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CP_SDUNet: road extraction using SDUNet and centerline preserving dice loss

10.11591/ijra.v14i2.pp260-272
Bayu Satria Persada , Muhammad Rifqi Priyo Susanto , Laksmita Rahadianti , Aniati Murni Arymurthy
Existing automatic road map extraction approaches on remote sensing images often fail because they cannot understand the spatial context of an image. Mainly because they could not learn the spatial context of the image and only knew the structure or texture of the image. These approaches only focus on regional accuracy instead of connectivity. Therefore, most approaches produce discontinuous outputs caused by buildings, shadows, and similarity to rivers. This study addresses the challenge of automatic road extraction, focusing on enhancing road connectivity and segmentation accuracy by proposing a network-based road extraction that uses a spatial intensifier module (DULR) and densely connected U-Net architecture (SDUNet) with a connectivity-preserving loss function (CP_clDice) called CP_SDUNet. This study analyzes the CP_clDice loss function for the road extraction task compared to the BCE Loss function to train the SDUNet model. The result shows that CP_SDUNet, performs best using an image size of 128×128 pixels and trained with the whole dataset with a combination of 20% clDice and 80% dice loss. The proposed method obtains a clDice score of 0.85 and an Interest over Union (IoU) score of 0.65 for the testing data. These findings demonstrate the potential of CP_SDUNet for reliable road extraction.
Volume: 14
Issue: 2
Page: 260-272
Publish at: 2025-06-01

BioTapSync: revolutionizing data synchronization with human touch

10.11591/ijai.v14.i3.pp2528-2536
Sohil Shah , Harshal Shah
Human Tap introduces a new way to ensure secure data transmission synchronization by integrating advanced technologies. Motivated by the need for secure and efficient communication, it uses both near field communication (NFC) and human field communication (HFC) to provide a wide range of secure communication solutions. The aim is to create a system with detailed specifications, including maximum coverage range, frequency of operation, type of communication, and data rate for each protocol. These specifications are tailored for various applications, such as credit card payments, e-ticket bookings, E-ZPass systems, and item tracking. A notable contribution of Human Tap is its ability to achieve microsecond-level accuracy for distances up to 2 cm by thoroughly analyzing the relationship between distance and time. This innovative synchronization method not only ensures a secure data transmission environment but also shows remarkable flexibility, effectively addressing the challenges of modern communication systems. Human Tap sets a new benchmark for secure and adaptable data transmission technologies, paving the way for future advancements in the field. The objective is to establish a robust and versatile data transmission method that can be adapted to a wide range of modern applications.
Volume: 14
Issue: 3
Page: 2528-2536
Publish at: 2025-06-01

IoT-based cricket environment system to maximize egg production and reduce mortality rate

10.11591/ijra.v14i2.pp281-289
Dominic Miracle Tjandrata , Suryadiputra Liawatimena
The deployment of Internet of things (IoT) technologies presents an opportunity to improve efficiency in cricket farming. This study investigates the implementation of an IoT-based system utilizing an ESP32 microcontroller, a suite of environmental sensors, and actuators. The system is supported by a ThingsBoard dashboard for data visualization and a Telegram bot for notifications. The setup was tested on a single cricket cage over a 28-day period and compared against a control group. Each cage contained 20 male and 100 female Cliring crickets. Key parameters analyzed included temperature, humidity, soil moisture, egg yield, food conversion ratio (FCR), and mortality rate. Findings show that the IoT-enabled cage consistently maintained optimal environmental conditions—temperature (20 to 32 °C), humidity (65% to 85%), and soil moisture (60% to 80%)—unlike the control, which experienced greater variability. The IoT cage yielded 87.28 grams of eggs, a 33.33% improvement over the control's 65.46 grams. Additionally, FCR improved from 2.53 to 2.01 grams per egg, and mortality rate dropped from 0.816 to 0.708. These results underscore the effectiveness of IoT systems in enhancing environmental stability, productivity, and survival rates in small- to medium-scale cricket farming operations.
Volume: 14
Issue: 2
Page: 281-289
Publish at: 2025-06-01

Designing high power efficient finite impulse response filters with three-four inexact adder-integrated Booth multiplier

10.11591/ijra.v14i2.pp204-213
Manju Inasu Kollannur , Oudaya Coumar Souprayen
Finite impulse response (FIR) filters are widely utilized in several applications in digital signal processing, including data transmission, photography, digital audio, and biomedicine. It is necessary to use high sample rates for FIR filters, while moderate sample rates are needed for low-power circuits. To solve these problems, a Booth multiplier based on three-four inexact adder-based multiplication (TFIE-BM) was proposed. The goal of the proposed TFIE-based FIR Booth multiplier is to lower area usage, latency, and power consumption. The proposed method utilizes the spotted hyena optimizer (SHO) to find the optimal filter coefficient (FC) by minimizing the pass power consumption and Transition bandwidth. Moreover, a high-performance three-four inexact adder (TIFE adder) has been introduced, which uses fewer XOR gates for sum and carry generation, indicating that the logic has been simplified to reduce hardware complexity. By increasing speed and decreasing the FIR filter's critical path delay, a modified Booth multiplier that uses a 5:2 compressor is introduced. The overall delay of the proposed approach is 23.4%, 18.7%, 12.3%, and 5.7% lower than that of the Radix-4 Booth multiplier, CSA Booth multiplier, hybrid multiplier, and traditional Booth multiplier, respectively.
Volume: 14
Issue: 2
Page: 204-213
Publish at: 2025-06-01

Ensemble model-based arrhythmia classification with local interpretable model-agnostic explanations

10.11591/ijai.v14.i3.pp2012-2025
Md. Rabiul Islam , Tapan Kumar Godder , Ahsan Ul-Ambia , Ferdib Al-Islam , Anindya Nag , Bulbul Ahamed , Nujhut Tanzim , Md. Estiak Ahmed
Arrhythmia can lead to heart failure, stroke, and sudden cardiac arrest. Prompt diagnosis of arrhythmia is crucial for appropriate treatment. This analysis utilized four databases. We utilized seven machine learning (ML) algorithms in our work. These algorithms include logistic regression (LR), decision tree (DT), extreme gradient boosting (XGB), K-nearest neighbors (KNN), naïve Bayes (NB), multilayer perceptron (MLP), AdaBoost, and a bagging ensemble of these approaches. In addition, we conducted an analysis on a stacking ensemble consisting of XGB and bagging XGB. This study examines various arrhythmia detection techniques using both a single base dataset and a composite dataset. The objective is to identify the optimal model for the combined dataset. This study aims to evaluate the efficacy of these models in accurately categorizing normal (N) and abnormal (A) heartbeats as binary classes. The empirical findings demonstrated that the stacking ensemble approach exhibited superior accuracy when used with the combined dataset. Arrhythmia classification models rely on this as a crucial component. The binary classification achieved an accuracy of 98.61%, a recall of 97.66%, and a precision of 97.77%. Subsequently, the local interpretable model-agnostic explanations (LIME) technique is employed to assess the prediction capability of the model.
Volume: 14
Issue: 3
Page: 2012-2025
Publish at: 2025-06-01

Addressing the challenges of harmonizing law and artificial intelligence technology in modern society

10.11591/ijai.v14.i3.pp2471-2478
Lamprini Seremeti , Sofia Anastasiadou , Andreas Masouras , Stylianos Papalexandris
The invasion of artificial intelligence (AI) in all forms of human activity causes the sudden change of social cohesion into a new hybrid reality, where the static rule of law maybe is overthrown by the instant violations of fundamental human rights, including the general rights of personhood, in its image, honor, and privacy, as well as, of general principles of law, including the principle of the “abuse of rights”, the principle of contractual autonomy, principles of tort liability, and general principles of intellectual property law. In that sense, AI disrupts the acquis due to the poor regulatory quality indicators covering unforeseen occurrences. We call this instantiation AI legal “coup d’état”. This paper constitutes a philosophical thesis statement which is in accordance with the global efforts to legally embed AI into societal systems. As part of an ongoing research on AI and law synergy, this paper focuses on proposing a theoretical framework utilizing category theory to align AI functionalities with traditional legal principles.
Volume: 14
Issue: 3
Page: 2471-2478
Publish at: 2025-06-01

Rooftops detection with YOLOv8 from aerial imagery and a brief review on rooftop photovoltaic potential assessment

10.11591/ijai.v14.i3.pp2282-2290
Md. Sabbir Ahmed , Md. Shohel Arman , Nusrat Tasnim , Md Hafizul Imran , Musabbir Smmak , Touhid Bhuiyan
Recent years have seen significant advancements in the switch from fossil fuel-based energy systems to renewable energy. Decentralized solar photovoltaic (PV) is one of the most promising energy sources since there is a lot of rooftop space, it is easy to install, and the cost of the PV panels is low. The determination of rooftop locations for PV installation is crucial for energy planning. With this context, this study aimed to detect the suitable rooftops of different shapes. The dataset of 5,076 building roofs used in this study was gathered by us utilizing a drone. This study identified ten distinct roof shapes accurately, including triangle, square, penta, hexa, hepta, octa, nona, deca, gabled roof, and hipped roof, using the most recent version of you only live once (YOLO), known as YOLOv8. Recent research revealed, YOLOv8 is more accurate than earlier YOLO models which is the reason of utilizing YOLOv8. Accuracy of this work of rooftops detection is 93.6%. Also, the precision, recall, and F1-score confidence curve showed good performances too. Finally, a brief review of the most recent studies on the evaluation of rooftop PV potential was conducted to provide insight into the use of solar energy.
Volume: 14
Issue: 3
Page: 2282-2290
Publish at: 2025-06-01

NAPLAM: a novel ledger-based algorithm for detection and mitigation of sinkhole attacks in routing protocol for low power and lossy networks-based Internet of things

10.11591/ijra.v14i2.pp248-259
Akshaya Dhingra , Vikas Sindhu , Lakshay Dhingra
The Internet of Things (IoT) is a network of connected physical objects that collect and share data over the Internet. However, routing attacks can disrupt data exchange, especially multi-node sinkhole attacks in low power and lossy IoT networks (LLNs). To support communication in LLN IoT, the IPv6-based routing protocol for LLNs (RPL) is used. Despite having several advantages, RPL also faces challenges like being vulnerable to attacks, having limited resources, compatibility, and scalability issues. Additionally, traditional security methods often do not work well for LLN-IoT devices because they lack the necessary computing power. To overcome these challenges, we have proposed a novel ledger-based framework called network and packet ledger to ascertain malicious devices using routing protocol for LLN (NAPLAM-RPL). This framework can effectively detect and mitigate multi-node sinkhole attacks in IoT networks. This paper also compares NAPLAM-RPL with similar protocols using the NetSim Simulator. The experimental analysis shows that NAPLAM-RPL improves network performance and outperforms existing methods like RF-trust, SoS-RPL, INTI, C-TRUST, and heartbeat algorithm in crucial areas, including packet delivery rate (PDR), throughput, End-to-End (E2E) delay, energy consumed, and detection accuracy.
Volume: 14
Issue: 2
Page: 248-259
Publish at: 2025-06-01

A new era of technological change in the restaurant industry: focusing on perceived values of robot servers

10.11591/ijra.v14i2.pp143-150
Jinsoo Hwang , Kyuhyeon Joo , Joonho Moon
The objective of this research is to examine the perceived values of robot servers, which include utilitarian and hedonic values, and how this influences willingness to pay more in the restaurant industry. This paper also examined the differences between the two sub-dimensions of perceived value, which are based on the demographic factors of the respondents. This research performed a data analysis based on a sample size of 295 participants, and the results indicated that the two sub-dimensions of perceived value play a crucial role in regard to the formation of willingness to pay more. Furthermore, the results showed that there were differences in perceived value in regard to the demographic factors.
Volume: 14
Issue: 2
Page: 143-150
Publish at: 2025-06-01

Application of satisfiability problem solvers for assessing the strength of hash algorithms

10.11591/ijece.v15i3.pp3191-3201
Kunbolat Algazy , Kairat Sakan , Andrey Varennikov , Nursulu Kapalova
This article presents a methodology for assessing the strength of cryptographic algorithms and provides experimental data obtained from studying the cryptographic strength of the developed hash function HBC-256 using modern satisfiability problem (SAT) solvers. Various SAT solvers implementing the conflict-driven clause learning (CDCL) algorithm, based on the Davis-Putnam-Logemann-Loveland (DPLL) algorithm, were used to conduct the cryptanalysis of the HBC-256 hash function. The most effective was the parallel SAT solver Parkissat, and thus it was used for more in-depth research. A series of experiments were conducted to determine how resistant the HBC-256 hashing algorithm is to preimage attacks for one, two, three, and four rounds. For this purpose, four sets of files were prepared using special propositional encoding tools, each set including 30 files in the standard of center for discrete mathematics and theoretical computer sciences (DIMACS) format. These files contain Boolean formulas in conjunctive normal form (CNF), used as input for modern SAT solvers. To obtain more accurate time measurements, the same experiment was repeated multiple times, after which the average time was determined. The results of this study show that SAT solvers encounter significant difficulties when attempting to solve the preimage search problem for the full-round version of the HBC-256 hash function, even when only 30 bits of the original message are unknown.
Volume: 15
Issue: 3
Page: 3191-3201
Publish at: 2025-06-01

Nusantara capital city sentiment analysis using support vector machine and logistic regression

10.11591/ijeecs.v38.i3.pp1708-1721
Valencia Eurelia Angelie Tania , Raymond Sunardi Oetama
The decision to move position the capital city of Indonesia to East Kalimantan has drawn people’s opinions, both pro and con, among the public, especially ahead of the presidential and vice-presidential elections. Discussions relevant to the relocation and construction of the capital city are increasingly crowded on social media, especially Twitter or X. This research aims to determine public sentiment regarding the development of the national capital to help the government and policymakers improve communication strategies, evaluate existing policies, and make more informed decisions based on public feedback. Public sentiment related to developing the Capital city of the Nusantara, including the presidential palace, toll road, and government offices, is analyzed. Support vector machine (SVM) and logistic regression (LR) algorithms are utilized for the sentiment classification. The results reveal that the SVM performs better in classifying sentiments in X data relevant to developing the Capital city of Nusantara, achieving an average accuracy of 91.97%.
Volume: 38
Issue: 3
Page: 1708-1721
Publish at: 2025-06-01

Electroencephalography classification technique based on statistical denoising and modified k-nearest neighbor algorithm with bipolar sigmoid rectified linear unit’s function

10.11591/ijece.v15i3.pp2786-2795
Thejaswini Bekkalale Mahalingegowda , Glan Devadhas George , Satheesha Tumakur Yoga , Kaliyamoorthy Ezhilarasan
Accurate classification of electroencephalography (EEG) data is much needed for early identification of diseases to treat various disorders. In this paper, we propose EEG classification technique based on statistical denoising & modified k-nearest neighbor (k-NN) algorithm with bipolar sigmoid rectified linear units (ReLU) function. The EEG data is subjected to statistical methods to remove the artifacts and then applied to modified k-NN algorithm to categorize the appropriate features giving preference to neighbors closer to one another considering the weighted votes of the k-nearest neighbors before selecting the class label based on the highest weighted vote. A customized activation function that combines these two functions called as hybrid function that uses various portions of each function in particular ranges is used in our work i.e., use of bipolar sigmoid for negative values and the ReLU function for positive values which helps to limit the signal in a particular range. The proposed algorithm's detection accuracy is tested for the confusion matrix of true positive (TP), false positive (FP), false negative (FN)and true negative (TN) and compared to the detection accuracy of other existing algorithms, demonstrating the algorithm's efficiency with a classification accuracy of almost 85 percent and sensitivity of 91% for standard Kaggle Dataset.
Volume: 15
Issue: 3
Page: 2786-2795
Publish at: 2025-06-01

Camera-based advanced driver assistance with integrated YOLOv4for real-time detection

10.11591/ijai.v14.i3.pp2236-2245
Keerthi Jayan , Balakrishnan Muruganantham
Testing object detection in adverse weather conditions poses significant chal lenges. This paper presents a framework for a camera-based advanced driver assistance system (ADAS) using the YOLOv4 model, supported by an electronic control unit (ECU). The ADAS-based ECU identifies object classes from real-time video, with detection efficiency validated against the YOLOv4 model. Performance is analysed using three testing methods: projection, video injection, and real vehicle testing. Each method is evaluated for accuracy in object detection, synchronization rate, correlated outcomes, and computational complexity. Results show that the projection method achieves highest accuracy with minimal frame deviation (1-2 frames) and up to 90% correlated outcomes, at approximately 30% computational complexity. The video injection method shows moderate accuracy and complexity, with frame deviation of 3-4 frames and 75%correlated outcomes. The real vehicle testing method, though demand ing higher computational resources and showing a lower synchronization rate (> 5 frames deviation), provides critical insights under realistic weather condi tions despite higher misclassification rates. The study highlights the importance of choosing appropriate method based on testing conditions and objectives, bal ancing computational efficiency, synchronization accuracy, and robustness in various weather scenarios. This research significantly advances autonomous ve hicle technology, particularly in enhancing ADAS object detection capabilities in diverse environmental conditions.
Volume: 14
Issue: 3
Page: 2236-2245
Publish at: 2025-06-01

An integrated framework for data breach on the dark web in brand monitoring data hunting

10.11591/ijece.v15i3.pp3162-3170
Siti Arpah Ahmad , Muhammad Al’Imran Mohd Khairuddin , Nor Shahniza Kamal Bashah , Nurul Aishah Ab Raman
In today's digital landscape, data breaches pose a substantial threat, with the dark web serving as a prevalent platform for malevolent actors to perpetrate such incidents. Currently, security analysts use various tools to solve the problem, which is very time-consuming. This paper introduces a novel framework that integrates data breach monitoring within the dark web, focusing on brand monitoring and data hunting. The framework starts from the scraping process and continues with the utilisation of the Splunk dashboard. The dashboard provides an exhaustive overview of data breaches related to brands for both manual inquiries and rule-based detection mechanisms. The framework comprises five phases: data sourcing, data collection, integration, monitoring, and visualisation. The visualisation phase encompasses alert generation, notification mechanisms, and reporting functionalities. Moreover, the monitoring phase provides real-time surveillance, advanced search capabilities, brand monitoring, and threat intelligence integration. The integration phase involves security information and event management (SIEM) systems and security orchestration, automation, and response (SOAR) systems. This paper's result contributes to enhancing the National Institute of Standards and Technology (NIST) cybersecurity framework, offering a comprehensive solution to the data breaches challenge within the dark web and the frontiers of knowledge and security practices.
Volume: 15
Issue: 3
Page: 3162-3170
Publish at: 2025-06-01

Fabric materials classification device using YOLOv8 algorithm

10.11591/ijeecs.v38.i3.pp1479-1488
Tuti Alawiya , Muhammad Ridho Isdi , Meqorry Yusfi , Harmadi Harmadi
The fashion industry in Indonesia significantly contributes to the country’s creative economy. However, public knowledge about various types of fabric materials is still limited, often leading to fraud. This research aims to develop a device that can classify fabric materials based on their structure using computer vision techniques. The device uses a digital microscope endoscope magnifier 1600x USB camera to capture fabric structure images and the YOLOv8 algorithm to classify 17 types of fabric materials from 1,700 raw image data. The research methodology includes collecting fabric image datasets, preprocessing data, and training the YOLOv8 model. The results show that the YOLOv8 model achieves an accuracy of 98%. The classification results are displayed on an LCD connected to NodeMCU ESP8266. System testing shows that the device effectively classifies fabric materials, sends the results to the database via API, and displays the results on the LCD. Overall, this device provides an effective solution for distinguishing types of fabrics and preventing fraud in fabric purchases.
Volume: 38
Issue: 3
Page: 1479-1488
Publish at: 2025-06-01
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