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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

LMS bot: enhanced learning management systems for improved student learning experiences using robotic process automation

10.11591/ijai.v14.i3.pp2044-2054
Mamidyala Durga Prasad , Nandini Balusu
In this paper, a workflow for bot is designed using robotic process automation (RPA) that is used to enhance learning management systems (LMS) by providing content from external sources along with educator made course content for better student learning experiences. Many students prefer to watch YouTube videos for learning, even if they have been taught the same content by an educator. YouTube is a dynamic platform where video rankings change based on viewer engagement, relevance, and newly included videos. This variability poses a challenge for educators seeking to include external videos, as the content environment within the LMS platform is unpredictable and can change significantly. The bot addresses the challenge by conducting periodic searches for related courses and topics on YouTube. It retrieves top-ranked videos based on relevance, which are then seamlessly integrated into external links within LMS. The LMS external links option enhances accessibility by offering videos sorted by popularity, ensuring students receive updated and relevant information seamlessly. The bot efficiently retrieves details of 750 videos from YouTube in just 17 seconds, showcasing its exceptional performance. Moreover, its capability to autonomously update LMS external links content weekly represents an added advantage. The bot is designed and tested using UiPath tool.
Volume: 14
Issue: 3
Page: 2044-2054
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

Enhancing tabular data analysis for classification of airline passenger satisfaction using TabNet deep neural network

10.11591/ijece.v15i3.pp3362-3372
Rachid Kaidi , Hicham Omara , Mohamed Lazaar , Mohammed Al Achhab
In an era of air travel, understanding and enhancing passenger satisfaction are pivotal to the success of airlines and the overall passenger experience. Analyzing airline passenger satisfaction using tabular data can pose various challenges, both when employing classical statistical methods and when leveraging machine learning and deep learning techniques. On the one hand, statistical approaches pose various challenges including limited feature engineering techniques, the assumption of linearity of the data sets and limited predictive power. Then again, the use of machine learning and deep learning techniques may face other challenges such as the problem of overfitting, difficulty in interpreting data, intensive resource requirements, and the generalization problem in deploying machine learning-based methods. This paper presents a novel deep learning approach utilizing TabNet to classify airline passenger satisfaction. Leveraging a comprehensive dataset comprising various passenger-related attributes, our TabNet-based model demonstrates exceptional performance in distinguishing between satisfied and dissatisfied passengers. Our model’s robustness in handling tabular data, underscores its power as a valuable tool for the aviation industry. Comparing out results to recent papers show that out model outperforms these studies in terms of accuracy, precision, recall and area under the curve. The results show that our TabNet Network model outperforms all implemented machine learning models by reaching respectively the following results: 96.47%, 96.41% and 96.24% for accuracy, F1-score and G-mean score.
Volume: 15
Issue: 3
Page: 3362-3372
Publish at: 2025-06-01

Skin cancer diagnosis using hybrid deep pre-trained convolutional neural networks

10.11591/ijai.v14.i3.pp2291-2301
Maha Ibrahim Khaleel , Amir Lakizadeh
As a variant of skin cancer, melanoma represents a substantial menace to the health and overall well-being of individuals. Statistics reveal that 55% of skin cancer patients succumb to this particular disease. However, early detection plays a crucial role in reducing mortality rates and saving lives. In the past several decades, there has been a rise in the adoption of deep learning algorithms, capturing the interest of researchers working in this field. One popular method involves utilizing pre-trained deep neural networks. In this study, a hybrid approach is employed to extract features from melanoma images. This approach integrates the utilization of pre-trained architectures, including AlexNet, ResNet-50, and GoogleNet. During the transfer training phase, these networks are fine-tuned to detect skin cancer by adjusting the learning rate. Subsequently, the maximum relevance minimum redundancy (MRMR) algorithm is employed to select optimal features based on the concepts of minimum redundancy and maximum relevance in order to minimize feature redundancy and enhance classification accuracy. The bagging technique is employed for the classification of various skin cancer types. The experimental results demonstrate the success of the suggested approach, yielding 98.9% accuracy. Furthermore, the results indicate the superiority of this method according to precision, recall, and F1-score in comparison with existing algorithms.
Volume: 14
Issue: 3
Page: 2291-2301
Publish at: 2025-06-01

Cuckoo search algorithm approach for optimal placement and sizing of distribution generation in radial distribution networks

10.11591/ijece.v15i3.pp2681-2696
Kayode Ojo , Seyi Fanifosi , Awelewa Ayokunle , Isaac Samuel
Radial distribution networks (RDNs) often experience power loss due to improper distribution generation (DG) allocation. Strategic DG placement can reduce power loss, minimize costs, and improve voltage profiles and stability. This research optimizes DG placement and sizing in RDNs using the cuckoo search algorithm (CSA). The objective function considers losses across all network branches, and CSA identifies optimal DG locations and sizes. Tested on IEEE 33-bus, IEEE 69-bus, and Nigeria's Imalefalafia 32-bus RDN, the Cuckoo Search technique results in optimal DG locations at buses 6, 50, and 18 with corresponding sizes of 2.4576, 1.852, and 2.718 MW, respectively. Voltage improvements are 0.9509, 0.9817, and 0.9821 p.u, while total active and reactive power losses for IEEE 33-bus are reduced by 49.03% and 45.00%, and for IEEE 69-bus by 63.67% and 61.14%. The CSA approach significantly enhances voltage profiles and reduces power losses in these networks.
Volume: 15
Issue: 3
Page: 2681-2696
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

Exploring patient-patient interactions graphs by network analysis

10.11591/ijai.v14.i3.pp1752-1762
Zaher Salah , Esraa Abu Elsoud , Kamal Salah
Understanding how patient demographics and shared experiences impact interactions is essential for strengthening pa/tient support networks and optimizing health outcomes as personalized healthcare becomes more and more important. To this end, this study explores the patient-patient interactions (PPIs) graph as a network and applies selected network analysis approaches to examine the PPIs network of accutane drug. Two main research questions are addressed by gaining deeper insight at the hidden patterns of reactivity and connectivity among interchanging nodes. There was a negative response to the first research question, which asked if patients react to others that have similar gender and/or age profiles in a consistent way. Patients tended to interact with people of different genders and ages, indicating a high degree of heterogeneity in the network. Negative responses were likewise given to the second research question, which asked if communities inside the network could identify patients based on gender or age profile. Network analysis approaches for community detection failed to distinguish between groups with similar demographic characteristics. Rather, groups seemed to emerge based on other factors, like similarity in patient opinions. The results imply that gender and age do not have a major influence on community membership. Future research will concentrate on applying more sophisticated graph mining techniques to expand these approaches to cover more and larger PPIs networks.
Volume: 14
Issue: 3
Page: 1752-1762
Publish at: 2025-06-01

Detection of partially occluded area in face image using U-Net model

10.11591/ijai.v14.i3.pp1863-1869
Jyothsna Cherapanamjeri , Bangole Narendra Kumar Rao
Occluded face recognition is important task in computer vision. To complete the occluded face recognition efficiently, first we need to identify the occluded region in face. Identifying the occluded region in face is a challenging task in computer vision. One case of face occlusion is nothing but wearing masks, sunglasses, and scarves. Another case of face occlusion is face is hiding the other objects like books, things, or other faces. In our research, identifying the occluded area which is corona virus disease of 2019 (COVID-19) masked area in face and generate segmentation map. In semantic segmentation, deep learning-based techniques have demonstrated promising outcomes. We have employed one of the deep learning-basedU-Net models to generate a binary segmentation map on masked region of a human face. It achieves reliable performance and reducing network complexity. We train our model on MaskedFace-CelebA dataset and accuracy is 97.7%. Results from experiments demonstrate that, in comparison to the most advanced semantic segmentation models, our approach achieves a promising compromise between segmentation accuracy and computing efficiency.
Volume: 14
Issue: 3
Page: 1863-1869
Publish at: 2025-06-01

The impact of innovative technology on shaping digital design skills in primary school students: a case study of Kazakhstan

10.11591/ijere.v14i3.32676
Saltanat Anapiyayeva , Gulnar Uaisova , Almash Turalbayeva , Sabira Nishanbayeva
The trajectory of digital progress in Kazakhstan has highlighted several challenges within the primary education system. A critical component of developing digital design skills (DDS) lies in the impact of innovative educational technologies on these skills. Despite the potential of such technologies to enhance DDS and engage students in digital literacy, the existing literature falls short in exploring this area comprehensively. The study aims to examine students’ DDS and examines how innovative educational learning technology affects these skills. This study used a quantitative research approach to measure innovative educational learning technology’s impact on primary school students’ DDS. The experiment involved 120 participants and uncovered several key insights. The deficiency in DDS and lack of motivation revealed by the study called for systematic changes in how digital literacy is taught. These changes included restructuring curricula, enhanced teacher training, access to digital resources, and more engaging, practical learning environments. The study demonstrated substantial improvements in students’ DDS following the introduction and testing of the author’s academic program with the experimental group (EG) participants. The findings from this study can serve as a foundation for developing strategies to enhance DDS in primary school and provide a methodological basis for adapting educational programs to support DDS development.
Volume: 14
Issue: 3
Page: 2389-2399
Publish at: 2025-06-01

Optimizing real-time data preprocessing in IoT-based fog computing using machine learning algorithms

10.11591/ijai.v14.i3.pp1900-1909
Nandini Gowda Puttaswamy , Anitha Narasimha Murthy
In the era of the internet of things (IoT), managing the massive influx of data with minimal latency is crucial, particularly within fog computing environments that process data close to its origin. Traditional methods have been inadequate, struggling with the high variability and volume of IoT data, which often leads to processing inefficiencies and poor resource allocation. To address these challenges, this paper introduces a novel machine learning-driven approach named real-time data preprocessing in IoT-based fog computing using machine learning algorithms (IoT-FCML). This method dynamically adapts to the changing characteristics of data and system demands. The implementation of IoT-FCML has led to significant performance enhancements: it reduces latency by approximately 0.26%, increases throughput by up to 0.3%, improves resource efficiency by 0.20%, and decreases data privacy overhead by 0.64%. These improvements are achieved through the integration of smart algorithms that prioritize data privacy and efficient resource use, allowing the IoT-FCML method to surpass traditional preprocessing techniques. Collectively, the enhancements in processing speed, adaptability, and data security represent a substantial advancement in developing more responsive and efficient IoT-based fog computing infrastructures, marking a pivotal progression in the field.
Volume: 14
Issue: 3
Page: 1900-1909
Publish at: 2025-06-01

Convolutional neural network based encoder-decoder for efficient real-time object detection

10.11591/ijai.v14.i3.pp1960-1967
Mothiram Rajasekaran , Chitra Sabapathy Ranganathan , Nagarajan Mohankumar , Rajeshkumar Sampathrajan , Thayalagaran Merlin Inbamalar , Nageshvaran Nandhini , Shanmugam Sujatha
Convolutional neural networks (CNN) are applied to a variety of computer vision problems, such as object recognition, image classification, semantic segmentation, and many others. One of the most important and difficult issues in computer vision, object detection, has attracted a lot of attention lately. Object detection validating the occurrence of the object in the picture or video and then properly locating it for recognition. However, under certain circumstances, such as when an item has issues like occlusion, distortion, or small size, there may still be subpar detection performance. This work aims to propose an efficient deep learning model with CNN and encoder decoder for efficient object detection. The proposed model is experimented on Microsoft Common Objects in Context (MS-COCO) dataset and achieved mean average precision (mAP) of about 54.1% and accuracy of 99%. The investigational outcomes amply showed that the suggested mechanism could achieve a high detection efficiency compared with the existing techniques and needed little computational resources.
Volume: 14
Issue: 3
Page: 1960-1967
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

Enhancing plagiarism detection using data pre-processing and machine learning approach

10.11591/ijai.v14.i3.pp1940-1950
Vrushali Bhuyar , Sachin N. Deshmukh
Modern technology and the internet have enhanced academic information accessibility, but this has led to a rising global concern about plagiarism. Researchers are actively exploring machine learning as a promising solution for detection. This study underscores the importance of robust data preprocessing for optimal machine learning algorithm performance. Using a dataset of 67 research papers, big five factors (OCEAN), and plagiarism rates, the study employed machine learning to detect plagiarism. The training process involved exposing algorithms to an 80% training subset, followed by evaluating their performance on the remaining 20% in the testing phase, assessing generalization capabilities. For the random forest regressor, bagging regressor, gradient boosting regressor, XGB regressor, and AdaBoost regressor, corresponding root mean squared error (RMSE) are 9.48, 10.66, 11.79, 12.53, and 12.79, respectively. This research contributes novel insights to existing literature by introducing a plagiarism detection model that innovatively integrates outlier detection, normalization, missing value imputation, and feature selection. The unique aspect lies in the effective combination of feature selection and missing value imputation, surpassing previous benchmarks and optimizing precision and efficiency. The approach is metaphorically likened to assembling puzzle pieces, highlighting the distinctive methodology employed in enhancing the performance of the plagiarism detection model using data preprocessing.
Volume: 14
Issue: 3
Page: 1940-1950
Publish at: 2025-06-01

Classification of customer complaints on social media for e-commerce in Indonesia

10.11591/ijece.v15i3.pp2977-2985
Achmad Rizki Aditama , Alfan Farizki Wicaksono
The e-commerce industry in Indonesia has experienced rapid growth, especially during the COVID-19 pandemic, which accelerated the shift to online platforms. The market is expected to grow by 105.5% from 2025 to 2030 due to increased internet and smartphone use. As e-commerce expands, companies must improve how they handle customer complaints to build trust and loyalty. Social media is a crucial channel for customer interactions, but it also includes non-complaint messages like positive comments, general questions, and spams that need to be filtered out. This research proposes a machine learning model to automatically classify social media interactions into complaints and non-complaints, focusing on Indonesian-language content. The modeling process utilized 10,600 data points collected from social media X. The best model, a bidirectional encoder representation from transformers (BERT) based classifier, achieved an F1-score of 98.3%. The McNemar test revealed significant performance differences between several models, with the BERT-based model outperforming others. This demonstrates that it is highly effective in distinguishing between complaints and non-complaints, making it a valuable tool for enhancing customer service in Indonesia's e-commerce sector.
Volume: 15
Issue: 3
Page: 2977-2985
Publish at: 2025-06-01
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