bluuebunny/crossref_metadata_2025 huggingface dataset:
A Case of Opsismodysplasia with a Novel INPPL1 Variant
Authors: Daşar Tuğba , Aypar Ebru , Utine Gülen Eda , Şimşek-Kiper Pelin Özlem
Published: January 2025

Introduction: Opsismodysplasia is a rare autosomal recessive genetic skeletal disorder characterized by short stature, short limbs, small hands and feet, delayed bone age, severe platyspondyly, metaphyseal cupping, and facial dysmorphism. Opsismodysplasia is caused by biallelic variants in the INPPL1 gene. Only 38 patients with a confirmed molecular diagnosis have been reported so far. Case Presentation: We present a 9-month-old male patient who was referred to our clinic with a suspicion of mucopolysaccharidoses due to facial features and radiographic findings, but urine glycosaminoglycans were within normal ranges. Audiologic and ophthalmologic assessments, transfontanelle ultrasound, and echocardiography were all normal. A renal cortical cyst with a diameter of 33 × 28 mm was detected in abdominal ultrasound. He had dysmorphic findings including relative macrocephaly, midface hypoplasia, depressed nasal bridge, anteverted nostrils, long philtrum, small hands and feet, and brachydactyly. His length was 63 cm (−3.7 SD) and his arm span was 58 cm. Delayed bone age, short metacarpals and phalanges, wide and irregular metaphysis, platyspondyly, anterior beaking of the vertebrae, T12 vertebral hypoplasia, and acetabular dysplasia were noted on X-rays. Exome sequencing revealed a novel homozygous c.147C>G (p.Ser49Arg) variant in INPLL1. Conclusion: Opsismodysplasia is an extremely rare skeletal disorder, and with this case, we further expand the clinical and molecular spectrum of opsismodysplasia.
An efficient course recommendation system for higher education students using machine learning techniques
Authors: M. Arcinas Myla , Meenakshi Meenakshi , S. Bahalkar Pranjali , Bhaturkar Deepali , Lalar Sachin , Pitambar Rane Kantilal , Garg Shaifali , Omarov Batyrkhan , Raghuvanshi Abhishek
Published: January 2025

Education institutions and teachers are in desperate need of automated, non-intrusive means of getting student feedback that would allow them to better understand the learning cycle and assess the success of course design. Students would benefit from a framework that intelligently guides their actions and provides exercises or resources to support and enhance their learning. The recommender system framework is a software agent that learns the user's preferences through a variety of channels and then utilizes that knowledge to provide product suggestions. A recommendation engine considers all potential user interests as background information, uses that knowledge to produce convincing recommendations, and then returns those ideas to the user. This article presents a feature selection and machine learning based course recommendation system for higher education students. principal component analysis (PCA) algorithm is used for feature selection. AdaBoost, k nearest neighbour (KNN), and Naïve Bayes algorithms are used to classify and predict student data. It is found that the AdaBoost algorithm is having better accuracy and F1 score for course recommendation to students. PCA AdaBoost is achieving an accuracy of 99.5%.
Developing digital capabilities through IT governance: a PLS-SEM analysis in Moroccan higher education institutions
Authors: Chahid Abdelilah , Ahriz Souad , El Guemmat Kamal , Mansouri Khalifa
Published: January 2025

This study examines the impact of information technology governance (ITG) on digital transformation (DT) in Moroccan higher education institutions, particularly emphasising the mediating role of absorptive capacity. Utilising a rigorous methodological framework, the research analyzes data collected from 110 staff members using structural equation modelling with the SmartPLS tool. The goal is to explore the complex dynamics between ITG practices and DT capability. The findings reveal a positive and statistically significant relationship between ITG mechanisms and absorptive capacity (AC) and between the latter and the success of DT. The study also identifies AC as a crucial mediator between ITG and digital capability (DC). It suggests universities should strengthen their AC and adopt open policies to increase their innovative potential. This contribution enriches the existing literature by empirically confirming the influence of certain IT governance variables on DC within Moroccan universities, offering valuable insights for academic researchers and practitioners involved in IT governance strategies and DT.
Automated tool for conducting emotion analysis studies in perception surveys
Authors: Elías Chanchí Golondrino Gabriel , Alejandro Ospina Alarcón Manuel , Estella Hernandez Londoño Claudia
Published: January 2025

Considering the growing need for companies to automate the analysis of customer opinions from different digital media, this paper outlines the development of an automated tool for emotion analysis in survey responses utilizing Ekman’s six-emotion model (joy, excitement, anger, sadness, fear, and boredom). The tool processes spreadsheets containing qualitative responses and generates the percentage distribution of emotions at both individual and aggregated levels. A case study conducted with 46 systems engineering students at the University of Cartagena during the COVID-19 pandemic showed that 'anger' was the most prevalent emotion (29.3%), followed by 'excitement' (19.4%), while 'boredom' was the least frequent (2.6%). The tool demonstrated an accuracy rate of 92% in classifying emotions, compared to 90% achieved through manual coding. These results highlight the tool’s effectiveness in automating emotion analysis, providing statistical and graphical reports that aid decision-making in academic and organizational contexts.
Efficient diabetic retinopathy detection using deep learning approaches and Raspberry Pi 4
Authors: Ajith Kumar Silpa , Kumar James Satheesh , Bharadwaj Sharath Chandra
Published: January 2025

Diabetic retinopathy (DR) is a leading cause of vision loss, predominantly affecting individuals aged 25-74 with diabetes mellitus. Timely medical intervention can protect against irreversible blindness in over 90% of cases, emphasizing effectively identifying and treating DR. In the scope of deep learning (DL), the possibility of using them in DR screening has garnered a lot of interest. Specifically, we adopted the densely connected convolutional networks (DenseNet) model because to its capacity to acquire complex features and learn from diverse datasets. Developing the computational model on retinal images labelled with varying phases of DR are obtained from databases such as Messidor and Kaggle. To enhance accessibility and user-friendliness, we integrated the DenseNet model into a Raspberry Pi 4, a compact, affordable and widely accessible computing platform. The proposed approach resulted in an impressive classification accuracy of 88%, demonstrating its proficiency in distinguishing between different phases of DR progression. The study aims to assist in the early detection and diagnosis of the disease, providing a potential resource that could help medical practitioners and ophthalmologists to evaluate the extent of DR in a timely manner.
Design and implementation of an industrial security system using color cameras
Authors: Ouhoud Amina , Hakim Guezzen Amine
Published: January 2025

This paper examines the design, development, and implementation of a modern industrial security system that integrates color cameras to enhance surveillance and improve safety. The system leverages cutting-edge technologies to detect intrusions and incidents with greater accuracy, which significantly strengthens the security of industrial sites. The study focuses on key stages of the project, including the design, installation, and operational processes, while addressing the challenges encountered during these phases. The integration of color cameras provides clearer and more precise monitoring, allowing for quicker detection and response to potential threats, thus reducing risks effectively. Our results demonstrate that the system greatly improves surveillance efficiency, providing a reliable and robust solution tailored to the security demands of industrial environments. This research offers in-depth insights into the system’s design and functionality, showcasing its critical role in safeguarding industrial facilities. Overall, the proposed solution is an essential tool for enhancing safety protocols and risk management strategies, contributing to more secure industrial sites.
Deflection enhancement of ferrite magnetic core-based microactuator
Authors: Pawinanto Roer Eka , Mulyanti Budi , Fauzan Jahril Nur , Subandi Ayub , Hasanah Lilik , Pangestu M. Assadillah , Yunas Jumril
Published: January 2025

Microactuators play a vital role in several microelectromechanical systems (MEMS) that generate forces or deflections necessary to accomplish functions such as scanning, tuning, manipulation, or delivery. Utilizing a ferrite magnetic core has shown the potential to enhance the deflection of the microactuator. However, the previous study presented a complex fabrication method with high power consumption unsuitable for micropump application. Herewith, we report the impact of ferrite core length on the deflection generated by a microactuator with a simple fabrication method. The deflection behavior shows that the corresponding magnetic core length is inverse to the deflection improvement. The force reduction generated led by a longer magnetic core because of the farther distance to the coil. Our study can be used as a reference to support the development of micropump or active micromixer devices, which require compact devices with simple fabrication and high deflection, achieving ultra-high flow rate and high mixing index.
Enhancing solar panel efficiency through dual-axis tracking and fresnel lens concentration: an image processing approach
Authors: Ghozi Witsqa Ramadhan Muhammad , Halim Levin , Wahab Faisal
Published: January 2025

Solar energy is currently utilized as an inexhaustible renewable energy source. Solar panels can convert solar energy into electrical energy that humans can use. The drawback of solar panels is that they cannot always be perpendicular to the sun, causing a decrease in the intensity of incoming light. Therefore, in this research, a solar tracking system with a fresnel lens was designed using image processing to increase the output of solar panels. In this research, programming was done using Python software for image processing using the hue, saturation, value (HSV) color, and space model, which was then connected with Arduino using the PyFirmata library to move the motor. In this research, solar panels with a fresnel lens and solar tracking were implemented. Data collection was performed on the output voltage of the solar panel. The research concludes that solar panels with solar tracker and fresnel lens have a higher average output voltage of 7.53 V than passive solar panels with an average output voltage of 6.38 V. Also, the average output voltage increased by 18.02% after implementing the solar tracking system and adding the fresnel lens.
The impact of BERT-infused deep learning models on sentiment analysis accuracy in financial news
Authors: Ndama Oussama , Bensassi Ismail , Mokhtar En-Naimi El
Published: January 2025

This study delves into the enhancement of sentiment analysis accuracy within the financial news domain through the integration of bidirectional encoder representations from transformers (BERT) with traditional deep learning models, including artificial neural networks (ANN), long short-term memory (LSTM) networks, gated recurrent units (GRU), and convolutional neural networks (CNN). By employing a comprehensive method encompassing data preprocessing, polarity analysis, and the application of advanced neural network architectures, we investigate the impact of incorporating BERT’s contextual embeddings on the models’ sentiment classification performance. The findings reveal significant improvements in model accuracy, precision, recall, and F1 scores when BERT is integrated, surpassing both traditional sentiment analysis models and contemporary natural language processing (NLP) transformers. This research contributes to the body of knowledge in financial sentiment analysis by demonstrating the potential of combining deep learning and NLP technologies to achieve a more nuanced understanding of financial news sentiment. The study’s insights advocate for a shift towards sophisticated, context-aware models, highlighting the pivotal role of transformer-based techniques in advancing the field.
Shallot disease classification system based on deep learning
Authors: Lidyawati Lita , Darlis Arsyad Ramadhan , Munawaroh Sofa Jauharotul
Published: January 2025

Shallot is one of the important horticultural commodities for society and has high economic value. The problem with shallot cultivation is disease attacks on plants, one of which is Fusarium wilt. With the condition that the shallot commodity at the farmer level has a high failure rate, it is hoped that this research can assist farmers in providing information about shallot plants that have diseased plant characteristics using deep learning system convolutional neural network (CNN) method by utilizing leaf images on shallot plants. This research was conducted using the ResNet-18 architecture, with a total of 400 data in the dataset divided into 2 categories, namely healthy and diseased Fusarium wilt. The device used to carry out the classification process in this research is a Jetson Nano 2 GB. The ratio used to form a model from the dataset is 80-20 (80% training data and 20% validation data). The accuracy results for the classification of shallot plant diseases using real-time leaf images during the day have an average accuracy value of 68% on healthy plants and 62% on Fusarium wilt plants, while at night it has an average accuracy value of 53% on healthy plants and 47% on Fusarium wilt plants.
A 0.7 GHz and 0.9 GHz efficient and compact dual-band rectifier for ambient radio frequency energy harvesting
Authors: Nor Azrin Raja Yunus Raja , Adam Ismahayati , Najib Mohd Yasin Mohd , Muhammad Surajo , Zuki Azman Wan Muhamad Wan , Amin Ahmed Ghaleb Abdulrahman
Published: January 2025

This study introduces a compact dual-band rectifier utilizing a single and multi-stub matching network (MN) technique. The rectifier consists of two branches, each incorporating a single block stub and two blocks stub to generate two frequency susceptance blocks, subsequently transformed into a meandered line. The proposed rectifier operates at two frequency bands of 0.7 GHz and 0.9 GHz and is fabricated on an RT/Duroid 5880 printed circuit board (PCB) with dimensions of 37×25×1.6 mm using an entire ground architecture. Simulation and measurement results show that the rectifier has a power conversion efficiency (PCE) of 67.77% and 66.35% at 0.7 GHz and 70.31% and 71.22% at 0.9 GHz with input power of 0 dBm, respectively. The rectified voltage is 1.79 V DC across a 5 kΩ load terminal (RL) with 5 dBm input power and is capable of sensing low input power down to -30 dBm. This feature makes the rectifier a promising solution for powering low-power devices from ambient energy.
Enhancing recommendation diversity in e-commerce using siamese network and cluster-based technique
Authors: Bahi Abderaouf , Gasmi Ibtissem , Bentrad Sassi , Khantouchi Ramzi
Published: January 2025

This study investigates the difficulty of improving product recommendations in e-commerce systems by tackling the common problem of poor diversity in suggestions. We present a novel approach that uses a siamese network architecture and ResNet for feature extraction to recommend visually similar elements while incorporating diversity through a cluster-based mechanism. The Siamese network is used to compare product pairs, allowing it to recommend both comparable and dissimilar items from distinct clusters. The model was evaluated using a variety of evaluation metrics, resulting in an accuracy of 88.5%, a precision of 90.2%, a recall of 87.1%, and an F1 score of 88.6%. Our results demonstrate that our strategy maintains a high level of relevance in suggestions while efficiently incorporating variety, hence improving the overall user experience in e-commerce applications.
Design and implementation of linear quadratic regulator control for two-wheeled self-balancing robot
Authors: Gilang Buana Putra Leonardus , Wahab Faisal , Agustinus Tamba Tua
Published: January 2025

This research aimed to develop a control system for a self-balancing robot (SBR) based on the mathematical model of an inverted pendulum on a two-wheeled cart. The linear quadratic regulator (LQR) control was implemented to maintain the SBR’s balance under normal conditions. A linearization approach was used to convert the dynamic model into a linear form, enabling the application of LQR. Testing was conducted through simulations and a physical SBR prototype equipped with an MPU6050 sensor and NEMA 17 motor. The test results demonstrated the effectiveness of the LQR control in maintaining the SBR’s balance and its responsiveness to disturbances. Although there are differences between the simulations and physical implementation, the system successfully maintained the SBR’s balance. In conclusion, the use of the inverted pendulum mathematical model and the implementation of LQR control successfully produced a stable and effective control system for SBR balance. By testing various values of the LQR parameters, optimal robot control parameters can be obtained.
Exploring 5G network performance: comparison of inner and outer city areas in Phetchaburi Province
Authors: Pornpongtechavanich Phisit , Daengsi Therdpong
Published: January 2025

The advancement of 5G technology has transformed various aspects of life, including tourism, by enabling people worldwide to communicate and travel with ease. Traveling to different places and countries is now seamless, removing language barriers and facilitating easy access to information on culture, accommodation, and tourist attractions. Additionally, access to applications that facilitate quicker language translation further enhances the travel experience. Phetchaburi Province holds significant importance as a global tourist destination. United Nations Educational, Scientific, and Cultural Organization (UNESCO) has recognized Phetchaburi as a member of the UNESCO creative cities network (UCCN), comprising one of 49 cities worldwide acknowledged for their creative city initiatives. Phetchaburi Province stands as the 5th city in Thailand to receive this designation. This research investigated 5G performance in Phetchaburi Province, both the inner and outer city, focusing on download and upload speeds. The results indicate that there is widespread 5G coverage throughout Phetchaburi Province, including urban and rural areas, especially for the 5G network with a good performance provided by one of the mobile network operators (MNOs). In addition, the statistical analysis reveals differences in 5G performances between the inner city and the outer city of Phetchaburi Province, particularly for download speeds (p-value0.001).
Accurate brain tumor classification with STN-NAM in ResNet50 using MRI
Authors: Topannavar Preeti Sadanand , Sachin Bendre Varsha , Khurge Deepti
Published: January 2025

Brain tumor is an abnormal cell growth that contains malignant and benign cells emerging from numerous cell types within brain. Magnetic resonance imaging (MRI) is utilized for brain tumor classification which provides high-resolution images. However, tumors exhibit different characteristics like shape, location, and size which make it challenging to accurately distinguish among different tumor types and accurately classify them. In this research, spatial transformer network and non-local attention mechanism (STN-NAM) is proposed in ResNet50 to accurately classify tumors. STN transforms spatial information while NAM identifies relationships among normal and lesion areas, which together accurately classify tumors. Initially, images are obtained from Figshare, Brats 2019, and Brats 2020 datasets. These images are pre-processed using a normalized median filter (NMF) to reduce salt and pepper noise. Then, normalization is performed to resize original image to a standard size which assists uniformity in image dimension. U-Net is employed to segment tumor regions and STN-NAM is performed to accurately classify tumors. In comparison to the existing techniques namely, multi-level attention network (MANet), mathematical model with 3D attention U-Net, and convolutional neural network (CNN), the STN-NAM achieves superior accuracy of 98.06%, 99.05%, and 98.66% in Figshare, Brats 2019, and Brats 2020 datasets, respectively.
Conceptualizing the ‘All You Can Eat’ game to promote healthy eating habits among young children
Authors: Yi-Chin Liu Noreena , Mohd Tuah Nooralisa , Ying Chen Yi Darren
Published: January 2025

Childhood obesity is a growing concern globally, with unhealthy eating habits being one of the leading causes. In response, researchers and game designers have investigated the use of serious games to encourage healthy eating among young children. Creating successful serious games to encourage children's good eating habits involves thoughtful consideration of elements such as age-appropriate content, game mechanics, and motivator strategies. The aim of this project is to create a serious game design that promotes and supports healthy eating habits in youngsters. This study evaluates children's existing understanding of nutrition by gathering their comments using a serious game as an example. Various gaming elements are recognized, leading to the creation of a board game named "All You Can Eat" (AYCE). The design evaluation process involves conducting questionnaire surveys and gathering feedback from both parents and children. The results will assist future research in creating and bringing to realisation the AYCE game. This research can be extended to a range of health topics beyond healthy eating habits, such as serious games for learning about cultures and ethics. Researchers, educators, and game designers collaborate to produce unique and interesting games aimed at promoting good eating habits and preventing youngsters’ obesity.
Forward and optimised IK utilising the Levenberg-Marquardt algorithm 6-DOF robot manipulator for sorting applications
Authors: Hafidz Hasnor Faiz Mohammad , M. Thamrin Norashikin , Syahirul Amin Megat Ali Megat , Zainal Abidin Idris
Published: January 2025

Colour sorting robots automate industries, but translating image data to robot movement is expensive and complicated. Vision sensors require a lot of processing power, which can slow down and strain the robot. Real-time colour sorting hardware and software integration complicates things. This work uses robotic operating system (ROS) to solve vision-guided colour sorting problems in Cartesian space. Ubuntu 20.04, ROS Noetic, a Raspberry Pi, a camera, and six servos. In Jupyter Lab, unified robotic description format (URDF) is used to build a virtual kinematic model, and Levenberg-Marquardt (LM) optimisation guides object manipulation. OpenCV image processing uses colour conversion, Canny edges, and midpoint estimation to detect coloured objects efficiently. The average servo movement error is 0.46 degrees, and the robot manipulator's final destination positioning error is 1.65 mm. The average object edge detection error is 0.33 mm, and the red, green, blue (RGB) colour distance is 57.84. ROS-based robot manipulator achieves impressive Cartesian space colour sorting accuracy despite image processing challenges, enabling real-world deployment.
Development of distance formulation for high-dimensional data visualization in multidimensional scaling
Authors: Marto Hasugian Paska , Mawengkang Herman , Sihombing Poltak , Efendi Syahril
Published: January 2025

This research aims to produce a new method called pasca-multidimensional scaling (pasca-MDS) by modifying the multidimensional scaling (MDS) method, the developed model comes as a solution to overcome the problem of data complexity by reducing its description dimension without losing important information. This model, offers an innovative approach in dealing with these problems. Pasca-MDS not only focuses on reducing the dimensionality of data, but also retains the essence of relevant information from each data point. As such, it allows for easier and more efficient analysis without compromising the accuracy of the information conveyed. The main advantage of pasca-MDS lies in its ability to produce simpler visual representations while maintaining the original structure of complex data. This provides clarity and ease in understanding the patterns or relationships hidden within. By using adjustment techniques after the MDS process, this model can provide more optimized results. This process allows the adjustment of data points to achieve a better representation in a lower dimensional space, resulting in a more intuitive and easy-to-understand interpretation. The developed distance formula has the ability to minimize stress compared to other distance formulas in MDS space, with the aim of improving the accuracy of high-dimensional data visualization.
Handwritten Kaganga script classification using deep learning and image fusion
Authors: Dwika Putra Erwin , Ermatita Ermatita , Abdiansah Abdiansah
Published: January 2025

Classification of traditional handwriting script and to preserve many cultures have been developed in some parts of the world, including image classification of handwriting Kaganga script. This study aims to propose a new combination model by implementing top-hat transform (THT) and contrast-limited adaptive histogram equalization (CLAHE) with discrete wavelet transform (DWT) to support the performance of the convolutional neural network (CNN) in Kaganga script classification. The top-hat transform and contrast-limited adaptive histogram equalization with discrete wavelet transform Fusion L2 convolutional neural network (DWT-THCL L2 CNN) models get the best accuracy from the CNN with L1 regularization, CNN with dropout regularization, CNN with L2 regularization and CNN with L2 regularization and CLAHE models. Based on the experimental results, the DWT-THCL L2 CNN model successfully increased training accuracy by 7.76%, validation accuracy by 5.11%, and testing accuracy by 3.73% from the CNN L1 model. The DWT-THCL L2 CNN model received a training accuracy of 99.87%, validation accuracy of 82.61%, and testing accuracy of 82.61%, while the CNN model with L1 regularization (L1 CNN) only received a training accuracy of 92.11%, validation accuracy of 77.50%, and testing accuracy of 78.88%.
Application of feature-based image matching method as an object recognition method
Authors: Karma I Gede Made , Darma I Ketut
Published: January 2025

In everyday life, objects are recognized based on the suitability of their characteristics to familiar objects. A feature matching process occurs when recognizing objects. This concept is what we want to apply and test in this research. Because various factors can influence the level of accuracy and success of an image matching method, the first step taken is to improve the accuracy level of the image matching method used. There are three feature-based image matching methods, which are implemented as object recognition methods. These three methods are the result of modifications of the image matching function method, normalized 2D cross correlation method and point feature matching which were later named PICMatch, NCMatch and FBMatch. As image matching methods, these three modified methods show performance with a success rate above 95%. However, when applied as an object recognition method, both individually and combined, the three methods only have a maximum accuracy of 7%. These results are obtained by matching the samples using one of the methods with the best match rate, in the order of application of the PICMatch, NCMatch, and FBMatch methods.
Refining CNN architecture for forest fire detection: improving accuracy through efficient hyperparameter tuning
Authors: Kurniawan Kurniawan , Perdana Windarto Agus , Solikhun Solikhun
Published: January 2025

Forest fire detection is one of the critical challenges in disaster mitigation and environmental management. This research aims to increase the accuracy of forest fire detection through improving the convolutional neural network (CNN) architecture. The main focus of research is on efficient hyperparameter tuning, which includes selecting and optimizing key parameters in CNN architectures such as convolutional layers, kernel size, number of neurons in hidden layers, and learning algorithms. By utilizing grid search techniques and heuristic-based optimization algorithms, the resulting CNN model shows significant improvements in detection accuracy compared to previous approaches. The evaluation was carried out using a pre-processed forest fire image dataset, and the results show that architectural refinement and appropriate hyperparameter tuning can substantially improve model performance. Evaluation results comparing two models, VGG16 and the proposed method, show significant improvements over the proposed method. The proposed method shows better capabilities with an accuracy of 95.31% and a precision of 97.22%. This research contributes to developing a more reliable and efficient forest fire detection system, which is expected to be used in real applications to reduce the impact of forest fires more effectively.
Enhancing SDN security with a feature-based approach using multiple k-means, Word2Vec, and neural network
Authors: Yzzogh Hicham , Benaboud Hafssa
Published: January 2025

In the rapidly evolving landscape of network management, software-defined networking (SDN) stands out as a transformative technology. It revolutionizes network management by decoupling the control and data planes, enhancing both flexibility and operational efficiency. However, this separation introduces significant security challenges, such as data interception, manipulation, and unauthorized access. To address these issues, this paper investigates the application of advanced clustering and classification algorithms for anomaly detection and traffic analysis in SDN environments. We present a novel approach that integrates multiple k-means clustering models with Word2Vec for feature extraction, followed by classification using a neural network (NN). Our method is rigorously benchmarked against a traditional NN model to comprehensively evaluate performance. Experimental results indicate that our approach outperforms the NN model, achieving an accuracy of 99.97% on the InSDN dataset and 98.65% on the CIC-DDoS2019 dataset, showcasing its effectiveness in detecting anomalies without relying on feature selection. These findings suggest that integrating clustering techniques with feature extraction algorithms can significantly enhance the security of SDN infrastructures.
Explainable deep learning for diagnosing acute lymphocytic leukemia using blood smear images
Authors: Theodorus Syaron Darmawan Sion , Husna Shabrina Nabila
Published: January 2025

Acute lymphocytic leukemia (ALL) is a rapidly progressing blood cancer that affects the lymphocytes. The diagnosis of ALL typically entails the examination of blood smears under a microscope, processes that are both time-consuming and susceptible to errors. Deep learning (DL) approaches have shown significant promise in automating the classification of ALL from microscopic images. However, the lack of transparency in these models hinders their widespread adoption in clinical settings. This study addresses this challenge by employing fine-tuned EfficientNetV2B3, a DL model, in conjunction with local interpretable model-agnostic explanations (LIME), a technique for explainable artificial intelligence (XAI) technique, to classify microscopic images of ALL. The C-NMC 2019 dataset, which has been augmented to ensure class balance, was utilized for training and evaluation. The proposed approach achieved impressive results, with an average recall, F1-score, and accuracy of 0.9795 and precision of 0.9796. The use of LIME effectively highlights relevant areas for prediction, accurately corresponding to the cell characteristics. The integration of DL and XAI techniques enhances the interpretability of ALL classification models, potentially increasing their trustworthiness and adoption in clinical practice. This study aims to further the development of diagnostic tools that are both precise and transparent for ALL.
Hybrid algorithm for optimized clustering and load balancing using deep Q reccurent neural networks in cloud computing
Authors: Vijay Kumar Nampally , Mohanty Satarupa , Kumar Pattnaik Prasant
Published: January 2025

Cloud services are among the technologies that are developing the fastest. Additionally, it is acknowledged that load balancing poses a major obstacle to reaching energy efficiency. Distributing the load among several resources in order to provide the best possible services is the main purpose of load balancing. The network's accessibility and dependability are increased through the usage of fault tolerance. An approach for hybrid deep learning (DL)-based load balancing is proposed in this paper. Tasks are first distributed in a round-robin fashion to every virtual machine. When assessing whether a virtual machine (VM) is overloaded or underloaded, the deep embedding cluster (DEC) also considers the central processing unit (CPU), bandwidth, memory, processing elements, and frequency scaling factors. For cloud load balancing, the tasks completed on the overloaded VM are assigned to the underloaded VM based on their value. To balance the load depending on many aspects like supply, demand, capacity, load, resource utilization, and fault tolerance, the deep Q recurrent neural network (DQRNN) is also suggested. Additionally, load, capacity, resource consumption, and success rate are used to evaluate the efficacy of this approach; optimum values of 0.147, 0.726, 0.527, and 0.895 are attained.
Optimized colon cancer classification via feature selection and machine learning
Authors: Haddou Bouazza Sara , Haddou Bouazza Jihad
Published: January 2025

The increasing dimensionality of gene expression data poses significant challenges in cancer classification, particularly in colon cancer. This study presents a novel filtering approach (FA) and a gene classifier (GC) to enhance gene selection and classification accuracy. Utilizing a dataset of 62 samples, our methods integrate statistical measures and machine learning classifiers, achieving classification accuracies of 96% and 97%, respectively. The FA effectively filters out noise and redundancy, allowing for accurate predictions with a minimal subset of genes, while the GC leverages multiple classifiers for optimal performance. These findings underscore the importance of robust feature selection in improving cancer diagnostics and suggest potential applications in personalized medicine. By addressing the limitations of existing methodologies, our work lays the groundwork for future research in cancer genomics, emphasizing the need for adaptive strategies to handle complex datasets.
Comparative analysis of incremental conductance MPPT for enhanced algorithm performance
Authors: Asnil Asnil , Nazir Refdinal , Krismadinata Krismadinata , Nasir Muhammad
Published: January 2025

The incremental conductance (INC) approach is limited in terms of its response speed, accuracy during steady state, and ability to handle oscillations. As a result, this algorithm is ineffective in situations with variations in solar radiation and temperature, particularly abrupt fluctuations. An enhanced variable step size INC approach is suggested to improve system efficiency and performance. The proposed algorithm is subjected to rigorous testing and analysis with other INC methods for better results. The research findings indicate that the proposed algorithm's tracking efficiency can be enhanced to 99.83% under varying radiation conditions and constant temperature. Additionally, the method utilizing the second model achieves a 99.83% tracking efficiency, while the method employing the first model achieves a tracking efficiency of 89.99%. In comparison, the conventional method achieves a tracking efficiency of 96.36%. Regarding radiation and temperature circumstances, the tracking efficiency value varies for different methods. Specifically, the tracking efficiency is 95.21% for the approach using the proposed algorithm’s, 92.94% for the method using the second model, 83,39% for the method using the first model, and 91.19% for the conventional way. Therefore, it can be inferred that the suggested Maximum power point tracking (MPPT) algorithm performs better than other algorithms under both test situations.
Correlation between input and output parameters of microbial fuel cell
Authors: Murugesu Ganesan V. , Khalid Saiful Nizam , Shareef Hussain , Khan Saad Saleem
Published: January 2025

This paper presents the correlation between open circuit voltage (OCV) and pH, temperature, and total dissolved solids (TDS) of an air cathode single chamber microbial fuel cell (MFC) using artificial neural network (ANN) and support vector machine (SVM) algorithms. Previous works used terminal voltages as output parameters to determine the correlation between MFCs' input and output parameters. However, OCV is the most important measurement that can determine the validity of the MFC. Thus, various tests were conducted to analyze the correlation between OCV and input parameters using ANN and SVM algorithms. Both techniques show a strong correlation between OCV and input parameters with the highest R2 values. The highest OCV value obtained from the experiment is 1.179 V at pH 5.26, temperature 299K, and TDS 3,124 ppm. Furthermore, an ANN model was developed to predict the OCV value based on pH, temperature, and TDS value.
TechTrolley-enhancing the retail experience
Authors: Chavan Dhananjay Rajendra , Sherekar Roshan Mahadev , Khudbhaiye Sarthak Praveen , Zalte Jaya
Published: January 2025

In the modern era, convenience and efficiency have become essential aspects of daily life, and grocery shopping is no exception. The traditional shopping experience, characterized by long queues and time-consuming checkout processes, can be frustrating and inefficient. To address these challenges, the TechTrolley has emerged as an innovative solution, leveraging Bluetooth and radio frequency identification (RFID) technology to revolutionize the grocery shopping experience. With the help of TechTrolley, customer can seamlessly complete the shopping by scanning and purchasing the products, controlling the trolley with the use of controller integrated in application, getting details of the products and price in the application and over LCD display embedded on the trolley, complete the checkout process at billing counter. With the need to implement, we need an RFID tag, ESP32, LCD display, L298N motor driver and battery to implement the motion features of a trolley, database for storing the user and product details, a bridge network through router to establish the network between admin, user and the trolley in order to invoke the real time updates.
Implementation and analysis of temperature and gas sensor datalogger in multi-stage condenser pyrolysis
Authors: Noor Muhammad Fathuddin , Sumarlan Sumardi Hadi , Hendrawan Yusuf , Argo Bambang Dwi , Abdillah Hartawan
Published: January 2025

The development of alternative energy sources is crucial for addressing contemporary energy and environmental challenges. This study presents the implementation and analysis of a temperature and gas sensor datalogger within a multi-stage condenser pyrolysis system, designed to assess the potential of pyrolytic liquid smoke derived from Cerbera odollam (bintaro) fruit waste. The datalogger system was developed to continuously capture and retain data on temperature, air humidity, and non-condensable gases throughout the pyrolysis process. The experimental research focused on evaluating the impact of varying reactor temperatures (250 °C, 300 °C, and 350 °C) and cooling fluid flow rates on the performance of the condenser and the production of bio-oil. Results indicated that reactor temperature significantly affects bio-oil yield, with the highest output of 190 mL obtained at 350 °C. Additionally, the temperature of the smoke entering each condenser and the cooling water’s temperature were found to influence the composition of the condensates produced by each stage. This study highlights the importance of integrating sensor technologies to optimize pyrolysis conditions, thereby enhancing the efficiency of energy production from bintaro fruit waste.
Development of newton dynamometer instrumentation integrated with smart counter applications based on Hooke's law
Authors: Yulkifli Yulkifli , Nofriandi Alwi , Audia Washilla , Asrizal Asrizal , Darvina Yenni , Muttaqin Afdhal , Jaafar Rosly
Published: January 2025

This research presents the development of an instrumentation system that employs ultrasonic sensors for Newton dynamometer applications. A key parameter measured is the change in spring length before and after loading. The methodology implemented in this study is based on Hooke's Law, applied within the instrumentation devices. The length change data is transmitted to a smartphone via a Bluetooth module integrated into the instrument. This allows for flexible data usage and input through a calculator-based application created with MIT App Inventor, tailored to the relevant supporting parameters. Before implementation, the sensors underwent characterization to assess the linearity of their output compared to a standard measuring tool, specifically a ruler. The linearity test yielded a coefficient of 0.9998, indicating excellent performance for this application. Additionally, the system achieved an average accuracy of 94.12% and an average precision of 99.94%.
Approach for modelling and controlling of autonomous cruise control system through machine learning algorithms
Authors: Kiruba R. , Samuel S. Prince , Kavitha N. , Srinivasan K. , Radhika V.
Published: January 2025

Automated cruise control installation is one of the utmost significant phases in the auto industry's pursuit of autonomous vehicles. The controller of choice is one of the key factors in determining whether a design will be durable and cost-effective. The model-based controller and a cutting-edge algorithmic optimization method are both presented inside the framework of this proposed study. The suggested controller may achieve the desired characteristics of the design, including a faster rise time, a faster settle time, a smaller peak overshoot, and a smaller steady-state error. A MATLAB-executed and -simulated system model using a control method based on a hybrid genetic algorithm and reinforcement learning has been used to effectively and automatically regulate the vehicle's velocity in compliance with all design parameters.
Comparative analysis of edge detection image processing techniques for efficient traffic signal management system
Authors: Bharathy Niranjana Chandrasekara , Dasappanavar Manju Shivanna , Byre Gowda Anne Gowda Aleri , Janardhanan Jijesh Jisha
Published: January 2025

Traffic monitoring and control pose significant challenges, particularly in metropolitan areas worldwide. Traditional methods such as timers, traffic police control, and sensor-based systems have become increasingly ineffective due to escalating traffic volumes. Image processing emerges as a promising technology for enhancing traffic control systems. This research aims to address the inefficiencies of current traffic management systems (TMS) by conducting a detailed comparative analysis of conventional image processing techniques, focusing on edge-based methods. Utilizing the open source computer vision (OpenCV) Library, we evaluated various edge detection techniques based on qualitative parameters like environmental lighting, object detection, and size, as well as quantitative parameters such as average traffic density, sharpness ratio (SR), abruptness, sensitivity factors (SF), processing time, frame processing time, and average frames per second (FPS). The study finds that the Canny edge detection technique outperforms others, with an average traffic density of 30.12 across 10 frames, superior SR, and minimal processing time of 99 seconds. This makes it highly effective for traffic control applications by generating high-quality edge maps with minimal noise. Our findings suggest that improving conventional image processing techniques and integrating deep learning algorithms can further enhance TMS, leading to more efficient urban mobility and reduced environmental impact.
Improving unmanned armoured mobile robot navigation accuracy using Kalman filter
Authors: Porang Tanya , Maneetham Dechrit , Crisnapati Padma Nyoman
Published: January 2025

Thailand's remarkable economic progress has earned it recognition as a development success story, but terrorism remains a significant threat, particularly in the southern provinces. To effectively combat terrorism, intelligent systems like military robots are being increasingly utilized worldwide, including the development of vision-based robots and unmanned military equipment. The Thai government aligns with this trend by aiming to manufacture military robots, including armored mobile robot (AMR) equipped with cameras for peacekeeping and surveillance purposes. The development of an automatic tank simulation robot control system integrates global position system (GPS) technology, digital compasses, and lidar sensors to enhance efficiency, directional control, and obstacle avoidance. The study investigates the implementation of Kalman filters to enhance the precision of navigation in AMRs used in military contexts. The suggested system combines GPS, Lidar sensors, and digital compasses, utilizing advanced sensor fusion algorithms to improve directional control and obstacle avoidance. The system's usefulness is demonstrated through field tests and simulations, especially in complicated contexts where conventional approaches may face difficulties. The findings help to the progress of military robotics by providing a strong answer for improving navigation accuracy in real-world situations.
Attention deficit and hyperactivity disorder classification in quantitative EEG signals using machine learning algorithms
Authors: Aliyah Syifani Ihfadza , Wijaya Sastra Kusuma , Ramli Yetty
Published: January 2025

Attention deficit and hyperactivity disorder (ADHD) classification method as a quantitative observation has been continually improved to assist medical practitioners. Currently, machine learning algorithms such as k-nearest neighbors (KNN), multilayer perceptron (MLP), and support vector machine (SVM) are widely used. This study proposed a feature extraction method for quantitative electroencephalography (qEEG) data derived from the continuous wavelet transform (CWT) to classify children with ADHD versus healthy subjects. Subsequently, this study compared the performance of the classification pipeline before and after the implementation of principal component analysis (PCA) on the features prior to processing with machine learning algorithms. The results revealed that the overall performance of the classifiers consistently improved after the implementation of PCA. The results highlight the varying impact of PCA on classifier performance, with KNN showing an improvement in testing accuracy from 61.84% to 69.21% following PCA implementation, while the other classifiers showed deterioration in performance. These findings suggest that while PCA may be beneficial for some classifiers, its impact on performance varies depending on the specific characteristics of the dataset and the classifier utilized. Moreover, this study provides insight for future implementation of the classification method for ADHD patients across a more specific clinical range of the spectrum.