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An machine learning-enhanced reconfigurable software defined radio architecture for adaptive 5G wireless systems

10.11591/ijict.v15i2.pp699-706
Vijaya Bhaskar Chalampalem , Sancarapu Nagaraju , Venkata Vara Prasad , R. Kiran Kumar , Shanmugham Balasundaram
This paper presents a machine learning (ML)-enhanced software defined radio (SDR) architecture optimized for adaptive 5G wireless communication. The system incorporates predictive ML algorithms to enable real-time modulation selection, finite impulse response (FIR) filter reconfiguration, and spectrum adaptation based on dynamic channel parameters such as bit error rate (BER), received signal strength indicator (RSSI) and signal-to-noise ratio (SNR). A decision tree classifier and a deep Q-learning agent dynamically determine optimal modulation schemes (BPSK, QPSK, 16-QAM, OQAM) and filter tap configurations (4/8/16 taps), ensuring efficient communication under varying network conditions. Implemented on a Xilinx Zynq SoC using Verilog for datapath design and Python for ML control via AXI4-Lite, the architecture achieves a maximum operating frequency of 182.4 MHz, 40.7% logic utilization, and only 122.3 mW power consumption. Compared to existing SDR implementations, the system demonstrates a 17% frequency improvement, 28% power reduction, and 21% area savings. Real-time electrocardiogram (ECG) transmission confirms the system’s adaptability, achieving BER < 10⁻³ at 22 dB SNR and < 10⁻⁵ at 26 dB. These results affirm the viability of the proposed ML-SDR for edge-based biomedical and ultra-reliable low-latency communications (URLLC) applications in 5G networks.
Volume: 15
Issue: 2
Page: 699-706
Publish at: 2026-06-01

Advanced machine learning for enhanced abdominal organ segmentation

10.11591/ijict.v15i2.pp759-768
Rohini Pawar , Rohini Jadhav , Rohit Jadhav
This research evaluates the ResUnet model’s performance in using computed tomography (CT) images to segment various abdominal organs. Weak boundaries, computing efficiency, and anatomical diversity are the current obstacles in abdominal multi-organ segmentation. By merging residual networks with U-Net, ResUnet overcomes obstacles by increasing precision and effectiveness, which qualifies it for use in medicine. The model’s effectiveness was assessed on a number of organs, and the segmentation accuracy was measured using the dice similarity coefficient (DSC). The ResUnet model’s ability to precisely segment organs with distinct borders was proved by its exceptional accuracy in segmenting important organs, such as the liver (mean DSC: 0.880) and spleen (mean DSC: 0.830). However, the model struggled to separate the esophagus correctly (mean DSC: 0.000) and struggled with smaller and more complex organs like the pancreas (mean DSC: 0.429) and gallbladder (mean DSC: 0.143). These results highlight the method’s limitations when handling organs with asymmetrical shapes or hazy borders.
Volume: 15
Issue: 2
Page: 759-768
Publish at: 2026-06-01

Mobile device application design for ThingSpeak interface using flutter

10.11591/ijict.v15i2.pp850-860
Moehammad Sauqy Ihza Zuliandra , Tigor Hamonangan Nasution , Ainul Hizriadi
The rapid development of internet of things (IoT) is prompting many people to design applications, particularly for monitoring applications based on mobile apps. This includes research designs to monitor electrical parameters from PV and the development of health monitoring applications. Previous research required a separate application to scan each IoT device. In this research, a mobile app-based IoT monitoring system was built using flutter. With this, people no longer need to design separate mobile apps for various IoT devices. This application utilizes the flutter framework, while the cloud component uses ThingSpeak. These research results show that data from multiple IoT devices can be transferred to the user’s mobile app. This application enables the monitoring of various IoT devices through a single mobile app, thereby enhancing the efficiency of IoT device design and management.
Volume: 15
Issue: 2
Page: 850-860
Publish at: 2026-06-01

Arobust outlier detection based filtering for noise removal in grayscale images

10.11591/ijict.v15i2.pp508-522
Ali Salem Al Rawash , Farah Aini Abdullah , Ahmad Kadri Junoh , Abdallah Alshbeel , Mohammed Banikhalid
Salt-and-pepper noise severely degrades the visual quality of digital images, par ticularly at high noise densities where conventional denoising techniques often fail. Median- and mean-based filters tend to oversmooth images and blur fine structures when the majority of pixels within a local window are corrupted. This paper proposes a robust dual-layer denoising framework for grayscale images that integrates rank-based prescreening, interquartile range (IQR)-based statis tical outlier detection using Tukey fences, and a lightweight post-processing sharpening stage. In the first layer, a rank-4 trimmed estimator suppresses ex treme impulse values and stabilizes local statistics. In the second layer, adap tive IQR thresholds are employed to detect and replace residual outliers, even in heavily corrupted neighborhoods. A final step involving selective sharpen ing combined with mild smoothing enhances edge details without amplifying residual noise. Extensive experiments on standard grayscale images (Lenna, Barbara, lake, boat, and living room) across salt-and-pepper noise levels from 10% to 90% demonstrate that the proposed approach consistently outperforms conventional methods, including mean, median, Gaussian, modified decision based unsymmetrical trimmed median filter (MDBUTMF), and pixel density based filter (BPDF). Quantitative evaluation indicates peak signal-to-noise ratio (PSNR) values reaching 38.23dB, structural similarity index (SSIM) values up to 0.99, and significant reductions in mean squared error (MSE), particularly at higher noise densities. These results confirm that the proposed framework ef fectively suppresses noise while preserving edges and textures, making it well suited for practical applications such as medical imaging, remote sensing, and surveillance.
Volume: 15
Issue: 2
Page: 508-522
Publish at: 2026-06-01

A systematic mapping study: exploring islamic inheritance in computing research

10.11591/ijict.v15i2.pp597-606
Ghader Reda Kurdi
Islamic inheritance, a fundamental component of Islamic jurisprudence governing asset allocation among heirs, presents challenges due to its complexity. Accessible resources are crucial to address these challenges, with computational technologies offering promising solutions. This systematic mapping study provides a comprehensive overview of research at the intersection of computing and Islamic inheritance, comprising 20 studies identified primarily through snowballing. It analyses publication trends, identifies primary application domains, explores computational technologies utilized, assesses empirical evaluation methods, and uncovers gaps, challenges, and limitations in the existing literature, ultimately determining areas necessitating further research. The findings suggest a significant presence of researchers from Southeast Asia, predominantly with backgrounds in computing. The studies focused on the computation of wealth distribution, employing various computational technologies. Furthermore, the findings emphasise the importance of interdisciplinary collaboration and empirical evaluation to enhance technological solutions in this domain.
Volume: 15
Issue: 2
Page: 597-606
Publish at: 2026-06-01

Evaluating user experience of a mobile website and redesigning its user interface using goal-directed design method

10.11591/ijict.v15i2.pp634-643
Aang Subiyakto , Muhammad R. Alghifari , Nuryasin N. , Muhammad Q. Huda , Nashrul Hakiem , Viva Arifin , Dwi Yuniarto , Hadi Rahman , Thosporn Sangsawang , Naeem Atanda Balogun
This study evaluated the usability of the user interface (UI) of a mobile website using its user experience (UX) perspectives. The website serves as an information portal intended for access via smartphones and other handheld devices. The objective of the study was to assess the usability of its current interface, redesign it using the goal-directed design (GDD) method, and compare the usability performance before and after the redesign. The study was conducted in five main steps using the cognitive walkthrough, think-aloud, post-study system usability questionnaire (PSSUQ), and interview techniques with five representative participants and 50 respondents. The most important findings of the study were that the redesigned mobile website showed improved usability of the website, as indicated by increased effectiveness and efficiency values, enhanced PSSUQ satisfaction scores, and more positive user feedback.
Volume: 15
Issue: 2
Page: 634-643
Publish at: 2026-06-01

Predicting battery life performance using artificial intelligence techniques in electric vehicles

10.11591/ijict.v15i2.pp805-812
Debani Prasad Mishra , Munavath Pavan Kalyan , Shivam Tyagi , Piyushjeet Piyushjeet , Shiv Grover , Surender Reddy Salkuti
Electric vehicles’ (EVs’ performance and sustainability are significantly influenced by the efficiency and lifespan of their lithium-ion batteries. This paper explores the critical factors affecting battery degradation, focusing on parameters such as charge cycles, thermal management, and voltage dynamics. Utilizing a dataset of 14 batteries, the study employs data-driven machine learning (ML) to predict the remaining useful life (RUL) of batteries. The ensemble-based regression model demonstrated superior predictive accuracy through comprehensive analysis, achieving R² values of 97.89% for training and 94.69% for testing. Feature importance analysis identified cycle index (CI) as the most critical determinant of battery health, followed by discharge time and voltage stability. Visualizations, including correlation heatmaps and residual plots, validate the robustness of the selected model. Additionally, sustainable charging strategies, such as steady current-steady voltage (also known as CC-CV), are highlighted for their role in enhancing battery longevity. This research offers actionable insights into battery management systems, providing a robust foundation for predictive maintenance and the development of sustainable electric mobility solutions.
Volume: 15
Issue: 2
Page: 805-812
Publish at: 2026-06-01

Early prediction of myocardial infarction using proposed score tree algorithm

10.11591/ijict.v15i2.pp813-822
Nusrat Parveen , Utkarsha Pacharaney , Gayatri Hegde , Mohammad Rafique , Sana Firoj Nalband , Shamim Akhtar , Satish Devane
Early detection and diagnosis of a diseases will have a big impact on the medical field and help to prevent loss of life. This study begins by gathering information on myocardial infraction patients from hospitals and focuses on earlier diagnostics. In fact, the pre-processed, confirmed data from a qualified doctor is used for this research. Early prediction of myocardial infarction (MI) is proposed by many researchers. They have used Kaggle datasets that is not recent, and they work on post MI. We have proposed early myocardial infraction detection works on unsupervised datasets. To identify myocardial infraction, numerous machines learning supervised algorithms, including decision tree (DT), random forest (RF), are employed in the literature. In this study, we use the score tree algorithm (STA), which operates on an unsupervised dataset, to present a unique early MI prediction method.
Volume: 15
Issue: 2
Page: 813-822
Publish at: 2026-06-01

Advanced IoT-integrated real-time fire detection and automated mitigation system

10.11591/ijict.v15i2.pp861-868
Rama Krishna Peddarapu , Ajimera Abhinav , Gnana Sathwika V. N. V. , Poosa Brijesh , Amrutha Varshini Ravula
In the field of industry and commerce safety, tackling the most challenging and ongoing fire threats requires the advance internet of things (IoT) integrated real-time fire detection and automated mitigation system. Leveraging IoT and multi-modal sensing in fire safety, the system combines flame, gas, and humidity sensors and cameras to provide continuous real time monitoring and appropriate management of the threats. Real-time automated hazard interventions, such as sprinkler system engagement and geocoded alerts to fire departments, significantly improve life safety outcomes of the system. Active damage mitigation IoT devices provide integrated damage mitigation safety and individual IoT device remote monitoring. In the scope of industry and commerce, this system is a demonstration of the impact of IoT on improving fire safety.
Volume: 15
Issue: 2
Page: 861-868
Publish at: 2026-06-01

A critical review of information retrieval techniques: current trends and challenges

10.11591/ijict.v15i2.pp456-464
Sanket D. Patil , Zahir Aalam
The realm of information retrieval is witnessing transformative advancements, driven by the integration of deep learning techniques, specialized algorithms, and domain-specific applications. Information retrieval systems play an important role in many applications including in the Artificial Intelligence powered systems that can be seen in many applications. Information Retrieval, generally, acts an important task in the knowledge discovery phase of any query based intelligent system. This paper presents a comprehensive review by conducting a detailed analysis of the technological nuances, dataset specifications, and pivotal findings. This detailed review has been done with the special emphasis on the kind of technology used to achieve accurate information retrieval, domain of the study, and the system’s ability to retain or work with tables and figures, among other parameters. Navigating through the rich tapestry of methodologies, the paper underscores the pivotal role of deep learning frameworks in revolutionizing traditional retrieval paradigms. Furthermore, it sheds light on the innovative integration of textual information, algorithmic advancements, and specialized datasets to enhance the efficacy and granularity of information retrieval mechanisms.
Volume: 15
Issue: 2
Page: 456-464
Publish at: 2026-06-01

Enhancing road damage detection performance using the YOLOv9 model

10.11591/ijict.v15i2.pp616-624
Muhammad Farkhan Adhitama , Sutikno Sutikno , Rismiyati Rismiyati
Roads are essential infrastructure that support community mobility, and their condition significantly impacts road user safety. However, manual road damage detection remains inefficient, time-consuming, costly, and prone to human error. To address this issue, this study proposed the YOLOv9 model for automated road damage detection and explored parameter combinations to optimize its performance. The proposed solution leverages the YOLOv9 model, which offers enhanced detection speed and accuracy compared to previous YOLO versions, due to its improved backbone and dynamic label assignment techniques. The method uses pre-trained weights and performs parameter tuning to adapt the model for identifying common road defects, including potholes, longitudinal, lateral, and alligator cracks. A publicly available dataset of road condition images was used for training and evaluation. Experimental results demonstrated that the optimized YOLOv9 model achieved a mean average precision (mAP) of 62.8%, indicating a promising ability to detect multiple types of road damage accurately. This study highlights the potential of YOLOv9 as an effective tool for road monitoring systems, contributing to proactive maintenance strategies and more efficient infrastructure management.
Volume: 15
Issue: 2
Page: 616-624
Publish at: 2026-06-01

Deep learning-based optimization techniques for network lifetime enhancement in wireless sensor networks

10.11591/ijict.v15i2.pp623-633
Abhay Raghunath Gaidhani , Amol D. Potgantwar
Wireless sensor networks (WSNs) are integral to applications like environmental monitoring, healthcare, and surveillance, yet they face the critical challenge of limited energy resources, which shortens the network's operational lifespan. Addressing this issue, this paper explores deep learning-based optimization techniques as a solution to enhance network lifetime by efficiently managing energy consumption. We present a detailed review of the existing challenges in WSNs and examine various deep learning methods, including neural networks, deep reinforcement learning (DRL), and generative adversarial networks, specifically tailored for WSN optimization. In this study, we introduce a new reinforcement learning (RL) based optimization algorithm to prolong the network lifetime. The proposed technique is intended to smartly distribute the energy consumption among the network elements, leading to desirable performance with regard to the battery lifetime. The paper ends with a summary of design aspects and future research directions to improve the WSN performance further based on deep learning.
Volume: 15
Issue: 2
Page: 623-633
Publish at: 2026-06-01

A review of sensemaking design elements: towards an affordances typology

10.11591/ijict.v15i2.pp488-496
Fadzlin Ahmadon , Murni Mahmud , Muna Azuddin
This study explores the intersection of interaction design and sensemaking within digital systems, aiming to identify and categorize key affordances that enhance user sensemaking. Starting with a focused literature review, key design elements such as tagging and annotation are identified, important for effective sensemaking in interaction design. Drawing on Maier's construct of affordances, the behaviours of these design elements are analyzed to derive specific affordances integral to enhancing user experience. The primary objective is to develop a generalized affordance typology that supports sensemaking across various digital systems. This typology organizes the derived affordances into broad themes such as effortless discovery, expressive freedom, collaborative engagement, cognitive support, insight enhancement, and user empowerment. This typology serves as a tool for interaction designers, facilitating the application of these themes in various design scenarios to create more intuitive and effective digital environment for sensemaking.
Volume: 15
Issue: 2
Page: 488-496
Publish at: 2026-06-01

Machine learning centered energy optimization in mobile edge computing: a review

10.11591/ijict.v15i2.pp465-476
Chandapiwa Mokgethi , Tshiamo Sigwele , Kabo Clifford Bhende , Aone Maenge , Selvaraj Rajalakshmi
Current literature reviews on machine learning-based approaches for mobile edge computing (MEC) energy optimization often lack in-depth gap analysis and fail to identify trends or offer actionable insights. Most focus narrowly on comparing MEC frameworks without critically evaluating or benchmarking prior research. This review contributes by addressings these gaps via analysis of existing reviews and related studies, with a focus on ML models, research objectives, evaluation metrics, datasets, tools, and gap identification. The review method follows a systematic literature review (SLR) using the PRISMA framework for transparency and reproducibility. Key findings reveal persistent challenges in energy consumption, computational overhead, cost, and poor performance in accuracy, QoS, latency, scalability, and carbon footprint. Deep reinforcement learning (DRL) emerges as the most commonly used model (55%), while TensorFlow (35%) is the most adopted tool, valued for its flexibility and robust community support. The AudioSet dataset is frequently used (28%) due to its compatibility. However, methodology limitations include dependency on study quality and exclusion of grey literature, context sensitivity. The review concludes by recommending advanced solutions such as serverless computing, liquid cooling, containerization, software-defined power, quantum computing, and blockchain to drive future MEC energy optimization.
Volume: 15
Issue: 2
Page: 465-476
Publish at: 2026-06-01

A new modified characteristic equation for optimal coordination of directional overcurrent relays

10.11591/ijict.v15i2.pp789-796
Neelakanteshwar Rao Battu , Surender Reddy Salkuti
The integration of distributed generation (DG) into power systems is increasing to meet the requirements of the utility system. Renewable energy sources are given priority due to their clean energy and high consistency advantages. Integration of DG into the system makes the bi-directional flow of current. Directional type overcurrent relays are usually used for protection of lines associated with bidirectional power flows. The installation of DGs, (especially, inverter-based) invites challenges to the existing protection schemes. A new modified characteristic equation-based approach is proposed in this paper to obtain the faster operational time of relays. The relay coordination scheme proposed in this paper is applied to an 8-bus test system integrated with the solar-based photovoltaic integrated distributed generator (PVIDG). The comparative analysis between the conventional and proposed approaches is done.
Volume: 15
Issue: 2
Page: 789-796
Publish at: 2026-06-01
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