVIDIA’s Jetson nano board to build a prototype. Our model is effective, promising and outperformed existing state of art techniques.
Volume: 38
Issue: 2
Page: 767-773
Publish at: 2025-05-01

Deep learning-based cryptanalysis in recovering the secret key and plaintext on lightweight cryptography

10.11591/ijeecs.v38.i2.pp1115-1123
Yulia Fatma , Muhammad Akmal Remli , Mohd Saberi Mohamad , Januar Al Amien
The development of machine learning (ML) technologies provide a new development direction for cryptanalysis. Several ML research in the field of cryptanalysis was carried out to identify the cryptographic algorithm used, find out the secret key, and even recover the secret message The first objective of this study is to see how much influence optimization and activation function have on the multi-layer perceptron (MLP) model in performing cryptanalysis. The second research objective, which is to compare the performance of cryptanalysis in recovering keys and the plaintext. Several experiments have been carried out, the observed parameters found that the use of the rectified linear unit (ReLU) activation function and the ADAM optimizer improves the performance of deep learning (DL)-based cryptanalysis as evidenced by a significantly smaller error rate. DL-based cryptanalysis works more effectively in recovering keys than recovering plaintext. DL-based cryptanalysis managed to recover the keys with an average loss of 0.007, an average of 49 epochs, and an average time of 0.178 minutes.
Volume: 38
Issue: 2
Page: 1115-1123
Publish at: 2025-05-01

A novel mobile application for personality assessment based on the five-factor model and graphology

10.11591/ijeecs.v38.i2.pp915-927
Ahmed Remaida , Zineb Sabri , Benyoussef Abdellaoui , Chakir Fri , Yassine Lakhchaf , Younès El Bouzekri El Idrissi , Mohammed Amine Lafraxo , Aniss Moumen
With the rising interest over the last decade, automated graphology has emerged as a promising filed of research, providing new insights on personality traits prediction on the basis of handwriting analysis. Although, few practical solutions to automate the extraction of handwriting features and personality prediction exist in the literature. This work aims to contribute to closing the gap in automated handwriting personality prediction by proposing a novel mobile application that uses robust feature extraction and machine learning models to predict big five personality traits. Our findings, based on high correlations between handwriting characteristics and personality traits, revealed convincing links. Notably, extraversion and extraversion have strong correlations with top margin feature, whereas agreeableness is expressed through line spacing. These findings emphasize the ability of automated graphology to properly interpret individual personalities. The proposed system achieved exceptional accuracy by using well known machine learning classifiers. The testing accuracy exceeded 92% in binary classification and 87% in multi-class case scenario, proving the adaptability and dependability of the system’s architecture. Our Android app promises to provide users with unprecedented insights into their personalities, establishing a robust tool for psychological assessment and self-discovery.
Volume: 38
Issue: 2
Page: 915-927
Publish at: 2025-05-01

Measuring political influence during elections using a deep learning approach

10.11591/ijeecs.v38.i2.pp1273-1288
Abderrazzak Cherkaoui , Omar El Beqqali
This contribution introduces a methodology for measuring political influence on Twitter during the 2020 U.S. presidential election campaign. The approach employs deep knowledge scores, which are generated through sentiment analysis of Tweets from users responding to influential users, coupled with an assessment of the strength of their interactions. The deep knowledge scores enable the categorization of three types of Twitter’s users engaging with influential users: influenced users, distrustful users, and connected users. Our approach, structured around a five-layer framework, effectively constructs networks of trust and distrust, and establishes the relationship between fluctuations in trust or distrust levels and the topics discussed by influential users.
Volume: 38
Issue: 2
Page: 1273-1288
Publish at: 2025-05-01

A recurrent network technique for energy optimization in 6G networks with dynamic device-to-device communication

10.11591/ijeecs.v38.i2.pp897-903
Sonia Aneesh , Alam N. Shaikh
Energy efficiency has become a paramount concern in the design and deployment of 6G networks, driven by the exponential growth of connected devices and increasing traffic demands. For domain experts grappling with dynamic device-to-device (D2D) communication scenarios, optimizing energy consumption while maintaining reliable connectivity poses a significant challenge. To address this issue, we propose a novel recurrent network technique that dynamically configures D2D communication patterns, adaptively allocating temporary base stations among network nodes to enable efficient data transmission while minimizing energy expenditure. Our simulations demonstrate substantial energy savings, extended node lifetimes, and reliable performance, with a 37% reduction in overall network energy consumption and a 65% increase in average node lifetime compared to traditional cellular communication scenarios. In conclusion, this innovative approach paves the way for sustainable and energy efficient 6G communication systems, benefiting society by reducing operational costs, minimizing environmental impact, and prolonging the usability of mobile devices.
Volume: 38
Issue: 2
Page: 897-903
Publish at: 2025-05-01

Enhanced vegetation encroachment detection along power transmission corridors using random forest algorithm

10.11591/ijeecs.v38.i2.pp1376-1382
Deepa Somasundaram , Nivetha Sivaraj , Shalinirajan Shalinirajan , Santhi Karuppiah , Sudha Rajendran
Vegetation encroachment along power transmission corridors poses significant risks to infrastructure safety and reliability, necessitating effective monitoring and management strategies. This study introduces an innovative methodology for detecting vegetation encroachment using a combination of manual and automatic processes integrated with the random forest algorithm. The issue of vegetation encroachment is critical as it can lead to power interruptions and safety hazards if not addressed promptly. The objective of this research is to develop a scalable and cost-effective solution for vegetation management in power infrastructure maintenance. The methodology involves manual patch extraction and labeling to ensure the accuracy of the training dataset, combined with automatic feature extraction techniques to capture relevant information from satellite imagery. Leveraging the random forest algorithm, the model constructs an ensemble of decision trees based on the extracted features, achieving robust classification accuracy. Findings from this study demonstrate that the proposed approach enables consistent and timely identification of vegetation encroachment in new satellite imagery. Stored model parameters facilitate efficient testing, enhancing the system's ability to provide proactive interventions. This scalable solution significantly reduces reliance on manual labor and offers a cost-effective method for continuous monitoring, ultimately contributing to the resilience and safety of power transmission infrastructure.
Volume: 38
Issue: 2
Page: 1376-1382
Publish at: 2025-05-01

RNN-driven integration of spatial, temporal, features for Indian sign language recognition and video captioning

10.11591/ijeecs.v38.i2.pp821-829
Ajay Manohar Pol , Shrinivas A. Patil
This paper presents a novel model that integrates spatial features from residual blocks and temporal features from FFT, alongside a sophisticated RNN architecture comprising BiLSTM, gated recurrent units (GRU) layers, and multi-head attention. Achieving nearly 99% accuracy on both WLASL and INCLUDE datasets, this model outperforms standard CNN pretrained models in feature extraction. Notably, the BiLSTM and GRU combination proves superior to other combinations such as LSTM and GRU. The BLEU score analysis further validates the model's efficacy, with scores of 0.51 and 0.54 on the WLASL and INCLUDE datasets, respectively. These results affirm the model's proficiency in capturing intricate spatial and temporal nuances inherent in sign language gestures, enhancing accessibility and communication for the deaf and hard-of-hearing communities. The comparison highlights the superiority of this paper's proposed model over standard approaches, emphasizing the significance of the integrated architecture. Continued refinement and optimization hold promise for further augmenting the model's performance and applicability in real-world scenarios, contributing to inclusive communication environments.
Volume: 38
Issue: 2
Page: 821-829
Publish at: 2025-05-01

End-user software engineering approach: improve spreadsheets capabilities using Python-based user-defined functions

10.11591/ijeecs.v38.i2.pp1024-1032
Tamer Bahgat Elserwy , Tarek Aly , Basma E. El-Demerdash
End-user computing enables non-developers to handle data and applications, boosting collaboration and productivity. Spreadsheets are a key example of end-user programming environments that are extensively utilized in business for data analysis. However, the functionalities of Excel have limitations compared to specialized programming languages. This study aims to address this shortcoming by proposing a prototype that integrates Python's features into Excel via standalone desktop Python-based user-defined functions (UDFs). This method mitigates potential latency concerns linked to cloud-based solutions. This study employs Excel-DNA (dynamic network access) and IronPython; Excel-DNA facilitates the creation of custom Python functions that integrate smoothly with Excel's calculation engine, while IronPython allows these Python UDFs to run directly within Excel. Core components include C# and visual studio tools office (VSTO) add-ins, which enable communication between Python and Excel. This approach grants users the chance to execute Python UDFs for various tasks such as mathematical computations and predictions — all within the familiar Excel environment. The prototype showcases seamless integration, enabling users to invoke Python-based UDFs just like built in Excel functions. This study enhances the capabilities of spreadsheets by harnessing Python's strengths within Excel. Future work may focus on expanding the Python UDF library and examining user experiences with this innovative approach to data analysis.
Volume: 38
Issue: 2
Page: 1024-1032
Publish at: 2025-05-01

Retraction Notice: Quay crane assignment in container terminals using a genetic algorithm

10.11591/csit.v6i1.p40-47
Aidi Sanaa , Torbi Imane , Mazouzi Mohamed
Notice of Retraction: A. Sanaa, T. Imane, and M. Mohamed, "Quay crane assignment in container terminals using a genetic algorithm," Computer Science and Information Technologies, vol. 6, no. 1, 2023, pp. 40-47, doi: 10.11591/csit.v6i1.p40-47." This article has been determined to have contravened IAES publication principles following a thorough and thoughtful review of its content by a duly established expert committee, following the report of Hizia Amani and Rachid Chaib. In particular, the content of the following source was copied without proper attribution in this article: H. Amani, L. Bouyaya, R. Chaib, F. Z. Djekrif, and M. Aizi, "Optimization of Quay Crane Scheduling Problem at the Port of Algeria," in I. Kabashkin, I. Yatskiv, and O. Prentkovskis, Eds. Cham: Springer International Publishing, 2023, pp. 232–241, doi: 10.1007/978-3-031-26655-3_21. Consequently, IAES has removed the content of this article from this online system. The authors concurred with the decision to retract when it was communicated. Additionally, the authors requested that the article be removed.The article is not suitable for research or citation. We apologize for any inconvenience this may have caused.
Volume: 6
Issue: 1
Page: 40-47
Publish at: 2025-04-05

Multimodal perception for enhancing human computer interaction through real-world affect recognition

10.11591/ijeecs.v38.i1.pp428-438
Karishma Raut , Sujata Kulkarni , Ashwini Sawant
Human-Computer Interaction can benefit from real-world affect recognition in applications like healthcare and assistive robotics. Human express emotions through various modalities, with audio-visual being the most significant. Using a unimodal approach, such as only speech or visual, is challenging in natural, dynamic environments. The proposed methodology integrated a pretrained model with a convolution neural network (CNN) to provide a robust initialization point and address the limited availability of facial expression data. The multimodal framework enhances discriminative power by combining visual scores with speech. This work addresses the challenges at each stage of the real-world affect recognition framework, including data preprocessing, feature extraction, feature fusion, and final classification. A 1D-CNN is employed for training on spectral and prosodic audio features, while deep visual features are processed using a 2D-CNN. The proposed system's performance was evaluated on the extended Cohn-Kanade (CK+), acted-facial-expressions in-the-wild (AFEW), and real-world-affective-face-database (RAF) datasets, which are commonly used in face recognition research. Experimental results indicate that 2% to 5% of visual data from natural settings were undetected, and the inclusion of the audio modality improved performance by providing relevant and supplementary information.
Volume: 38
Issue: 1
Page: 428-438
Publish at: 2025-04-01

Link adaptation techniques for throughput enhancement in LEO satellites: a survey

10.11591/ijeecs.v38.i1.pp262-271
Habib Idmouida , Khalid Minaoui Minaoui
In addition to the rapid geometric change of low earth orbit (LEO) satellites, the earth-to-space channel suffers from various attenuations that affect the communication link. To overcome this challenge, the link adaptation technique emerges as a key solution to optimize the transmission performance of LEO satellites, especially the data throughput. The existing contributions in the literature remain scattered across the research board, and a comprehensive survey of this research area still lacks at this stage. The present survey examines various link adaptation methods, mainly variable coding and modulation, adaptive coding and modulation, and hybrid methods using artificial intelligence. In addition, this study explains how this technique leverages a set of recommended standards and cost-effective technologies, such as software-defined radio (SDR) and field programmable gate arrays (FPGA), to fine-tune transmission strategies. Lastly, the paper provides a comparative study of the current research on this field and sheds light on future directions, where the need for higher data throughput makes emerging learning-based techniques and new experimental standards a necessity.
Volume: 38
Issue: 1
Page: 262-271
Publish at: 2025-04-01

Optimizing FBMC/OQAM: Hermite filter and DFT-based precoding for PAPR reduction

10.11591/ijeecs.v38.i1.pp76-87
Anupriya Anupriya , Vikas Nandal
In the ever-evolving landscape of wireless communication, there is a persistent quest for modulation schemes that optimize spectral efficiency, reduce interference, and enhance overall system performance. This paper introduces a novel modulation technique that synergistically improves on the strengths of filter bank multi-carrier (FBMC). A distinctive feature of our approach is the deployment of the Hermite prototype filter in the FBMC system, diverging from traditional FBMC architectures. An advanced precoding strategy leveraging a pruned discrete fourier transform (pDFT) paired with scaling is also introduced. This combination promises reduced inter-symbol interference and heightened spectral efficiency. As the management of the peak-to-average power ratio (PAPR) is a significant challenge in FBMC systems to addressing this iterative particle swarm optimization (IPSO) algorithm is proposed. Evaluations are carried out to demonstrate the efficiency of the proposes scheme in reducing PAPR substantially for FBMC/OQAM framework. Experiments are conducted and comparisons are performed among several prominent multicarrier modulation schemes. The results from the experiments indicate that the application of IPSO algorithm with Hermite functions and applied to an FBMC/OQAM system using pruned DFT has been successful in reducing the PAPR also a 6-13% decrease in error rate has been shown across varying QAM orders regardless of SNR level.
Volume: 38
Issue: 1
Page: 76-87
Publish at: 2025-04-01

A hybrid combination of improved mayfly optimization based modified perturb and observe for solar based water pumping system

10.11591/ijeecs.v38.i1.pp50-62
Dattatray Surykant Sawant , Yerramreddy Srinivasa Rao , Rajendra Ramchandra Sawant
In recent years, solar water pumping systems (WPS) have been fuel-free and environmentally beneficial because they have gained a lot of attention in the agricultural and industrial sectors. Traditional water pumps consume higher amount of energy which make it as frequently unreliable, low efficiency and needs high maintenance. For WPS applications, Brushless DC (BLDC) motors are far superior options than other induction motors because of their high efficiency, high dependability, and low maintenance needs. Thus, in this research, the major goal is to develop a more efficient, reliable, and maintenance-free solar WPS solution. This paper describes a sensorless control strategy that reduces the need for hall sensors and increases system’s overall reliability. Solar system power is typically impacted by partial shadowing and cannot reach the maximum available power because the traditional perturbed and observe (P&O) algorithm fails. This paper integrates the modified P&O (MP&O) algorithm with an improved mayfly optimization (IMO) name called IMO-MP&O to address these issues by efficiently extracts the maximum power from solar. From the results, it clearly shows that IMO-MP&O achieved higher efficiency of 99.58% than the existing P&O MPPT which is analyzed the MATLAB sim-power-system toolboxes.
Volume: 38
Issue: 1
Page: 50-62
Publish at: 2025-04-01
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