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31,456 Article Results

Integrating F-filter and K-Means SMOTE to enhance LGWUM-based turnover prediction

10.11591/ijict.v15i3.pp1078-1086
Risyda Miftahur Rahmah , Sigit Priyanta
High employee turnover poses a significant challenge for organizations. While uplift modeling offers a prescriptive analytics approach by estimating differential treatment effects to optimize retention programs, its performance is often hindered by irrelevant features and imbalanced class distributions. To address these issues, this study proposes an employee turnover model utilizing lai’s generalized weighted uplift method (LGWUM), enhanced with F-Filter feature selection and K-Means SMOTE for a refined feature space and balanced treatment representation. Evaluated across three HR datasets with four engineered uplift classes (CN, CR, TN, TR), the integrated framework significantly improves uplift performance, yielding increased Qini coefficients of 0.0755 and 0.0870 on Datasets 2 and 3, respectively. Furthermore, top-decile probability distribution analysis confirms a clearer separation between positive and negative responders, with XGBoost demonstrating the most robust and reliable uplift discrimination across the models.
Volume: 15
Issue: 3
Page: 1078-1086
Publish at: 2026-09-01

Development of highway vehicle detection using background subtraction and Haar cascade methods

10.11591/ijict.v15i3.pp1004-1015
Ni Gusti Ayu Dasriani , Anthony Anggrawan , Khasnur Hidjah , Christofer Satria , I Nyoman Yoga Sumadewa
Vehicle recognition is a critical component of traffic analysis and the progress of advanced transportation systems, underscoring the importance of automated, real-time methods that reduce the need for manual observation. While the field has seen notable innovations in deep learning-centric detection technologies, many of these approaches require considerable computational strength and are not well-suited for real-time application in resource-constrained environments. In response to this limitation, the present study introduces a streamlined vehicle detection framework that combines background subtraction for motion-oriented foreground extraction with a Haar cascade classifier for object identification in traffic video sequences. The system is evaluated using real-world highway traffic recordings under different illumination conditions, including both day and night scenarios. The experiment's findings show that the system achieves an overall accuracy of 82.08%, with a precision of 85.33%, a recall of 66.67%, and an F1-score of 74.86%. The system also demonstrates consistent performance across different lighting conditions. These findings indicate a trade-off between detection accuracy and computational efficiency, where the proposed approach prioritizes practical deployment feasibility. Overall, the results suggest that classical computer vision techniques remain viable alternatives for real-time traffic monitoring in environments with limited computational resources.
Volume: 15
Issue: 3
Page: 1004-1015
Publish at: 2026-09-01

Attention-enhanced VGG-16 architecture for precision weed detection in wheat fields

10.11591/ijict.v15i3.pp1303-1312
Akanksha Bodhale , Seema Verma , Aishwary Bodhale
This study delineates five advanced convolutional neural network designs that employ deep learning for the classification of wheat weeds. The dataset consists of greyscale photos taken in agricultural fields, enhanced with RGB lighting and cropped to 256×256×3 pixels. Normalization, batch-wise augmentation, contrast stretching, or histogram equalization were all part of the preprocessing that improved picture quality and model learning efficiency. These enhancements developed feature extraction by increasing picture contrast and homogeneity. A VGG16-based model with further Conv2D layers and spatial attention is one of the five models recommended. An additional design, inspired by ResNet50 that applies residual blocks and worldwide average pooling. A hybrid RNN integrated ICNA-CNN or LSTM for spatial-temporal content learning is another. Lastly, InceptionResNetV2 is enhanced with CNN layers and a query-key attention mechanism. We used the Adam optimizer and categorical cross-entropy to estimate the loss throughout the training for the round. At the end, all the models had ReLU activation, batch normalization, MaxPooling2D, and a dropout charge of 0.5 to keep them from overfitting. Upon evaluating their performance based on accuracy, recall, reliability, and training loss, VGG16 emerged as the standout model, achieving an impressive 99.07% precision and a remarkably low training loss of just 0.0079. The study determined that VGG16 is the optimal choice for precise wheat–weed categorization because to its superior generalizability and accuracy.
Volume: 15
Issue: 3
Page: 1303-1312
Publish at: 2026-09-01

An AI-powered knowledge graph-based question answering system for Charak Samhita: integrating sanskrit NLP and graph data science

10.11591/ijict.v15i3.pp1197-1207
Sharayu Mirasdar , Mangesh Bedekar
Originating in India, Ayurveda is an ancient medical system focused on holistic healing that considers the mind, body, and spirit. This study utilizes knowledge graph (KG) technology to develop a KG model for an Ayurveda question-and-answer system. The system includes modules for knowledge extraction from चरकसंहिता, कायहचहकत्सा, भैषज्यरत्नावली and द्रव्यगुण संग्रि, construction of KG from this extracted knowledge and construction of AI supported Question answer system. In the methodology, domain-specific KG is constructed in Neo4j. Entities such as diseases (Vyadhi व्याधी), symptoms (Lakshana लक्षण), doshas (दोष), herbs, and treatments are incorporated. Advanced Sanskrit natural language processing (NLP) pipelines using ByT5-Sanskrit, SanskritBERT, and fine-tuned BioBERT facilitate named entity recognition (NER) and relation extraction. Graph-based reasoning models such as graph attention networks (GAT) and graph reasoning enhanced language models (GREASELM) enhance multi-hop reasoning across Ayurvedic concepts. Evaluation was conducted using a gold-standard annotated dataset of Charak Samhita verses mapped to disease–symptom–treatment relationships. Performance metrics included precision, recall, F1-score, mean reciprocal rank (MRR), and overlap coefficient. Superior accuracy can be seen in the proposed model as compared to baseline BERT-QA and subgraph QA approaches. This research has integrated Sanskrit computational linguistics and KG science. The approach mentioned in this paper has mentioned a framework that is scalable, interpretable and culturally significant. With the focus on Ayurveda, the methodology also mentions the potential for developing cross-cultural medical questions–answering systems, thereby bridging ancient wisdom with modern technological approaches.
Volume: 15
Issue: 3
Page: 1197-1207
Publish at: 2026-09-01

Smart road maintenance: real-time surface damage detection and mapping with YOLOv8

10.11591/ijict.v15i3.pp1331-1339
Preety Singh , Bommireddipalli Likhitha , Kolla Sahithi , Donthireddy Ganesh Reddy , Dammalapati Keerthana , Kolli Prasanna Adarsh
The degradation of road surfaces presents considerable obstacles for the management of urban infrastructure. This study presents a deep learning model based on YOLOv8 that can find many types of road faults, like potholes, longitudinal cracks, transverse cracks, and alligator cracks, using pictures, video streams, and live webcam feeds. The suggested system can find things with an accuracy of 91.2%, and the confidence levels range from 60% to 95%. Streamlit has been utilized to develop a web interface that makes it easier to use in real life. It makes it easy for users to choose inputs and provides outputs with notes and boundary boxes. GPS makes it possible to find problems with great accuracy, and a graphical dashboard presents damage categories and confidence levels in real time. Road maintenance is considerably more efficient with automated detection, location mapping, and easy-to-understand visualization. It is also easier to keep a check on smart municipal infrastructure. The results demonstrate that using computer vision and geospatial analytics together could make it easier to automatically check road conditions and make better decisions about how to run a city.
Volume: 15
Issue: 3
Page: 1331-1339
Publish at: 2026-09-01

Bridging the linguistic divide: recent developments in machine translation for Indian languages

10.11591/ijict.v15i3.pp1272-1289
Jayanand A. Kamble , Shivajirao M. Jadhav , Vinod J. Kadam
Significant advances have been achieved in machine translation (MT) in recent times, particularly state of the art (SOTA) models for languages like English and Indian having distinct grammatical structures and limited monolingual training data. This paper analyses various recent state-of-the-art variants of large language models (LLMs) and neural machine translation (NMT) for Indian languages in comparison to statistical machine translation (SMT). It tackles key questions, such as idiomatic expressions, morphologically complex grammar or the scarceness of parallel corpora. Furthermore, it studies bytewise BPE, compares translation models in terms of BLEU scores using separate and shared-vocabulary representation with copy actions between the BPE translations, and analyses how multitask learning (Caruana (1997)) and attention mechanisms can contribute to the quality of translation. In summary, it provides directions for future work by suggesting new avenues of research including better curated datasets, more efficient approaches for lowresource languages and culturally aware translations.
Volume: 15
Issue: 3
Page: 1272-1289
Publish at: 2026-09-01

Deep reinforcement learning inspired optimization framework using Optuna for brain tumor detection

10.11591/ijict.v15i3.pp1352-1363
Aashutosh Kharb , Prachi Chaudhary
Accurate brain tumor detection is essential for effective clinical diagnosis; however, the performance of deep learning models is highly sensitive to manually selected architectures and hyperparameters. To address this challenge, this paper presents a reinforcement learning–inspired automated optimization framework for brain tumor detection that eliminates manual trial-and-error tuning of hyperparameters. The proposed approach integrates EfficientNetB0 as a fixed feature extractor (base model) with an Optuna-based reinforcement learning strategy to jointly optimize the classifier architecture and key training hyperparameters, including learning rate, batch size, dropout rate, and network depth. Unlike existing studies that rely on static or heuristically tuned models, the proposed framework dynamically adapts model configurations based on validation feedback. Experiments conducted on the BraTS 2020 MRI dataset demonstrate that the optimized model achieves an accuracy of 92%, an F1-score of 92%, and a ROC–AUC of 0.96. Additional evaluations on imbalanced and cross-dataset settings show stable minority-class performance and good generalization. The results confirm that the proposed automated optimization framework offers a robust, scalable, and clinically relevant solution for brain tumor detection, representing a significant advancement over manually tuned deep learning approaches.
Volume: 15
Issue: 3
Page: 1352-1363
Publish at: 2026-09-01

Machine learning models for predicting daily profitability in Mauritanian digital banking operations: a case study

10.11591/ijict.v15i3.pp925-934
Mohamed Lemine Sidibba , Mohamedou Cheikh Tourad , Ahmad Outfarouin , Nema Sidi Mohamed Mawloud , Mohamedade Farouk Nanne
This article presents a case study on predicting daily profitability in Mauritanian digital banking operations using machine learning models. We utilized a dataset containing detailed information on daily operations to assess predictive models and predict profitability. The study assesses various machine learning models like logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron classifier (MLPClassifier), in order to identify the most precise model for predicting daily profitability. A systematic approach guides the analysis of banking transactions by performing detailed preprocessing operations which include type conversions, feature selection and missing value management. The dataset receives systematic partitioning into three parts for training, validation and testing to establish model reliability. LR had a recall rate of 99% and an F1-score of 99%, SVM had a recall rate of 53% and an F1-score of 54%, KNN had a recall rate of 96% and an F1-score of 95%, and MLPClassifier had a recall rate of 94% and an F1-score of 97%. The findings show that the LR model performed better than the other models in terms of both recall and F1-score. Future research will investigate both deep learning approaches and hybrid models to enhance prediction accuracy.
Volume: 15
Issue: 3
Page: 925-934
Publish at: 2026-09-01

A pilot review of the Google cluster workload trace 2019, methodology and its alternatives: analysis of workload in large scale data centres

10.11591/ijict.v15i3.pp1167-1178
Akash Patel , Amit Nayak , Khushi Patel , Anand Patel
Cloud data centres require shared services that are highly available, elastic capacity, managed operations, and robust recovery capabilities to facilitate the next generation of efficient, reliable, and diverse connected computing environments. Nevertheless, large-scale cloud infrastructures continue to fail regularly despite their availability, scalability, and cost efficiency, primarily due to low resource utilisation and inadequate early-stage failure management. The key to effective resource management and minimizing failures in such settings is understanding the nature of the workload and its failure modes. The current review considers the Google cluster workload trace 2019 to investigate workload and failure patterns and to generalise the results of 24 articles. The analysis is also compared with other major datasets, such as Microsoft Azure Trace, Tencent Trace, and Alibaba Trace. The paper establishes the relevance of Google cluster traces, describes the key contents of the 2019 dataset, and contrasts prior literature with respect to research objectives, trace datasets, significant results, and limitations. Moreover, it briefly describes methods for analyzing and modeling cluster traces and identifies gaps in the research that should be addressed to advance the study of cluster traces.
Volume: 15
Issue: 3
Page: 1167-1178
Publish at: 2026-09-01

Classification of encryption attacks and strategies for mitigation

10.11591/ijict.v15i3.pp1238-1253
Anas Maaifi , Khalid Zine-Dine
Cryptography is essential for securing digital communications, yet it remains vulnerable to various malicious attacks. These attacks can be classified based on the type of cryptography they target symmetric or asymmetric. This paper presents a comprehensive classification of encryption attacks, examining the specific vulnerabilities associated with each cryptographic approach. By analyzing these attack vectors, the study shows the importance of understanding weaknesses in cryptographic systems. Furthermore, it proposes several mitigation strategies to strengthen defenses and enhance protection of sensitive information in the digital domain.
Volume: 15
Issue: 3
Page: 1238-1253
Publish at: 2026-09-01

Car parking lot availability detection using the faster R-CNN method

10.11591/ijict.v15i3.pp1038-1046
Andi Riansyah , Alif Hakim Al Faruq , Badieah Badieah
Parking demand continues to rise as private vehicle use increases, making timely information about available spaces essential for efficient parking management. Many existing monitoring approaches still rely on fixed slot sensors or visual detectors that report accuracy without examining how confidence settings affect the final availability decision. This work investigates Faster region-based convolutional network (Faster R-CNN) with a ResNet-50 backbone for image-based parking availability detection using a public parking-lot dataset annotated in Pascal visual object classes (VOC) format. The experiment evaluates several confidence thresholds to determine how each setting changes the balance among accuracy, precision, recall, and F1-score. The most balanced setting was obtained at a threshold of 0.5, where the model achieved 95% accuracy and 97.3% for precision, recall, and F1-score. These results show that threshold configuration is an important factor in reducing missed detections and false alarms, although validation using real campus CCTV data and direct comparison with lightweight detectors remain necessary before practical deployment.
Volume: 15
Issue: 3
Page: 1038-1046
Publish at: 2026-09-01

Wavelet-based spectrum sensing with improved thresholding for enhanced detection in cognitive radio networks

10.11591/ijict.v15i3.pp1123-1134
Nur Hanis Abdul Rani , Mas Haslinda Mohamad , Nurusolihah Zamri , Nor Khairiah Ibrahim
Cognitive radio (CR) technology is an adaptive, intelligent radio and network technology that can automatically detect available channels in a wireless spectrum. Spectrum sensing is the most important component in CR due to its ability to sense and recognize parameters related to the radio channel characteristics. However, there are some spectrums that are not used known as spectrum holes. It is challenging to accurately identify these spectrum holes, especially when employing traditional energy detection techniques, which suffer from incorrect threshold selection at low signal-to noise ratio (SNR) levels. This work suggests a wavelet-based spectrum sensing technique in conjunction with an enhanced thresholding method to improve detection accuracy and decrease noise to overcome this constraint. MATLAB simulations are used for evaluating three threshold functions: hard, soft, and improved. The results indicate that the improved threshold achieves superior denoising performance and a higher detection probability compared to the traditional energy detection method. In this study, the energy detection technique was also implemented for comparison with the wavelet-based approach. The findings reveal that wavelet-based sensing consistently provides a higher detection probability (𝑃𝐷𝐸𝑇), demonstrating its effectiveness and reliability for cognitive radio (CR) application.
Volume: 15
Issue: 3
Page: 1123-1134
Publish at: 2026-09-01

Design and analysis of low-k dielectric TSV liners for noise mitigation in high-frequency 3D ICs

10.11591/ijict.v15i3.pp1188-1196
Pathakunta Guru Prathap Reddy , Sravan Abhilash Kothapalli
Moore’s Law has driven the development of very large-scale integration (VLSI) technology, allowing continuous transistor scaling to increase speed, density, and performance. However, as two-dimensional (2D) integrated circuits (ICs) near their physical and performance boundaries, and 2.5D ICs still face interconnect delay and power issues, three-dimensional (3D) integration has become a practical solution. In 3D ICs, multiple active layers are vertically stacked and connected via through-silicon vias (TSVs), providing short, high-bandwidth interconnects between layers. Electrical TSVs are essential for signal transmission, but also cause noise coupling between adjacent TSVs, where an aggressive TSV can induce interference in a nearby TSV. This coupling can impair signal integrity, increasing delay and power consumption. To mitigate this, low-dielectric-constant (low-k) materials are used to reduce capacitive coupling. In this study, materials such as benzocyclobutene (BCB), Perylene-N, and Teflon AF 1600 are compared with conventional SiO₂. Generally, TSVs are two structures — single-liner and stacked-liner — which are analysed at 10 GHz and 1 THz frequencies. At 10 GHz, the single-liner structure incorporating SiO₂ exhibits a noise reduction of about 6.56 dB, whereas the stacked-liner configuration using Teflon AF 1600 provides a noticeably greater reduction of 8.40 dB. As the operating frequency increases to 1 THz, the advantage of the low-k dielectric becomes more evident, yielding 9.63 dB noise reduction for the single-liner and 12.04 dB for the stacked-liner structure. These results indicate that low-k materials effectively suppress capacitive coupling and mitigate high-frequency interference in 3D ICs. The stacked-liner design contributes additional isolation by creating a secondary dielectric barrier, which further minimizes electric field interaction between neighboring interconnects. Thus, the integration of low-k dielectrics with optimized liner architectures significantly enhances signal integrity and overall electromagnetic performance in advanced high-frequency 3D IC systems.
Volume: 15
Issue: 3
Page: 1188-1196
Publish at: 2026-09-01

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

Performance evaluation of a solar-driven IoT water quality monitoring system using descriptive and ANOVA analysis

10.11591/ijict.v15i3.pp1385-1394
Suziana Ahmad , Arfah Ahmad , Muhammad Uwais Mohammad Raffee , Amirul Syafiq Sadun , Aminurrashid Noordin , Mohd Firdaus Mohd Ab Halim
Water quality monitoring is vital for protecting aquatic ecosystems and ensuring sustainable water resource management. Traditional manual sampling methods are often costly, time-consuming, and unsuitable for real-time assessment. This study presents a newly designed solar-powered IoT-based water quality monitoring system for remote and continuous data collection. The system utilizes an ESP32 microcontroller integrated with pH, temperature, and total dissolved solids (TDS) sensors, powered by a 10W solar panel. Data is transmitted to a cloud-based platform wirelessly, enabling remote access and visualization via a mobile app. Performance evaluation included descriptive statistics and one-way ANOVA across four sampling sites. ANOVA results showed statistically significant differences (p < 0.05) in water quality parameters among locations, confirming the system’s sensitivity. Sensor accuracy was validated against standard meters, revealing mean relative errors below 5% for pH and TDS. The system reliably provides real-time, accurate data, supporting proactive water quality management. Integrating IoT with renewable energy offers a cost-effective, scalable, and energy-efficient solution for environmental monitoring in remote or resource-limited areas.
Volume: 15
Issue: 3
Page: 1385-1394
Publish at: 2026-09-01
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