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31,456 Article Results

Design of a pyramidal slotted wide-band patch antenna for directive wireless communications over Ka-Band

10.11591/ijict.v15i3.pp1135-1142
Safa Nasssr Nafea
A wide Band for directive wireless communications over Ka-Band was presented. The proposed antenna printed on Rogers RT/Duroid 5880 substrate and achieved a moderate gain of 6.80 dB at resonating freqenc of 28 GHz. The pyramidal antenna achieved a wide operating bandwidth of 11.55 GHz. The proposed antenna achieved high size reduction percentage with a compact overall dimension of (4.8×6.9×1.5) mm3. The antenna achieved the wide operating bandwidth based on two factors; the first is selection of low loss dielectric material to ensure etter antennas performance. The other is etching pyramidal with 2×2 squared – shapes slots array etched from patch’s surface to reduce stored reactive energy which decreases overall ualit factor of and improves the -10 dB bandwidth obviously.
Volume: 15
Issue: 3
Page: 1135-1142
Publish at: 2026-09-01

Advanced encryption standard with asymmetric key exchange for text encryption

10.11591/ijict.v15i3.pp1263-1271
Ravindra K Reddy , Vijayalakshmi P
When creating communication systems, securing data is crucial and improved randomization in creating secret keys contributes to more secure systems. Unfortunately, the symmetric ciphers used for data encryption, such as the advanced encryption standard (AES), may be subject to attacks that exploit timing measurements to deduce the secret key used for encryption, resulting in a significant lack of research on the security of hybrid implementations (usually defined as incorporating AES and asymmetric ciphers) for AES encryption. We introduce a new hybrid encryption method that combines the AES encryption standard with elliptic curve cryptography (ECC). In this hybrid form of encryption, ECC is utilized to facilitate secure encryption and transmission of the AES key and cycle through 16 rounds of AES to encrypt the majority of the data. We provide a performance comparison between the new algorithm and the AES-128 encryption standard. Our findings indicate that the new hybrid encryption method produced an average encryption time of 0.0002 seconds for 10MB files, significantly faster than the AES-128 encryption standard, which takes an average of 0.0016 seconds. The new hybrid method exhibits significant resistance to cryptanalytical attempts, experiencing an average avalanche effect of 49.84%, while maintaining the AES nonlinearity value at 112. As such, we conclusively state that the new hybrid encryption method utilizing ECC and AES will provide effective security for user data against timing side-channel attacks while remaining an efficient method for performing encryption.
Volume: 15
Issue: 3
Page: 1263-1271
Publish at: 2026-09-01

Intelligent engineering framework for managing hospital cardiac arrest resources

10.11591/ijict.v15i3.pp1290-1302
Chams Eddine Fathoun , Mohamed Ridda Laouar , Safa Abid , Sean B. Eom
In-hospital cardiac arrest in intensive care remains frequent (often cited incidence roughly 0.5%-7.8% of admissions), while causes differ in what staff and equipment must be ready. We ask whether vital-sign trajectories from a standard EHR can classify which of three cardiac-related mechanisms is most salient arrhythmia, acute myocardial infarction (AMI), or respiratory failure or hypoxia so ICU resources can be aligned with risk. Using MIMIC-IV, we extracted diagnoses and charted vitals in the 12 hours before the index event, applied cleaning, aggregation, label encoding, sequence padding, and class balancing (3,000 cases per class), then trained and compared eXtreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and logistic regression (LR) with 5-fold cross-validation on an 80/20 split. XGBoost performed best (about 93% accuracy; sensitivity 89.15%; specificity 90.43%; AUC-ROC 0.94). Feature importance highlighted heart rate, oxygen saturation, and blood pressure patterns consistent with bedside monitoring practice. The study supports mechanism-oriented triage labels derived from widely recorded vitals, as a complement to generic early warning scores, for prioritizing telemetry, respiratory support, and cardiology pathways. External validation and prospective evaluation are needed before deployment.
Volume: 15
Issue: 3
Page: 1290-1302
Publish at: 2026-09-01

A multi-expert approach to content-based image retrieval using feature fusion and late re-ranking

10.11591/ijict.v15i3.pp1376-1384
Ali Abdulazeez Mohammed Baqer Qazzaz , Yousif Samer Mudhafar
As digital data rapidly grows, content-based image retrieval (CBIR) has become important for optimizing collections of visual data. This work proposes a retrieval framework which operates in two stages and improves accuracy by using systematic fusion of features. In the first stage, first-stage wide-scope descriptors called bag-of-visual-words (BoVW), scattering wavelet transform (SWT), discrete cosine transform (DCT), and principal component analysis (PCA) retrieve initial candidate images. The second stage undertakes detailed re-ordering of candidate images by implementing the local binary pattern (LBP), histogram of oriented gradients (HOG), and singular value decomposition (SVD) descriptors to re-evaluate similarity scores. Each individual descriptor returned results for mean average precision for the top 10 retrieved images (mAP, top-10) of between 0.63 and 0.79 and the fused framework achieved 0.88, which is evidence of the viability of complementary feature integration. These findings support the hypothesis that while multiple descriptors performed well and delivered high retrieval accuracy, hierarchical fusion of multiple handcrafted descriptors does not involve the computational costs associated with deep learning methods.
Volume: 15
Issue: 3
Page: 1376-1384
Publish at: 2026-09-01

Probabilistic inventory modeling for chlorine gas using minitab and python: a comparative study of demand distributions

10.11591/ijict.v15i3.pp1026-1037
Oki Dwipurwani , Fitri Maya Puspita , Siti Suzlin Supadi , Evi Yuliza
The availability of chlorine gas (Cl2) is a critical component in the drinking water disinfection process at the regional drinking water company (PDAM), as it plays a vital role in ensuring microbiological safety. Disruptions in the chlorine gas supply may lead to interruptions in water distribution and pose significant public health risks. This study investigates the application of a probabilistic (Q, r) inventory model for managing chlorine gas stock, incorporating several probability distributions that satisfy the underlying model assumptions. The resulting optimal inventory policies derived from each distribution are then compared. Chlorine gas demand forecasting is also performed using the seasonal autoregressive integrated moving average (SARIMA) model. The objective of this research is to generate an optimal inventory policy and accurate demand forecasts, with the entire implementation carried out in Python software. The results show that the best model obtainis the SARIMA (0,1,0)(0,1,1)12 model, with a MAPE value of 5.48%, and that the chlorine gas demand data follow normal, gamma, exponential, and erlang probability distributions. The comparison results show that the optimal policy of the gamma probabilistic model provides the best results, as well as being better than Normal and exponential policies in previous studies.
Volume: 15
Issue: 3
Page: 1026-1037
Publish at: 2026-09-01

A multi-cancer detection framework using deep learning and hybrid machine learning approaches

10.11591/ijict.v15i3.pp1443-1452
Karan Singh , Amruta Pawar , Drishya Tomar , Amrita Yadav , Aditi Chhabria , Vaibhav Narawade
The diagnostic solutions offered by the present artificial intelligence (AI) solutions suffer from non-generalizability and heavy reliance on complex models. In an attempt to solve these issues, we propose a lightweight yet versatile method consisting of a combination of ResNet50 transfer learning and hybrid machine learning. Image features are extracted using dermoscopy, magnetic resonance imaging (MRI), and histopathological images. These are subjected to principal component analysis (PCA) dimensionality reduction followed by classification using support vector machine (SVM), random forest (RF), logistic regression (LR), and XGBoost algorithms. This segregation of the two processes improves efficiency. The hybrid approach using ResNet50 + LR yielded an accuracy of 91.01% in the case of breast cancer detection compared to 86.26% of a baseline convolutional neural network (CNN). Also, ResNet50 gave an accuracy of 96.61% in diagnosing skin cancer. Custom CNN provided an accuracy of 99.42% for lung cancer and 96.33% for brain tumor detection.
Volume: 15
Issue: 3
Page: 1443-1452
Publish at: 2026-09-01

Navigating digital parenting: a bibliometric exploration of trends on children’s digital soothing practices

10.11591/ijict.v15i3.pp1431-1442
Rita Wong Mee Mee , Noor Hanim Harun , Lim Seong Pek , Suzulaikha Mohamed , Tengku Shahrom Tengku Shahdan , Nurul Asyiqin Jalil , Anisa Ahmad , Tirzah Zubeidah Zachariah
The digital age has transformed parenting practices, with an increasing reliance on digital devices for managing children’s behavior, particularly as calming tools. This study addresses the growing phenomenon of digital parenting, highlighting its implications on child development and family dynamics. Despite the benefits of digital media, concerns persist regarding its overuse for emotional regulation, which may impede children’s self-regulation skills and parent-child interactions. This study aims to explore the evolution of research on digital parenting using bibliometric analysis. A comprehensive dataset was extracted from the Scopus database, focusing on publications from 2020 to 2024 within the Social Sciences domain. The inclusion criteria included peer-reviewed, open-access articles written in English. A systematic methodology ensured the analysis of performance metrics, trends, and co-authorship patterns. Results indicate a significant increase in scholarly attention to digital parenting, with 837 articles meeting the inclusion criteria. Leading contributions emerged from journals such as Sustainability Switzerland and Education Sciences, with prolific authors and institutions from the United Kingdom and the United States dominating the field. The analysis underscores the interdisciplinary nature of the topic, reflecting contributions from education, media studies, and child development. This study offers valuable theoretical insights and practical recommendations, emphasizing balanced digital media use and informed parenting strategies to foster healthier family dynamics.
Volume: 15
Issue: 3
Page: 1431-1442
Publish at: 2026-09-01

Advanced materials for crosstalk and power optimization in TSV-enabled 3D ICs

10.11591/ijict.v15i3.pp1143-1153
Tappeta Chinna Sanjeeva Rayudu , Merrin Prasanna Nagadasari
The continued scaling of semiconductor devices has exposed the limitations of traditional two-dimensional (2D) integrated circuit architectures. To address performance bottlenecks and interconnect constraints, the industry is increasingly adopting three-dimensional (3D) integration technologies. through-silicon vias (TSVs) are a fundamental enabler of this advancement, facilitating vertical signal transmission between stacked silicon layers. Despite their benefits, TSVs face critical challenges related to crosstalk, power dissipation, and signal delay issues that are especially pronounced in dense via arrays. This research explores the use of multi-walled carbon nanotube (MWCNT) based TSVs insulated with different dielectric liners, including silicon dioxide (SiO₂), PPC, polyimide, and benzocyclobutene (BCB). HSPICE simulations are used to evaluate crosstalk noise, power dissipation, power delay product (PDP), and energy delay product (EDP) across varying TSV pitches. Among the materials studied, BCB demonstrates the most promising results. Specifically, MWCNT TSVs with BCB at a 10,000 μm pitch achieve up to 58% reduction in functional crosstalk, 75% in dynamic crosstalk, 78% in power dissipation, and a 52% improvement in PDP compared to single-walled CNT (SWCNT) based TSVs. These findings confirm the suitability of combining MWCNT cores with low-k BCB liners for enhancing performance, energy efficiency, and signal reliability in advanced 3D integrated circuits.
Volume: 15
Issue: 3
Page: 1143-1153
Publish at: 2026-09-01

AI-powered cardiovascular risk prediction using deep learning images in IoT-blockchain systems

10.11591/ijict.v15i3.pp1016-1025
Deepika Prabhakar , Agusthiyar Ramu
The increasing prevalence of cardiovascular diseases (CVDs) necessitates the development of intelligent, secure, and scalable diagnostic systems capable of accurate and early disease prediction. The importance of this research is to develop a robust and secure system for predicting CVD risk from echocardiogram imaging integrated within an internet of things (IoT) framework and enhanced by blockchain technology. Due to non-invasive feature identification problems and dimensionality, prediction accuracy is degraded due to higher false positives, which leads to lower precision and recall rates. To resolve this problem, implement AI-powered CVD risk prediction based on smart-featured deep learning in Echocardiogram images for an IoT-blockchain environment. The first phase contains data analysis echocardio-dataset vision transformation technique is functional to find the risk level of the disease. The adaptive gaussian filter is applied for normalization process and design a spread-spectral canny edge morphological segmentation and SURF-scaled invariant feature selection for dimensionality scaling. Then, visual geometry network (VGNet) convolutional neural network (CNN) is applied for disease classification. In second phase, the advanced blockchain technology will provide a decentralized and immutable record of patient data, thereby ensuring data integrity and security. The proposed system produces higher performance by analysing the sensitivity specificity as well by ensuring the disease detection level. The blockchain provides higher security to safe in repository for image data for carrying sensitive data with a platform for sharing and validating predictive insights among healthcare providers, researchers, and patients, thus fostering collaborative healthcare efforts.
Volume: 15
Issue: 3
Page: 1016-1025
Publish at: 2026-09-01

Smart drones for human detection in disaster response

10.11591/ijict.v15i3.pp1179-1187
Menakadevi Nanjundan , Y. L. Ajay Kumar , V. R. Seshagiri Rao , Nagarjuna Telagam , Seetha Chaithanya , Manikadan S.
Natural disasters require swift, coordinated responses to minimise human casualties and infrastructure damage. This paper presents a novel AI-assisted drone system designed to enhance disaster relief efforts through advanced human detection and a distributed emergency Wi-Fi network. This drone system, equipped with state-of-the-art machine learning algorithms and thermal imaging, excels at locating and identifying individuals even in challenging conditions, such as smoke, debris, or low visibility. The drone fleet operates autonomously, dynamically forming an ad-hoc network that adapts to the evolving needs of the disaster zone. By integrating real-time data processing with efficient network management, our system provides a critical lifeline for communication and a powerful tool for rescuers to navigate and respond effectively. The detection and communication times are observed to be within the range of 5 to 8 seconds for this proposed system, which is widely used in disaster response.
Volume: 15
Issue: 3
Page: 1179-1187
Publish at: 2026-09-01

Ensemble deep learning framework for accurate and robust cervical cancer classification

10.11591/ijeecs.v43.i3.pp834-846
Sankarasubramanian Meenakshi
Early and accurate cervical cancer diagnosis (CCD) is essential for improving treatment outcomes and reducing mortality. However, manual examination of cervical cytology images is labor-intensive, time-consuming, and subject to inter-observer variability. To address these limitations, this paper proposes an ensemble deep learning framework for automated cervical cancer classification. The proposed methodology integrates multiple convolutional neural network (CNN) models to exploit their complementary feature extraction capabilities, thereby improving classification accuracy and robustness. Image preprocessing and data augmentation techniques are employed to enhance image quality, reduce noise, and increase the diversity of the training dataset, enabling better model generalization. The outputs of the individual CNN models are combined through an ensemble decision strategy to generate more reliable diagnostic predictions than those obtained from a single classifier. Experimental results demonstrate that the proposed framework consistently outperforms conventional deep learning methods in terms of accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The ensemble approach effectively reduces classification errors while maintaining stable performance across different cervical cell categories. These findings indicate that the proposed framework can serve as a reliable computer-aided diagnostic tool for cervical cancer screening, supporting clinicians in early diagnosis and improving the efficiency, consistency, and reliability of clinical decision making.
Volume: 43
Issue: 3
Page: 834-846
Publish at: 2026-09-01

Deep learning–driven automated prediction and data analysis of the Indian stock index

10.11591/ijeecs.v43.i3.pp780-791
Vijaykumar Bidve , Ketki Kshirsagar , Jayashree Tamkhade , Sunil Sangve , Geetha R Chillarge , Aryani Gangadhara , Shailesh Kediya , Priyanka Paygude
This study focuses on the Indian stock index (NIFTY50), employing advanced deep learning methodologies to enhance predictive accuracy and support intelligent trading decisions. The research investigates the relationship between the volatility index (VIX) and NIFTY50, analyzing how fluctuations in VIX influence market direction and futures performance. By monitoring real-time variations in VIX during trading hours, the study aims to capture intraday volatility patterns and provide actionable insights for investors. To achieve this, the proposed framework leverages long short term memory (LSTM) networks, a sophisticated deep learning technique well-suited for sequential data analysis and financial forecasting. The methodology encompasses data collection, preprocessing, feature engineering, and the design of an LSTM-based predictive model, followed by rigorous evaluation and back-testing. Experimental results demonstrate the effectiveness of the approach in forecasting NIFTY50 movements, highlighting the potential of LSTM networks to outperform traditional statistical and machine learning models. The findings contribute to the growing body of research on AI-driven financial prediction, offering a paradigm shift in the application of deep learning for stock market analysis and paving the way for more reliable, automated decision-making in investment strategies.
Volume: 43
Issue: 3
Page: 780-791
Publish at: 2026-09-01

HEC: a hybrid evolutionary-CELF algorithm for enhanced influence maximization in social networks

10.11591/ijeecs.v43.i3.pp808-816
Jyoti Sunil More , Vaishali V. Sarbhukan (Bodade) , Reena Lokare
In the realm of influence maximization within social networks, the hybrid evolutionary-CELF (HEC) algorithm offers a novel approach by integrating evolutionary optimization techniques with the efficiency of the cost-effective lazy forward (CELF) algorithm. This paper presents the HEC algorithm, which enhances the process of selecting a seed set of nodes to maximize influence spread by combining the CELF algorithm’s computational efficiency with evolutionary strategies such as crossover and mutation. We demonstrate that this hybrid approach outperforms existing methods, including greedy algorithms, CELF, and cuckoo search, in terms of both influence spread and computational efficiency. Experimental results on various network structures highlight the superior performance of the HEC algorithm in balancing exploration and exploitation. The proposed method provides a robust solution for large-scale influence maximization problems, offering significant improvements in effectiveness and efficiency.
Volume: 43
Issue: 3
Page: 808-816
Publish at: 2026-09-01

SqueezeNet-driven AI framework for OCR of handwritten English characters and digits

10.11591/ijeecs.v43.i3.pp928-939
Areen Arabiat , Muneera Altayeb
Optical character recognition (OCR) plays a pivotal role in automated data entry, document digitization, and intelligent user interfaces, particularly as digital content continues to expand across diverse applications. Handwritten character recognition remains a challenging task due to variations in writing styles, noise, and structural complexity. This study investigates the use of artificial intelligence (AI) and machine learning (ML) strategies for classifying handwritten English characters and digits, employing the orange3 platform and a dataset of 2,728 images sourced from Kaggle, partitioned into 70% training and 30% testing sets. A SqueezeNet-based feature extraction approach was implemented to generate 1,000 discriminative features, which were subsequently used to train multiple ML classifiers, including support vector machine (SVM), decision tree (DT), and random forest (RF). Experimental evaluation revealed that the RF classifier achieved the highest accuracy of 99.3%, outperforming DT (90.3%) and SVM (89.9%). These findings highlight the effectiveness of lightweight deep learning architectures such as SqueezeNet in enhancing OCR performance, while demonstrating the robustness of ensemble learning methods in achieving near-perfect classification accuracy. The proposed framework underscores the potential of combining efficient feature extraction with ML classifiers to advance OCR systems for handwritten text recognition in real-world applications.
Volume: 43
Issue: 3
Page: 928-939
Publish at: 2026-09-01

Mitigation of transmission impairments in MMW-RoF systems using digital compensation techniques: A 17.88 GHz bandwidth study at 160 GHz and 90 GHz

10.11591/ijeecs.v43.i3.pp737-748
Sura Mousa Ali
The unprecedented growth of next-generation wireless communication systems has positioned the transmission of millimeter-wave (MMW) signals over fiber-optic links as a critical research area. As 5G and beyond networks continue to demand ultra-high data rates, massive connectivity, and low latency communication, fiber-wireless convergence has emerged as a key enabling technology for realizing scalable, high-capacity, and cost-effective fronthaul infrastructures. This paper addresses these challenges by designing and simulating an radio-over-fiber (RoF) system using VPIphotonics (Ver. 11.1) and Python (Ver. 3.7.6). The proposed compensation framework incorporates digital signal processing (DSP), Algorithms for dispersion compensation, and a phase noise compensator. The system is planned to operate with a bandwidth of 17.88 GHz, a centre frequency of 160 GHz, and a 90 GHz mmWave. Signal transmission is tested over fibre lengths of 50 km and 70 km, evaluating performance metrics such as error vector magnitude (EVM), signal to noise (SNR), symbol error rate (SER), bit error rate (BER), and data rate consistency for different configurations, including 2x2 MIMO. For 2x2 MIMO, EVM values of approximately 3%, 28.3 dBm SNR, 1E-8 SER, and 2E-09 BER over 50 km and over 70 km channel length achieved 4% EVM, 25.89 dBm SNR, 1E-05 SER, and 2E-06 BER at a data rate of 56.656 Gb/s. This compensator ensured robust performance, enhanced signal integrity, and reliable communication over long distances, meeting the developing demands of modern high-speed communication systems.
Volume: 43
Issue: 3
Page: 737-748
Publish at: 2026-09-01
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