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31,456 Article Results

Improving breast cancer prediction through explainable artificial intelligence - A transdisciplinary approach

10.11591/ijeecs.v40.i1.pp288-296
Reena Lokare , Jyoti Sunil More , Vaishali V. Sarbhukan , Mansing Rathod , Sarita Rathod , Sunita Patil
Artificial intelligence (AI) technology has shown tremendous contributions in various applications like speech recognition, expert systems, computer vision, robotics, and gaming. machine learning (ML) and deep learning (DL) algorithms under AI address problems such as prediction, classification, and regression. AI has touched many domains. The results or the predictions generated by these algorithms are not easily accepted by the user. Especially, the Healthcare domain is facing a great challenge in accepting the results or the predictions with the concern, Are AI results reliable, correct, and ethical? Doctors or medical practitioners are not ready to treat patients based on results or suggestions generated by AI algorithms. Hence, a technology that can explain how the results returned by AI algorithms are trustworthy, transparent, and interpretable was strongly needed. This need has given rise to the latest technology-explainable artificial intelligence (XAI). With the use of XAI, all the predictions, classifications made by AI algorithms are explainable, auditable, comprehensive, validating, and socially acceptable. This paper discusses explaining the results of breast cancer prediction as a case study. The results show that such an explanation will build trust in the doctors and hence will increase the acceptance of the AI-based systems.
Volume: 40
Issue: 1
Page: 288-296
Publish at: 2025-10-01

Predict glucose values with DE algorithm optimized T-LSTM

10.11591/ijeecs.v40.i1.pp530-544
QingXiang Bian , Azizan As’array , XiangGuo Cong , Khairil Anas bin Md Rezali , Raja Mohd Kamil bin Raja Ahmad , Mohd Zarhamdy Md. Zain
The prevalence of diabetes is rising. According to the International Diabetes Federation (IDF) predictions, the number of diabetic patients worldwide will reach 608 million in 2030, accounting for approximately 11.3% of the total number of people in the world. To monitor and predict the future 1 hour glucose have a great significance meaning for patients. This research utilizes a differential evolution (DE) algorithm, an optimized hybrid model transformer and long short-term memory (T-LSTM) technologies to analyze historical data from continuous blood glucose monitoring (CGM) systems and equipment calibration values. The aim is to predict future blood sugar levels in patients, thereby helping to prevent episodes of hypoglycemia and hyperglycemia. The study tested the model using the CGM data from 8 patients at the Suzhou Municipal Hospital in Jiangsu Province, China. Results show that this DE-optimized T-LSTM model outperforms traditional models. The model's accuracy is evaluated using mean squared error (MSE), with MSE values recorded at 15, 30, and 45 minutes being 0.96, 1.54, and 2.31, respectively.
Volume: 40
Issue: 1
Page: 530-544
Publish at: 2025-10-01

MoBiSafe: an obfuscated single factor authentication mode to enhance secured USSD channel transaction in Nigeria

10.11591/ijeecs.v40.i1.pp426-436
Amaka Patience Binitie , Sebastina Nkechi Okofu , Margaret Dumebi Okpor , Kizito Eluemunor Anazia , Arnold Adimabua Ojugo , Francesca Avwuru Egbokhare , Annie Egwali , Peace Oguguo Ezzeh , Rita Erhovwo Ako , Victor Ochuko Geteloma , Tabitha Chukwudi Aghaunor , Eferhire Valentine Ugbotu , Sunny Innocent Onyemenem
The flexibility of the unstructured supplementary service data (USSD) across mobile phones has caused its adoption surge as a payment channel. Its usage accommodates financial inclusivity and extends customer reach irrespective of their specific phone capabilities. With data conveyed on the USSD channel in plaintext–this has raised vulnerability issues with shoulder surfing attacks. The use of password yielded extra layer of security as authentication to USSD-based services. But, the rise in password guess attacks has necessitated a new scheme. This study is a randomized-obfuscated single factor authentication (SFA) mode via a 5-digit PIN-entry as requisite for the USSD channel. It yields a list via which users select a key-array that corresponds to their PIN as concealed in a 10-digit array. Expert assess of MoBiSafe’s usability and security against shoulder-surf yielded 10.1 msecs and 2.26 msecs respectively to outperform existing models that utilize direct/indirect PIN-entry as in USSD transactions. And this was found to be both secure, usable and acceptable.
Volume: 40
Issue: 1
Page: 426-436
Publish at: 2025-10-01

Phishing URL prediction – two-phase model using logistic regression and finite state automata

10.11591/ijeecs.v40.i1.pp356-365
Nisha T N , Dhanya Pramod
The human factor in security is more important when they become the carriers of attacks on enterprises. Phishing attacks can be classified as insider attacks when the employees unintentionally participate in the attack propagation. Since complete user training is a myth, enterprises must implement detection tools for phishing attacks on their network perimeters. This research discusses a two-phase model for phishing URL detection, in which the first phase identifies the properties of URLs that detect phishing and their relative weight using logistic regression. The second phase checks the probability of a new URL being categorized as phishing using the knowledge achieved during the first phase using the dynamically created Finite state machines. The model defines a malicious score (MS), which can be used to check any URL in real-time to identify whether it is phishing or not. The model described in this work has been experimented with different benchmarking datasets to verify the performance. The model provided a decent result in classifying a URL as phishing or naive. The malicious score (MS) defined by this model can be used to evaluate any URL and can be used as a filtering mechanism for end-point phishing URL detection. The key contribution is towards developing a two-phase model which evaluates the URL with the help of self-crafted features without reliance on a feature set. This accommodates the model's hyper-competitive phishing URL detection area in cyber security.
Volume: 40
Issue: 1
Page: 356-365
Publish at: 2025-10-01

Machine identification codes of color laser printers: revisiting privacy and security

10.11591/ijeecs.v40.i1.pp137-145
Shreya Arora , Rajendra Kumar Sarin , Pooja Puri
Forging legal documents has been easier and faster with the advancement of technology. Printer identification has become a critical field for tracing criminals and validating the authenticity of documents. The current study uses a non-destructive method to detect and identify covert embedded hidden information (machine identification codes (MIC)). Samples were collected from popular brands, including Xerox and HP color laser printers, to attain this aim. Their printouts were then scanned at 600 dpi using a Konica Minolta scanner. Scanned images were subjected to graphic editors for linear and non-linear adjustments. Following this, yellow-toner dots were observed as a base pattern. Grayscale imaging with a computational approach to analyze the yellow dot patterns was utilized for intensity-focused analysis, with edge detection algorithms applied using Python to enhance and highlight the converted patterns in printed documents. The printouts from Xerox printers exhibited repeating patterns. However, no such detailed information was observed in prints from HP printers, even when analyzed using binary code for deductions. A notable variation was detected in the yellow tracking dots among both brands, which can be instrumental in identifying the origin of printouts and scanned images for forensic investigations. This methodology provides conclusive and dependable accuracy.
Volume: 40
Issue: 1
Page: 137-145
Publish at: 2025-10-01

Distributed formation control with obstacle and collision avoidance for humanoid robot

10.11591/ijeecs.v40.i1.pp108-117
Faisal Wahab , Bambang Riyanto Trilaksnono
Formation control has become a popular research topic in recent years. A common challenge in formation control is ensuring that robots can avoid obstacles and maintain a safe distance from one another to prevent collisions while forming a formation. In this research, a distributed formation control approach for a multi-robot system (MRS) with obstacle and collision avoidance is presented. The distributed formation control architecture is based on a consensus algorithm and consists of four layers: consensus tracking, consensus-based formation control, behavior, and physical robot layers. The system was implemented and evaluated through both simulations and experiments. Humanoid robots were used as the platform for these implementations. The result of the simulations and experiments show that the distributed formation control system successfully guided the robots into desired formation while also avoiding obstacles and preventing collisions with other robots.
Volume: 40
Issue: 1
Page: 108-117
Publish at: 2025-10-01

Voltage stability investigation: enabling large-scale renewable energy integration in Tamil Nadu’s grid

10.11591/ijeecs.v40.i1.pp47-56
Chelladurai Chandarahasan , Edwin Sheeba Percis
The integration of renewable energy sources (RES) such as wind and solar, characterized by their inherent intermittency and variability, poses notable challenges to the stability and reliability of power systems. This research addresses these challenges by conducting a detailed load flow, voltage sensitivity and stability analysis at a significant extra high voltage (EHV) pooling station under high-RES penetration. Employing advanced simulation methodologies, the study evaluates the effects of RES integration on voltage profiles and the system’s capacity to sustain equilibrium under steady-state operations. The findings highlight that substantial RES integration induces considerable voltage fluctuations and reactive power disparities. To counter these effects, static var compensators (SVCs) were deployed, demonstrating their efficacy in enhancing voltage stability and providing essential reactive power support. The study confirms the pivotal role of SVCs in alleviating voltage sensitivity to RES fluctuations, thereby promoting a more stable and reliable power supply. This research underscores the importance of strategic reactive power management devices in enabling a seamless transition towards a renewable-dominant energy landscape.
Volume: 40
Issue: 1
Page: 47-56
Publish at: 2025-10-01

Wind farm integration with the objective of transmission expansion power in South Africa

10.11591/ijeecs.v40.i1.pp34-46
Nomihla Wandile Ndlela , Katleho Moloi , Musasa Kabeya
Growing renewable energy (RE) use mitigates climate change. The integration of large-scale intermittent renewable energy resources (RER) like wind energy into electrical networks has increased during the past decade. However, careful planning is needed to accommodate the long-term energy demand increase. Transmission network expansion planning (TNEP) is the methodical and profitable process of increasing power infrastructure to meet predicted electricity demand while preserving reliability. This article is for those interested in integrating renewable energy sources (RES) into HVTL to increase power availability and decrease losses. The Eros-VuyaniNeptune 400 kV transmission powerline connecting KwaZulu-Natal to the Eastern Cape is used in this study. It was implemented during the transfer of affected residents in the Ingquza Hill Local Municipality, which includes Lusikisiki and Flagstaff villages. This study connects the existing Metro wind farm to the Vuyani substation, which is connected to the Eros substation through a 400 kV transmission line. This research enhanced transmission line power while preserving grid stability with a 27 MW wind farm, and also increased external grid reserve capacity for future usage or unexpected power demand. This paper outlines TNEP’s significant advances using classic (mathematical) and advanced (heuristic and meta-heuristic) optimization approaches.
Volume: 40
Issue: 1
Page: 34-46
Publish at: 2025-10-01

A centroid-based algorithm for measuring and tracking vehicle speed from a monocular camera using the YOLOv8 object detector

10.11591/ijeecs.v40.i1.pp437-449
Pankaj Kumar Gautam , Sanjeev Kumar
Accurate real-time vehicle speed measurement is crucial for enhancing road safety and advance intelligent transportation systems (ITS). This paper proposes a centroid-based tracking algorithm that integrates YOLOv8, a state-of-the-art object detector, with DeepSORT for robust multi-object tracking. By leveraging YOLOv8’s anchor-free detection and DeepSORT’s appearance-based association, the proposed method effectively mitigates occlusions and minimizes identity switches. Evaluations on the VS13 benchmark dataset reveal a 2–5% improvement in measurement accuracy as compared to existing solutions, while maintaining real-time performance at 30 FPS. The method demonstrates consistent reliability across different vehicle models, speeds, and lighting conditions, underscoring its adaptability to real-world traffic scenarios. Moreover, larger bounding boxes enhance tracking stability, reducing false detections. Overall, the approach’s low computational overhead and high accuracy position it as a practical solution for ITS applications in constrained environments.
Volume: 40
Issue: 1
Page: 437-449
Publish at: 2025-10-01

Accurate plate number recognition based on Sobel edge detection and weighted morphological structuring element

10.11591/ijeecs.v40.i1.pp307-315
Rostam Affendi Hamzah , Ahmad Fauzan Kadmin , Khairul Azha A Aziz , Mohd Saad Hamid , Lim Then Sean
Number plate recognition (NPR) became a very important in our daily life because of the unlimited increase of cars and transportation systems which make it impossible to be fully managed and monitored by humans, examples are like traffic monitoring, tracking stolen cars, managing parking toll, red-light traffic violation enforcement, border, and customs checkpoints. Yet it is a very challenging problem, due to the diversity of plate formats, different scales, rotations, and non-uniform illumination conditions during image acquisition. The proposed system is simple but efficient for recognizing plate numbers. The Sobel edge detection and morphological process are used in the proposed framework with weighted structuring element. This approach simplifies the task by using the bounding box method for character segmentation to segment all the characters on the plate number. Template matching is then applied to recognize the numbers and characters. This approach produces accurate results as shown by the experimental process.
Volume: 40
Issue: 1
Page: 307-315
Publish at: 2025-10-01

A comprehensive analysis of smartphones and tablets in graphic design and digital art

10.11591/ijeecs.v40.i1.pp146-155
Jirawat Sookkaew , Nakharet Chaikaew , Nakarin Chaikaew
This paper discusses how smartphones and tablets have changed creativity and graphic design. These portable tools and easy apps have transformed the creative process, allowing artists, designers, and students to create high-quality work anywhere. Mobile design apps promote creativity, accessibility, and skill development across broad user groups, according to the study. Unlike desktop tools, it addresses key constraints. Mobile apps sometimes struggle with smaller screens, restricted processing power, and reduced capabilities for complicated tasks like multi-layer editing and advanced graphics. These restrictions may inhibit expert designers working on complex, precise designs. Even Nevertheless, mobile technology like larger screens, stylus support, and cloud-based solutions are making mobile devices more feasible for creative work. The findings emphasise the relevance of integrating mobile technology into education and professional workflows and its complementarity to desktop solutions for resource-intensive jobs. In the developing digital landscape, our dual-platform approach maximizes creativity and flexibility.
Volume: 40
Issue: 1
Page: 146-155
Publish at: 2025-10-01

A hybrid intelligent model for prediction of coronary artery diseases using TabNet and multiclass SVM

10.11591/ijeecs.v40.i1.pp156-163
Niveditha Honnemadu Rudreshgowda , Balakrishna Kempegowda , Anitha Sammilan
Cardiovascular disease is one of the significant fatality-causing diseases in this era by affecting the heart and blood vessels. Cardio diseases are classified into coronary heart disease (CHD), heart failure, valve disease, and arrhythmias. Medical diagnosis of heart disease and treating the patient is a challenging process, where early detection can lead to decreased fatality. In this research, hybrid model-based prediction of CHD detection is developed by TabNet and multiclass support vector machine (SVM). We created our datasets for experimentation by visiting the hospitals in the Mysore and Mandya regions of Karnataka, India. Datasets consist of 16 features; the features are pre-processed to normalize, encode, and handle missing values to extract the aggregate features using TabNet, and the multiclass SVM model is trained to classify the disease based on the classes. The proposed hybrid model prediction performance was evaluated using various metrics such as accuracy, recall, precision, and F1-score.
Volume: 40
Issue: 1
Page: 156-163
Publish at: 2025-10-01

A multi-path routing protocol for IoT-based sensor networks

10.11591/ijeecs.v40.i1.pp225-235
Udaya Suriya Rajkumar Dhamodharan , Krishna Prasad Karani , Saranya Pichandi , Kavitha Palani , Sathiyaraj Rajendran
Internet of things (IoT) based sensors are to link a big number of low-cost and power-integrated devices in a reliable manner. Numerous military and adventurous applications are regulated by communication among IoT sensors. The multi-path routing protocol (MRP) approach presented in this research to enhance secure routing in IoT sensors is significant. This technique makes use of data transfer routing and the relationships between network components. It finds the most efficient route between the nodes that minimizes communication overhead and is both reliable and economical in terms of shortest duration. The particle swarm optimization (PSO) technique is used to find the shortest path that is most cost-effective. To reach the target node, end-to-end data transmission must transit via intermediary nodes, which are provided by the routing path node history. The optimal path is chosen by MRP from PSO, and it traces the path to identify the intermediate nodes. In the unlikely event of a crisis, MRP offers the most affordable backup route for data transfer. When compared to earlier techniques, the outcomes of these current approaches enhance network efficiency, balance energy consumption among nodes, and routing attacks.
Volume: 40
Issue: 1
Page: 225-235
Publish at: 2025-10-01

Autoencoder-based Gaussian mixture model for diagnosing early onset of diabetic retinopathy

10.11591/ijeecs.v40.i1.pp164-172
Priyanka Sreenivas , Kavita V. Horadi , Kalpa Rajashekar
The current study presents a simplified yet innovative solution towards effective early diagnosis of diabetic retinopathy (DR) that leads to irreversible blindness. A review of current literature shows a considerable number of machine learning and deep learning approaches have been presented; however, there are significant issues with the early detection of DR. Hence, the proposed study deploys a novel architecture using an autoencoder that extracts a hidden representation of retinal images while binary classification is carried out using a Gaussian mixture model. The prime contribution is the joint integration of deep learning with statistical modelling towards efficient feature extraction and anomaly detection, supporting early determination of DR. The study outcome shows a proposed system to significantly exhibit 96.5% accuracy, 94.2% sensitivity, and 98.3% specificity on two standard benchmarked datasets in comparison to existing models frequently used for the diagnosis of DR.
Volume: 40
Issue: 1
Page: 164-172
Publish at: 2025-10-01

FaceSynth: text-to-face generation using CLIP and its variants with generative adversarial networks

10.11591/ijai.v14.i5.pp3588-3598
Priyadharsini Ravisankar , Shruthi Dhanvanth , Vaishnave Jenane Padmanabhan
In recent years, there have been massive developments in the field of generative AI, especially in generative adversarial networks (GANs). GANs generate original images that haven't been seen during training and have had several advancements like StyleGAN, StyleGAN2, and StyleGAN2-adaptive discriminator augmentation (ADA). Contrastive language-image pre-training (CLIP), by OpenAI, is a visual linguistic model that has been trained to associate texts with images. Recently, new CLIP variants were developed, such as metadata-curated language-image pre-training (MetaCLIP), released by Facebook and trained on a larger dataset, and Multilinigual-CLIP, which adapts CLIP to multiple languages. We compare CLIP and its variants in text-to-face synthesis with a custom StyleGAN2-ADA model and a pre-trained StyleGAN2 model. Our training-free algorithm starts with an initial image latent code that is iteratively manipulated to match a given text description. It achieves this by minimizing the distance between the text and image embedding in the multi-modal embedding space of the CLIP models. An examination of CLIP and its variants showed that MetaCLIP outperformed its competitors in LPIPS similarity and closeness of the synthesized image to the actual prompt. CLIP produced the most realistic images with the best FID score and multilingual-CLIP presented a choice of input text language and generated decent images.
Volume: 14
Issue: 5
Page: 3588-3598
Publish at: 2025-10-01
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